# WWDC.ai Full Agent Context

Generated WWDC session summaries and source links. Apple hosts the original videos, resources, and transcript source material.

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## Navigation

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- Concise index: https://wwdc.ai/llms.txt

## WWDC 2026

- Year index: https://wwdc.ai/2026.md

### Keynote

- Session ID: wwdc2026-101
- Page: https://wwdc.ai/2026/101
- Markdown: https://wwdc.ai/2026/101.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/101/
- Category: Essentials
- Description: Apple's WWDC 2026 keynote introduces platform responsiveness updates, expanded child safety tools, Siri AI, and new Apple Intelligence developer APIs.
- Duration: 76:14
- Apple HLS stream: https://events-delivery.apple.com/0601eknundukyswuegvjcidvurtztakr/vod_main_vdhwwrygnmzydhppixlrqwduiqffztdw/vod_main_vdhwwrygnmzydhppixlrqwduiqffztdw.m3u8

Apple's WWDC 2026 keynote introduces platform responsiveness updates, expanded child safety tools, Siri AI, and new Apple Intelligence developer APIs.

TLDR:
- Apple focused this release cycle on responsiveness, reliability, search, and design refinements across platforms, including Liquid Glass tuning and macOS Golden Gate updates.
- Child safety expands with Child Account setup, Ask to Browse, Time Allowances, redesigned Screen Time, Communication Safety for violent content, and developer APIs such as Declared Age Range API.
- Apple Intelligence gets a new architecture using Apple Foundation models, Private Cloud Compute, a system orchestrator, App Actions, personal context, on-screen awareness, and web knowledge.
- Siri AI is the headline feature: richer conversations, a dedicated Siri app, Visual Intelligence, system-wide writing tools, and deeper app integration through App Intents, Spotlight, Foundation Models framework, and Core AI.

## Platform releases emphasize refinement and responsiveness

Apple framed the 2026 platform releases around three priorities: making the platforms more responsive and easier to use, expanding trust and safety for kids, and advancing Apple Intelligence and Siri. The updates apply across iOS, iPadOS, watchOS, tvOS, visionOS, and macOS, with the next macOS named macOS Golden Gate.

The platform improvements are less about a single new UI direction and more about system-wide polish: readability, performance, search stability, network transitions, and feature refinements in apps such as Photos, Health, Maps, and Home.

- Liquid Glass is tuned for better readability by diffusing complex background content more effectively and adding more depth and separation.
- A new Liquid Glass setting lets users adjust the appearance from ultra clear to fully tinted; apps that already adopted Liquid Glass inherit these customizations.
- macOS adds a more uniform toolbar, edge-to-edge sidebars, restored sidebar icon color, and a consistent tighter window corner radius.
- App icons gain additional Liquid Glass layers for sharper, more defined artwork across Dock, Home Screen, and clear appearances.

## System performance, search, and everyday workflows

Apple highlighted foundational optimizations in memory usage, CPU utilization, networking, display rendering, app launch, file transfer, and search indexing. Several improvements apply automatically to third-party apps or system services developers rely on.

A rebuilt search foundation powers Spotlight, Photos, and Mail using a more stable, efficient, and comprehensive search index. The system reindexes existing content after update and indexes new content nearly immediately, improving the odds that user content appears when searched.

- iPhone and iPad apps launch up to 30% faster through preloading of key app data, including for third-party apps.
- New photos appear in Photos up to 70% faster, AirDrop transfers are up to 80% faster, and iPad external-drive file browsing and transfers are up to five times faster.
- An optimized CPU scheduler comes to older iPhones back to iPhone 11; iOS 27 supports the same iPhone models as iOS 26.
- Network transitions between Wi-Fi and cellular are smarter, and Messages adds per-message send indicators for slow transfers.
- Mail gains a new ranking system for Top Hits to surface more relevant search results.

## Expanded trust and safety for kids

Apple expanded child safety around four parent concerns: what content kids can see, who they can communicate with, when they have access, and how parents can guide the child's digital journey. The company positioned Child Accounts as the foundation because they immediately enable age-based safeguards across the system.

The new controls are informed by clinical, child development, and online safety research, including collaboration with the American Academy of Pediatrics on guidance for families. Apple also emphasized developer responsibility for age-appropriate in-app experiences.

- A Child Account enables safeguards such as blocking adult websites, restricting media by age, and applying age-based App Store restrictions; existing accounts can be converted.
- A setup assistant lets parents start with essential apps, a recommended set, or specific allowed apps, then expand access over time.
- Ask to Browse extends the Ask to Buy model to Safari website access on iPhone, iPad, and Mac; Ask to Browse and Ask to Buy are on by default for kids under 13 and can be enabled for teens.
- Communication Safety now intervenes before kids see gore or violent content in shared images or videos, in addition to existing nudity protections.
- Time Allowances provide recommended daily limits for Entertainment, Games, and Social Media, and schedules let parents define app availability for school, weekends, or other routines.

## Apple Intelligence architecture and Siri AI

The next generation of Apple Intelligence is built around new Apple Foundation models created through collaboration with Google technologies behind Gemini, adapted for on-device processing and Private Cloud Compute. Apple described server and on-device models with stronger reasoning, image understanding and generation, and multiple modalities.

A new system orchestrator coordinates personal context understanding, broad world knowledge, App Actions, on-screen awareness, and Spotlight's semantic index. Apple emphasized that requests run on device or through Private Cloud Compute, where data is used only to execute the request and is not stored or made accessible to Apple.

Siri AI is the new Apple Intelligence-powered Siri. It supports richer conversations, personal context, app actions, on-screen awareness, image understanding, and web-backed answers. Conversations can be revisited in a dedicated Siri app with privately synced iCloud history.

- Siri AI can answer current-information questions, set reminders, control Music, reason over what is on screen, find personal data from Messages or Photos, and take actions in apps.
- On supported products with the most advanced on-device model, Siri gains more expressive voices, voice customization, and higher-accuracy system-wide dictation.
- On macOS, Siri is integrated into Spotlight and system-wide context menus for questions about images, files, selected text, and local content.
- On iOS, a swipe from the Dynamic Island can start typed search or Siri conversation; Siri AI also comes to iPadOS, watchOS, visionOS, CarPlay, and AirPods with platform-specific experiences.
- Siri AI starts in English and expands to more languages; it will launch for customers in beta later in the year and is not initially available in the EU on iOS and iPadOS or in China while Apple addresses regulatory requirements.

## Apple Intelligence in apps and system features

Apple Intelligence is integrated into Safari, Passwords, Messages, Mail, Calendar, Phone, Home, Shortcuts, Image Playground, and Photos. Many features use on-device intelligence, while image generation and some heavier capabilities use Private Cloud Compute.

Safari can organize tabs into topics, monitor pages with Notify Me using natural-language requests, and generate custom page adaptations through Describe an extension. Passwords can use Apple Intelligence and Safari to update eligible weak or compromised passwords to strong passwords with a tap.

- Messages offers contextual one-tap suggestions such as creating reminders or notes, and can search Photos for images based on conversation context.
- Mail suggestions can invoke favorite apps, including third-party apps; Calendar can create or edit events from natural language; Phone Call Context surfaces relevant on-device information such as confirmation codes based on who is being called.
- Home groups related accessory notifications, summarizes compatible camera clips, searches camera history by captured events, and supports 4K clips on supported cameras.
- Shortcuts can assemble automations from natural-language descriptions and refine them through follow-up descriptions.
- Image Playground adds higher-quality image generation, photorealistic styles, natural-language image transformation, touch-based edits, dimensions for different uses, and integration into Messages backgrounds, contact posters, and Lock Screen wallpapers.
- Photos adds upgraded Clean Up, Extend for expanding image boundaries, and Spatial Reframing, which combines on-device spatial models with Private Cloud Compute image generation to adjust composition while preserving the original scene.

## Developer-facing APIs, tools, and availability

Developers are expected to integrate with Apple Intelligence through existing platform technologies as well as new AI frameworks. Apple specifically called out App Intents, Spotlight indexing, the Foundation Models framework, Image Playground API, Declared Age Range API, and a new Core AI framework.

Apple also previewed Xcode improvements for agentic coding and testing. The coding assistant can localize an entire app, interact with simulated devices, be extended with custom skills, use a chosen model or agent including Gemini, and connect to tools such as Figma and GitHub. The new Device Hub unifies simulated and real devices for testing and iteration.

- Apps that index content into Spotlight can make that content available for Siri queries, as shown with Line conversations.
- Apps that adopt App Intents can expose actions to Siri, as shown with Structured creating a calendar event from a user request.
- Foundation Models framework gains image input in addition to text, custom skills, and access to models running on servers through the same Swift API.
- Core AI lets apps bring other models to run locally with Apple silicon across Apple platforms.
- Image Playground API exposes the new Image Playground capabilities to developers.
- Child safety APIs and resources include tools for nudity and violent-content protection, parent approval of new contacts, and Declared Age Range API for privacy-preserving age-range-aware experiences.

Chapters:
- 0:00 Introduction: Tim Cook opens WWDC by recognizing the developer community and framing Apple's platform strategy around integrated hardware, software, and shared technologies. Craig Federighi introduces the three release priorities: platform improvements, trust and safety, and a major Apple Intelligence and Siri update.
- 5:04 Platform improvements: Apple presents design refinements to Liquid Glass and macOS Golden Gate, then details performance, networking, file transfer, search, and app improvements across platforms. Highlights include faster app launches, improved search infrastructure for Spotlight, Photos, and Mail, broader CPU scheduler support back to iPhone 11, and new features in Photos, Health, AirPods, visionOS, and Maps.
- 16:56 Trust and safety: Apple expands child safety around Child Accounts, content and communication controls, website approval, time limits, schedules, and a redesigned Screen Time experience. The chapter also calls on developers to build age-appropriate app experiences using Apple's child safety APIs and resources, including Declared Age Range API.
- 27:53 Apple Intelligence and Siri: Apple introduces a new Apple Intelligence architecture using Apple Foundation models, Private Cloud Compute, a system orchestrator, personal context, App Actions, on-screen awareness, and web knowledge. The chapter debuts Siri AI, Visual Intelligence, stronger writing tools, Apple Intelligence features across apps, new developer APIs and frameworks, Xcode improvements, availability constraints, and beta timing.

### Platforms State of the Union

- Session ID: wwdc2026-102
- Page: https://wwdc.ai/2026/102
- Markdown: https://wwdc.ai/2026/102.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/102/
- Category: Essentials
- Description: Apple's WWDC26 platform overview covers Apple Intelligence APIs, Liquid Glass refinements, SwiftUI and Swift updates, and agentic coding in Xcode 27.
- Duration: 61:38
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-102/eng_3aa42d16b39e/wwdc2026-102-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/102/2/abb4bd38-dfae-46cf-985f-160769b92d41/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/102/2/abb4bd38-dfae-46cf-985f-160769b92d41/downloads/wwdc2026-102_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/102/2/abb4bd38-dfae-46cf-985f-160769b92d41/downloads/wwdc2026-102_sd.mp4?dl=1

Apple's WWDC26 platform overview covers Apple Intelligence APIs, Liquid Glass refinements, SwiftUI and Swift updates, and agentic coding in Xcode 27.

TLDR:
- Foundation Models gains multimodal prompts, server-model support, Dynamic Profiles, evaluation/debugging tools, and planned open sourcing; Core AI adds an on-device runtime for custom models.
- App Intents integrates apps with Siri AI, Spotlight semantic indexing, system schemas, and View Annotations so users can find and act on app content with natural language.
- Platform updates include Liquid Glass refinements, resizable iOS apps in iPhone Mirroring and on iPad, major SwiftUI interaction/performance improvements, and Swift 6.4 workflow enhancements.
- Xcode 27 focuses on agentic coding and daily productivity with faster project loading, iCloud-synced settings, customizable themes/toolbars, Device Hub, improved Previews, Xcode Cloud setup, and plugin-based agents.

## Apple Intelligence in apps

The session positions Apple Intelligence as both an in-app capability and a system integration surface. Apple Foundation Models now power Apple Intelligence using models adapted for on-device execution and Private Cloud Compute, and the Foundation Models framework expands beyond the local model to support image input and server models through a single Swift API.

The Foundation Models framework adds multimodal prompting with text and images, Vision framework integration for tools such as OCR and barcode reading, support for server models such as Claude and Gemini through providers that conform to the Language Model protocol, and access to Apple Foundation Models running in Private Cloud Compute with no cloud API cost for qualifying developers with fewer than 2 million first-time App Store downloads.

Dynamic Profiles are new declarative APIs for adaptive AI experiences. They let a LanguageModelSession switch models, instructions, tools, and behavior over time while preserving a continuous transcript, enabling patterns such as skills, sub-agents, and context-aware model routing.

- New tooling includes the Evaluations framework for prompt and feature validation, an upgraded Foundation Models instrument for visualizing model behavior, and the `fm` command-line tool for prompting from the terminal.
- Additional announced Foundation Models capabilities include a Python SDK, tool calling with images, and a private RAG tool powered by Core Spotlight.
- The Foundation Models framework is planned to become open source later in the summer, enabling the same Swift APIs to run in apps and on Swift servers.
- Core AI is a new framework for bringing custom models on device, with Swift APIs, PyTorch conversion/optimization tools, ahead-of-time compilation, Core AI instruments, and a tensor-value visual debugger tied back to Python source.

### Dynamic Profiles concept

The demonstrated pattern starts with a `LanguageModelSession`, selects a Dynamic Profile based on app state, and swaps among profiles for brainstorming, tutorial generation, and lightweight jargon explanations while sharing one transcript.

## System intelligence with App Intents

App Intents is the bridge between apps and system intelligence. By describing app content and capabilities with schemas, apps can contribute personal context to the Spotlight semantic index, expose system-understandable actions, and become available through Siri AI, Shortcuts, widgets, the Action button, and other system surfaces.

Entity schemas describe the content and concepts an app works with, while intent schemas describe actions the app can perform. Because schemas are system-defined, Siri can understand natural requests without developers defining every phrase, and apps can benefit from future language-understanding improvements.

The new View Annotations API lets an app associate visible UI with entities, so users can refer to on-screen content naturally, such as "this photo" or "the second message," and pass that referenced content into app intents.

- Entities in the demo conform to `IndexedEntity` so they can be indexed into Spotlight.
- `@AppEntity` is used for schema-conforming content such as messages, contacts, and conversations.
- `@AppIntent` is used for actions, such as conforming a send-message intent to a system schema.
- Combining Spotlight semantic indexing, intent schemas, and View Annotations enables natural-language discovery and action from inside and outside the app.

### Schema adoption pattern

The transcript describes using `@AppEntity` for content entities and `@AppIntent` for actions so Siri can reason over app content and perform supported actions.

```swift
@AppEntity
struct MessageEntity: IndexedEntity { /* app content indexed into Spotlight */ }

@AppIntent
struct SendMessageIntent { /* action conforming to a system intent schema */ }
```

## Design, adaptability, SwiftUI, and Swift

Liquid Glass continues to be refined across the 27 releases. Apps already using Liquid Glass receive improved readability, diffusion, edge separation, specular highlights, and accessibility adaptation automatically when running on the new releases. Users also gain a settings slider to tune Liquid Glass from ultra clear to fully tinted.

App adaptability becomes more important because iOS apps can now be resized in iPhone Mirroring and on iPad after rebuilding with the latest SDK. SwiftUI, Auto Layout, scene lifecycle support, size classes, trait collections, the new resizable iOS simulator, and Previews are the recommended path for validating layouts across dynamic sizes and aspect ratios.

SwiftUI gains richer interactions, better performance, and new app capabilities. Highlights include reorderable containers outside lists, swipe actions in any scrollable container, improved text selection, faster nested stack layouts, lazy `@State` initialization under the hood, HTTP caching in `AsyncImage`, adaptive toolbar controls, a new document infrastructure, and the Spatial Preview framework for Mac apps streaming spatial previews to Apple Vision Pro.

Swift 6.4 focuses on day-to-day workflow improvements: targeted warning suppression or promotion to errors, `anyAppleOS` availability shorthand, `await` in `defer`, and better diagnostics for expressions that previously failed with the generic type-checking timeout message.

- macOS 27 supports the `show borders` environment value, allowing custom controls to adapt to that accessibility setting.
- Standard sidebars, toolbars, lists, labels, menus, and app icons receive multiple design refinements automatically or through existing APIs.
- After recompiling with Xcode 27, apps automatically use the new Liquid Glass design because support for opting into the old design is being removed.
- macOS Tahoe was reiterated as the final release supporting Intel Macs; developers can now ship Apple silicon-only binaries on the Mac App Store.

### SwiftUI container interactions

SwiftUI adds reorder support for containers such as grids and stacks, and extends swipe actions beyond lists using a scrollable container modifier.

```text
ForEach(items) { item in
    ItemView(item)
}
.reorderable()

ScrollView {
    /* rows with .swipeActions { ... } */
}
.swipeActionsContainer()
```

### Adaptive toolbar priorities

The session describes new toolbar controls such as visibility priority, overflow menu grouping, and a pinned trailing placement for adaptive layouts.

```text
ToolbarItem { ShareButton() }
    .visibilityPriority(.high)

ToolbarItem(placement: .topBarPinnedTrailing) {
    ShareButton()
}
```

## Xcode 27 and agentic coding

Xcode 27 emphasizes both the daily developer experience and integrated intelligence. It is smaller, Apple silicon-only, faster at loading projects, more reliable during debug sessions, and has a console designed to handle heavier logging. Settings can sync through iCloud, new projects can be created with minimal setup, and toolbars and themes are more customizable.

Xcode Cloud setup is streamlined from inside Xcode, with no separate App Store Connect setup shown in the demo, and builds are described as up to twice as fast with support for Apple Vision Pro and Metal apps on Apple silicon. Previews can now generate variations for arbitrary properties, such as all cases of an enum, not just common environment variants.

Device Hub replaces Simulator and unifies simulators with physical devices. It supports simulator controls, system-setting changes, high-fidelity gestures, dynamic resizing for iOS app layout testing, and interaction with connected hardware from the same interface.

Agentic coding is integrated throughout planning, implementation, validation, localization, and crash fixing. Agents can understand projects, search documentation, render previews with variants, interact with the simulator, run tests, use playgrounds, localize strings in context, inspect Organizer crash data, reproduce issues, and validate fixes.

- Xcode supports Model Context Protocol tools and adds Agent Client Protocol support so compatible agents can be brought into Xcode.
- Built-in integrations include agents from Anthropic, OpenAI, and Google; ACP support and Gemini integration are shipping in an Xcode 26 update, with more in Xcode 27.
- Xcode 27 ships with Apple-authored skills, documentation, and MCP tools for areas such as SwiftUI, accessibility, universal sizing, testing, and performance.
- Plugins can contain skills, MCP tools, and ACP-based agent integrations; they can be installed from the command line or by pasting a Git URL into Xcode.

### Agent planning workflow

The demonstrated workflow asks an agent to plan first, review and refine that plan, then let Xcode and the agent implement, preview, run, and test the feature.

```text
/plan
Implement a choose-your-own-adventure story feature using the latest Foundation Models APIs. Include a diagram and ask clarifying questions before coding.
```

## Other developer tools and where to go next

The session also highlights broader developer tooling updates. Reality Composer Pro 3 has been rebuilt for production-ready 3D experiences with RealityKit, character animations, more realistic lighting, and live previews using Mac Virtual Display. Game Porting Toolkit receives a major update with AI skills for coding agents, and new Metal command-line tools give agents direct control during development and debugging.

Apple points developers to the rest of the WWDC26 program for deeper coverage of Apple Intelligence, Xcode 27, design, SwiftUI, and other platform technologies, plus Group Labs, online panels, Q&A sessions, Developer Forums, Meet with Apple events, and Developer Centers.

- Use Core AI for custom on-device models in apps.
- Use MLX for experimenting with, training, researching, or fine-tuning generative models, including local inference; it now supports Metal 4, GPU Neural Accelerators, and multi-Mac training with RDMA over Thunderbolt.
- Use Foundation Models when an app needs Apple's on-device model, Private Cloud Compute, or server-model integration behind a native Swift API.
- Use App Intents schemas and View Annotations when the goal is Siri AI, Spotlight semantic search, and natural-language actions over app content.

### Keynote (ASL)

- Session ID: wwdc2026-111
- Page: https://wwdc.ai/2026/111
- Markdown: https://wwdc.ai/2026/111.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/111/
- Category: Essentials
- Description: Apple's WWDC 2026 keynote introduces macOS Golden Gate, iOS 27 platform refinements, expanded child safety tools, Siri AI, and new Apple Intelligence developer APIs.
- Duration: 76:14
- Apple HLS stream: https://events-delivery.apple.com/0601eknundukyswuegvjcidvurtztakr/vod_asl_xywfnqqbxvfzvwqytojhyuvrfwjxgkqr/vod_asl_xywfnqqbxvfzvwqytojhyuvrfwjxgkqr.m3u8

Apple's WWDC 2026 keynote introduces macOS Golden Gate, iOS 27 platform refinements, expanded child safety tools, Siri AI, and new Apple Intelligence developer APIs.

TLDR:
- Apple focused this release cycle on responsiveness, reliability, Liquid Glass refinements, improved search infrastructure, and broad OS support including iOS 27 on iPhone 11 and later supported iOS 26 models.
- Child safety updates center on Child Accounts, setup guidance, Ask to Browse, expanded Communication Safety, Time Allowances, Schedules, and a redesigned Screen Time experience.
- The next generation of Apple Intelligence uses new Apple Foundation models, Private Cloud Compute, a system orchestrator, personal context, App Actions, on-screen awareness, and broad world knowledge to power Siri AI and app features.
- Developers can integrate with Apple Intelligence through App Intents, Spotlight indexing, Foundation Models framework updates, the new Core AI framework, Image Playground API, and enhanced Xcode agentic tooling.

## Platform refinements across Apple OS releases

Apple framed the 2026 software releases around making existing platform experiences more responsive, reliable, readable, and easier to use rather than only adding new features. The next macOS release is named macOS Golden Gate.

Liquid Glass receives foundation-level tuning for better readability, depth, and separation over complex backgrounds. Users get a new setting to adjust Liquid Glass from ultra clear to fully tinted, and apps that already adopted Liquid Glass inherit those customizations automatically.

- macOS adds a more uniform toolbar treatment, edge-to-edge sidebars, restored sidebar icon color, and a consistent tighter window corner radius.
- iPhone and iPad app launches are described as up to 30% faster, including third-party apps, through preloading of key app data.
- Photos library updates, AirDrop transfers, and iPad external-drive file browsing receive performance improvements.
- The newer CPU scheduler is optimized further and brought to older models back to iPhone 11; iOS 27 supports the same iPhone models as iOS 26.
- Search infrastructure for Spotlight, Photos, and Mail is rebuilt with a more stable, efficient, comprehensive index and improved Mail Top Hits ranking.

## Everyday feature updates

The keynote also highlighted targeted improvements across built-in experiences, many of which affect how users interact with apps and content across platforms.

- iPhone network transitions between Wi-Fi and cellular are intended to be more seamless, reducing cases where users manually disable Wi-Fi.
- Messages shows per-message send indicators when low bandwidth slows large media delivery.
- Photos Shared Albums can include Android and Windows contributors and support full-resolution sharing.
- Health adds Cycle Tracking support for perimenopause and menopause, including notifications, symptom logging, and educational information.
- AirPods gain custom EQ; Apple Vision Pro can convert panoramas into spatial scenes and environments; Maps Flyover uses aerial imagery and vision intelligence models for sharper city rendering.

## Child safety and parental controls

Apple expanded child safety features around the idea that parents should choose what is appropriate for each child, with recommendations informed by clinical, child development, and online safety experts. The first recommended step is creating or converting to a Child Account, which enables age-tailored safeguards across the system.

The keynote emphasized that developers also have responsibility for age-appropriate in-app experiences and pointed to APIs and resources for safer app design.

- A new setup flow lets parents start children with only essential, recommended, or specifically chosen apps, then expand access over time.
- Ask to Buy remains the app approval path, while Ask to Browse extends approval requests to new websites in Safari across iPhone, iPad, and Mac.
- Communication Safety expands beyond nudity detection to intervene before children view gore or violent content in shared images or videos.
- Time Allowances provide recommended daily limits for Entertainment, Games, and Social Media, and Schedules let parents choose which apps are available at different times such as school hours.
- Developers can use child-safety APIs for nudity and violent content protection, contact approval flows, and the Declared Age Range API to tailor experiences in a privacy-preserving way.

## Apple Intelligence architecture and Siri AI

The next generation of Apple Intelligence is built around new Apple Foundation models created through a collaboration with Google using technologies behind Gemini. Apple adapted these models for on-device execution and Private Cloud Compute, including a more capable on-device model for supported Apple silicon systems.

A new system orchestrator coordinates personal context understanding, Spotlight's semantic index, broad world knowledge, App Actions, and on-screen awareness. Apple reiterated that requests use on-device processing or Private Cloud Compute, where data is not stored or accessible to Apple and is only used to execute the request.

- Siri AI is a rebuilt Siri powered by Apple Intelligence, available through familiar entry points such as "Hey Siri," the side button, typing, and platform-specific integrations.
- Siri AI supports richer conversations, personal context, app actions, screen awareness, image understanding, broad world knowledge, and a dedicated Siri app for revisiting synced conversational history.
- On supported devices, Siri gets more expressive voices and improved system-wide dictation accuracy.
- Siri AI extends to CarPlay, AirPods, iPadOS, macOS through Spotlight and context menus, watchOS, and visionOS with a spatial Siri presence.
- Siri AI starts in English, expands to more languages later, launches for customers in beta later in 2026, and is not initially available in the EU on iOS and iPadOS; Siri AI and other new Apple Intelligence features are not available in China while Apple works through regulatory requirements.

## Apple Intelligence in system apps

Apple Intelligence powers new features in Safari, Passwords, communication apps, Home, Shortcuts, Image Playground, and Photos. The session repeatedly ties these features to privacy-preserving processing, including on-device execution and Private Cloud Compute for more powerful model-backed tasks.

- Safari can group tabs into topics, keep related tabs organized as browsing continues, monitor pages with Notify Me, and create custom page-adapting extensions from natural language with Describe an extension.
- Passwords can automatically update eligible weak or compromised accounts to strong passwords by using Apple Intelligence and Safari to navigate supported websites on the user's behalf.
- Messages, Mail, Calendar, and Phone gain contextual suggestions, natural-language event creation and editing, and Call Context for surfacing relevant information such as confirmation codes when calling businesses.
- Home can summarize related accessory notifications, generate camera clip descriptions, connect relevant footage across cameras, search camera recordings by what was captured, and support 4K recorded clips on compatible cameras.
- Shortcuts can assemble automations from natural-language descriptions, then revise them through additional natural-language changes.
- Image Playground gains photorealistic and flexible style generation, natural-language image edits, touch-based object selection, more output dimensions, system integrations, and developer access through the Image Playground API. Photos gains upgraded Clean Up, Extend, and Spatial Reframing for perspective and composition adjustments.

## Developer technologies and tools

For developers, the keynote positioned Apple Intelligence as something apps can participate in through existing platform mechanisms and new model APIs. Native app experiences are expected to be enhanced by intelligence rather than replaced by generic AI surfaces.

Apple also previewed Xcode and testing workflow improvements, with more detail deferred to the Platforms State of the Union and technical sessions.

- Apps can expose capabilities to Siri through App Intents so users can ask for actions directly.
- Apps can index content into Spotlight so Apple Intelligence and Siri can help users find relevant app information, as shown with a messaging-app example.
- The Foundation Models framework now supports images as input in addition to text, custom skills to extend model capabilities, and server models through the same Swift API.
- The new Core AI framework lets apps run other local models with the power of Apple silicon across Apple platforms.
- Xcode's coding assistant can localize an app, interact with simulated devices, use custom skills, choose models and agents including Gemini, and connect to tools such as Figma and GitHub.
- The new Device Hub unifies real and simulated devices, supports simulated multi-touch gestures, one-click appearance changes, and dynamic resizing while iterating.

Chapters:
- 0:00 Introduction: Apple opens WWDC by highlighting the developer community, App Store activity, Apple Developer Academies, and the role of platform integration. The keynote sets up three themes: platform improvements, trust and safety, and Apple Intelligence with a new Siri.
- 5:04 Platform improvements: Apple announces macOS Golden Gate and describes refinements across the platforms, including Liquid Glass readability and personalization, macOS visual structure changes, app launch and transfer performance improvements, CPU scheduler work, better network transitions, rebuilt search infrastructure, and selected app feature updates.
- 16:56 Trust and safety: Apple expands child safety around Child Accounts, parent-guided setup, web and app approval flows, Communication Safety for violent content, Time Allowances, Schedules, and a redesigned Screen Time interface. The chapter also calls out developer responsibility and APIs such as the Declared Age Range API for privacy-preserving age-appropriate experiences.
- 27:53 Apple Intelligence and Siri: Apple presents a new Apple Intelligence architecture using updated Apple Foundation models, on-device processing, Private Cloud Compute, a system orchestrator, personal context, App Actions, and on-screen awareness. Siri AI, Visual Intelligence, app integrations, image and photo generation/editing features, developer APIs, Xcode assistant updates, and Device Hub are introduced with availability notes.

### Platforms State of the Union (ASL)

- Session ID: wwdc2026-112
- Page: https://wwdc.ai/2026/112
- Markdown: https://wwdc.ai/2026/112.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/112/
- Category: Essentials
- Description: Apple's 2026 platform overview covers Apple Intelligence APIs, Liquid Glass refinements, SwiftUI and Swift updates, and Xcode 27 agentic coding.
- Duration: 61:38
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-112/eng_bf4c8f76a82c/wwdc2026-112-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/112/1/0e1d49e8-277b-49f9-aaff-d937c5956d86/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/112/1/0e1d49e8-277b-49f9-aaff-d937c5956d86/downloads/wwdc2026-112_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/112/1/0e1d49e8-277b-49f9-aaff-d937c5956d86/downloads/wwdc2026-112_sd.mp4?dl=1

Apple's 2026 platform overview covers Apple Intelligence APIs, Liquid Glass refinements, SwiftUI and Swift updates, and Xcode 27 agentic coding.

TLDR:
- Foundation Models expands beyond on-device text to image input, Private Cloud Compute/server models, Dynamic Profiles, evaluation/debugging tools, and planned open source availability.
- App Intents gains deeper Siri integration through schemas, Spotlight semantic indexing, and View Annotations so users can find, reference, and act on app content with natural language.
- Platform updates include Liquid Glass refinements, resizable iOS apps in iPhone Mirroring and on iPad, SwiftUI interaction/performance improvements, Swift 6.4 workflow changes, and Apple silicon-only Mac distribution.
- Xcode 27 focuses on faster daily use and agentic coding: Device Hub, improved Previews, Xcode Cloud setup, iCloud-synced settings, plugins, MCP tools, ACP agents, localization, testing, and crash-fix workflows.

## Apple Intelligence in apps

Apple Intelligence is presented as a system-wide layer that developers can connect to in two complementary ways: building intelligence directly into apps, and exposing app content and actions to system intelligence. Apple Foundation Models power Apple Intelligence and are available through the Foundation Models framework.

The Foundation Models framework expands from on-device model access to multimodal prompts with text and images, Vision framework tools such as OCR and barcode reading, and server model support. Developers can call models such as Claude, Gemini, and other providers through Swift packages that conform to the Language Model protocol. Apple also announced access to Apple Foundation Models running in Private Cloud Compute with no cloud API cost for developers under the stated first-time App Store download threshold.

Dynamic Profiles are new declarative APIs for adaptive AI sessions. A session can switch profiles, models, tools, and instructions over time while sharing a continuous transcript, enabling agent-like workflows without forcing a single fixed model configuration.

- Use Foundation Models for native Swift access to on-device, Private Cloud Compute, and third-party/server language models.
- Use image input and Vision-integrated tools when prompts need multimodal context or precise extraction such as OCR/barcodes.
- Use Dynamic Profiles to change model choice, instructions, and tools based on app state within one continuous session.
- Use the Evaluations framework, upgraded Foundation Models instrument, and FM command line tool to test, visualize, debug, and prompt models.

### Dynamic Profiles are used to vary model behavior by app state

The session describes starting with a LanguageModelSession, selecting a Dynamic Profile, and switching between profiles such as brainstorming, tutorial generation, and glossary explanation. The exact API surface was demonstrated conceptually rather than as a complete copyable listing.

## Custom models, Core AI, and MLX

Core AI is a new on-device model framework with a memory-safe Swift API, tuning capabilities, ahead-of-time compilation, dedicated Instruments support, and a visual debugger that can trace tensor values back to original Python source. Python-based tools help convert and optimize PyTorch models for the Core AI runtime.

Apple positions Core AI as the right choice when an app brings its own model and needs efficient on-device inference across Apple devices. MLX remains the open source array framework for experimentation, research, fine-tuning, and local inference servers, with support for Metal 4, GPU Neural Accelerators, and multi-Mac training over RDMA via Thunderbolt.

- Choose Core AI for shipping custom on-device models inside apps with no server dependency or token cost.
- Choose Foundation Models when the app needs language-model features through Apple's native Swift API and can use on-device, Private Cloud Compute, or server language models.
- Choose MLX for research, training, fine-tuning, and local inference workflows rather than app-level platform integration.

## App Intents, Siri, and semantic app integration

App Intents is the bridge between apps and Apple Intelligence. Entity schemas describe app content and concepts; intent schemas describe actions the app can perform. Because schemas are system-defined, Siri can understand natural user requests without developers hard-coding exact phrases.

Entities can be contributed to the Spotlight semantic index so Apple Intelligence can reason over app content with attribution back to the app. View Annotations let developers associate visible views with entities, allowing users to reference on-screen content naturally, such as "this photo" or "the second message," and pass that entity into app intents.

The session's Origami app example used message, contact, and conversation entities, a send-message intent schema, Spotlight indexing, and view annotations so Siri could answer questions from app messages and send follow-up content through the app.

- Conform app content to relevant App Intents entity schemas so Siri can discover and reason about it.
- Index supported entities into Spotlight to provide private semantic search and personal context.
- Conform actions to intent schemas to expose capabilities through Siri, Shortcuts, widgets, the Action button, and system workflows.
- Annotate visible UI with entities so natural references to on-screen content can become actionable.

### Entity and intent macros used for App Intents integration

The session calls out @AppEntity for content entities, IndexedEntity for Spotlight indexing, and @AppIntent for actions such as sending a message.

```swift
@AppEntity
struct MessageEntity: IndexedEntity { ... }

@AppIntent
struct SendMessageIntent { ... }
```

## Design, adaptability, SwiftUI, and Swift

Liquid Glass receives rendering and consistency refinements: improved diffusion behind complex content, darker edges, brighter specular highlights, a user setting from ultra clear to fully tinted, accessibility adaptations, macOS support for the "show borders" environment value, edge-to-edge sidebars, standard toolbar scroll-edge behavior, and updated icon rendering. Apps already using Liquid Glass receive many improvements automatically.

iOS apps become resizable in iPhone Mirroring and on iPad after rebuilding with the latest SDK. Apple recommends using SwiftUI, Auto Layout, size classes, trait collections, and testing with the resizable iOS simulator and Previews rather than designing only for fixed devices or orientations.

SwiftUI gains reorderable containers, swipe actions in arbitrary containers, more flexible text selection, toolbar adaptation controls, prominent tabs, new document infrastructure, and the Spatial Preview framework for Mac apps streamed to Apple Vision Pro. SwiftUI also gets performance improvements in controls, nested stack layouts, State initialization, AsyncImage HTTP caching, and type-checking for content builders.

Swift 6.4 focuses on workflow improvements: suppressing warnings in specific regions, promoting warnings to errors where strictness is needed, simplifying platform availability with anyAppleOS, allowing await in defer, and improving diagnostics for expressions that previously failed with generic type-checking timeouts.

- Recompile with Xcode 27 to adopt the new design path; the old design opt-out is being removed.
- macOS Tahoe was the final Intel Mac release; Mac App Store apps can now ship Apple silicon-only binaries.
- Use resizable simulator, Previews, and iPhone Mirroring to validate layouts across dynamic sizes and aspect ratios.
- Use SwiftUI's new toolbar controls to keep important actions visible and move lower-priority actions into overflow as space shrinks.

### SwiftUI reorderable and swipeable custom containers

The session highlights .reorderable(), .reorderContainer(), .swipeActions(), and .swipeActionsContainer() as ways to add drag reordering and swipe actions outside standard lists.

```text
ForEach(projects) { project in
    ProjectCell(project)
        .swipeActions { ... }
        .reorderable()
}
.swipeActionsContainer()
.reorderContainer()
```

### Swift 6.4 platform availability shorthand

Swift 6.4 introduces anyAppleOS to avoid repeating the same availability version across Apple platforms.

```text
@available(anyAppleOS 27, *)
```

## Xcode 27 and agentic development

Xcode 27 improves the core IDE experience with faster project loading, fixes for crashes and hangs, more reliable debugging, faster expression evaluation, a console that handles heavier logging, a smaller Apple silicon-only install, iCloud-synced settings, quicker project creation for prototyping, customizable toolbars, and app-wide themes.

Xcode Cloud setup is simplified directly inside Xcode, without requiring separate App Store Connect setup in the demonstrated flow. Builds are described as up to twice as fast and add support for Apple Vision Pro and Metal apps on Apple silicon. Previews can now generate variations for any property, such as showing all enum states of a view at once.

Device Hub replaces Simulator and unifies simulator and physical-device workflows. It supports device settings, dark mode, Dynamic Type changes, screenshots, rotation, pinch/scroll interactions, dynamic simulator resizing, and interaction with physical devices from the Mac.

Agentic coding is deeply integrated across planning, implementation, validation, localization, and issue fixing. Xcode agents can understand a project, search documentation, render previews with variants, interact with the simulator, run tests, use playgrounds, localize strings with code/UI context, inspect Organizer crash data, reproduce issues, make fixes, and validate them. Xcode supports Model Context Protocol tools, built-in integrations for Anthropic, OpenAI, and Google agents, and Agent Client Protocol for compatible third-party agents.

- Use /plan-style workflows with agents to review implementation plans before code is changed.
- Use agent tools for build/test, simulator interaction, preview validation, localization, accessibility, resizability, and crash triage.
- Extend Xcode with plugins containing skills, MCP tools, and ACP-compatible agents.
- Connect external tools such as Figma and GitHub through the same plugin and MCP-oriented architecture when supported.

### Plugin installation can be driven from Xcode or command line

The session states that plugins can be installed by command line or by pasting a git URL into Xcode; no exact command syntax was provided.

## Additional tools and developer resources

Reality Composer Pro 3 has been rebuilt around production-ready 3D authoring with RealityKit, including character animations, more realistic lighting, and live previews via Mac Virtual Display. Game developers also get a major Game Porting Toolkit update that adds AI skills for coding agents, plus new Metal command line tools that give agents direct control during development and debugging.

Apple points developers to more than 100 WWDC sessions for deeper coverage of Apple Intelligence, Xcode 27, design, and the platform-specific updates, along with Group Labs, online panels, Q&A sessions, Developer Forums, Meet with Apple events, and Developer Centers.

- Use Reality Composer Pro 3 for production-oriented RealityKit 3D workflows.
- Use the updated Game Porting Toolkit and Metal command line tools for game porting and agent-assisted debugging on Apple platforms.
- Use Developer Forums, Group Labs, and Meet with Apple for follow-up technical questions beyond the keynote-level overview.

### Announcing Apple's next big step for Siri and iPhone

- Session ID: wwdc2026-121
- Page: https://wwdc.ai/2026/121
- Markdown: https://wwdc.ai/2026/121.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/121/
- Category: Essentials
- Description: Preview of iOS 27's Siri AI and Apple Intelligence features for iPhone, including conversational assistance, image creation, Safari organization, and privacy protections.
- Duration: 1:31
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-121/eng_5563778676d7/wwdc2026-121-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/121/1/f1e6baa3-3c16-4944-abec-3525818a2702/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/121/1/f1e6baa3-3c16-4944-abec-3525818a2702/downloads/wwdc2026-121_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/121/1/f1e6baa3-3c16-4944-abec-3525818a2702/downloads/wwdc2026-121_sd.mp4?dl=1

Preview of iOS 27's Siri AI and Apple Intelligence features for iPhone, including conversational assistance, image creation, Safari organization, and privacy protections.

TLDR:
- Introduces Siri AI in iOS 27 as a more conversational personal assistant with a dedicated app for asking questions and revisiting past conversations.
- Highlights Apple Intelligence features for photos, including extending, reframing, cleaning up images, and creating photorealistic imagery in Image Playground.
- Mentions Safari organization and notification improvements through topics and Notify Me.
- Emphasizes privacy and security protections, including one-tap automatic updates for compromised passwords and keeping personal information private.

## Siri AI on iPhone

Siri AI is positioned as a more capable personal assistant for iPhone in iOS 27. The announcement emphasizes natural conversation, asking Siri AI broad questions, and returning to prior interactions through a new dedicated app.

Examples shown include Siri AI providing nutritional details about a meal and handling details for a soccer watch party, framing the feature around task assistance rather than developer-facing APIs.

- More conversational Siri AI experience on iPhone.
- Dedicated app for asking questions and revisiting conversations.
- Metadata also describes natural language help for editing and writing emails, texts, and documents.

## Apple Intelligence for photos and image creation

Apple Intelligence is described as adding next-generation image capabilities across the system. The session highlights extending, reframing, and cleaning up photos, along with creating photorealistic images in Image Playground.

Image Playground is presented as useful for personal system surfaces such as wallpapers, Contact Posters, and backgrounds.

- Photo transformations include extend, reframe, and clean up.
- Image Playground gains photorealistic image generation.
- Generated imagery can be used in places like Contact Posters and wallpapers.

## Safari organization and timely updates

Safari is mentioned as becoming more organized with topics and more timely with Notify Me. The transcript does not describe implementation details or developer APIs for these features.

- Safari topics are positioned as an organization feature.
- Notify Me is positioned as a timely notification feature.

## Privacy and password protection

The announcement emphasizes that Apple Intelligence protects privacy and keeps personal information private. It also calls out a security workflow for compromised passwords, where passwords can be automatically updated with a tap.

- Apple Intelligence is framed as protecting private information.
- Compromised passwords can be updated automatically with one tap.
- No specific developer integration points are described.

### WWDC26 Platforms State of the Union Recap

- Session ID: wwdc2026-122
- Page: https://wwdc.ai/2026/122
- Markdown: https://wwdc.ai/2026/122.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/122/
- Category: Essentials
- Description: Five-minute WWDC26 recap covering Apple Intelligence updates, Liquid Glass design, SwiftUI improvements, Spatial Preview, Xcode 27, Device Hub, and agentic coding.
- Duration: 4:32
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-122/eng_0b80b02cbc5e/wwdc2026-122-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/122/3/acc0a465-b6fa-446b-8f3f-dc122d862f47/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/122/3/acc0a465-b6fa-446b-8f3f-dc122d862f47/downloads/wwdc2026-122_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/122/3/acc0a465-b6fa-446b-8f3f-dc122d862f47/downloads/wwdc2026-122_sd.mp4?dl=1

Five-minute WWDC26 recap covering Apple Intelligence updates, Liquid Glass design, SwiftUI improvements, Spatial Preview, Xcode 27, Device Hub, and agentic coding.

TLDR:
- Apple Intelligence is rebuilt with new Apple Foundation Models, expanded Foundation Models framework capabilities, Core AI for on-device models, App Intents, and View Annotations.
- Platform updates include Liquid Glass design refinements, sharper app icon rendering, resizable iOS apps on larger displays, and better Simulator and preview resizing workflows.
- SwiftUI gains broader drag-to-reorder and swipe actions, faster nested layouts, automatic async image caching, improved toolbar control, and Spatial Preview for Apple Vision Pro workflows.
- Xcode 27 focuses on daily productivity and agentic coding: faster and smaller Apple Silicon-only Xcode, iCloud settings sync, Device Hub, faster Xcode Cloud, and extensible coding agents.

## Intelligence across apps and the system

Apple Intelligence is described as rebuilt from the ground up, with Apple working with Google and technologies behind the Gemini family of models to create the latest Apple Foundation Models for integrated Apple Intelligence experiences.

The Foundation Models framework expands beyond prior capabilities with image input and support for cloud models. For complex tasks that need more advanced frontier models, apps can integrate with a cloud model provider selected by the developer.

Dynamic profiles are positioned as a way to build AI agents and skills with less code by changing tools and instructions at runtime.

- Core AI is a new framework for running on-device models in apps, built into the OS and optimized for Apple Silicon.
- App Intents connect app actions and user content to Apple Intelligence and Siri natural-language interactions.
- The new View Annotations API lets users act on visible on-screen content by asking.

## Design and platform refinements

The recap highlights a new design language with Liquid Glass, emphasizing consistency, personalization, and readability across the platforms.

On macOS, windows share a tighter corner radius. App icons receive sharper rendering automatically, and Icon Composer can add new refraction effects.

- iOS apps are now resizable, helping users make better use of larger displays when running iOS apps on iPad or on Mac using iPhone Mirroring.
- Resizable iOS Simulator and previews make size-class and layout testing easier across multiple dimensions.
- Icon Composer and app icon rendering updates are part of the design workflow changes.

## SwiftUI and spatial development updates

SwiftUI receives performance and interaction improvements aimed at making common app layouts and controls more flexible.

- Drag to reorder and swipe actions now work in any container.
- Nested layouts resize up to twice as fast.
- Async image caching happens automatically.
- Toolbars provide finer control over which items remain visible as available space shrinks.
- For Apple Vision Pro, the Spatial Preview framework streams 3D models from a Mac into the surrounding space.

## Xcode 27 daily development improvements

Xcode 27 is presented around two themes: the daily development experience and agentic coding. It is Apple Silicon-only and 30% smaller, with faster project loading.

Developer environment improvements include iCloud settings sync, a fully customizable toolbar, and app-wide color themes such as Emerald, Neon Noir, and Coral Reef.

- Xcode Cloud setup is easier, builds can be up to twice as fast, and support is added for Apple Vision Pro and apps using Metal on Apple Silicon.
- Device Hub replaces Simulator and brings virtual and physical devices into one place.
- Device Hub supports workflows such as pinch to zoom, live resizing, and controlling real hardware from the Mac.

## Agentic coding in Xcode

Xcode expands AI coding assistance by integrating agents from leading model providers, including Anthropic, OpenAI, and Google. Agent conversations behave like files that can be opened, split, stacked, and managed in the Navigator.

Agents can plan before writing code so developers can review the approach first. They can also run tests, use playgrounds, customize previews, and drive a running app by tapping, scrolling, swiping, and typing for end-to-end validation.

- Xcode 27 includes built-in specialists based on Apple engineering and design expertise for SwiftUI, accessibility, sizing, testing, and performance.
- Plugins can add skills, MCP tools, and agents through the agent-client protocol.
- Figma and GitHub provide plugins with one-click setup.

### Secure your apps with App Attest

- Session ID: wwdc2026-201
- Page: https://wwdc.ai/2026/201
- Markdown: https://wwdc.ai/2026/201.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/201/
- Category: Privacy & Security
- Description: Use DeviceCheck App Attest to verify genuine Apple hardware, detect modified app builds, secure server payloads, and feed fraud signals into risk assessment.
- Duration: 20:02
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-201/eng_1c230dc02157/wwdc2026-201-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/201/4/d3eb2e5b-5104-4aee-a754-9985008a5b06/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/201/4/d3eb2e5b-5104-4aee-a754-9985008a5b06/downloads/wwdc2026-201_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/201/4/d3eb2e5b-5104-4aee-a754-9985008a5b06/downloads/wwdc2026-201_sd.mp4?dl=1

Use DeviceCheck App Attest to verify genuine Apple hardware, detect modified app builds, secure server payloads, and feed fraud signals into risk assessment.

TLDR:
- App Attest protects server workflows from modified clients by proving an app key was generated for your app on genuine Apple hardware and by surfacing app identity signals.
- Adopt the flow server-first: generate a Secure Enclave-bound key, attest it with a server challenge, validate and store the attestation on your server, then use assertions for protected requests.
- Newer platform signals include macOS 27 support, key access control validation for macOS security state, and iOS 27 authenticator-data extensions for launch validation category and bundle version.
- Treat unsupported responses, new keys, assertion counter anomalies, and the receipt-based fraud metric as risk signals; avoid blocking users without broader assessment.

## Threat model and protections

App Attest is aimed at server-side fraud caused by modified copies of your app that still send valid-looking requests. Examples include a quiz app submitting falsified answers or a game client injected with cheat UI and then re-signed.

The core value is not local self-defense; it is giving your server cryptographic evidence about the app, device, and request so the server can reject or scrutinize suspicious traffic.

- App identity is represented by the relying party identifier: your Apple Developer Team Identifier plus the app bundle identifier.
- Attestations can prove the key was generated on genuine Apple hardware and expose app-related properties useful for detecting tampering.
- Assertions use an already-attested key to protect subsequent payloads sent from the app to your server.
- New iOS 27 signals include launch validation category and bundle version in authenticator-data extensions.

## Availability and key generation

App Attest is supported across major Apple platforms, including macOS 27 and later, but not in every app or extension context. Gate use with the App Attest support check and consider unexpected unsupported results as one input to fraud detection.

Key generation creates a Secure Enclave-bound key pair for the app. The private key remains in the Secure Enclave, and App Attest returns a key ID derived from the public key for the app to store.

- Use one key per user for account-based apps, or one key for the app on a device; do not share keys across users.
- Store App Attest key IDs in Keychain.
- Keys survive app updates but are invalidated by app reinstall or device restore, including iCloud restore.
- Keys are per-device and do not sync across a user's devices.

### Generate a Secure Enclave-bound key

Creates an App Attest key pair and returns the key ID that should be persisted in Keychain.

```swift
import DeviceCheck

let keyID = try await DCAppAttestService.shared.generateKey()
```

## Attestation flow and server validation

Attestation should be initiated and controlled by your server. The server vends a challenge, the app calls the attestation API with the key ID and challenge hash, and the app sends the returned attestation object back to the server for validation and storage.

Your server, not the app, must validate attestations. A compromised client cannot be trusted to validate its own integrity. Perform attestation outside critical user flows when possible, retry later on failures, and use exponential backoff instead of hard-coded retry loops.

- Validate the Apple anonymized attestation format, certificate chain, nonce, key ID, and relying party identifier.
- Validate the attestation receipt, including relying party ID, attested key, and server challenge, then store the receipt for fraud metric requests.
- On macOS 27 and later, validate the key access control property, also described as the ACL Blob OID, to confirm Secure Enclave-enforced security conditions such as full security mode and System Integrity Protection.
- Unpack authenticator data according to the documentation and W3C authenticator-data model; on iOS 27 and later, inspect extensions for launch validation category and bundle version.

### Request an attestation

The client passes the key ID and a hash of server-controlled challenge data, then sends the returned attestation to the server.

```swift
import DeviceCheck

let keyId: String = ...
let clientDataHash: Data = ...
let attestation = try await DCAppAttestService.shared.attestKey(
    keyId: keyId,
    clientDataHash: clientDataHash
)
```

## Assertions for protected requests

After the server validates an attestation and stores the public key, the app can generate assertions for ongoing protected communication. Assertions are generated locally and do not require a round trip to Apple servers.

Use assertions on demand for sensitive payloads, such as authentication-related requests or premium-content access. Generating assertions performs cryptographic work, so avoid excessive generation in tight loops or high-frequency lifecycle paths.

- Embed the assertion object in the payload sent to your server.
- Validate the assertion signature with the authenticator data, server challenge, and public key from the attestation.
- Track the assertion counter per user or key on the server and require it to be strictly increasing.
- Treat steady or decreasing counters as possible replay or compromise signals.
- Handle iOS 27 authenticator-data extensions in assertions the same way as in attestations.

### Generate an assertion

Signs challenge-bound client data with the previously attested key so the server can verify the payload.

```swift
import DeviceCheck

let keyId: String = ...
let clientDataHash: Data = ...
let assertion = try await DCAppAttestService.shared.generateAssertion(
    keyId: keyId,
    clientDataHash: clientDataHash
)
```

## Pitfalls, fraud metric, and rollout guidance

Do not reject every new key for an existing user. App reinstall and device restore can legitimately invalidate a key and require key rotation, so keep prior attestations long enough to evaluate risk rather than immediately discarding them.

The fraud metric helps detect a compromised device acting as an attestation broker. It is an approximate 30-day count of unique attested keys associated with your app on a particular device, retrieved by your server from the App Attest data server using a stored attestation receipt.

- If an attestation or assertion is rejected, degrade App Attest-protected functionality gracefully and consider limited access with heightened monitoring.
- Use unsupported App Attest responses, key rotation patterns, suspicious extension values, assertion counter anomalies, and the fraud metric as inputs to a broader risk profile.
- The fraud metric response receipt contains a signature, certificate chain, receipt payload, risk metric field, not-before refresh time, and expiration time.
- Rebuild with the latest SDKs, identify flows that benefit from attestations and assertions, set up server validation and counter tracking, and integrate the fraud metric into your risk pipeline.

Resources:
- W3C Authenticator Data: https://www.w3.org/TR/webauthn-3/#sctn-authenticator-data
- About System Integrity Protection on your Mac: https://support.apple.com/en-us/102149
- DeviceCheck: https://developer.apple.com/documentation/DeviceCheck

Chapters:
- 0:00 Introduction: Introduces App Attest as a defense against modified app clients that send valid-looking but fraudulent requests, such as falsified quiz submissions or cheated game scores.
- 1:35 Protections: Explains how App Attest proves execution on genuine Apple hardware, surfaces app modification signals such as relying party identity, launch validation category, and bundle version, and uses assertions to protect payloads.
- 4:04 Availability: Covers platform support, including macOS 27 and later, notes that not every app extension type is supported, and recommends gating usage with isSupported while treating unexpected unsupported responses as a possible fraud signal.
- 5:02 Key generation: Shows generating a Secure Enclave-bound App Attest key ID and storing it in Keychain. It also covers key lifetime, per-device behavior, and recommended key cardinality for account-based and device-based apps.
- 6:12 Attestation: Walks through the server-challenge attestation flow and the server-side validation requirements for the attestation format, certificate chain, receipt, authenticator data, macOS key access control property, and iOS 27 extensions.
- 12:10 Assertion: Describes using an attested key to generate assertions for protected server payloads. The server validates the signature and strictly increasing assertion counter to help prevent replay and detect suspicious clients.
- 14:58 Common pitfalls: Warns against rejecting new keys outright for existing users because reinstall and restore can legitimately rotate keys. Recommends graceful degradation and broader risk assessment before blocking users.
- 16:27 Fraud metric: Explains the receipt-based fraud metric as an approximate 30-day count of unique attested keys for an app on a device, useful for identifying possible broker devices when incorporated into a risk pipeline.
- 19:07 Next steps: Recommends rebuilding with the latest SDKs, choosing sensitive flows for attestations and assertions, implementing server validation and counter tracking, storing receipts, and incorporating the fraud metric into risk assessment.

### Read between the strokes with PencilKit

- Session ID: wwdc2026-203
- Page: https://wwdc.ai/2026/203
- Markdown: https://wwdc.ai/2026/203.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/203/
- Category: App Services
- Description: Use PencilKit in iOS 27 and related platforms to recognize handwriting, index and search drawings, convert paths, track strokes, and slice strokes.
- Duration: 15:10
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-203/eng_0717c2908401/wwdc2026-203-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/203/4/eb979cd5-af5b-4091-87ec-4839e8d131b9/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/203/4/eb979cd5-af5b-4091-87ec-4839e8d131b9/downloads/wwdc2026-203_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/203/4/eb979cd5-af5b-4091-87ec-4839e8d131b9/downloads/wwdc2026-203_sd.mp4?dl=1

Use PencilKit in iOS 27 and related platforms to recognize handwriting, index and search drawings, convert paths, track strokes, and slice strokes.

TLDR:
- PKStrokeRecognizer brings on-device handwriting recognition to PencilKit on iOS, iPadOS, macOS, and visionOS 27, with recognized text, indexable content, and search results.
- Recognition can use device languages or explicit preferredLanguages, supports subsets of strokes by stroke ID, and exposes supportedLanguages and recognizerVersion for language handling and re-indexing.
- PKStrokePath can convert to and from Bézier paths, making handwriting recognition usable with custom canvases that do not use PKCanvasView.
- New model APIs add stable stroke identity, selection change access, controllable wet-ink render groups, programmatic erasing, and parametric substroke extraction.

## Handwriting recognition with PKStrokeRecognizer

PKStrokeRecognizer is a Swift actor, so recognition work is asynchronous and thread-safe by design. It can recognize an entire PKDrawing or a subset of strokes identified by stroke IDs.

By default, the recognizer uses the device's languages to interpret handwriting. Apps can set preferredLanguages when the app has stronger context, such as a flashcard or language-learning workflow. The supportedLanguages property exposes the available recognition languages; the session notes 29 supported languages as of iOS 27. In Simulator, handwriting recognition is limited to languages that use Latin characters.

Recognition runs entirely on device using an offline model included with the operating system, and is available on iOS, iPadOS, macOS, and visionOS 27.

- Use recognizedText() when the app needs the single most likely transcription.
- Use updateDrawing(_:) before requesting recognition results.
- Use stroke IDs to scope recognition to part of a drawing when needed.
- Consider preferredLanguages for multilingual or language-specific app contexts.

### Recognize the most likely text

Updates the recognizer with a drawing and displays the best recognized text result.

```swift
import PencilKit

let recognizer = PKStrokeRecognizer()
await recognizer.updateDrawing(drawing)
myLabel.text = await recognizer.recognizedText()
```

## Indexing, search, and accessibility

Indexable content returns a single string representing the contents of the full drawing. Unlike recognized text, it can include multiple candidate interpretations, which is useful when handwriting is ambiguous or when multiple languages are active.

For persisted indexes, store recognizerVersion alongside indexedContent. When loading previously indexed content, compare the stored version with the current recognizer version and re-index when the recognition model has changed. Avoid calling recognition on every stroke unless the feature needs that immediacy; throttling can reduce power use.

The search API takes a target string and returns results with bounds for likely matches in the drawing. Those bounds can drive highlights, navigation, and assistive features. The session specifically calls out pairing search() with UIFindInteraction and UIFindInteractionDelegate to provide the system find UI over a drawing canvas.

- Use indexableContent for Spotlight-style indexing.
- Use search(_:) for interactive handwritten text search and match highlighting.
- Connect recognition results to VoiceOver so handwritten content can be spoken aloud.
- Use search result locations to help assistive features navigate to words in a drawing.

### Create indexable handwriting content

Gets a searchable text representation of the full drawing for app indexing.

```swift
import PencilKit

let recognizer = PKStrokeRecognizer()
await recognizer.updateDrawing(drawing)
if let indexedContent = await recognizer.indexableContent {
    index(text: indexedContent)
}
```

### Find and highlight handwritten text

Searches handwritten content and uses result bounds to highlight matches.

```swift
import PencilKit

let recognizer = PKStrokeRecognizer()
await recognizer.updateDrawing(drawing)
let results = await recognizer.search("apple")
for result in results {
    highlight(bounds: result.bounds)
}
```

## Path conversion for custom canvases

PencilKit stores stroke paths as cubic uniform B-splines, while many custom drawing systems store strokes as Bézier paths. In iOS 27, PKStrokePath supports conversion between PencilKit stroke paths and Bézier paths.

PencilKit handles the geometry during conversion, but Bézier paths do not carry PencilKit-specific values such as size, opacity, or force. Apps converting from Bézier data must provide those properties for the resulting control points.

Converting from PKStrokePath to a Bézier path and back preserves control point locations, enabling apps to store PencilKit strokes in a Bézier-based format and reconstruct them without losing path fidelity. Apps with their own canvas can convert existing Bézier strokes into PKStrokePaths, build a PKDrawing, and pass it to PKStrokeRecognizer without adopting PKCanvasView.

- Use path conversion to bring handwriting recognition to non-PKCanvasView canvases.
- Preserve or synthesize PencilKit stroke properties when importing Bézier paths.
- Refer to the WWDC20 PencilKit drawing model session for background on PKStrokePath storage.

## Deeper drawing model access

iOS 27 adds model-level access that supports more customized drawing experiences. PKStroke and PKStrokePath conform to Identifiable, with a stable UUID that survives transforms, edits, and undo operations.

With stable stroke identity, apps can track individual strokes over time and control selection state on PKCanvasView. A new canvasViewSelectionDidChange delegate callback lets apps respond when the user changes the canvas selection.

PencilKit also exposes control over wet-ink render grouping. When certain inks are drawn together quickly, PencilKit can composite strokes as if the ink is still wet by using the same renderGroupID; in iOS 27 that grouping is controllable.

- Track strokes across edits using stable Identifiable IDs.
- Control PKCanvasView selection state programmatically.
- Observe user selection changes with canvasViewSelectionDidChange.
- Adjust renderGroupID behavior for wet-ink compositing use cases.

## Stroke slicing, erasing, and substrokes

The new slicing APIs cover two related workflows: programmatic erasing and substroke extraction. PencilKit already represents partial erasure with stroke masks; iOS 27 lets apps apply that operation programmatically by providing a PKStrokePath as the eraser.

Programmatic erasing cuts through the drawing and can split a single stroke into multiple independent strokes with their own masks, matching what would happen if the user erased on the canvas. This can be expensive for complex drawings, so apps with many strokes should consider doing erasing work off the main UI path.

Substroke extraction lets apps obtain a section of a PKStroke or PKStrokePath using parametric ranges along the path. PencilKit maintains consistency for ink features such as pencil texture particles that are positioned relative to the full stroke. The session demonstrates using substrokes to replay handwriting order for Chinese character practice, with PencilKit rendering implemented in Metal for smooth animation.

- Use programmatic erasing to cut strokes using an eraser path.
- Use parametric range subscripting on PKStroke or PKStrokePath to extract precise substrokes.
- Consider performance carefully when slicing drawings with many strokes.
- Use substrokes for animations, editing tools, and stroke-order visualizations.

Resources:
- Controlling stroke rendering for animation and editing: https://developer.apple.com/documentation/PencilKit/controlling-stroke-rendering-for-animation-and-editing
- Recognizing handwriting and converting it to text: https://developer.apple.com/documentation/PencilKit/recognizing-handwriting-and-converting-to-text
- Building a handwriting recognition experience with PencilKit: https://developer.apple.com/documentation/PencilKit/building-a-handwriting-recognition-experience-with-pencilkit
- PencilKit: https://developer.apple.com/documentation/PencilKit

Chapters:
- 0:00 Introduction: Introduces new PencilKit APIs in iOS 27 for handwriting recognition and deeper drawing model access, available across iOS, iPadOS, macOS, and visionOS 27. The session frames the APIs with a handwriting practice app that checks Chinese and English writing.
- 3:25 Handwriting recognition: Explains PKStrokeRecognizer as an asynchronous Swift actor with recognized text, indexable content, and search capabilities. Covers language configuration, on-device offline recognition, model versioning for indexing, throttling, UIFindInteraction integration, and accessibility uses.
- 8:38 Path conversion: Describes conversion between PKStrokePath's cubic uniform B-spline representation and Bézier paths. This lets apps with custom Bézier-based canvases create PKDrawings and use PencilKit handwriting recognition without relying on PKCanvasView.
- 10:21 Improved model access: Covers stable Identifiable IDs for PKStroke and PKStrokePath, programmatic access to PKCanvasView selection, a selection change delegate callback, and controllable wet-ink render groups through renderGroupID.
- 11:25 Stroke slicing: Introduces programmatic erasing using a PKStrokePath eraser and substroke extraction with parametric ranges on PKStroke and PKStrokePath. Notes performance considerations for complex drawings and shows substrokes used to replay handwriting stroke order.
- 13:48 Next steps: Summarizes how to adopt the new APIs: use PKStrokeRecognizer, convert existing Bézier paths, track strokes with stable identity, respond to selection changes, and use slicing for erasing and animation.

### What's new in WebKit for Safari 27

- Session ID: wwdc2026-204
- Page: https://wwdc.ai/2026/204
- Markdown: https://wwdc.ai/2026/204.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/204/
- Category: Safari & Web
- Description: Safari 27 adds Customizable Select, HTML Model across Apple platforms, immersive visionOS environments, Web Extension packaging, and major WebKit quality fixes.
- Duration: 16:32
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-204/eng_58f8ca52b0b9/wwdc2026-204-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/204/5/0226f57f-eb7b-4c8d-91cb-0ac8f245d88b/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/204/5/0226f57f-eb7b-4c8d-91cb-0ac8f245d88b/downloads/wwdc2026-204_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/204/5/0226f57f-eb7b-4c8d-91cb-0ac8f245d88b/downloads/wwdc2026-204_sd.mp4?dl=1

Safari 27 adds Customizable Select, HTML Model across Apple platforms, immersive visionOS environments, Web Extension packaging, and major WebKit quality fixes.

TLDR:
- WebKit prioritized quality this year, with over 1,100 fixes and improvements across compatibility, layout foundations, SVG, standards alignment, and feature integration.
- Safari 27 adds Customizable Select so real HTML select controls can be heavily styled while preserving built-in form control behavior and accessibility.
- The HTML Model element expands beyond visionOS to iOS, iPadOS, and macOS, with visionOS 27 adding immersive website environments through an API modeled on Fullscreen.
- Safari Web Extension Packager lets developers package and distribute Safari Web Extensions through App Store Connect from any browser on any operating system.

## WebKit quality and interoperability focus

Safari 27 is framed around a large quality push rather than only new feature delivery. WebKit shipped over 1,100 fixes and improvements since the previous fall, targeting real-world site compatibility, old engine foundations, standards conformance, and interactions between features.

Examples include a compatibility workaround for sites that still use JavaScript String.fromCharCode with characters beyond 16 bits, a rewrite of block-in-inline layout code, more than 75 SVG improvements, updates to CSS random() scoping after CSS Working Group discussion, and support for min(), max(), and clamp() inside the HTML sizes attribute.

- Test projects in current Safari Technology Preview or Safari beta builds.
- Use Safari release notes for the full list of WebKit changes.
- File reproducible browser engine issues through bugs.webkit.org or Feedback Assistant.

## CSS Grid Lanes

CSS Grid Lanes shipped in Safari 26.4 and enables masonry-style layouts in pure CSS without JavaScript. It builds on CSS Grid track definitions and can lay content in either direction.

Safari Web Inspector helps debug these layouts. The session specifically calls out Order Numbers for visualizing item order and tuning flow tolerance for a better keyboard tabbing experience.

- Use Grid Lanes for masonry-like layouts and other variable-size item flows.
- Explore configurations and demos in the CSS Grid Lanes Field Guide at gridlanes.webkit.org.
- Watch the related "Learn CSS Grid Lanes" session for implementation details.

## Customizable Select

Safari 27 adds Customizable Select support, making it possible to create highly styled select controls while keeping the accessibility and robustness of a real HTML form control.

The starting point is applying appearance: base-select to the select element. Applying the same appearance to the ::picker pseudo-element enables styling of the popup menu. Additional pseudo-elements such as ::checkmark and ::picker-icon target parts of the control, and options can contain additional HTML such as subtext or images.

- Style the closed select control and the opened picker UI with CSS.
- Use Grid, Flexbox, and other CSS layout features inside option UI where appropriate.
- Prefer this over replacing select with custom JavaScript controls when native form behavior and accessibility matter.

### Enable Customizable Select styling

The session identifies appearance: base-select on select and ::picker as the entry point for styling the control and its popup menu.

```text
select {
  appearance: base-select;
}

select::picker {
  appearance: base-select;
}
```

## HTML Model and immersive environments

The HTML Model element, previously introduced for Safari in visionOS, comes to iOS, iPadOS, and macOS in Safari 27. It joins the family of HTML media elements for embedding 3D models directly in web pages.

Developers can keep markup simple or use source elements to provide multiple model formats. Attributes such as environmentmap and stagemode can control lighting and default interaction behavior. JavaScript can target the model element, and iOS and iPadOS users can use AR Quick Look to see a product or object in their own space.

In visionOS 27, model-based sites can launch immersive website environments. The new Immersive API is described as working like the Fullscreen API, letting a user enter an immersive model experience from Safari.

- Use HTML Model for product previews, object visualization, and other 3D web experiences.
- Use AR Quick Look on iOS and iPadOS when users should view a model in their own space.
- Use the related sessions for model optimization, JavaScript control, and visionOS immersive environment details.

## Web Extensions and MapKit JS

Safari Web Extensions continue moving toward a cross-browser model: one codebase, one set of scripts, and one manifest using interoperable HTML, CSS, and JavaScript. Safari 14 added Safari Web Extension support, and Apple later helped establish the W3C WebExtensions Community Group.

The new Safari Web Extension Packager removes the requirement to use Xcode or even a Mac for distribution. Developers can package and distribute Safari Web Extensions through App Store Connect from any web browser on any operating system.

MapKit JS is also highlighted as a way to embed privacy-preserving interactive maps in websites and web apps. It works across browsers and operating systems.

- Use Safari Web Extension Packager when distributing an existing cross-browser extension to Safari users.
- Submit Safari Web Extensions through App Store Connect.
- Use MapKit JS for interactive Apple Maps on the web with privacy-preserving behavior.

Resources:
- WebKit.org - CSS Grid Lanes Field Guide: https://gridlanes.webkit.org/
- Packaging and distributing Safari Web Extensions with App Store Connect: https://developer.apple.com/documentation/SafariServices/packaging-and-distributing-safari-web-extensions-with-app-store-connect
- WebKit.org - Report issues to the WebKit open-source project: https://bugs.webkit.org/
- Learn more about MapKitJS: https://developer.apple.com/maps/web/
- Safari Technology Preview: https://developer.apple.com/safari/technology-preview/
- Submit feedback: http://feedbackassistant.apple.com/

Chapters:
- 0:00 Introduction: Introduces WebKit updates for Safari, including already-shipped technologies such as CSS Grid Lanes, Navigation API, and Largest Contentful Paint, plus Safari 27 beta features such as Customizable Select and img sizes=auto.
- 1:07 A year of quality improvements: Explains WebKit's major quality push: over 1,100 fixes and improvements across compatibility, engine foundations, SVG, standards alignment, and cross-feature integration. Examples include emoji input compatibility, block-in-inline layout rewrites, SVG 2 alignment, CSS random() scoping changes, and min()/max()/clamp() support in sizes.
- 9:06 CSS Grid Lanes: Shows CSS Grid Lanes as a pure-CSS way to build masonry-style layouts, shipped in Safari 26.4. It points developers to the Field Guide and Safari Web Inspector tools for understanding item order and tuning layout behavior.
- 10:06 Customizable Select: Covers Safari 27 support for styling native select controls with appearance: base-select, the ::picker pseudo-element, and additional pseudo-elements such as ::checkmark and ::picker-icon. It also notes support for richer HTML inside options while retaining native accessibility and form-control behavior.
- 11:24 HTML Model element: Explains that the HTML Model element is coming to iOS, iPadOS, and macOS in Safari 27 after debuting in Safari for visionOS. Developers can embed 3D models, provide multiple sources, use attributes such as environmentmap and stagemode, target models with JavaScript, and integrate AR Quick Look.
- 12:51 Immersive Website Environments: Introduces visionOS 27 immersive website environments, where a model can open into a full immersive experience from Safari. The new Immersive API is described as similar to the Fullscreen API.
- 13:38 Web Extensions: Describes the move toward interoperable cross-browser Web Extensions using one codebase, scripts, and manifest. Safari Web Extension Packager now enables packaging and App Store Connect distribution from any browser on any operating system, without requiring a Mac or Xcode.
- 15:18 MapKit JS: Briefly highlights MapKit JS for embedding privacy-preserving interactive maps in websites and web apps. It works across browsers and operating systems.
- 15:40 Next steps: Directs developers to webkit.org for Safari release information, related WWDC sessions for deeper coverage, and bugs.webkit.org for issue reporting.

### Enhance your presence on the App Store

- Session ID: wwdc2026-205
- Page: https://wwdc.ai/2026/205
- Markdown: https://wwdc.ai/2026/205.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/205/
- Category: App Store, Distribution & Marketing
- Description: Use new App Store image and video placements, Asset Library, and preview workflows to improve product pages, search results, and Apple Ads creatives.
- Duration: 8:14
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-205/eng_af80ab85f696/wwdc2026-205-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/205/4/47ee16f9-fba0-48a3-9d60-065befef7a95/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/205/4/47ee16f9-fba0-48a3-9d60-065befef7a95/downloads/wwdc2026-205_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/205/4/47ee16f9-fba0-48a3-9d60-065befef7a95/downloads/wwdc2026-205_sd.mp4?dl=1

Use new App Store image and video placements, Asset Library, and preview workflows to improve product pages, search results, and Apple Ads creatives.

TLDR:
- App product pages can now include a Product Page Header that uses marketing images or videos beyond screenshots and app previews.
- Search results can show a custom image or video instead of default screenshots, helping communicate core value before users tap through.
- Asset Library in App Store Connect centralizes screenshots, previews, in-app event media, and new creative assets across platforms, sizes, and placements.
- Approved creative assets can be reused for Product Page Header and Search Results without additional review, and uploads can be automated with the App Store Connect API.

## New visual placements on the App Store

The session introduces new App Store placements for richer app marketing visuals. Product pages can now include a Product Page Header, which is the first visual element people see when landing on an app page. This header can use images or videos that express the app's brand or visual identity, rather than being limited to app screenshots and previews.

Search Results can also use a custom image or video instead of default screenshots. These assets should clearly communicate the app's core value and encourage people to tap through for more detail.

- Product Page Header supports images and videos outside normal screenshots and app previews.
- Search Results can show an impactful custom image or video instead of default screenshots.
- Product Page Header, app icon, and screenshots should work together to explain the app experience.
- These placements are available for product page header and search result visuals on iOS 27 and iPadOS 27.

## Use assets consistently across acquisition paths

The same marketing visuals can be reused across product pages, search results, Custom Product Pages, and Apple Ads campaigns to create a consistent acquisition experience. For example, an app promoting yoga classes on its website can link to a Custom Product Page that uses matching header visuals, then deep-link people into the yoga offering after installation.

The session recommends tailoring assets for different contexts, such as search keywords or campaign audiences, while keeping the user journey coherent from discovery through install.

- Use Apple Ads to set up creative assets for Today tab or Search Results campaigns.
- Use Custom Product Pages to align visuals with a specific website banner, keyword, audience, campaign, or offering.
- Use deep links after download when appropriate to continue the same experience promoted in the acquisition creative.
- Consider matching Product Page Header and Search Results assets for a seamless customer experience.

## Test and optimize visuals

Product Page Optimization in App Store Connect can be used to test different visual approaches and learn which assets perform best with an audience. The examples include testing whether users respond better to an app logo, a core value proposition, or a new feature.

The guidance applies broadly across app categories. Outdoor apps might use aspirational imagery, travel apps might promote destinations, and games might show gameplay or characters.

- Use Product Page Optimization to compare alternate header or search-result visuals.
- Choose assets that communicate the app's value quickly and clearly.
- Adapt the creative strategy to the category and user intent.

## Submit assets for review

There are two review paths for these assets. The familiar path is through the app version page: upload the assets with a version, preview the product page appearance, and submit the version for App Review. When the approved version is released, the Product Page Header and Search Results assets go live.

The new path is through Asset Library in App Store Connect. In this flow, creative assets can be uploaded and submitted for review independently, without updating an app version or deciding exactly where the assets will be used later.

- Version-page flow: upload assets on the app version page, preview them, then submit the version for review.
- Asset Library flow: upload creative assets directly to Asset Library and submit them standalone for review.
- The new preview functionality shows how the app will look on the App Store with these assets.
- Preview supports iPhone and iPad, different orientations, and different languages.

## Manage creative assets with Asset Library

Asset Library is a centralized place in App Store Connect for managing app assets across platforms, sizes, and placements. It includes existing screenshots, preview videos, in-app event media, and the new marketing images and videos, which App Store Connect calls creative assets.

A key benefit is reuse after approval. Once creative assets are approved, they are available in Asset Library and can be used across Product Page Header and Search Results without additional review. This enables faster changes, such as replacing a summer hiking header with an already approved winter hiking asset and publishing that change directly to the App Store.

- Centralizes screenshots, preview videos, in-app event media, and creative assets.
- Stores assets across platforms, sizes, and placements.
- Approved assets can be reused for Product Page Header and Search Results without another review.
- Pre-approving assets gives teams flexibility to update visuals in real time.

## Automation and next steps

Teams can automate Asset Library upload and submission workflows with the App Store Connect API. Apple Ads setup flows can also be automated with the Apple Ads Platform API, which includes open-source client libraries for Swift and other languages.

Recommended next steps are to prepare the images and videos that showcase the app, upload them to Asset Library, use them across the product page and search results, and preview the app before submitting assets or versions for review.

- Prepare brand, feature, or campaign-specific images and videos.
- Upload assets to Asset Library for reuse across Product Page, Search Results, and related placements.
- Preview App Store presentation before submission.
- Use App Store Connect API and Apple Ads Platform API for automated asset and ad workflows where needed.

Resources:
- Design your own ads with creative assets: https://ads.apple.com/app-store/h/help/design-your-own-ads-with-creative-assets
- App Store - What's New: https://developer.apple.com/app-store/whats-new/
- Creating your Product Page: https://developer.apple.com/app-store/product-page/

Chapters:
- 0:06 Introduction: Introduces the importance of first impressions on the App Store and previews new ways to use images and videos beyond existing screenshots and app previews.
- 0:52 New asset placements: Explains Product Page Header and Search Results visual placements, including use with Apple Ads, Custom Product Pages, deep links, and Product Page Optimization.
- 4:24 Meet Asset Library: Introduces Asset Library in App Store Connect as a centralized place for screenshots, preview videos, in-app event media, and creative assets, with standalone review and post-approval reuse.
- 7:35 Next steps: Recommends preparing images and videos, uploading them to Asset Library, using them across product page and search results, and previewing the app before review submission.

### What's new in managing Apple devices

- Session ID: wwdc2026-206
- Page: https://wwdc.ai/2026/206
- Markdown: https://wwdc.ai/2026/206.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/206/
- Category: Business & Education
- Description: Updates for Apple device management in 2026: Apple Business APIs, declarative management, app controls, Platform SSO, and education workflows.
- Duration: 23:02
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-206/eng_f011a468bfe0/wwdc2026-206-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/206/4/e49f983e-700d-4d52-ae6b-a0fa1ea89fd0/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/206/4/e49f983e-700d-4d52-ae6b-a0fa1ea89fd0/downloads/wwdc2026-206_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/206/4/e49f983e-700d-4d52-ae6b-a0fa1ea89fd0/downloads/wwdc2026-206_sd.mp4?dl=1

Updates for Apple device management in 2026: Apple Business APIs, declarative management, app controls, Platform SSO, and education workflows.

TLDR:
- Apple Business is expanded as an all-in-one platform with new APIs for Blueprints, configurations, users, groups, app license information, and audit events.
- Declarative device management is positioned as the standard, with new managed Mac migration, credential assets, status items, system health reporting, enhanced log collection, and Content Caching controls.
- macOS 27 gains declarative app configuration, package cleanup, consolidated privacy consent prompts, Safari website permission management, and binary execution controls.
- Platform SSO adds required Touch ID as a second factor, web-based login and QR-code flows, FileVault support for Authenticated Guest Mode, while education updates add Authenticated Guest Mode for Shared iPad and guided browsing in Classroom.

## Apple services for business and education

Apple Business is presented as a new all-in-one platform for organizations, available in more than 200 countries and regions. It includes zero-touch deployment, Managed Apple Accounts, and built-in device management capabilities intended to help businesses start managing Apple devices more quickly.

New Apple Business APIs support automation across Blueprints, configurations, users and groups, app license information, and audit events. These join existing APIs for servers, devices, inventory assignment to device management servers, and AppleCare warranty details.

- Volume licensing is being extended to subscriptions in App Store apps, allowing IT administrators to purchase, manage, and assign app subscriptions through device management workflows.
- The subscription licensing mechanism will be available later in Apple Business and Apple School Manager.
- For subscription implementation details, the session points to "Offer subscriptions to groups and organizations."

## Declarative device management is the standard

The session emphasizes that declarative device management is no longer a future direction but the standard model for Apple device management. New capabilities rely on the declarative data model, status channel, assets, and configurations to reduce polling and make state changes more efficient.

A new managed migration feature for Mac lets IT migrate user data while preserving device management enrollment and settings. A declarative configuration is deployed immediately after enrollment, and IT controls which accounts, files, and security and privacy settings are migrated. Migration Assistant reports declarative management status so administrators can monitor progress.

- New declarative controls are available for Apple Intelligence, Siri, and keyboard settings, with more granular controls for individual Apple Intelligence and Siri features.
- Credential-backed configurations are moving from monolithic configuration profiles toward declarative assets, allowing multiple configurations to reference a single certificate, identity, or password asset.
- When a credential changes, the server updates the asset and the device updates all dependent configurations.
- New declarative status items include enrollment type, awaiting device configuration, return-to-service state, Shared iPad state, current push token, Lockdown Mode state, and more.

## Fleet health, support, and Content Caching

iOS and iPadOS 27 can report device system health through a declarative management status item. Reported components include hardware areas such as baseband, camera, Face ID, Touch ID, and others, giving IT teams a fleet-wide view of device health.

AppleCare support workflows are streamlined with the new TriggerEnhancedLogCollection command for organization-owned devices on iOS, iPadOS, tvOS, and macOS 27. Declarative status can be used to monitor the enhanced log collection process.

- macOS 27 adds a declarative configuration to control the Content Caching service on Mac.
- New declarative status items report Content Caching service state for monitoring cache server health.
- Content cache servers can send their own reports to an arbitrary HTTPS endpoint, enabling more advanced monitoring consoles.
- Declarative status adoption is described as a subscription model: the server subscribes to status items, and devices send changes as they occur.

## App management, privacy prompts, and binary controls

Declarative app configuration, previously available on iOS, iPadOS, and visionOS, comes to macOS 27. It supports secure provisioning of managed apps with credentials and configuration, including hardware-bound keys and Managed Device Attestation for authenticating apps and extensions with enterprise services.

macOS 27 also lets administrators remove all files and directories installed by a declarative management package when the package configuration is removed, reducing leftover data after managed software is no longer needed.

- The session recommends that enterprise app developers adopt the ManagedApp framework for managed app configuration and enterprise integration.
- iOS, iPadOS, and macOS 27 introduce a consolidated privacy consent prompt for managed apps and Safari websites, showing the organization, app or website, administrator justification, requested components, and app-provided justifications.
- If users choose Allow, recommended privacy defaults are applied and additional prompts are avoided; if users choose Not Now, standard prompts appear when access is requested.
- macOS 27 adds declarative binary execution controls using the Endpoint Security framework to allow or deny binaries and terminate processes associated with denied binaries.
- Binary matching rules use code-signing properties, and administrators can automatically allow managed apps without writing rules for each one.
- App privacy controls and binary blocking live in a new declarative app.settings configuration; Safari website permissions are part of the existing safari.settings configuration.

## Identity integrations and Platform SSO

macOS 27 enhances Platform SSO with a new login and unlock experience that clearly presents organization credentials. Administrators can require Touch ID in addition to a password on organization devices, making Touch ID a built-in second factor enforced at login, screen unlock, and FileVault unlock.

A new web-based authentication option for Platform SSO allows identity providers to render modern authentication flows in a secure system-managed web view at the login window and screen unlock. Supported flows include one-time codes, conditional access prompts, QR-code sign-in, custom challenge-response flows, and offline authentication.

- The web view runs in a tightly controlled operating-system context.
- For QR-code sign-in, the camera runs in a secure system process isolated from the web view; the page receives decoded QR data, not image frames or a raw camera feed.
- Web authentication works across login window, screen unlock, and FileVault unlock.
- Authenticated Guest Mode on macOS 27 can unlock FileVault-protected Macs, allowing temporary shared sessions while preserving full-disk encryption.
- The session names Authentik, ClassLink, and Identity Automation as identity developers working on web login and QR-code support for Platform SSO.

## Education updates

Authenticated Guest Mode is coming to Shared iPad later in the release. When enabled, iPad starts in a temporary session and presents a login screen where users sign in with a Managed Apple Account using native or federated authentication with Single Sign-On support.

When the user signs out from the lock screen, local data and the Managed Apple Account are automatically removed. The temporary session shares device capacity with the system without hard quotas, making storage use more flexible.

- Classroom gains guided browsing for keeping students focused on specific websites or tabs.
- Teachers can lock students to one or more websites, or to a single tab for an immediate focal point.
- Teachers can configure websites directly or use prepared bookmarks.
- Teachers can limit navigation inside or outside websites and grant access to camera and microphone, while students retain agency over whether those remain enabled.
- Guided browsing can be applied to one student or many students, opening the guided browser with the selected websites on student devices.

Chapters:
- 0:00 Introduction: The session frames the update areas: Apple services, declarative device management, app management, identity management, and education technologies.
- 0:41 Apple services: Apple Business is introduced as an expanded all-in-one platform available in over 200 countries and regions. The chapter covers new Apple Business APIs and a forthcoming volume licensing mechanism for App Store subscriptions in Apple Business and Apple School Manager.
- 2:34 Device management: Declarative device management is described as the current standard, with new managed Mac migration, granular Apple Intelligence and Siri controls, credential assets, expanded status reporting, system health status, enhanced log collection, and Content Caching management.
- 9:33 App management: macOS 27 gains declarative app configuration with hardware-bound keys and Managed Device Attestation support, package file cleanup, consolidated privacy prompts for apps and Safari websites, and binary execution controls using Endpoint Security.
- 14:56 Identity Integrations: Platform SSO on macOS 27 adds a redesigned organization login experience, required Touch ID as a second factor, secure web-based authentication including QR-code flows, offline support, and FileVault support for Authenticated Guest Mode.
- 19:46 Education: Authenticated Guest Mode comes to Shared iPad with temporary sessions and Managed Apple Account sign-in. Classroom adds guided browsing so teachers can lock students to selected websites or tabs and manage navigation during class.

### Deliver workout insights with HealthKit workout zones

- Session ID: wwdc2026-207
- Page: https://wwdc.ai/2026/207
- Markdown: https://wwdc.ai/2026/207.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/207/
- Category: Health & Fitness
- Description: Use HealthKit workout zones in iOS 27 and watchOS 27 to read completed workout zone data, handle live zone changes, and configure preferred or custom zones.
- Duration: 12:15
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-207/eng_f066c93f7b91/wwdc2026-207-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/207/5/8627c1d4-7a34-46f2-8491-f0d1c138edd1/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/207/5/8627c1d4-7a34-46f2-8491-f0d1c138edd1/downloads/wwdc2026-207_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/207/5/8627c1d4-7a34-46f2-8491-f0d1c138edd1/downloads/wwdc2026-207_sd.mp4?dl=1

Use HealthKit workout zones in iOS 27 and watchOS 27 to read completed workout zone data, handle live zone changes, and configure preferred or custom zones.

TLDR:
- HealthKit now supports heart rate and cycling power workout zones, automatically calculating time-in-zone from workout samples when users authorize the relevant data types.
- Completed workouts expose zone data through `zoneGroupsByType` on `HKWorkout` or `HKWorkoutActivity`, including the zone configuration, source, boundaries, and per-zone durations.
- Live workouts can receive zone-change updates through `HKLiveWorkoutBuilderDelegate.didUpdateWorkoutZone`, enabling active-zone UI, alerts, and live coaching.
- Apps can use preferred zone thresholds from Health Settings or provide a custom `HKWorkoutZoneConfiguration` before `beginCollection`; custom configurations are workout-scoped and must be normalized carefully when comparing workouts.

## Workout zones in HealthKit

Workout zones turn raw metrics such as heart rate samples or cycling power into intensity ranges that are easier to use for training guidance. Heart rate zones are personalized using factors such as age and resting heart rate, while cycling power zones are based on functional threshold power.

In iOS 27 and watchOS 27, HealthKit integrates heart rate and cycling power zones directly. HealthKit calculates time spent in each zone from incoming workout samples, letting apps build post-workout summaries, live coaching, and training dashboards without implementing the core zone accounting themselves.

- Request HealthKit authorization for the relevant data before accessing zone information, such as workouts, heart rate, and cycling power.
- Heart rate and cycling power zones share a similar HealthKit structure; switch the `HKQuantityType` to retrieve the metric you need.
- Zones are useful for classifying effort, tracking whether a workout stayed within a target intensity, and balancing recovery or load.

## Read zones from completed workouts

After a workout finishes, zone data is available from the `zoneGroupsByType` dictionary on either an `HKWorkout` or an individual `HKWorkoutActivity`. This supports both whole-workout summaries and per-activity views for multi-sport workouts.

An `HKWorkoutZoneGroup` contains a `configuration` and `zoneDurations`. The configuration describes the quantity type, the source of the thresholds, and the ordered zones. Zone boundaries are contiguous and non-overlapping; the first zone has no lower bound and the last has no upper bound. The durations array gives the time spent in each zone, ordered by threshold values.

- Use `HKQuantityType(.heartRate)` for heart rate zones or the cycling power quantity type for cycling power zones.
- Use `zoneDurations` to render time-in-zone charts or classify the completed workout's intensity.
- Inspect the configuration source to understand whether thresholds came from the system, the user, or the app.

### Reading heart rate zones from a completed workout

Looks up the completed workout's heart rate zone group and converts its zone count and durations into app display data.

```swift
if let heartRateZoneGroup = workout.zoneGroupsByType?[HKQuantityType(.heartRate)] {
    let zones = ZoneDisplayData(
        zoneCount: heartRateZoneGroup.configuration.zones.count,
        currentZoneIndex: nil,
        durations: heartRateZoneGroup.zoneDurations.map(\.duration)
    )
}
```

## Handle live zone updates

During a live workout, HealthKit processes incoming samples and determines the current zone. When the current zone changes, HealthKit sends an update to the workout builder delegate.

Use `HKLiveWorkoutBuilderDelegate` and implement `didUpdateWorkoutZone` to respond to zone transitions. Each update includes the current and previous zones, the full zone group with cumulative totals, and a timestamp for the last processed sample. The timestamp can support a running timer for time in the current zone.

- Live updates are sent on zone changes, such as moving from Zone 2 to Zone 3.
- Use the current zone to highlight UI, guide pacing, or notify someone when they leave a target zone.
- Use the cumulative zone group to keep live time-in-zone totals in sync with HealthKit.

### Handling live zone updates

Responds to HealthKit live zone changes by rebuilding display data and updating UI state on the main actor.

```swift
func workoutBuilder(_ workoutBuilder: HKLiveWorkoutBuilder,
                    didUpdateWorkoutZone zoneUpdate: HKLiveWorkoutZoneUpdate) {
    guard let zoneGroup = zoneUpdate.zoneGroup else {
        return
    }

    if let currentIndex = zoneUpdate.currentZoneDuration?.zone.index {
        let data = ZoneDisplayData(
            zoneCount: zoneGroup.configuration.zones.count,
            currentZoneIndex: currentIndex,
            durations: zoneGroup.zoneDurations.map(\.duration)
        )

        Task { @MainActor in
            self.heartRateZones = data
        }
    }
}
```

## Preferred zones from Health Settings

By default, HealthKit uses preferred workout zone thresholds from Health Settings. These can be calculated automatically by the system from available user metrics or manually configured by the user. Preferred zones sync across devices through HealthKit, giving users a consistent experience across apps.

Before starting a workout that depends on zone information, query for a preferred zone configuration on `HKHealthStore` or `HKWorkoutBuilder` to confirm one exists.

- Preferred zone sources may be system-calculated or manually set by the user.
- Use preferred zones when your app does not need a proprietary zone model.
- Checking for a preferred configuration lets your app decide whether to proceed, prompt the user, or supply a workout-specific custom configuration.

### Check whether a preferred heart rate zone configuration exists

Queries the workout builder for the preferred heart rate zone configuration before starting collection.

```text
if try await builder.zoneConfiguration(for: HKQuantityType(.heartRate)) == nil {
    // Provide a custom configuration or handle the missing preference.
}
```

## Custom workout zone configurations

Use custom zones when your app's training model differs from the user's preferred Health Settings zones, such as a proprietary coaching platform. Create an `HKWorkoutZoneConfiguration` from a quantity type and compatible boundary quantities, then set it on the `HKWorkoutBuilder` before collection begins.

Custom zone configurations are scoped to the individual workout and are not persisted by HealthKit. If your app needs to reuse or sync them, it is responsible for storing and syncing that configuration.

HealthKit requires between 3 and 9 zones. Because different workouts may have different zone counts and thresholds, do not compare zone indexes directly across workouts without considering their boundaries.

- Boundary units must match and be compatible with the configuration's `HKQuantityType`.
- The first zone starts at 0 and the final zone is unbounded.
- Call `setCustomZoneConfiguration(_:for:)` before `beginCollection(at:)`.
- When comparing time-in-zone across workouts with different zone definitions, normalize using the original samples and the target bucket definitions rather than assuming, for example, Zone 3 means the same thing everywhere.

### Create heart rate zone boundaries and configuration

Builds a heart rate zone configuration from beats-per-minute thresholds.

```swift
let defaultHeartRateZoneThresholds = [91.0, 114.0, 136.0, 158.0]
let bpmUnit = HKUnit.count().unitDivided(by: HKUnit.minute())
let boundaries = defaultHeartRateZoneThresholds.map {
    HKQuantity(unit: bpmUnit, doubleValue: $0)
}

let heartRate = HKQuantityType(.heartRate)
let defaultConfiguration = try HKWorkoutZoneConfiguration(
    quantityType: heartRate,
    zoneBoundaries: boundaries
)
```

### Set a custom configuration before collection

Applies the custom zone configuration to the workout builder before starting HealthKit data collection.

```swift
try await builder.setCustomZoneConfiguration(defaultConfiguration, for: heartRate)

let startDate = Date()
try await builder.beginCollection(at: startDate)
```

Resources:
- Tracking heart rate zones for workouts: https://developer.apple.com/documentation/HealthKit/tracking-heart-rate-zones-for-workouts
- Accessing workout zone data: https://developer.apple.com/documentation/HealthKit/accessing-workout-zone-data

Chapters:
- 0:01 Introduction: Introduces HealthKit workout zones for heart rate and cycling power in iOS 27 and watchOS 27. Explains how zones convert biometric samples into actionable workout guidance and previews completed-workout access, live updates, preferred zones, and custom configurations.
- 2:17 Accessing workout zones: Shows how to retrieve zone data from completed `HKWorkout` or `HKWorkoutActivity` instances using `zoneGroupsByType`. Covers `HKWorkoutZoneGroup`, configuration metadata, zone boundaries, source, and per-zone durations for building summary charts.
- 6:19 Live zone updates: Explains how live workouts receive zone-change notifications through `HKLiveWorkoutBuilderDelegate.didUpdateWorkoutZone`. Describes using current and previous zones, cumulative zone data, and sample timestamps to update UI or notify users during a workout.
- 8:11 Preferred zones: Describes HealthKit's default use of preferred zone thresholds from Health Settings, including system-calculated and manually configured zones that sync across devices. Recommends checking for a preferred zone configuration before starting a zone-aware workout.
- 9:00 Custom zones: Shows how to create and set a custom `HKWorkoutZoneConfiguration` on an `HKWorkoutBuilder` before beginning collection. Covers constraints such as compatible boundary units, 3 to 9 zones, workout-scoped persistence, and the need to normalize when comparing workouts with different zone definitions.

### What's new in Wallet

- Session ID: wwdc2026-209
- Page: https://wwdc.ai/2026/209
- Markdown: https://wwdc.ai/2026/209.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/209/
- Category: App Services
- Description: Learn how iOS 27 Wallet passes add Poster Generic, new barcode formats, featured actions, and Pass Designer/Pass Builder workflows.
- Duration: 15:49
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-209/eng_f4959c77a413/wwdc2026-209-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/209/5/25eb40d5-b64d-4677-bc99-f5c3a30d386a/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/209/5/25eb40d5-b64d-4677-bc99-f5c3a30d386a/downloads/wwdc2026-209_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/209/5/25eb40d5-b64d-4677-bc99-f5c3a30d386a/downloads/wwdc2026-209_sd.mp4?dl=1

Learn how iOS 27 Wallet passes add Poster Generic, new barcode formats, featured actions, and Pass Designer/Pass Builder workflows.

TLDR:
- iOS 27 adds the Poster Generic pass style for artwork-forward membership, loyalty, and store cards; include a Generic style fallback for iOS 26 and earlier.
- Wallet passes gain EAN-13, Code 39, Codabar, and ITF barcode formats using the existing `barcodes` array; provide fallback barcodes or a prominent manual credential ID for older OS versions.
- Featured actions let any pass style expose up to two prioritized actions below the pass face using a top-level `featuredActions` array.
- New tooling includes Pass Designer for WYSIWYG pass templates and Pass Builder for server-side personalization, signing, validation, and command-line generation.

## iOS 27 pass updates

iOS 27 expands Wallet passes with a new visual pass style, more barcode choices, a flexible pass actions API, and new tooling for template-driven pass generation. The changes target common pass use cases such as membership cards, loyalty programs, store cards, and operational workflows where passes need to be customized and distributed at scale.

- Poster Generic is a new pass style for bold artwork-driven pass faces.
- New barcode formats are EAN-13, Code 39, Codabar, and Interleaved 2 of 5 (ITF).
- Featured actions expose relevant actions below the pass face for all pass styles.
- Pass Designer creates `.pkpasstemplate` files; Pass Builder personalizes and signs them on Mac or Linux.

## Poster Generic pass style

Poster Generic is adopted by adding the `posterGeneric` top-level style key to `pass.json`. Its pass face can include a background image, primary logo, header fields, primary fields, a single footer field, and an optional barcode. If more than one footer field is supplied, only the first is displayed.

Poster Generic requires iOS 27 or later. To keep passes addable on iOS 26 and earlier, include the existing `generic` style key alongside `posterGeneric`, with relevant fields for each style.

- Good fit: membership cards, loyalty programs, store cards, and passes where colorful artwork should dominate.
- Compatibility guidance: include a `generic` fallback for customers on iOS 26 and earlier.
- Layout constraint: only one footer field is shown on the pass face.

### Adopt Poster Generic with Generic fallback

Use `posterGeneric` for iOS 27 and provide `generic` fields for earlier iOS versions.

```text
{
  "posterGeneric": {
    "headerFields": [
      { "key": "memberID", "label": "Guest No.", "value": "102035" }
    ],
    "footerFields": [
      { "key": "membershipType", "value": "Family Pass" }
    ]
  },
  "generic": {
    "headerFields": [
      { "key": "memberID", "label": "Guest No.", "value": "102035" }
    ],
    "footerFields": [
      { "key": "membershipType", "value": "Family Pass" }
    ]
  }
}
```

## New barcode formats and fallback strategy

Wallet passes in iOS 27 support four additional barcode formats through the existing `Barcode` object and `barcodes` array in `pass.json`: EAN-13, Code 39, Codabar, and ITF. For example, Codabar is specified with `PKBarcodeFormatCodabar`.

Older iOS releases do not support these new formats. If a pass only provides a new iOS 27 barcode type, no barcode is rendered on iOS 26 and earlier. The recommended approach is to list barcodes in priority order, starting with the preferred new format and falling back to a supported format such as QR.

- Provide multiple barcodes when possible, ordered by preference.
- If multiple formats are not possible, show the credential ID prominently in a `primaryField` or `headerField`.
- Train staff for manual-entry workflows so an unscannable pass does not block the customer.

### Codabar with QR fallback

Lead with the preferred new barcode type and include a supported fallback for iOS 26 and earlier.

```text
{
  "barcodes": [
    {
      "format": "PKBarcodeFormatCodabar",
      "message": "123456789",
      "messageEncoding": "iso-8859-1"
    },
    {
      "format": "PKBarcodeFormatQR",
      "message": "123456789",
      "messageEncoding": "iso-8859-1"
    }
  ]
}
```

## Featured actions

Featured actions are a new iOS 27 API for showing relevant actions below the pass face across all pass styles. Add a top-level `featuredActions` key to `pass.json`; each action has a unique identifier, an action type, and a value such as a URL. Wallet renders the action with an appropriate icon and localized call to action.

- Each pass can contain up to two featured actions.
- Actions should be supplied in priority order.
- Use only the most meaningful and relevant actions for the customer.
- Supported action types and expected values are documented in the Wallet Passes documentation.

### Add a membership benefits action

Defines a featured action that links the user to membership offers or benefits.

```text
{
  "featuredActions": [
    {
      "identifier": "my-offer-id",
      "type": "membershipBenefits",
      "url": "www.example.com/offers"
    }
  ]
}
```

## Pass Designer workflow

Pass Designer is a Mac WYSIWYG editor that provides a true-to-iOS rendering while designing a pass. It can configure identity and signing settings, pass style, images, barcodes, fields, and semantics when supported by the selected pass style.

The demonstrated workflow creates a Poster Generic dog daycare membership pass: choose the Poster Generic style, add a dog portrait background, configure header and primary fields, use a PDF417 barcode for membership check-in, add a primary logo, set label colors, and add a footer. Pass Designer saves the result as a `.pkpasstemplate` file for later personalization.

- Use Pass Designer to experiment with Poster Generic before changing production pass generation.
- Use placeholder values in template fields that will later be replaced server-side.
- A missing label on the first Poster Generic primary field produces a bold title-like value presentation.

## Pass Builder for server-side personalization and signing

Pass Builder is a Swift on Server package for Mac and Linux. It takes templates created in Pass Designer and provides APIs to personalize, validate, sign, and build distributable passes. It also includes the `buildpass` command-line executable.

On the server, load a `.pkpasstemplate` with `PassPackage`, set field values, set images, configure barcodes and featured actions, then sign the pass with a pass signing certificate and the WWDR intermediate certificate. Pass Builder handles generating the manifest, creating the detached signature, packaging the bundle, and writing the `.pkpass` output.

Pass Builder can also be used from other programming languages. The session calls out native Java bindings generated by `swift-java`, protobuf definitions for the Pass Package format, and the `buildpass` command-line executable for personalization and signing.

- Add Pass Builder as a Swift package dependency to the server package manifest and target.
- Use `PassPackage` to access template contents and `package.pass` to edit `pass.json` fields.
- Use `PassCertificate` and `PassSigner` to sign the personalized package for distribution.
- Consult the Pass Builder documentation for `buildpass` command-line usage.

### Add Pass Builder to a Swift package

Registers Pass Builder as a dependency for a Swift server target.

```swift
// Package.swift
import PackageDescription

let package = Package(
  name: "MyServer",
  products: [
    .library(name: "MyServer", targets: ["MyServer"])
  ],
  dependencies: [
    .package(path: "./path/to/PassBuilder")
  ],
  targets: [
    .target(
      name: "MyServer",
      dependencies: [
        .product(name: "PassBuilder", package: "PassBuilder")
      ]
    )
  ]
)
```

### Personalize and sign a pass template

Loads a template, customizes fields, image, barcode, and featured action, then signs the pass for distribution.

```swift
import PassBuilder

func createPass(for doggo: MemberModel) async throws -> URL {
  var package = PassPackage(url: "template.pkpasstemplate")

  package.pass.fields.setValue(doggo.name, forKey: "DOG_NAME")
  package.pass.fields.setValue(doggo.favoriteToy, forKey: "LOVES")
  package.pass.fields.setValue(doggo.id, forKey: "MEMBER_ID")

  package.background = PassImage(url: doggo.photoURL)
  package.pass.barcodes = [
    Pass.Barcode(message: doggo.id, format: .pdf417)
  ]
  package.featuredActions = [
    Pass.Action(id: "action-1", type: "viewMembership", url: doggo.membershipURL)
  ]

  let passCertificate = try PassCertificate(url: "pass.p12", password: "s3cr3t")
  let wwdrCertificate = try PassCertificate(url: "wwdr.cer")
  let signer = PassSigner(
    passCertificate: passCertificate,
    wwdrCertifiate: wwdrCertificate
  )

  let destinationURL = URL(string: "/www/passes/" + doggo.id)!
  try signer.signPass(package, writingTo: destinationURL)
  return destinationURL
}
```

Resources:
- Pass Builder: https://developer.apple.com/videos/play/wwdc2026/209/github.com/apple/pass-builder
- Wallet: https://developer.apple.com/documentation/PassKit/wallet

Chapters:
- 0:01 Introduction: Introduces iOS 27 Wallet pass updates: Poster Generic, four new barcode types, featured actions, and new developer tools for designing and building passes.
- 0:40 Poster Generic: Explains the new Poster Generic pass style, its pass-face components, footer-field limitation, and the need to include a Generic fallback for iOS 26 and earlier.
- 2:36 Barcodes: Covers the new EAN-13, Code 39, Codabar, and ITF barcode formats and recommends priority-ordered fallback barcodes or a manual credential ID workflow for older iOS versions.
- 4:27 Featured actions: Introduces top-level `featuredActions` for all pass styles, with unique identifiers, action types, values such as URLs, and a limit of two prioritized actions per pass.
- 5:46 Developer tools: Sets up the new developer tooling suite for Mac and server platforms, centered on Pass Designer and Pass Builder.
- 5:47 Pass Designer: Demonstrates designing a Poster Generic pass in Pass Designer using fields, images, colors, a PDF417 barcode, and saving the result as a `.pkpasstemplate`.
- 10:40 Pass Builder: Shows adding Pass Builder to a Swift server package, loading a pass template, personalizing fields and assets, configuring a barcode and featured action, then signing the pass with certificates.
- 13:50 Personalizing a pass template: Describes non-Swift integration options, including Java bindings via `swift-java`, protobuf definitions for the Pass Package format, and invoking `buildpass` to personalize and sign passes.
- 15:01 Next steps: Recommends trying Pass Designer, evaluating Poster Generic, planning barcode fallbacks, and choosing the most meaningful featured actions for customers.

### What's new in Apple In-App Purchase

- Session ID: wwdc2026-210
- Page: https://wwdc.ai/2026/210
- Markdown: https://wwdc.ai/2026/210.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/210/
- Category: App Store, Distribution & Marketing
- Description: Learn the new StoreKit and App Store Connect workflows for monthly annual-commitment subscriptions, offer code redemption, and IAP review submissions.
- Duration: 13:26
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-210/eng_632939f53992/wwdc2026-210-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/210/4/f029ab19-6670-48c6-b9b1-88ac6692cdda/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/210/4/f029ab19-6670-48c6-b9b1-88ac6692cdda/downloads/wwdc2026-210_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/210/4/f029ab19-6670-48c6-b9b1-88ac6692cdda/downloads/wwdc2026-210_sd.mp4?dl=1

Learn the new StoreKit and App Store Connect workflows for monthly annual-commitment subscriptions, offer code redemption, and IAP review submissions.

TLDR:
- Monthly subscriptions with a 12-month commitment let customers pay monthly for a one-year auto-renewable subscription; configure them in App Store Connect for new or existing annual products.
- StoreKit adds pricing terms and billing-plan purchase options so apps can merchandise monthly commitment plans, display commitment pricing, and purchase a specific billing plan.
- Transaction, RenewalInfo, App Store Server API, and App Store Server Notifications V2 payloads expose billing-plan and commitment metadata for entitlement and lifecycle handling.
- Offer code redemption now returns a VerificationResult and accepts RedeemOption values, and App Store Connect is moving IAP review submissions into the unified reviewSubmissions workflow.

## Monthly subscriptions with a 12-month commitment

iOS 26.5 introduces a new pricing option for one-year auto-renewable subscriptions: customers can pay monthly while committing to a 12-month subscription. Developers can add this billing plan to new or existing annual subscriptions in App Store Connect.

After compiling against the 26.5 SDK, eligible customers in available markets can subscribe to the new billing plan in apps running on iOS, iPadOS, macOS, tvOS, or visionOS 26.4. Each auto-renewable subscription has at least one pricing term with the default `.upFront` billing plan type; products configured with the new option also return a `.monthly` pricing term.

- Configure availability for "monthly with a 12-month commitment" on a one-year subscription in App Store Connect.
- Offers can be configured separately per billing plan type, such as a free trial only for the commitment plan.
- Billing plan metadata is returned only when available for the customer's storefront.

## Merchandising and purchasing with StoreKit

StoreKit adds `PricingTerms` on `SubscriptionInfo` for presenting available billing plans. StoreKit views can prefer a specific pricing term, while custom UI can fetch products with the Product API, read pricing terms, and display both monthly price and total commitment price.

To purchase a monthly commitment plan from custom UI, pass the new `.billingPlanType(.monthly)` purchase option. As with other StoreKit flows, verify the transaction, unlock the entitlement, and finish the transaction.

- Use `SubscriptionStoreView` with `.preferredSubscriptionPricingTerms` for StoreKit-managed subscription UI.
- Use `product.subscription?.pricingTerms` for custom merchandising.
- Display both `billingDisplayPrice` and `commitmentInfo.price` when presenting commitment pricing.
- Use `.billingPlanType(.monthly)` when initiating purchase for the monthly commitment plan.

### Prefer monthly commitment pricing in a StoreKit subscription view

Filters the subscription store to merchandise the monthly billing plan when the product has monthly commitment pricing available.

```swift
import StoreKit
import SwiftUI

struct SubscriptionStore: View {
    var body: some View {
        SubscriptionStoreView(groupID: "3F19ED53") {
            // Custom marketing content
        }
        .preferredSubscriptionPricingTerms { _, subscriptionInfo in
            subscriptionInfo.pricingTerms.first {
                $0.billingPlanType == .monthly
            }
        }
    }
}
```

### Read pricing terms and purchase a monthly billing plan

Custom UI can select the monthly pricing term, present required pricing context, and purchase that billing plan explicitly.

```swift
import StoreKit

var product: Product? // Fetch and assign product

let pricingTerms = product?.subscription?.pricingTerms
    .first { $0.billingPlanType == .monthly }

if let pricingTerms {
    let monthlyPrice = pricingTerms.billingDisplayPrice
    let totalCommitmentPrice = pricingTerms.commitmentInfo.price
    // Display both prices to the customer
}

let result = try? await product?.purchase(options: [.billingPlanType(.monthly)])

switch result {
    // Verify the transaction, grant access, and finish the transaction
}
```

## Subscription management, entitlements, and testing

The App Store automatically presents a one-time disclosure sheet before a customer first subscribes to a commitment plan. It explains the number of payments required and includes cancellation guidance. Apple's subscription management UI also shows available plans, remaining payments, and the commitment renewal date.

Apps can present subscription management directly with SwiftUI's `.manageSubscriptionsSheet` or UIKit's `showManageSubscriptions` API. For app-side entitlement and progress UI, use the latest transaction: upfront billing plans have `commitmentInfo == nil`, while monthly commitment plans provide progress, price, and commitment expiration metadata.

StoreKit Testing in Xcode 26.5 supports creating a monthly-with-12-month-commitment billing plan for a one-year auto-renewable subscription in a StoreKit configuration file. Developers can configure commitment pricing, create offers per billing plan type, test purchases with the transaction manager, and inspect `commitmentInfo` in the Transaction inspector.

- Always use the latest transaction for an accurate expiration date.
- `Transaction` exposes billing-plan-specific metadata for the current billing period.
- `RenewalInfo` exposes `renewalBillingPlanType` and commitment renewal information for the overall commitment.
- Commitment-related StoreKit fields are available starting with OS 26.4.

### Present Apple's subscription management sheet

Lets customers manage subscriptions from inside the app, including monthly commitment plan status.

```swift
import SwiftUI
import StoreKit

struct ManageSubscriptionsButton: View {
    let subscriptionGroupID: String
    @State var presentingManageSubscriptionsSheet = false

    var body: some View {
        Button("Manage Subscriptions") {
            presentingManageSubscriptionsSheet = true
        }
        .manageSubscriptionsSheet(
            isPresented: $presentingManageSubscriptionsSheet,
            subscriptionGroupID: subscriptionGroupID
        )
    }
}
```

## Server-side monitoring and subscription lifecycle

App Store Server APIs and App Store Server Notifications V2 add fields to signed transaction and renewal info objects for monthly subscriptions with a 12-month commitment. Notifications continue to report lifecycle events, including monthly renewals, during the commitment.

Decoded `JWSTransaction` payloads can include `billingPlanType` and `commitmentInfo` so servers can identify billing-period purchases and relate them to the total commitment. Decoded `JWSRenewalInfo` payloads can include renewal preferences after the commitment ends, and these commitment fields are present only while the subscription is currently in a commitment.

- Use server fields to track the current billing period number, total billing periods, commitment expiration, and commitment price.
- Use renewal info to detect the customer's post-commitment renewal product, status, price, and billing plan type.
- The Retention Messaging API also supports this payment option for auto-renewable subscriptions.

### Decoded signed transaction fields for a monthly commitment plan

Shows the billing-period transaction in the context of the full 12-month commitment.

```text
{
  "expiresDate": 1783503660000,
  "price": 10990,
  "productId": "plus.pro.annual",
  "purchaseDate": 1780911660000,
  "type": "Auto-Renewable Subscription",
  "billingPlanType": "MONTHLY",
  "commitmentInfo": {
    "billingPeriodNumber": 1,
    "totalBillingPeriods": 12,
    "commitmentExpiresDate": 1812447660000,
    "commitmentPrice": 131880
  }
}
```

### Decoded renewal info fields for post-commitment renewal

Shows renewal preferences that apply when the current commitment completes.

```text
{
  "renewalBillingPlanType": "MONTHLY",
  "commitmentInfo": {
    "commitmentAutoRenewProductId": "plus.standard.annual",
    "commitmentAutoRenewStatus": 0,
    "commitmentRenewalDate": 1812447660000,
    "commitmentRenewalPrice": 10990,
    "commitmentRenewalBillingPlanType": "BILLED_UPFRONT"
  }
}
```

## Offer codes, Bundles and Suites, and App Review submission

The OfferCodeRedemption API now accepts a set of `RedeemOption` values and returns a `VerificationResult` when redemption completes. On success, the app receives a transaction in the verification result; on failure, the app receives an error describing why redemption failed. A UIKit variant is available through `presentOfferCodeRedeemSheet`, and Xcode 27 can test offer code redemption for applicable product types.

Bundles and Suites are a new subscription offering model. A Bundle groups individually purchasable subscriptions into a single purchase, typically at a better combined price. A Suite groups subscriptions that only exist together in the suite and usually provide service across related apps. API testing for Bundles and Suites starts in Xcode 27, with more program details coming later in 2026.

App Store Connect's enhanced submission experience lets developers group multiple In-App Purchase products as review items in a single App Review submission. IAPs can be submitted alongside other review item types such as in-app events, custom product pages, and product page optimizations. The App Store Connect API's `reviewSubmissions` collection is expanding to support In-App Purchase, subscription, and subscription group resources, replacing older IAP/subscription submission resources over time.

- Update offer code redemption call sites to handle `VerificationResult`.
- Use the enhanced App Store Connect review workflow to keep related review items together.
- Start migrating App Store Connect API automation toward `reviewSubmission` and `reviewSubmissionItems` resources.

### Redeem an offer code and handle VerificationResult

Uses the updated SwiftUI offer code redemption API, which returns a verification result instead of only presenting the sheet.

```swift
import SwiftUI
import StoreKit

struct OfferCodeRedemption: View {
    @State var presentingOfferCodeSheet = false

    var body: some View {
        Button("Redeem Offer Code") {
            presentingOfferCodeSheet = true
        }
        .offerCodeRedemption(options: [], isPresented: $presentingOfferCodeSheet) { result in
            switch result {
            case .success(let verificationResult):
                switch verificationResult {
                    // Verify the transaction, grant access, and finish it
                }
            case .failure(let error):
                // Handle error
            }
        }
    }
}
```

Resources:
- In-App Purchase types: https://developer.apple.com/help/app-store-connect/reference/in-app-purchases-and-subscriptions/in-app-purchase-types
- Managing the life cycle of monthly subscriptions with a 12-month commitment: https://developer.apple.com/documentation/StoreKit/managing-lifecycle-of-monthly-subscriptions-with-a-12-month-commitment-
- Supporting monthly subscriptions with a 12-month commitment: https://developer.apple.com/documentation/StoreKit/supporting-monthly-subscriptions-with-a-12-month-commitment
- App Store Server Notifications V2: https://developer.apple.com/documentation/AppStoreServerNotifications/App-Store-Server-Notifications-V2
- Supporting offer codes in your app: https://developer.apple.com/documentation/StoreKit/supporting-offer-codes-in-your-app
- Implementing a store in your app using the StoreKit API: https://developer.apple.com/documentation/StoreKit/implementing-a-store-in-your-app-using-the-storekit-api

Chapters:
- 0:01 Introduction: Introduces updates for In-App Purchases: expanded subscription pricing options, offer code redemption API changes, and an enhanced App Store Connect submission experience.
- 0:51 Overview of monthly subscriptions with a 12-month commitment: Explains the new option that lets customers pay monthly for an annual auto-renewable subscription, available for new or existing one-year subscriptions in App Store Connect.
- 1:42 Set up in App Store Connect: Shows configuring availability for the monthly 12-month commitment billing plan on an annual subscription and setting offers separately by billing plan type.
- 2:28 Merchandise with StoreKit: Covers StoreKit pricing terms, StoreKit views, custom Product API merchandising, purchase options, subscription management UI, transaction fields, renewal fields, and Xcode testing for the new billing plan.
- 6:55 Monitor subscriptions with App Store Server APIs: Describes new signed transaction and renewal info fields for commitment plans, including lifecycle monitoring through App Store Server APIs and App Store Server Notifications V2.
- 8:50 Bundles and Suites: Introduces Bundles as groups of individually purchasable subscriptions sold together and Suites as groups of subscriptions that only exist together, with API testing beginning in Xcode 27.
- 9:26 Offer code redemption: Explains the updated offer code redemption API, which accepts RedeemOption values and returns a VerificationResult, with SwiftUI, UIKit, and Xcode 27 testing support.
- 10:35 Enhanced submission experience: Shows the unified App Store Connect submission workflow for grouping In-App Purchase products and other review items into a single App Review submission, including API migration guidance.
- 12:38 Next steps: Recommends adding new billing plans, updating offer code redemption call sites, testing in Xcode 27 and sandbox, and submitting updated products through the enhanced App Review experience.

### Rev up your CarPlay app

- Session ID: wwdc2026-212
- Page: https://wwdc.ai/2026/212
- Markdown: https://wwdc.ai/2026/212.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/212/
- Category: System Services
- Description: Learn iOS 27 CarPlay updates for video browsing, richer media UI, voice control overlays, navigation panels, route sharing, and simulator testing.
- Duration: 16:20
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-212/eng_7e9cbc74b8b1/wwdc2026-212-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/212/4/c594f5de-1012-4f5a-bad4-95ca200f5f58/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/212/4/c594f5de-1012-4f5a-bad4-95ca200f5f58/downloads/wwdc2026-212_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/212/4/c594f5de-1012-4f5a-bad4-95ca200f5f58/downloads/wwdc2026-212_sd.mp4?dl=1

Learn iOS 27 CarPlay updates for video browsing, richer media UI, voice control overlays, navigation panels, route sharing, and simulator testing.

TLDR:
- iOS 27 adds CarPlay video browsing for supported vehicles; video apps must support AirPlay video streaming and use the CarPlay framework for the browsing UI.
- CarPlay framework gains richer list/media UI: portrait and landscape images, thumbnails with overlays/progress/sports scores, details headers, and an automatic MiniPlayer for Now Playing.
- Voice Control template is now available across CarPlay app categories and can be presented full-screen or as an overlay, with buttons and navigation bar actions.
- Navigation apps get custom panels in the Map template and Route sharing, which can send route segments to supported vehicles and receive proposed waypoints such as EV charging stops.

## New CarPlay app categories and video browsing

iOS 27 expands CarPlay with support for voice-based conversational apps and CarPlay video apps in vehicles that support the video in car feature. Existing apps that support AirPlay video streaming can already play video to the CarPlay display when the vehicle allows it; the new work is to provide an in-CarPlay browsing experience using the CarPlay framework.

CarPlay video apps only appear on the CarPlay Home Screen when the connected vehicle supports video in car. If content is useful as both video and audio, include both the CarPlay video app entitlement and the CarPlay audio app entitlement so the app can appear even when video browsing is not available.

- CarPlay video apps must support AirPlay video streaming.
- Use the CarPlay framework to build the browsing UI shown on the CarPlay display.
- The car can indicate that video playback is unavailable at any time; in that case, video content plays audio-only.
- Apps can conditionally expose video UI, such as a Videos tab, by checking whether the CarPlay session configuration supports video.

## Richer media UI in the CarPlay framework

iOS 27 adds UI improvements that apply broadly to CarPlay apps, especially media browsing experiences. Lists can show images in portrait or landscape aspect ratios, and card-style thumbnail elements can include overlays, playback progress, and sports information.

Use CPPlaybackConfiguration to describe playable items. Set the preferred presentation to video for items that should play as video, or audio for audio-first items. Keep playback configuration up to date as playback state changes so thumbnails and details headers show accurate progress and action state.

- Thumbnail overlays can be text badges, custom images, playback progress, or sports team/score information.
- Details headers present one prominent item above a list, combining a thumbnail, title, body text, playback configuration, and action buttons.
- The first action button in a details header is automatically combined with current playback progress.
- The Now Playing MiniPlayer appears automatically for apps that show Now Playing.

### Disable the MiniPlayer

Use this when an app should show the Now Playing icon in the navigation bar instead of the automatic MiniPlayer.

```text
CPNowPlayingTemplate.shared.allowsMiniPlayer = false
```

## Voice control templates and audio behavior

The Voice Control template is available to all CarPlay app categories in iOS 27. It can show a prompt and animated conversation-state icon, and apps can add up to two action buttons plus leading and trailing navigation bar buttons.

Voice Control can also be presented as an overlay on another template, such as a Map template. Overlay presentations support the same kinds of text and buttons as the full-screen presentation, but should use shorter text to fit the reduced space.

- Use action buttons for follow-up actions such as starting navigation or placing a call when those are relevant to the conversation.
- Open URLs through CPTemplateApplicationScene to perform CarPlay requests such as navigation or calling.
- Use CPInterfaceController to show the Voice Control template as an overlay.
- For spoken interactions, provide audio feedback for waiting or processing states.
- Configure AVAudioSession with the play-and-record category, default mode, and no mixing for voice conversations.

## Navigation panels and Route sharing

Navigation apps gain more control over the primary interface area of CPMapTemplate. Instead of relying only on trip and route option presentation flows, apps can build and push panels that keep the map visible while showing custom navigation UI.

Route sharing lets supported vehicles coordinate driver-assistance and vehicle guidance features with a CarPlay navigation app's route. The app shares route segments as geographic coordinates when the trip changes; a supported vehicle can use that route for features such as automatic lane changes, route-aware guidance, or EV charging stop suggestions.

- Panels can combine CarPlay framework objects such as trips, grids, route choices, route details, waypoints, and list items.
- Panel button configuration defines bottom actions such as Go or End.
- Route sharing requires iOS 26.4 or later and a supported vehicle.
- Drivers approve Route sharing per vehicle during pairing; navigation apps must also opt in on the Map template.
- Apps can disable Route sharing for individual trips that are not eligible.
- If a vehicle proposes a waypoint, the navigation app can return travel estimates and let the Map template prompt the driver, or manage waypoint confirmation itself.

### Enable route sharing

Opt in from the Map template so the navigation app can share eligible routes with supported vehicles.

```swift
func mapTemplateShouldProvideRouteSharing(_ mapTemplate: CPMapTemplate) -> Bool {
    true
}
```

### Disable route sharing for a trip

Use this on trips that the app determines should not be shared with the vehicle.

```text
trip.routeSegmentsAvailableForRegion = false
```

## Testing with CarPlay Simulator

CarPlay Simulator supports testing different screen sizes and vehicle configurations, including vehicles that support video. The session demonstrates using Simulator to validate conditional video UI, thumbnails, playback progress, overlays, the MiniPlayer, Voice Control overlay presentation, details headers, and video playback behavior.

CarPlay Simulator is available in Device Hub. Navigation developers should use the new diagnostic tools for Route sharing, and video app developers can download CarPlay Simulator through the Additional Tools for Xcode package.

- Use Simulator vehicle configuration to verify whether video-only apps appear on the CarPlay Home Screen.
- Test video browsing UI and audio-only fallback behavior against vehicle capabilities.
- Use Route sharing diagnostics when implementing navigation route segment sharing.

Resources:
- CarPlay for developers: https://developer.apple.com/carplay

Chapters:
- 0:00 Introduction: Introduces iOS 27 CarPlay updates across app categories, including framework improvements, navigation-specific features, and simulator changes.
- 0:42 Apps in CarPlay: Reviews supported CarPlay app categories and adds voice-based conversational apps and video browsing in supported vehicles. Explains requirements and availability rules for CarPlay video apps, including AirPlay video streaming and entitlements.
- 2:51 CarPlay framework: Covers new CarPlay framework UI capabilities: richer list images, thumbnails with overlays and playback metadata, details headers, the Now Playing MiniPlayer, and Voice Control template updates including overlay presentation.
- 11:54 Navigation apps: Explains new Map template panels for navigation UI and Route sharing for coordinating a navigation app's route with supported vehicles. Describes route segments, proposed waypoints, driver approval, app opt-in, and per-trip disabling.
- 15:19 CarPlay Simulator: Shows CarPlay Simulator support for testing screen sizes, vehicle configurations, video capability, and Route sharing diagnostics. Notes that Simulator is available in Device Hub and through Additional Tools for Xcode for video app development.

### Translate your app using agents in Xcode

- Session ID: wwdc2026-213
- Page: https://wwdc.ai/2026/213
- Markdown: https://wwdc.ai/2026/213.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/213/
- Category: Developer Tools
- Description: Use Xcode 27 coding agents to translate String Catalogs with app context, review localized UI, and guide terminology with project instructions.
- Duration: 14:52
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-213/eng_3cd38c1bc6b7/wwdc2026-213-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/213/4/be1ee662-a447-4df4-89a5-5411447c0eeb/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/213/4/be1ee662-a447-4df4-89a5-5411447c0eeb/downloads/wwdc2026-213_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/213/4/be1ee662-a447-4df4-89a5-5411447c0eeb/downloads/wwdc2026-213_sd.mp4?dl=1

Use Xcode 27 coding agents to translate String Catalogs with app context, review localized UI, and guide terminology with project instructions.

TLDR:
- Xcode 27 can use coding agents to translate String Catalogs, adding target languages, building targets to discover strings, and populating catalogs with translated values.
- Agents receive Xcode-provided context such as code usage, similar terminology, existing translations, plural requirements, and String Catalog comments to improve localization consistency.
- Localization review should include runtime/debug runs, agent-rendered UI checks for truncation, clipping, and right-to-left layout issues, plus native-speaker feedback through tools like TestFlight.
- Best results come from making all user-facing strings localizable, providing translation guidance such as glossaries in project documentation, and choosing models suited to long, context-heavy translation tasks.

## What Xcode agents add to localization

The session introduces Xcode 27 support for translating String Catalogs directly with coding agents. The core idea is that software localization needs more than generic translation: ambiguous words, plural forms, tone, product terminology, and UI context all affect the correct result.

Xcode provides agents with localization context that String Catalogs have accumulated over recent releases, including where strings are used in code, how they are used, generated comments, similar terminology, and existing translations in other languages.

- Useful for translating an entire project into a new language, such as Canadian French.
- Also useful when adding a new feature to an already-localized app and translating just the new or changed strings.
- Agents can reuse established terminology from existing String Catalog entries to keep translations consistent across later work.

## Adding translations with an agent

The demonstrated workflow starts from a normal Xcode agent conversation. After the developer asks for a translation, the agent asks Xcode to prepare the project for localization. Xcode adds the requested language to project settings, builds all targets for supported platforms to discover localizable strings, and updates or creates String Catalogs.

By default, newly discovered strings go into a String Catalog named Localizable. Developers can use custom table names to organize strings into separate catalogs.

- SwiftUI controls such as Text and Button expose many strings for localization automatically.
- Xcode builds targets so strings discovered through compilation are included before translation begins.
- The agent reads the relevant String Catalogs, splits work into batches, and delegates translation work to subagents.
- Xcode tells subagents which plural variations are required for the destination language.

### SwiftUI localizable string with a comment

SwiftUI text literals can be made localizable with developer comments that help translators and agents understand intent.

```text
Text("Hello, world!", comment: "A standard greeting")
```

### Organizing strings with a custom table name

A custom table name places the string in a matching String Catalog, such as Greetings, instead of the default Localizable catalog.

```text
Text("Hello, world!", tableName: "Greetings", comment: "A standard greeting")
```

## Iterating on terminology and new features

After the initial Canadian French translation, the session shows a developer-driven terminology change: replacing a default translation for "landmarks" with "attraits" and renaming the app to "Attraits phares." The agent finds relevant strings across catalogs and updates the translations.

The session also shows adding a new feature and localizing it in one agent task. Because Xcode can surface existing project terminology, the new Canadian French pluralized string reuses the chosen "attraits" terminology without requiring the agent to read the previous conversation.

- Use agents not only for first-pass translation, but also for targeted terminology changes.
- Existing String Catalog translations become useful context for future feature work.
- Pluralized strings should be reviewed in both source and target languages, because plural categories vary by language.

## Review localized UI before shipping

Adding translated strings is only part of localization. Translations can be longer, taller, or flow in a different direction than the source language, so localized UI must be reviewed at runtime.

The demonstrated review workflow changes the run language in the scheme options, builds and runs the app in Canadian French, then asks an agent to render the new UI in that language and look for truncation. The agent identifies a truncation issue in the new feature label.

- Check longer languages such as Canadian French for horizontal truncation.
- Check tall scripts such as Thai for vertical clipping.
- Check right-to-left languages such as Arabic for incorrect alignment and layout direction issues.
- For ambiguous problems, decide whether to fix the implementation, redesign the UI, or request a shorter translation.
- Use TestFlight to collect feedback, screenshots, and suggestions from native speakers before release.

## Best practices for agent-assisted translation

All user-facing strings must be localizable before agents can translate them. SwiftUI helps by making many UI strings localizable by default, but other code often needs explicit localization APIs.

Agents use Apple language-specific style guides that ship in Xcode, and they can also follow project-specific translation guidance. The session recommends adding translation guidance to AGENTS.md or referring from AGENTS.md to a dedicated TRANSLATION.md file so the extra context is read only for translation tasks.

Translation is a long-running, context-heavy task. The session recommends considering models with large context windows and strong extended-task performance, and consulting model-provider documentation for language coverage because quality may vary by language and model.

- Provide a glossary for preferred translations of key terms.
- List words that must remain untranslated, such as product names and trademarks.
- Describe the desired tone, audience, or domain-specific style, such as banking versus children's apps.
- When processing Xcode-exported localizations, use the XLIFF state qualifier to identify machine-translated strings.

### Localizing strings outside SwiftUI views

Use localization APIs for user-facing strings that are not automatically exposed through SwiftUI views.

```text
String(localized: "Hello, world!", comment: "A standard greeting")
LocalizedStringResource("Hello World!", bundle: #bundle, comment: "A standard greeting")
```

### Machine-translation marker in exported XLIFF

The leveraged-mt state qualifier indicates a translation provided by an agent in exported localization files.

```text
<trans-unit id="Grand Canyon" xml:space="preserve">
  <source>Grand Canyon</source>
  <target state="translated" state-qualifier="leveraged-mt">Grand Canyon</target>
  <note>Name of the 'Grand Canyon' landmark.</note>
</trans-unit>
```

Resources:
- Localizing your app using agents: https://developer.apple.com/documentation/Xcode/localizing-your-app-using-agents
- Expanding Your App to New Markets: https://developer.apple.com/localization/

Chapters:
- 0:00 Introduction: Introduces agent-assisted translation in Xcode 27 and explains why software localization needs app context for ambiguous terms, style, and usage. String Catalogs provide context such as where and how strings are used.
- 1:49 Add translations: Shows asking an Xcode agent to translate an app into Canadian French. Xcode adds the language, builds targets to discover strings, creates or updates String Catalogs, and gives subagents context for translating entries including plural variations.
- 8:11 Review and iterate: Demonstrates running the app in the translated language, refining terminology, localizing a new feature, and using agent-rendered previews to detect truncation. It also stresses native-speaker feedback through TestFlight.
- 11:02 Best practices: Covers making all user-facing strings localizable, using APIs such as String(localized:) outside SwiftUI defaults, and providing translation guidance through AGENTS.md or TRANSLATION.md. It also discusses model selection, language quality variation, and the XLIFF leveraged-mt marker.

### Get started with the HTML Model Element

- Session ID: wwdc2026-215
- Page: https://wwdc.ai/2026/215
- Markdown: https://wwdc.ai/2026/215.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/215/
- Category: Safari & Web
- Description: Use the native HTML &lt;model&gt; element to embed USDZ 3D content in Safari, add fallbacks and interactions, enable AR, and optimize assets for production.
- Duration: 15:52
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-215/eng_b41863da1f4b/wwdc2026-215-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/215/4/b7d159c9-ee29-45d9-80f5-87b6a1c90565/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/215/4/b7d159c9-ee29-45d9-80f5-87b6a1c90565/downloads/wwdc2026-215_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/215/4/b7d159c9-ee29-45d9-80f5-87b6a1c90565/downloads/wwdc2026-215_sd.mp4?dl=1

Use the native HTML &lt;model&gt; element to embed USDZ 3D content in Safari, add fallbacks and interactions, enable AR, and optimize assets for production.

TLDR:
- The HTML &lt;model&gt; element is now described as available across Safari on iOS, iPadOS, macOS, and visionOS, with native rendering and stereoscopic support on visionOS.
- Use USDZ for the best starting point: it packages geometry, materials, textures, and animations in one file and works with the element's loading, playback, AR, and optimization workflows.
- Production pages should include image fallback content, handle the element's ready promise, optionally load the W3C polyfill where native support is absent, and test both native and polyfilled behavior.
- Interactivity ranges from one-attribute orbit controls to custom DOMMatrix transforms, requestAnimationFrame transitions, baked animation playback, AR Quick Look, and visionOS spatial experiences.

## What the HTML Model Element Provides

The HTML &lt;model&gt; element brings 3D content to web pages using a native element, similar in spirit to using &lt;img&gt; for images. Apple introduced it on visionOS and describes expanded support across iOS, iPadOS, and macOS, using the same markup across Apple platforms.

The session positions &lt;model&gt; as a native alternative to JavaScript libraries such as model-viewer for supported browsers. A polyfill is available for browsers without native support, but not every capability can be polyfilled; stereoscopic display on spatial platforms is called out as a native-only capability.

- Use native &lt;model&gt; when Safari platform integration and visionOS stereoscopic rendering matter.
- Use a polyfill for broader browser reach, but test native and polyfilled behavior separately.
- Track the emerging W3C model element specification if building long-lived web 3D infrastructure.

## Prepare USDZ Assets

The recommended asset path is Capture, Convert, Create: scan real-world objects with iPhone, convert existing 3D files, or author from scratch in tools such as Blender. The session also mentions generating models from images or text prompts with tools such as Tripo3D and Meshy.ai.

USDZ is the recommended starting format. It is Universal Scene Description packaged into a single file and can include geometry, materials, textures, and animations. Safari supports other formats too, but the examples use USDZ throughout.

- Start with USDZ for the best Model element experience in Safari.
- Bake animations into the USDZ file if you want &lt;model&gt; to play them directly.
- Use AOUSD resources for USDZ specification and pipeline information.

## Load Models, Fallbacks, and Readiness

A model can be loaded directly with the src attribute or with a nested &lt;source&gt; element that includes a MIME type. Because 3D assets can be large, the page should provide loading UI and use the ready promise to know when the asset is ready to display.

Fallback content belongs inside the &lt;model&gt; element. Older Safari versions and browsers that do not support &lt;model&gt; can render an inner image, preserving a useful product view. For unsupported browsers, conditionally importing a polyfill can provide as much of the API as JavaScript can emulate.

- Use &lt;img&gt; inside &lt;model&gt; as the simplest fallback.
- Use model.ready.then(...) to hide loading indicators once the model is available.
- Use model.ready.catch(...) to handle load failure and show fallback UI.
- Check window.HTMLModelElement before loading a polyfill.

### Load a USDZ model with a fallback image

Embeds a USDZ model while preserving an image fallback for browsers without native support or when fallback content is needed.

```text
<model id="mallet" src="mallet.usdz">
  <img src="mallet.png" alt="Rubber mallet with wooden handle">
</model>
```

### Wait for the model to be ready and conditionally polyfill

Uses the ready promise for loading state and loads a polyfill only when the native element is not defined.

```swift
<model id="mallet" src="mallet.usdz"></model>

<script>
  const model = document.getElementById("mallet");
  model.ready
    .then(result => {
      // Hide the loading indicator
    })
    .catch(error => {
      // Loading failed, show fallback
    });
</script>

<script type="module">
  if (!window.HTMLModelElement) {
    import("model-element-polyfill.js").then(() => {
      // Polyfill ready to use
    });
  }
</script>
```

## Styling and Interaction

The model renders in its own virtual space, so it does not automatically pick up the page background. Set background-color directly on the &lt;model&gt; element to visually integrate it with the page; the background is rendered as fully opaque even when a transparent color is specified.

For basic interaction, stagemode="orbit" lets visitors rotate the model side to side, springing back from vertical tilt and rescaling the model to reduce clipping during rotation. For custom controls, remove orbit behavior and assign a DOMMatrix to entityTransform from JavaScript.

- Prefer stagemode="orbit" for simple product exploration with minimal code.
- Use entityTransform for exact view changes such as Side or Reset buttons.
- When manually transforming a model, account for bounding boxes and clipping; the model can rotate out of the visible area.
- Use requestAnimationFrame to animate custom transform transitions smoothly.

### Enable built-in orbit interaction and set a matching background

Adds built-in rotation behavior and sets the model's own opaque background color.

```text
<model id="mallet" src="mallet.usdz" stagemode="orbit"></model>

<style>
  model {
    background-color: #f4f1ec;
  }
</style>
```

### Apply a custom orientation with entityTransform

Uses DOMMatrix rotation around the Y axis to switch the model to a specific view, then restores the initial transform.

```text
<model id="boot" src="boot.usdz"></model>
<button id="button-side">Side</button>
<button id="button-reset">Reset</button>

<script>
  const model = document.getElementById("boot");
  const initialTransform = model.entityTransform;

  document.getElementById("button-side").addEventListener("click", () => {
    const transform = new DOMMatrix();
    transform.rotateSelf(0, 135, 0);
    model.entityTransform = transform;
  });

  document.getElementById("button-reset").addEventListener("click", () => {
    model.entityTransform = initialTransform;
  });
</script>
```

## Animation, AR, and Spatial Experiences

If the USDZ file includes baked animation, the Model element can play the first animation track. The playbackRate property controls speed and direction: positive values play forward, negative values reverse, and larger magnitudes play faster.

To let users place the object in their environment on iOS and iPadOS, wrap the model in an &lt;a rel="ar"&gt; link to the same USDZ resource. On visionOS, &lt;model&gt; supports stereoscopic rendering and is also used by immersive website environments that place the visitor inside a 3D scene.

- Use model.play() to start baked animation playback.
- Set model.playbackRate before playback to control direction and speed.
- Wrap &lt;model&gt; in &lt;a rel="ar" href="..."&gt; for AR Quick Look on iOS and iPadOS.
- Consult the immersive website environments session for the visionOS environment API details.

### Play a baked animation forward or in reverse

Controls the first animation track baked into the USDZ file using playbackRate and play().

```text
<model id="bottle" src="bottle.usdz"></model>
<button id="button-play" onclick="play(5)">Play</button>
<button id="button-reverse" onclick="play(-5)">Reverse</button>

<script>
  const model = document.getElementById("bottle");

  function play(rate) {
    model.playbackRate = rate;
    model.play();
  }
</script>
```

### Enable AR Quick Look

Links the model to AR Quick Look on iOS and iPadOS while keeping the inline 3D model on the page.

```text
<a rel="ar" href="bottle.usdz">
  <model id="boot" src="bottle.usdz"></model>
</a>
```

## Optimize for Production and Next Steps

Large 3D assets can make web pages feel slow, so the session recommends optimizing USDZ files before shipping. The usdcrush command-line tool is shown reducing a boot model from 7.9 MB to 1.9 MB with no perceived visual quality loss in the demo.

For catalogs that need fallback images or thumbnails, usdrecord can render images directly from a 3D file, including options such as output format and custom camera rendering when the file contains a camera. Both usdcrush and usdrecord are described as installed on macOS as part of the USD tool suite.

- Run usdcrush on USDZ assets before production deployment.
- Use usdrecord to generate fallback images or thumbnails from source 3D assets.
- Review the WWDC24 USD and MaterialX session for deeper USD tooling coverage.
- Provide feedback through WebKit, Feedback Assistant, and the W3C Immersive Web Community Group.

### USD tooling mentioned for production pipelines

The session names usdcrush for size reduction and usdrecord for rendering thumbnails or fallback images; exact flags depend on the asset and output needs.

```text
usdcrush boot.usdz
usdrecord input.usdz thumbnail.png
```

Resources:
- WebKit.org - Theater Ticket Sales immersive website environment demo for Apple Vision Pro: https://webkit.org/demos/model-demos/ticket-sales.html
- The HTML model element in Apple Vision Pro: https://webkit.org/blog/17118/a-step-into-the-spatial-web-the-html-model-element-in-apple-vision-pro/
- GitHub: model element samples: https://immersive-web.github.io/model-element-samples/
- WebKit.org - Report issues to the WebKit open-source project: https://bugs.webkit.org/
- AOUSD - Alliance for OpenUSD: https://aousd.org/
- w3.org - Model element: https://immersive-web.github.io/model-element
- Submit feedback: http://feedbackassistant.apple.com/

Chapters:
- 0:00 Introduction: Introduces the HTML &lt;model&gt; element as a native way to add 3D content to web pages, now described as spanning iOS, iPadOS, macOS, and visionOS. It compares the native element with model-viewer and frames the element as an emerging web standard with polyfill support.
- 2:22 Prepare the USDZ model asset: Covers ways to obtain 3D assets, including scanning with iPhone, converting existing files, authoring in tools like Blender, and generating from images or prompts. Recommends USDZ because it packages geometry, materials, textures, and animations in a single file.
- 4:18 Loading and fallbacks: Shows loading a model with src or a nested &lt;source&gt;, adding an inner &lt;img&gt; fallback, using the ready promise for loading/error UI, and conditionally loading a polyfill. It emphasizes testing native and polyfilled behavior and notes that some native spatial features cannot be polyfilled.
- 6:14 Model background: Explains that the model renders in its own virtual space and does not inherit the page background. Developers should set background-color directly on the &lt;model&gt; element, with the caveat that the background is rendered opaque.
- 6:48 Interactions: Demonstrates stagemode="orbit" for built-in rotation, spring-back behavior, and clipping protection. Then shows custom controls using entityTransform and DOMMatrix, with guidance to disable orbit mode and account for possible clipping.
- 8:26 Transition animation: Builds a smooth custom rotation transition using requestAnimationFrame, current angle state, easing, and cancellation of in-flight animations. The example animates between a side view and reset orientation by repeatedly updating entityTransform.
- 10:08 Animation playback: Shows playback of animations baked into a USDZ file through the element's play() method and playbackRate property. Positive playback rates play forward, negative rates reverse, and the value magnitude controls speed.
- 10:52 AR and spatial: Enables AR Quick Look by wrapping the model in an &lt;a rel="ar"&gt; link to the USDZ asset on iOS and iPadOS. Also describes visionOS stereoscopic rendering and points to immersive website environments for placing visitors inside 3D scenes.
- 12:29 Optimize assets for production: Uses usdcrush to reduce USDZ file size substantially in the demo without perceived quality loss. Introduces usdrecord for generating thumbnails or fallback images from 3D assets and notes that both tools are part of the macOS USD tool suite.
- 14:53 Next steps: Recommends creating or generating a model, adding a &lt;model&gt; tag to a site, optimizing assets with USD tools, and testing across platforms. It also encourages participation in W3C Immersive Web standards work and review of related spatial web sessions.

### Create web extensions for Safari

- Session ID: wwdc2026-216
- Page: https://wwdc.ai/2026/216
- Markdown: https://wwdc.ai/2026/216.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/216/
- Category: Safari & Web
- Description: Build, test, package, and distribute Safari web extensions with Manifest V3, declarativeNetRequest, content scripts, storage, and native messaging.
- Duration: 26:42
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-216/eng_07ab48930540/wwdc2026-216-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/216/5/4fceecc8-1e28-465c-b894-fd0d03067c18/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/216/5/4fceecc8-1e28-465c-b894-fd0d03067c18/downloads/wwdc2026-216_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/216/5/4fceecc8-1e28-465c-b894-fd0d03067c18/downloads/wwdc2026-216_sd.mp4?dl=1

Build, test, package, and distribute Safari web extensions with Manifest V3, declarativeNetRequest, content scripts, storage, and native messaging.

TLDR:
- Safari web extensions are built with HTML, CSS, and JavaScript, run across Safari on iOS, iPadOS, macOS, and visionOS, and can be loaded temporarily in Safari for development without Xcode.
- Use Manifest V3 plus WebExtensions APIs such as declarativeNetRequest, scripting, permissions, storage, and background pages or service workers to block, redirect, modify pages, and persist state.
- Safari's permissions model puts users in control of host access; optional host permissions let extensions request access at runtime only for sites the user adds.
- Extensions can be packaged through App Store Connect or Xcode, distributed with TestFlight and the App Store, and connected to native app capabilities through native messaging.

## Safari web extension basics

Safari web extensions are packaged inside an app but authored with standard web technologies: HTML, CSS, and JavaScript. The session builds a distraction-blocking extension that works across Safari on iOS, iPadOS, macOS, and visionOS.

Every extension starts with a Manifest V3 `manifest.json` file that identifies the extension and declares its capabilities. Safari can load an unpacked extension folder for development after enabling web developer features and allowing unsigned extensions in Safari Settings.

- Use the manifest for name, description, version, icons, UI entry points, permissions, host permissions, background scripts, and content scripts.
- Add an SVG icon so Safari can scale it for toolbar and Extensions Settings contexts.
- Use an action popup for compact toolbar UI, or an options page for larger settings UI.

### Minimal Manifest V3 file

The manifest is the required JSON file that tells Safari what the extension is.

```text
{
  "manifest_version": 3,
  "name": "Shiny OnTrack",
  "description": "Stay on track while you browse the web",
  "version": 1.0
}
```

### Options page UI entry point

An options page gives the extension a full-page settings surface instead of a small toolbar popup.

```text
{
  "manifest_version": 3,
  "name": "Shiny OnTrack",
  "description": "Stay on track while you browse the web",
  "version": 1.0,
  "icons": { "512": "images/icon.svg" },
  "options_ui": { "page": "options.html" }
}
```

## Blocking and redirecting network requests

The extension uses the `declarativeNetRequest` API to block, modify, or redirect network requests. Rules include an ID, priority, action, and matching condition. Static rules can be declared up front, but dynamic rules are useful when the sites are chosen by the user at runtime.

Blocking a navigation does not require page access, but redirecting a network request does. For redirects, the extension switches to `declarativeNetRequestWithHostAccess` and asks for optional host permissions when the user adds a site.

- Use `declarativeNetRequest` for simple block rules that do not need host access.
- Use `declarativeNetRequestWithHostAccess` when rules need host access, such as redirecting to an extension page.
- Declare `optional_host_permissions` when access should be requested lazily at runtime.
- Request both the host and subdomain origins when adding a user-selected domain.

### Dynamic redirect rule

Redirect rules can send blocked navigations to a custom extension page instead of Safari's default blocked/error page.

```text
function createRedirectRule(host) {
  return {
    id: hostToRuleID(host),
    priority: 1,
    action: {
      type: "redirect",
      redirect: { extensionPath: "/blocked.html" }
    },
    condition: {
      urlFilter: `||${host}`,
      resourceTypes: ["main_frame"]
    }
  }
}

await browser.declarativeNetRequest.updateDynamicRules({
  addRules: hosts.map(createRedirectRule)
})
```

### Request optional host access at runtime

Optional host permissions let the extension ask for access only when the user adds a site.

```text
const granted = await browser.permissions.request({
  origins: [`*://${host}/*`, `*://*.${host}/*`]
})

if (!granted) return
```

## Modifying webpages with content scripts

Content scripts let an extension read and modify a page. The sample uses them to inject a 10-minute countdown timer on distracting sites when the user selects the light blocking mode.

Content scripts can be static in the manifest when match patterns are known ahead of time, or registered dynamically with the `scripting` API when the target hosts are user-defined. Dynamically registered scripts can persist across Safari relaunches, but the session notes that they should be re-registered after extension updates.

- Add the `scripting` permission to dynamically register content scripts.
- Use match patterns for both the selected domain and its subdomains.
- Set `persistAcrossSessions: true` to keep registered scripts after Safari relaunches.
- Use a background page or service worker to respond to lifecycle events such as extension updates.

### Register a persistent content script dynamically

Dynamic registration is appropriate when the extension does not know target sites until the user adds them.

```text
function contentScript(host) {
  return {
    id: `cs-${host}`,
    js: ["content.js"],
    css: ["content.css"],
    matches: [`*://${host}/*`, `*://*.${host}/*`],
    persistAcrossSessions: true
  }
}

await browser.scripting.registerContentScripts(hosts.map(contentScript))
```

### Re-register content scripts after updates

Registered content scripts persist across Safari restarts but should be restored after extension updates.

```text
browser.runtime.onInstalled.addListener(async (details) => {
  if (details.reason !== "update") return
  const hosts = await getHosts()
  await registerScripts(hosts)
})
```

## Persisting settings and extension state

The sample initially stores blocklist state in memory, which disappears when the extension reloads. The `storage` API fixes this by persisting hosts and blocking mode in `browser.storage.local`. Safari also supports session storage for temporary in-memory data that does not need to survive restarts.

- Use `browser.storage.local` for durable extension preferences and user state.
- Use session storage for short-lived data that should not be written to disk.
- Store both the list of blocked hosts and the selected blocking mode.
- When the mode changes, update stored state and recreate or remove blocking rules accordingly.

### Storage helpers for hosts and mode

The extension persists its blocklist and selected mode with `browser.storage.local`.

```text
export async function updateHosts(hosts) {
  await browser.storage.local.set({ hosts })
}

export async function getHosts() {
  const { hosts = [] } = await browser.storage.local.get("hosts")
  return hosts
}

export async function saveBlockMode(mode) {
  await browser.storage.local.set({ blockMode: mode })
}

export async function getBlockMode() {
  const { blockMode = "full" } = await browser.storage.local.get("blockMode")
  return blockMode
}
```

## Packaging, TestFlight, and App Store distribution

Safari web extensions must be packaged inside a containing app. The session shows two packaging paths: using App Store Connect to create/package the app without Xcode, or using Xcode after generating a project with the Safari Web Extension Packager.

In App Store Connect, the developer creates an app, chooses supported platforms, sets a bundle identifier, uploads the extension resources through the Safari Web Extension Packager, tests with TestFlight, and submits the build for App Review. Choosing iOS and macOS makes the extension available on iPhone, iPad, Mac, and as a compatible app on Apple Vision Pro.

- Use TestFlight to distribute beta builds and collect feedback before App Store submission.
- For Xcode-based distribution, archive the app and ensure the build number is higher than any previously uploaded build.
- Use App Store Connect's distribution metadata, screenshots, description, selected build, and review submission workflow.

### Generate an Xcode project from extension resources

The Safari Web Extension Packager creates and opens an Xcode project containing the app and web extension.

```text
xcrun safari-web-extension-packager --copy-resources /path/to/ShinyOnTrack
```

## Native messaging with the containing app

Native messaging lets JavaScript in the web extension communicate with the containing app through a Safari app extension handler. This is useful when the extension needs platform features unavailable to web APIs.

The sample adds biometric authentication before allowing changes to the blocklist. The extension sends a `requestBioAuth` message from its background page, and the generated `SafariWebExtensionHandler` uses Local Authentication to evaluate biometric authentication and return a success value.

- Declare the `nativeMessaging` permission in the manifest.
- Send messages from extension JavaScript with `browser.runtime.sendNativeMessage`.
- Handle incoming messages in `SafariWebExtensionHandler`, using `SFExtensionMessageKey` to read and reply.
- Use native APIs such as `LocalAuthentication` inside the containing app or app extension side of the flow.

### Send a native message from the extension

The background page asks the native side to perform biometric authentication and returns whether it succeeded.

```text
export async function requestBioAuth() {
  const message = { message: "requestBioAuth" }
  const response = await browser.runtime.sendNativeMessage(message)
  return response?.success
}
```

### Reply from SafariWebExtensionHandler

The app extension returns a message payload to the web extension through `SFExtensionMessageKey`.

```swift
private func reply(context: NSExtensionContext, success: Bool) {
  let response = NSExtensionItem()
  response.userInfo = [SFExtensionMessageKey: ["success": success]]
  context.completeRequest(returningItems: [response], completionHandler: nil)
}
```

Resources:
- w3.org - W3C WebExtensions Community Group: https://www.w3.org/community/webextensions/
- Packaging and distributing Safari Web Extensions with App Store Connect: https://developer.apple.com/documentation/SafariServices/packaging-and-distributing-safari-web-extensions-with-app-store-connect
- WebKit.org - Report issues to the WebKit open-source project: https://bugs.webkit.org/
- Submit feedback: http://feedbackassistant.apple.com/
- MDN Web Docs - Web Extensions API: https://developer.mozilla.org/en-US/docs/Mozilla/Add-ons/WebExtensions/API

Chapters:
- 0:00 Introduction: Introduces Safari web extensions as HTML, CSS, and JavaScript extensions packaged inside apps and built on cross-browser WebExtensions work. The sample extension blocks distracting sites with a 10-minute light mode and a full redirect mode across Apple platforms.
- 3:23 Get started: Creates the initial Manifest V3 file, adds an SVG icon, loads the unpacked extension in Safari, and adds UI through either a toolbar action popup or an options page. The options page starts with a simple HTML page before being replaced with the extension settings interface.
- 7:23 Block content: Adds declarative net request permissions and dynamic rules to block user-selected hosts. Then switches to redirect rules, explains when host access is required, and uses optional host permissions with `browser.permissions.request` to ask users for access at runtime.
- 14:40 Modify webpages: Uses content scripts to inject a countdown timer into pages for light blocking mode, registering scripts dynamically with the `scripting` API. Adds `storage` persistence for hosts and blocking mode, then uses a background page to re-register content scripts after extension updates.
- 19:53 Package and distribute: Shows how App Store Connect can create the containing app and package the uploaded extension resources without requiring a Mac. Covers platform selection, bundle identifier setup, TestFlight beta distribution, metadata, build selection, and App Review submission.
- 22:33 Communicate with your app: Uses the Safari Web Extension Packager to generate an Xcode project and adds native messaging between the extension and containing app. The sample sends a biometric authentication request from JavaScript and handles it in `SafariWebExtensionHandler` using Local Authentication.
- 26:04 Next steps: Wraps up by recommending the sample project, MDN WebExtensions documentation, Feedback Assistant, and WebKit bug reporting while testing Safari 27 extensions.

### Enhance the accessibility of your reading app

- Session ID: wwdc2026-219
- Page: https://wwdc.ai/2026/219
- Markdown: https://wwdc.ai/2026/219.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/219/
- Category: Accessibility & Inclusion
- Description: Build accessible long-form reading experiences with VoiceOver, Speak Screen, text navigation, page turning, selection, and UITextInput.
- Duration: 20:00
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-219/eng_ec8f0406fdb0/wwdc2026-219-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/219/4/da70a3a7-e193-4513-904f-991788c1fa81/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/219/4/da70a3a7-e193-4513-904f-991788c1fa81/downloads/wwdc2026-219_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/219/4/da70a3a7-e193-4513-904f-991788c1fa81/downloads/wwdc2026-219_sd.mp4?dl=1

Build accessible long-form reading experiences with VoiceOver, Speak Screen, text navigation, page turning, selection, and UITextInput.

TLDR:
- Use standard text controls first: UITextView, SwiftUI TextEditor, selectable SwiftUI Text, and NSTextView already provide granular navigation and accessible selection through UITextInput.
- Connect separate paragraphs or text elements with text navigation APIs, or SwiftUI accessibilityLinkedGroup, so VoiceOver can move by line, word, or character across element boundaries.
- For paginated reading, mark the final readable element with the causesPageTurn trait and implement accessibilityScroll so VoiceOver and Speak Screen can continue read-all across pages.
- For custom-rendered or scanned text, adopt UITextInput fully, including text ranges, selection geometry, tokenizer behavior, and optional UITextInteraction for standard selection visuals.

## What makes reading accessibility different

Long-form reading is not the same problem as navigating controls. A good reading app lets assistive technologies move fluidly through text, continue reading without interruptions, and select text with the same fidelity users expect from system text views.

The session focuses on three goals for apps that display paginated or multi-paragraph content: granular text navigation, continuous reading, and comprehensive text selection. The examples center on VoiceOver and Speak Screen, with benefits extending to Accessibility Reader.

- Granular navigation means VoiceOver and Speak Screen can move by line, word, character, and other text granularities.
- Continuous reading means read-all gestures can proceed through paragraphs and pages without stopping at artificial layout boundaries.
- Accessible selection means users can select text and discover selection-related actions through assistive technologies.

## Start with standard text views when possible

Apple's standard text components already implement the high-fidelity text accessibility behavior needed for reading. UIKit UITextView, SwiftUI TextEditor, selectable SwiftUI Text, and AppKit NSTextView provide line, word, and character navigation plus accessible text selection.

UIAccessibilityReadingContent remains useful for full-page reading support, but this session emphasizes UITextInput because it is the protocol used by native text views and can also be adopted by custom views.

- Use UITextView on iOS when the layout can be represented with system text views.
- Use SwiftUI TextEditor or Text with textSelection enabled for accessible SwiftUI text.
- Use NSTextView, or the SwiftUI text views above, for macOS apps.
- Prefer these components before building custom-rendered text, because they supply much of the accessibility behavior automatically.

## Link separate text elements into one reading flow

Separate paragraph views can break VoiceOver's line-by-line navigation: when the user reaches the end of one element, VoiceOver may not know which text element should come next. Text navigation APIs let each element expose its next and previous accessible text element so navigation continues across boundaries.

In SwiftUI, accessibilityLinkedGroup links multiple selectable text elements in the same group to provide equivalent text navigation behavior. The transcript notes this SwiftUI modifier as starting in iOS 27. On macOS AppKit, accessibilitySharedTextUIElements provides a similar result.

- Set accessibilityNextTextNavigationElement and accessibilityPreviousTextNavigationElement for adjacent UIKit text elements.
- Use the same accessibilityLinkedGroup id and namespace for SwiftUI text elements that should be treated as one linked reading group.
- Audit with the VoiceOver lines rotor to confirm users can move from the last line of one paragraph to the first line of the next.

### Connect UIKit paragraph views for VoiceOver text navigation

Each paragraph identifies its adjacent text element, allowing VoiceOver to continue granular navigation across separate views.

```swift
import UIKit

class TravelGuidePageController: UIViewController {
    var paragraphs: [TravelGuideParagraph] = []

    func configureNavigationElements() {
        for (index, paragraph) in paragraphs.enumerated() {
            if index + 1 < paragraphs.count {
                paragraph.accessibilityNextTextNavigationElement = paragraphs[index + 1]
            }
            if index - 1 >= 0 {
                paragraph.accessibilityPreviousTextNavigationElement = paragraphs[index - 1]
            }
        }
    }
}
```

### Link selectable SwiftUI Text elements

Text elements with the same linked group id and namespace participate in a shared navigation sequence.

```swift
import SwiftUI

struct PageView: View {
    @Namespace private var pageNamespace
    var paragraphs: [String]
    var pageNumber: Int

    var body: some View {
        Text(paragraphs[0])
            .textSelection(.enabled)
            .accessibilityLinkedGroup(id: pageNumber, in: pageNamespace)

        Text(paragraphs[1])
            .textSelection(.enabled)
            .accessibilityLinkedGroup(id: pageNumber, in: pageNamespace)
    }
}
```

## Support read-all across pages

Paginated content can interrupt read-all experiences in VoiceOver and Speak Screen. Marking the final text element on a page with the causesPageTurn trait tells assistive technologies that reaching this element can trigger a page transition.

Pair causesPageTurn with accessibilityScroll so the app can move to the next or previous page and post a page-scrolled notification. This lets Speak Screen and VoiceOver continue reading into the next page automatically, instead of stopping at the bottom of the current page.

- Apply causesPageTurn to the last readable element on a page in UIKit or SwiftUI.
- Implement accessibilityScroll(_:) to perform the page turn requested by the assistive technology.
- Post UIAccessibility.Notification.pageScrolled with a useful page status string after moving pages.

### Advance pages during continuous reading

The final paragraph advertises that it can cause a page turn, while accessibilityScroll performs the page navigation and announces the result.

```swift
import UIKit

class TravelGuidePageController: UIViewController {
    override func viewDidLoad() {
        super.viewDidLoad()
        lastParagraphView.accessibilityTraits.insert(.causesPageTurn)
    }

    override func accessibilityScroll(_ direction: UIAccessibilityScrollDirection) -> Bool {
        moveToPage(direction)
        let scrollString = "Page \(currentPage) of \(pages.count)"
        UIAccessibility.post(notification: .pageScrolled, argument: scrollString)
        return true
    }
}
```

## Expose selection actions through the edit rotor

System text views already provide accessible text selection. If the app adds selection-specific features, such as saving selected content, make them discoverable through VoiceOver's edit rotor rather than only through visual controls.

Use UIAccessibilityCustomAction and set its category to UIAccessibilityCustomAction.editCategory for actions associated with text editing or selection. Preserve any custom actions from the superclass.

- Use the text selection rotor to test expanding and shrinking selection by word, line, or other granularities.
- Use the edit rotor for actions that operate on the current text selection.
- Do not categorize selection actions as generic actions when they belong with editing behavior.

### Add a selection-related action to the edit rotor

The custom action appears in VoiceOver's edit rotor because it is categorized as an editing action.

```swift
import UIKit

class TravelGuideParagraph: UITextView {
    override var accessibilityCustomActions: [UIAccessibilityCustomAction]? {
        get {
            let saveAction = UIAccessibilityCustomAction(name: "Save Recommendation") { _ in
                self.saveRecommendation()
            }
            saveAction.category = UIAccessibilityCustomAction.editCategory
            return (super.accessibilityCustomActions ?? []) + [saveAction]
        }
        set { }
    }

    private func saveRecommendation() -> Bool {
        // Save the current selection.
        return true
    }
}
```

## Make custom-rendered text accessible with UITextInput

Custom typography, shared rendering engines, and scanned pages can remove the accessibility behavior that system text views provide. For custom-rendered or image-based text, adopt UITextInput on the accessibility element to give assistive technologies the same model of text, ranges, selection, and geometry that native text controls expose.

A complete UITextInput implementation must manage text ranges, return substrings for queried ranges, compute selection rectangles, and provide a tokenizer for navigation by line, sentence, word, and character. The session emphasizes implementing the protocol in its entirety to receive the full accessibility benefit.

UITextInteraction can be added to a custom text view for familiar selection handles and highlights. Notify the input delegate when selectedTextRange changes so the system updates selection visuals.

- Implement selectionRects(for:) using the geometry of the rendered text or scanned image.
- Implement text(in:) so assistive technologies can retrieve the exact string for a requested range.
- Provide a UITextInputTokenizer, such as a custom tokenizer based on UITextInputStringTokenizer when appropriate.
- Combine UITextInput with causesPageTurn and text navigation APIs for custom paginated reading content.

### Adopt UITextInput for scanned or custom-rendered text

The custom view exposes text geometry, text extraction, tokenization, and selection changes so assistive technologies can navigate and select custom content.

```swift
import UIKit

class ScannedPage: UIView, UITextInput {
    override init(frame: CGRect) {
        super.init(frame: frame)
        let interaction = UITextInteraction(for: .nonEditable)
        interaction.textInput = self
        addInteraction(interaction)
    }

    func selectionRects(for range: UITextRange) -> [UITextSelectionRect] {
        var rects: [UITextSelectionRect] = []
        let startLine = lineIndex(for: range.start)
        let endLine = lineIndex(for: range.end)
        for line in startLine...endLine {
            rects.append(selectionRectFromImage(for: range, in: line))
        }
        return rects
    }

    func text(in range: UITextRange) -> String? {
        let nsRange = nsRange(from: range)
        guard let range = Range(nsRange, in: scannedText) else { return nil }
        return String(scannedText[range])
    }

    var tokenizer: any UITextInputTokenizer {
        CustomHandwritingTokenizer(textInput: self)
    }

    weak var inputDelegate: UITextInputDelegate?

    var selectedTextRange: UITextRange? {
        willSet { inputDelegate?.selectionWillChange(self) }
        didSet { inputDelegate?.selectionDidChange(self) }
    }
}
```

Resources:
- accessibilityNextTextNavigationElement: https://developer.apple.com/documentation/ObjectiveC/NSObject-swift.class/accessibilityNextTextNavigationElement
- editCategory: https://developer.apple.com/documentation/UIKit/UIAccessibilityCustomAction/editCategory
- accessibilityLinkedGroup(id:in:): https://developer.apple.com/documentation/SwiftUI/View/accessibilityLinkedGroup(id:in:)
- causesPageTurn: https://developer.apple.com/documentation/SwiftUI/AccessibilityTraits/causesPageTurn
- UITextInput: https://developer.apple.com/documentation/UIKit/UITextInput
- Accessibility for UIKit: https://developer.apple.com/documentation/UIKit/accessibility-for-uikit

Chapters:
- 0:01 Introduction: Introduces the accessibility challenges of long-form reading apps and frames reading as fluid text navigation rather than ordinary UI control navigation. The session scope covers characteristics of a good reading experience, extending UIKit and SwiftUI text views, and supporting custom text.
- 1:26 Characteristics: Defines the core goals for accessible reading: granular navigation, continuous reading, and comprehensive text selection. VoiceOver and Speak Screen are demonstrated as primary assistive technologies for evaluating those goals.
- 3:45 Standard views: Explains how UITextView, SwiftUI TextEditor, selectable SwiftUI Text, and NSTextView provide accessible text behavior through UITextInput. It then shows how to link separate text elements, enable page turning during read-all, and expose selection actions through the edit rotor.
- 14:05 Custom text: Covers custom-rendered and scanned text, where system text view behavior is not available automatically. Adopting UITextInput fully, including selection geometry, text range lookup, tokenization, and optional UITextInteraction, restores granular navigation, selection, and continuous reading support.

### Refine accessibility for custom controls

- Session ID: wwdc2026-220
- Page: https://wwdc.ai/2026/220
- Markdown: https://wwdc.ai/2026/220.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/220/
- Category: Accessibility & Inclusion
- Description: Make SwiftUI custom controls work with VoiceOver and other assistive technologies using labels, values, adjustable actions, passthrough gestures, custom actions, and Direct Touch.
- Duration: 16:25
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-220/eng_d67bc112dc5b/wwdc2026-220-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/220/4/945f8d34-8427-4476-ae75-34edc4a9c3f9/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/220/4/945f8d34-8427-4476-ae75-34edc4a9c3f9/downloads/wwdc2026-220_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/220/4/945f8d34-8427-4476-ae75-34edc4a9c3f9/downloads/wwdc2026-220_sd.mp4?dl=1

Make SwiftUI custom controls work with VoiceOver and other assistive technologies using labels, values, adjustable actions, passthrough gestures, custom actions, and Direct Touch.

TLDR:
- Use the visual affordances of a control as an accessibility checklist: purpose, value, available actions, and feedback.
- For custom slider-like controls, expose a label and value, add the adjustable trait, implement accessibility adjustable actions, and announce meaningful value changes.
- Use the VoiceOver passthrough gesture plus a tuned accessibility activation point for fine-grained direct manipulation of a focused control.
- For complex controls, prefer custom actions for multi-axis or repeated operations, and use Direct Touch for gesture-heavy regions while still offering alternative actions when possible.

## Accessibility model for custom controls

Custom controls often communicate through visual cues: shape, position, handles, axes, gestures, and immediate visual feedback. Assistive technologies do not automatically receive all of that implicit information, so custom controls need explicit accessibility metadata and interactions.

The session frames custom-control accessibility around four questions: can someone understand the control's purpose, read its current value, know what actions are available, and receive feedback when interaction changes state?

- Use an accessibility label to name the purpose of the control.
- Expose an accessibility value when the control represents state or a measurement.
- Add traits and actions that match the interaction model, such as adjustable behavior for a one-dimensional value.
- Provide feedback through value updates, announcements, sound, haptics, or other output that assistive technologies can perceive.

## Make a custom slider behave like a standard adjustable control

The coffee dispenser example starts as a custom vertical fill control that visually indicates ounces of coffee and responds to dragging. VoiceOver initially cannot describe the control or adjust it in the same way as a standard SwiftUI slider.

The fix is to mark the view as an accessibility element, provide a label and value, add the adjustable trait, and implement increment and decrement behavior with an accessibility adjustable action. This gives VoiceOver users the familiar swipe-up/swipe-down adjustment model.

- Use `.accessibilityElement()` when the custom drawing should be treated as one accessible control.
- Use `.accessibilityLabel` for the control name and `.accessibilityValue` for the current state.
- Use `.accessibilityAddTraits(.adjustable)` and `.accessibilityAdjustableAction` for slider-like controls.
- Handle both `.increment` and `.decrement` in the adjustable action closure.

### Expose a custom coffee dispenser as an adjustable control

Adds purpose, value, the adjustable trait, and VoiceOver increment/decrement behavior to a custom slider-like control.

```swift
import SwiftUI

struct CoffeeDispenserView: View {
    @State var coffee: Double = 0.0

    var body: some View {
        CoffeeSlider(value: coffee)
            .accessibilityElement()
            .accessibilityLabel("Coffee Dispenser")
            .accessibilityValue("\(Int(coffee)) ounces")
            .accessibilityAddTraits(.adjustable)
            .accessibilityAdjustableAction { direction in
                switch direction {
                case .increment:
                    increaseCoffeeAmount()
                case .decrement:
                    decreaseCoffeeAmount()
                }
            }
    }
}
```

## Support precise manipulation with passthrough gestures

VoiceOver's passthrough gesture lets someone double-tap and hold, then send touch events directly to the focused control. This is useful when the adjustable action is too coarse, such as changing the coffee amount by fractions of an ounce.

The passthrough gesture begins at the control's accessibility activation point. For a vertical fill control, setting that point to the current fill level makes the gesture begin where the visible handle or value is, leaving useful room to move in either direction.

During passthrough interaction, avoid announcing every tiny value change. The example tracks the last spoken value and throttles announcements so feedback stays meaningful rather than noisy.

- Use `.accessibilityActivationPoint` to align direct manipulation with the control's current visual state.
- Post accessibility announcements when the value changes enough to matter.
- Throttle announcements; the example waits at least about 0.3 seconds and skips unchanged values.

### Place the activation point at the current fill level

Starts VoiceOver passthrough interaction at the current coffee level instead of the default center point.

```swift
import SwiftUI

struct CoffeeDispenserView: View {
    @State var coffee: Double = 0.0

    var body: some View {
        CoffeeSlider(value: coffee)
            .accessibilityActivationPoint(
                UnitPoint(x: 0.5, y: 1 - coffee)
            )
    }
}
```

### Announce meaningful value changes

Posts accessibility announcements during direct manipulation while avoiding excessive speech.

```swift
import SwiftUI

struct CoffeeDispenserView: View {
    @State var coffee: Double = 0.0

    var body: some View {
        CoffeeSlider(value: coffee)
            // ...
            .onChange(of: coffee) { _, newValue in
                if sufficientTimeSinceLastAnnouncement() && valueHasChanged() {
                    cacheLastSpokenValue(newValue)
                    AccessibilityNotification
                        .Announcement(newValue)
                        .post()
                }
            }
    }
}
```

## Use custom actions for multi-dimensional controls

An equalizer pad has two axes: frequency and amplitude. The adjustable trait only models a single increment/decrement axis, so it is not the best fit for navigating a two-dimensional surface.

Custom actions expose operations that assistive-technology users can discover, select, and activate. In the equalizer example, four actions move the handle up, right, down, and left by fixed steps, clamped to the chart bounds.

- Use custom actions when a control has more than one meaningful operation or axis.
- Give each action a descriptive label such as "Move Up" or "Move Right."
- Update one dimension at a time and provide state feedback through the accessibility value or the control's own output.
- Custom actions are also useful as alternatives for users who cannot perform complex gestures.

### Add directional custom actions to a two-axis control

Exposes a two-dimensional pad through familiar, discrete VoiceOver actions instead of forcing it into a single adjustable axis.

```swift
import SwiftUI

struct EqualizerView: View {
    var body: some View {
        EqualizerPad()
            .accessibilityActions("Move Up") {
                increaseY(by: 10)
            }
            .accessibilityActions("Move Right") {
                increaseX(by: 10)
            }
            .accessibilityActions("Move Down") {
                decreaseY(by: 10)
            }
            .accessibilityActions("Move Left") {
                decreaseX(by: 10)
            }
    }
}
```

## Use Direct Touch for gesture-heavy interactive regions

Some controls are defined by repeated or varied gestures, such as an interactive virtual cat that responds to patting, tapping, and pinching. A one-time passthrough gesture may not be the best model because users may want to perform several gestures in sequence.

The Direct Touch API marks a region where touch events pass to the control instead of being interpreted by VoiceOver. The `.requiresActivation` option prevents accidental interaction while exploring: the user must activate the region before direct touch begins, and it remains active until focus moves elsewhere.

The session cautions that not everyone can perform direct touch gestures. When possible, provide custom actions or other accessible alternatives in addition to Direct Touch.

- Use `.accessibilityDirectTouch([.requiresActivation])` for regions that need direct gesture input after explicit activation.
- Consider `.silentOnTouch` for controls that provide their own audio feedback and should not be interrupted by VoiceOver speech.
- Still expose labels, values, and alternative actions where possible.
- Direct Touch can help VoiceOver users access the same gesture vocabulary as sighted users, but it should not be the only interaction path when alternatives are feasible.

### Enable Direct Touch for an interactive surface

Names the interactive region, reports the cat's current reaction, and allows direct gesture input after activation.

```swift
import SwiftUI

struct VirtualCat: View {
    var cat: CatModel

    var body: some View {
        InteractiveCatSurface()
            .accessibilityLabel("Virtual Cat")
            .accessibilityValue(cat.currentReaction.description)
            .accessibilityDirectTouch([.requiresActivation])
    }
}
```

Resources:
- Accessible controls: https://developer.apple.com/documentation/SwiftUI/Accessible-controls
- Accessible descriptions: https://developer.apple.com/documentation/SwiftUI/Accessible-descriptions
- Accessibility fundamentals: https://developer.apple.com/documentation/SwiftUI/Accessibility-fundamentals
- Creating accessible views: https://developer.apple.com/documentation/SwiftUI/creating-accessible-views

Chapters:
- 0:01 Introduction: Introduces the need to make custom UI controls accessible so assistive-technology users can access the core interactions an app provides. The session sets up guiding principles followed by examples of increasingly complex controls.
- 1:02 Guiding principles: Explains how visual controls communicate purpose, value, actions, and feedback, then maps those cues to accessibility labels, values, traits, actions, activation points, and announcements. A coffee dispenser control is improved from an inaccessible custom view into an adjustable VoiceOver control with support for precise passthrough interaction.
- 8:41 Complex controls: Applies the same principles to a two-dimensional equalizer pad using custom actions and to a gesture-heavy virtual cat surface using Direct Touch. The chapter emphasizes choosing interaction models that match the control and providing alternatives when direct gestures are not accessible to everyone.

### Prepare your tvOS apps for Dynamic Type

- Session ID: wwdc2026-221
- Page: https://wwdc.ai/2026/221
- Markdown: https://wwdc.ai/2026/221.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/221/
- Category: Accessibility & Inclusion
- Description: Learn how to support tvOS 27 Large Text by replacing fixed typography and constraints with Dynamic Type styles and adaptive layouts.
- Duration: 10:08
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-221/eng_d780e5adc1fe/wwdc2026-221-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/221/5/ada10ebd-34f8-4f57-92b5-4b3cd6281267/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/221/5/ada10ebd-34f8-4f57-92b5-4b3cd6281267/downloads/wwdc2026-221_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/221/5/ada10ebd-34f8-4f57-92b5-4b3cd6281267/downloads/wwdc2026-221_sd.mp4?dl=1

Learn how to support tvOS 27 Large Text by replacing fixed typography and constraints with Dynamic Type styles and adaptive layouts.

TLDR:
- tvOS 27 adds system-wide Large Text support; UIKit and SwiftUI standard controls can scale automatically when apps use Dynamic Type-compatible text styles.
- Common fixes include removing hard-coded font sizes, widths, heights, and rigid constraints that cause truncation or clipping at larger text sizes.
- SwiftUI apps can inspect `dynamicTypeSize` to adjust grids, carousels, and card layouts for accessibility sizes.
- UIKit apps should use preferred text styles, enable `adjustsFontForContentSizeCategory`, and respond to `UITraitPreferredContentSizeCategory` changes for layout updates.

## Large Text on tvOS 27

Large Text support is available system-wide on tvOS 27, bringing Dynamic Type-style text scaling to tvOS apps. People enable it in Settings under Accessibility, Display, Text Size, with sizes ranging from Large through Accessibility XXXL.

Apps that support Larger Text can indicate that support in their Accessibility Nutrition Labels for tvOS in the App Store. Standard UIKit and SwiftUI components such as labels, buttons, and navigation tab bars handle much of the scaling automatically, but custom UI needs review.

## Find common Dynamic Type issues

The main problems to look for are fixed font sizes, fixed dimensions, and rigid constraints. These often appear in custom text elements or media-detail layouts designed around predictable dimensions, and they can cause text truncation, clipped controls, or inadequate spacing when text grows.

The recommended migration is to use semantic text styles and flexible layout constraints so text can grow with the user's setting.

- Search for hard-coded font sizes and replace them with standard text styles.
- Search for hard-coded width and height constraints that limit text growth.
- Check padding and spacing around controls after scaling, not just the text itself.
- Test at the largest Large Text sizes, not only the default size.

### Use standard SwiftUI text styles and flexible width

Replace a fixed font and fixed 300-point width with a semantic `caption` style and a flexible maximum width.

```text
VStack(spacing: 20) {
    Text("Signup information")
        .font(.caption.bold())
        .lineLimit(1)
        .foregroundStyle(.secondary)
        .frame(maxWidth: .infinity, alignment: .leading)

    HStack(alignment: .top, spacing: 40) {
        /* ... */
    }
}
```

### Use Dynamic Type text styles in UIKit

UIKit labels need a preferred text style plus `adjustsFontForContentSizeCategory` so they update when the content size category changes.

```text
// Before: hard-coded size
titleLabel.font = UIFont.boldSystemFont(ofSize: 28)

// After: Dynamic Type-compatible style
titleLabel.font = UIFont.preferredFont(forTextStyle: .headline)
titleLabel.adjustsFontForContentSizeCategory = true
```

## Adapt media grids and carousels

Media interfaces often need more than font scaling. A shelf or carousel that shows six posters at a standard text size may not leave enough horizontal room for larger titles. In SwiftUI, read `dynamicTypeSize` from the environment and use it to adjust layout parameters such as the number of visible cells.

The session's example reduces a horizontal movie shelf from six cells to four cells at accessibility sizes, giving each poster title more width. For very long text, consider a custom marquee strategy.

### Adjust visible cells based on Dynamic Type size

Use `dynamicTypeSize.isAccessibilitySize` to give each carousel item more room when larger text is enabled.

```swift
struct MovieShelf: View {
    @Environment(\.dynamicTypeSize) private var dynamicTypeSize

    var body: some View {
        ScrollView(.horizontal) {
            LazyHStack(spacing: 40) {
                ForEach(Asset.allCases) { asset in
                    Button {
                        /* ... */
                    } label: {
                        asset.portraitImage
                        Text(asset.title)
                    }
                    .containerRelativeFrame(
                        .horizontal,
                        count: dynamicTypeSize.isAccessibilitySize ? 4 : 6,
                        spacing: 40
                    )
                }
            }
        }
    }
}
```

## Switch layouts for accessibility sizes

Some content cards need a structural layout change at larger text sizes. A horizontal card with an image, title, and subtitle can become cramped when text scales. Switching to a vertical layout lets text use the full card width and allows cells to grow taller.

SwiftUI can use `AnyLayout` to choose between `HStackLayout` and `VStackLayout`. UIKit can update a `UIStackView` axis based on `preferredContentSizeCategory.isAccessibilityCategory` and register for trait changes so the layout updates while the app is running.

### Conditional SwiftUI layout with AnyLayout

Switch from horizontal to vertical card content when accessibility text sizes are active.

```swift
struct CardContentView: View {
    @Environment(\.dynamicTypeSize) private var dynamicTypeSize
    var asset: Asset

    var body: some View {
        let layout = dynamicTypeSize.isAccessibilitySize
            ? AnyLayout(VStackLayout(alignment: .leading, spacing: 10))
            : AnyLayout(HStackLayout(alignment: .top, spacing: 10))

        layout {
            /* ... */
        }
    }
}
```

### UIKit adaptive stack view layout

Respond to content size category changes by updating `UIStackView.axis`.

```swift
class AdaptiveLayoutViewController: UIViewController {
    let stackView = UIStackView()

    override func viewDidLoad() {
        super.viewDidLoad()
        updateLayout()

        let sizeTraits: [UITrait] = [UITraitPreferredContentSizeCategory.self]
        registerForTraitChanges(sizeTraits, action: #selector(updateLayout))
    }

    private func updateLayout() {
        if traitCollection.preferredContentSizeCategory.isAccessibilityCategory {
            stackView.axis = .vertical
        } else {
            stackView.axis = .horizontal
        }
    }
}
```

## Implementation checklist

- Use standard text styles instead of hard-coded font sizes.
- Replace fixed widths and heights with flexible constraints where text needs to grow.
- Test the app systematically with Large Text enabled on tvOS, including the largest accessibility sizes.
- Adapt grids, shelves, carousels, and cards when scaling alone does not preserve legibility.
- Declare Larger Text support in the app's Accessibility Nutrition Labels for tvOS when the app properly supports it.

Resources:
- Applying custom fonts to text: https://developer.apple.com/documentation/SwiftUI/Applying-Custom-Fonts-to-Text
- Scaling fonts automatically: https://developer.apple.com/documentation/UIKit/scaling-fonts-automatically

Chapters:
- 0:01 Introduction: Introduces system-wide Large Text support on tvOS 27 and explains that Dynamic Type works similarly to iOS. Covers where users enable larger text and notes that standard UIKit and SwiftUI components can scale automatically.
- 2:46 Identify common issues: Shows how hard-coded font sizes and fixed constraints prevent custom UI from scaling correctly. Demonstrates replacing fixed typography with standard text styles and replacing fixed widths with flexible constraints, plus the UIKit equivalent.
- 6:13 Adapt your layout: Explains when scaling text is not enough and layouts need to respond to larger sizes. Demonstrates adjusting carousel item counts with `dynamicTypeSize`, switching SwiftUI card layouts with `AnyLayout`, and updating UIKit stack view axes in response to content size category changes.

### Meet the new MetricKit

- Session ID: wwdc2026-222
- Page: https://wwdc.ai/2026/222
- Markdown: https://wwdc.ai/2026/222.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/222/
- Category: System Services
- Description: Use iOS 27's Swift-first MetricKit APIs to collect metrics, diagnostics, and StateReporting context for faster performance triage.
- Duration: 17:43
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-222/eng_f9fd58f39baf/wwdc2026-222-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/222/4/86b76599-f095-4bd8-8004-f1dbd1bacb84/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/222/4/86b76599-f095-4bd8-8004-f1dbd1bacb84/downloads/wwdc2026-222_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/222/4/86b76599-f095-4bd8-8004-f1dbd1bacb84/downloads/wwdc2026-222_sd.mp4?dl=1

Use iOS 27's Swift-first MetricKit APIs to collect metrics, diagnostics, and StateReporting context for faster performance triage.

TLDR:
- iOS 27 introduces a rebuilt, Swift-first MetricKit API centered on MetricManager, with new capabilities exclusive to the new API surface.
- MetricKit provides daily metric reports for app health signals such as launch time, hangs, CPU, memory, GPU, disk writes, network transfers, display metrics, and the new Metal frame rate metric.
- Diagnostic reports are delivered immediately for failures such as crashes and hangs; iOS 27 adds memory exception diagnostics and a crash termination category for correlating diagnostics with metrics.
- StateReporting lets apps tag meaningful app states and metadata so MetricKit can break metrics down by user flow, configuration, or experiment instead of reporting only blended app-wide averages.

## What changed in MetricKit

MetricKit is the collection layer in a performance workflow: collect data, analyze trends, triage problems, fix them, and monitor the results. It provides two complementary data types: metrics for overall performance trends and diagnostics for identifying code paths behind specific failures.

In iOS 27, MetricKit has been rebuilt with a modern Swift-first API. The new MetricManager-based APIs are the future of the framework, and the new capabilities discussed in the session are exclusive to them. Apps still using MXMetricManager should migrate to MetricManager to access these features.

- Metrics cover app health signals such as launch time, hangs, animation/display behavior, CPU, GPU, memory, disk writes, network transfers, and Metal frame rate.
- Diagnostics cover events such as crashes, hangs, and memory exceptions, with structured data useful for triage.
- MetricKit can now intersect metrics with app state reported through StateReporting.

## Collect and analyze metric reports

MetricKit continuously collects metrics as people use an app and delivers them in daily reports. Each report includes a full-day entry and may include smaller interval entries, typically spanning a few hours, when metrics are available for those windows.

Metrics are organized into groups such as .cpu, .memory, .display, and .gpu. Reports are Codable, so a common production workflow is to encode the complete report and send it to a server for aggregation across devices. Server-side analysis should establish baselines and monitor statistically meaningful changes over time.

- Set up MetricManager at app startup to avoid losing data from delayed subscription.
- Keep the MetricManager alive so its async streams continue delivering reports.
- Use intervalEntries for full-day and smaller-window metric data.
- Filter by metricGroup and switch on metric cases when extracting specific values such as peak memory.

### Receive and encode metric reports

Subscribes to MetricKit metric reports and encodes each Codable report for server-side ingestion.

```swift
import MetricKit

let manager = MetricManager()

for await report in manager.metricReports {
    let jsonData = try JSONEncoder().encode(report)
    sendToServer(jsonData)
}
```

### Inspect memory metrics

Shows the report structure: iterate interval entries, filter by metric group, then switch on individual metric cases.

```swift
import MetricKit

for await report in manager.metricReports {
    for entry in report.intervalEntries {
        let memoryMetrics = entry.values.filter { $0.metricGroup == .memory }
        for metric in memoryMetrics {
            switch metric {
            case .peakMemory(let peak):
                processPeakMemory(peak)
            default:
                break
            }
        }
    }
}
```

## Use diagnostics for triage

Diagnostics are captured on device when something goes wrong and are delivered immediately through MetricKit. They help move from a trend, such as rising crashes or hangs, to a concrete code path that can be investigated.

Crash diagnostics include a symbolicated backtrace plus metadata such as exception type and termination reason. In iOS 27, crash diagnostics also include a termination category indicating how the crash was counted in metrics, making it easier to correlate abnormal termination trends with individual diagnostics. iOS 27 also adds memory exception diagnostics for app or extension termination due to exceeding memory limits.

- Listen to diagnosticReports as soon as the app launches, ideally from a detached task or dedicated service.
- Encode DiagnosticReport values with JSONEncoder when forwarding to an analytics backend.
- Switch on report.result to handle crashes, hangs, and other diagnostics differently.

### Receive and encode diagnostic reports

Receives immediate diagnostic reports and forwards the structured Codable payload to a server.

```swift
import MetricKit

let manager = MetricManager()

for await report in manager.diagnosticReports {
    let jsonData = try JSONEncoder().encode(report)
    sendToServer(jsonData)
}
```

### Extract crash and hang details

Accesses structured diagnostic details, including crash backtrace, termination reason, and termination category.

```swift
import MetricKit

for await report in manager.diagnosticReports {
    switch report.result {
    case .crash(let crash):
        let backtrace = crash.callStackTree
        let reason = crash.terminationReason
        let category = crash.terminationCategory
        processCrash(backtrace: backtrace, reason: reason, category: category)
    case .hang(let hang):
        processHangDiagnostic(hang)
    default:
        break
    }
}
```

## Add app context with StateReporting

App-wide metrics can hide where a problem actually occurs. StateReporting lets an app report meaningful states, such as the active tab, user flow, app configuration, or experiment variant, so MetricKit can aggregate metrics separately for each state.

States are scoped to domains. A domain represents a function or area of the app and can have only one active state at a time. Separate domains allow multiple dimensions to be active concurrently, such as active tab and database batch-size experiment. State reporting uses a transition model: the app reports the state it is moving to, and MetricKit tracks how long it remains there.

- Create a StateReportingDomain, usually named with a reverse-DNS string.
- Register enabled state reporting domains when constructing MetricManager.
- Report stable, meaningful transitions rather than transient UI events.
- Use @ReportableMetadata for structured state metadata such as list size or sort order.
- MetricReport.stateEntries contains state-aware metrics; it is empty when no states are reported.

### Report app state transitions

Enables a StateReporting domain for MetricKit and reports a transition into a named app state.

```swift
import MetricKit
import StateReporting

let domain = StateReportingDomain("com.metrickitsample.tabs")
let manager = MetricManager(enabledStateReportingDomains: [domain])

let reporter = StateReporter.reporter(for: domain.rawValue)
reporter.reportTransition(to: "Reports")
```

### Attach structured metadata to states

Adds stable structured metadata to a reported state so performance data can be segmented by app-defined configuration.

```swift
import StateReporting

@ReportableMetadata
struct ViewConfiguration {
    let listSize: String
    let isSorted: Bool
}

let reporter = StateReporter.reporter(
    for: domain.rawValue,
    stableMetadata: ViewConfiguration.self
)

reporter.reportTransition(
    to: "Reports",
    stableMetadata: ViewConfiguration(listSize: "large", isSorted: false)
)
```

## Encode and interpret state-aware reports

After states are reported, MetricKit surfaces StateEntry values alongside interval entries. Each StateEntry contains metrics aggregated across time spent in an individual state, enabling comparisons such as scroll hitch rate in one tab versus another.

When sending reports to a server, the encoded report can be grouped by state reporting domain. This helps analytics pipelines process interval entries and state entries by domain and state.

- Use narrowly scoped domains so each app area has interpretable state data.
- Avoid creating too many states; overly granular state models can make the data harder to interpret and there are upper limits to reduce overhead.
- Plan states so a regression points to a fixable area or configuration.
- Validate reported states with the Points of Interest instrument before shipping.

### Encode reports grouped by StateReporting domain

Configures JSONEncoder so encoded MetricReport data is grouped by each StateReporting domain and state.

```swift
import MetricKit

for await report in manager.metricReports {
    let encoder = JSONEncoder()
    let formatKey = MetricReport.encodingFormatKey
    encoder.userInfo[formatKey] = MetricReport.EncodingFormat.byStateReportingDomain
    let jsonData = try encoder.encode(report)
    sendToServer(jsonData)
}
```

Resources:
- Getting started with StateReporting: https://developer.apple.com/documentation/StateReporting/getting-started-with-statereporting
- Analyzing app performance with MetricKit: https://developer.apple.com/documentation/MetricKit/analyzing-app-performance-with-metrickit
- Monitoring app performance with MetricKit: https://developer.apple.com/documentation/MetricKit/monitoring-app-performance-with-metrickit
- Track performance by app state using MetricKit: https://developer.apple.com/documentation/MetricKit/track-performance-by-app-state-using-metrickit
- MetricKit: https://developer.apple.com/documentation/MetricKit

Chapters:
- 0:01 Introduction: Introduces MetricKit as the collection component of a performance optimization workflow and explains the difference between metrics and diagnostics. Covers the iOS 27 rebuild with a Swift-first API, contextual metrics by app state, Metal frame rate metrics, and memory exception diagnostics.
- 4:07 Metrics: Explains daily metric reports, full-day and smaller interval entries, metric groups, and individual metric values. Shows how to receive MetricReport values through MetricManager, encode them for a server, and inspect specific metrics such as peak memory.
- 7:13 Diagnostics: Describes immediate diagnostic delivery for failures such as crashes and hangs, including symbolicated backtraces and termination metadata. Demonstrates receiving, encoding, and switching over diagnostic report results, including crash termination category in iOS 27.
- 10:03 Context: Shows how StateReporting adds app-defined context so MetricKit can segment metrics by user flow, configuration, tab, or experiment. Covers domains, state transitions, structured metadata with @ReportableMetadata, stateEntries, grouped encoding, and best practices for defining useful states.

### Live Activities essentials

- Session ID: wwdc2026-223
- Page: https://wwdc.ai/2026/223
- Markdown: https://wwdc.ai/2026/223.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/223/
- Category: App Services
- Description: Build and tune Live Activities with ActivityKit, WidgetKit, push updates, Dynamic Island landscape layouts, StandBy, small family, and App Intents.
- Duration: 15:16
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-223/eng_9c0d55151553/wwdc2026-223-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/223/4/9098c495-ea8b-44f9-b852-f6eb64840161/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/223/4/9098c495-ea8b-44f9-b852-f6eb64840161/downloads/wwdc2026-223_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/223/4/9098c495-ea8b-44f9-b852-f6eb64840161/downloads/wwdc2026-223_sd.mp4?dl=1

Build and tune Live Activities with ActivityKit, WidgetKit, push updates, Dynamic Island landscape layouts, StandBy, small family, and App Intents.

TLDR:
- Model Live Activity data with `ActivityAttributes` for static values and nested `ContentState` for dynamic values so updates stay efficient.
- Build Lock Screen and Dynamic Island presentations in a WidgetKit `ActivityConfiguration`, using `ActivityViewContext` to read attributes and current state.
- Start and update activities locally with ActivityKit, or use ActivityKit push notifications; choose broadcast updates for large shared events and device-targeted pushes for specific activities.
- Optimize for iOS 27 Dynamic Island landscape width, StandBy backgrounds, Apple Watch/CarPlay small family layouts, and quick actions using `LiveActivityIntent`.

## Where Live Activities appear

Live Activities provide glanceable, time-sensitive updates for ongoing tasks and events. The session uses a coffee order Live Activity to show changing order phases, estimated ready time, and a short post-order rating interaction.

On iPhone, Live Activities appear on the Lock Screen and in the Dynamic Island. In iOS 27, Dynamic Island compact and minimal presentations are visible in both portrait and landscape. Live Activities also appear in StandBy, in the Apple Watch Smart Stack, in the macOS menu bar, and on the CarPlay Dashboard.

- Design the experience around the key information people need over time.
- Use larger presentations for richer status and context, and compact/minimal presentations for only the most essential signal.
- Consult the Human Interface Guidelines and the "Design dynamic Live Activities" session for design guidance.

## Model static and dynamic data

Live Activities treat static and dynamic data differently. Static values go in a type conforming to `ActivityAttributes`; only values in the nested `ContentState` can change during the activity lifetime.

For the coffee order, the shop name, drink, and order ID are static. The order phase, estimated ready date, and optional rating are dynamic.

- Keep the model small and update-focused so ActivityKit updates are fast.
- Put server identifiers in attributes when they do not change, so intents and updates can associate UI actions with the right backend object.
- Use `staleDate` in `ActivityContent` when the current content should be considered out-of-date; the example leaves it unset for the coffee order.

### Define Live Activity attributes and content state

Static order metadata lives on `DrinkOrderAttributes`; mutable status values live in `ContentState`.

```swift
import ActivityKit
import Foundation

public struct DrinkOrderAttributes: ActivityAttributes {
    let shopName: String
    let drink: Drink
    let orderID: UUID

    public struct ContentState: Codable, Hashable {
        var phase: DrinkOrder.Phase = .waiting
        var estimatedReadyDate: Date
        var rating: DrinkOrder.Rating?
    }
}
```

## Build the WidgetKit presentations

A Live Activity UI is implemented in a widget extension with WidgetKit. `ActivityConfiguration` binds the widget to an `ActivityAttributes` type and supplies SwiftUI views for the Lock Screen and Dynamic Island.

Each view receives an `ActivityViewContext`, which contains the immutable attributes and the latest content state. The Dynamic Island requires separate compact leading, compact trailing, minimal, and expanded-region views.

- Use the content closure for the Lock Screen-style presentation.
- Provide compact and minimal Dynamic Island views that reduce the interface to the most important information.
- Use `DynamicIslandExpandedRegion` closures for richer expanded Dynamic Island layouts.

### Create Live Activity and Dynamic Island views

`ActivityConfiguration` defines the Lock Screen presentation and all Dynamic Island variants for one activity type.

```swift
import ActivityKit
import SwiftUI
import WidgetKit

struct DrinkOrderLiveActivity: Widget {
    var body: some WidgetConfiguration {
        ActivityConfiguration(for: DrinkOrderAttributes.self) { context in
            ActivityView(context: context)
        } dynamicIsland: { context in
            DynamicIsland {
                DynamicIslandExpandedRegion(.leading) {
                    ExpandedLeadingView(context: context)
                }
                DynamicIslandExpandedRegion(.center) {
                    ExpandedCenterView(context: context)
                }
                DynamicIslandExpandedRegion(.trailing) {
                    ExpandedTrailingView(context: context)
                }
                DynamicIslandExpandedRegion(.bottom) {
                    ExpandedBottomView(context: context)
                }
            } compactLeading: {
                CompactLeadingView(context: context)
            } compactTrailing: {
                CompactTrailingView(context: context)
            } minimal: {
                MinimalView(context: context)
            }
        }
    }
}
```

## Start and update activities

ActivityKit can start a Live Activity directly while the app is running in the foreground. The flow is to check authorization, create attributes, create the initial `ContentState`, wrap it in `ActivityContent`, and call `Activity.request`.

While the activity is running, call `update` with a new `ActivityContent`. Activities can also be started or updated by push notifications, and can be scheduled to start at a specific time.

- Use local ActivityKit updates when the app is running and has the current state.
- Use ActivityKit push notifications for background updates.
- Use broadcast updates when many people are following the same shared event, such as a game score.
- Use device-targeted push notifications for per-user or per-device activity updates by obtaining the activity push token.

### Start and update a Live Activity locally

The local ActivityKit path checks authorization, requests a new activity, then updates dynamic state with `activity.update`.

```swift
func launchLiveActivity(order: DrinkOrder) throws {
    guard ActivityAuthorizationInfo().areActivitiesEnabled else { return }

    let attributes = DrinkOrderAttributes(
        shopName: "Coffee Shop",
        drink: order.drink,
        orderID: order.id
    )
    let estimatedReadyDate = Date.now + (15 * 60)
    let contentState = DrinkOrderAttributes.ContentState(
        phase: .waiting,
        estimatedReadyDate: estimatedReadyDate
    )
    let activityContent = ActivityContent(state: contentState, staleDate: nil)

    let activity = try Activity.request(attributes: attributes, content: activityContent)
}

await activity.update(
    ActivityContent(
        state: DrinkOrderAttributes.ContentState(
            phase: .preparing,
            estimatedReadyDate: estimatedReadyDate
        ),
        staleDate: nil
    )
)
```

## Optimize presentations and add actions

The session focuses on several refinements for real-world surfaces. In iOS 27 landscape, Dynamic Island compact and minimal views cannot grow in width, so compact views should switch to narrower alternatives when `isDynamicIslandLimitedInWidth` is true.

StandBy uses the Lock Screen view scaled up, so backgrounds may need different treatment. Use `showsWidgetContainerBackground` to decide when to apply the regular widget background, and use `activityBackgroundTint` to provide an edge-to-edge tint in StandBy.

For Apple Watch Smart Stack and CarPlay, declare support for the small activity family and branch on `activityFamily` to provide a dedicated small layout. For quick contextual actions, associate Live Activity buttons with App Intents conforming to `LiveActivityIntent`.

- Use an icon or abbreviated UI when the Dynamic Island is width-constrained.
- Use `.supplementalActivityFamilies([.small])` before branching to a `.small`-specific view.
- Use `LiveActivityIntent` for actions such as rating an order from the Live Activity UI.

### Adapt compact Dynamic Island UI for limited width

`isDynamicIslandLimitedInWidth` lets compact views switch to a narrower layout in landscape.

```swift
struct CompactTrailingView: View {
    @Environment(\.isDynamicIslandLimitedInWidth) var isDynamicIslandLimitedInWidth
    var context: ActivityViewContext<DrinkOrderAttributes>

    var body: some View {
        if isDynamicIslandLimitedInWidth {
            StepProgressIconView(context: context)
        } else if context.state.phase.showsTimer {
            EstimatedReadyView(context: context, font: .system(.body).monospacedDigit())
                .multilineTextAlignment(.trailing)
                .frame(maxWidth: maximumTimerLabelWidth)
        } else {
            OrderPhaseLabelView(context: context, font: .caption2.bold(), color: .brown)
                .multilineTextAlignment(.trailing)
        }
    }
}
```

### Support small family and Live Activity button intents

The small family enables adapted Apple Watch and CarPlay layouts; `LiveActivityIntent` powers quick actions from the Live Activity.

```swift
struct DrinkOrderLiveActivity: Widget {
    var body: some WidgetConfiguration {
        ActivityConfiguration(for: DrinkOrderAttributes.self) { context in
            ActivityView(context: context)
        } dynamicIsland: { context in
            // Dynamic Island views...
        }
        .supplementalActivityFamilies([.small])
    }
}

struct RateDrinkIntent: LiveActivityIntent {
    static var title: LocalizedStringResource = "Rate Drink"

    @Parameter(title: "Order ID") var orderID: String
    @Parameter(title: "Positive") var isPositive: Bool

    func perform() async throws -> some IntentResult {
        await updateLocalDatastore(
            rating: isPositive ? .great : .poor,
            dismissPolicy: .after(.now + 15)
        )
        return .result()
    }
}
```

Resources:
- Human Interface Guidelines: Live Activities: https://developer.apple.com/design/human-interface-guidelines/live-activities
- Starting and updating Live Activities with ActivityKit push notifications: https://developer.apple.com/documentation/ActivityKit/starting-and-updating-live-activities-with-activitykit-push-notifications
- ActivityKit: https://developer.apple.com/documentation/ActivityKit

Chapters:
- 0:01 Introduction: Introduces Live Activities as glanceable, real-time surfaces for ongoing events and tasks. Covers where they appear, including Lock Screen, Dynamic Island, StandBy, Apple Watch, macOS menu bar, and CarPlay, with iOS 27 support for Dynamic Island in portrait and landscape.
- 1:53 Create and update: Walks through a coffee order Live Activity: designing the data model, separating static `ActivityAttributes` from dynamic `ContentState`, building WidgetKit presentations, and starting or updating the activity with ActivityKit. Also explains local updates, scheduled starts, push notification starts/updates, broadcast updates, and targeted push-token updates.
- 9:51 Optimize: Shows refinements for constrained and alternate surfaces: limited-width Dynamic Island layouts in landscape, StandBy background treatment, and small family layouts for Apple Watch and CarPlay. Ends by adding interactive rating buttons backed by a `LiveActivityIntent`.

### Expand the capabilities of your Virtualization app

- Session ID: wwdc2026-224
- Page: https://wwdc.ai/2026/224
- Markdown: https://wwdc.ai/2026/224.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/224/
- Category: System Services
- Description: Use macOS 27 Virtualization APIs for guest provisioning, USB passthrough, custom vmnet networks, DiskImageKit images, and custom Virtio devices.
- Duration: 20:29
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-224/eng_27a866e6384c/wwdc2026-224-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/224/5/33a91529-8caf-409e-9c54-1b8952744651/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/224/5/33a91529-8caf-409e-9c54-1b8952744651/downloads/wwdc2026-224_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/224/5/33a91529-8caf-409e-9c54-1b8952744651/downloads/wwdc2026-224_sd.mp4?dl=1

Use macOS 27 Virtualization APIs for guest provisioning, USB passthrough, custom vmnet networks, DiskImageKit images, and custom Virtio devices.

TLDR:
- Automate first-boot macOS guest setup with VZMacGuestProvisioningOptions, including account creation, auto-login, and Remote Login over SSH.
- Pass USB accessories through to macOS or Linux VMs with Accessory Access and VZUSBPassthroughDevice, while preserving user control over attach and detach.
- Build advanced VM networking with vmnet, including shared custom networks, DHCP configuration, port forwarding, and cross-process network sharing.
- Use DiskImageKit and custom Virtio devices for more efficient VM storage stacks and high-performance host/guest communication in Linux guests.

## Session scope

The session covers new and existing APIs for building more capable Virtualization apps on macOS 27. It targets apps that run full desktop VMs, developer workflow tooling, and command-line automation for repeatable test environments.

The main areas are macOS guest provisioning, USB passthrough with Accessory Access, custom network topologies with vmnet, efficient layered disk images with DiskImageKit, and custom Virtio devices for Linux guests.

- Virtualization apps can support macOS and Linux virtual machines.
- Some features are macOS 27 additions, while vmnet integration is described as available in macOS 26 and later.
- Other mentioned improvements include iCloud support for desktop-style VM experiences, EFI Secure Boot for Linux VMs, and additional Metal support in macOS guests.

## macOS guest provisioning

Virtualization can now pass provisioning options to a virtual Mac when it starts for the first time after macOS installation. Setup Assistant uses these values to create the user account and apply requested settings without manual interaction.

Provisioning is only honored for an unconfigured guest. If a user account already exists, provisioning options passed on later boots are ignored. Apps should treat passwords carefully, such as sourcing them from Keychain, configuration, or environment variables rather than hardcoding them.

- Create VZMacGuestProvisioningOptions with full name, username, and password.
- Optionally enable automatic login and Remote Login over SSH.
- Attach the provisioning options to VZMacOSVirtualMachineStartOptions before starting the VM.

### Provision a macOS guest on first boot

Creates a macOS user during Setup Assistant and optionally enables auto-login and SSH.

```swift
import Virtualization

let provisioningOptions = VZMacGuestProvisioningOptions()
provisioningOptions.fullName = fullName
provisioningOptions.username = username
provisioningOptions.password = password
provisioningOptions.logsInAutomatically = true
provisioningOptions.enablesRemoteLogin = true

let startOptions = VZMacOSVirtualMachineStartOptions()
try startOptions.setGuestProvisioning(provisioningOptions)
try await virtualMachine.start(options: startOptions)
```

## USB passthrough with Accessory Access

Accessory Access lets apps make USB accessories available to macOS and Linux VMs while keeping people in control of their physical devices. Users explicitly attach devices to apps through the Accessory Access menu extra, can detach them at any time, and the framework supports hot plugging.

An app registers a listener with USB matching criteria. When the user attaches a matching accessory to the app, the listener receives the accessory and can attach it to a VM as a VZUSBPassthroughDevice. Virtual machine mutations must be performed on the VM's queue.

- Use AAUSBAccessoryMatchingCriteria to filter by class/subclass, vendor ID, product ID, or other criteria.
- An empty matching criteria array expresses interest in all USB devices.
- Add the Claim USB Accessory capability to the Xcode target.
- Handle attach and detach events gracefully; consult Accessory Access documentation for supported device types.

### Register an Accessory Access listener

Registers interest in USB accessories and receives any devices already attached to the app.

```swift
import AccessoryAccess

let criteria: [AAUSBAccessoryMatchingCriteria] = []
let accessories = try await AAUSBAccessoryManager.shared.registerListener(
    self,
    matchingCriteria: criteria
)

for accessory in accessories {
    // Handle previously attached accessories.
}
```

### Attach a USB accessory to a VM

Converts an attached AAUSBAccessory into a Virtualization USB passthrough device and hot-plugs it into the VM.

```swift
import AccessoryAccess
import Virtualization

class AccessoryListener: NSObject, AAUSBAccessoryListener {
    func usbAccessoryDidConnect(_ usbAccessory: AAUSBAccessory) {
        virtualMachine.queue.async {
            do {
                let configuration = VZUSBPassthroughDeviceConfiguration(device: usbAccessory)
                let device = try VZUSBPassthroughDevice(configuration: configuration)
                self.virtualMachine.usbControllers.first?.attach(device: device) { error in
                    // Handle error if necessary.
                }
            } catch {
                // Handle error.
            }
        }
    }
}
```

## Advanced network topologies with vmnet

For basic cases, Virtualization provides isolated, NAT, and bridge networking. For more controlled VM-to-VM and VM-to-host/external-network behavior, vmnet can create custom networks for macOS and Linux VMs.

A vmnet network can be configured with parameters such as DHCP settings and TCP/UDP host port forwarding rules. Multiple VMs can join the same custom network by using the same vmnet network object when configuring their network device attachments.

- Create a vmnet network configuration, customize it, then create a vmnet network object.
- Wrap the vmnet network in VZVmnetNetworkDeviceAttachment and assign it to a VZVirtioNetworkDeviceConfiguration.
- vmnet network objects are reference-counted and are not persisted after the app exits; persist your own settings if you need repeatability.
- Use vmnet_network_copy_serialization and vmnet_network_create_with_serialization to transfer a vmnet network across XPC, useful when VMs run in separate processes.

### Create a custom vmnet-backed network device

Connects a VM to a custom vmnet network using a Virtio network device.

```swift
import Virtualization
import vmnet

var status: vmnet_return_t = .VMNET_FAILURE

guard let networkConfiguration = vmnet_network_configuration_create(.VMNET_SHARED_MODE, &status) else {
    // Handle error.
}

guard let network = vmnet_network_create(networkConfiguration, &status) else {
    // Handle error.
}

let attachment = VZVmnetNetworkDeviceAttachment(network: network)
let networkDeviceConfiguration = VZVirtioNetworkDeviceConfiguration()
networkDeviceConfiguration.attachment = attachment

virtualMachineConfiguration.networkDevices = [networkDeviceConfiguration]
let virtualMachine = VZVirtualMachine(configuration: virtualMachineConfiguration)
```

## Efficient storage with DiskImageKit

DiskImageKit is a macOS 27 framework for managing efficient disk images. It supports the Apple Sparse Image Format, ASIF, introduced in macOS 26, and can also work with raw disk images.

Instead of copying an entire raw disk for snapshots, DiskImageKit can build a stack of layers. The base layer can be any supported format, while upper layers are ASIF cache or overlay layers. Cache layers improve reads from slower underlying storage; overlay layers implement copy-on-write semantics for snapshots and independent VM writes.

Read-only layers can be shared across concurrent stacks, which lets multiple VMs reuse common base content while keeping their writes separate. Keep stacks shallow where possible because deeper stacks have a performance cost. When cloning a VM, remember to duplicate non-disk-image state such as a virtual Mac auxiliary storage file or an EFI variable store file.

- ASIF images are sparse: missing blocks read as zero-filled data instead of occupying physical storage.
- Cache layers store data read from lower layers for faster subsequent reads.
- Overlay layers receive writes without modifying lower layers.
- VZDiskImageStorageDeviceAttachment lets Virtualization use a DiskImage as VM storage.

### Use a layered DiskImageKit stack as VM storage

Builds a base/cache/overlay image stack and exposes it to a VM through a Virtio block device.

```swift
import DiskImageKit
import Virtualization

let baseImage = try DiskImage(opening: .open(url: baseLayerURL, mode: .readOnly))
let cacheImage = try baseImage.appending(.asifLayer(url: cacheLayerURL, type: .cache))
let overlayImage = try DiskImage(opening: .open(url: overlayLayerURL))
let stackedImage = try cacheImage.appending(overlayImage)

let attachment = try VZDiskImageStorageDeviceAttachment(diskImage: stackedImage)
let storageDeviceConfiguration = VZVirtioBlockDeviceConfiguration(attachment: attachment)
virtualMachineConfiguration.storageDevices = [storageDeviceConfiguration]

let virtualMachine = VZVirtualMachine(configuration: virtualMachineConfiguration)
```

## Custom Virtio devices

macOS 27 adds APIs for implementing custom Virtio devices in Virtualization. This is intended for Linux guests that have custom drivers and need specialized, high-throughput, low-latency host/guest communication.

Virtio uses shared memory buffers organized into queues. The guest driver notifies the host device when data is enqueued, and the host can trigger interrupts to notify the guest. Apps configure a custom device identity, PCI class/subclass, queue count, and a delegate provider, then process queue elements in a device delegate.

- Use VZCustomVirtioDeviceConfiguration to describe the custom Virtio device.
- Use VZCustomVirtioDeviceDelegateProvider and VZCustomVirtioDeviceConfigurationDelegate to receive the created device and set its runtime delegate.
- Implement VZCustomVirtioDeviceDelegate to handle queue notifications and process VZVirtioQueue elements.
- A matching custom guest driver is required; standard guest OS drivers will not automatically know how to use your custom device.

### Configure a custom Virtio device

Defines a custom Virtio device, including device identity, PCI classification, queue count, and delegate provider.

```swift
import Virtualization

let deviceConfiguration = VZCustomVirtioDeviceConfiguration()
deviceConfiguration.deviceID = 4
deviceConfiguration.pciClassID = 0x10
deviceConfiguration.pciSubclassID = 0x00
deviceConfiguration.virtioQueueCount = 1
deviceConfiguration.provider = VZCustomVirtioDeviceDelegateProvider(
    deviceQueue: deviceQueue,
    delegate: provider
)

virtualMachineConfiguration.customVirtioDevices = [deviceConfiguration]
let virtualMachine = VZVirtualMachine(configuration: virtualMachineConfiguration)
```

### Attach delegates and process Virtio queue elements

Sets the device delegate after creation and drains queue elements when the guest notifies the host.

```swift
import Virtualization

class DeviceConfigurationDelegate: NSObject, VZCustomVirtioDeviceConfigurationDelegate {
    func customVirtioConfiguration(
        _ deviceConfiguration: VZCustomVirtioDeviceConfiguration,
        didCreateDevice device: VZCustomVirtioDevice
    ) {
        device.delegate = deviceDelegate
        self.device = device
    }
}

class DeviceDelegate: NSObject, VZCustomVirtioDeviceDelegate {
    func customVirtioDevice(_ device: VZCustomVirtioDevice,
                            didReceiveNotificationFor queue: VZVirtioQueue) {
        while let element = queue.nextElement() {
            // Process element.
            element.returnToQueue()
        }
    }
}
```

Resources:
- DiskImageKit: https://developer.apple.com/documentation/DiskImageKit
- Accessory Access: https://developer.apple.com/documentation/AccessoryAccess
- vmnet: https://developer.apple.com/documentation/vmnet
- Virtual I/O Device (VIRTIO) Version 1.4: https://docs.oasis-open.org/virtio/virtio/v1.4/virtio-v1.4.html
- Virtualization: https://developer.apple.com/documentation/Virtualization

Chapters:
- 0:01 Introduction: Introduces advanced Virtualization app capabilities for desktop VM experiences, developer workflows, and automation. The session previews guest provisioning, Accessory Access USB passthrough, vmnet networking, DiskImageKit storage, and custom Virtio devices.
- 1:04 macOS guest provisioning: Shows how VZMacGuestProvisioningOptions can automate first-boot Setup Assistant for virtual Macs by creating a user account and optionally enabling auto-login and Remote Login. It also notes that provisioning is ignored after the guest has already been set up and that passwords should be handled securely.
- 4:34 Accessory Access: Explains how Accessory Access lets users explicitly attach USB accessories to VM apps, with hot-plug support and visibility through the menu extra. The implementation registers an AAUSBAccessoryListener and attaches granted accessories as VZUSBPassthroughDevice instances on the VM queue.
- 8:26 Advanced network topologies: Describes using vmnet with Virtualization to create custom networks, control VM communication, configure DHCP, and add TCP or UDP port forwarding. It covers creating a vmnet network, wrapping it in VZVmnetNetworkDeviceAttachment, sharing it across VMs, and serializing it across XPC.
- 11:35 DiskImageKit: Introduces DiskImageKit for efficient VM disk images using ASIF, sparse storage, and layered base/cache/overlay stacks. It explains cache behavior, copy-on-write overlays, shared read-only layers, Virtualization integration through VZDiskImageStorageDeviceAttachment, and the cost of deep stacks.
- 15:57 Custom Virtio: Covers custom Virtio devices for Linux VMs using VZCustomVirtioDeviceConfiguration, delegates, queues, and interrupts. It explains the need for custom guest drivers and shows the host-side pattern for receiving queue notifications, processing elements, and returning them to the queue.

### Create live communication experiences

- Session ID: wwdc2026-226
- Page: https://wwdc.ai/2026/226
- Markdown: https://wwdc.ai/2026/226.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/226/
- Category: App Services
- Description: Use LiveCommunicationKit to present VoIP conversations in system UI, handle incoming and outgoing calls, and manage group membership and merging.
- Duration: 17:18
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-226/eng_dbc00fb03729/wwdc2026-226-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/226/4/f8343d5b-0c78-4396-be05-956666fb4ae0/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/226/4/f8343d5b-0c78-4396-be05-956666fb4ae0/downloads/wwdc2026-226_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/226/4/f8343d5b-0c78-4396-be05-956666fb4ae0/downloads/wwdc2026-226_sd.mp4?dl=1

Use LiveCommunicationKit to present VoIP conversations in system UI, handle incoming and outgoing calls, and manage group membership and merging.

TLDR:
- LiveCommunicationKit centers real-time communication around `ConversationManager`, `Conversation`, handles, capabilities, and delegate-delivered actions.
- Incoming conversations use PushKit VoIP pushes, then must be reported to `ConversationManager` before the push delegate method returns.
- Outgoing conversations should be started with `StartConversationAction` and routed through the same delegate path as system-initiated actions.
- Group conversations require accurate `members` and `activeRemoteMembers`, and advanced flows like merge and unmerge are handled through capabilities and delegate actions.

## Why adopt LiveCommunicationKit

LiveCommunicationKit gives real-time communication apps system-integrated conversation UI, including full-screen Lock Screen presentation, Dynamic Island support, Phone app Recents, contact details, Spotlight, and Siri entry points where applicable. Apple positions it as the modern replacement for traditional approaches such as `CXProvider` for apps that need richer live conversation integration.

A conversation represents one live interaction and exists only while participants are in it. It is described by handles for participants and capabilities for what the conversation supports.

- Handles have a kind, value, and display name; supported kinds include phone number, email address, and generic string.
- Phone number and email handles let the system match saved contacts and show contact names and photos.
- Display names are used when a handle cannot be matched to a contact.
- Capabilities control which system UI controls and gestures are enabled, such as video, pausing, merging, and unmerging.

## Conversation lifecycle and manager architecture

Apps drive the lifecycle through a single `ConversationManager` and its delegate. The app reports new conversations and conversation events through the manager, while the system sends user-initiated work back to the app as `ConversationAction` values through the delegate.

The lifecycle moves from ringing/idle, to joining while app setup completes, to joined when audio or video capture begins, and finally through leaving/left when the app tears down media and connections. Pausing, resuming, route changes, and capability changes are also reported through manager events so the system UI stays synchronized.

- Create the `ConversationManager` for the lifetime of the app, typically at launch.
- Configure ringtone, icon image data, group limits, Recents behavior, video support, and supported handle types.
- Use one delegate action path for interactions from both system UI and in-app UI.
- Enable Audio and Voice over IP background modes for conversations that continue while backgrounded or locked.

### Set up a conversation manager

Configures how the app's conversations appear and behave across system UI, then installs the delegate that handles actions.

```swift
import LiveCommunicationKit

let configuration = ConversationManager.Configuration(
    ringtoneName: "SampleRingtone.caf",
    iconTemplateImageData: UIImage(named: "SampleIcon")?.pngData(),
    maximumConversationGroups: 1,
    maximumConversationsPerConversationGroup: 2,
    includesConversationInRecents: true,
    supportsVideo: true,
    supportedHandleTypes: [.phoneNumber, .emailAddress]
)

let manager = ConversationManager(configuration: configuration)
manager.delegate = self
```

## Incoming conversations with PushKit

Incoming conversations are delivered with a Voice over IP push. The app decodes the payload, builds a `Conversation.Update` with the caller handle and capabilities, and reports the new incoming conversation to the `ConversationManager`. PushKit wakes the app when it is not already running.

A key constraint is that the app must report the conversation before the PushKit delegate method returns; otherwise the system will terminate the app. When the user answers, the system sends a `JoinConversationAction`, and the app performs async media setup before fulfilling the action.

- Include a stable conversation UUID and caller handle in the push payload.
- Report capabilities such as `.video`, `.pausing`, and `.merging` in the initial update when supported.
- In the join handler, verify the conversation exists, report connecting, set up media, report connected, then fulfill the action.
- Fail actions promptly when the conversation is missing or setup fails so the system can clean up.

### Report an incoming conversation from a VoIP push

Decodes the incoming VoIP push, constructs a conversation update, and reports it to the system.

```swift
import LiveCommunicationKit
import PushKit

final class SamplePushHandler: NSObject, PKPushRegistryDelegate {
    func pushRegistry(
        _ registry: PKPushRegistry,
        didReceiveIncomingVoIPPushWith payload: PKPushPayload,
        metadata: PKVoIPPushMetadata
    ) async {
        guard let (handle, uuid) = parseConversationPayload(from: payload) else { return }
        let capabilities = [.video, .pausing, .merging]
        let update = Conversation.Update(members: [handle], capabilities: capabilities)
        try? await manager.reportNewIncomingConversation(uuid: uuid, update: update)
    }
}
```

### Handle a join action

Routes system-delivered actions through the manager delegate so each action type has one implementation path.

```swift
final class SampleDelegate: ConversationManagerDelegate {
    func conversationManager(
        _ manager: ConversationManager,
        perform action: ConversationAction
    ) {
        switch action {
        case let action as JoinConversationAction:
            handleJoinAction(action)
        default:
            action.fail()
        }
    }
}
```

## Outgoing conversations and redialing

When the user starts a conversation from inside the app, the app should also report that conversation to the system. It creates a `StartConversationAction`, calls `manager.perform`, and handles the resulting action in the same delegate path used for system-initiated work.

After conversations end, Recents and Spotlight can be used to redial when the app supports the start call intent. The intent is delivered to the app's scene as an `NSUserActivity`. Apple also recommends donating the app's own intent at the end of each conversation so Siri can surface the app's representation.

- Use stable handles instead of transient tokens if the conversation should support redialing from Recents.
- Set `includesConversationInRecents` to `false` for ephemeral one-time rooms that should not be redialable.
- Add `StartConversationAction` handling to the same delegate switch used for join and end actions.

### Create and perform a start action

Starts an outgoing conversation from app UI while letting the system update its conversation UI and forward the work to the delegate.

```swift
let startAction = StartConversationAction(
    conversationUUID: UUID(),
    handles: [
        Handle(
            type: .phoneNumber,
            value: "+1-650-555-0199",
            displayName: "Ryan Notch"
        )
    ],
    isVideo: false
)

try await manager.perform([startAction])
```

## Group conversations, membership, and merging

Group conversations distinguish between everyone invited and everyone currently active. `members` is the full invited list, while `activeRemoteMembers` is the subset with media actively flowing. Keeping both updated lets the system show accurate group state.

For advanced call management, declare capabilities such as `.merging` and `.unmerging`. When the user merges conversations from system UI, the delegate receives a `MergeConversationAction` with the UUIDs of both conversations; the app combines its media streams, reports updated membership, and fulfills or fails the action.

- Build group start actions with handles for all invited remote participants.
- Report `localMember`, `members`, `activeRemoteMembers`, and capabilities whenever group state changes.
- Merging and unmerging follow the same action/delegate pattern as join, end, and start.
- Validate both source and target conversations before attempting to merge.

### Report group membership updates

Keeps system UI synchronized with invited members, active remote members, and supported group capabilities.

```swift
let update = Conversation.Update(
    localMember: adam,
    members: [david, ryan],
    activeRemoteMembers: [david, ryan],
    capabilities: [.merging, .pausing, .unmerging]
)

manager.reportConversationEvent(
    .conversationUpdated(update),
    for: conversation
)
```

### Handle merge actions

Validates both conversations, combines media streams asynchronously, reports the merged update, and fulfills the action.

```swift
extension SampleDelegate {
    func handleMergeAction(_ action: MergeConversationAction) {
        let sourceUUID = action.conversationUUID
        let targetUUID = action.conversationUUIDToMergeWith

        guard manager.conversations.contains(where: { $0.uuid == sourceUUID }),
              manager.conversations.contains(where: { $0.uuid == targetUUID }) else {
            return action.fail()
        }

        Task {
            do {
                let update = try await combineStreams(from: sourceUUID, into: targetUUID)
                manager.reportConversationEvent(.conversationUpdated(update), for: target)
                action.fulfill()
            } catch {
                action.fail()
            }
        }
    }
}
```

Resources:
- Initiating VoIP conversations with LiveCommunicationKit: https://developer.apple.com/documentation/LiveCommunicationKit/initiating-voip-conversations-with-livecommunicationkit
- Responding to VoIP Notifications from PushKit: https://developer.apple.com/documentation/PushKit/responding-to-voip-notifications-from-pushkit
- LiveCommunicationKit: https://developer.apple.com/documentation/LiveCommunicationKit

Chapters:
- 0:01 Introduction: Introduces LiveCommunicationKit as the modern way to integrate real-time communication apps with Lock Screen, Dynamic Island, Recents, contacts, Siri, and system controls. Defines conversations, handles, capabilities, lifecycle states, and the `ConversationManager`/delegate action architecture.
- 7:56 Incoming conversations: Shows how an incoming conversation is delivered with a PushKit VoIP push, decoded into a handle and UUID, reported to `ConversationManager`, and then joined through `JoinConversationAction`. Covers the requirement to report before the PushKit delegate returns and the pattern for async media setup and action fulfillment.
- 11:29 Outgoing conversations: Explains starting in-app conversations with `StartConversationAction` and `manager.perform`, then routing the start through the same delegate action logic used elsewhere. Also covers redialing from Spotlight or Recents, start call intent support, and intent donation after conversations.
- 13:18 Groups: Covers group conversation modeling with full invited `members` and currently active `activeRemoteMembers`, plus reporting membership updates through `Conversation.Update`. Demonstrates merge support using the `.merging` capability and `MergeConversationAction`, with unmerging following the same delegate pattern.

### Create UI prototypes using agents in Xcode

- Session ID: wwdc2026-227
- Page: https://wwdc.ai/2026/227
- Markdown: https://wwdc.ai/2026/227.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/227/
- Category: Design
- Description: Use Xcode coding agents and previews to prototype SwiftUI screens, realistic states, and tunable interactions without giving up design judgment.
- Duration: 18:11
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-227/eng_3785695f5baa/wwdc2026-227-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/227/4/f96c1da6-a49b-4d9a-8612-340d198d201b/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/227/4/f96c1da6-a49b-4d9a-8612-340d198d201b/downloads/wwdc2026-227_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/227/4/f96c1da6-a49b-4d9a-8612-340d198d201b/downloads/wwdc2026-227_sd.mp4?dl=1

Use Xcode coding agents and previews to prototype SwiftUI screens, realistic states, and tunable interactions without giving up design judgment.

TLDR:
- Use Xcode coding agents as prototyping collaborators: write specific prompts, ask for multiple named Swift previews, compare directions, then remix promising elements.
- Bring prototypes closer to real use by asking agents for plausible sample data, reusable sample models, and previews for edge cases such as empty states, long text, and unbounded lists.
- For interaction design, create custom tuning panels in Xcode previews to adjust animation phases, spring/ease parameters, delays, staggered entrances, layout options, and app states.
- The core guidance is to shorten feedback loops while retaining human judgment; agents can generate real native code, but they should not decide the experience for you.

## Prototype with agents, but keep design judgment human

The session frames prototyping as a way to explore many design ideas quickly before an app's direction hardens. Xcode coding agents can generate native code from a description, while Xcode previews let developers inspect and interact with UI without rebuilding and running the app for every change.

The recommended workflow is collaborative rather than delegating design decisions. Agents are useful for producing starting points, variations, sample content, and tuning utilities, but the developer or designer remains responsible for deciding what serves people best.

- Use coding agents to request code changes or features directly in Xcode conversations.
- Use Xcode previews by ensuring the Swift file has a preview view and opening the canvas.
- Prefer prototyping key screens and moments in real native code so promising work can be carried forward.
- Avoid treating agent output as authoritative; evaluate, revise, and discard aggressively.

## Explore UI possibilities by going wide first

A vague prompt such as asking for a UI for a regularly meeting book club can anchor the prototype on arbitrary layout, navigation, and feature choices. The session's example shows how an agent may invent features such as polling or photo galleries, which can create feature creep and make later refinement harder.

Better prompts are specific about the app's intended features, desired mood, and exploration strategy. The key tactic is to ask for multiple named variations, each with its own Swift preview, so the generated ideas can be compared side by side and referenced in follow-up prompts.

- Define the core features before prompting, rather than letting the agent infer them.
- Include stylistic cues such as atmosphere, color palette, typography, or metaphor.
- Ask for multiple divergent options early, not just one implementation.
- Follow up by naming promising variations and asking the agent to remix specific elements.
- Repeat the cycle: go wide, remix, refine.

### Prompt pattern for UI variation exploration

The session recommends prompts that specify features, stylistic direction, multiple options, and separately named Swift previews.

```text
Create multiple UI variations for a book club app.
Include these specific features: current book, meeting details, discussion, member progress, and prior books.
Explore distinct visual directions, such as warm coffee-shop, editorial typography, and progress/racetrack metaphors.
Give each variation a unique name and its own Swift preview so I can compare them.
```

### Prompt pattern for remixing generated ideas

After reviewing generated directions, reference the named previews and ask the agent to combine only the useful parts.

```text
Create new hybrid iterations using only these elements I liked:
- the clear current-book and meeting section from Cozy
- the pleasing typography from Editorial
- the standings/progress-board idea
- the current book image treatment
Give each iteration a unique name and its own Swift preview.
```

## Make prototypes feel lived in with realistic content and edge cases

Once the structure is promising, agents can help populate prototypes with plausible sample data so the UI can be evaluated before real users have enough personal content in the app. For the book club example, sample discussions, books, members, meeting descriptions, and book covers reveal layout and interaction issues that blank placeholders hide.

The session emphasizes thinking through edge cases yourself and asking for multiple previews that cover them. Agents can help author sample models, but the prompt should make the content relevant to the app's audience and easy to reuse or edit.

- Ask for many previews, not one happy-path state.
- Cover empty states, such as no scheduled meeting or no selected book.
- Stress-test long text, long conversations, many members, many previous books, and other unbounded UI areas.
- Specify whether text should truncate, wrap, or trigger a layout change.
- Ask for sample models in a reusable, readable file so future prototypes can reuse or modify them.
- Use realistic content to identify missing flows such as blank-slate UI, account management, and calls to action.

### Prompt pattern for realistic sample states

The recommended prompt names concrete edge cases, requires reusable sample data, and asks for descriptive previews.

```text
Populate the prototype with plausible book-club sample content and reusable sample models in a separate, easy-to-edit file.
Create multiple named Swift previews for these states:
- active club with current book and meeting
- no meeting scheduled
- long meeting description
- many participants on the leaderboard
- long discussion thread
- empty or first-run state
Keep the sample discussions centered on books and make each preview easy to refer to in follow-up revisions.
```

## Tune key moments with dedicated preview controls

Static layout is only part of the prototype. SwiftUI interactions, animations, transitions, friction, inertia, device-motion responses, and haptics shape how an app feels. The session focuses on animation tuning, especially ease animations and spring animations.

Rather than repeatedly editing scattered constants in code, create a custom tuning panel that exposes the parameters relevant to the interaction. Xcode agents can build these panels, and Xcode previews can display them alongside the UI in a wider canvas so parameters can be changed without context switching.

- Ease animations can tune acceleration, deceleration, and duration.
- Spring animations expose parameters such as stiffness, damping, and mass.
- Break complex animations into named phases so the panel, prompt, and code share a vocabulary.
- Use tuning panels for animation parameters, app states, colors, font styles, visual offsets, and other configuration choices.
- Prefer side-by-side preview layouts over modal or obstructing controls when tuning visual changes.
- Inspect phases independently to diagnose issues such as excessive delay or staggered entrance timing.

### Prompt pattern for an animation tuning panel

The session recommends asking the agent for a purpose-built panel that exposes animation phases and tunable parameters in preview.

```text
Build a tuning panel for this transition and show it side by side with the app UI in a wide Xcode preview.
Break the animation into phases:
1. transition from overview to detail page
2. staggered entrance of subsequent rows
Expose controls for duration, delay, stagger timing, spring/ease style, and any relevant presets such as bouncy.
Let me isolate each phase and replay the transition while adjusting values.
```

## Workflow takeaways

The overall practice is to use Xcode agents to reduce iteration cost, not to replace taste or product thinking. The most productive prompts are explicit, comparative, and structured around feedback loops: generate alternatives, inspect them in previews, identify concrete issues, and ask for targeted revisions.

The session closes by pointing developers to "Xcode, agents, and you" for more on using agents in Xcode.

- Use agents for breadth, sample data, and tooling around iteration.
- Use previews to compare states and interactions quickly.
- Use human judgment to choose the right direction and refine it into a people-centered experience.
- Consult the related session "Xcode, agents, and you" for broader Xcode agent guidance.

Chapters:
- 0:00 Introduction: Introduces prototyping as a way to iterate intentionally and explains how Xcode coding agents and Xcode previews work together to generate native UI code and inspect it quickly. It also cautions that agents should be treated as collaborators, with the developer retaining final design judgment.
- 2:56 Exploring UI possibilities: Shows why vague prompts can lead to arbitrary layouts and invented features, then demonstrates a better strategy: specify features and style, request many named Swift previews, and remix the best elements from generated variations.
- 7:31 Making your app feel lived in: Explains how to use agents to add plausible sample content and generate previews for realistic states and edge cases. The chapter highlights empty states, long text, unbounded lists, reusable sample models, and how real-looking content reveals layout and product issues.
- 11:19 Tuning key moments: Covers dynamic UI refinement, including ease and spring animations, friction and inertia, device motion, and haptics, then focuses on animation tuning. It recommends building custom tuning panels in Xcode previews, breaking animations into phases, and adjusting parameters side by side with the UI.
- 17:31 Next steps: Summarizes the main message that agents are collaborators for exploration and iteration, not replacements for design judgment. It points developers to "Xcode, agents, and you" for more about working with agents in Xcode.

### What's new in assessment on macOS

- Session ID: wwdc2026-230
- Page: https://wwdc.ai/2026/230
- Markdown: https://wwdc.ai/2026/230.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/230/
- Category: Business & Education
- Description: Use Automatic Assessment Configuration on macOS 27 to precheck device state, control accessibility, customize system UI, and lock down exam processes.
- Duration: 14:01
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-230/eng_2fb35740c287/wwdc2026-230-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/230/4/03914f48-0bbe-4f2d-bb09-3ae676579cf2/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/230/4/03914f48-0bbe-4f2d-bb09-3ae676579cf2/downloads/wwdc2026-230_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/230/4/03914f48-0bbe-4f2d-bb09-3ae676579cf2/downloads/wwdc2026-230_sd.mp4?dl=1

Use Automatic Assessment Configuration on macOS 27 to precheck device state, control accessibility, customize system UI, and lock down exam processes.

TLDR:
- Automatic Assessment Configuration on macOS 27 adds precondition checks for hardened exam devices, including SIP, MDM enrollment, single signed-in user, account type, Lockdown Mode, and iCloud Private Relay.
- Assessment apps can allow or restrict built-in accessibility features per approved accommodations; allowing a feature does not turn it on, it only permits use if the user already enabled it.
- New system-experience controls let apps selectively expose the Menu Bar, Apple menu items, menu extras, Dock, input technologies, Finder, and Open/Save access to allowlisted files and directories.
- Runtime lockdown can limit execution to the assessment app, explicitly allowlisted participant apps, and essential system processes, while blocking Shortcuts and Automator user scripts.

## Framework scope and requirements

Automatic Assessment Configuration helps education and certification apps create a locked-down testing environment on macOS 27. The session focuses on new controls for validating the Mac before an exam, shaping the allowed system experience, managing accessibility accommodations, and limiting process execution during an assessment.

Apps that use the framework need the restricted Automatic Assessment Configuration entitlement, requested through the Apple Developer portal. Assessment parameters are configured through an AEAssessmentConfiguration object.

- Use the framework's APIs rather than building parallel lockdown behavior.
- Drive app state from the framework's session transition callbacks rather than assuming begin/end calls take effect immediately.
- Retest full exam workflows on each macOS beta and file Feedback for regressions.

## Precondition checks before starting an exam

Precondition checks let an assessment app refuse to start until the device satisfies required security and deployment conditions. These checks are intended to ensure the Mac is in a hardened, tamper-resistant state and that Apple privacy or security features do not interfere with assessment infrastructure requirements.

- Require System Integrity Protection to be enabled.
- Require the Mac to be MDM enrolled.
- Require only a single signed-in user account.
- Require a specific account type, such as a standard account.
- Require Lockdown Mode and iCloud Private Relay to be disabled.

### Configure assessment preconditions

Creates an assessment configuration that requires SIP, MDM enrollment, a single standard user account, and disables Lockdown Mode and iCloud Private Relay for the session.

```swift
import AutomaticAssessmentConfiguration

func makeAssessmentConfiguration() -> AEAssessmentConfiguration {
    let configuration = AEAssessmentConfiguration()
    configuration.allowLockdownMode = false
    configuration.allowPrivateRelay = false
    configuration.requiresSIP = true
    configuration.requiresManagedDevice = true
    configuration.requiresSingleUser = true
    configuration.requiresUserAccountType = .standard
    return configuration
}
```

## Accessibility controls for approved accommodations

macOS accessibility features remain an important part of equitable assessment delivery. By default, currently enabled accessibility features continue to work during an assessment session, even though the Menu Bar and Dock are hidden.

The framework lets apps decide which accessibility features remain available. This is useful because some features can be customized with user-generated content, so an assessment provider may allow only the features tied to approved accommodations. Setting an accessibility property to true does not enable that feature; it only permits it to be used if enabled by the user.

- Allow required accommodations instead of treating accessibility as an afterthought.
- Restrict features that are not approved for the current student or assessment.
- If a restricted feature is running when the session begins, the system can quit it and prevent relaunch during the session.

### Allow most accessibility features while blocking Switch Control

Permits selected built-in accessibility features while preventing Switch Control from being used during the assessment.

```swift
import AutomaticAssessmentConfiguration

func makeAssessmentConfiguration() -> AEAssessmentConfiguration {
    let configuration = AEAssessmentConfiguration()
    configuration.allowsAccessibilityVoiceOver = true
    configuration.allowsAccessibilitySwitchControl = false
    configuration.allowsAccessibilityAlternativeInputMethods = true
    configuration.allowsAccessibilityBackgroundSounds = true
    configuration.allowsAccessibilityHoverText = true
    configuration.allowsAccessibilityLiveSpeech = true
    configuration.allowsAccessibilitySpokenContent = true
    configuration.allowsAccessibilityVoiceControl = true
    configuration.allowsAccessibilityZoom = true
    return configuration
}
```

## Customize the macOS assessment experience

Assessment apps can tailor how students interact with macOS during a session. The framework can expose a filtered Menu Bar and Apple menu, allow specific menu extras, disable input methods that may reveal answers, show a filtered Dock, and constrain Finder or Open/Save panel access to approved locations.

Menu extras are not forced on by the configuration; allowed extras remain available only if already present in the Menu Bar. Finder appears as a Dock anchor, but it is not accessible unless explicitly added as a participant.

- Allow only necessary Menu Bar items, such as Battery, Clock, or Volume.
- Filter the Apple menu, including hiding all configurable items except About This Mac by using an empty allowlist.
- Disable Dictation, AutoFill, structural input, and the emoji picker when they could reveal spelling, reference data, symbols, or composition hints.
- Use allowedDirectoriesAndFiles to constrain Finder and standard Open/Save panels to designated files or directories.

### Allow a filtered Menu Bar and Apple menu

Shows the Menu Bar while limiting visible menu extras and Apple menu contents.

```swift
import AutomaticAssessmentConfiguration

func makeAssessmentConfiguration() -> AEAssessmentConfiguration {
    let configuration = AEAssessmentConfiguration()
    configuration.allowsMenuBar = true
    configuration.allowedMenuBarItems = [ .battery, .clock, .volume ]
    configuration.allowedAppleMenuItems = [ .sleep ]
    return configuration
}
```

### Restrict input helpers and file access

Prevents selected input technologies and limits Finder/Open/Save access to an allowlisted directory.

```swift
import AutomaticAssessmentConfiguration

func makeAssessmentConfiguration() -> AEAssessmentConfiguration {
    let configuration = AEAssessmentConfiguration()
    configuration.allowsDictation = false
    configuration.allowsAutoFill = false
    configuration.allowsStructuralInput = false
    configuration.allowsEmojiKeyboard = false
    configuration.allowedDirectoriesAndFiles = [ URL(fileURLWithPath: "~/Documents/") ]
    return configuration
}
```

## Restrict application and script execution

The framework can restrict the runtime environment so nonessential user processes are stopped when the assessment begins. This helps reduce risks from apps or background processes that might capture the screen, log keystrokes, transmit data, or otherwise interact with the exam environment.

When allowOnlyParticipantsToRun is enabled, only the main assessment app, explicitly allowlisted participant apps, and essential system processes may run. The session also shows blocking user script execution so Shortcuts and Automator actions are stopped and cannot execute during the secure exam.

- Explicitly allowlist participant apps needed by the assessment workflow, such as Finder when file access is required.
- Use runtime restrictions only for what the assessment actually requires; unnecessary restrictions can degrade the test-taker experience.
- Validate behavior with real assessment workflows, not only isolated API checks.

### Limit running apps and block user scripts

Restricts process execution to the assessment app, allowlisted participants, and essential system processes, while blocking Shortcuts and Automator scripts.

```swift
import AutomaticAssessmentConfiguration

func makeAssessmentConfiguration() -> AEAssessmentConfiguration {
    let configuration = AEAssessmentConfiguration()
    configuration.allowOnlyParticipantsToRun = true
    configuration.allowsUserScriptExecution = false
    return configuration
}
```

Resources:
- Automatic Assessment Configuration: https://developer.apple.com/documentation/AutomaticAssessmentConfiguration

Chapters:
- 0:00 Introduction: Introduces enhancements to Automatic Assessment Configuration in macOS 27 for creating secure assessment environments. Covers the required restricted entitlement and previews preconditions, accessibility, system customization, process restrictions, and best practices.
- 1:34 Precondition checks: Explains checks an app can require before an exam starts, including SIP, MDM enrollment, single signed-in user, account type, Lockdown Mode, and iCloud Private Relay. Shows configuring these requirements on AEAssessmentConfiguration so students are alerted when the device does not qualify.
- 3:00 Accessibility restrictions: Describes how built-in macOS accessibility features can remain available for approved accommodations while restricting features that may include user-generated content. Demonstrates allowing many accessibility features while blocking Switch Control during an assessment.
- 4:33 System experience customization: Shows how to customize the assessment environment by filtering the Menu Bar, menu extras, Apple menu, input technologies, Dock, Finder, and standard Open/Save panels. Demonstrates allowlisting Menu Bar items, disabling Dictation/AutoFill/structural input/emoji input, enabling the Dock, and limiting file access to approved directories.
- 9:16 Application launch restrictions: Covers runtime restrictions that stop nonessential processes and allow only the assessment app, allowlisted participant apps, and essential system processes. Also shows blocking Shortcuts and Automator script execution during the assessment.
- 10:51 Best practices: Recommends relying on the framework instead of custom equivalents, applying only necessary restrictions, treating accessibility as a requirement, using transition callbacks, and retesting on each macOS beta. The guidance is aimed at both new integrations and apps hardening existing Assessment Mode support.
- 12:35 Next steps: Summarizes adoption steps: validate device integrity, enable accessibility accommodations, customize system access, block nonessential processes, and test with real exam workflows. Positions the framework as a unified API for securing and tailoring macOS assessment experiences.

### Run local agentic AI on the Mac using MLX

- Session ID: wwdc2026-232
- Page: https://wwdc.ai/2026/232
- Markdown: https://wwdc.ai/2026/232.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/232/
- Category: AI & Machine Learning
- Description: Build fully local agentic AI workflows on Mac with MLX-LM Server, OpenAI-compatible agents, continuous batching, and distributed inference.
- Duration: 13:37
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-232/eng_4c61416dd929/wwdc2026-232-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/232/4/f309be4a-8e5b-4c0f-843a-fcbd84c5e2d1/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/232/4/f309be4a-8e5b-4c0f-843a-fcbd84c5e2d1/downloads/wwdc2026-232_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/232/4/f309be4a-8e5b-4c0f-843a-fcbd84c5e2d1/downloads/wwdc2026-232_sd.mp4?dl=1

Build fully local agentic AI workflows on Mac with MLX-LM Server, OpenAI-compatible agents, continuous batching, and distributed inference.

TLDR:
- MLX-LM Server exposes local language models through an OpenAI-compatible chat completions API, so tools like OpenCode and Xcode can use a Mac-hosted model instead of a cloud endpoint.
- A local agentic loop can run commands, read files, call APIs, inspect results, and iterate while keeping model inference and code context on the Mac.
- MLX improves local agent performance with M5 Neural Accelerators for prompt processing, continuous batching for concurrent subagents, and distributed inference across multiple Macs.
- Setup is three steps: install `mlx-lm`, launch `mlx_lm.server` with a tool-calling model, then configure the agent's base URL to `http://127.0.0.1:8080/v1`.

## Local agentic AI on Mac

The session explains how to run agentic AI workflows entirely on a Mac using MLX. Unlike a simple chat interaction, an agent loops between the user request, the language model, and tools such as shell commands, file reads, GitHub CLI calls, APIs, build tools, and test commands.

Running this loop locally gives developers privacy, offline availability, low latency, and no per-token API usage cost. Network access is still possible for tool calls, such as fetching GitHub pull requests, but model inference and local project context stay on the machine.

- Agentic loop: user → agent → model → tools → observations → model → repeat until done.
- Useful for coding workflows that require reading a project, building, fixing compile errors, or summarizing repository changes.
- Demonstrated with OpenCode summarizing MLX pull requests using a locally hosted model and standard command-line tools.

## The four-layer MLX agent stack

The local stack has four layers. MLX is the Apple silicon array framework handling low-level computation, Metal acceleration, and memory management. MLX-LM adds model loading, inference, quantization, fine-tuning, CLI tools, and a Python API for language models.

MLX-LM Server provides the agent-facing layer: a persistent OpenAI-compatible HTTP server with support for structured tool calling and reasoning models. At the top, any agent or coding tool that speaks the OpenAI chat completions protocol can connect to the local server.

- Foundation: MLX, optimized for Apple silicon.
- Model layer: MLX-LM, with support for thousands of Hugging Face models.
- Server layer: MLX-LM Server, an OpenAI-compatible local endpoint.
- Agent layer: OpenCode, Xcode, custom scripts, or other OpenAI-compatible agent frameworks.
- Related ecosystem tools mentioned include Ollama, LM Studio, and vLLM.

## Set up a local agent

Setup is intentionally small: install MLX-LM, start `mlx_lm.server` with a model that supports tool calling, then configure the agent to use the local server as its model provider. Starting with a small model is recommended for validating the setup.

The server listens on localhost and exposes a `/v1/chat/completions` API compatible with existing OpenAI-style clients. The agent generally does not need to know whether the model is local or cloud-hosted; it just needs the base URL and model name.

- Install `mlx-lm` with `pip`.
- Launch `mlx_lm.server` with a tool-calling model.
- Point the agent provider configuration at `http://127.0.0.1:8080/v1`.

### Install MLX-LM, start the server, and test chat completions

Starts a local MLX-LM Server and verifies the OpenAI-compatible chat completions endpoint.

```bash
# Step 1: Install MLX-LM
pip install mlx-lm

# Step 2: Start the server
mlx_lm.server --model mlx-community/Qwen-3.5-4B-8bit

# Step 3: Point your agent to the server
curl -X POST \
  http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"default_model","messages":[{"role":"user","content":"Hello!"}]}'
```

### Configure OpenCode for a local MLX provider

Defines an OpenAI-compatible local provider and routes OpenCode's primary and small-model traffic to MLX-LM Server.

```text
{
  "$schema": "https://opencode.ai/config.json",
  "model": "mlx/default_model",
  "small_model": "mlx/default_model",
  "provider": {
    "mlx": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "MLX (local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "default_model": {
          "name": "Default MLX Model"
        }
      }
    }
  }
}
```

## Performance: prompt processing, batching, and scale-out

Agentic workloads repeatedly process large tool outputs and accumulated context. The session emphasizes that these sessions can involve hundreds of thousands of tokens, and most of that work is prompt processing rather than token generation.

On M5, MLX can target Neural Accelerators for matrix multiplication, which the session states is four times faster than M4 for this work. With MLX multiplication and attention kernels, that improvement translates closely to prompt-processing speedups without requiring code changes or special flags.

MLX-LM Server also uses continuous batching so concurrent agent or subagent requests can be grouped dynamically on the GPU. For models too large for one Mac, MLX distributed inference can shard a model across multiple Macs over Thunderbolt or Ethernet.

- Neural Accelerators help agents read codebases and process tool results faster on supported M5 hardware.
- Continuous batching lets new requests join in-progress batches instead of waiting for one request to finish.
- Distributed inference can run larger models and parallelize prompt processing across Macs.
- Starting with macOS 26.2, Thunderbolt RDMA provides low-latency, high-bandwidth communication for distributed MLX inference.

### Launch distributed inference with MLX

Runs MLX-LM Server through `mlx.launch`, using a hostfile to shard the model across multiple Macs.

```text
mlx.launch --hostfile hosts.json \
  --backend jaccl \
  /remote/path/to/mlx_lm.server \
  --model mlx-community/Qwen-3.5-122B-A3B-8bit
```

## Coding demos with OpenCode and Xcode

The session demonstrates using OpenCode with a local MLX server to generate a SwiftUI drawing app from a blank Xcode project. The agent inspects the project structure, plans the implementation, writes files, runs builds, and fixes compile errors using standard development tools such as `xcodebuild`.

A second demo connects Xcode directly to the already-running MLX server. In Xcode Settings, the Intelligence tab can add a locally hosted chat provider by selecting the local port, such as 8080. Xcode then uses the local model to inspect project files, understand build errors, and make targeted fixes.

- OpenCode demo: generate and iterate on a SwiftUI iPad drawing app entirely on-device.
- Xcode demo: add a Locally Hosted chat provider pointing at the MLX server port.
- Local coding agents can use normal project files and build tools while keeping code context on the Mac.

Resources:
- MLX Swift LM on GitHub: https://github.com/ml-explore/mlx-swift-lm
- MLX Swift Examples: https://github.com/ml-explore/mlx-swift-examples
- MLX Examples: https://github.com/ml-explore/mlx-examples
- MLX Swift: https://github.com/ml-explore/mlx-swift
- MLX LM - Python API: https://github.com/ml-explore/mlx-lm
- MLX Explore - Python API: https://github.com/ml-explore/mlx
- MLX Framework: https://mlx-framework.org/
- MLX: https://ml-explore.github.io/mlx/

Chapters:
- 0:00 Introduction: Introduces running agentic AI workflows entirely on Mac with MLX, without cloud inference or API keys. Frames the benefits as privacy, local availability, and using Apple silicon hardware directly.
- 0:32 The chat and agentic loop: Contrasts simple chat with the agentic loop, where an agent asks the model what to do, calls tools, observes results, and iterates. Demonstrates a local OpenCode agent summarizing MLX repository pull requests using MLX for inference and command-line tools for data access.
- 2:42 Local agentic AI stack: Explains the four layers: MLX, MLX-LM, MLX-LM Server, and the agent. MLX-LM Server provides an OpenAI-compatible endpoint with structured tool calling so tools like OpenCode, Xcode, and custom agents can connect.
- 4:36 Setting up your own agent: Shows the three-step setup: install MLX-LM, start `mlx_lm.server` with a tool-calling model, and point the agent to the localhost endpoint. An OpenCode configuration example defines a local MLX provider and model.
- 5:39 Making agents fast: Covers prompt processing as a major cost in agentic workflows because tool outputs and accumulated context are repeatedly reprocessed. MLX can use M5 Neural Accelerators and specialized kernels to speed this work without developer code changes.
- 6:53 Concurrency and distributed inference: Describes continuous batching in MLX-LM Server for serving multiple agent or subagent requests concurrently. Also explains distributed inference across multiple Macs for larger models and faster prompt processing, including Thunderbolt RDMA support starting with macOS 26.2.
- 9:20 More examples: Demonstrates local agentic coding by generating a SwiftUI drawing app from a blank Xcode project, building it, fixing errors, and iterating on UI behavior. Then shows Xcode connected to the local MLX server as a locally hosted provider to diagnose and fix a bug.
- 13:01 Next steps: Recaps the full local stack and the main performance features: Neural Accelerators, continuous batching, and distributed inference. Recommends getting started by installing MLX-LM, launching the server, and connecting an agent to the local endpoint.

### Explore distributed inference and training with MLX

- Session ID: wwdc2026-233
- Page: https://wwdc.ai/2026/233
- Markdown: https://wwdc.ai/2026/233.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/233/
- Category: AI & Machine Learning
- Description: Scale MLX inference and fine-tuning across multiple Macs with RDMA over Thunderbolt 5, JACCL, mlx.launch, and MLX LM.
- Duration: 22:06
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-233/eng_fcd0290597bf/wwdc2026-233-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/233/4/379c319a-5718-4fd2-aac6-2f97180c5892/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/233/4/379c319a-5718-4fd2-aac6-2f97180c5892/downloads/wwdc2026-233_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/233/4/379c319a-5718-4fd2-aac6-2f97180c5892/downloads/wwdc2026-233_sd.mp4?dl=1

Scale MLX inference and fine-tuning across multiple Macs with RDMA over Thunderbolt 5, JACCL, mlx.launch, and MLX LM.

TLDR:
- MLX can distribute inference and fine-tuning across multiple Apple silicon Macs using RDMA over Thunderbolt 5 and Apple's open-source JACCL communication library.
- Use `mlx.distributed_config` to generate a hostfile and configure a Thunderbolt RDMA cluster, then wrap existing MLX LM commands with `mlx.launch`.
- MLX LM supports tensor parallelism for faster inference, pipeline parallelism for some large models, and data-parallel LoRA fine-tuning with scaled batch size.
- Distributed APIs are available at several levels: MLX LM Python helpers, lower-level MLX distributed primitives in Python/Swift/C++, and standalone JACCL C++ collectives.

## Distributed stack on Apple silicon

Distributed MLX workloads use a layered stack: Thunderbolt 5 provides the physical interconnect, RDMA over Thunderbolt moves data directly between machine memories with low CPU and OS overhead, JACCL provides collective communication primitives, and MLX uses those primitives for distributed inference and training.

JACCL is an open-source collective communication library from Apple. It supports communication patterns needed for distributed ML, such as sending data between machines and combining results across a group, but can also be used for non-ML distributed workloads.

- RDMA over Thunderbolt 5 is supported starting in macOS 26.2.
- JACCL uses RDMA over Thunderbolt for low-latency communication across Apple silicon Macs.
- MLX and MLX LM build on JACCL to shard models, launch distributed jobs, and coordinate communication.

## Set up a multi-Mac MLX cluster

The session demonstrates a four-node M3 Ultra cluster connected with Thunderbolt 5. Topology matters because communication cost has both latency and transfer-time components: small messages are latency-bound, while large messages are bandwidth-bound.

JACCL supports mesh and ring topologies. A full mesh gives direct one-hop communication between every pair of machines and is preferred for latency-sensitive patterns such as tensor parallelism. A ring uses fewer ports and cables and can use multiple cables per neighbor to improve bandwidth. With a mesh, JACCL can choose mesh or ring routing depending on message size and operation.

After connecting the machines, enable RDMA over Thunderbolt in System Settings on every Mac and reboot. `mlx.launch` starts distributed programs over SSH using a JSON hostfile, after which the nodes communicate over the Thunderbolt links.

- The hostfile contains one entry per node with SSH hostname, local-network IPs for JACCL coordination, and RDMA device names for Thunderbolt peer connections.
- `mlx.distributed_config` can generate the hostfile, check SSH reachability, probe Thunderbolt topology, and optionally configure Thunderbolt links for RDMA.
- Environment variables can be embedded in the hostfile; the session uses `MLX_METAL_FAST_SYNCH=1` for faster GPU-to-CPU synchronization in distributed jobs.
- Use `--backend jaccl` for mesh and `--backend jaccl-ring` for ring.

### Generate a mesh hostfile for four Macs

Creates a cluster configuration for `mlx.launch`, embeds `MLX_METAL_FAST_SYNCH=1`, and uses the JACCL mesh backend.

```text
mlx.distributed_config \
  --hosts m3-ultra-0,m3-ultra-1,m3-ultra-2,m3-ultra-3 \
  --output "m3-ultra-jaccl.json" \
  --env MLX_METAL_FAST_SYNCH=1 \
  --auto-setup \
  --backend jaccl
```

### Hostfile shape

Each node declares the SSH hostname, coordination IP, and RDMA interfaces for its Thunderbolt peer links.

```text
[
  {
    "ssh": "m3-ultra-0",
    "ips": ["192.168.1.10"],
    "rdma": [null, "rdma_en5", "rdma_en4", "rdma_en3"]
  }
]
```

## Run distributed LLM inference with MLX LM

The simplest migration path is to keep the existing MLX LM command and wrap it with `mlx.launch`. The launcher connects to each host from the hostfile, starts the executable on each node, and MLX LM shards the model and coordinates distributed inference.

The session compares Qwen 3.6 27B on one M3 Ultra versus four M3 Ultras and reports nearly 3x token generation rate on the cluster. It also demonstrates running Kimi 2.6, a one-trillion-parameter model whose 8-bit weights alone require about one terabyte of memory, across four Macs.

- All required libraries, including MLX, must be installed on every Mac.
- The executable path passed after `--` must be valid on each remote node.
- Tensor parallelism is the default MLX LM sharding strategy.
- Pipeline parallelism can be requested with `--pipeline`, but not all models support it.

### Wrap a single-device chat command with mlx.launch

The distributed command is the same MLX LM chat invocation launched across the cluster.

```text
# Single-device LLM inference
mlx_lm.chat --model "Qwen/Qwen3.6-27B" --max-tokens 2048

# Distributed LLM inference across the cluster
mlx.launch --hostfile "m3-ultra-jaccl.json" -- \
  /remote/path/to/mlx_lm.chat --model "Qwen/Qwen3.6-27B" --max-tokens 2048
```

### Select tensor or pipeline parallelism

Tensor parallelism splits layers by width; pipeline parallelism splits the model by depth where supported.

```text
# Tensor parallelism (default)
mlx.launch --hostfile "m3-ultra-jaccl.json" -- \
  /remote/path/to/mlx_lm.chat --model "moonshotai/Kimi-K2.6" \
  --max-tokens 2048

# Pipeline parallelism
mlx.launch --hostfile "m3-ultra-jaccl.json" -- \
  /remote/path/to/mlx_lm.chat --model "moonshotai/Kimi-K2.6" \
  --max-tokens 2048 \
  --pipeline
```

## Parallelism strategies and distributed fine-tuning

MLX LM uses model parallelism for large-model inference. Pipeline parallelism assigns groups of layers to different machines, reducing communication to boundary activations but not speeding up per-token execution because tokens still pass through layer groups sequentially. Tensor parallelism splits each layer by width so machines process the same token concurrently, improving inference speed at the cost of communication at every layer and token.

For fine-tuning, MLX LM uses data parallelism: each Mac holds a model replica, receives different batches, computes gradients locally, and averages gradients across machines. The session demonstrates LoRA fine-tuning Qwen 3.5 9B and scales `--batch-size` by the number of devices so each machine processes the same number of samples per step.

- Tensor parallelism benefits from low-latency mesh communication because every layer can require cross-node communication.
- Pipeline parallelism has simpler communication but does not inherently accelerate single-token inference.
- Data-parallel training can process data up to roughly N times faster with N machines, depending on workload and overhead.
- The demonstrated four-node fine-tuning run processes about 600 tokens/s versus about 180 tokens/s on one M3 Ultra.

### Run distributed LoRA fine-tuning

The distributed LoRA command uses `mlx.launch`; MLX LM handles data sharding and gradient averaging.

```text
# Single-device fine-tuning
mlx_lm.lora --model "Qwen/Qwen3.5-9B" \
  --data "mlx-community/wikisql" \
  --train --batch-size 4

# Distributed fine-tuning: scale batch size by number of devices
mlx.launch --hostfile "hostfile.json" -- \
  /remote/path/to/mlx_lm.lora --model "Qwen/Qwen3.5-9B" \
  --data "mlx-community/wikisql" \
  --train --batch-size 16
```

## Python, Swift, C++, and standalone JACCL APIs

Beyond the CLI, MLX exposes distributed functionality through Python, Swift, and C++ APIs. At the MLX LM level, Python code can initialize a distributed group, choose tensor or pipeline groups, load a sharded model, and generate as if using a single-device model while MLX LM handles communication.

Lower-level MLX APIs expose layer sharding and collective operations such as all-reduce. JACCL can also be used directly from C++ without MLX for distributed applications that need collective communication but are not necessarily machine-learning workloads.

- Use `mx.distributed.init(strict=True, backend="jaccl")` to initialize a Python distributed group with the JACCL backend.
- Use `sharded_load` from MLX LM to load a distributed model for inference.
- Use MLX distributed collectives such as `all_sum` from Python, Swift, or C++.
- Use standalone JACCL C++ APIs when an application needs collective communication without MLX.

### Distributed inference with MLX LM Python API

Initializes distributed communication, loads a tensor-sharded model, and prints generated text from rank 0.

```swift
import mlx.core as mx
from mlx_lm import stream_generate
from mlx_lm.utils import sharded_load

group = mx.distributed.init(strict=True, backend="jaccl")
tensor_group, pipeline_group = group, None

model, tokenizer = sharded_load("moonshotai/Kimi-K2.6", pipeline_group, tensor_group)

for response in stream_generate(model, tokenizer, prompt, max_tokens=1024):
    if group.rank() == 0:
        print(response.text, end="", flush=True)
```

### Shard a layer and use a collective

Shows lower-level MLX control over tensor-parallel layer sharding and an all-reduce sum.

```swift
import mlx.core as mx
import mlx.nn as nn

group = mx.distributed.init(strict=True, backend="jaccl")

layer = nn.Linear(1024, 1024)
sharded_layer = nn.layers.distributed.shard_linear(
    layer, strategy="all-to-sharded", group=group
)

data = mx.random.normal((1, 1, 1024))
output = sharded_layer(data)
mx.eval(output)

values = mx.full((4,), float(group.rank()), dtype=mx.float32)
result = mx.distributed.all_sum(values, group=group)
mx.eval(result)
```

Resources:
- MLX Swift LM on GitHub: https://github.com/ml-explore/mlx-swift-lm
- MLX Swift Examples: https://github.com/ml-explore/mlx-swift-examples
- MLX Examples: https://github.com/ml-explore/mlx-examples
- MLX Swift: https://github.com/ml-explore/mlx-swift
- MLX LM - Python API: https://github.com/ml-explore/mlx-lm
- MLX Explore - Python API: https://github.com/ml-explore/mlx
- MLX Framework: https://mlx-framework.org/
- MLX: https://ml-explore.github.io/mlx/

Chapters:
- 0:00 Introduction: Introduces why local ML workloads become distributed when model size, context length, compute, memory, or bandwidth exceeds one machine. Frames the session around scaling MLX across multiple Macs via CLI tools, Python APIs, and Swift integration.
- 2:09 Distributed communication: Explains the distributed communication stack: Thunderbolt 5 as interconnect, RDMA over Thunderbolt for direct memory movement, JACCL for collective communication, and MLX as the ML framework using that backend.
- 4:32 Setting up your cluster: Walks through building a four-M3-Ultra cluster, including topology trade-offs between mesh and ring, enabling RDMA over Thunderbolt, and using `mlx.distributed_config` plus `mlx.launch` to generate and use a cluster hostfile.
- 10:33 Distributed inference and fine-tuning: Shows how to wrap `mlx_lm.chat` with `mlx.launch` to run the same LLM chat command across a cluster. Demonstrates a Qwen 3.6 27B inference run with nearly three times the token generation rate compared with a single M3 Ultra.
- 13:35 Model parallelism strategies: Compares pipeline parallelism, which splits layers by depth, with tensor parallelism, which splits layers by width and is the MLX LM default. Demonstrates running a one-trillion-parameter Kimi 2.6 model across four Macs.
- 15:53 Distributed fine-tuning: Explains data-parallel fine-tuning where each machine holds a model replica, processes different batches, and averages gradients. Demonstrates distributed LoRA fine-tuning of Qwen 3.5 9B with more than 3x throughput versus one M3 Ultra.
- 18:34 CLI, Python, Swift, and C++ APIs: Moves beyond CLI examples to MLX LM Python sharded loading, lower-level MLX sharding and collective primitives, Swift and C++ distributed operations, and standalone JACCL C++ collectives.
- 20:45 Next steps: Recaps the stack from RDMA over Thunderbolt through MLX and MLX LM, and points developers toward local agentic AI, custom parallelism strategies, training loops, documentation, and the MLX LM distributed server.

### Design immersive environments for visionOS apps and the spatial web

- Session ID: wwdc2026-234
- Page: https://wwdc.ai/2026/234
- Markdown: https://wwdc.ai/2026/234.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/234/
- Category: Design
- Description: Design photoreal immersive visionOS and spatial web environments with intentional pre-production, high-quality capture, 3D cleanup, motion, and Spatial Audio.
- Duration: 15:58
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-234/eng_c2dc30c8d8ea/wwdc2026-234-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/234/4/88f2dbdd-e1b1-4b50-9fa0-69a32ac768b2/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/234/4/88f2dbdd-e1b1-4b50-9fa0-69a32ac768b2/downloads/wwdc2026-234_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/234/4/88f2dbdd-e1b1-4b50-9fa0-69a32ac768b2/downloads/wwdc2026-234_sd.mp4?dl=1

Design photoreal immersive visionOS and spatial web environments with intentional pre-production, high-quality capture, 3D cleanup, motion, and Spatial Audio.

TLDR:
- Immersive environments are not flat panoramas: they need depth, parallax, motion, lighting, and audio to feel like believable places in visionOS.
- Pre-production should define intent, viewer use cases, composition, layers, removable elements, motion needs, and spatial audio opportunities before expensive capture or CG work begins.
- Production guidance emphasizes source imagery quality: leveled tripod capture, 360° coverage, bracketed exposures, secondary cameras, photogrammetry/LiDAR reference, lighting charts, and over-capturing resolution.
- Post-production turns panoramas into textured 3D meshes, cleans up distractions, preserves fidelity with A/B checks, and uses efficient shader techniques such as UV flow maps, scrolling masks, flip books, layered sine waves, and normal maps.

## What makes an environment immersive

visionOS system environments are described as photorealistic natural landscapes built for spatial computing, not simple backgrounds. Unlike flat media or a panoramic image, an immersive environment should respond to perspective changes with depth and parallax.

The session frames the design process around three phases: pre-production, production, and post-production. Examples from Apple environments such as Mount Hood, the Moon, Jupiter, Yosemite, Thorsmork, and Bora Bora illustrate how intent, source capture, 3D asset work, sound, and motion combine into a convincing illusion.

- Design for a primary viewpoint, but account for the full 360° scene.
- Expect a fully immersed viewer to see approximately 81° of the scene in their field of view.
- Treat sound and motion as core immersion elements, not decorative add-ons.
- Use visual reference to guide both artistic direction and technical shader controls.

## Pre-production: define intent and composition

Start by answering why the environment exists, what qualities it should bring to life, and how the viewer will use the space. A media-focused environment may need terrain sculpted for a large screen and a carefully researched viewing center line, while a presentation-practice environment may intentionally omit sound and concentrate lighting on a stage.

Scouting is important for real locations. Decide where the viewer should be placed, what appears in front of them, and what they see when turning around. Scouting also reveals unwanted elements such as roads, dense vegetation, or other distractions that should be replaced or removed later.

For inaccessible or invented environments, collect reference materials such as photography, videography, mission imagery, or data sources. The Moon environment used Apollo photography; Yosemite planning used Digital Elevation Models and Earth-orbit information to study lighting and determine capture dates and times.

- Visualize the scene in layers from background to foreground.
- Identify elements that need motion and where spatial audio may be attached.
- Flag features to omit, replace, simplify, or emphasize before production starts.
- Use pre-production discoveries to update requirements; for Jupiter, a scale model led to a design requirement for passage of time.

## Production: capture source material for 3D work

High-quality primary photography makes the later 3D asset process easier. For Yosemite, the team chose a viewpoint with clear mid-distance framing because foreground detail can be added with CG, while obstructing elements are harder to remove.

Plan for season, weather, and time of day, especially around sunrise and sunset where lighting changes quickly. The recommendation is to spend more time on location and shoot more material than expected, because every image can become useful for 3D asset creation.

- Use a tripod with the camera leveled 1 meter off the ground and ensure deep depth of field.
- If possible, trigger a second camera 2 meters off the ground at the same time to help fill areas not visible from the primary view.
- Use a rig and lens setup that covers all views and can produce a stitched 360° panorama.
- Shoot bracketed exposures to capture detail across high dynamic range from sun to shadow.
- For visionOS, target sharpness is 40 pixels per degree; an ideal 360° panorama target is 14,400 × 7,200 pixels.

## Secondary capture and reference data

Secondary photography, photogrammetry, LiDAR, lighting reference, and video reference provide measurements and context for CG work. Point clouds can be meshed as starting points for assets and can reveal real-world distances or terrain slopes that affect modeling decisions.

Lighting references such as Macbeth charts, chrome spheres, and gray spheres should be captured at the same time as primary photography so CG assets can be integrated consistently. Video of moving scene elements helps shader authors reproduce believable motion, and sound notes help source appropriate audio.

- Use photogrammetry and LiDAR point clouds for geometry starts and distance measurements.
- Capture lighting references alongside the main panorama photography.
- Record motion reference for water, vegetation, clouds, or other dynamic elements.
- If photographic panorama capture is impossible, create a rendered panorama in a digital content creation tool for full scene control.

## Post-production: clean up, texture, and preserve fidelity

Post-production begins with building a high-resolution source panorama, then cleaning it up. Obvious removals include the camera rig, footprints, and people; composition-driven removals may include busy vegetation, dominant bushes, or other elements identified during pre-production.

To create parallax and depth, the refined panorama is moved onto textures in UV space for a 3D mesh. Areas not visible in the panorama need to be filled with secondary photography or CG renders. Fidelity checks are critical because texture transfer can introduce sharpness, color, value, or data-loss problems.

- Use iterative digital matte painting and CG rendering to refine the panorama.
- Maintain color balance and lighting consistency throughout cleanup.
- A/B compare the 3D asset against the refined panorama to verify texture quality.
- Keep scene element sharpness consistent with nearby objects so scale feels correct.
- Flop the scene, or test extreme gamma and gain values, to reveal composition issues, value inconsistencies, or texture-transfer losses.

## Motion, lighting, and Spatial Audio on a real-time budget

A textured mesh forms the visual base, but immersion also depends on sound and motion. Spatial audio emitters can be placed at meaningful scene locations, such as a rippling-water sound positioned where a river flows around rocks.

For real-time rendering, the session emphasizes artful approximations over expensive dynamic simulation. Bora Bora is used as an example with evolving clouds, connected cloud shadows, palm movement, waves, and time-varying water appearance, all designed to achieve the visual intent within a real-time rendering budget.

- Use UV flow maps for evolving clouds or wind-driven vegetation at low cost.
- Use scrolling masks to darken terrain textures for cloud shadows instead of rendering dynamic lights.
- Use pre-rendered flip book textures to approximate dense, soft palm shadows.
- Use hierarchical vertex animation and layered sine waves to combine low-frequency trunk/frond sway with higher-frequency leaflet motion.
- Layer normal maps, scrolling textures, and hue/saturation/brightness modulation to simulate water movement and light interaction.

Chapters:
- 0:00 Introduction: Introduces visionOS immersive environments as photoreal spatial landscapes with depth, parallax, motion, and audio rather than flat panoramas. Outlines the pre-production, production, and post-production workflow using Apple system environments as examples.
- 1:17 Pre-production: Explains how to define intent, use cases, viewer placement, composition, layers, removable elements, motion, and audio before building. Covers scouting real-world locations, using reference for inaccessible places, and iterating when planning reveals better requirements.
- 5:07 Production: Covers on-location capture practices for high-quality source imagery, including tripod height, 360° panorama coverage, bracketed exposures, secondary camera views, and target resolution. Also discusses secondary photography, photogrammetry, LiDAR, lighting reference, motion reference, and rendered panoramas when photography is not possible.
- 8:41 Post-production: Describes cleaning and refining the panorama, transferring it to textures on a 3D mesh, filling unseen areas, and checking fidelity with A/B comparisons and image stress tests. Adds spatial audio and efficient real-time motion techniques such as UV flow maps, scrolling masks, flip books, layered sine waves, and composited water effects.
- 15:07 Next steps: Summarizes the design mindset: make intentional choices, build a composition where every element belongs, and connect the scene with sound and motion. Encourages iteration, experimentation, and being willing to change ideas when unexpected results improve the environment.

### What's new in image understanding

- Session ID: wwdc2026-237
- Page: https://wwdc.ai/2026/237
- Markdown: https://wwdc.ai/2026/237.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/237/
- Category: AI & Machine Learning
- Description: Use Vision tap-to-segment, Foundation Models image inputs and image tool calling, and Vision on watchOS for richer app image understanding.
- Duration: 15:46
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-237/eng_78dd4531ef7b/wwdc2026-237-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/237/6/a3bdea1e-5c1d-44bc-8c21-9e1958774bd3/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/237/6/a3bdea1e-5c1d-44bc-8c21-9e1958774bd3/downloads/wwdc2026-237_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/237/6/a3bdea1e-5c1d-44bc-8c21-9e1958774bd3/downloads/wwdc2026-237_sd.mp4?dl=1

Use Vision tap-to-segment, Foundation Models image inputs and image tool calling, and Vision on watchOS for richer app image understanding.

TLDR:
- Vision adds tap-to-segment with iterative masks from taps, boxes, lassos, and scribbles, plus refinement by including or excluding points.
- Foundation Models can now accept image attachments directly in prompts for descriptive and generative image tasks such as captions, scene analysis, recipes, or suggestions.
- Foundation Models tool calling now supports image arguments through ImageReference, enabling custom image tools and built-in Vision tools such as barcode reading and OCR.
- Vision is now available on watchOS, with saliency analysis shown as a practical way to crop photos around prominent subjects for small screens.

## Overview

The session covers new image-understanding capabilities across Vision and the Foundation Models framework. Vision gains an interactive tap-to-segment API for isolating arbitrary objects, while Foundation Models gains image input support for large language model prompts.

The session also shows how image-based tool calling lets a language model delegate specific image tasks to external code or Vision tools, and how Vision can now be used in watchOS apps.

- Use Vision when you need fast, task-specific computer vision APIs such as segmentation, saliency, barcode scanning, OCR, pose estimation, detection, classification, tracking, or facial analysis.
- Use Foundation Models when the task benefits from flexible language reasoning over an image, such as captioning, describing a scene, or generating suggestions from visual context.
- Combine them with tool calling when a model needs deterministic or specialized image-processing capabilities.

## Segment images with tap-to-segment

Vision's new tap-to-segment API lets apps isolate any object in an image, not just people or predefined categories. Users can select objects with a point tap, bounding box, lasso, scribble, or combinations of those interactions.

Segmentation is iterative. After generating an initial mask, apps can refine the result by adding included points or excluding parts of the mask. The resulting mask is exposed as a pixel buffer indicating which pixels belong to the selected object.

- Create an ImageRequestHandler for the source image.
- Use GenerateIterativeSegmentationRequest with an initial seed such as a normalized point inside the object.
- Perform the request to receive a segmentation observation and pixel-buffer mask.
- Refine by adding included or excluded points and performing the same request again.
- Vision coordinates are normalized from 0 to 1 with the origin in the lower-left corner.
- For lasso input, use a stroke width at least 1% of the image width; very thin strokes may produce poor results.
- Before the first segmentation request on a device, the model asset must be downloaded; use downloadAssets and assetStatus to manage readiness.

### Segment and refine an object mask

Creates an iterative segmentation request from a seed point, performs it on an image, and refines the mask by adding another included point.

```swift
// Generate a segmentation mask of an object with a seed point
let handler = ImageRequestHandler(image)
let request = GenerateIterativeSegmentationRequest(seed: point)
let observation = try await handler.perform(request)
let mask = observation?.pixelBuffer

// Refine the mask with a new point
request.addIncludedPoint(newPoint)
let refinedObservation = try await handler.perform(request)
```

## Image inputs in Foundation Models

Foundation Models now supports image attachments in prompts. This enables large language model workflows that reason over images, such as generating captions, interpreting notes in a photo, describing a room, or creating a recipe from a fridge image.

The API uses the Foundation Models prompt builder syntax: provide text instructions and attach the image. The model then responds with generated content derived from both the text prompt and the image.

- Foundation Models is well suited to open-ended descriptive or reasoning tasks over images.
- Vision remains preferable for fixed, optimized computer-vision tasks, especially when performance matters or when processing video frames in real time.
- The two approaches can be combined by giving a Foundation Models session access to tools backed by Vision.

### Generate an image caption

Builds a prompt containing text instructions and an image attachment, then asks the language model session to generate a caption.

```swift
import FoundationModels

let prompt = Prompt {
    "Generate a caption for this image"
    Attachment(image)
}

let response = try await session.respond(to: prompt)
let caption = response.content
```

## Image-based tool calling

Tool calling lets a language model invoke external code when it needs information it cannot reliably produce by itself. This year, tool arguments can include image references, so a model can ask a tool to analyze an image already present in the session.

Image tool arguments use ImageReference rather than passing the full image directly. The reference is valid only in the context of the transcript where it was generated, so the tool resolves it through session history before converting the attachment to a pixel buffer or other analyzable representation.

- Define a Tool with @Generable arguments containing an ImageReference.
- Use @SessionProperty(\.history) to access the current session history.
- Create a Transcript from history and resolve the ImageReference in that transcript.
- Convert the resolved image attachment to a pixel buffer before passing it to custom image-analysis code.
- When prompting for image-based tool calls, label attached images so the model can identify which image to pass to the tool.

### Create a custom image-based tool

Shows the key pattern for image tool calling: receive an ImageReference, resolve it against session history, convert the attachment to image data, and run app-specific analysis.

```swift
import FoundationModels

struct PlantIdentifierTool: Tool {
    @SessionProperty(\.history) var history

    @Generable
    struct Arguments {
        var image: ImageReference
    }

    func call(arguments: Arguments) async throws -> String {
        let imageReference = arguments.image
        let transcript = Transcript(history)

        guard let imageAttachment = imageReference.resolve(in: transcript) else {
            throw AppError.imageNotFound
        }

        let image = try imageAttachment.pixelBuffer()
        return classifyPlant(image)
    }
}
```

### Use built-in Vision tools with a language model session

Configures a Foundation Models session with a Vision barcode reader tool and labels the attached flyer image so the model can pass it to the tool.

```swift
import FoundationModels
import Vision

let session = LanguageModelSession(model: model, tools: [BarcodeReaderTool()])

let response = try await session.respond(generating: EventInfo.self) {
    "Get the date, location, and website from this flyer"
    Attachment(image)
        .label("flyer")
}
```

## Built-in Vision tools and custom Vision-backed tools

Vision provides built-in tools for common model gaps. The barcode reader tool helps models read barcodes and QR codes, and the OCR tool helps read fine or dense text in more than 30 languages.

Apps can also build their own tools on top of Vision's broader image-analysis APIs. The session specifically mentions segmentation, facial analysis, pose estimation, detection, image classification, trajectory analysis, and object tracking as examples of Vision capabilities that can be exposed to a model through custom tools.

- Import Vision and FoundationModels when using Vision tools with a LanguageModelSession.
- Pass the desired tools when creating the session.
- Prefer a Vision tool when a model needs a precise capability such as barcode scanning or OCR rather than free-form visual reasoning.

## Vision on watchOS

Vision is now available on watchOS. The session demonstrates using saliency analysis in a watch app that displays wildlife photos, where the full image may be hard to understand on a small display.

GenerateObjectnessBasedSaliencyImageRequest identifies salient objects in an image. The app can use the most prominent object's bounding box as a crop rectangle so the watch UI emphasizes the subject rather than showing an unhelpful full-frame image.

- Create a saliency request with GenerateObjectnessBasedSaliencyImageRequest.
- Perform the request on a CGImage.
- Read salientObjects from the returned observation.
- Use the first or most prominent salient object as a NormalizedRect crop.

### Generate a crop around a prominent subject

Uses Vision saliency analysis to find a prominent object and return its normalized bounding rectangle for cropping, including on watchOS.

```swift
func generateImageCrop(in image: CGImage) async throws -> NormalizedRect? {
    let request = GenerateObjectnessBasedSaliencyImageRequest()
    let observation = try await request.perform(on: image)
    let prominentObjects = observation.salientObjects
    return prominentObjects.first
}
```

Resources:
- Segmenting objects using taps, scribbles or rectangles: https://developer.apple.com/documentation/Vision/segmenting-objects-using-taps-scribbles-or-rectangles
- Implementing saliency-based image cropping in iOS and watchOS: https://developer.apple.com/documentation/Vision/implementing-saliency-based-image-cropping-in-iOS-and-watchOS

Chapters:
- 0:00 Introduction: Introduces the year's image-understanding updates: Vision tap-to-segment, Foundation Models image inputs, image-based tool calling, and Vision availability on watchOS.
- 1:36 Segment images with tap-to-segment: Explains how GenerateIterativeSegmentationRequest can segment arbitrary objects from taps, boxes, lassos, and scribbles, then refine masks with additional include or exclude points. Covers normalized coordinates, lasso stroke-width guidance, and downloading the segmentation model asset before first use.
- 5:50 Image inputs for Foundation Models: Shows how Foundation Models prompts can include image attachments for tasks like captioning, scene understanding, decorating suggestions, and recipe generation. Compares LLM-based image analysis with Vision's fast, specialized computer-vision APIs.
- 7:57 Image-based tool calling: Describes how tool calling now supports image arguments through ImageReference, allowing custom tools to resolve images from session history and analyze them. Demonstrates built-in Vision barcode and OCR tools and stresses labeling image attachments for tool use.
- 13:09 Vision on watchOS: Shows Vision running on watchOS through a wildlife app example that uses objectness-based saliency to crop photos around prominent subjects for a small watch display.
- 14:39 Next steps: Recaps the four main capabilities and points developers to sample apps for tap-to-segment and Vision on watchOS, plus related Vision and Foundation Models sessions.

### Build intelligent Siri experiences with App Schemas

- Session ID: wwdc2026-240
- Page: https://wwdc.ai/2026/240
- Markdown: https://wwdc.ai/2026/240.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/240/
- Category: AI & Machine Learning
- Description: Use App Entities, App Schemas, IndexedEntity, Transferable, and testing tools to make Siri understand app content, actions, and context.
- Duration: 27:23
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-240/eng_776d55ebeff5/wwdc2026-240-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/240/4/d46aac11-3990-42cd-bb33-4ce5e958b902/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/240/4/d46aac11-3990-42cd-bb33-4ce5e958b902/downloads/wwdc2026-240_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/240/4/d46aac11-3990-42cd-bb33-4ce5e958b902/downloads/wwdc2026-240_sd.mp4?dl=1

Use App Entities, App Schemas, IndexedEntity, Transferable, and testing tools to make Siri understand app content, actions, and context.

TLDR:
- Siri's Apple Intelligence integration is built on App Intents: model app content as App Entities, conform them to App Schemas, and expose actions as schema-backed intents.
- Adopt IndexedEntity for semantic entity resolution and content Q&A through Spotlight indexing; use EntityStringQuery when data cannot be indexed ahead of time.
- For cross-app and contextual workflows, connect visible UI to entities with NSUserActivity or view annotations, then use Transferable, IntentValueRepresentation, and IntentValueQuery for content transfer.
- Build complete schema-domain integrations, follow Xcode's related-schema diagnostics and Fix-Its, and validate progressively with AppIntentsTesting, Shortcuts, Spotlight, and Siri.

## Siri integration starts with App Intents and App Entities

Siri's newer Apple Intelligence-powered capabilities use App Intents as the structured description of what an app can do and what content it manages. The session uses a sample messaging app, UnicornChat, to show the pieces required for Siri to find content, perform actions, understand onscreen context, and participate in cross-app workflows.

An AppEntity is not a replacement data model. It is a system-facing representation of existing app content: what the thing is, how it is identified, and which properties are meaningful. For Siri to reason about the category of content, entities should conform to an App Schema, such as messages, contacts, or documents.

- Model meaningful app nouns as App Entities, such as contacts, conversations, messages, documents, events, photos, or albums.
- Conform entities to App Schemas so Siri understands the type of thing, not just app-specific identifiers.
- Use schema-backed entities as the shared foundation for Siri actions, search, onscreen context, and transfer between apps.

## Entity resolution, semantic search, and IndexedEntity

Entity resolution maps what a person says to real app entities. Exact names are not enough for natural language: users often describe content by meaning, relationship, or context. IndexedEntity lets entities participate in the system semantic index so Siri can match based on meaning, understand relationships, and answer questions over app content.

When app data cannot be indexed ahead of time - for example, because it is too large, server-backed, or changes too frequently - EntityStringQuery provides a fallback. Siri passes the spoken string to the app, and the app returns matching entities. This gives control over search behavior but does not provide the same semantic understanding as IndexedEntity.

- Prefer IndexedEntity when content can be indexed into Spotlight and should support semantic matching or content Q&A.
- Mark searchable properties with indexingKey, such as a message body's text content.
- Use EntityStringQuery for dynamic or non-indexable datasets where the app must perform lookup itself.

### Contribute message content with IndexedEntity

A schematized message entity adopts IndexedEntity and marks its body as searchable content for Spotlight and Siri.

```swift
@AppEntity(schema: .messages.message)
struct MessageEntity: IndexedEntity {
    // The text content of the message
    @Property(indexingKey: \.textContent)
    var body: AttributedString?
}
```

### Resolve entities from arbitrary text with EntityStringQuery

Use a string query when Siri should hand the app an input string and let app-specific lookup logic return matching entities.

```swift
struct ContactQuery: EntityStringQuery {
    func entities(matching string: String) async throws -> [ContactEntity] {
        let predicate = #Predicate<Person> { person in
            person.name.localizedStandardContains(string)
        }
        let descriptor = FetchDescriptor<Person>(predicate: predicate)
        let matches = try modelContext.fetch(descriptor)
        return matches.map(\.entity)
    }
}
```

## Make actions executable by Siri with App Schema domains

General App Intents can appear across system surfaces such as Shortcuts, Spotlight, and Widgets. To make an action directly executable by Siri through natural language, conform the intent to an App Schema. Schemas define the action structure Siri expects, including parameters and behavior that map to natural language commands.

Schemas are grouped into App Schema domains, such as Messages. Adopting a domain means implementing a set of predefined contracts that match a category of tasks. In UnicornChat, adopting the Messages domain's sendMessage schema maps Siri's recipient and message-content parameters onto the app's existing send flow, then returns the sent message as an entity.

- Use App Intents to expose app actions to the system.
- Use App Schemas to make those actions understandable and executable by Siri.
- Adopt complete schema domains that match the app's core workflows instead of inventing custom Siri-only intent shapes.
- Return entities from actions when the resulting app content should remain available to the system.

## Support onscreen awareness and cross-app content transfer

Many Siri requests depend on what the user is looking at, such as "forward this message" or "call this contact." Onscreen awareness connects visible UI to App Entities so Siri can resolve references like "this," "that," or "the last one." Use NSUserActivity when a screen has one primary item, and view annotations when multiple meaningful items are visible, such as rows in a list.

Cross-app workflows also require content transfer. Transferable and IntentValueRepresentation describe how an entity can be exported into a system value that another app can consume. When content enters an app, IntentValueQuery can resolve incoming values to existing entities, while an importing representation can create new entities.

- Annotate views with entity identifiers for lists or other screens showing multiple meaningful items.
- Adopt Transferable on entities that should move into actions from other apps.
- Use IntentValueQuery to match incoming transferred values to existing app content.
- Use IntentValueRepresentation(importing:) when incoming transferred values should create new app content.

### Annotate visible rows with App Entity identifiers

Each visible message row is connected to its MessageEntity so Siri can resolve contextual references to onscreen content.

```text
List {
    ForEach(messages) { message in
        MessageRow(message: message)
            .appEntityIdentifier(
                EntityIdentifier(
                    for: MessageEntity.self,
                    identifier: message.id
                )
            )
    }
}
```

### Export and import content with Transferable

A contact entity can be exported as an IntentPerson and imported back into the app by creating a new contact entity when needed.

```swift
extension ContactEntity: Transferable {
    static var transferRepresentation: some TransferRepresentation {
        IntentValueRepresentation(
            exporting: \.person,
            importing: { intentPerson in
                let contact = Contact(importing: intentPerson)
                ContactManager.shared.contacts.append(contact)
                return contact.entity
            }
        )
    }
}
```

### Resolve incoming intent values to existing entities

IntentValueQuery maps incoming IntentPerson values from another app to existing ContactEntity values in the current app.

```swift
struct ContactEntityQuery: IntentValueQuery {
    func values(for input: [IntentPerson]) async throws -> [ContactEntity] {
        let names = input.map(\.displayName)
        let descriptor = FetchDescriptor<Contact>()
        let contacts = try model.mainContext.fetch(descriptor)
        let matches = contacts.filter { contact in
            names.contains { name in
                contact.name.localizedStandardContains(name)
            }
        }
        return matches.map(\.entity)
    }
}
```

## Best practices and testing workflow

High-quality Siri integrations usually require related schemas to work together. Xcode can diagnose incomplete schema-domain adoption at build time. In the UnicornChat example, adopting sendMessage produces a build error until the related draftMessage schema is also adopted; Xcode offers a Fix-It that generates the intent definition, required parameters, and stub implementation.

Testing should move from isolated business logic to full end-to-end natural language behavior. Start with AppIntentsTesting for intent logic, then inspect intent shape in Shortcuts, validate indexing and linking in Spotlight, and finally test complete Siri flows involving natural language, entity resolution, onscreen context, and cross-app behavior.

- Treat related-schema diagnostics as design guidance, not just compiler errors.
- Use Xcode Fix-Its to scaffold missing schema adoptions, then fill in app-specific entity mapping, dependencies, input processing, and UI behavior.
- Run UI-mutating intent work on the main actor when the action changes app UI state.
- Test early and repeatedly: AppIntentsTesting → Shortcuts → Spotlight → Siri.

Resources:
- Integrating your messaging app with Apple Intelligence: https://developer.apple.com/documentation/AppIntents/integrating-your-messaging-app-with-apple-intelligence
- Donating your app's data and actions to the system: https://developer.apple.com/documentation/AppIntents/donating-your-apps-data-and-actions-to-the-system
- Making app entities available in Spotlight: https://developer.apple.com/documentation/AppIntents/making-app-entities-available-in-spotlight
- Making actions and content discoverable by Apple Intelligence: https://developer.apple.com/documentation/AppIntents/making-actions-and-content-discoverable-by-apple-intelligence
- Providing contextual cues to Apple Intelligence and Siri: https://developer.apple.com/documentation/AppIntents/providing-contextual-cues-to-apple-intelligence-and-siri
- Apple Intelligence and Siri AI: https://developer.apple.com/documentation/AppIntents/apple-intelligence-and-siri-ai
- Messages: https://developer.apple.com/documentation/AppIntents/app-schema-domain-messages
- App schema domains: https://developer.apple.com/documentation/AppIntents/app-schema-domains

Chapters:
- 0:00 Introduction: Introduces Siri capabilities powered by Apple Intelligence and frames App Intents as the integration foundation. The agenda covers Siri changes, content modeling, actions, cross-app workflows, and best practices.
- 1:06 What's new in Siri: Siri can access app entities, perform actions through intents, and understand onscreen context. The UnicornChat sample app is introduced as the running example.
- 4:06 Contributing content with App Entities: Explains AppEntity as a structured representation of existing app content, including identity and meaningful properties. Entities should conform to App Schemas so Siri understands their category, such as messages or contacts.
- 6:21 Entity resolution and IndexedEntity: Shows how Siri resolves spoken references to app entities and why semantic matching is needed for natural language. IndexedEntity enables Spotlight-backed semantic indexing, while EntityStringQuery supports app-controlled lookup when indexing is not feasible.
- 9:49 Making actions available: Distinguishes general App Intents from schema-backed intents that Siri can execute directly. App Schema domains group related action contracts so apps can provide complete task experiences.
- 12:03 Adopting a schema domain in UnicornChat: Walks through adopting the Messages domain's sendMessage schema in UnicornChat. The app maps schema parameters to its send flow and returns the newly sent message as an entity, allowing Siri to send a message without opening the app.
- 15:39 Moving content across apps: Introduces cross-app workflows as a combination of onscreen awareness and content transfer. Siri needs to identify referenced visible content and pass it to actions in another app.
- 16:00 Working across apps: onscreen awareness: Explains how to connect visible UI to App Entities using NSUserActivity for a primary item or view annotations for multiple items. Then covers Transferable, IntentValueRepresentation, and IntentValueQuery for exporting, resolving, or importing content across apps.
- 21:09 Best practices: Recommends adopting complete schema sets rather than isolated schemas. Xcode can surface missing related schemas at build time and generate scaffolding with Fix-Its.
- 24:18 Testing your integration: Presents a progressive testing strategy: validate intent logic with AppIntentsTesting, inspect configuration in Shortcuts, verify indexing in Spotlight, and test complete natural-language behavior in Siri.
- 26:21 Next steps: Summarizes the implementation path: model and index entities, adopt matching App Schema domains, enable Transferable import and export, and test early across AppIntentsTesting, Shortcuts, Spotlight, and Siri.

### What's new in the Foundation Models framework

- Session ID: wwdc2026-241
- Page: https://wwdc.ai/2026/241
- Markdown: https://wwdc.ai/2026/241.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/241/
- Category: AI & Machine Learning
- Description: Foundation Models gains Private Cloud Compute, vision, model-provider abstraction, Dynamic Profiles, evaluations, CLI/Python tooling, and open source utilities.
- Duration: 21:13
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-241/eng_e0f02872b5a9/wwdc2026-241-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/241/6/900558cb-1997-490a-9aac-2461b209e578/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/241/6/900558cb-1997-490a-9aac-2461b209e578/downloads/wwdc2026-241_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/241/6/900558cb-1997-490a-9aac-2461b209e578/downloads/wwdc2026-241_sd.mp4?dl=1

Foundation Models gains Private Cloud Compute, vision, model-provider abstraction, Dynamic Profiles, evaluations, CLI/Python tooling, and open source utilities.

TLDR:
- The on-device SystemLanguageModel is rebuilt with better reasoning and tool calling, token-counting/context-size APIs, improved guardrails, and image attachments for vision prompts.
- PrivateCloudComputeLanguageModel adds Apple server models with a 32K context window, configurable reasoning levels, no app-managed API keys, privacy guarantees, and watchOS 27 support.
- A new LanguageModel protocol lets LanguageModelSession run against Apple, local CoreAI/MLX, and partner model packages from Anthropic and Google while preserving the same downstream session APIs.
- Dynamic Profiles declaratively switch instructions, tools, models, and reasoning settings inside one session; new Evaluations, fm CLI, Python SDK, and open source utilities support development and testing.

## Foundation Models expands across models and modalities

The release opens the Foundation Models framework and adds a utilities package for emerging LLM building blocks. The core theme is broader model choice, deeper OS integrations, and new primitives for agentic app experiences.

The on-device model is rebuilt with stronger logic and tool calling. Developers should use the context-size and token-counting APIs introduced in iOS 26.4 to adapt prompts, instructions, and transcripts to the current hardware. Guardrails were refined in iOS 26.4 and continue to improve in iOS 27.

The on-device model also gains image understanding through prompt attachments. Attachments can be created from UIImage, NSImage, CGImage, Core Image types, CoreVideo pixel buffers, and file URLs. Images can use arbitrary size and aspect ratio, but larger images consume more tokens and increase latency.

### Inspect context size and token count

Use model-specific context and token-counting APIs before assembling prompts, instructions, or transcripts.

```swift
let model = SystemLanguageModel()
print(model.contextSize) // 8192

let count = try await model.tokenCount(
    for: "What are the Japanese characters for origami?"
)
print(count)
```

### Attach an image to a prompt

Image attachments extend the existing prompt-builder style for multimodal prompting.

```swift
let response = try await session.respond {
    "What animal is this?"
    Attachment(UIImage(...))
}
```

## Private Cloud Compute and model abstraction

PrivateCloudComputeLanguageModel gives apps access to Apple server models through Foundation Models. It has a 32,000-token context window and supports reasoning levels, where deeper reasoning can improve answer quality at the cost of more compute.

Using PCC does not require app-managed account setup, authentication, or API keys. The session notes that prompts are not stored, privacy claims are independently verifiable, and PCC enables Foundation Models on watchOS 27. PCC requires an entitlement, and Apple points developers to the dedicated PCC session for details.

The new LanguageModel protocol opens the model abstraction layer. SystemLanguageModel and PrivateCloudComputeLanguageModel already conform, and Apple is open sourcing CoreAILanguageModel and MLXLanguageModel for local models on Apple Neural Engine and Mac GPU. Anthropic and Google are publishing Swift packages for their models; when using third-party server models, use secure authentication such as OAuth and store tokens in Keychain, not in the app binary.

- LanguageModelSession can now be backed by local or server models through LanguageModel.
- ContextOptions(reasoningLevel:) controls how much a reasoning-capable model can think before responding.
- Sessions and responses expose usage data, including total input/output tokens, cached input tokens, and reasoning tokens.
- Third-party providers may involve separate authentication and per-token billing.

### Use reasoning and inspect token usage

Reasoning and usage APIs help tune quality, latency, and cost across Apple and third-party models.

```swift
let response = try await session.respond(
    to: "Recommend a craft that doesn't require scissors.",
    contextOptions: ContextOptions(reasoningLevel: .light)
)

print(response.usage.input.totalTokenCount)
print(response.usage.input.cachedTokenCount)
print(response.usage.output.totalTokenCount)
print(response.usage.output.reasoningTokenCount)
```

## Built-in system tools for vision and local RAG

Foundation Models adds system-provided tools backed by other OS frameworks. BarcodeReaderTool reads information from barcodes, and OCRTool extracts structured text from images. These tools complement native image understanding when a model needs specialized visual processing.

A Spotlight-powered search tool enables fully local Retrieval-Augmented Generation. The session describes RAG as providing the model with up-to-date personal or domain knowledge through a Spotlight index and processed queries, without requiring a remote retrieval service.

- Use BarcodeReaderTool for barcode information in images.
- Use OCRTool when structured text extraction improves visual reasoning.
- Use the Spotlight search tool to build local RAG over indexed content.
- For deeper implementation details, Apple points to image-understanding and Core Spotlight LLM search sessions.

## Dynamic Profiles for agentic sessions

Dynamic Profiles are a declarative primitive for changing the active context of a LanguageModelSession. Instead of manually juggling multiple sessions, a DynamicProfile describes the currently active Profile: its instructions, tools, model, and configuration.

The craft app example switches between a craft-analysis mode and a brainstorming mode. Analysis uses instructions and tools to inspect journal images and save detected craft details. Brainstorming can switch to Private Cloud Compute with deep reasoning while preserving conversation history.

A DynamicProfile resolves to one active Profile at any given time. Conditionals choose the active branch, and the framework handles the transition. Apple recommends considering privacy boundaries, model capabilities, and cost when profiles switch models or reasoning levels.

### Define a simple profile

A DynamicProfile can provide the current instructions and tools when creating a session.

```swift
struct CraftProfile: LanguageModelSession.DynamicProfile {
    var body: some DynamicProfile {
        Profile {
            Instructions {
                """
                You are an expert crafting assistant.
                Record craft project image analyses
                using the recordImageAnalysis tool.
                """
            }
            RecordImageAnalysisTool()
        }
    }
}

let session = LanguageModelSession(profile: CraftProfile())
```

### Switch profiles and configure model behavior

Profile branches can change instructions, tools, model, and reasoning level while maintaining a single session history.

```swift
struct CraftProfile: LanguageModelSession.DynamicProfile {
    let states: CraftProjectStates

    var body: some DynamicProfile {
        switch states.mode {
        case .craftAnalysis:
            Profile {
                Instructions { /* ... */ }
                RecordImageAnalysisTool()
                SwitchModeTool(states: states)
            }
        case .brainstorm:
            Profile {
                Instructions { /* ... */ }
                BrainstormRecordTool()
            }
            .model(states.privateCloudCompute)
            .reasoningLevel(.deep)
        }
    }
}
```

## Evaluation, command-line, Python, and open source support

The new Evaluations framework measures the quality of intelligence features. It is intended to quantify accuracy as prompts change and help developers understand the statistical impact of prompt and model changes before shipping.

On macOS 27, the fm command line tool exposes Apple Foundation Models from the terminal. It supports on-device and PCC usage, including interactive experimentation with fm chat and shell-script workflows for summarization, extraction, generation, and image-based file naming.

The Foundation Models Python SDK exposes the same on-device model as the Swift framework for data scientists and researchers. The open source Foundation Models framework utilities package adds building blocks such as transcript-management profile modifiers, a skill API for procedural knowledge loading, and a Chat Completions-compatible language model. The core framework is also being open sourced so Foundation Models can run wherever Swift runs, including Linux servers.

- Use Evaluations to compare prompt and behavior changes statistically.
- Use fm for terminal workflows and quick model experiments on macOS 27.
- Use the Python SDK when working in Python-centric research or data workflows.
- Use utilities for emerging LLM patterns that may evolve between OS releases.

### Use the Foundation Models Python SDK

The Python SDK provides availability checking and response generation against the on-device model.

```swift
import apple_fm_sdk as fm

model = fm.SystemLanguageModel()

is_available, reason = model.is_available()
if is_available:
    session = fm.LanguageModelSession(model=model)
    response = await session.respond(prompt="Hello!")
    print(response)
```

Resources:
- Expanding generation with tool calling: https://developer.apple.com/documentation/FoundationModels/expanding-generation-with-tool-calling
- Analyzing images with multimodal prompting: https://developer.apple.com/documentation/FoundationModels/analyzing-images-with-multimodal-prompting
- Composing dynamic sessions with instructions and profiles: https://developer.apple.com/documentation/FoundationModels/composing-dynamic-sessions-with-instructions-and-profiles
- Adding server-side intelligence with Private Cloud Compute: https://developer.apple.com/documentation/FoundationModels/adding-server-side-intelligence-with-private-cloud-compute

Chapters:
- 0:00 Introduction: The session previews the release: open sourcing the Foundation Models framework, adding a utilities package, expanding model choices, adding system tools, introducing Dynamic Profiles, and improving tooling.
- 2:34 New on-device model: The rebuilt on-device model improves intelligence, logic, and tool calling. Developers are encouraged to use context-size and token-counting APIs and benefit from refined guardrails.
- 3:21 Vision: image understanding: The on-device model can accept image attachments in prompts using common Apple image representations and file URLs. Arbitrary sizes and aspect ratios are supported, with token and latency costs increasing for larger images.
- 4:20 Private Cloud Compute: PrivateCloudComputeLanguageModel provides access to larger Apple server models with a 32K context window and configurable reasoning levels. It avoids app-managed API keys, emphasizes privacy, and brings Foundation Models capabilities to watchOS 27.
- 6:46 Model abstraction layer: The new LanguageModel protocol lets LanguageModelSession run against local or server-backed models. Apple models conform, and CoreAILanguageModel and MLXLanguageModel are being open sourced for local model execution.
- 7:32 Partner model integrations: Anthropic and Google provide Swift packages for their models, which can be swapped into sessions through the model abstraction layer. The chapter covers secure auth guidance and token usage reporting, including cached and reasoning tokens.
- 9:40 System tools: Vision and Spotlight: Foundation Models gains built-in Vision-backed BarcodeReaderTool and OCRTool, plus a Spotlight-powered search tool for fully local RAG over indexed content.
- 10:57 Dynamic Profiles for agentic apps: Dynamic Profiles are introduced as a declarative way to switch instructions and tools inside a single session. The craft app example moves between image analysis and brainstorming modes, including tool-driven mode switching.
- 13:46 Composing models and configurations: Profile branches can specify different models and configurations, such as using the on-device model for quick analysis and PCC with deep reasoning for brainstorming. A DynamicProfile resolves to a single active Profile at a time.
- 15:30 Evaluations framework: The Evaluations framework measures intelligence feature quality and helps quantify the accuracy impact of prompt changes and other tweaks.
- 16:02 The fm command line tool: macOS 27 adds the fm CLI for accessing on-device models and PCC from the terminal. It supports interactive chat and shell-script workflows such as summarization, extraction, generation, and image-based file naming.
- 17:13 Foundation Models Python SDK: The Python SDK exposes the same on-device model as the Swift framework, including availability checks and response generation for Python-based workflows.
- 17:55 Open source and framework utilities: The utilities package provides emerging LLM building blocks such as transcript management, skill APIs, and Chat Completions interfacing. The core framework is also open sourced to support Swift environments including Linux servers.
- 19:24 Next steps: The session recommends exploring the sample app, learning Dynamic Profiles, and adopting the Evaluations framework. It also points to related deep dives on PCC, evaluations, Xcode instrumentation, and dynamic profiles.

### Build agentic app experiences with the Foundation Models framework

- Session ID: wwdc2026-242
- Page: https://wwdc.ai/2026/242
- Markdown: https://wwdc.ai/2026/242.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/242/
- Category: AI & Machine Learning
- Description: Use DynamicProfile, DynamicInstructions, history transforms, lifecycle modifiers, and tool orchestration to build agentic Foundation Models app flows.
- Duration: 21:43
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-242/eng_e20a5200d723/wwdc2026-242-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/242/4/7f05515d-be1a-43a0-9962-a1f77f115666/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/242/4/7f05515d-be1a-43a0-9962-a1f77f115666/downloads/wwdc2026-242_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/242/4/7f05515d-be1a-43a0-9962-a1f77f115666/downloads/wwdc2026-242_sd.mp4?dl=1

Use DynamicProfile, DynamicInstructions, history transforms, lifecycle modifiers, and tool orchestration to build agentic Foundation Models app flows.

TLDR:
- DynamicProfile lets a LanguageModelSession switch instructions, tools, model, temperature, reasoningLevel, and other options per app phase or agent.
- DynamicInstructions, historyTransform, custom modifiers, lifecycle modifiers, and session properties support reusable context engineering and transcript management.
- The session covers baton-pass and phone-a-friend orchestration patterns, plus Skills from the Foundation Models framework utilities package.
- ToolCallingMode, transcriptErrorHandlingPolicy, mutable transcripts, KV cache behavior, Instruments, and Evaluations are key when building advanced agentic workflows.

## Dynamic profiles as agent configuration

DynamicProfile is a new Foundation Models framework API for changing the active configuration of a LanguageModelSession. A profile can represent an app phase or agent, with its own instructions, tools, model, sampling options, temperature, reasoningLevel, and other modifiers.

The sample Origami craft app has brainstorming, planning, and reviewing phases. They share context, but each phase has different needs: brainstorming favors creativity, planning benefits from deeper reasoning, and reviewing can use the on-device SystemLanguageModel to avoid unnecessary server calls. The body of a DynamicProfile is re-evaluated on each prompt, so changing app state can swap the session persona.

- Use PrivateCloudComputeLanguageModel where server model capabilities are needed, such as broad craft knowledge or deep tutorial planning.
- Use SystemLanguageModel where an on-device model is sufficient, such as reviewing in-progress craft photos and giving technique advice.
- Initialize a session directly with a dynamic profile using the new LanguageModelSession initializer.

### Define per-phase model configuration

A single session can route between distinct agents by re-evaluating the profile from app state.

```swift
struct CraftProfile: LanguageModelSession.DynamicProfile {
    var orchestrator: CraftOrchestrator

    var body: some DynamicProfile {
        switch orchestrator.mode {
        case .brainstorming:
            Profile {
                BrainstormFacilitator(orchestrator: orchestrator)
            }
            .model(orchestrator.pccLanguageModel)
            .temperature(1)

        case .planning:
            Profile {
                TutorialAuthor(orchestrator: orchestrator)
            }
            .model(orchestrator.pccLanguageModel)
            .reasoningLevel(.deep)

        case .reviewing:
            Profile {
                CraftCoach()
            }
            .model(orchestrator.systemLanguageModel)
        }
    }
}

let session = LanguageModelSession(profile: CraftProfile(orchestrator: orchestrator))
```

## Reusable dynamic instructions and utilities

DynamicInstructions groups related instructions and tools into a reusable component. Nesting one DynamicInstructions component inside another concatenates its instructions and tools, which makes domain packages such as an OrigamiExpert reusable across profiles.

Apple also introduced the open-source Foundation Models framework utilities package. It contains components for agentic experiences, including history-management modifiers and the Skills pattern for procedural context loading, and can evolve between OS releases.

- Use DynamicInstructions for reusable instruction/tool bundles.
- Conditionally include domain expertise or tools based on app state, such as adding origami-specific support only for origami projects.
- Use Skills from the utilities package when procedural context should be activated selectively.

### Compose instructions and tools

DynamicInstructions can package instructions, tools, and conditional capabilities for reuse across profile declarations.

```swift
struct BrainstormFacilitator: DynamicInstructions {
    var orchestrator: CraftOrchestrator

    var body: some DynamicInstructions {
        Instructions {
            "You are a warm and friendly expert crafting brainstorm facilitator."
        }

        GenerateProjectTitle()

        if orchestrator.techniques.contains(.origami) {
            OrigamiExpert()
        }
    }
}
```

### Represent procedural context as skills

The Skills utility is presented as a pattern for procedural context loading.

```swift
struct CraftingSkills: LanguageModelSession.DynamicInstructions {
    var activations: SkillActivations

    var body: some DynamicInstructions {
        Skills(activations: activations) {
            Skill(
                name: "origami_folds",
                description: "Details about specific types of folds",
                prompt: """
                Valley Fold: Paper is folded toward you, creating a V-shaped crease
                Mountain Fold: Paper is folded away from you, creating an inverted V
                ...
                """
            )
            Skill(...)
            Skill(...)
        }
    }
}
```

## Transcript management and shared session state

A session transcript is the model's context. Dynamic profiles make it practical to trim, summarize, or redact context when switching between models with different context limits, privacy boundaries, or focus requirements.

historyTransform applies a stateless, per-request transform over the session history before prompting the model. It does not permanently mutate the session transcript, so it is preferred for lossless, profile-specific context shaping. For stateful changes shared by all profiles, use session properties such as the built-in history property.

- Use historyTransform to drop irrelevant entries, completed tool calls, or private content for a specific profile.
- Use DynamicProfileModifier to package transforms as reusable custom modifiers.
- Use lifecycle modifiers such as onResponse to run imperative code at response boundaries.
- Use @SessionPropertyEntry for custom state shared across profiles and tools, such as a conversation summary.
- Be careful: the built-in history session property is lossy and changes apply across all profiles.

### Drop completed tool calls with a custom profile modifier

Custom modifiers hide transcript-transform complexity and make profile configuration reusable.

```swift
struct DroppingToolCallsProfileModifier: LanguageModelSession.DynamicProfileModifier {
    func body(content: Content) -> some DynamicProfile {
        content
            .historyTransform { history in
                guard let latestResponseIndex = lastResponseEntryIndex(history) else {
                    return history
                }
                let filteredHistory = history[0..<latestResponseIndex].filter { entry in
                    isToolCallsOrToolOutput(entry)
                }
                return filteredHistory + history[latestResponseIndex...]
            }
    }
}

extension LanguageModelSession.DynamicProfile {
    func droppingCompletedToolCalls() -> some DynamicProfile {
        self.modifier(DroppingToolCallsProfileModifier())
    }
}
```

### Store a shared summary in a session property

A lifecycle modifier can summarize earlier transcript entries, store the summary, and trim shared history after a response.

```swift
extension SessionPropertyValues {
    @SessionPropertyEntry var summary: String?
}

struct CraftProfile: LanguageModelSession.DynamicProfile {
    @SessionProperty(\.history) var history
    @SessionProperty(\.summary) var summary
    var orchestrator: CraftOrchestrator

    var body: some DynamicProfile {
        switch orchestrator.mode {
        case .planning:
            Profile {
                TutorialAuthor(orchestrator: orchestrator)
                if let summary {
                    Instructions { "Summary: \(summary)" }
                }
            }
            .onResponse {
                if history.count > 50, let responseIndex = lastResponse(history.prefix(40)) {
                    summary = try await summarize(history[0..<responseIndex])
                    history = history[responseIndex...]
                }
            }
        }
    }
}
```

## Agent orchestration patterns

The session describes two common orchestration patterns: baton-pass and phone-a-friend. Both rely on tool calling, but they differ in transcript visibility and who produces the final answer.

In baton-pass, multiple profiles share the full transcript. A tool changes the active profile variable, and the receiving profile carries the request to completion. In phone-a-friend, a tool creates a short-lived child session with an independent transcript; the child returns tool output, and the parent profile always produces the final response.

- Use baton-pass for collaboration between profiles that should see the same full session history.
- Use phone-a-friend for consultation where an isolated specialist session should provide intermediate output.
- Represent handoffs and consultations as tools so the model can decide when to invoke them.

### Baton-pass profile switch

A tool call changes the active profile, handing control to another agent that shares the transcript.

```swift
struct CraftProfile: LanguageModelSession.DynamicProfile {
    var orchestrator: CraftOrchestrator

    var body: some DynamicProfile {
        switch orchestrator.mode {
        case .brainstorm:
            Profile {
                BrainstormInstructions()
                BatonPassTool()
            }
            .onToolCall { orchestrator.mode = .tutorial }
            .model(orchestrator.serverModel)

        case .tutorial:
            Profile {
                TutorialInstructions()
                BatonPassTool()
            }
            .onToolCall { orchestrator.mode = .brainstorm }
            .model(orchestrator.systemModel)
        }
    }
}
```

### Phone-a-friend child session

A tool can spawn a short-lived specialist session and return its response as tool output to the parent session.

```swift
struct PhoneFriendTool<P: LanguageModelSession.DynamicProfile>: Tool {
    func call(arguments: GeneratedContent) async throws -> String {
        let session = LanguageModelSession(profile: profile())
        let response = try await session.respond(to: arguments)
        return response.content
    }
}
```

## Tool calling mode and transcript errors

ToolCallingMode controls whether a model may, must, or must not call tools. The default allowed mode preserves existing behavior. disallowed prevents tool use, and required forces the model to produce tool calls, which is useful for systems that represent actions as tools.

When tool calling is required, the model is effectively in a loop. The app must provide an exit condition, such as changing the tool calling mode after a specific tool call or using a final-answer tool that throws to abort the loop and return control to the app.

By default, a thrown tool error or cancellation rolls the transcript back. transcriptErrorHandlingPolicy can be set to .revertTranscript or .preserveTranscript. If preserving the transcript after an error, the app is responsible for restoring it to a usable state.

- Set tool calling mode as a profile modifier or through GenerationOptions when calling respond(to:).
- Use .required only with a clear exit path.
- Set transcriptErrorHandlingPolicy on a profile or directly on LanguageModelSession.
- LanguageModelSession.transcript is mutable, but only modify it when isResponding is false.

### Require tools for a profile or a single generation

Tool calling mode can be part of a profile or a per-request generation option.

```swift
public struct ToolCallingMode: Sendable {
    public static let allowed: ToolCallingMode
    public static let disallowed: ToolCallingMode
    public static let required: ToolCallingMode
}

struct OrigamiExpert: LanguageModelSession.DynamicProfile {
    var body: some LanguageModelSession.DynamicProfile {
        Profile {
            Instructions("You are an origami expert")
            QueryOrigamiDatabaseTool()
            ShowDirectionsTool()
        }
        .toolCallingMode(.required)
    }
}

let response = try await session.respond(
    to: "Write out the instructions for folding a paper crane.",
    options: GenerationOptions(toolCallingMode: .required)
)
```

### Preserve transcript after errors

Preserving the transcript supports advanced cancellation/resume flows, but requires manual cleanup if the transcript is left in a bad state.

```swift
struct OrigamiExpert: LanguageModelSession.DynamicProfile {
    let state: OrigamiAppState

    var body: some LanguageModelSession.DynamicProfile {
        Profile {
            Instructions("Answer questions about how to fold origami")
            QueryOrigamiDatabaseTool()
        }
        .transcriptErrorHandlingPolicy(.preserveTranscript)
    }
}

let session = LanguageModelSession()
session.transcriptErrorHandlingPolicy = .preserveTranscript

extension LanguageModelSession {
    public struct TranscriptErrorHandlingPolicy: Sendable {
        public static let revertTranscript: TranscriptErrorHandlingPolicy
        public static let preserveTranscript: TranscriptErrorHandlingPolicy
    }
}
```

## Performance, accuracy, and evaluation

Advanced transcript mutation affects both latency and model quality. KV caches are an important optimization for large language models. Appending to the transcript generally preserves cache state and minimizes time-to-first-token, while removing entries, changing attached tools, or updating instructions can invalidate caches and increase latency.

Rewriting history can also confuse the model. For example, adding a title-generation tool after earlier title-generation turns may cause the model to infer that direct generation is still acceptable. Context engineering changes should be measured rather than assumed.

- Use the upgraded Foundation Models Instrument in Xcode to detect cache invalidations and measure latency.
- Expect different models to have different caching behavior; measure the specific configuration.
- Use the Evaluations framework to build eval sets and quantify accuracy effects of transcript and context strategies.
- Prefer append-only behavior when possible, and mutate transcript deliberately when the benefit outweighs performance and accuracy risk.

Resources:
- Composing dynamic sessions with instructions and profiles: https://developer.apple.com/documentation/FoundationModels/composing-dynamic-sessions-with-instructions-and-profiles

Chapters:
- 0:00 Introduction: Introduces DynamicProfile as a set of Foundation Models APIs for context management, model boundaries, and flexible agentic abstractions. Also announces the open-source Foundation Models framework utilities package.
- 2:47 The example app and agents: Uses an Origami craft app with brainstorming, planning, and reviewing phases to show why app stages can be modeled as agents. Each agent has shared context but distinct goals, tools, models, and generation options.
- 3:47 Declaring a dynamic profile: Shows a CraftOrchestrator-driven DynamicProfile and starts with the brainstorming profile. The profile includes instructions, a title-generation tool, and conditional origami-specific capabilities.
- 4:45 Dynamic instructions: Introduces DynamicInstructions as reusable bundles of instructions and tools. The OrigamiExpert component can be nested inside other instruction declarations to concatenate its capabilities.
- 5:36 Configuring models per phase: Configures PrivateCloudComputeLanguageModel with temperature for brainstorming and deep reasoningLevel for planning, while reviewing uses SystemLanguageModel. The DynamicProfile body is re-evaluated per prompt, allowing the session persona to change with app mode.
- 7:21 Transcript management and history transforms: Explains that the transcript is the session's model context and may need trimming, focusing, or redaction when switching models. historyTransform provides a non-mutating, per-request window over history, such as dropping tool calls before prompting an on-device model.
- 8:50 Custom modifiers: Wraps a historyTransform in a DynamicProfileModifier and exposes it through a DynamicProfile extension. The utilities package also provides ready-made history-management modifiers.
- 9:39 Lifecycle modifiers and session properties: Uses lifecycle modifiers such as onResponse to run imperative code at response boundaries. Introduces built-in and custom session properties, including shared history and a summary property for preserving context after trimming.
- 12:52 Orchestration: baton-pass: Describes baton-pass as a collaborative pattern where profiles share the full transcript and a tool changes which profile is active. The receiving profile produces the final response.
- 14:06 Orchestration: phone-a-friend and skills: Describes phone-a-friend as a consultation pattern where a tool creates a short-lived child session with an isolated transcript and returns its output to the parent. Also mentions the Skills utility for procedural context loading.
- 15:18 Tool calling mode: Introduces ToolCallingMode with allowed, disallowed, and required modes, set as a profile modifier or GenerationOptions. Required mode creates a tool-call loop, so apps must provide an exit condition.
- 17:12 Transcript error handling: Explains transcript behavior after thrown tool errors or cancellation. transcriptErrorHandlingPolicy can revert or preserve the transcript, and mutable transcripts must only be changed when the session is not responding.
- 18:27 Performance, accuracy, and evaluations: Covers KV cache implications of transcript mutation: appending generally preserves cache while rewriting history, tools, or instructions may invalidate it. Recommends measuring with the Foundation Models Instrument and using the Evaluations framework for accuracy impacts.
- 21:24 Next steps: Recaps dynamic profiles, transcript management, orchestration, tool calling mode, and KV cache considerations. Suggests trying the sample app, Foundation Models utilities, PCC, and the Xcode instrument.

### Debug and profile agentic app experiences with Instruments

- Session ID: wwdc2026-243
- Page: https://wwdc.ai/2026/243
- Markdown: https://wwdc.ai/2026/243.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/243/
- Category: AI & Machine Learning
- Description: Use the enhanced Foundation Models instrument in Xcode 27 to trace prompts, tool calls, instruction handoffs, and latency in agentic LLM app flows.
- Duration: 14:20
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-243/eng_eada70aacc9a/wwdc2026-243-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/243/4/127c397a-8124-4f3d-ad18-ac2a1d275803/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/243/4/127c397a-8124-4f3d-ad18-ac2a1d275803/downloads/wwdc2026-243_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/243/4/127c397a-8124-4f3d-ad18-ac2a1d275803/downloads/wwdc2026-243_sd.mp4?dl=1

Use the enhanced Foundation Models instrument in Xcode 27 to trace prompts, tool calls, instruction handoffs, and latency in agentic LLM app flows.

TLDR:
- The Foundation Models instrument helps inspect agentic app behavior across sessions, requests, model inferences, instructions, prompts, responses, and tool calls.
- LLM app debugging requires a different mindset: outputs are probabilistic, multi-model data flow can fail silently, and observability is essential.
- A demo bug is diagnosed by finding that a Dynamic Instructions prompt referenced a tutorial-switching tool that was not included in the active toolset.
- Performance work starts from model inference metrics: time-to-first-token, tokens-per-second, total latency, token usage, and duration breakdowns.

## Why agentic LLM flows need different debugging tools

Foundation Models apps can use on-device and server-based generative AI for natural-language understanding, content generation, and context-aware responses. Agentic experiences become harder to debug when they adapt instructions and tools dynamically before each request.

The session frames three LLM-specific challenges: non-deterministic output makes hardcoded string assertions unreliable; multi-model or multi-step flows require robust data handoff; and failures can be difficult to locate without visibility into what each model received, decided, and returned.

- DynamicInstructions re-evaluates before every request so the model has the current instruction and tool context.
- Tool-call loops add intermediate steps: prompt, model reasoning, tool call, tool execution, model response, and possibly another loop.
- Each extra model inference or tool call adds latency and another potential failure point.

## Profiling with the Foundation Models template

Profiling starts from Xcode by choosing Product &gt; Profile, selecting the Foundation Models Instruments template, and recording while exercising the app. The instrument captures prompt and response data during the trace, so trace files can contain sensitive information and should be handled carefully.

The demonstrated app is a craft journaling companion with an interactive brainstorming feature. It uses one instruction set for idea generation and another for tutorial generation, both using the server model on Private Cloud Compute.

- Use the Foundation Models template in Instruments to record framework activity while running the app.
- Prompt and response logging is off in production but enabled for the duration of the trace.
- Keep trace files secure because they may include sensitive prompt, response, or context data.

## Inspecting instructions, requests, inferences, and tool calls

The Instruments UI combines timeline lanes, a detail view, and an inspector. The Foundation Models instrument includes lanes that summarize session structure and latency, while the tree detail view organizes recorded activity into sessions, requests, model inferences, instructions, prompts, responses, and tool calls.

In the demo, the timeline immediately shows that only one instruction set stayed active across the experience, even though the design required a handoff from brainstorming to tutorial generation. Drilling into the tree reveals the root cause: the prompt referenced a switchToTutorialMode tool, but the active instruction's configured toolset only included the idea-generation tool.

- The Instructions lane shows how long each instruction set and tool configuration was active.
- The Model Inference lane uses yellow bars for input prompt processing and orange bars for response generation.
- Selecting a tree node opens inspector details for the associated instruction, prompt, response, error, tool call, duration, and token usage.
- The info column can help identify errors, long durations, and large token counts.

## Diagnosing and validating an agentic handoff bug

The failure mode in the demo is silent: the model continues accepting input and making tool calls, but the app never switches from brainstorming mode to tutorial mode. Instruments makes the missing tool configuration visible by showing the instruction's available tools next to the prompt that expected a tutorial-switching tool.

After adding the missing SwitchToTutorialMode tool to the brainstorming Dynamic Instructions toolset, a second trace confirms the fix. The Instructions lane now shows two distinct instruction sets, and the tree view shows the switch tool call passing the selected craft as an argument before the next request runs under tutorial-generation instructions.

- Compare the intended instruction lifecycle with the Instructions lane.
- Inspect the instruction node to verify that prompt text and configured tools match.
- Validate fixes by recording a new trace, not just by observing UI behavior.
- Use the tree hierarchy to confirm when a tool call occurred and which context was passed into the following request.

## Performance metrics to optimize LLM experiences

The model inference inspector includes duration visualizations and token usage metrics. These are the starting point for reducing latency, spotting regressions, and understanding where time and resources are being spent.

- Time to First Token measures how long the model takes to begin generating after receiving the prompt. If it is high, shorten the prompt.
- Tokens per Second measures generation speed and is useful for benchmarking prompt configurations and catching regressions.
- Total Latency measures the full request-to-final-response time. Use streaming to reduce perceived wait by showing partial results sooner.
- Token usage and duration breakdowns on model inference nodes help identify expensive prompts or unexpectedly slow calls.

## Requirements and next steps

To use the improved Foundation Models instrument, install Xcode 27 and update the profiling device to the latest OS releases. The instrument supports any model used through the Foundation Models framework.

After debugging behavior and latency, use evaluation workflows to measure prompt quality and response quality more systematically. The session points developers to related Foundation Models and agentic app sessions for deeper background.

- Review Foundation Models framework updates before applying these workflows to new app features.
- Use Instruments for runtime observability and the Evaluations framework for structured quality measurement.
- Consult the documentation guide on analyzing runtime performance for Foundation Models apps.

Resources:
- Analyzing the runtime performance of your Foundation Models app: https://developer.apple.com/documentation/FoundationModels/analyzing-the-runtime-performance-of-your-foundation-models-app

Chapters:
- 0:00 Introduction: Introduces the enhanced Foundation Models instrument in Instruments for debugging and profiling features built with the Foundation Models framework. Sets up the focus on DynamicInstructions, tool access, prompt/response inspection, and agentic flows.
- 1:57 LLM app development mindset: Explains why LLM app development differs from traditional deterministic code: output is probabilistic, multi-model communication adds complexity, and observability is needed to locate failures. Describes the tool-call loop and how each step can add latency or fail.
- 3:59 Inspect and diagnose an agentic experience: Introduces the craft companion demo app, including brainstorming and tutorial-generation modes. The feature uses separate instruction sets and tools, with server model usage on Private Cloud Compute.
- 5:02 Recording a trace with Instruments: Shows the workflow for profiling from Xcode using Product &gt; Profile and the Foundation Models template. Notes that traces can include sensitive prompt and response data because logging is enabled during recording.
- 6:04 Navigating the Instruments UI: Walks through the timeline, detail view, inspector, lanes, and tree hierarchy for Foundation Models activity. Uses the trace to find a silent failure where a prompt referenced a tutorial-switching tool that was not included in the active instruction toolset, then validates the fix in a second recording.
- 12:07 Performance metrics: Covers key model inference metrics: Time to First Token, Tokens per Second, Total Latency, duration breakdowns, and token usage. Explains how these metrics guide prompt shortening, benchmarking, regression detection, and use of streaming.
- 13:04 Next steps: Summarizes the debugging workflow and recommends exploring evaluation for prompt quality after runtime bugs are fixed. Notes the requirements of Xcode 27 and current OS releases, and that the instrument works with models used through the Foundation Models framework.

### LLM search using Core Spotlight

- Session ID: wwdc2026-246
- Page: https://wwdc.ai/2026/246
- Markdown: https://wwdc.ai/2026/246.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/246/
- Category: AI & Machine Learning
- Description: Use SpotlightSearchTool with Foundation Models to build grounded, conversational Core Spotlight search with hydration, guidance, pipelines, and evaluations.
- Duration: 16:25
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-246/eng_cecff104e0a8/wwdc2026-246-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/246/4/b390ab9d-d231-4cf5-9d1b-e4270ef5012b/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/246/4/b390ab9d-d231-4cf5-9d1b-e4270ef5012b/downloads/wwdc2026-246_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/246/4/b390ab9d-d231-4cf5-9d1b-e4270ef5012b/downloads/wwdc2026-246_sd.mp4?dl=1

Use SpotlightSearchTool with Foundation Models to build grounded, conversational Core Spotlight search with hydration, guidance, pipelines, and evaluations.

TLDR:
- SpotlightSearchTool lets a LanguageModelSession search your app's Core Spotlight index so answers are grounded in donated app content instead of only model knowledge.
- For high-quality answers, donate useful searchable metadata and implement CSSearchableIndexDelegate hydration so the model can recover full CSSearchableItem data when compact index representations are not readable.
- Tune the tool with guidance profiles, contact resolution, and custom Generable pipeline stages for app-specific reasoning such as sentiment or aggregate computations.
- Use the Evaluations framework, ModelSampleProtocol datasets, trajectory expectations, and custom metrics such as result coverage to test tool-calling and response quality.

## Conversational search grounded in app content

The session shows how to turn Core Spotlight indexing into a retrieval-augmented experience for a Foundation Models session. A language model can answer broad questions from its own world knowledge, but questions about the user's app data need grounding in the app's Core Spotlight index.

SpotlightSearchTool adopts the Foundation Models Tool protocol. The model can decide to call it, generate search arguments, receive a description of the result set, and use that output to produce a grounded response. The app must already donate searchable content to Core Spotlight or index entities for Apple Intelligence.

- Running example: a hiking trails app with trails, locations, completion dates, and personal notes.
- SpotlightSearchTool is available on iOS, iPadOS, macOS, and visionOS.
- Use prior Core Spotlight indexing practices: donate searchable items, manage reindexing, and include metadata that supports semantic and structured search.

### Ask a Foundation Models session

A plain LanguageModelSession can answer from model knowledge; adding SpotlightSearchTool grounds questions in indexed app content.

```swift
let response = try await session.respond(to: "What are some nice hikes near water?")
```

## Configure SpotlightSearchTool and add it to a session

Adopting SpotlightSearchTool has three parts: configure the kind of search the model can perform, provide additional context while search is active, and decide how to display both generated responses and raw search results.

Configuration starts by importing CoreSpotlight and FoundationModels, creating the tool, selecting a model such as SystemLanguageModel or a model from the Model Provider APIs, and passing the tool into LanguageModelSession.

- The default tool searches the app's Core Spotlight index.
- A custom configuration can specify sources, such as file paths in the app sandbox.
- The model may call the tool one or more times before generating the final answer.

### Create a default or file-backed SpotlightSearchTool

The default initializer is enough for the app's Core Spotlight index; configuration can narrow sources such as files.

```swift
import CoreSpotlight
import FoundationModels

let tool = SpotlightSearchTool()

let fileTool = SpotlightSearchTool(
    configuration: .init(
        sources: [.files]
    )
)
```

### Add the tool to a LanguageModelSession

Once included in the session, the model can call SpotlightSearchTool when it needs app-indexed context.

```swift
import CoreSpotlight
import FoundationModels

let tool = SpotlightSearchTool()
let session = LanguageModelSession(
    model: model,
    tools: [tool],
    instructions: instructions
)

let response = try await session.respond(to: "What hikes have I gone on?")
```

## Hydrate full items and display partial results

Some Spotlight metadata, including text content and HTML, may be stored in compact searchable representations that are not recoverable in a form the model can read. To expose complete item data during model search, implement the new delegate method that returns full CSSearchableItem values for identifiers.

For UI, the session response is usually appropriate for an assistant-style answer. For list-style search result displays, consume searchResults from SpotlightSearchTool directly. Replies arrive as an async sequence and can contain batches of results before the tool call completes.

- Implement searchableItems(forIdentifiers:) on CSSearchableIndexDelegate to hydrate full items on demand.
- Attach extra attributes during hydration when they are useful for model reasoning but not appropriate to donate for regular search.
- Use each reply's queryToken to detect a new tool query, because one model response may involve multiple Spotlight searches.

### Implement an index delegate for full item recovery

The delegate lets SpotlightSearchTool recover complete searchable items by unique identifier for model reasoning.

```swift
import CoreSpotlight

class IndexDelegate: NSObject, CSSearchableIndexDelegate {
    func searchableItems(forIdentifiers identifiers: [String]) async -> [CSSearchableItem] {
        let entries = await mystore.fetchEntries(ids: identifiers)
        return entries.map { makeSearchableItem(from: $0) }
    }
}
```

### Track partial search replies by query token

Search replies are streamed in batches; query tokens keep UI state aligned with the model's tool calls.

```swift
import CoreSpotlight
import FoundationModels

let tool = SpotlightSearchTool()

for await reply in tool.searchResults {
    if reply.queryToken != currentToken {
        currentToken = reply.queryToken
        // Start a new display section or refresh the current results UI.
    }

    switch reply.content {
    case .items(let searchItems):
        // Display this batch of CSSearchableItem results.
        break
    default:
        break
    }
}
```

## Customize search guidance, reference resolution, and pipelines

SpotlightSearchTool exposes semantic search over text and structured search over metadata such as dates, people, and locations. Guidance profiles constrain that capability set so the model sees only what is relevant to the app, which is especially important for limited-context on-device models.

Reference resolution supplies context that is not directly in the search index. If indexed metadata includes person relationships, a ContactResolver can return identities for the current user so references such as "me" or a participant can be matched against indexed metadata.

For complex requests, the model can use pipeline search: a combination of index queries and computation stages. Apps can register custom Generable stages, such as a happiness score over hiking notes, and the tool may return pipeline outputs as partial results.

- Use GuidanceProfile to enable capabilities like text matching, dates, people, and specific attributes.
- Use focused guidance for simpler searches or constrained model contexts.
- Custom stages declare input and output SearchPipelineDataType values and are registered in the tool configuration.
- Partial reply content can include items, scored items, grouped items, counts, tables, statistics, and text, each with an LLM-generated label.

### Set a dynamic guidance profile

Guidance profiles scope the search interface the model uses for generation.

```swift
import CoreSpotlight
import FoundationModels

let profile = SpotlightSearchTool.GuidanceProfile(
    textMatch: true,
    dates: true,
    people: false,
    attributes: [.title, .altitude, .completionDate]
)

let tool = SpotlightSearchTool(
    configuration: .init(
        guide: .init(level: .dynamic(profile))
    )
)

let focusedTool = SpotlightSearchTool(
    configuration: .init(
        guide: .init(level: .focused(.items))
    )
)
```

### Provide user identity with a ContactResolver

A contact resolver lets the tool match user references to person metadata in the search index.

```swift
import CoreSpotlight
import FoundationModels

struct MyContactResolver: ContactResolver {
    func userIdentity() -> ResolvedContact {
        var contact = ResolvedContact(displayName: "Jane Doe")
        contact.emailAddresses = ["jane@example.com", "jdoe@work.com"]
        contact.names = ["Jane", "JD"]
        return contact
    }
}

tool.contactResolver = MyContactResolver()
```

### Define and register a custom pipeline stage

Custom stages let the app run domain-specific computation over result sets for model-generated pipelines.

```swift
import CoreSpotlight
import FoundationModels

@Generable
struct HappinessStage: CustomStage {
    static var name = "happiness"
    static var description = "Scores hike by how happy the author was"
    static var inputTypes: [SearchPipelineDataType] = [.items]
    static var outputTypes: [SearchPipelineDataType] = [.scoredItems]

    @Guide(description: "Minimum happiness score (0.0-1.0) to include in results")
    var threshold: Double?

    func execute(on input: SearchPipelineData) async throws -> SearchPipelineData {
        return SearchPipelineData(payload: .scoredItems(sorted))
    }
}

let tool = SpotlightSearchTool(
    configuration: .init(
        customStages: [.happinessBoost(threshold: 0.5)]
    )
)
```

## Evaluate tool-calling and result quality

The Evaluations framework helps measure how well the model calls SpotlightSearchTool and how meaningful the response is. The hiking trails example focuses on result coverage: given indexed data, did the model's search and response include the expected items?

The evaluation setup defines samples that include natural-language inputs, expected outputs, trajectory expectations, and expected searchable item identifiers. Seed samples can be expanded with Sample Generation APIs, then loaded into a test target that indexes items, runs the evaluation, and asserts metrics.

- Adopt ModelSampleProtocol for the evaluation dataset.
- Use TrajectoryExpectation to assert that the response includes a SpotlightSearchTool call.
- Use custom metrics such as ResultCoverage to compare expected identifiers with returned or final-response items.
- Evaluations can be used to iterate on searchable metadata, guidance profiles, custom stages, and model choice.

### Define an evaluation sample type

Samples pair user prompts with expected model behavior and expected Spotlight result identifiers.

```swift
import Evaluations

struct TrailRequest: ModelSampleProtocol {
    typealias ExpectedValue = String
    typealias Expectation = TrajectoryExpectation

    var input: ModelSampleInput
    var output: ModelSampleOutput<String, TrajectoryExpectation>
    var expectedIdentifiers: [String]
}
```

### Expect a Spotlight tool call

Trajectory expectations verify that the model used SpotlightSearchTool as part of the response.

```swift
import Evaluations

TrajectoryExpectation(
    unordered: [
        ToolExpectation(
            "searchSpotlight",
            arguments: [.keyOnly(argumentName: "query")]
        )
    ]
)
```

### Run an evaluation test with a result coverage threshold

A test can index fixture data, run the search evaluation, and fail when aggregate quality drops below a chosen threshold.

```swift
@Test("Trail search evaluation meets quality thresholds")
func trailSearchEval() async throws {
    let items = try Self.loadItems()
    let samples = try Self.loadSamples()

    try await Self.indexDelegate.indexSearchableItems(items)

    let tool = Self.makeSearchTool()
    let evaluation = TrailSearchEvaluation(
        tool: tool,
        dataset: ArrayLoader(samples: samples)
    )

    let result = try await evaluation.run()
    let coverageMean = result.aggregateValue(.mean(of: Metric("ResultCoverage")))

    #expect(coverageMean >= 0.5, "Result coverage should be at least 50% across queries")
}
```

Resources:
- Spotlight search tool: https://developer.apple.com/documentation/CoreSpotlight/Spotlight-search-tool
- Making your indexed content available to Foundation Models: https://developer.apple.com/documentation/CoreSpotlight/making-your-indexed-content-available-to-foundation-models

Chapters:
- 0:00 Introduction: Introduces conversational search built by making app content available to Foundation Models through Core Spotlight. The hiking trails app example sets up indexed trails, completed hikes, and personal notes as the data source.
- 1:41 Grounding answers with Spotlight tool-calling: Explains why model-only answers are insufficient for app-specific questions and introduces SpotlightSearchTool as a Foundation Models Tool that searches the app's Core Spotlight index. Also notes the prerequisite of donating searchable content.
- 4:00 Configure and add SpotlightSearchTool: Shows how to import CoreSpotlight and FoundationModels, create SpotlightSearchTool with optional configuration, choose a model, and add the tool to LanguageModelSession. Describes the tool-calling trajectory from generated query to grounded response.
- 6:44 Displaying results and partial replies: Contrasts the final session response for assistant UIs with streamed SpotlightSearchTool search results for list UIs. Search replies arrive in batches and should be grouped or refreshed using query tokens.
- 6:46 Provide full items with an index delegate: Explains that some indexed metadata is stored compactly and cannot be read back by the model. Implementing searchableItems(forIdentifiers:) on CSSearchableIndexDelegate lets the tool recover complete CSSearchableItem values and attach extra model-useful attributes.
- 8:12 Customizing with guidance profiles: Covers GuidanceProfile as a way to scope SpotlightSearchTool capabilities such as text matching, dates, people, and specific attributes. Focused guidance is recommended for simpler searches and smaller on-device model contexts.
- 11:02 Reference resolution with a contact resolver: Shows how a ContactResolver supplies identity information for the current user when prompts reference people. The tool can then match those references against person metadata in the search index.
- 11:24 Custom pipeline stages: Introduces pipeline search for complex queries that combine index retrieval with computation over result sets. Apps can register Generable custom stages, such as a happiness scoring stage, and handle pipeline output data types in partial replies.
- 12:47 Evaluating response quality: Demonstrates using the Evaluations framework to measure tool-calling and response quality. The example defines ModelSampleProtocol datasets, expected identifiers, trajectory expectations, generated sample variations, and a result coverage metric.
- 15:53 Next steps: Recommends exploring the hiking trails sample code, adding custom functionality, and building an evaluation suite. The closing guidance is to provide high-quality indexed content and let the model and tools perform the search and reasoning.

### Principles of great design

- Session ID: wwdc2026-250
- Page: https://wwdc.ai/2026/250
- Markdown: https://wwdc.ai/2026/250.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/250/
- Category: Design
- Description: A practical framework for designing Apple-platform experiences with purpose, agency, responsibility, familiarity, flexibility, simplicity, craft, and delight.
- Duration: 17:16
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-250/eng_d9bf38d42da6/wwdc2026-250-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/250/4/ad804f32-2805-48aa-891c-8c742579acab/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/250/4/ad804f32-2805-48aa-891c-8c742579acab/downloads/wwdc2026-250_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/250/4/ad804f32-2805-48aa-891c-8c742579acab/downloads/wwdc2026-250_sd.mp4?dl=1

A practical framework for designing Apple-platform experiences with purpose, agency, responsibility, familiarity, flexibility, simplicity, craft, and delight.

TLDR:
- Design is framed as intentional decision-making: every feature asks for a person's time, attention, and trust, so omission is as important as addition.
- Great Apple-platform experiences give people agency through choice, direct exploration, undo, and careful confirmation for destructive actions.
- Responsible design minimizes data requests, asks at the right moment with context, and anticipates misuse or harm, especially when adding AI capabilities.
- Familiarity, flexibility, simplicity, craft, and delight help teams choose conventions, adapt across contexts and abilities, reduce friction, polish details, and build emotional connection.

## Design starts with purpose

The session defines design as making something with intention, not just how something looks or behaves. A product should focus on what matters most to people and deliver something they will truly value.

Every feature has a cost: it asks for time, attention, and trust. Before sketching or writing code, evaluate whether a feature has real purpose, and be willing to decide what not to include.

- Use purpose as an early filter for product and feature decisions.
- Prefer experiences that serve people, respect their lives, adapt to their contexts, and are clear and considered.
- There is no formula for combining the principles; design judgment is required when principles appear to conflict.

## Agency and forgiveness

Agency means putting people in control. Interfaces should avoid forcing people through predetermined paths when exploration would serve them better, and should let people proceed at their own pace.

Because agency means people can make mistakes, good design includes forgiveness. Undo, recovery paths, and selective confirmations help people feel safe enough to explore.

- Offer meaningful choices instead of blocking the person's goal.
- Make it easy to undo actions such as sending, changing, or deleting.
- Confirm destructive actions when the cost of a mistake is high.
- Use interruptions sparingly; they are most appropriate when preventing a major mistake.

## Responsibility, privacy, and safety

Responsibility on Apple platforms starts with treating privacy as a human right. A design should not request personal information or permissions at launch without context or an immediate need.

Responsible teams also consider who could be harmed by a feature and how it could be misused. For AI-powered features, this includes anticipating unexpected or inaccurate model output and adding safeguards where appropriate.

- Ask only for data that is necessary.
- Request permissions at the moment they are needed, with clear context for why.
- Evaluate possible misuse: who could be harmed, how harm could happen, and how to prevent it.
- For risky AI features, consider previews, confirmations, disclaimers, or removing the feature if the safety risk outweighs its value.

## Familiarity and flexibility

Familiarity lets people apply existing knowledge from the real world and from other interfaces. Metaphors, platform conventions, consistent behavior, and consistent placement help people predict what will happen.

Flexibility recognizes that people use products in different contexts, on different devices, with different abilities and preferences. A design should adapt to the strengths of each platform and, when one solution cannot fit everyone, allow personalization.

- Use familiar metaphors for common actions, such as trash for delete, and avoid assigning familiar icons surprising meanings.
- Keep behavior consistent: things that look the same should work the same.
- Design for platform context: quick touch interactions on iPhone, deeper workflows and precise pointer control on Mac, and hands-free or wearable contexts when relevant.
- Consider audience differences such as age, language, experience level, and reliance on accessibility features.
- Support personalization when appropriate, such as rearranging or hiding controls to fit individual workflows.

## Simplicity and craft

Simplicity is not the same as minimalism. A minimal-looking interface can still be hard to use if functionality is hidden or workflows require too much effort. Simplicity means reducing friction so people can find what they need and complete tasks naturally.

Craft is the attention to detail that makes software feel trustworthy. Performance, alignment, layout behavior, typography, color, graphics, iconography, animation, reliability, and security all contribute to whether an experience feels solid or fragile.

- Use plain language, avoid jargon, remove redundancy, and reduce unnecessary steps.
- Build clear hierarchy with order, spacing, and contrast so the most important element is obvious.
- Make affordances understandable: people should know what they can interact with and how.
- Let every element earn its place, but add context when it makes decisions easier, such as progress and remaining time on media controls.
- Iterate and maintain the design over time as platforms, hardware, and user needs evolve.

## Delight as the result of care

Delight is described as a satisfying, enriching emotional connection, not as decorative effects added at the end. It comes from identifying the emotion the experience should create-such as relaxation, confidence, or excitement-and reinforcing it through the whole design.

When a product combines intention, agency, safety, familiar patterns, flexibility, simplicity, and craft, delight becomes the natural result. The session points developers and designers to the Human Interface Guidelines design principles page for deeper guidance.

- Do not rely on superficial flourishes to create delight.
- Design the emotional outcome intentionally.
- Use the full set of principles together to create experiences people trust and enjoy.

Resources:
- Human Interface Guidelines: Design principles: https://developer.apple.com/design/human-interface-guidelines/design-principles

Chapters:
- 0:00 Intro: Design is introduced as making something with intention rather than merely defining appearance or behavior. The speakers emphasize that every feature asks for time, attention, and trust, so deciding what not to build is a core design act.
- 1:08 Purpose: Purpose is presented as the first foundational principle: build experiences that genuinely serve people and deliver value. The chapter notes that there is no fixed formula for combining principles, so designers must use judgment when tradeoffs arise.
- 1:52 Agency: Agency means giving people control over how they explore and use an experience. The chapter explains how choices, undo, recovery, and careful confirmations help people feel confident and free to try things.
- 3:38 Responsibility: Responsibility centers on acting in people's best interest, starting with privacy and respectful permission requests. It also covers safety, misuse analysis, and the need for safeguards or feature removal when AI or other capabilities could cause real harm.
- 6:04 Familiarity: Familiarity uses people's existing knowledge, metaphors, platform conventions, and consistency to make interfaces predictable. The chapter warns against surprising uses of familiar symbols and stresses that similar-looking controls should behave similarly.
- 8:52 Flexibility: Flexibility means adapting to different contexts, devices, abilities, and preferences. The chapter highlights designing for each platform's strengths and offering personalization when a single layout or workflow cannot serve everyone.
- 11:13 Simplicity: Simplicity is defined as removing friction, not merely making an interface look minimal. The chapter covers concise language, clear hierarchy, meaningful affordances, distilling information, and adding context when it makes an experience easier to understand.
- 13:42 Craft: Craft is the detailed execution that makes software feel trustworthy, including typography, color, iconography, animation, performance, reliability, and secure foundations. The chapter frames craft as an ongoing commitment to iteration and maintenance as platforms evolve.
- 15:47 Delight: Delight is described as the emotional result of a well-considered experience, not a decorative flourish. The chapter concludes by tying all the principles together and pointing to the Human Interface Guidelines design principles page.

### Communicate your brand identity on iOS

- Session ID: wwdc2026-251
- Page: https://wwdc.ai/2026/251
- Markdown: https://wwdc.ai/2026/251.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/251/
- Category: Design
- Description: Practical iOS design guidance for expressing brand through content, color, typography, components, motion, and iconography without breaking platform conventions.
- Duration: 17:26
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-251/eng_45666e53e3ea/wwdc2026-251-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/251/4/52fb4c75-99ba-419f-90d6-bfef374ac966/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/251/4/52fb4c75-99ba-419f-90d6-bfef374ac966/downloads/wwdc2026-251_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/251/4/52fb4c75-99ba-419f-90d6-bfef374ac966/downloads/wwdc2026-251_sd.mp4?dl=1

Practical iOS design guidance for expressing brand through content, color, typography, components, motion, and iconography without breaking platform conventions.

TLDR:
- Treat iOS branding as a balance: keep navigation and common actions familiar, then express identity where it strengthens content and product meaning.
- With Liquid Glass in iOS 26, think in layers: standard UI controls float above a content layer that is the best place for distinctive brand moments.
- Use color, typography, motion, and imagery with purpose; support Dark Mode and Dynamic Type so brand expression adapts to user preferences and accessibility needs.
- Custom iconography can work well when it remains recognizable, scalable, and platform-aware; SF Symbols is often the simplest native option.

## Branding should serve the iOS experience

The session frames brand identity as more than visual consistency across a website, marketing, retail, or another platform. On iOS, people expect apps to feel like iOS, so brand expression should be adapted to the platform rather than copied identically from other contexts.

The core guidance is to feel familiar where it matters and bespoke where it helps. Navigation, actions, and conventional workflows should preserve recognizable iOS patterns, while distinctive content, visuals, language, and motion can communicate the product's personality.

- Avoid forcing brand treatments that conflict with established iOS behavior.
- Use brand to clarify the product experience, not to make users relearn basic interaction patterns.
- Evaluate whether a design choice reinforces the product or merely replaces a familiar system convention.

## Use native UI as the foundation

With Liquid Glass in iOS 26, the session describes apps as having two conceptual layers: a UI layer for global navigation and actions, and a content layer beneath it. The UI layer should generally rely on platform components so people can move through the app quickly and confidently.

Standard components such as tab bars, top toolbars, grid views, grouped tables, sheets, and context menus provide familiar behavior and interactions. Custom components are appropriate when they support a high-impact, product-specific experience, but utilitarian controls usually benefit from standard system treatment.

- Use native tab bars and toolbars for predictable navigation and actions when possible.
- Reserve custom components for places where they make unique content or workflows clearer.
- Audit functional areas of the app for opportunities to replace bespoke UI with standard components.
- Context menus are highlighted as a useful standard component for screen-level actions, settings, grouped actions, secondary menus, and modal presentation.
- SwiftUI provides many standard components and interactions out of the box, reducing the need to build and maintain custom equivalents.

## Make the content layer the brand canvas

The content layer is presented as the strongest place to express identity. This includes imagery, video, words, data visualizations, illustrations, animation, and the way information responds as people scroll, tap, and interact.

Examples include Crumbl using full-bleed videos for weekly flavors, Moonlitt using immersive edge-to-edge night-sky color and 3D moon-position elements, NYT Cooking using transitions to connect recipe comments to their source, and Gentler Streak using springy motion to make activity recaps feel active and approachable.

- Use rich media only when it has a clear product purpose, not as decoration.
- Treat voice and tone as part of brand; copy can make an app feel playful, trustworthy, safe, active, or refined.
- Use transitions and animation to reinforce hierarchy and connect interaction states.
- Prioritize performance: delayed loads and dropped frames can damage the perceived quality of the app experience.

## Use color intentionally

The session recommends moving strong brand color moments into the content area rather than using solid, bulky toolbar or tab bar backgrounds. In the iOS 26 design language, Liquid Glass controls can float above the content layer and pick up brand color dynamically.

Color should communicate meaning. Good uses include hierarchy, grouping, action affordances, status, feedback, unread indicators, badges, and selected states. Slack is used as an example of sparse, purposeful tint usage across primary actions and state indicators.

- Prefer brand color in scrollable content rather than permanently occupying top and bottom UI chrome.
- Use accent or tint color to communicate actions, status, feedback, and selection.
- Support Dark Mode with a refined low-light palette.
- Consider brand touchpoints outside the app, such as widgets, when they provide recognizable and useful information.
- Exercise restraint so color remains meaningful rather than overwhelming.

## Typography must remain flexible and legible

Typography can be a strong brand signal, especially in large, memorable moments such as headers or product names. Crumbl is cited for using its custom Crumbl Sans typeface in marketing and selected iOS app moments.

The main implementation concern for custom fonts is scaling. Dynamic Type is built into Apple system fonts, but custom fonts require explicit support and testing so layouts remain legible at larger accessibility sizes. The session emphasizes accommodating larger text with layout changes such as wrapping instead of truncation.

- Use custom fonts where they add identity, but test them with Dynamic Type and accessibility text sizes.
- Do not assume fixed label sizes; allow multi-line layouts where needed.
- Remember that standard components also adapt at larger Dynamic Type sizes.
- System typography is a valid brand choice: San Francisco supports more than 150 languages and includes variants such as SF Pro, SF Compact, SF Mono, SF Rounded, and New York.
- Gentler Streak is cited as an app that achieves hierarchy and personality using system fonts and variants.

## Keep iconography recognizable and platform-aware

Custom iconography is encouraged when it is cohesive, simple, and scalable. NYT Cooking is highlighted for icons with sharper edges and line-weight variation that work in tab bars, toolbars, and inline content actions without becoming overly detailed.

Platform conventions still matter. A custom share icon style should respect the platform's expected sharing metaphor on iOS, Android, and the web rather than enforcing one identical mark everywhere. The session also cautions against overusing logos inside an app because users already know which app they opened and screen space is better used for relevant information.

- Design icons to be identifiable at small sizes.
- Keep icon style consistent across controls and content actions.
- Respect platform-specific conventions for common actions such as sharing.
- Use SF Symbols when custom iconography is unnecessary; it provides over 7,000 symbols, dynamic scaling like text, multiple weights, accessibility support, localization support, and Xcode integration.
- Use logos sparingly and unobtrusively; NYT Cooking is cited for showing its logo only on the Home tab and fading it on scroll.

Resources:
- Human Interface Guidelines: https://developer.apple.com/design/human-interface-guidelines

Chapters:
- 0:00 Intro: Introduces the goal of expressing brand identity while preserving familiar iOS patterns. The session previews examples across components, content, color, typography, and iconography.
- 2:24 Components: Explains the iOS 26 Liquid Glass framing of a UI layer for navigation and actions above a content layer for unique product expression. It recommends native components for conventional tasks and custom components only where they materially improve distinctive app experiences.
- 6:41 Content: Positions content as the main canvas for brand identity, including imagery, video, copy, data visualization, transitions, and animation. Examples from Crumbl, Moonlitt, NYT Cooking, and Gentler Streak show content-driven brand expression that supports product meaning.
- 11:49 Color: Recommends moving strong brand color into the content area rather than permanent toolbar or tab bar backgrounds. Color should be used with restraint for hierarchy, actions, status, feedback, selection, Dark Mode support, and related touchpoints such as widgets.
- 11:50 Typography: Covers custom and system typography as brand tools, with emphasis on legibility and adaptability. Custom fonts need Dynamic Type support and testing, while San Francisco variants can provide hierarchy and personality without leaving the system typographic language.
- 14:17 Iconography: Encourages custom iconography when it is simple, cohesive, recognizable, and scalable, while respecting platform conventions for common actions. SF Symbols is presented as a strong built-in alternative, and logos should be used sparingly inside the app.

### Design no-code games with Reality Composer Pro 3

- Session ID: wwdc2026-252
- Page: https://wwdc.ai/2026/252
- Markdown: https://wwdc.ai/2026/252.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/252/
- Category: Design
- Description: Use Reality Composer Pro 3 ScriptGraph to prototype RealityKit games with visual nodes, physics, custom events, reusable subgraphs, and SwiftUI attachments.
- Duration: 18:53
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-252/eng_3338c0935615/wwdc2026-252-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/252/6/572c2388-69f6-4e57-9eba-c71b65f5f6ed/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/252/6/572c2388-69f6-4e57-9eba-c71b65f5f6ed/downloads/wwdc2026-252_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/252/6/572c2388-69f6-4e57-9eba-c71b65f5f6ed/downloads/wwdc2026-252_sd.mp4?dl=1

Use Reality Composer Pro 3 ScriptGraph to prototype RealityKit games with visual nodes, physics, custom events, reusable subgraphs, and SwiftUI attachments.

TLDR:
- ScriptGraph is Reality Composer Pro 3's node-based, event-driven visual scripting system for building RealityKit interactions without writing gameplay code.
- The session builds a visionOS squirrel game around a draggable nut using Input Target, Collision, Hover Effect, Scripting, Physics Body, On Drag, Set Transform, Add Force, and Set PhysicsBodyComponent nodes.
- Advanced workflows include public input variables and overrides, prototyped subgraphs for reusable logic, custom node libraries/events for communication between ScriptGraphs, and material-parameter changes from graph logic.
- ScriptGraph scene events can bridge into Swift/Xcode so a SwiftUI attachment, such as a speech bubble, reacts to no-code graph events.

## ScriptGraph basics

ScriptGraph in Reality Composer Pro 3 is node-based visual scripting for RealityKit content. Logic starts from event nodes, transforms event data through math or control-flow nodes, and then uses set/action nodes to modify entities, components, variables, physics, materials, or events.

The workflow is designed for fast iteration: build graph logic in Reality Composer Pro, press Play to test it in the viewport, and use device preview on Vision Pro to evaluate spatial interactions in context.

- Use event nodes such as drag or pinch events to start graph execution.
- Use Set nodes to write data back into components, variables, and other graph state.
- Use math and logic nodes to adjust gesture data before applying it.
- Live preview on device is described as available later in the year; Reality Composer Pro can also run the experience through Xcode when SwiftUI integration is needed.

## Building a draggable nut interaction

The sample game begins with a squirrel and a nut represented as simple textured planes. To make the nut interactive, the entity is configured with components that make it targetable, define its hit volume, show hover feedback, and attach ScriptGraph logic.

- Add an Input Target Component so the nut can receive gaze/input interaction.
- Add a Collision Component to define the interactive target size.
- Add a Hover Effect Component so the nut highlights when gazed at.
- Create a Script Graph asset, add a Scripting Component to the nut, and assign the graph to that component.
- Start with an On Drag event and connect its scene location or translation data into transform or physics logic.

## Iteration with public variables and overrides

The first drag implementation directly connects On Drag data to a Set Transform node. After testing on Vision Pro, the drag feel is adjusted by multiplying drag translation with a public ScriptGraph input variable named dragSpeed.

Public variables appear in the entity's Scripting Component. Changing one there creates an override, meaning multiple entities can share the same graph while each uses different variable values.

- Create an input variable in the ScriptGraph Inspector, for example dragSpeed of type number.
- Mark it public so it can be edited from the Scripting Component on the entity.
- Use an Input node to feed the variable into graph logic, such as a Multiply by Number node.
- Use overrides to tune per-entity behavior without duplicating the ScriptGraph.

## Physics-driven dragging

To make the nut feel dynamic and throwable, the graph moves from direct transform updates to physics. A Physics Body Component puts the nut into the physics simulation, and an Add Force node applies force based on how the drag changes over time.

Because the drag event does not directly provide the desired delta, the graph stores the current target position in a variable, subtracts the previous position from the current position, stores the result as dragDelta, and feeds a scaled version into Add Force. Gravity and damping are adjusted while the nut is being held to improve control.

- Add a Physics Body Component to the nut entity.
- Store drag translation/position in a graph variable such as targetPosition.
- Compute dragDelta by comparing the current and previous drag positions.
- Pass a scaled dragDelta into Add Force to make the nut move and be tossable.
- Use Set PhysicsBodyComponent to disable gravity while dragging and increase linear damping for less finicky motion; restore settings when dropped.

## Reusable graph logic and cross-entity events

As graphs grow, Reality Composer Pro 3 supports organizing selected nodes into subgraphs. A subgraph can be converted into a prototyped subgraph so it appears in the asset browser and add-node menu for reuse across scripts.

The squirrel gets its own ScriptGraph and reacts to the nut through a custom event. A Custom Node Library defines a custom event named nutIsDragged with a nutPosition property. The nut graph sends that event with the nut's world position, and the squirrel graph listens for it to rotate toward the nut.

- Use Compose Subgraph to collapse a group of nodes into a named unit, such as logic that detects when a Bool changes.
- Use Convert to Prototyped Subgraph to make reusable node logic available in other graphs.
- Create a Custom Node Library for custom events used across ScriptGraphs.
- Use Send "nutIsDragged" from the nut graph and On "nutIsDragged" from the squirrel graph.
- Use Set Material Parameter to drive a public Shader Graph material input, such as a Bool named isNutDragged, to swap the squirrel's expression texture.

## SwiftUI attachments through scene events

ScriptGraph can also communicate with Swift. The session uses a Send Scene Event node named squirrelTalk with a String value named sayThis. Xcode code subscribes to that scene event, stores the string, and uses it to show a SwiftUI speech-bubble attachment over the squirrel entity.

Reality Composer Pro 3 can create an Xcode project for running the experience when SwiftUI interface elements are needed. The graph remains responsible for gameplay timing, while SwiftUI renders the bubble.

- Switch preview mode to Run with Xcode when SwiftUI integration is needed.
- Send a scene event from ScriptGraph, for example squirrelTalk, with a string payload such as sayThis.
- Subscribe to the event from Swift and update SwiftUI state.
- Render the speech bubble using a SwiftUI Attachment with the event payload as text.

### Subscribe to a ScriptGraph scene event and show a SwiftUI attachment

Swift listens for the squirrelTalk scene event sent by ScriptGraph, extracts the sayThis string payload, and uses it as the text for a SwiftUI attachment.

```text
if let scene = entity.scene {
    scene.subscribe(forEventName: "squirrelTalk", on: { event in
        if let sayThis: String = try? event.value("sayThis") {
            self.sayThis = sayThis
        }
    })
    .store(in: &cancellables)
}

attachments: {
    Attachment(id: "squirrelTalk") {
        SquirrelTalkAttachmentView(text: sayThis)
    }
}
```

Chapters:
- 0:00 Introduction: Introduces Reality Composer Pro 3 as a fast iteration tool for mocking up RealityKit game ideas without writing code. The session focuses on building a game from scratch with ScriptGraph and then layering in more advanced techniques.
- 1:02 Meet ScriptGraph: Defines ScriptGraph as Reality Composer Pro's node-based visual scripting system for event-driven game logic. Examples include pinch-driven animation, drag gestures, custom events, and direct testing in Reality Composer Pro and on Vision Pro.
- 1:50 A wish...: Sets up the game concept: a sleeping squirrel has a nut, night is approaching, and the player steals the nut to guide the squirrel home. The target interaction is a spatial Vision Pro experience where the player reaches out and drags the nut.
- 2:36 Build the game: Builds the core nut interaction by adding input, collision, hover, scripting, and physics components, then wiring On Drag, Set Transform, variables, math, Add Force, and Set PhysicsBodyComponent nodes. The chapter emphasizes testing in the Reality Composer Pro viewport and tuning on Vision Pro with public variables and overrides.
- 11:16 Advanced techniques: Shows how to keep graph logic organized with subgraphs and prototyped subgraphs, then uses a custom event to let the nut ScriptGraph communicate with the squirrel ScriptGraph. It also demonstrates changing material parameters from a graph and bridging ScriptGraph scene events into SwiftUI attachments for a speech bubble.
- 18:32 Next steps: Wraps up by pointing developers to Reality Composer Pro 3, deeper advanced workflow material, and the Squirrel sample project from the Apple Developer website.

### Meet the Music Understanding framework

- Session ID: wwdc2026-253
- Page: https://wwdc.ai/2026/253
- Markdown: https://wwdc.ai/2026/253.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/253/
- Category: Audio & Video
- Description: Use MusicUnderstandingSession to analyze audio on device for key, rhythm, structure, pace, instrument activity, and loudness across Apple platforms.
- Duration: 16:40
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-253/eng_090d0e62fc29/wwdc2026-253-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/253/5/db1c3715-aaaf-42db-8e9e-66d2a0011430/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/253/5/db1c3715-aaaf-42db-8e9e-66d2a0011430/downloads/wwdc2026-253_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/253/5/db1c3715-aaaf-42db-8e9e-66d2a0011430/downloads/wwdc2026-253_sd.mp4?dl=1

Use MusicUnderstandingSession to analyze audio on device for key, rhythm, structure, pace, instrument activity, and loudness across Apple platforms.

TLDR:
- Music Understanding provides on-device, offline audio analysis across six dimensions: key, rhythm, structure, pace, instrument activity, and loudness.
- Apps create a MusicUnderstandingSession from an AVAsset or custom audio provider, then call analyze() or targeted analyze(for:) for only the result types they need.
- Results are time-based, Codable, and Sendable, using CMTime, CMTimeRange, TimedValue, and RangedValue to drive synced UI, video edits, animations, or export workflows.
- The Music Understanding Lab sample app demonstrates visualizing each analysis result and combining structure with pace to generate music-synced video timing.

## What Music Understanding provides

Music Understanding is a framework for on-device musical intelligence across Apple platforms. It handles signal processing and model inference for apps, works offline, and keeps analyzed audio on device.

The framework analyzes six areas: key, rhythm, structure, pace, instrument activity, and loudness. Apple uses it in Final Cut Pro features such as beat detection and music-synchronized montage creation.

- Key identifies the tonic and major/minor mode over time.
- Rhythm returns beats, bars, and an optional global beats-per-minute value.
- Structure returns hierarchical time ranges for sections, segments, and phrases.
- Pace estimates how fast or energetic the music feels over time.
- Instrument activity reports whether instruments are present and how strongly they are active.
- Loudness reports integrated, momentary, short-term, and peak measurements.

## Create a session and run analysis

Apps interact with MusicUnderstandingSession. Initialize it with an AVAsset for file-based analysis, or with a custom audio provider for buffer-based input. Calling analyze() requests every supported analysis type by default.

For best performance, request only the analysis types your app needs with the targeted analyze(for:) API. When using targeted analysis, unrequested fields in SessionResult are nil.

- For AVURLAsset input, set AVURLAssetPreferPreciseDurationAndTimingKey to true for the most accurate timing results.
- SessionResult fields are optional because targeted analysis may omit result types.
- All MusicUnderstanding results are Codable, so apps can export or cache analysis results as JSON.

### Analyze an imported audio file

Creates an AVURLAsset from a selected audio file, initializes a MusicUnderstandingSession, and awaits all analysis results.

```swift
import MusicUnderstanding

.fileImporter(isPresented: $isPresented, allowedContentTypes: [.audio]) { result in
    switch result {
    case .success(let url):
        let asset = AVURLAsset(
            url: url,
            options: [AVURLAssetPreferPreciseDurationAndTimingKey: true]
        )
        let session = try await MusicUnderstandingSession(asset: asset)
        let results = try await session.analyze()
    }
}
```

### SessionResult shape

Each analysis dimension has its own optional result field.

```swift
import MusicUnderstanding

public struct SessionResult: Codable, Sendable {
    public let instrumentActivity: InstrumentActivityResult?
    public let key: KeyResult?
    public let loudness: LoudnessResult?
    public let pace: PaceResult?
    public let rhythm: RhythmResult?
    public let structure: StructureResult?
}
```

## Time-based result model

The framework consistently associates musical values with media time. TimedValue attaches a value to a single CMTime, while RangedValue attaches a value to a CMTimeRange.

These types make the results straightforward to render against a playhead, use for beat-synced visuals, align edits to song structure, or drive audio-reactive animations.

- Use TimedValue for sampled values such as loudness or instrument intensity over time.
- Use RangedValue for values that apply across an interval, such as key or pace.
- Structure results use arrays of CMTimeRange for sections, segments, and phrases.

### TimedValue and RangedValue

Standard containers used throughout MusicUnderstanding to bind values to media time.

```swift
import MusicUnderstanding

public struct TimedValue<Value>: Codable, Equatable, Sendable
where Value: Codable & Equatable & Sendable {
    public let time: CMTime
    public let value: Value
}

public struct RangedValue<Value>: Codable, Equatable, Sendable
where Value: Codable & Equatable & Sendable {
    public let range: CMTimeRange
    public let value: Value
}
```

## Analysis result types

Each result type exposes data at the level needed for common music-aware app features. Rhythm and structure can drive timeline UI and edit alignment; key can support cataloging or DJ workflows; pace and instrument activity can drive visual intensity or automated clip timing.

Loudness uses LUFS for perceived volume. Integrated loudness represents overall loudness, momentary and short-term values are sampled every 100 milliseconds, and peak reports the maximum audio level in decibels.

- RhythmResult includes beat timestamps, bar timestamps, and optional beatsPerMinute. beatsPerMinute can be nil until enough audio has been processed to identify at least two beats.
- StructureResult includes sections, segments, and phrases as hierarchical CMTimeRange arrays.
- InstrumentActivityResult includes coarse presence ranges and detailed per-instrument activity values from 0 to 1.
- LoudnessResult includes integrated, momentary, shortTerm, and peak values.

### Rhythm, structure, and pace results

Core timing results for beat grids, structural timelines, and perceived-energy changes.

```swift
import MusicUnderstanding

public struct RhythmResult: Codable, Sendable {
    public let beats: [CMTime]
    public let bars: [CMTime]
    public let beatsPerMinute: Float?
}

public struct StructureResult: Codable, Sendable {
    public let sections: [CMTimeRange]
    public let segments: [CMTimeRange]
    public let phrases: [CMTimeRange]
}

public struct PaceResult: Codable, Sendable {
    public let ranges: [MusicUnderstandingSession.RangedValue<Double>]
}
```

### Loudness result

Loudness exposes overall, time-varying, and peak measurements for audio-level visualizations or analysis.

```swift
import MusicUnderstanding

public struct LoudnessResult: Codable, Sendable {
    public let integrated: MusicUnderstandingSession.TimedValue<Float>
    public let momentary: [MusicUnderstandingSession.TimedValue<Float>]
    public let shortTerm: [MusicUnderstandingSession.TimedValue<Float>]
    public let peak: MusicUnderstandingSession.TimedValue<Float>
}
```

## Streaming input, loudness, and export workflows

MusicUnderstandingSession also provides a streaming loudness API. LoudnessResult values are delivered through an AsyncSequence for every 100 milliseconds of audio analyzed by the framework.

For custom input, an AudioProvider conforms to AsyncSequence and yields AVReadOnlyAudioPCMBuffer objects. It must yield a final nil to indicate completion.

The Music Understanding Lab sample app exports all analysis data as JSON and demonstrates combining structure with pace to create video clips that start on section boundaries and change duration with the music's energy.

- Use the loudness AsyncSequence when UI needs incremental loudness updates during analysis.
- Use Codable results for JSON export, caching, or precomputing analysis data to bundle with app content.
- For pace-driven video timing, the session suggests deriving clip duration from the pace value so higher-energy sections use shorter clips.

### Custom audio provider shape

A buffer-based provider can feed audio into MusicUnderstandingSession instead of using an AVAsset.

```swift
import MusicUnderstanding

struct AudioProvider: AsyncSequence, AsyncIteratorProtocol {
    func makeAsyncIterator() -> Self { self }

    mutating func next() async -> AVReadOnlyAudioPCMBuffer? {
        // Return the next audio buffer, or nil to signal completion.
    }
}
```

### Export analysis results as JSON

MusicUnderstanding result types are Codable, enabling JSON export or caching.

```swift
import MusicUnderstanding

let session = try await MusicUnderstandingSession(asset: asset)
let results = try await session.analyze()
let encoder = JSONEncoder()
try encoder.encode(results)
```

Resources:
- Creating visuals with Music Understanding analysis results: https://developer.apple.com/documentation/MusicUnderstanding/create-visuals-using-musicunderstanding-analysis-results
- MusicUnderstanding: https://developer.apple.com/documentation/MusicUnderstanding

Chapters:
- 0:00 Introduction: Introduces Music Understanding as an on-device, offline framework for musical intelligence across Apple platforms. It highlights Apple use cases in Final Cut Pro for beat detection and automatic montage synchronization.
- 1:39 Musical features: Defines the six analysis areas: key, rhythm, structure, pace, instrument activity, and loudness. It explains the musical hierarchy from beats and bars through phrases, segments, and sections.
- 3:19 Framework integration: Explains the main integration model: create a MusicUnderstandingSession from an AVAsset or custom audio provider, then call analyze and await results. It notes that all result types are analyzed by default, while targeted analysis improves performance.
- 3:55 Music Understanding Lab: Walks through the sample app that visualizes each result type, including key, rhythm, structure, pace, instrument activity, and loudness. It also shows JSON export, streaming loudness, and a pace-and-structure-based video synchronization example.

### Integrate MusicKit into your app

- Session ID: wwdc2026-254
- Page: https://wwdc.ai/2026/254
- Markdown: https://wwdc.ai/2026/254.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/254/
- Category: Audio & Video
- Description: Use MusicKit to authorize music access, present subscription offers, pick Apple Music or library content, control playback, and fetch catalog songs.
- Duration: 21:06
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-254/eng_b172d7c5c8c2/wwdc2026-254-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/254/5/d4b2c60a-8a2a-41d1-a55a-0fd60d927798/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/254/5/d4b2c60a-8a2a-41d1-a55a-0fd60d927798/downloads/wwdc2026-254_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/254/5/d4b2c60a-8a2a-41d1-a55a-0fd60d927798/downloads/wwdc2026-254_sd.mp4?dl=1

Use MusicKit to authorize music access, present subscription offers, pick Apple Music or library content, control playback, and fetch catalog songs.

TLDR:
- Configure MusicKit by enabling the MusicKit service for the App ID, adding the Media Library capability in Xcode, and requesting `MusicAuthorization` before accessing music content.
- Use `.musicSubscriptionOffer` with `MusicSubscription.current` and `MusicSubscription.subscriptionUpdates` to show an in-app Apple Music offer only when a person can become a subscriber.
- Use the SwiftUI `.musicPicker` modifier to select `Song` values from a unified Apple Music catalog and library UI; without a subscription, it shows only the person's library.
- Choose between `SystemMusicPlayer` and `ApplicationMusicPlayer`, build playback UI from observable player state and queue data, and use `MusicCatalogResourceRequest` with `.findEquivalents` for storefront-aware catalog content.

## Project setup, authorization, and subscription state

MusicKit is a Swift framework for Apple platforms that integrates with Swift concurrency and SwiftUI. To make MusicKit requests, the app needs a developer token. The session shows the automatic-token path: enable the MusicKit checkbox for the app's App ID in the developer portal, then make sure Xcode is signed into the same developer account.

Before browsing or playing a person's music content, request access with `MusicAuthorization.request()`. Add the Media Library capability in Xcode and provide a usage description; that text appears in the MusicKit permission alert.

An Apple Music subscription is not required for MusicKit, but without one the app can access only purchased or synced music. If the person can become a subscriber, present an in-app subscription offer using `.musicSubscriptionOffer` and keep subscription state current with `MusicSubscription.current` and `MusicSubscription.subscriptionUpdates`.

- Enable MusicKit for the App ID in App Services so developer tokens can be generated automatically.
- Add the Media Library capability and describe why the app needs access to music content.
- Use `MusicSubscription.canBecomeSubscriber` to decide whether to show a subscription button.
- `MusicSubscriptionOffer.Options` can include partner-program information and a `messageIdentifier`, such as `.playMusic`.

### Present an Apple Music subscription offer

Shows the in-app Apple Music subscription UI with a message tailored for playing music.

```swift
@State var showSubscriptionOffer = false

let options = MusicSubscriptionOffer.Options(
    messageIdentifier: .playMusic
)

@ViewBuilder var musicSubscriptionButton: some View {
    Button("Subscribe to Apple Music", systemImage: "music.note") {
        showSubscriptionOffer = true
    }
    .musicSubscriptionOffer(isPresented: $showSubscriptionOffer, options: options)
}
```

### Track subscription updates

Fetches the current subscription and listens for changes after authorization.

```swift
@State var subscription: MusicSubscription?

var body: some View {
    VStack {
        if let subscription, subscription.canBecomeSubscriber {
            musicSubscriptionButton
        }
    }
    .task(id: isAuthorized) {
        self.subscription = try? await MusicSubscription.current
        for await subscription in MusicSubscription.subscriptionUpdates {
            self.subscription = subscription
        }
    }
}
```

## Music items and Music Picker

MusicKit models content as music items. Items such as `Album`, `Song`, `Genre`, `Station`, and `Playlist` are value types with attributes, relationships, and associations. Attributes are direct properties like an album title or content rating; relationships describe strongly related content such as an album's tracks; associations describe related content with weaker ties, such as other versions of an album.

The Music Picker is exposed as a SwiftUI view modifier and presents a unified interface for the Apple Music catalog and the person's library. If the person does not have an Apple Music subscription, the picker is still usable, but it shows library items rather than both library and catalog content.

The picker can bind to a single selected item or to an array for multi-selection. It can also select container content such as albums or playlists from their detail pages.

- Use `.musicPicker(isPresented:selection:)` to present the picker from any SwiftUI view.
- Bind selection to `Song?` for a single song or to an array for multiple selections.
- The picker handles browsing and search across supported MusicKit content sources.
- Subscription status affects catalog availability, not whether the picker itself can be shown.

### Add a Music Picker for a song

Presents the Music Picker and stores the selected `Song` in SwiftUI state.

```text
@State var showMusicPicker = false
@State var selectedSong: Song? = nil

@ViewBuilder var musicPickerButton: some View {
    Button("Pick some Music", systemImage: "music.note.list") {
        showMusicPicker = true
    }
    .musicPicker(isPresented: $showMusicPicker, selection: $selectedSong)
}
```

## Players, queues, and playback behavior

MusicKit provides `SystemMusicPlayer` and `ApplicationMusicPlayer`, both subclasses of `MusicPlayer`. `SystemMusicPlayer` controls the system Music app: apps can set its queue, but cannot inspect the full queue beyond the current item. `ApplicationMusicPlayer` plays from within the app and gives full read/write access to its queue.

Both players support playback state such as repeat and shuffle, and both can control whether playback affects the Music app's listening history through `affectsListeningHistory`. By default, playback generally appears in Recently Played, while respecting the Music app's Use Listening History setting.

`SystemMusicPlayer` continues playing if the app backgrounds or quits because it controls the system Music app. To get comparable background playback with `ApplicationMusicPlayer`, enable the Audio Background Mode capability.

- Create queues from playable items such as songs, albums, and playlists.
- Use special queue initializers for container types to lazily load their contents.
- Call `prepareToPlay()` when upcoming content is known to reduce delay before audible playback.
- Call `play()` to start and `pause()` to stop playback.
- Observe player state and queue objects directly in SwiftUI for playback UI.

## Building playback UI in SwiftUI

The workout app UI reads `ApplicationMusicPlayer.shared.queue` to display the current entry's artwork, title, and subtitle. `ArtworkImage` renders MusicKit artwork when available, while a placeholder can cover the empty state.

Playback controls derive their state from `ApplicationMusicPlayer.shared.state`. The play/pause button checks `playbackStatus`, calls `pause()` when already playing, and otherwise calls async `play()`. Previous and next controls call async skip methods on the player.

- Use `queue.currentEntry?.artwork` with `ArtworkImage` for now-playing artwork.
- Use `queue.currentEntry?.title` and optional `subtitle` for now-playing labels.
- Use `state.playbackStatus == .playing` to derive play/pause UI.
- Use `skipToPreviousEntry()` and `skipToNextEntry()` for transport controls.

### Display current artwork and song information

Uses the application player queue as the source of truth for now-playing artwork and metadata.

```swift
@State var queue = ApplicationMusicPlayer.shared.queue

var body: some View {
    VStack {
        if let artwork = queue.currentEntry?.artwork {
            ArtworkImage(artwork, width: 200, height: 200)
        } else {
            RoundedRectangle(cornerRadius: 16)
                .fill(.quaternary)
                .frame(width: 200, height: 200)
        }

        if let currentSong = queue.currentEntry {
            Text(currentSong.title)
                .font(.title3.bold())
            if let subtitle = currentSong.subtitle {
                Text(subtitle)
                    .font(.subheadline)
                    .foregroundStyle(.secondary)
            }
        }
    }
}
```

### Play, pause, previous, and next controls

Builds simple transport controls around `ApplicationMusicPlayer` state and async player operations.

```swift
let player = ApplicationMusicPlayer.shared
@State var state = ApplicationMusicPlayer.shared.state

var isPlaying: Bool {
    state.playbackStatus == .playing
}

var controls: some View {
    HStack {
        Button("Back", systemImage: "backward.fill") {
            Task { try await player.skipToPreviousEntry() }
        }

        Button(isPlaying ? "Pause" : "Play",
               systemImage: isPlaying ? "pause.fill" : "play.fill") {
            if isPlaying {
                player.pause()
            } else {
                Task { try await player.play() }
            }
        }

        Button("Next", systemImage: "forward.fill") {
            Task { try await player.skipToNextEntry() }
        }
    }
}
```

## Catalog requests and storefront equivalency

Catalog requests let an app query Apple Music independently of the person's library, which is useful for curated shelves or quick-start recommendations. `MusicCatalogResourceRequest` is a structured request for a specific resource type, such as `Song`. Requests can configure options, requested relationships and associations, and result limits.

Calling async `response()` returns a `MusicCatalogResourceResponse` with strongly typed results in a `MusicItemCollection`. Collections support pagination: when `hasNextBatch` is true, fetch more items with async `nextBatch()`.

Resource availability can vary by account settings, storefront, region, and explicit-content restrictions. A requested resource may have an equivalent item with a different ID in another region, or a clean equivalent when explicit content is unavailable. The `.findEquivalents` option enables that equivalency behavior, and callers should handle missing results because the catalog is not guaranteed to return every requested item.

- Use structured catalog requests for Apple Music API content in MusicKit.
- Use `matching: \.id, memberOf: ids` to request specific known catalog IDs.
- Use `.findEquivalents` for cross-storefront or content-restriction equivalents.
- Use `response.item(for:)` to retrieve the returned item corresponding to an input ID.
- Always handle unavailable catalog resources.

### Fetch songs by catalog IDs with equivalency

Requests a set of songs, allows storefront/content equivalents, and safely maps returned items back to requested IDs.

```swift
func fetchSongs(songIDs: [MusicItemID]) async throws -> (featured: Song?, other: [Song]) {
    var request = MusicCatalogResourceRequest<Song>(matching: \.id, memberOf: songIDs)
    request.options = [.findEquivalents]

    let response = try await request.response()

    let featuredSongID = songIDs[0]
    let featuredSong = response.item(for: featuredSongID)

    let others: [Song] = songIDs[1...].compactMap { songID in
        response.item(for: songID)
    }

    return (featuredSong, others)
}
```

Resources:
- Integrating MusicKit into your app: https://developer.apple.com/documentation/MusicKit/integrating-musickit-into-your-app
- Apple Services Performance Partner Program: https://performance-partners.apple.com/home
- MusicKit: https://developer.apple.com/documentation/musickit

Chapters:
- 0:00 Introduction: Introduces MusicKit as a Swift framework for Apple platforms and frames the session around adding music selection and playback to a workout app using SwiftUI and Swift concurrency.
- 2:11 Project setup and authorization: Covers enabling MusicKit for the App ID, ensuring Xcode uses the same developer account, adding the Media Library capability and permission description, requesting `MusicAuthorization`, and presenting an Apple Music subscription offer when appropriate.
- 7:10 Music items and music picker: Explains MusicKit music items, including attributes, relationships, and associations, then shows how to add the SwiftUI `.musicPicker` modifier for single or multiple song selection from the catalog and library.
- 10:54 Music players and playback: Compares `SystemMusicPlayer` and `ApplicationMusicPlayer`, explains queues and playback preparation, and builds SwiftUI now-playing artwork, metadata, and transport controls using observable player state.
- 16:26 Catalog requests: Shows how structured catalog requests fetch Apple Music content, including `MusicCatalogResourceRequest`, response collections, pagination, and `.findEquivalents` for storefront or content-restriction equivalents.
- 20:11 Next steps: Recaps the Music Picker, Apple Music catalog playback, and additional MusicKit APIs, then points to related sessions on MusicKit, Apple Music API, and SwiftUI observation.

### Discover generated subtitles and subtitle styles

- Session ID: wwdc2026-256
- Page: https://wwdc.ai/2026/256
- Markdown: https://wwdc.ai/2026/256.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/256/
- Category: Audio & Video
- Description: Use on-device generated subtitles and subtitle style preview to improve AVKit/AVFoundation video accessibility across Apple platforms.
- Duration: 11:00
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-256/eng_8cf794319620/wwdc2026-256-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/256/4/d28efb5e-5550-468d-b1d1-caec51ce55e6/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/256/4/d28efb5e-5550-468d-b1d1-caec51ce55e6/downloads/wwdc2026-256_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/256/4/d28efb5e-5550-468d-b1d1-caec51ce55e6/downloads/wwdc2026-256_sd.mp4?dl=1

Use on-device generated subtitles and subtitle style preview to improve AVKit/AVFoundation video accessibility across Apple platforms.

TLDR:
- Apple AI-generated subtitles require no app-side opt-in for supported playback: they can transcribe English audio or translate from English authored subtitles using on-device models.
- Generated subtitles work with HTTP Live Streaming, live streams, video on demand, and file-based media, while authored subtitles remain preferred and unchanged.
- Use AVPlayerViewController on iOS or AVPlayerView on macOS for built-in subtitle selection and style preview, or AVLegibleMediaOptionsMenuController for existing custom players.
- For custom style preview UI, use AVPlayerLayer caption preview APIs with Media Accessibility caption appearance profile IDs, then set the active system-wide caption style.

## Generated subtitles fill language and accessibility gaps

Generated subtitles are created live and locally on device during media playback. They help when content does not include a subtitle language the viewer needs, while preserving authored subtitles as the preferred source when available.

The session distinguishes authored subtitles from generated subtitles: authored subtitles are manually created as part of the media package alongside video and audio, while generated subtitles are additional tracks produced at playback time.

- Speech transcription generates subtitles from source audio using an on-device Speech-To-Text model.
- Language translation generates new subtitle languages from existing subtitles using an on-device translation model.
- Authored subtitles remain unchanged and are preferred over generated alternatives.

## Availability and supported media

Apps do not need to implement anything to enable generated subtitles for supported playback scenarios. The system exposes them automatically during video playback when device, OS, language, and media conditions are supported.

- Supported playback scenarios include HTTP Live Streaming, including live streams such as TV channels.
- Video on demand content is supported, including movies, shows, travel videos, and live events such as sports.
- File-based content is supported, including app-bundled videos and downloaded media.
- Supported content types include professional content such as movies and series, and customer-created content such as iPhone camera captures and social media videos.
- Starting in iOS, macOS, tvOS, and visionOS 27, English subtitles can be generated from English audio; on iOS and macOS, multiple subtitle languages can also be generated from English subtitles.

## Present subtitle selection UI

Even though generated subtitles are available automatically, apps should provide subtitle selection UI during playback so people can discover and choose authored or generated options.

- Use AVPlayerViewController on iOS for fully implemented player controls and subtitle selection.
- Use AVPlayerView on macOS for similar built-in functionality.
- Use AVLegibleMediaOptionsMenuController when adding subtitle selection to an existing player UI without adopting full player controls.
- Implement custom media selection controls when the UI needs to match the rest of the app.

## Subtitle style preview

Subtitle style preview lets people change caption and subtitle styling during playback instead of leaving the app for Settings. The style menu can show built-in and custom caption styles and preview how each style will look over video.

AVPlayerViewController on iOS and AVPlayerView on macOS fully implement subtitle style preview. AVLegibleMediaOptionsMenuController can add this behavior to an existing player UI. For custom UIs, AVPlayerLayer can display a preview, and AVCaptionRenderer can provide a preview when the app handles rendering.

- Subtitle and caption styles are represented by Media Accessibility caption appearance profile IDs.
- When previewing a style, AVPlayerLayer hides existing subtitles so they do not interfere with the preview.
- Passing nil for preview text uses localized system text.
- The preview position is an offset from the default preview location, which helps avoid overlapping custom controls.
- After selection, stop the preview and set the chosen profile as the active system caption style.

### Implement subtitle style preview with AVPlayerLayer

Fetch system caption style profile IDs, preview a selected style over the video, stop the preview when selection ends, and set the chosen style as the active system caption appearance.

```swift
import AVFoundation
import MediaAccessibility

func updateProfileList() {
    subtitleStyleProfileIDs = MACaptionAppearanceCopyProfileIDs() as? [String] ?? []
}

func showPreviewStyle(subtitleStyleProfileID: String) {
    playerLayer.setCaptionPreviewProfileID(
        subtitleStyleProfileID,
        position: .zero,
        text: nil
    )
}

func stopPreviewStyle() {
    playerLayer.stopShowingCaptionPreview()
}

func setSubtitleStyle(subtitleStyleProfileID: CFString) {
    MACaptionAppearanceSetActiveProfileID(subtitleStyleProfileID)
}
```

## Demo behavior and user experience

The demo shows a video with English subtitles where generated Italian subtitles appear in the subtitle menu alongside authored options. Generated options are marked with a sparkle symbol and labeled as translated.

The style menu then previews styles during playback. Selecting a style temporarily replaces existing subtitles with a localized placeholder rendered in the candidate style; dismissing the menu applies the chosen style to the active subtitles.

- Generated subtitle choices should be discoverable in the same place as authored subtitle choices.
- Style preview helps users evaluate readability before committing to a caption style.
- Providing both subtitle selection and style preview improves accessibility for users who rely on captions for comprehension.

Resources:
- What's new in HTTP Live Streaming: https://developer.apple.com/streaming/Whats-new-HLS.pdf

Chapters:
- 0:00 Introduction: Introduces subtitles as an accessibility and comprehension feature, then frames two topics: Apple AI-generated subtitles during playback and subtitle style preview.
- 1:10 Media authoring: Explains how video, audio, and manually created authored subtitles are combined into a final media package, often with multiple audio and subtitle languages.
- 2:14 Subtitle generation methods: Covers the two generated subtitle paths: speech transcription from audio and language translation from existing subtitles. Authored subtitles are still preferred and remain unchanged.
- 3:03 Availability and support: Describes automatic support for generated subtitles across HLS, live streams, video on demand, and file-based media, plus supported content categories, devices, OS versions, and language cases.
- 4:31 Presenting subtitles in your app: Compares options for subtitle selection UI: AVPlayerViewController, AVPlayerView, AVLegibleMediaOptionsMenuController, or fully custom media selection controls.
- 5:39 Subtitle style preview: Explains in-playback caption style selection and preview, including built-in support in AVPlayerViewController and AVPlayerView and custom implementation paths using AVPlayerLayer or AVCaptionRenderer.
- 8:55 Demo: Shows generated Italian subtitles selected from a subtitle menu and then demonstrates style preview with built-in and custom styles before applying the chosen style.
- 10:20 Next steps: Recommends exploring generated subtitles, ensuring apps provide subtitle selection UI, and implementing subtitle style preview for better accessibility.

### What's new in Xcode 27

- Session ID: wwdc2026-258
- Page: https://wwdc.ai/2026/258
- Markdown: https://wwdc.ai/2026/258.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/258/
- Category: Developer Tools
- Description: Explore Xcode 27's customizable workspace, editor-based coding agents, Device Hub, localization, Organizer metrics, Instruments, and Xcode Cloud updates.
- Duration: 28:00
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-258/eng_7e89be5579ea/wwdc2026-258-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/258/4/66bc9c90-649b-4a16-a2bb-1e6f16b1ec73/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/258/4/66bc9c90-649b-4a16-a2bb-1e6f16b1ec73/downloads/wwdc2026-258_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/258/4/66bc9c90-649b-4a16-a2bb-1e6f16b1ec73/downloads/wwdc2026-258_sd.mp4?dl=1

Explore Xcode 27's customizable workspace, editor-based coding agents, Device Hub, localization, Organizer metrics, Instruments, and Xcode Cloud updates.

TLDR:
- Xcode 27 adds a redesigned, customizable toolbar; richer workspace themes; subtler predictive inline issues; and fast untitled project and standalone Swift-file workflows for prototyping.
- Coding agent conversations now live in editor tabs and splits, support a /plan workflow before code changes, show changed files and artifacts, and can help localize apps with String Catalogs.
- Device Hub centralizes simulator and physical-device workflows, including accessibility settings, screenshots, rotation, iPhone Mirroring resize testing, files, data containers, and app configurations.
- Post-launch workflows improve with Organizer metric goals and agent-generated recommendations, Instruments Top Functions for finding expensive code paths, and a simpler Xcode Cloud onboarding flow.

## Workspace customization and lower-distraction editing

Xcode 27 refreshes the workspace around a redesigned toolbar and more expressive themes. Controls that were previously in the jump bar, including history navigation and editor controls, move into the toolbar, while build activity appears under the window title. The branch picker moves to the bottom bar so long branch names fit more naturally.

The toolbar is fully customizable: developers can add, remove, and reorder items. The right side includes controls for tabs, editor panes, editor settings, and a three-way mode chooser for canvas previews and playgrounds, Assistant Editor related content, and source-control review.

Themes are managed from the new Appearance panel. Presets and palette sliders drive text color intensity and background intensity, including subtle light themes and more vibrant dark themes. Individual colors can be overridden and reset, and font settings for code, prose, and console act as a palette for generated editor fonts.

- Per-workspace themes can be assigned independently, making side-by-side projects easier to distinguish.
- Font settings are saved separately from themes, so visual themes can be swapped without changing typography.
- Predictive live issues now appear with a subtle theme-aware background while typing.
- After a build, unresolved predictions become full-intensity warnings or errors; resolved predictions disappear.

## Faster prototyping with untitled projects and standalone Swift files

Xcode 27 reduces friction when testing an idea. Creating a project from the File menu can immediately produce a new untitled project without first requiring naming and save-location decisions. Developers can explore the idea, then either save and name the project or discard it.

The project template choices shown include SwiftUI App, macOS Command Line Tool, Swift package, and Playground for a standalone Swift file with a Playground macro. Xcode 27 can also open a standalone Swift file in a workspace window and still show playground results and UI previews in the canvas, even when the file is not part of a project.

- Use untitled projects as a temporary blank canvas for feature experiments or app ideas.
- Use standalone Swift files for lightweight sharing, previews, and playground-style iteration.
- Choose Swift package when the goal is a reusable library rather than an app.

## Coding agents integrated into the editor

Coding agent conversations move into the editor pane in Xcode 27. Agent conversations can be arranged like other editors with tabs and split panes, making it easier to keep implementation work, review, and conversation context visible together.

The toolbar includes an entry point for starting a new agent conversation or task. Agent output includes a view of codebase changes, plus produced files, artifacts, and screenshots. The coding assistant sidebar lists parallel conversations and tasks, including unread messages and tasks that need input.

- Use the /plan command when the agent should inspect context and propose an approach before modifying files.
- Agents can gather project context and use sub-agents in parallel while planning.
- Plans can be reviewed, refined with inline feedback, or approved for implementation.
- For deeper agent workflows, the session points to "Xcode, agents, and you."

### Plan before implementation

The /plan command invokes the planning tool so the agent gathers context and proposes a plan before making code changes.

```text
/plan Add statistics for different paper airplane designs. Consider existing views, data models, previews, and simulator behavior before changing code.
```

## Device Hub for simulator and physical-device evaluation

When launching an app on a simulator, Xcode 27 opens it in Device Hub. Device Hub provides a compact device-sized window with quick actions such as Home, screenshot, and rotation, and it can expand to expose more controls and an Inspector.

The Inspector supports app evaluation across important configurations, including accessibility settings such as increased contrast, larger Dynamic Type sizes, and dark appearance. On macOS 27, iPhone Mirroring supports a resize mode, and Device Hub can be used to test different aspect ratios and content sizes.

Device Hub is not limited to simulators. Its sidebar shows a combined list of simulators and paired physical devices, and a running app on a physical device can be viewed and controlled from the Mac.

- Use Device Hub to evaluate form factors, accessibility settings, appearance changes, and resizing behavior.
- SwiftUI standard views and custom layouts that already support resizable iPad and Mac windows can help iPhone apps behave well in iPhone Mirroring resize scenarios.
- Device Hub also supports workflows around files, data containers, and app configurations.
- For more detail, the session recommends "Get the most out of Device Hub."

## Localization with agents and String Catalogs

Xcode 27 can use coding agents to help set up localization. In the demonstrated workflow, the agent reads the app code, prepares string literals for localizable references, creates a String Catalog containing UI strings, and translates those strings into a requested language.

String Catalogs remain the focused place for per-language review. Developers can add a language from the String Catalog, select it, and use the new Generate Translations button. The agent works in the background, and progress can be checked either in the agent conversation or by watching String Catalog entries fill in.

The session emphasizes testing localized builds even when the developer does not read every language, because layout problems and truncated text are often visible. TestFlight is recommended for collecting feedback from native speakers.

- Start with one or two languages so localization and layout issues are easier to spot.
- Ask the agent to ensure existing strings are ready for localization before generating translations.
- Review translations in the String Catalog and run the app to catch awkward layouts or truncation.
- Related sessions: "Translate your app using agents in Xcode" and "Code-along: Explore localization with Xcode."

## Post-launch diagnostics, performance, and CI/CD

Organizer in Xcode 27 is redesigned to surface high-impact issues first. The Overview combines diagnostics and metrics so a metric spike and relevant diagnostic reports can be investigated from one place. New metrics include storage and broader animation hitches, and app recommendations have evolved into Metric Goals.

The storage metric breaks down documents, data, and binary size, helping identify where app footprint reductions matter. The updated hitches metric covers more than scrolling, including animations involving Liquid Glass and SwiftUI views. Metric Goals are calibrated against technically and functionally similar apps and include historical baselines for the app itself.

Organizer can also generate recommendations using coding agents. From Organizer, developers can choose Generate Recommendations, select a project, and let the agent analyze diagnostic data while iterating toward possible fixes.

Instruments adds Top Functions, a view for quickly identifying where time is spent in a selected recording range. The session demonstrates using it on a CPU profile to find an expensive app function causing an animation hitch, then confirming the fix with another Instruments run.

Xcode Cloud onboarding is streamlined in Xcode 27. The setup flow can connect a project to a remote source repository and start a first build so unit and UI tests run automatically on commits. Xcode Cloud also integrates with TestFlight and App Store delivery.

- Metric Goals cover launch time plus expanded areas such as hang rate, disk writes, battery, storage, and hitches.
- Top Functions is especially useful for expensive operations repeated many times.
- Use follow-up Instruments recordings to verify that a change actually improved performance.
- Related sessions include "Build, deliver, and automate with Xcode Cloud," "Profile, fix, and verify: Improve app responsiveness with Instruments," and "Debug and profile agentic app experiences with Instruments."

### Quick Open a costly function from Xcode

After identifying an expensive function in Instruments Top Functions, use Quick Open in Xcode to jump directly to a file, function, or other code symbol.

```text
Command-Shift-O
```

Resources:
- Xcode updates: https://developer.apple.com/documentation/Updates/Xcode

Chapters:
- 0:07 Introduction: The session frames Xcode 27 around customizable workspaces, fast project iteration, coding agents, Device Hub, and tools for improving apps after launch.
- 1:01 Workspace & Toolbar: Xcode 27 introduces a redesigned toolbar with moved navigation and editor controls, build activity under the title, a coding-agent entry point, editor mode controls, and full toolbar customization.
- 2:13 Themes: The new Appearance panel provides preset themes, palette sliders for text and background intensity, individual color overrides, font palettes, and per-workspace theme selection.
- 5:04 Inline Issues: Predictive live issues now use a subtle theme-aware appearance while typing, then become full-intensity build warnings or errors only if they remain after building.
- 6:08 New Project Workflows: Xcode 27 supports instant untitled projects for experimentation and standalone Swift files that can show playground results and UI previews without being part of a project.
- 8:40 Coding Agents in the Editor: Coding agent conversations now appear in editor panes with tabs and splits, expose code changes and artifacts, and support /plan for context gathering and planning before implementation.
- 9:37 Device Hub: Device Hub opens simulator runs in a dedicated device window with quick actions, an Inspector for accessibility and appearance testing, iPhone Mirroring resize evaluation, and support for physical devices.
- 13:13 Localization: Coding agents can prepare code for localization, create String Catalogs, translate UI strings, and generate additional language translations from the String Catalog while developers review and test builds.
- 16:57 Organizer: Organizer gains a redesigned Overview, new storage and animation hitch metrics, Metric Goals calibrated to similar apps and historical baselines, and agent-generated recommendations for diagnostics.
- 21:07 Instruments & Top Functions: Instruments adds Top Functions to surface expensive code paths in a selected trace range, demonstrated by finding and fixing a costly animation-related function causing a hitch.
- 25:48 Xcode Cloud: Xcode Cloud setup is simplified so a project can connect to a remote repository and start cloud builds and tests on commits, with delivery integration for TestFlight and the App Store.
- 27:51 Next steps: The closing recap positions Xcode 27 as supporting the app lifecycle from prototyping and agent collaboration through localization, diagnostics, performance work, and cloud delivery.

### Xcode, agents, and you

- Session ID: wwdc2026-259
- Page: https://wwdc.ai/2026/259
- Markdown: https://wwdc.ai/2026/259.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/259/
- Category: Developer Tools
- Description: Use Xcode 27 coding agents to explore projects, plan and build features, refine UI, and orchestrate localization and accessibility work.
- Duration: 24:03
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-259/eng_b93f768b8bb0/wwdc2026-259-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/259/4/f4d40bb5-32db-418f-8a6e-396c77044afb/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/259/4/f4d40bb5-32db-418f-8a6e-396c77044afb/downloads/wwdc2026-259_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/259/4/f4d40bb5-32db-418f-8a6e-396c77044afb/downloads/wwdc2026-259_sd.mp4?dl=1

Use Xcode 27 coding agents to explore projects, plan and build features, refine UI, and orchestrate localization and accessibility work.

TLDR:
- Xcode agents can inspect project context, open files, selections, build settings, and Apple documentation to explain a codebase and preserve findings as project documentation.
- Plan mode, queued messages, agent questions, artifacts, builds, previews, and tests help keep feature implementation aligned with developer intent.
- Design refinement workflows combine Swift Charts, realistic previews, image attachments, and inline annotations for targeted UI changes.
- Orchestration lets developers describe high-level goals such as localization and accessibility while Xcode discovers tools and coordinates sub-agents in parallel.

## Agent workflows in Xcode 27

The session demonstrates Xcode 27's expanded coding-agent workflow through a workout tracking app. The development flow is organized around four activities: exploring an unfamiliar project, building a new feature, refining the visual design, and orchestrating broader tasks across multiple conversations.

A key theme is staying in control: the developer supplies product direction, reviews plans and artifacts, steers implementation with follow-up messages, and validates changes with Xcode's build, preview, and test tools.

- Xcode 26.3 introduced coding agents and tools for complex multi-step tasks; Xcode 27 expands the tool set and redesigns agent interaction.
- Agents can work from project context including source code, build settings, open files, and active selections.
- Conversation output is split between the transcript, which shows reasoning progress and tool activity, and artifacts, which show created files, edits, diffs, and previews.

## Explore a project and capture knowledge

Agents can be used as a guided entry point into a codebase. In the demo, a new conversation is opened in a separate editor pane, and the agent is asked to summarize data models, describe the current view hierarchy, and provide a walkthrough. The walkthrough includes architecture details, data flow, key source references, and links into relevant files.

The exploration work is then preserved by asking the agent to draft architecture documents and place them in the project. This turns one conversation's discovery loop into reusable project knowledge that can help the original developer or teammates get up to speed later.

- Use an agent walkthrough to identify where to start in a new or unfamiliar project.
- Ask the agent to create architecture documents from its findings so the knowledge base evolves with the source code.
- Reference those documents in later conversations to give the agent fast, project-specific context.
- Use Apple Document Search when adopting unfamiliar Apple APIs or checking current framework guidance.

## Plan, build, and validate features

For the insights feature, Xcode is put into plan mode before any implementation begins. The developer provides the desired new tab, high-level requirements, device-specific presentation details learned during exploration, and a request for previews. Plan mode turns implementation into a reviewable discussion rather than immediately generating code.

Queued messages let the developer add requirements while the agent is still working. In the demo, a follow-up asks for metric ideas, the agent proposes options, and the developer selects a per-exercise view and a top-level summary. Once the markdown plan is approved, Xcode implements it and surfaces diffs and new files as artifacts.

Validation is part of the workflow. The agent uses Xcode's build tool, receives build errors, iterates, updates architecture documents, renders previews, and later writes and runs unit tests for the SwiftData model changes.

- Use plan mode when the architecture and scope matter more than immediate code generation.
- Review and edit the markdown plan before approving implementation.
- Inspect source edits and new files as artifacts while they are produced.
- Use builds, previews, and tests to let the agent validate work incrementally.

## Refine UI with previews, attachments, and annotations

The refinement phase adds Swift Charts visualizations to the insights view. The agent first explores chart styles suitable for the app's data, then generates previews using artificial workout data so the developer can compare options in context. The selected chart is a volume-over-time visualization.

The developer then attaches a sketch made in Freeform to communicate the desired chart style. Xcode's preview rendering allows the agent to iteratively verify the generated UI. Final tweaks use inline annotations placed directly in source code to request a fade-in animation and theme-matching trend line color at precise locations.

- Use realistic preview data to evaluate visual options rather than relying on text descriptions.
- Attach images, sketches, or documents to communicate design intent that is hard to express in text.
- Use inline annotations when the change belongs at a specific source location; surrounding code becomes part of the context.
- Keep subjective choices such as chart type, animation, and color under developer direction.

## Orchestrate larger goals with tools and sub-agents

The orchestration section shows high-level tasks that span many files: localizing the app into Filipino and improving accessibility. Instead of manually invoking each tool, the developer describes the goal and Xcode discovers the relevant tools, such as machine translation support, then coordinates work across sub-agents.

Localization and accessibility run in parallel conversations. The localization workflow finds user-facing strings, translates them, and configures the strings catalog. The accessibility workflow adds VoiceOver labels and accessibility identifiers to interactive elements. The developer remains able to check progress and review the final app behavior.

- Describe broad goals such as localization or accessibility instead of micromanaging each edit.
- Xcode can discover tools automatically based on the task.
- Sub-agents can divide work and call specialized tools under the main workflow.
- Always review results, especially translated strings and accessibility behavior.

Resources:
- Writing code with intelligence in Xcode: https://developer.apple.com/documentation/Xcode/writing-code-with-intelligence-in-xcode

Chapters:
- 0:00 Introduction: Introduces coding agents in Xcode 27 and frames the session around exploring a project, planning and building a feature, refining UI, and orchestrating multi-step work.
- 1:14 Meet the app: Presents the workout tracking app used throughout the session, including workout tracking and history features, and identifies the need for an insights view.
- 2:06 Explore: Shows how agents can explain a project's data models and view hierarchy, create reusable architecture documents, and use Apple Document Search to research SwiftUI and SwiftData APIs.
- 7:38 Build: Demonstrates plan mode, queued follow-up messages, reviewable plans, implementation artifacts, build validation, preview rendering, and unit tests while adding the insights feature.
- 13:44 Refine: Uses Swift Charts, generated previews with realistic data, a sketch attachment, and inline annotations to refine the insights UI while preserving developer control over design choices.
- 18:25 Orchestrate: Shows high-level localization and accessibility prompts running in parallel, with Xcode discovering tools and coordinating sub-agents to translate strings and add VoiceOver/accessibility support.
- 22:09 Next steps: Recaps the agent workflows used across the session and points developers to Xcode 27, agentic tools, and related sessions on UI prototyping and translation with agents.

### Get the most out of Device Hub

- Session ID: wwdc2026-260
- Page: https://wwdc.ai/2026/260
- Markdown: https://wwdc.ai/2026/260.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/260/
- Category: Developer Tools
- Description: Use Device Hub in Xcode 27 to control, organize, configure, diagnose, and reproduce issues across physical devices and simulators.
- Duration: 17:09
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-260/eng_0e69231894a5/wwdc2026-260-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/260/4/87d4b48f-1dfb-413f-a4f8-44d80b0f3432/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/260/4/87d4b48f-1dfb-413f-a4f8-44d80b0f3432/downloads/wwdc2026-260_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/260/4/87d4b48f-1dfb-413f-a4f8-44d80b0f3432/downloads/wwdc2026-260_sd.mp4?dl=1

Use Device Hub in Xcode 27 to control, organize, configure, diagnose, and reproduce issues across physical devices and simulators.

TLDR:
- Device Hub ships with Xcode 27 as a standalone app for working with both physical devices and simulators without launching Xcode.
- It provides compact and full-window modes with live device interaction, contextual hardware controls, zoom, physical-size viewing, resize mode, and keyboard capture.
- The sidebar and inspector support device inventory management, pairing, app/container management, profiles, appearance settings, simulated conditions, and diagnostics.
- A bug-reproduction workflow shows how to collect logs and app data from a real device, then mirror device model, app data, orientation, location, and text size on a simulator; devicectl covers automation and CI use cases.

## What Device Hub is

Device Hub is a new app that ships alongside Xcode 27. It is intended as a central place to work with physical devices and simulators, whether you are developing, testing configurations, or managing a device inventory.

When running to a simulator from Xcode, Device Hub launches automatically and shows a live, interactive compact window. The same toolset is designed to apply consistently across devices and simulators.

- Compact mode focuses on the device screen plus essential controls.
- Full-window mode expands into a sidebar, central canvas, and inspector for broader control and configuration.
- Device controls are contextual: examples include Apple TV navigation and play/pause, Apple Vision Pro environment and camera movement, and Apple Watch side button and Digital Crown controls.

## Control devices from the canvas

The canvas in the full Device Hub window provides a live display of the selected device or simulator. Developers can click, drag, scroll, and use natural trackpad gestures directly against the displayed screen.

The canvas also exposes controls that are not available in compact mode, making it useful for UI inspection, hardware-keyboard testing, and resizability work.

- Zoom in or out, or snap to one-to-one physical size to evaluate real-world dimensions.
- Use resize mode to freely transform an app's dimensions; the session points to "Modernize your UIKit app" for more detail.
- Capture keyboard routes Mac keystrokes directly to the device, which helps test key commands and hardware keyboard support.
- Switch back to compact mode when only the live screen and essential controls are needed.

## Organize device and simulator inventory

The sidebar shows the full inventory of connected devices and available simulators in one place. It is built for workflows that involve many devices, multiple simulator sizes, or paired device sets.

- Use the filter menu to control which devices are visible.
- Sort and group the inventory using several sidebar options.
- Context-click devices for quick actions such as restarting or pairing an iPhone and Apple Watch simulator together.
- Open multiple devices as tabs or as standalone compact windows to compare layouts across screen sizes.

## Configure settings, apps, profiles, and diagnostics

The inspector area contains panels for changing device configuration and collecting information needed to investigate issues. The session emphasizes avoiding repeated trips through on-device Settings by changing common test parameters directly in Device Hub.

- Device settings include appearance options such as Dark Mode and text size, condition simulation such as location, and audio options for levels and I/O.
- Diagnostic reports include crashes, spins, and other logged diagnostics, making this a starting point for hangs and crashes.
- Info shows quick device details such as storage, model, and serial number.
- Apps supports installing, uninstalling, managing apps, and downloading or replacing app data containers.
- Profiles manages configuration profiles and provisioning profiles.

## Reproduce bugs by mirroring the reporter's environment

The demo workflow starts with a location-based workout app bug: recovery advice text is clipped in landscape. The first developer collects enough context from a real iPhone and Apple Watch setup for another developer to reproduce the problem on a simulator.

The reproduction succeeds only after matching a combination of factors: corresponding device model, app data container, landscape orientation, simulated location, and large text size. Device Hub is presented as the place to collect and apply those variables consistently.

- Pair nearby devices from the sidebar, including pairing an Apple Watch with the Mac.
- Install a Core Location logging configuration profile through the Profiles panel, then reboot the iPhone for privacy reasons.
- Capture evidence such as screenshots, sysdiagnose output, copied text, and the app data container.
- On the simulator, verify the model in the Info panel, replace the app's data container through the Apps inspector, rotate the simulator, simulate the reported location, and match accessibility-related appearance settings such as text size.

## Automation with devicectl

For scripting and automation, the session recommends devicectl, a command-line tool based on the same underlying technology as Device Hub. It is positioned for test environments, scripts, and CI workflows.

- Use devicectl to list devices, install apps, change settings such as light or dark appearance, retrieve device information, and capture diagnostics.
- Use the json-output option when structured output is needed for scripts or CI integration.
- Consult the Device Hub documentation and devicectl documentation for exact commands and options.

### devicectl capabilities mentioned

The session names devicectl operations-listing devices, installing apps, changing settings, getting device information, and capturing diagnostics-but does not show exact command syntax.

Resources:
- Device Hub: https://developer.apple.com/documentation/Xcode/device-hub

Chapters:
- 0:00 Introduction: Introduces Device Hub as an app for working with both devices and simulators, then outlines the session: overview, feature walkthrough, and a practical debugging workflow.
- 1:04 Device Hub overview: Explains that Device Hub ships with Xcode 27 but can be used independently of Xcode. Shows compact mode, automatic launch when running to a simulator, contextual device controls, and the transition to the full window.
- 3:00 Control: Covers the central canvas for live device interaction across devices and simulators. Highlights direct input, contextual controls, zoom, one-to-one physical sizing, resize mode, keyboard capture, and compact-mode switching.
- 4:39 Organize: Shows how the sidebar presents the full device and simulator inventory. Developers can filter, sort, group, use context-menu actions, and open multiple devices in tabs or compact windows for comparison.
- 6:04 Configure: Walks through inspector panels for settings, simulated conditions, audio, diagnostics, device information, app management, and profile management. Emphasizes quick changes such as appearance and text size without navigating device Settings.
- 8:08 Reproducing a bug: Demonstrates a real workflow for a clipped-text bug in a workout app. One developer pairs devices, installs a logging profile, captures screenshots, sysdiagnose, copied text, and app data; another reproduces the issue on a simulator by matching model, data, orientation, location, and text size.
- 15:52 devicectl: Introduces devicectl as the command-line counterpart for automation. It can manage devices, install apps, change settings, gather device information, capture diagnostics, and emit structured output for scripts or CI.
- 16:30 Next steps: Points developers to Xcode 27, Device Hub documentation, devicectl documentation, the UIKit resizability session, and the WWDC 2019 Simulator session for deeper follow-up.

### Build, deliver, and automate with Xcode Cloud

- Session ID: wwdc2026-261
- Page: https://wwdc.ai/2026/261
- Markdown: https://wwdc.ai/2026/261.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/261/
- Category: Developer Tools
- Description: Set up Xcode Cloud builds, tests, TestFlight distribution, webhooks, and additional Git repositories directly from Xcode.
- Duration: 13:49
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-261/eng_36a8fed8a390/wwdc2026-261-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/261/7/35c49f2b-3f0a-4956-826b-d54d9fed678e/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/261/7/35c49f2b-3f0a-4956-826b-d54d9fed678e/downloads/wwdc2026-261_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/261/7/35c49f2b-3f0a-4956-826b-d54d9fed678e/downloads/wwdc2026-261_sd.mp4?dl=1

Set up Xcode Cloud builds, tests, TestFlight distribution, webhooks, and additional Git repositories directly from Xcode.

TLDR:
- Xcode Cloud is positioned as cloud CI/CD for Apple apps, running builds and tests on ephemeral virtual machines across devices and OS versions.
- The session walks through onboarding iOS and macOS products from Xcode's Cloud report navigator by connecting the source repository and starting default workflows.
- Distribution setup can create the App Store Connect app record, register Bundle ID and SKU, and generate a TestFlight distribution workflow from Xcode.
- Webhooks and additional repositories extend workflows by sending build lifecycle payloads to external services and granting builds access to shared dependencies.

## Xcode Cloud fundamentals

Xcode Cloud is a continuous integration and delivery service built into Xcode for Apple-platform development. It builds and tests apps in the cloud, then supports delivery to TestFlight and the App Store when the app is ready for feedback or release.

The session frames Xcode Cloud as a way to keep up with faster iteration, including agent-assisted coding in Xcode, by moving recurring build and test work off the local machine.

- Builds and tests can run in parallel across multiple devices and OS versions.
- Cloud automation helps catch regressions, bugs, and performance issues before they reach customers.
- Xcode Cloud can be used for both new projects and existing apps already using Xcode workflows.

## Onboard an app from Xcode

Onboarding starts in Xcode's Report navigator under the Cloud tab. The onboarding assistant shows products in the workspace, uses the Developer Team from Signing & Distribution settings, and guides repository connection.

Xcode Cloud needs access to the source repository to build the app. The exact connection steps depend on the source provider. Once connected, Xcode Cloud creates a product and a default workflow, and the first build can be started from Xcode.

- Open Report navigator → Cloud → Get Started.
- Select the product and confirm the Developer Team.
- Connect the source repository when prompted.
- Finish onboarding, then start the first build from the Cloud section.

## Repository and source handling

Xcode Cloud builds run on ephemeral virtual machines. Source code is fetched only when a build starts and is discarded when the build finishes. The session states that source code is not stored and Apple has no way to access it.

When multiple apps share a workspace and repository access has already been granted, adding another product can skip the repository connection step. The example onboards a macOS companion app from the same workspace by creating another workflow.

- Use the More button in the Cloud navigator to create another workflow for another product.
- Products in the same workspace can share repository access already granted to Xcode Cloud.
- Additional workflows can later be expanded to cover more scenarios, platforms, and edge cases.

## Set up TestFlight distribution

Xcode Cloud can configure distribution from Xcode. For the iOS app example, Set Up Distribution creates the app record on App Store Connect and prepares an internal TestFlight distribution workflow.

The setup assistant collects required App Store Connect properties such as app name, Bundle ID, and SKU. Xcode Cloud indicates when values are already taken so they can be changed without leaving the assistant.

- From the Cloud navigator, secondary-click the product and choose Set Up Distribution.
- Provide app name, Bundle ID, and SKU for the App Store Connect record.
- Xcode Cloud creates the app record, verifies it, and registers the Bundle ID and SKU in the background.
- For workflow-based setup, create an archive action; archiving is required for TestFlight distribution.

## Automate with webhooks

Webhooks let Xcode Cloud send build event payloads to an external service, making them useful for dashboards, notifications, issue tracking, and other automation. The example configures a webhook named Dashboard with a publicly resolvable payload URL.

Xcode Cloud supports hooks for build creation, build start, and build completion. After a build runs, the Webhooks view shows delivery history and status indicators for the delivered events.

- Open Manage Webhooks for a product in the Cloud navigator.
- Add a webhook with a name and publicly resolvable Payload URL.
- Run a build to test the webhook configuration.
- Review delivery history to confirm lifecycle events were sent successfully.

## Add repositories for shared dependencies

As a project grows, shared code may be split into separate Git repositories. Xcode Cloud repository management lets a product include additional repositories so cloud builds can fetch dependencies such as a shared framework.

The example adds a style framework repository through Manage Repositories. The primary repository appears first, and additional Git remote URLs can be added below it. If access to the remote provider has already been granted, no additional authorization is required.

- Open Manage Repositories for the Xcode Cloud product.
- Use Add in the Additional section.
- Paste the Git remote URL for the dependency repository.
- Confirm that future builds have access to all required repositories.

Chapters:
- 0:00 Introduction: Introduces Xcode Cloud as CI/CD built into Xcode for building, testing, and distributing Apple apps. The session scope covers core concepts, onboarding, distribution, webhooks, and repository management.
- 1:13 Essential concepts: Explains why cloud CI helps teams keep pace with rapid iteration by running builds and tests away from the local machine. Xcode Cloud can test in parallel across devices and OS versions and deliver builds to TestFlight and the App Store.
- 2:07 Getting started: Demonstrates onboarding an iOS app through the Cloud tab in Xcode's Report navigator, connecting the source repository, and starting a first build. It also shows adding a macOS app from the same workspace by creating another workflow.
- 6:42 Distribution: Shows distribution setup from Xcode, including creating an App Store Connect app record and preparing an internal TestFlight workflow. The workflow manager path also demonstrates that an archive action is required for TestFlight distribution.
- 9:21 Webhooks: Configures a webhook with a name and payload URL so Xcode Cloud can send build lifecycle payloads to an external service. The delivery history view confirms created, started, and completed build events were sent successfully.
- 11:22 Additional repositories: Shows how to add another Git repository to an Xcode Cloud product so builds can access shared dependencies such as a framework split into its own repository. Existing provider authorization can be reused when access has already been granted.
- 13:00 Next steps: Recaps onboarding apps to build, test, distribute, and then extending workflows with webhooks and additional repositories. It points developers to additional Xcode Cloud sessions for practical workflows, automation, and distribution.

### What's new in Swift

- Session ID: wwdc2026-262
- Page: https://wwdc.ai/2026/262
- Markdown: https://wwdc.ai/2026/262.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/262/
- Category: Swift
- Description: Swift 6.3 and 6.4 add everyday language cleanup, stronger testing and subprocess APIs, broader interoperability, and safer performance tuning.
- Duration: 32:45
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-262/eng_16587e71353a/wwdc2026-262-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/262/5/d430e425-34fc-4ed5-b590-507ac593453a/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/262/5/d430e425-34fc-4ed5-b590-507ac593453a/downloads/wwdc2026-262_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/262/5/d430e425-34fc-4ed5-b590-507ac593453a/downloads/wwdc2026-262_sd.mp4?dl=1

Swift 6.3 and 6.4 add everyday language cleanup, stronger testing and subprocess APIs, broader interoperability, and safer performance tuning.

TLDR:
- Swift 6.4 reduces boilerplate with optional `some`/`any` syntax cleanup, `anyAppleOS` availability, `@diagnose`, `weak let`, explicit `~Sendable`, and more accessible memberwise initializers.
- Library updates include task cancellation shields, `Dictionary.mapKeyedValues`, a standard-library `FilePath`, Swift Testing severity/cancellation/XCTest interop, Subprocess 1.0, and Foundation `ProgressManager`.
- Swift expands beyond Apple platforms with `@C` exports, improved Swift-Java and Android SDK support, VSCode/OpenVSX tooling, WebAssembly/JavascriptKit improvements, and a larger Embedded Swift subset.
- Performance work focuses on explicit optimizer control with `@inline(always)` and `@specialized`, plus ownership features such as `Iterable`, `borrow`/`mutate` accessors, `UniqueBox`, `UniqueArray`, `Ref`, and `MutableRef`.

## Everyday Swift language improvements

Swift 6.3 and 6.4 focus heavily on making intent clearer with less boilerplate. Swift 6.4 removes the need to parenthesize `some` or `any` when used with optional types, warns when a throwing `Task` error is silently ignored, and removes the old restriction on calling async functions from `defer`.

Concurrency and access-control ergonomics also improve: immutable weak references can be written as `weak let`, types can explicitly opt out of `Sendable` with `~Sendable`, and structs that mix internal and private stored properties now receive a usable internal memberwise initializer in addition to the private full initializer.

- Use `anyAppleOS` to consolidate matching availability across Apple platforms, with platform-specific carve-outs when needed.
- Use `#if os(anyAppleOS)` for conditional compilation across Apple OSes.
- Use `@diagnose` to ignore, enable, warn, or error specific diagnostic groups inside one declaration.
- Use module selectors, written `Module::name`, when imported modules contain conflicting type, method, or property names.

### Condense Apple-platform availability with `anyAppleOS`

`anyAppleOS` replaces repeated macOS/iOS/watchOS/tvOS/visionOS availability when the versioning aligns.

```swift
extension Mission {
  @available(anyAppleOS 27, *)
  func showStatus() { ... }

  @available(anyAppleOS 27, *)
  @available(tvOS, unavailable)
  func launch() { ... }

  #if os(anyAppleOS)
  func makeLiveActivityWidget() -> some Widget { ... }
  #endif
}
```

### Resolve module name conflicts with `::`

The left side of `::` is always treated as a module name, avoiding ambiguity with same-named types or extension members.

```swift
import Rocket
import GiftShopToys

let rocket = Rocket::SaturnV()
launchPadTechnician.HumanResources::fire()
```

### Control diagnostics locally

`@diagnose` lets a declaration suppress, enable, or promote selected diagnostic groups without changing the whole project.

```swift
@diagnose(DeprecatedDeclaration, as: ignored, reason: "Temporary migration")
func makeLegacyMission() -> Mission { ... }

@diagnose(StrictMemorySafety, as: warning)
func uplinkCommand(...) { ... }

@diagnose(ErrorInFutureSwiftVersion, as: error)
func fetchPosition() -> (x: Double, y: Double, z: Double) { ... }
```

## Standard library, testing, Subprocess, and Foundation

The standard library adds targeted APIs for common correctness and portability problems. A task cancellation shield allows a short critical section to finish or roll back work even if the surrounding task is cancelled. `mapKeyedValues` transforms dictionary values while also receiving each key, and the standard-library `FilePath` brings cross-platform path manipulation from Swift System into everyday Swift.

Swift Testing in Swift 6.4 gains more control over test outcomes. `Issue.record` can report warnings that do not fail CI, `Test.cancel` can dynamically cancel individual cases in parameterized tests, and `swift test` can repeat tests until pass or fail with a maximum repetition count. XCTest and Swift Testing now interoperate in both directions, with interoperability issues reported as warnings by default and promotable to failures in Xcode build settings.

Subprocess 1.0 refines process execution with simpler execution types, improved errors, output streaming through `AsyncBufferSequence`, line-by-line `strings()` reading that respects grapheme cluster boundaries, and better cross-platform process semantics. Foundation adds `ProgressManager` for async-aware progress composition and reporting, while Swift-Foundation continues migrating implementation to Swift with faster `Data`, `NSURL`, and `CFURL` behavior.

### Use a task cancellation shield for short critical work

Inside the shield, cancellation checks return false; keep the shielded region short and limited to finishing or rolling back started work.

```swift
extension EmergencyTransponder {
  func sendSOS() {
    withTaskCancellationShield {
      radio.send(makeSOSPacket())
    }
  }
}
```

### Transform dictionary values with access to keys

`mapKeyedValues` avoids manually rebuilding a dictionary when the new value depends on both key and old value.

```swift
func makeCalendarDisplayNames(for missions: [Mission: LaunchWindow]) -> [Mission: String] {
  missions.mapKeyedValues { mission, launchWindow in
    makeDisplayName(for: mission, in: launchWindow)
  }
}
```

### Record warnings and dynamically cancel Swift Testing cases

Swift Testing can now distinguish non-fatal warnings from failing expectations and cancel cases at runtime.

```swift
@Test(arguments: allRockets)
func testBurn(rocket: Rocket) throws {
  if rocket.engineType == .solid {
    try Test.cancel("\(rocket.name) has solid fuel")
  }

  rocket.burn(for: .seconds(150))
  let remaining = rocket.propellantKg / rocket.totalPropellantKg
  if remaining < 0.10 {
    Issue.record("Remaining fuel below reserve target", severity: .warning)
  }
  #expect(remaining > 0.02, "Propellant critically low - abort")
}
```

## Interoperability and Swift beyond Apple platforms

Swift 6.4 extends language interoperability so Swift can be adopted incrementally in existing systems. The new `@C` attribute exposes Swift functions to C for C-compatible types, and `@implementation` can be used when implementing an existing C declaration in Swift. Safe interop features translate array-and-count style C APIs into safer Swift spans, and C++20 spans also bridge with Swift spans.

Swift-Java now supports calling async and throwing Swift functions from Java, captures more of Swift generics including constrained extensions, and can conform Java classes to Swift protocols. This improves Swift use from Java and Kotlin, including on Android, where an official Swift SDK is available from swift.org.

Tooling expands as well: the Swift VSCode extension integrates with Swiftly for installing toolchains from swift.org and is available on OpenVSX for editors such as VSCodium, Cursor, Kiro, and Antigravity. Swift can also compile to WebAssembly, and JavascriptKit has improved safer Swift-to-JavaScript bridging; the session cites Goodnotes benchmarks showing safe bridging 35-40x faster than the older dynamic bridging path.

- Use `@C` only with C-compatible signatures; the compiler prevents incompatible Swift-only types from being exported.
- Use `@implementation` when the C declaration already exists and Swift is providing the implementation.
- Use generated C interop headers when Swift introduces a new exported C-callable function.
- Use Swiftly-enabled editor tooling to install the right open-source toolchain for targets such as WebAssembly or embedded platforms.

### Swift-to-C export workflow

The session demonstrates replacing C launch-window functions with Swift implementations using `@C`; existing C declarations use `@implementation`, while new Swift functions are emitted into the generated C interop header.

## Embedded Swift and constrained environments

Embedded Swift continues to grow while remaining a subset of the full language. Swift 6.4 adds support for existential types, allowing values of multiple conforming types to be stored or passed through protocol-typed APIs in embedded contexts. It also adds untyped throws using the same underlying machinery that supports existentials.

Debuggability improves without increasing runtime binary size. Embedded Swift stores type-layout metadata needed by the debugger in DWARF debug info, which improves coredump debugging on constrained hardware where a live process may not be available.

Diagnostics in the `EmbeddedRestrictions` warning group identify language features unavailable in embedded contexts. For libraries that support both full and embedded Swift, `@diagnose` can tune these diagnostics on specific declarations.

### Use `@diagnose` with embedded restrictions

The exact policy depends on the library, but `@diagnose` is the mechanism discussed for controlling embedded-restriction diagnostics locally.

```swift
@diagnose(EmbeddedRestrictions, as: warning)
func fullSwiftOnlyEntryPoint() { ... }
```

## Performance tuning with optimizer control and ownership

Swift's optimizer normally decides when code duplication is worth it, but Swift 6.3 and 6.4 add explicit controls for performance-sensitive code. `@inline(always)` complements the existing `@inline(never)` by forcing inlining when possible; for class methods, `final` may be needed because dynamically dispatched methods cannot always be inlined. `@specialized` asks the compiler to generate a concrete implementation of a generic function for important type constraints.

The larger performance theme is avoiding unnecessary copies without falling back to unsafe pointers. Swift's ownership model represents safe shared read access as borrows and safe exclusive write access as mutations, with compile-time exclusivity checking. Swift 6.4 extends more protocols and generic patterns into this world: `Equatable`, `Comparable`, and `Hashable` work with noncopyable types, `Equatable` and `Comparable` also work with non-escapable types, and associated types can be `~Copyable` or `~Escapable`.

The new `Iterable` protocol lets `for` loops borrow elements instead of copying them out, which supports noncopyable elements and can avoid reference-counting costs for object and copy-on-write values. New `borrow` and `mutate` accessors let computed properties provide read-only shared access or exclusive in-place mutation without copying. The standard library also adds `UniqueBox`, `UniqueArray`, `Continuation`, `Ref`, and `MutableRef` for safe high-performance ownership patterns.

- Prefer straightforward Swift first; these features are intended for hot paths, libraries, embedded code, or other constrained environments.
- Use `@inline(always)` and `@specialized` selectively because they can increase code size and may hurt performance if overused.
- `Iterable` is used by `for` loops when `Sequence` is unavailable; if both exist, `Sequence` is preferred because mutation/exclusivity behavior differs.
- `Ref` and `MutableRef` are non-escapable, allowing Swift to know when the borrowed or mutable access ends.

### Request optimizer decisions explicitly

`@inline(always)` forces inlining when possible, while `@specialized` generates a concrete generic specialization for important call patterns.

```swift
@inline(always)
func makeInts(randomized: Bool) -> [256 of Int] { ... }

@specialized(where Values == [UInt8])
func histogram<Values>(of values: Values) -> [256 of Int]
where Values: Sequence<UInt8> { ... }
```

### Use `borrow` and `mutate` accessors to avoid copies

The property can expose storage for read or in-place mutation without copying a large or noncopyable value through `get` and `set`.

```text
@safe public struct UniqueBox<Value: ~Copyable>: ~Copyable {
  private let valuePointer: UnsafeMutablePointer<Value>

  public var value: Value {
    borrow { valuePointer.pointee }
    mutate { &valuePointer.pointee }
  }
}
```

### Hoist repeated dictionary access with `MutableRef`

`MutableRef` keeps a mutable access open across the loop, avoiding repeated dictionary lookups without an unsafe pointer or helper `inout` trick.

```swift
func updateCount<Key: Hashable>(for key: Key, from sets: [Set<Key>], in counts: inout [Key: Int]) {
  var countRef = MutableRef(&counts[key, default: 0])
  for set in sets {
    if set.contains(key) {
      countRef.value += 1
    }
  }
}
```

## Open-source Swift ecosystem direction

The session closes by emphasizing that these features are developed in open source across Apple OSes, Linux, Windows, Android, and other environments. Swift Build, the build system from Xcode, is now the default build-system backend for Swift Package Manager, improving consistency between package builds and Xcode builds.

New and continuing workgroups include build and packaging, networking, Windows, and Android. The Android workgroup released the first Swift SDK for Android as part of Swift 6.3, enabling shared Swift code between Android and iOS apps. Developers are encouraged to follow and participate through the Swift Forums.

Resources:
- Swift Blog: https://www.swift.org/blog/
- Explore documentation on swift.org: https://www.swift.org/documentation/
- Swift Forums: https://forums.swift.org/

Chapters:
- 0:07 Introduction: Introduces Swift 6.3 and 6.4 updates across language improvements, library updates, cross-platform support, performance tuning, and open-source development.
- 0:44 Everyday Language Improvements: Covers quality-of-life changes such as optional `some`/`any` syntax cleanup, better diagnostics for ignored throwing tasks, async `defer`, `weak let`, explicit `~Sendable`, and improved memberwise initializers.
- 1:55 anyAppleOS Availability: Shows how `@available(anyAppleOS ...)` and `#if os(anyAppleOS)` reduce repeated platform availability annotations while still allowing platform-specific exceptions.
- 3:02 @diagnose Attribute: Explains how `@diagnose` changes selected warning behavior inside a single declaration, including suppressing deprecations, enabling strict memory safety warnings, or promoting future-version warnings to errors.
- 3:52 Module Selectors (::): Introduces `Module::name` syntax for unambiguously selecting APIs from a module when imports contain conflicting type or member names, and recommends it for conflicts rather than intentional API design.
- 5:59 Library Updates: Transitions from language changes to updates in the standard library, Swift Testing, Subprocess, and Foundation.
- 6:16 Standard Library: Highlights task cancellation shields, `Dictionary.mapKeyedValues`, and the new standard-library `FilePath` type for cross-platform path handling.
- 7:31 Swift Testing Updates: Describes warning-severity issues, dynamic test cancellation, flaky-test repetition in `swift test`, and improved two-way interoperability between Swift Testing and XCTest.
- 9:29 Subprocess 1.0: Announces Subprocess 1.0 with refined execution APIs, better errors, output streaming, line-by-line string reading, and improved cross-platform process semantics.
- 10:14 Foundation: Introduces `ProgressManager` for async-aware progress reporting and summarizes Swift-Foundation migration work that improves `Data`, `NSURL`, and `CFURL` performance and memory use.
- 11:59 Beyond Apple Platforms: Frames Swift 6.4 improvements for broader software stacks, including services, devices, web, Android, and embedded environments.
- 12:35 Swift-C Interoperability (@C attribute): Explains exporting Swift functions to C with `@C`, using `@implementation` for existing C declarations, and relying on safe span-based interop for C-compatible APIs.
- 15:09 Swift-Java: Summarizes Swift-Java improvements for async and throwing Swift calls from Java, constrained extensions, protocol conformance from Java classes, Kotlin/Android use, and the Swift SDK for Android.
- 16:03 Editor support: Covers Swift VSCode extension updates, Swiftly toolchain installation, OpenVSX availability, and onboarding features for creating, testing, running, and documenting Swift projects.
- 16:44 WebAssembly (Wasm) & JavascriptKit: Describes compiling Swift to WebAssembly and JavascriptKit improvements that make Swift-to-JavaScript bridging safer and much faster, with Goodnotes cited as a real-world web reuse example.
- 18:08 Embedded Swift: Details Embedded Swift additions including existential types, untyped throws, DWARF debug metadata for coredump debugging, and `EmbeddedRestrictions` diagnostics.
- 19:59 Performance Tuning: Introduces advanced performance work focused on explicit optimizer control and ownership-system features that avoid unnecessary copies while preserving safety.
- 21:29 Optimizer Control: @inline(always) & @specialized: Explains when inlining and specialization help or hurt, then introduces `@inline(always)` and `@specialized(where ...)` as explicit controls for hot generic or call-site-specific code.
- 24:29 Ownership System & Noncopyable Types: Reviews borrowing and mutation as safe alternatives to copies or unsafe pointers, then notes expanded protocol support for noncopyable and non-escapable types.
- 26:18 Iterable Protocol & Borrow/Mutate Accessors: Introduces `Iterable` for borrow-based `for` loops over batched spans and new `borrow`/`mutate` accessors for computed properties that read or mutate storage without copying.
- 28:57 New Standard Library Types: UniqueBox, UniqueArray, Ref: Presents standard-library ownership tools including `UniqueBox`, `UniqueArray`, safe temporary allocation with `OutputSpan`, single-resume `Continuation`, and `Ref`/`MutableRef` for non-escapable borrowed access.
- 31:11 The Future of Swift: Summarizes open-source ecosystem progress including Swift Build as Swift Package Manager's default backend, workgroups for build, networking, Windows, and Android, and participation through Swift Forums.

### Build real-time apps and services with gRPC and Swift

- Session ID: wwdc2026-265
- Page: https://wwdc.ai/2026/265
- Markdown: https://wwdc.ai/2026/265.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/265/
- Category: Swift
- Description: Use gRPC Swift, Protobuf, Swift concurrency, and HTTP/2 transports to build typed unary and bidirectional streaming app-to-server APIs.
- Duration: 24:25
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-265/eng_bbac5458fd46/wwdc2026-265-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/265/4/05249c6d-4136-4164-a8d0-5db0bbb22c7f/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/265/4/05249c6d-4136-4164-a8d0-5db0bbb22c7f/downloads/wwdc2026-265_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/265/4/05249c6d-4136-4164-a8d0-5db0bbb22c7f/downloads/wwdc2026-265_sd.mp4?dl=1

Use gRPC Swift, Protobuf, Swift concurrency, and HTTP/2 transports to build typed unary and bidirectional streaming app-to-server APIs.

TLDR:
- gRPC Swift generates typed Swift clients, servers, and Protobuf messages from .proto service definitions, reducing hand-written networking code.
- The session builds an iOS go-kart app that first calls a unary ListRaces RPC, then adds a bidirectional FollowRace stream for live kart positions and standings.
- Client lifecycle matters: share a GRPCClient across views, connect lazily, reuse connections, and shut down gracefully when the app backgrounds.
- A Swift gRPC server can be containerized, deployed to a cloud HTTP/2 endpoint, and targeted from the app over TLS.

## Why gRPC Swift

gRPC models APIs as remote functions with typed inputs and outputs instead of hand-written HTTP endpoint code. A .proto file becomes the source of truth, and generated Swift code provides the client, server protocol, and message types.

The package is built around Swift concurrency and supports unary, client streaming, server streaming, and bidirectional streaming RPCs. Protobuf provides both the service definition language and a compact binary message format.

- Use gRPC when a typed service contract and generated Swift call sites are preferable to manually maintaining request/response code.
- Use unary RPCs for single request/single response operations such as fetching a schedule.
- Use streaming RPCs for live data, telemetry, commentary, subscriptions, or other ongoing exchanges.

### Unary service definition in Protobuf

Defines a typed ListRaces function and the request/response messages used by generated Swift code.

```swift
edition = "2024";
import "google/protobuf/timestamp.proto";

service SwiftKartService {
  rpc ListRaces(ListRacesRequest) returns (ListRacesResponse);
}

message ListRacesRequest {
  int32 limit = 1 [default = 100];
}

message ListRacesResponse {
  repeated Race races = 1;
}

message Race {
  string name = 1;
  string location = 2;
  google.protobuf.Timestamp start_time = 3;
  int32 laps = 4;
  string championship = 5;
}
```

## Generate and call a gRPC client from an app

The app adds grpc-swift-nio-transport for SwiftNIO-based HTTP/2 networking and grpc-swift-protobuf for the GRPCProtobufGenerator build plugin. In Xcode, the plugin is added to the target's Run Build Tool Plug-ins phase and scans the target for .proto files.

For an app target, the generator can be configured to emit only messages and client stubs. The generated service client wraps a lower-level GRPCClient that knows how to connect to the server.

- Import GRPCCore, GRPCNIOTransportHTTP2, and SwiftProtobuf in app code that calls the generated client.
- Create a transport using an address or DNS target and a transport security mode.
- Instantiate the generated service client with SwiftKartService.Client(wrapping: client), build a request, await the RPC, and map Protobuf messages into view state.

### Generator config for an app target

Generates client and message code but omits server code for the iOS app.

```text
{
  "generate": {
    "clients": true,
    "servers": false,
    "messages": true
  }
}
```

### Call ListRaces from SwiftUI

Creates a local gRPC client, calls the generated unary RPC, and updates SwiftUI state.

```swift
.task {
  do {
    try await withGRPCClient(
      transport: .http2NIOTS(
        address: .ipv4(host: "127.0.0.1", port: 8080),
        transportSecurity: .tls
      )
    ) { client in
      let kart = SwiftKartService.Client(wrapping: client)
      let response = try await kart.listRaces(ListRacesRequest())
      self.races = response.races.map { race in
        RaceInfo(
          name: race.name,
          location: race.location,
          startTime: race.startTime.date,
          championship: race.championship,
          laps: Int(race.laps),
          drivers: race.drivers
        )
      }
    }
  } catch {
    print("gRPC error: \(error)")
  }
}
```

## Manage the client lifecycle

Creating a new gRPC client each time a view appears forces each view to establish its own connection, adding avoidable latency. The session moves the client behind a shared ClientManager, injects it through the SwiftUI environment, and disconnects when the scene enters the background.

The manager connects lazily, stores connection state behind Synchronization.Mutex, starts the client's connection task with runConnections(), and shuts down with beginGracefulShutdown().

- Create one shared manager at the app entry point and pass it with .environment(manager).
- Read it in child views using @Environment(ClientManager.self).
- On scenePhase == .background, call manager.disconnect() to free resources.
- Use manager.withClient &#123; ... &#125; instead of constructing a new client in each view.

### Propagate and background-disconnect a shared client manager

Shares the client manager across views and disconnects it when the app backgrounds.

```swift
@main
struct SwiftKartApp: App {
  let manager = ClientManager()
  @Environment(\.scenePhase) private var scenePhase

  var body: some Scene {
    WindowGroup {
      RaceScheduleView()
        .environment(manager)
    }
    .onChange(of: scenePhase) { _, newPhase in
      if newPhase == .background {
        manager.disconnect()
      }
    }
  }
}
```

### Use the injected manager

Reuses the shared client connection instead of constructing a per-view client.

```swift
@Environment(ClientManager.self) var manager

.task {
  do {
    try await manager.withClient { client in
      let kart = SwiftKartService.Client(wrapping: client)
      let response = try await kart.listRaces(ListRacesRequest())
      // map response into view state
    }
  } catch {
    print("gRPC error: \(error)")
  }
}
```

## Protobuf messages and streaming RPCs

Protobuf messages are generated as Swift types through SwiftProtobuf. When sent over gRPC, messages are serialized to a binary representation that uses numeric field identifiers rather than field names, making the payload smaller than equivalent JSON in the session's example.

The live race feature uses a bidirectional streaming RPC. The client sends subscription updates indicating which event types it wants, and the server continuously sends matching race events back.

- A unary RPC sends one request and receives one response.
- A client streaming RPC sends many requests and receives one response.
- A server streaming RPC sends one request and receives many responses.
- A bidirectional streaming RPC allows both sides to send any number of messages.

### Bidirectional FollowRace definition

Adds a client-to-server subscription stream and a server-to-client event stream.

```text
service SwiftKartService {
  rpc ListRaces(ListRacesRequest) returns (ListRacesResponse);
  rpc FollowRace(stream FollowRaceRequest) returns (stream FollowRaceResponse);
}

message FollowRaceRequest {
  string race_name = 1;
  repeated RaceEventType event_types = 2;
}

enum RaceEventType {
  RACE_EVENT_TYPE_UNSPECIFIED = 0;
  RACE_EVENT_TYPE_KART_LOCATIONS = 1;
  RACE_EVENT_TYPE_STANDINGS = 2;
}

message FollowRaceResponse {
  oneof event {
    KartLocations locations = 1;
    Standings standings = 2;
  }
}
```

### Create and serialize a SwiftProtobuf message

Shows generated Swift message types and binary serialization.

```swift
var race = Race()
race.name = "Duck Pond Dash"
race.location = "Apple Park, Cupertino"
race.startTime = .init(roundingTimeIntervalSince1970: 1_781_198_600)
race.laps = 6
race.championship = "Corporate Cup"
race.drivers = ["Monty", "Pepper", "Mycroft", "Pancakes", "Duke", "Kiko", "Sissi", "Bo"]
try race.serializedBytes()
```

## Implement the Swift server and live app stream

On the server, GRPCServer is initialized with an HTTP/2 transport and an array of service implementations. The service type conforms to a generated protocol; unary RPCs are async functions, while streaming RPCs use async sequences for incoming requests and RPCWriter for outgoing responses.

The FollowRace implementation reads the first request to determine the race and initial subscriptions, uses a mutex-protected set of event types shared across tasks, filters tracker events, writes matching responses, and keeps consuming request messages so the client can update its subscriptions.

- The server-side request parameter for FollowRace is RPCAsyncSequence&lt;FollowRaceRequest, any Error&gt;.
- The response parameter is RPCWriter&lt;FollowRaceResponse&gt;.
- A task group allows request consumption and event production to proceed concurrently.
- The client-side generated call has separate closures for writing request messages and reading response messages.

### Start a gRPC Swift server

Runs a local Swift gRPC server over HTTP/2.

```swift
let server = GRPCServer(
  transport: .http2NIOPosix(
    address: .ipv4(host: "127.0.0.1", port: 8080),
    transportSecurity: .plaintext
  ),
  services: [Service()]
)

try await server.serve()
```

### Server service protocol implementation

Implements the generated server protocol for a unary RPC.

```swift
struct Service: SwiftKartService.SimpleServiceProtocol {
  private let database = RaceDB()

  func listRaces(
    request: ListRacesRequest,
    context: ServerContext
  ) async throws -> ListRacesResponse {
    var response = ListRacesResponse()
    response.races = await database.listRaces(atMost: request.limit)
    return response
  }
}
```

## Deploy and target the production service

The Swift server is packaged into a Linux container image using a multi-stage Containerfile. The builder stage compiles the server in release mode, and the runtime stage copies only the executable into a smaller swift:slim image.

The example deploys to Google Cloud Run with HTTP/2 enabled and unauthenticated access allowed, then updates the app's transport target to the deployment DNS name and switches transport security to TLS.

- Most cloud platforms can host the server, but deployment commands and configuration differ.
- HTTP/2 must be enabled for the gRPC service endpoint used in the demo.
- For the deployed client target, use a DNS host and TLS instead of a local address and plaintext transport.

### Containerfile for the Swift server

Builds the server executable in one stage and runs it from a smaller Swift runtime image.

```text
FROM swift:latest AS builder
WORKDIR /app
COPY Package.swift Package.resolved .
COPY Sources/ Sources/
RUN swift build -c release --product server
RUN cp "$(swift build -c release --show-bin-path)/server" /usr/bin/server

FROM swift:slim
COPY --from=builder /usr/bin/server /usr/bin/server
EXPOSE 8080
ENTRYPOINT ["/usr/bin/server"]
```

### Deploy to Cloud Run and point the app at the service

Deploys the service with HTTP/2 and configures the app to connect to the deployed TLS endpoint.

```text
gcloud run deploy wwdc-demo-server \
  --image us-central1-docker.pkg.dev/wwdc26/wwdc-demo-server/wwdc-demo-server:latest \
  --region us-central1 \
  --use-http2 \
  --allow-unauthenticated

static func makeTransport() throws -> HTTP2ClientTransport.TransportServices {
  try .http2NIOTS(
    target: .dns(host: "wwdc-demo-server-863666503339.us-central1.run.app"),
    transportSecurity: .tls
  )
}
```

Resources:
- About gRPC: https://grpc.io/
- gRPC Swift Extras: https://github.com/grpc/grpc-swift-extras
- gRPC Swift Protobuf: https://github.com/grpc/grpc-swift-protobuf
- gRPC Swift NIO Transport: https://github.com/grpc/grpc-swift-nio-transport
- gRPC Swift: https://github.com/grpc/grpc-swift
- Swift on Server: https://www.swift.org/server/

Chapters:
- 0:00 Introduction: Introduces the problem of hand-written networking code and positions generated service clients from a specification as a safer workflow. The session scope covers simple requests, streaming RPCs, service implementation, and cloud deployment with gRPC Swift.
- 1:39 Meet gRPC: Defines gRPC as a CNCF remote procedure call framework where APIs are described as functions with inputs and outputs. It contrasts the model with HTTP-endpoint-oriented APIs while preserving the benefit of generated code.
- 2:13 App overview and demo setup: Introduces a go-karting iOS app with prebuilt views and mock data. The demo goal is to replace static race information with live data from a Swift gRPC backend.
- 3:30 Defining the ListRaces RPC: Creates a .proto service definition with a unary ListRaces RPC, request and response messages, field numbers, repeated fields, and a Protobuf timestamp well-known type.
- 4:30 Setting up Xcode to generate gRPC code: Adds grpc-swift-nio-transport and grpc-swift-protobuf dependencies, enables the GRPCProtobufGenerator build plugin, and configures generation for client and message code. The generated client is then used to call the local server from SwiftUI.
- 7:50 Managing the gRPC client lifecycle: Replaces per-view client creation with a shared ClientManager injected through the SwiftUI environment. The manager reuses connections and disconnects when the app enters the background.
- 9:36 Protobuf message format and binary efficiency: Shows SwiftProtobuf message construction and explains Protobuf binary serialization using field numbers. The session notes the reduced payload size compared with JSON and its value for mobile and service-to-service communication.
- 12:33 Implementing a bidirectional streaming RPC: Adds a FollowRace bidirectional streaming RPC for live kart locations and standings. The server implementation uses async sequences, RPCWriter, a task group, and a mutex-protected subscription set; the app uses an AsyncStream to send subscription changes and updates UI state from streamed responses.
- 20:11 Deploying the service: Packages the Swift server into a multi-stage container image and deploys it to Google Cloud Run with HTTP/2 enabled. The app is updated to connect to the deployed DNS endpoint over TLS.
- 23:11 Next steps: Recaps the end-to-end gRPC Swift workflow and points to production-oriented capabilities such as Swift OTel integration, Swift service lifecycle, custom transports, name resolvers, and client-side load balancing. The open-source project and tutorials are recommended for further exploration.

### Migrate to Swift Testing

- Session ID: wwdc2026-267
- Page: https://wwdc.ai/2026/267
- Markdown: https://wwdc.ai/2026/267.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/267/
- Category: Swift
- Description: Adopt Swift Testing incrementally alongside XCTest using interoperability modes, migration patterns, parameterized tests, and exit tests.
- Duration: 21:25
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-267/eng_c82547cdf0f7/wwdc2026-267-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/267/4/d54e4861-10d9-4d4d-9952-3fe311cd2dc4/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/267/4/d54e4861-10d9-4d4d-9952-3fe311cd2dc4/downloads/wwdc2026-267_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/267/4/d54e4861-10d9-4d4d-9952-3fe311cd2dc4/downloads/wwdc2026-267_sd.mp4?dl=1

Adopt Swift Testing incrementally alongside XCTest using interoperability modes, migration patterns, parameterized tests, and exit tests.

TLDR:
- Swift Testing can coexist with XCTest in the same test target; keep existing XCTests in place and write new tests with @Test, #expect, and related Swift Testing APIs.
- Xcode 27 enables test framework interoperability by default so Swift Testing tests can surface XCTest assertion failures, and XCTests can use supported Swift Testing issue APIs.
- Interoperability modes control cross-framework issue behavior: Limited warns, Complete preserves errors, Strict traps on XCTest cross-framework issues, and None disables reporting temporarily.
- After migration, use Swift Testing features such as parameterized tests for parallel input combinations and exit tests for code paths that intentionally crash.

## Migration approach

The recommended strategy is incremental migration rather than rewriting all existing XCTests. Leave current XCTest cases in place, start writing new tests with Swift Testing, and migrate tests that you touch frequently or that benefit from Swift Testing features.

A test target can contain both XCTest and Swift Testing tests, but Swift Testing tests do not go inside XCTest classes. XCTest remains necessary for UI automation, performance testing APIs, and Objective-C exception testing, where Swift cannot safely handle exceptions.

- Import the Testing framework and declare tests with the @Test macro.
- Use raw identifiers with backticks for readable test names containing spaces or punctuation.
- Use #expect for most assertions; use Issue.record for unconditional failures that replace XCTFail.

### Basic Swift Testing test

Shows the core Swift Testing shape: import Testing, mark a function with @Test, and assert behavior with #expect.

```swift
import Testing
@testable import DemoApp

@Test
func `Default climate: tropical`() async throws {
    let fruit = Fruit(name: "Coconut")
    #expect(fruit.climate == .tropical)
}
```

## Test framework interoperability

Test framework interoperability lets code from one test framework safely report issues while running inside a test owned by the other framework. This is useful when new Swift Testing tests reuse existing helper functions that still call XCTest APIs, or when shared helpers are migrated to Swift Testing APIs while still being used by XCTests.

A common migration path is to update helper functions from XCTest issue reporting to Swift Testing issue reporting. For helpers that previously forwarded file and line to XCTFail, migrate to SourceLocation and Issue.record.

- Cross-framework issues from XCTest in Swift Testing tests are handled according to the selected interoperability mode.
- Cross-framework issues from Swift Testing in XCTests remain errors in all interoperability modes.
- Supported interoperability includes XCTest assertions, Swift Testing #expect and #require, Swift Testing known-issue APIs for XCTest assertion failures, and Test.cancel for skipping XCTest cases.

### XCTest helper before migration

An existing helper wraps XCTFail and can be reused during migration, but may create cross-framework issues when called from Swift Testing tests.

```swift
func assertUnique(_ fruits: [Fruit], file: StaticString = #filePath, line: UInt = #line) {
    var uniqueNames = Set<String>()
    for name in fruits.map(\.name) {
        if !uniqueNames.insert(name).inserted {
            XCTFail("Duplicate name: \(name)", file: file, line: line)
        }
    }
}
```

### Swift Testing helper after migration

Replacing XCTFail with Issue.record lets the same helper report failures correctly from both Swift Testing tests and XCTests under interoperability.

```swift
import Testing

func assertUnique(_ fruits: [Fruit], sourceLocation: SourceLocation = ...) {
    var uniqueNames = Set<String>()
    for name in fruits.map(\.name) {
        if !uniqueNames.insert(name).inserted {
            Issue.record("Duplicate name: \(name)", sourceLocation: sourceLocation)
        }
    }
}
```

## Interoperability modes and configuration

Interoperability modes determine how cross-framework issues are reported. Test plans created before Xcode 27 inherit Limited mode, while new projects use Complete mode. The mode can be changed in Xcode Test Plan Settings under Test Execution.

Swift Package projects also support interoperability modes. With the Swift 6.4 toolchain, limited mode is enabled by default for packages using older tools versions; updating the package to swift-tools-version 6.4 or newer changes the default behavior to Complete. The mode can also be overridden from the command line.

- Limited: cross-framework issues from XCTest are warnings, so the Swift Testing test can still pass.
- Complete: those issues remain errors, making failures harder to miss.
- Strict: cross-framework issues from XCTest stop the test with a fatal error, helping find places to replace XCTest APIs.
- None: opts out of interoperability and should only be temporary because it can hide real failures.

### Run Swift Package tests in strict mode

Use the SWIFT_TESTING_XCTEST_INTEROP_MODE environment variable with a lowercase mode name to override package test behavior.

```text
SWIFT_TESTING_XCTEST_INTEROP_MODE=strict swift test
```

## Common migration patterns

Several XCTest patterns have direct Swift Testing replacements, but the preferred Swift Testing style often moves intent into the test declaration or into individual assertions.

For skipped tests, Test.cancel can replace XCTSkip-style logic, including in XCTests through interoperability. In new Swift Testing tests, prefer enabled or disabled traits so test availability is visible before the test body runs. For halting after a failed assertion, replace XCTest's continueAfterFailure = false style with #require at the specific point where execution should stop.

- Use Test.cancel when cancellation must happen from inside the test body.
- Prefer @Test(.enabled(if:reason)) or disabled traits for Swift Testing enablement rules.
- Use #expect for non-halting checks and #require for checks that must stop the test on failure.

### Skip migration: body cancellation vs. trait

Traits keep test enablement logic at the declaration instead of hiding it inside the test body.

```swift
let isFall = false

// Interoperable Swift Testing API
func testSwallowFallMigration() async throws {
    if !isFall {
        try Test.cancel("Wrong season for migration")
    }
    // ...
}

// Prefer this for new Swift Testing tests
@Test(.enabled(if: isFall, "Wrong season for migration"))
func `Swallow fall migration`() async throws {
    // ...
}
```

### Use #require to halt after a failure

#require throws when it fails, stopping the test only at the checks that must be fatal.

```swift
func testExample() async throws {
    #expect(Fruit.banana.climate == .temperate)
    try #require(Fruit.banana == Fruit.plantain)
    XCTFail("This is never reached")
}
```

## Swift Testing features worth adopting

Parameterized tests replace loop-based tests with separate generated test cases for each input combination. Swift Testing runs test cases in parallel by default, and each failing argument combination is visible in the Test navigator, which improves both speed and diagnostics.

Exit tests cover code paths that intentionally terminate the process, such as preconditionFailure. Swift Testing runs the exit-test body in a child process, allowing the code to crash without disrupting the rest of the suite, then verifies the process exit condition. Exit tests are supported on macOS, Linux, FreeBSD, and Windows.

- Use @Test(arguments:) to express repeated test inputs instead of nested loops.
- Each argument combination becomes an individual test case with clearer failure reporting.
- Use #expect(processExitsWith:) when the expected behavior is process termination.

### Convert nested loops to a parameterized test

The @Test(arguments:) form generates combinations for birds and counts and can run them in parallel.

```swift
struct BirdTests {
    @Test(arguments: Aviary.birds, 40...100)
    func `Birds flap wings successfully`(bird: Bird, count: Int) async throws {
        try await bird.flapWings(count: count)
    }
}
```

### Cover a precondition failure with an exit test

The initializer is expected to terminate the child process, and the test passes when the process exits with failure.

```swift
extension BirdTests {
    @Test
    func `Bird with empty name crashes`() async throws {
        await #expect(processExitsWith: .failure) {
            _ = Bird(name: "")
        }
    }
}
```

Chapters:
- 0:07 Introduction: Introduces the goal of migrating from XCTest to Swift Testing and previews incremental adoption, interoperability, and advanced Swift Testing features.
- 1:08 Swift Testing basics: Reviews @Test, raw-identifier test names, #expect, and Issue.record, and explains where XCTest remains necessary.
- 2:50 Migration strategy: Recommends leaving most existing XCTests unchanged while adding new Swift Testing tests in the same target and migrating old tests gradually.
- 5:48 Test framework interoperability: Explains cross-framework issues and demonstrates why existing XCTest helpers such as those wrapping XCTFail need interoperability when called from Swift Testing tests.
- 7:43 Interoperability modes: Covers Limited, Complete, Strict, and None modes, how they affect cross-framework XCTest issues, and how to configure them in Xcode test plans or Swift packages.
- 13:02 Common migration patterns: Shows replacements for common XCTest patterns, including using Test.cancel or traits for skipped tests and #require for halting after a failed check.
- 15:34 Parameterized tests: Refactors nested-loop test logic into @Test(arguments:) so Swift Testing can create separate, parallel test cases with clearer failing-input reporting.
- 18:02 Exit tests: Demonstrates testing a preconditionFailure path by running the crashing code in a child process and expecting a failing process exit.
- 20:04 Next steps: Recaps the migration path, notes Swift Testing's open-source SwiftLang project and community process, and points to further Swift Testing resources.

### Profile, fix, and verify: Improve app responsiveness with Instruments

- Session ID: wwdc2026-268
- Page: https://wwdc.ai/2026/268
- Markdown: https://wwdc.ai/2026/268.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/268/
- Category: Developer Tools
- Description: Use Instruments 27 to diagnose app hangs with Time Profiler, Top Functions, Run Comparisons, Swift executors, and System Trace.
- Duration: 26:43
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-268/eng_72b6ca3ffcb8/wwdc2026-268-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/268/4/7d94575d-e65b-4033-811f-199586ac587a/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/268/4/7d94575d-e65b-4033-811f-199586ac587a/downloads/wwdc2026-268_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/268/4/7d94575d-e65b-4033-811f-199586ac587a/downloads/wwdc2026-268_sd.mp4?dl=1

Use Instruments 27 to diagnose app hangs with Time Profiler, Top Functions, Run Comparisons, Swift executors, and System Trace.

TLDR:
- Start responsiveness investigations with Time Profiler, profile a release build, and decide whether the main thread is CPU-saturated or blocked while idle.
- Use OS signpost intervals, Top Functions, flame graphs, and Run Comparisons to isolate scattered CPU overhead and verify fixes across traces.
- Use the Swift executors instrument to find Main Actor congestion; move non-UI async work to the global executor with `@concurrent` when safe.
- Use System Trace and the Inspector to diagnose low-CPU hangs caused by synchronous system calls such as large file writes on the main thread.

## Diagnostic flow for hangs and frame drops

The session presents a practical responsiveness workflow: capture a release-profile trace, identify the affected time interval, inspect main-thread CPU behavior, then choose the right Instruments view for the symptom.

High CPU on the main thread means the app is executing work that takes too long. Fixes are either algorithmic optimization or moving unavoidable work off the UI path. Low CPU during a UI freeze usually means the main thread is blocked waiting on a resource such as file I/O, a lock, or IPC; Time Profiler alone cannot explain those waits.

- Profile from Xcode with Product &gt; Profile so Instruments records a release build rather than a debug build.
- Use the Swift Concurrency template when the app uses Swift Concurrency; it still includes Time Profiler.
- Add `os_signpost` intervals around user-visible workflows so traces and run comparisons can be filtered to equivalent regions.
- Use Time Profiler for active CPU work, Swift executors for actor/executor contention, and System Trace for blocked or off-core thread time.

### Add a signpost interval around lasso selection

Signpost intervals appear in the Points of Interest track and provide reliable ranges for filtering and comparing profiling data.

```swift
import os.signpost

let signposter = OSSignposter(subsystem: "Demo App", category: .pointsOfInterest)
var lassoIntervalState: OSSignpostIntervalState? = nil

func lassoSelectionUpdated() {
    lassoIntervalState = signposter.beginInterval("Lasso Selection")
    // Update selection in canvas...
}

func lassoSelectionEnded() {
    // Finalize lasso selection...
    signposter.endInterval("Lasso Selection", lassoIntervalState!)
}
```

## Analyze CPU saturation with Time Profiler

Time Profiler samples call stacks at regular intervals, producing a call tree where weight represents how often a function appears in samples and self weight represents time spent executing directly in that function. Instruments can render the same sampling data as an outline call tree, a flame graph, or Top Functions.

Flame graphs are useful for scanning expensive call paths, but costs for widely used functions can be split across many branches. Top Functions removes the call hierarchy, merges scattered nodes, and sorts functions by self weight, making hidden hotspots easier to spot.

- In the lasso-selection example, the main thread stayed near 100% CPU, indicating expensive code rather than system blocking.
- Top Functions identified `swift_project_boxed_opaque_existential` as the largest self-time contributor.
- The fix was to reduce existential overhead in drawing code by using concrete types and generics where performance mattered.
- Run Comparisons verified the optimized trace against the baseline over the same signposted interval; green marks improvements and red marks regressions or newly introduced work.

### Existential parameter with runtime overhead in a hot path

An existential can hold any conforming type, but accessing the underlying value may require runtime work that can matter in tight loops.

```swift
protocol Foo { }
struct TypeA: Foo { }
struct TypeB: Foo { }

func bar(_ foo: any Foo) { }
```

### Alternatives that give the compiler more type information

Concrete overloads, generics, and enums can avoid some existential costs and enable stronger optimization in performance-critical code.

```swift
protocol Foo { }
struct TypeA: Foo { }
struct TypeB: Foo { }

func bar(_ a: TypeA) { }
func bar(_ b: TypeB) { }

func bar<T: Foo>(_ generic: T) { }

enum FooValue {
    case a(TypeA)
    case b(TypeB)
}

func bar(_ value: FooValue) { }
```

## Find executor contention with the Swift executors instrument

The Swift executors instrument visualizes the Main Actor, the global concurrent executor, and custom executors. It helps connect UI responsiveness problems to Swift tasks running on specific executors.

In the scrolling example, several `renderThumbnail` tasks ran for hundreds of milliseconds on the Main Actor. Because the code was called from SwiftUI, it inherited the Main Actor context, causing thumbnail rendering to compete with UI updates and interactions.

- Filter to the hang, then inspect the Main Actor and main-thread CPU usage.
- If the main thread is busy and long-running tasks appear on the Main Actor, move non-UI work off the actor rather than letting it compete with UI events.
- Adding `@concurrent` to the task body routes the work to the global executor; the Swift compiler checks for race-safety issues introduced by the change.
- The updated trace showed thumbnail rendering moving from the Main Actor track to the global executor track, enabling parallel rendering and preventing UI hangs.

### Thumbnail rendering inherited by the Main Actor

Because the task is created from Main Actor-isolated code, the work can remain on the Main Actor and block UI progress.

```swift
let drawingData = note.drawingData
let canvasImages = note.decodeCanvas()

thumbnail = await Task(name: "Render Thumbnail") {
    await renderThumbnail(
        drawingData: drawingData,
        canvasImages: canvasImages,
        size: CGSize(width: 300, height: 240)
    )
}.value
```

### Move thumbnail rendering off the Main Actor

`@concurrent` moves the task body to the global executor so CPU-heavy non-UI work does not monopolize the Main Actor.

```swift
let drawingData = note.drawingData
let canvasImages = note.decodeCanvas()

thumbnail = await Task(name: "Render Thumbnail") { @concurrent in
    await renderThumbnail(
        drawingData: drawingData,
        canvasImages: canvasImages,
        size: CGSize(width: 300, height: 240)
    )
}.value
```

## Diagnose low-CPU hangs with System Trace

A UI hang with low main-thread CPU indicates the thread may be blocked, not slow. System Trace shows thread states, system calls, on-core execution, off-core blocking, and runnable time after a resource becomes available.

In the save workflow, the main thread used only about 20% CPU during a micro-hang. System Trace showed a large write system call with translucent off-core segments, and the Inspector revealed a write of more than 1.7 GB that took over 500 ms, with almost 300 ms spent waiting for disk.

- Use System Trace when Time Profiler shows little CPU activity during a hang.
- Pin the main thread in the Inspector and inspect activity lanes for blank/off-core time.
- Select syscall intervals to see their full duration across active and blocked portions.
- Use Inspector details such as syscall arguments, file descriptor, buffer address, size, and timing to identify synchronous blocking work.

### Synchronous file save on the main thread

The atomic `data.write` call blocks the caller until storage responds; on the main thread this freezes UI interactions.

```swift
let encoder = PropertyListEncoder()
encoder.outputFormat = .binary

guard let data = try? encoder.encode(snapshots) else { return }

let id = signposter.beginInterval("Writing To File")
try? data.write(to: fileURL, options: .atomic)
signposter.endInterval("Writing To File", id)
```

### Move encoding and file I/O to a concurrent task

Running the save work on the concurrent thread pool removes the write syscall from the main thread and unblocks the Main Actor.

```swift
Task { @concurrent in
    let encoder = PropertyListEncoder()
    encoder.outputFormat = .binary

    guard let data = try? encoder.encode(snapshots) else { return }

    let id = signposter.beginInterval("Writing To File")
    try? data.write(to: fileURL, options: .atomic)
    signposter.endInterval("Writing To File", id)
}
```

## Verification and practical takeaways

The workflow is iterative: profile, narrow the interval, form a hypothesis from the right instrument, fix the code, then verify with another trace. Instruments 27 improves that loop with the Inspector, Top Functions, Swift executors visualization, and Run Comparisons saved in the document.

The final guidance is to match the tool to the symptom: Top Functions and Run Comparisons for CPU overload, Swift executors for actor congestion, and System Trace for idle or blocked threads.

- Always profile a release build for actionable performance data.
- Use signpost intervals to compare equivalent workflows across runs and reduce measurement noise.
- Treat new red functions in Run Comparisons carefully; they may be regressions or simply new code introduced by a refactor.
- Consult related material for deeper dives: CPU profiling with Instruments, Swift Concurrency adoption, hang analysis, and Swift generics.

Resources:
- Analyzing CPU profiles with call tree views: https://developer.apple.com/documentation/Xcode/analyzing-cpu-profiles-with-call-tree-views

Chapters:
- 0:00 Introduction: Introduces Instruments 27 as a way to reason across app code, Swift runtime behavior, operating system services, and hardware when improving responsiveness. Frames the session around CPU saturation, sampling visualization, execution contention, and system blocking.
- 1:12 Diagnostic flow: Defines the triage workflow for hangs and frame drops: start with Time Profiler, inspect main-thread CPU usage, then distinguish busy CPU work from idle blocking. Demonstrates profiling a release build from Xcode and using `OSSignposter` intervals to mark the lasso-selection workflow.
- 7:06 Sampling data visualization: Explains how Time Profiler samples call stacks and how call trees, flame graphs, and Top Functions expose different views of the same data. Uses Top Functions to identify existential unwrapping overhead, then verifies a generics/concrete-types refactor with Run Comparisons.
- 16:01 Execution contention: Uses the Swift executors instrument to show thumbnail-rendering tasks congesting the Main Actor and causing scrolling hangs. Fixes the issue by adding `@concurrent` so rendering moves to the global executor, freeing UI work and allowing parallel thumbnail generation.
- 20:29 System blocking: Shows that a save-related hang has low CPU usage, indicating the main thread is blocked rather than executing slow code. Uses System Trace and the Inspector to identify a large synchronous file write on the main thread and moves encoding and writing to a concurrent task.
- 26:07 Next steps: Recaps the tool-to-symptom mapping: Top Functions and Run Comparisons for CPU overload, Swift executors for actor congestion, and System Trace for blocking. Reinforces release-build profiling and signpost intervals as essential practices for accurate measurement.

### What's new in SwiftUI

- Session ID: wwdc2026-269
- Page: https://wwdc.ai/2026/269
- Markdown: https://wwdc.ai/2026/269.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/269/
- Category: SwiftUI & UI Frameworks
- Description: SwiftUI 2026 updates for Liquid Glass, document apps, reorderable containers, toolbar control, AsyncImage caching, @State, and ContentBuilder.
- Duration: 28:15
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-269/eng_21f9a1cb9e79/wwdc2026-269-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/269/4/9215cf93-1308-4706-91e8-34d4e40939d1/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/269/4/9215cf93-1308-4706-91e8-34d4e40939d1/downloads/wwdc2026-269_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/269/4/9215cf93-1308-4706-91e8-34d4e40939d1/downloads/wwdc2026-269_sd.mp4?dl=1

SwiftUI 2026 updates for Liquid Glass, document apps, reorderable containers, toolbar control, AsyncImage caching, @State, and ContentBuilder.

TLDR:
- Apps built for the 2027 releases automatically adopt the refreshed Liquid Glass appearance, with new controls for active-window styling, prominent tabs, toolbar overflow, pinned toolbar items, and minimize-on-scroll behavior.
- SwiftUI's expanded document APIs add creation sources, WritableDocument/ReadableDocument, snapshot-based writing, background DocumentWriter/DocumentReader work, progress reporting, and multi-format export such as PNG.
- New interaction APIs support reordering in List, LazyVGrid, and other containers, swipe actions outside List via swipeActionsContainer(), and item-binding confirmation dialogs and alerts.
- Performance improvements include default HTTP caching for AsyncImage, customizable URLRequest/URLSession support, lazy initialization for @State-stored Observable classes, and faster type checking through ContentBuilder.

## Refreshed look, resizability, and toolbars

SwiftUI apps built for the 2027 releases automatically receive the updated Liquid Glass appearance. Liquid Glass responds to the system tint slider, custom Liquid Glass elements can be marked interactive on macOS, and iPad apps get inactive-window dimming similar to Mac.

The session emphasizes testing resizable layouts in Xcode 27 Live Previews, which now include resize handles. Apps that mix UIKit and SwiftUI should prefer size classes and correct screen-geometry handling instead of relying on idiom-based assumptions.

- Use the appearsActive environment value to adjust custom UI when a window is inactive.
- Use .labelStyle(.titleAndIcon) to opt important menu items into showing icons in menu bars that otherwise minimize icon use.
- Use Tab(role: .prominent) for a visually distinct tab such as a cart or primary destination.
- Use toolbar visibility priority, ToolbarOverflowMenu, and topBarPinnedTrailing to control what remains visible when toolbar space is constrained.
- Use toolbarMinimizeBehavior(.onScrollDown, for: .navigationBar) to reclaim vertical space while scrolling.

### Keep important toolbar actions visible

Prioritize editing actions, force infrequent actions into overflow, and pin Share so it is not hidden at narrow sizes.

```text
StickerPageView()
    .toolbar {
        ToolbarItemGroup {
            UndoButton()
            RedoButton()
        }
        .visibilityPriority(.high)

        ToolbarOverflowMenu {
            ChoosePhotoButton()
            ExportAsImageButton()
            ClearAllStickersButton()
        }

        ToolbarItem(placement: .topBarPinnedTrailing) {
            ShareButton()
        }
    }
```

### Minimize the navigation toolbar while scrolling

Lets the system move the navigation bar out of the way as content scrolls down.

```text
ScrollView {
    StickerListView()
}
.toolbarMinimizeBehavior(.onScrollDown, for: .navigationBar)
```

## Document-based app APIs

SwiftUI's document model is expanded for the 2027 releases. A document app still starts from a DocumentGroup, and the document object can use Observation so dependent views update only when the properties they read change.

DocumentCreationSource lets an app expose multiple creation paths in a launch scene. The chosen source is passed through the document creation context, enabling flows such as creating a blank document or creating one from a photo and immediately presenting a photo picker.

- Declare custom DocumentCreationSource values and attach them to NewDocumentButton instances.
- Use the DocumentGroup creation closure's context parameter to initialize the document differently for each source.
- Adopt WritableDocument to describe writable content types, create an immutable snapshot, and provide a DocumentWriter.
- Adopt ReadableDocument for the corresponding read path, with disk work handled by a DocumentReader.
- Add more writable content types, such as .png, and branch in the writer based on UTType conformance.

### Custom document creation sources

Expose multiple new-document buttons and pass the selected source into document initialization.

```swift
@main
struct Stickers: App {
    var body: some Scene {
        DocumentGroupLaunchScene("Create a Sticker Page") {
            NewDocumentButton("New Sticker Page", source: .blank)
            NewDocumentButton("Sticker Page from Photo...", source: .photo)
        }

        DocumentGroup { document in
            StickerPageDocumentView(document)
        } newDocument: { configuration, context in
            StickerPageDocument(configuration: configuration, context: context)
        }
    }
}

extension DocumentCreationSource {
    static let blank = Self(id: "blank")
    static let photo = Self(id: "photo")
}
```

### Snapshot-based writable document

The writer runs asynchronously and nonisolated, can diff against the previous snapshot, and can report progress while writing.

```swift
@Observable
final class StickerDocument: WritableDocument {
    static let writableDocumentTypes: [UTType] = [.stickerDocument]

    @MainActor
    func snapshot(contentType: UTType) async throws -> sending PageSnapshot {
        makeSnapshot()
    }

    func writer(configuration: sending WriteConfiguration) -> sending Writer {
        Writer(contentType: configuration.contentType)
    }
}

struct Writer: DocumentWriter {
    typealias Snapshot = PageSnapshot
    let contentType: UTType

    nonisolated func write(
        snapshot: sending PageSnapshot,
        to destination: URL,
        previous: sending PageSnapshot?,
        progress: consuming Subprogress
    ) async throws {
        // Compare snapshot and previous, report progress, and write to disk.
    }
}
```

## Reordering, swipe actions, and presentations

Reorderable containers let users drag to rearrange items in List, LazyVGrid, and other SwiftUI containers. SwiftUI handles the drag interaction and animations; the app receives a ReorderDifference and applies it to its data source.

Swipe actions are no longer limited to List. A scroll view with a LazyVStack can coordinate row swipe actions by adding swipeActionsContainer(). Confirmation dialogs and alerts also gain the item-binding pattern familiar from sheets.

- Add .reorderable() to the repeated item content and .reorderContainer(for:) to the containing view.
- Use the ReorderDifference to update the backing collection; the sample helper uses OrderedDictionary from Swift Collections.
- Reordering is available for watchOS for the first time through these APIs.
- Use .swipeActions on arbitrary row views and .swipeActionsContainer() on the containing scroll area.
- Use confirmationDialog(_:item:) or alert(_:item:) when presentation should be driven by an optional selected model value.

### Reorder a list or grid

The same reorderable API pattern works across different containers.

```text
List {
    ForEach(stickers) { sticker in
        StickerListItemView(sticker: sticker)
    }
    .reorderable()
}
.reorderContainer(for: Sticker.self) { difference in
    difference.apply(to: &stickers)
}

LazyVGrid(columns: columns) {
    ForEach(stickers) { sticker in
        StickerListItemView(sticker: sticker)
    }
    .reorderable()
}
.reorderContainer(for: Sticker.self) { difference in
    difference.apply(to: &stickers)
}
```

### Swipe actions outside List

Coordinates swipe actions for custom scrollable layouts.

```text
ScrollView {
    LazyVStack {
        ForEach(stickers) { sticker in
            StickerListItemView(sticker: sticker)
                .swipeActions {
                    DeleteButton(sticker: sticker)
                }
        }
    }
}
.swipeActionsContainer()
```

## AsyncImage and @State performance

AsyncImage now supports standard HTTP caching by default, respecting server cache headers without app changes. Apps built with Xcode 27 can customize image loading by supplying a URLRequest and by configuring the URLSession used by AsyncImage.

@State has been converted from a DynamicProperty to a macro. When an Observable class is initialized into @State, SwiftUI now initializes it lazily and only once for the lifetime of the view instead of creating and discarding new instances on repeated view initialization. This behavior is back-ported to the OS releases where @Observable first appeared, starting with iOS 17, macOS 14, and aligned releases.

- Use URLRequest for per-request behavior such as cachePolicy.
- Use asyncImageURLSession(_:) with a custom URLSession and URLCache for longer-lived download configuration.
- If a @State property has a default value and is also assigned in init, remove the default value to avoid the new macro initialization error.

### Customize AsyncImage caching

Use a request-level cache policy and a custom session cache for AsyncImage downloads.

```swift
@Observable
class StickerStore {
    static let imageSession: URLSession = {
        let config = URLSessionConfiguration.default
        config.urlCache = URLCache(
            memoryCapacity: 64 * 1024 * 1024,
            diskCapacity: 256 * 1024 * 1024
        )
        return URLSession(configuration: config)
    }()
}

ForEach(pets) { pet in
    AsyncImage(request: URLRequest(
        url: pet.imageURL,
        cachePolicy: .returnCacheDataElseLoad
    ))
}
.asyncImageURLSession(StickerStore.imageSession)
```

### Fix @State macro initialization

When assigning @State in init, omit the default value from the property declaration.

```swift
struct StickerPageView: View {
    @State private var page: StickerPage
    let title: String

    init(title: String) {
        self.page = StickerPage(title: title)
        self.title = title
    }

    var body: some View { /* ... */ }
}
```

## ContentBuilder and agent skills

SwiftUI improves compile-time performance for complex nested views by unifying common builders under ContentBuilder. This reduces overload search paths for common constructs such as Section, Group, and ForEach, helping avoid expensive type-checking in large view expressions.

ContentBuilder is an evolution of ViewBuilder and can be used with any minimum deployment target when building with Xcode 27. Xcode 27 also includes SwiftUI agent skills: a SwiftUI Specialist Skill for best practices and a What's New in SwiftUI Skill for adopting the 2027 APIs.

- Use @ContentBuilder for builder functions that assemble SwiftUI content.
- Build with Xcode 27 to get the type-checking improvements even when targeting earlier OS releases.
- Agent skills are available in the Xcode 27 Coding Assistant and can be exported for other tools with xcrun agent skills export.

### Use ContentBuilder

ContentBuilder provides a unified builder path for SwiftUI content.

```swift
@ContentBuilder
func stickerLibraryView() -> some View {
    // ...
}
```

### Export Xcode agent skills

Exports the Xcode 27 agent skills as Markdown files that can be imported into other workflows.

```text
xcrun agent skills export
```

Resources:
- State(): https://developer.apple.com/documentation/SwiftUI/State()
- ContentBuilder: https://developer.apple.com/documentation/SwiftUI/ContentBuilder
- Swift Collections on GitHub: https://github.com/apple/swift-collections

Chapters:
- 0:00 Introduction: The session introduces a sticker app used to demonstrate SwiftUI updates across appearance, document support, interaction, and performance. It previews Liquid Glass changes, document APIs, presentation improvements, and data-flow enhancements.
- 2:12 Refreshed look and feel: SwiftUI apps built for the 2027 releases automatically adopt the refreshed Liquid Glass design, including inactive-window styling and interactive custom elements. The chapter also covers Xcode 27 resize previews, prominent tabs, toolbar visibility priority, overflow menus, pinned toolbar placement, and minimize-on-scroll behavior.
- 8:06 Document-based apps: The document section introduces DocumentCreationSource for custom creation flows and expanded document APIs for reading and writing. It explains WritableDocument, ReadableDocument, snapshot-based writing, asynchronous nonisolated DocumentWriter work, progress reporting, previous-snapshot diffing, and adding PNG export with UTType checks.
- 15:18 Presentation and interaction: New reorderable container APIs allow drag reordering in List, LazyVGrid, and other containers, with app-side updates driven by a ReorderDifference. The chapter also shows swipe actions on arbitrary views with swipeActionsContainer and item-binding confirmation dialogs and alerts.
- 19:58 Data flow and performance: AsyncImage gains default HTTP caching and new customization points for URLRequest and URLSession. @State becomes a macro with lazy initialization for Observable classes, and ContentBuilder improves type-checking performance for complex SwiftUI view hierarchies.
- 27:25 Next steps: The closing recommendations are to build with Xcode 27, inspect the refreshed appearance, evaluate the new document APIs for document-based apps, and try the SwiftUI agent skills. The session also notes that the skills can be exported with xcrun agent skills export.

### Code-along: Build powerful drag and drop in SwiftUI

- Session ID: wwdc2026-271
- Page: https://wwdc.ai/2026/271
- Markdown: https://wwdc.ai/2026/271.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/271/
- Category: SwiftUI & UI Frameworks
- Description: Build SwiftUI drag-and-drop interactions with reorderable content, multi-item drag containers, preview formations, and move-aware drop configuration.
- Duration: 15:21
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-271/eng_b276942c7e75/wwdc2026-271-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/271/5/07f08d32-e28e-476f-8ebe-a3600b2e917c/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/271/5/07f08d32-e28e-476f-8ebe-a3600b2e917c/downloads/wwdc2026-271_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/271/5/07f08d32-e28e-476f-8ebe-a3600b2e917c/downloads/wwdc2026-271_sd.mp4?dl=1

Build SwiftUI drag-and-drop interactions with reorderable content, multi-item drag containers, preview formations, and move-aware drop configuration.

TLDR:
- SwiftUI adds a reordering API built around `reorderable` and `reorderContainer`, letting views move items within and across collections by applying a `ReorderDifference`.
- `dragContainer` can customize what data is lifted for a drag, enabling multi-item drags such as moving a stack of Solitaire cards from a single gesture.
- `dragPreviewsFormation` and `dropPreviewsFormation` control how multiple dragged items are visually grouped, with options such as stack-like previews.
- `dragConfiguration`, `dropDestination`, and `dropConfiguration` let sources and destinations negotiate move/copy behavior and reject invalid drops based on app rules.

## Context: SwiftUI drag and drop expands beyond Transferable

The session builds a Solitaire game to demonstrate new SwiftUI drag-and-drop capabilities available in the 2027 releases. It assumes the existing model where app data conforms to `Transferable` and views use `draggable` and `dropDestination` to move content through the system.

The new pieces focus on app-local interactions: reordering ordered content, dragging multiple items from a single gesture, controlling drag/drop preview formation, and configuring whether a transfer should be a move or a copy.

- Use `Transferable` for data that participates in drag and drop.
- Use the new reordering APIs when content has an order that people should be able to rearrange.
- Compose lower-level drag/drop modifiers with `reorderContainer` when the default behavior needs app-specific rules.

## Enable reordering with `reorderable` and `reorderContainer`

The basic reordering model has two parts: mark the items that can be reordered with `reorderable`, then scope the interaction with `reorderContainer`. At the end of an operation, SwiftUI provides a difference that the app applies to its model.

For a single collection preview, the closure can directly apply the difference to an array. For the Solitaire board, one `reorderContainer` wraps all piles and uses a collection identifier type, `Card.Group`, so cards can move across multiple piles.

- Apply `reorderable()` to the `ForEach` or item collection that should participate in reordering.
- Apply `reorderContainer(for:)` around the shared container and update model state from the provided difference.
- When multiple reorderable collections share a container, pass a unique `collectionID` for each collection.
- Exclude non-draggable content, such as face-down cards, by rendering it in a separate `ForEach` without `reorderable`.

### Simple reorderable preview

Marks the card views reorderable and applies the resulting reorder difference to the backing array.

```text
#Preview {
    @Previewable @State var cards = [
        CardValue(rank: .ace, suit: .clubs),
        CardValue(rank: .ace, suit: .diamonds),
        CardValue(rank: .ace, suit: .hearts),
        CardValue(rank: .ace, suit: .spades)
    ]

    HStack {
        ForEach(cards) { card in
            CardFaceView(card: card)
        }
        .reorderable()
    }
    .reorderContainer(for: CardValue.self) { difference in
        cards.apply(difference: difference)
    }
}
```

### Multiple piles in one reorder container

Scopes reordering across all Solitaire piles and lets game logic handle moves between collections.

```text
HStack(alignment: .top, spacing: spacing) {
    ForEach(0..<7) { index in
        PileView(game: game, index: index)
            .frame(width: cardWidth)
    }
}
.reorderContainer(for: CardValue.self, in: Card.Group.self) { difference in
    game.moveCards(difference: difference)
}
```

## Model reorderable subsets correctly

The session avoids advanced filtering by changing the view structure: face-down cards are rendered separately from face-up cards. Only the face-up slice receives `reorderable`, so dragging a face-down card starts no reordering interaction.

- Use view composition to express what is reorderable instead of accepting every item and later rejecting some of them.
- Use stable item identity for reorderable views; the sample uses `id: \.value` for the face-up cards.
- Provide the collection identifier with `reorderable(collectionID:)` when a container manages multiple collections.

### Only face-up cards are reorderable

Splits the pile into non-reorderable face-down cards and reorderable face-up cards.

```swift
PileLayout {
    let index = firstFaceUpIndex

    ForEach(cards[..<index]) { card in
        CardView(card: card)
    }

    ForEach(cards[index...], id: \.value) { card in
        CardView(card: card)
    }
    .reorderable(collectionID: Card.Group.pile(index))
}
```

## Drag multiple items with `dragContainer` and preview formations

A `reorderContainer` implicitly provides drag-container and drop-destination behavior, but the app can add its own `dragContainer` to customize what transferable data is included in a drag. In the Solitaire sample, dragging a card can lift that card plus the cards stacked above it.

The visual grouping of dragged items is configurable. `dragPreviewsFormation(.stack)` changes the lifted preview, and `dropPreviewsFormation(.stack)` keeps the same appearance when the drag is over destinations.

- Place `dragContainer(for:)` near the `reorderContainer` when customizing reorder drags.
- Use the same item type for the `reorderContainer` and `dragContainer` so they work together.
- Return all transferable items that should participate in the drag from the `dragContainer` closure.
- Use `dragPreviewsFormation` and `dropPreviewsFormation` to make multi-item drags visually match the app's interaction model.

### Lift a stack of cards from one dragged card

Customizes the reorder drag so one card identifier expands into a stack of cards, then displays those previews as a stack.

```text
.reorderContainer(for: CardValue.self, in: Card.Group.self) { difference in
    game.moveCards(difference: difference)
}
.dragContainer(for: CardValue.self) { cardID in
    game.cardStack(startingAt: cardID)
}
.dragPreviewsFormation(.stack)
```

### Keep drop previews consistent

Applies the same stacked preview formation over drop destinations.

```text
VStack {
    // playing area
}
.dropPreviewsFormation(.stack)
```

## Configure move semantics and validate drops

The remaining Solitaire interaction moves a card from the remainder deck into a pile. By default, SwiftUI suggests copy semantics for a separate drag source and drop destination. The source uses `dragConfiguration(DragConfiguration(allowMove: true))` to express that a move is allowed, but the destination has the final say.

The destination adds `dropDestination` so the reorder container can accept inserted cards from outside the container, then adds `dropConfiguration` to compute the intended reorder destination, require `.move`, and reject invalid Solitaire moves with `.forbidden`.

- Use `dragConfiguration` on the source to express transfer intent, such as allowing move.
- Use `dropDestination` when a reorder container should accept new items, not only moves already inside the container.
- Use `session.reorderDestination(for:in:)` to read the destination computed for inserted reorder items.
- Use `dropConfiguration` to choose the `DropOperation`, provide a destination, and enforce app-specific validation.

### Allow moving a card from the remainder deck

Configures the source card so a drag can represent a move instead of only a copy.

```text
CardFaceView(card: currentCard.value)
    .draggable(containerItemID: currentCard.value)

.dragContainer(for: CardValue.self) { cardID in
    [cardID]
}
.dragConfiguration(DragConfiguration(allowMove: true))
```

### Accept and validate inserted cards

Lets the destination insert outside cards only when the session supports move and the game rules allow the computed pile destination.

```swift
.dropDestination(for: CardValue.self) { newCards, session in
    if let destination = session.reorderDestination(
        for: CardValue.self,
        in: Card.Group.self
    ) {
        game.insertCards(newCards, to: destination)
    }
}
.dropConfiguration { session in
    let alignedX = session.location.x - 0.5 * spacing
    let pile = Int(alignedX / (cardWidth + spacing))
    let destination = ReorderDifference<CardValue, Card.Group>.Destination(
        position: .end,
        collectionID: .pile(pile)
    )

    let allowed = session.suggestedOperations.contains(.move)
        && game.validateMove(session: session, destination: destination)
    let operation: DropOperation = allowed ? .move : .forbidden

    return DropConfiguration(operation: operation, destination: destination)
}
```

Resources:
- Making a card game with drag, drop, and reordering in SwiftUI: https://developer.apple.com/documentation/SwiftUI/Making-a-card-game-with-drag-drop-and-reordering-in-swiftui
- Drag and drop: https://developer.apple.com/documentation/UIKit/drag-and-drop

Chapters:
- 0:00 Introduction: Introduces expanded SwiftUI drag-and-drop APIs in the 2027 releases: reordering, multi-item drag containers, and drag/drop configuration. The Solitaire sample provides the running example, building on `Transferable`, `draggable`, and `dropDestination`.
- 1:42 Reordering: Demonstrates `reorderable` and `reorderContainer` first in a simple preview, then across multiple Solitaire piles using `Card.Group` collection identifiers. Face-down cards are excluded by placing them in a separate non-reorderable `ForEach`.
- 6:50 Drag multiple items: Adds `dragContainer` to customize the items included in a reorder drag so dragging one card can lift a stack. Uses `dragPreviewsFormation(.stack)` and `dropPreviewsFormation(.stack)` to keep multi-card previews visually consistent.
- 9:59 Drag configuration: Uses `dragConfiguration` to allow move semantics from the remainder deck, then adds `dropDestination` and `dropConfiguration` to accept inserted cards only when move is supported and game rules permit the destination. Invalid drops are returned as `.forbidden`.
- 14:29 Next steps: Recaps the progression from basic reordering to fully customized drag-and-drop behavior composed from SwiftUI modifiers. Encourages using reordering, multi-item dragging, and configuration to match app-specific interaction rules.

### Use SwiftUI with AppKit and UIKit

- Session ID: wwdc2026-272
- Page: https://wwdc.ai/2026/272
- Markdown: https://wwdc.ai/2026/272.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/272/
- Category: SwiftUI & UI Frameworks
- Description: Incrementally adopt SwiftUI in AppKit or UIKit apps using Observation, hosting views and menus, gesture recognizer representables, and SwiftUI scenes.
- Duration: 14:03
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-272/eng_d54908c12996/wwdc2026-272-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/272/5/e1e4aa9a-cbe2-4f83-9cea-3dcaae19afd6/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/272/5/e1e4aa9a-cbe2-4f83-9cea-3dcaae19afd6/downloads/wwdc2026-272_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/272/5/e1e4aa9a-cbe2-4f83-9cea-3dcaae19afd6/downloads/wwdc2026-272_sd.mp4?dl=1

Incrementally adopt SwiftUI in AppKit or UIKit apps using Observation, hosting views and menus, gesture recognizer representables, and SwiftUI scenes.

TLDR:
- Use `@Observable` models to let AppKit and UIKit automatically invalidate drawing, layout, constraints, and other UI updates based on property reads.
- Embed new SwiftUI components in an existing AppKit hierarchy with `NSHostingView`; the session demonstrates replacing slider-based drawing with a SwiftUI `Canvas` color picker.
- Reuse existing AppKit gesture recognizers in SwiftUI with `NSGestureRecognizerRepresentable`, and build AppKit main-menu content from SwiftUI using `NSHostingMenu`.
- Add complete SwiftUI scenes from an `NSApplicationDelegate` with `NSHostingSceneRepresentation`, including `MenuBarExtra` and `Settings` scenes.

## Incremental SwiftUI adoption strategy

SwiftUI is intended to coexist with AppKit and UIKit, so existing apps can adopt it feature by feature rather than through a rewrite. The session uses a macOS lighting-control app as the running example, but the general approach applies across Apple platforms.

The recommended migration path starts with shared observable models, then moves individual controls, menus, gestures, and whole scenes to SwiftUI where they make sense.

- Start by moving shared mutable UI state into `@Observable` model types.
- Use SwiftUI for new or substantially rewritten UI components, especially when drawing and interaction code would change significantly.
- Keep the existing AppKit or UIKit lifecycle and view hierarchy where it is still working.
- Adopt SwiftUI scenes for new windows, settings, or menu bar extras without converting the whole app.

## Observation in AppKit and UIKit

AppKit can automatically track reads from `@Observable` model properties during supported view and controller update methods. When those properties change, the relevant views update without manually setting `needsDisplay`.

The example replaces manual invalidation between hue, saturation, and brightness sliders with a shared `ColorModel`. AppKit tracks property reads inside drawing methods such as `NSSliderCell.drawKnob` and redraws when accessed values change.

- AppKit observation applies to `NSView.draw(_:)`, drawing paths called by view drawing such as `NSSliderCell` drawing methods, `updateConstraints()`, `layout()`, `updateLayer()`, and related `NSViewController` methods.
- UIKit has similar Observation tracking across `UIView`, `UIViewController`, and additional UIKit types such as controls and collection view cells.
- Back deploy AppKit integration to macOS 15 with `NSObservationTrackingEnabled` in `Info.plist`.
- Back deploy UIKit integration to iOS 18 with `UIObservationTrackingEnabled`; tracking is enabled by default in the 2026 releases and later.

### Observable model shared by AppKit and SwiftUI

Adding `@Observable` makes mutable properties participate in Observation so AppKit, UIKit, and SwiftUI can react to changes.

```swift
import Observation

@Observable
@MainActor
final class ColorModel {
    var hue: Double = 0.6
    var saturation: Double = 1.0
    var brightness: Double = 1.0
}
```

## Host SwiftUI controls inside AppKit

When a component's rendering and interaction model changes substantially, rebuilding it in SwiftUI can be simpler than evolving the existing AppKit view. The sample replaces three custom slider cells with a circular HSB color picker drawn using SwiftUI `Canvas`.

`Canvas` provides an immediate-mode drawing model similar to AppKit or UIKit drawing: each redraw receives a fresh `GraphicsContext`, and code issues strokes, fills, transforms, and filters. Existing Core Graphics drawing can be reused through `withCGContext`.

- The SwiftUI color picker uses the same `@Observable` `ColorModel` as the previous AppKit implementation.
- The picker draws a hue ring, saturation and brightness semicircles, and a center color preview.
- The SwiftUI view is embedded in the existing `NSView` hierarchy using `NSHostingView`, which is itself an `NSView` subclass.

### Embed a SwiftUI view in AppKit

Use `NSHostingView` to place a SwiftUI component inside an AppKit view hierarchy.

```swift
NSHostingView(
    rootView: HSBColorPicker(model: model)
)
```

### SwiftUI Canvas shape of the color picker

The session's full sample draws with `Canvas`; this abbreviated version shows the immediate-mode drawing structure.

```swift
struct HSBColorPicker: View {
    var model: ColorModel

    var body: some View {
        Canvas { context, size in
            let metrics = PickerMetrics(size: size)
            drawPicker(in: &context, metrics: metrics,
                       hue: model.hue,
                       saturation: model.saturation,
                       brightness: model.brightness)
        }
        .contentShape(Circle())
        .aspectRatio(1, contentMode: .fit)
    }
}
```

## Bring AppKit gestures and menus into SwiftUI

Existing AppKit gesture recognizers do not need to be rewritten as SwiftUI gestures. `NSGestureRecognizerRepresentable` lets a SwiftUI view create and handle an `NSGestureRecognizer` subclass, then attach it with the standard `.gesture` modifier.

The sample adds a Force Click gesture that resets saturation and brightness to full intensity. Because Force Click is not available on every input device, the same action is also exposed through a SwiftUI-built main menu with keyboard shortcuts.

- Implement `makeNSGestureRecognizer(context:)` to create the AppKit recognizer.
- Implement `handleNSGestureRecognizerAction(_:context:)` to respond when the recognizer fires.
- Attach the representable gesture to a SwiftUI view using `.gesture`.
- Build menu content as a SwiftUI `View`, then wrap it in `NSHostingMenu` and assign it as an AppKit `NSMenuItem` submenu.

### Wrap an AppKit gesture recognizer for SwiftUI

`NSGestureRecognizerRepresentable` reuses an existing AppKit recognizer from a SwiftUI view.

```swift
struct ForceClickReset: NSGestureRecognizerRepresentable {
    var model: ColorModel

    func makeNSGestureRecognizer(context: Context) -> ForceClickGestureRecognizer {
        ForceClickGestureRecognizer()
    }

    func handleNSGestureRecognizerAction(_ recognizer: ForceClickGestureRecognizer,
                                         context: Context) {
        withAnimation {
            model.saturation = 1
            model.brightness = 1
        }
    }
}
```

### Host SwiftUI menu content in the AppKit main menu

`NSHostingMenu` is an `NSMenu` subclass, so it can be inserted into an existing AppKit menu tree.

```swift
let colorMenu = NSHostingMenu(rootView: ColorMenu(model: colorModel))
colorMenu.title = "Color"

let colorMenuItem = NSMenuItem()
colorMenuItem.submenu = colorMenu
mainMenu.addItem(colorMenuItem)
```

## Add SwiftUI scenes from an AppKit lifecycle

SwiftUI scenes can be added dynamically from an existing AppKit app without moving to the SwiftUI app lifecycle. The sample uses `NSHostingSceneRepresentation` from `NSApplicationDelegate.applicationWillFinishLaunching` to add a `MenuBarExtra` scene and a `Settings` scene.

The menu bar extra provides quick access to light controls. The settings window contains a toggle bound to app state that inserts or removes the menu bar extra, and `NSHostingSceneRepresentation` exposes an environment action for opening settings programmatically.

- Create an `NSHostingSceneRepresentation` with one or more SwiftUI scenes.
- Register it with `NSApplication.shared.addSceneRepresentation(...)`.
- Use `MenuBarExtra(..., isInserted:)` to let state control whether the menu bar item is present.
- Use the representation's environment `openSettings()` action from an `@IBAction` when an AppKit menu command should open the SwiftUI settings scene.

### Register SwiftUI scenes from an AppKit app delegate

`NSHostingSceneRepresentation` bridges SwiftUI scenes into an app that still uses `NSApplicationDelegate`.

```swift
func applicationWillFinishLaunching(_ notification: Notification) {
    let scenes = NSHostingSceneRepresentation {
        LightMenuBarExtra(appModel: model)
        LightSettings(appModel: model)
    }

    NSApplication.shared.addSceneRepresentation(scenes)
    openSettingsAction = { scenes.environment.openSettings() }
}

@IBAction func openSettings(_ sender: Any?) {
    openSettingsAction?()
}
```

Chapters:
- 0:00 Introduction: Introduces SwiftUI as a framework designed to coexist with AppKit and UIKit for incremental adoption. The sample app is a macOS lighting controller used to demonstrate Observation, hosting SwiftUI views, gestures, menus, and scenes.
- 2:33 Observation in AppKit: Shows how adding `@Observable` to a shared model lets AppKit automatically track property reads in drawing and update methods, removing manual `needsDisplay` invalidation. Notes supported AppKit and UIKit integration points and the back-deployment Info.plist keys.
- 5:41 Hosting SwiftUI in AppKit: Rebuilds a color picker as a SwiftUI `Canvas` when the desired drawing and interaction model changes significantly. Embeds the resulting SwiftUI view into the existing AppKit hierarchy with `NSHostingView`.
- 7:48 AppKit gestures in SwiftUI: Demonstrates reusing an existing `NSGestureRecognizer` subclass in SwiftUI with `NSGestureRecognizerRepresentable`. The example adds a Force Click gesture that resets saturation and brightness while coexisting with the SwiftUI drag gesture.
- 9:16 SwiftUI in the main menu: Builds main-menu content as a SwiftUI `View` using buttons, keyboard shortcuts, and a palette picker. Inserts it into the AppKit menu bar by wrapping the view in `NSHostingMenu` and assigning it to an `NSMenuItem` submenu.
- 11:30 SwiftUI scenes in AppKit: Adds complete SwiftUI scenes from an AppKit `NSApplicationDelegate` using `NSHostingSceneRepresentation`. The sample registers a `MenuBarExtra` for quick controls and a `Settings` scene that can toggle the menu bar item and be opened from an AppKit action.
- 13:04 Next steps: Recaps the incremental adoption path: use `@Observable`, consider SwiftUI for new components, reuse existing gesture recognizers, and add new scenes with SwiftUI. Emphasizes that apps do not need to become entirely SwiftUI to benefit from these APIs.

### What's new in SwiftData

- Session ID: wwdc2026-274
- Page: https://wwdc.ai/2026/274
- Markdown: https://wwdc.ai/2026/274.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/274/
- Category: App Services
- Description: Learn SwiftData updates for sectioned @Query fetches, Codable attributes, and store/history observation outside SwiftUI.
- Duration: 12:53
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-274/eng_7baf39fdd097/wwdc2026-274-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/274/4/87fb1efb-9956-414e-8c99-f2579fe86da2/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/274/4/87fb1efb-9956-414e-8c99-f2579fe86da2/downloads/wwdc2026-274_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/274/4/87fb1efb-9956-414e-8c99-f2579fe86da2/downloads/wwdc2026-274_sd.mp4?dl=1

Learn SwiftData updates for sectioned @Query fetches, Codable attributes, and store/history observation outside SwiftUI.

TLDR:
- @Query gains a sectionBy: parameter for grouping SwiftData fetch results in SwiftUI while keeping the wrapped value as the fetched model array.
- @Attribute(.codable) lets SwiftData persist Codable types it can't schema-inspect, such as MapKit's MKMapItem.Identifier, with important filtering, sorting, and migration limits.
- ResultsObserver brings Query-like fetching and change observation to non-SwiftUI code using Swift Observation.
- HistoryObserver exposes persistent-history changes through an observable eventCounter for sync, extensions, and other transaction-driven workflows.

## Section SwiftData fetches in SwiftUI

SwiftData's Query supports sectioned fetching by passing a key path to the new sectionBy: parameter. The key path starts at the model type and resolves to a String used as the section identity.

The @Query wrapped value remains an array of models, so existing code can continue to consume the fetched results. To render sections, access the property wrapper with its underscore-prefixed name and iterate its sections collection.

- Use @Query(sort:sectionBy:) to fetch sorted results grouped by a model key path.
- Each section exposes an id, which is the value produced by the sectionBy key path.
- Each section is itself a collection of models, suitable for an inner ForEach in SwiftUI.
- The example groups Trip models by destination while sorting by startDate.

### Sectioned fetching with @Query

Access _trips.sections to render grouped results while trips remains the normal array of fetched Trip models.

```swift
struct TripListView: View {
    @Query(sort: \Trip.startDate, sectionBy: \.destination)
    var trips: [Trip]

    var body: some View {
        List(selection: $selection) {
            ForEach(_trips.sections) { section in
                Section(section.id) {
                    ForEach(section) { trip in
                        TripListItem(trip: trip)
                    }
                }
            }
        }
    }
}
```

## Persist custom and third-party types with Codable

SwiftData normally builds a schema by inspecting model properties. Types from other frameworks can fail schema generation when SwiftData can't inspect them, such as a non-@Model class type.

Marking an attribute with @Attribute(.codable) tells SwiftData to persist the type's encoded representation instead of deriving a schema for it. This is intended as an escape hatch for types you don't own.

- Useful for external Codable types such as MKMapItem.Identifier.
- Codable attributes are opaque to SwiftData.
- They can't be used in predicates for filtering or in sort descriptors for sorting.
- Shape changes inside the Codable type don't trigger SwiftData migration; the type's Codable implementation must remain forward- and backward-compatible.
- For types you define, prefer @Model or supported value types to preserve filtering, sorting, indexing, and migration behavior.

### Storing a Codable MapKit identifier

Use @Attribute(.codable) for the MapKit identifier while ordinary SwiftData-supported values remain modeled normally.

```swift
import SwiftData

@Model
class Trip {
    struct Location: Codable {
        var latitude: Double
        var longitude: Double
    }

    var name: String
    var destination: String
    var startDate: Date
    var endDate: Date
    var location: Location?

    @Attribute(.codable)
    var mapItemIdentifier: MKMapItem.Identifier?
}
```

## Observe query results outside SwiftUI

@Query remains the first choice for SwiftUI views because it fetches data and automatically refreshes the view when relevant store changes occur. For non-SwiftUI code, ResultsObserver provides similar fetch-and-observe behavior using Swift Observation.

ResultsObserver supports familiar query primitives including filtering, sorting, and sectioning. The session demonstrates using it from a MapCameraController that recalculates MapCameraBounds whenever the Trip results change.

- Create a ResultsObserver for the model type and model context.
- Use withContinuousObservation(options: [.didSet]) to run code after observed results change.
- Store the ObservationTracking.Token to keep the observation alive for the lifetime of the owning object.
- Use this pattern for state objects, delegate-based architectures, games, controllers, or other non-view code that depends on SwiftData results.

### Recompute map bounds when trips change

A non-view controller observes Trip results and updates derived map state whenever the store changes.

```swift
@Observable
@MainActor
final class MapCameraController {
    private let modelResultsObserver: ModelResultsObserver<Trip>
    var bounds: MapCameraBounds?
    private var token: ObservationTracking.Token?

    init(modelContext: ModelContext) throws {
        modelResultsObserver = try ModelResultsObserver<Trip>(modelContext: modelContext)
        token = withContinuousObservation(options: [.didSet]) { [weak self] event in
            self?.bounds = self?.calculateBounds(trips: modelResultsObserver.results)
        }
    }

    private func calculateBounds(trips: [Trip]) -> MapCameraBounds? {
        /* ... */
    }
}
```

## Observe persistent history for sync and external changes

SwiftData records persistent-history transactions when the store is saved. Transactions describe what changed, where the change came from, and include tokens that can be used with ModelContext.fetchHistory() to fetch newer transactions.

HistoryObserver watches persistent history and increments a single observable eventCounter when new transactions are available. Code observes that counter, then fetches and processes the relevant history.

- Use HistoryObserver when you need transaction-level awareness rather than just current query results.
- Filter observation by model type or transaction author when only some changes matter.
- A server sync workflow can observe only app-authored transactions to avoid replaying server-originated changes back to the server.
- Call ModelContext.fetchHistory() from the processing step to fetch and handle transactions.

### Trigger server sync from history changes

Observing eventCounter tells Swift Observation what to track; processChanges can then fetch history and upload changes.

```swift
@SyncActor
final class ServerSync {
    private let observer: HistoryObserver
    private var token: ObservationTracking.Token?

    func start() throws {
        self.observer = try HistoryObserver(authors: ["App"], modelContainer: modelContainer)
        token = withContinuousObservation(options: .didSet) { [weak self] _ in
            _ = self?.observer.eventCounter
            self?.processChanges()
        }
    }

    private func processChanges() {
        // Fetch and process history transactions.
    }
}
```

## Adoption guidance

Use the highest-level API that matches the part of the app you're building: @Query in SwiftUI views, ResultsObserver for live fetched results elsewhere, and HistoryObserver for persistent-history-driven workflows.

For persistence modeling, keep Codable attributes narrow. They are best suited to external framework types that SwiftData cannot inspect, not as a replacement for well-modeled app-owned data.

- Start with @Query for SwiftUI list/detail UI.
- Use sectionBy: when grouped presentation is needed directly from a fetch.
- Use @Attribute(.codable) only when native SwiftData modeling is not practical.
- Use ResultsObserver for derived state from current store contents.
- Use HistoryObserver plus ModelContext.fetchHistory() for synchronization, app extensions, and transaction processing.

Resources:
- SwiftData: https://developer.apple.com/documentation/SwiftData
- Adopting SwiftData for a Core Data app: https://developer.apple.com/documentation/CoreData/adopting-swiftdata-for-a-core-data-app

Chapters:
- 0:00 Introduction: Introduces SwiftData enhancements in Apple's 2027 releases: sectioned Query fetches, custom type persistence, and APIs for observing model and history changes.
- 0:53 Sectioning your fetches: Shows how @Query can group fetched Trip models using sectionBy: with a key path such as destination. The sectioned data is accessed through the underscore-prefixed query wrapper and rendered with nested SwiftUI ForEach and Section views.
- 2:56 Using custom types: Explains why SwiftData can fail to infer a schema for third-party class types such as MKMapItem.Identifier and how @Attribute(.codable) persists their encoded representation. It also covers the limitations of opaque Codable storage for filtering, sorting, and migration.
- 6:26 Observing data stores with ResultsObserver: Introduces ResultsObserver as Query-like fetching and change observation for code outside SwiftUI. The example uses Swift Observation and an observation token to update map camera bounds when Trip results change.
- 9:41 Observing history with HistoryObserver: Describes SwiftData persistent history transactions and the new HistoryObserver API. HistoryObserver increments eventCounter when new transactions arrive, enabling workflows such as server sync with ModelContext.fetchHistory().
- 12:20 Next steps: Recaps the main adoption points: section fetches, use Codable attributes for external types, react to result changes with ResultsObserver, and react to persistent-history changes with HistoryObserver.

### Code-along: Add persistence with SwiftData

- Session ID: wwdc2026-275
- Page: https://wwdc.ai/2026/275
- Markdown: https://wwdc.ai/2026/275.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/275/
- Category: App Services
- Description: Add SwiftData persistence to an existing SwiftUI app by converting state to @Model schemas, relationships, model containers, and targeted @Query fetches.
- Duration: 22:35
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-275/eng_88080fba886b/wwdc2026-275-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/275/4/7c64f887-3c3c-4bdf-8472-72d6b96f8e3d/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/275/4/7c64f887-3c3c-4bdf-8472-72d6b96f8e3d/downloads/wwdc2026-275_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/275/4/7c64f887-3c3c-4bdf-8472-72d6b96f8e3d/downloads/wwdc2026-275_sd.mp4?dl=1

Add SwiftData persistence to an existing SwiftUI app by converting state to @Model schemas, relationships, model containers, and targeted @Query fetches.

TLDR:
- Migrates the Wishlist SwiftUI sample from an in-memory DataSource to SwiftData-backed persistence using @Model, ModelContainer, ModelContext, and @Query.
- Shows schema design decisions: make persisted properties mutable and Codable, convert an enum-based Goal into model classes, and model Trip-to-Activity as a relationship.
- Demonstrates targeted SwiftUI queries with #Predicate, FetchDescriptor, SortDescriptor, fetch limits, and initializer-built Query values for filtering, search, and recency lists.
- Covers practical cleanup: remove redundant state-management helpers, rely on autosave, surface runtime errors, and restore side effects such as dateEdited updates with continuous observation.

## Migration goal: replace in-memory app state with SwiftData

The session starts from the Wishlist sample app, a SwiftUI travel-planning app that stores trips, activities, goals, and search state in an environment-provided DataSource. That design works for a small demo, but filtering and sorting happen in memory, edits are lost on relaunch, and memory use grows with hardcoded data.

The migration path is framed as three steps: identify relevant dynamic state, define SwiftData model schemas for that state, and declare relationships so views can query persistent data efficiently through a model context.

- Relevant app state includes trip collections, goal statuses, trips, activities, photos, and search results.
- The DataSource responsibilities are replaced by SwiftData models, ModelContext operations, relationships, and SwiftUI queries.
- Persistent storage changes the design tradeoff: fetch only the data each view needs instead of loading and filtering broad in-memory collections.

## Define SwiftData schemas with @Model

The Activity and Trip types are converted from observable app types into SwiftData models by importing SwiftData and applying @Model. SwiftData supplies observable conformance for model types, so the previous Observable macro is no longer needed.

Model properties that are loaded from persistent storage must be writable, so values such as creationDate need to be declared with var rather than let. Non-model value properties that should be stored in database columns, such as TripCollection, must conform to Codable.

The original Goal enum is refactored because an enum represents a closed set of cases and cannot store per-instance persistent progress. The model becomes a class with stored progress state, and TripGoal and ActivityGoal subclasses represent the two goal categories using SwiftData model inheritance.

- Use @Model for persistent classes such as Activity, Trip, Goal, TripGoal, ActivityGoal, and TripImage.
- Remove didSet observers from model properties while bringing the project back to a buildable schema, then reintroduce needed side effects later through observation.
- Store goal progress directly on the model, including completedCount and an isComplete-style persisted value that can be queried.

### Convert an Activity to a SwiftData model

@Model makes Activity persistent and observable through SwiftData; simple stored properties become part of the schema.

```swift
import Foundation
import SwiftData

@Model
class Activity {
    var name: String
    var isComplete: Bool = false
    var dateCreated = Date.now
    var dateEdited = Date.now
}
```

### Make persisted value types Codable

TripCollection can be stored as a model property because SwiftData can encode it into a database column.

```text
enum TripCollection: String, CaseIterable, RawRepresentable, Codable {
    case springEscapes
    case summerVibes
    case fallGetaways
    case winterRetreats
}
```

## Model relationships and app-wide container setup

Wishlist previously approximated relationships using dictionaries and ID-based lookups. The SwiftData version declares relationships directly on model types, so a Trip can own many Activity models and activity-driven views can navigate back to parent trip details.

The Trip model uses @Relationship with a cascade delete rule and an inverse to Activity.trip. Deleting a trip clears its associated activities. The session also changes image storage: thumbnailData is inlined for efficient list and carousel UI, while full-resolution image handling is moved to a separate TripImage model.

After schemas are defined, the app creates a ModelContainer for all model types, seeds sample data if needed, and attaches the container to the SwiftUI scene with .modelContainer(container). At that point, Query can access the schema from the view hierarchy.

- TripEditModel becomes unnecessary because SwiftUI can bind directly to SwiftData models.
- DataSource becomes unnecessary because ModelContext, queries, and relationships handle storage, filtering, sorting, traversal, and updates.
- The model container is the integration point between the SwiftData schema and the SwiftUI scene.

### Declare Trip-to-Activity relationship

An array property models the to-many relationship, and the cascade rule removes child activities when a trip is deleted.

```swift
import Foundation
import SwiftData

@Model
class Trip {
    var name: String
    var collection: TripCollection
    var photo: TripImage
    var thumbnailData: Data?

    @Relationship(deleteRule: .cascade, inverse: \Activity.trip)
    var activities: [Activity] = []

    private(set) var creationDate = Date.now
    var subtitle: String?
    var isComplete: Bool = false
}
```

### Create and install the ModelContainer

The app registers the full schema once and makes it available to SwiftUI queries through the scene modifier.

```swift
import SwiftUI
import SwiftData

@main
struct WishlistApp: App {
    let container: ModelContainer = {
        do {
            let modelContainer = try ModelContainer(
                for: Trip.self, Activity.self, TripImage.self,
                     Goal.self, TripGoal.self, ActivityGoal.self
            )
            try SampleData.seedIfNeeded(in: modelContainer.mainContext)
            return modelContainer
        } catch {
            fatalError("Could not create model container: \(error)")
        }
    }()

    var body: some Scene {
        WindowGroup {
            ContentView()
                .preferredColorScheme(.dark)
        }
        .modelContainer(container)
    }
}
```

## Replace DataSource reads with targeted @Query fetches

The view layer is updated to ask SwiftData for exactly the data each subview needs. Query is described as equivalent to fetching from a model context, with the SwiftUI advantage that the view automatically updates when query results change.

The session emphasizes performance: persistent storage may live outside the app address space, so fetching broad sets and filtering in memory can waste I/O and memory. Predicates, sort descriptors, and fetch limits should be used to shape each query before data is loaded into the view.

- GoalsView uses separate queries for achieved and upcoming goals.
- RecentTripsPageView fetches the five most recent trips in reverse chronological order.
- TripCollectionView constructs its Query in init so the predicate can capture the selected TripCollection.
- SearchResultsListView switches between recent-trip fallback results and name-based Trip and Activity predicates.

### Fetch achieved and upcoming goals

Two focused queries avoid fetching all goals and separating them in memory.

```text
@Query(
    filter: #Predicate<Goal> { $0.isAchieved },
    sort: \Goal.dateAchieved,
    order: .reverse
)
private var achievedGoals: [Goal]

@Query(
    filter: #Predicate<Goal> { !$0.isAchieved },
    sort: \Goal.sortOrder
)
private var upcomingGoals: [Goal]
```

### Build a query from initializer input

The selected collection is captured in the predicate before the query is sent to storage.

```text
init(tripCollection: TripCollection, cardSize: TripCard.Size, namespace: Namespace.ID) {
    _trips = Query(
        filter: #Predicate<Trip> { $0.collection == tripCollection },
        sort: \Trip.name
    )
    self.tripCollection = tripCollection
    self.cardSize = cardSize
    self.namespace = namespace
}
```

### Search trips and activities

Search uses different query plans for empty and non-empty search text, including a predicate that only returns activities attached to a trip.

```swift
if searchText.isEmpty {
    _trips = Query(
        FetchDescriptor(
            sortBy: [SortDescriptor(\Trip.creationDate, order: .reverse)],
            fetchLimit: 3
        )
    )
    _activities = Query(filter: #Predicate<Activity> { _ in false })
} else {
    let tripSearchPredicate = #Predicate<Trip> {
        $0.name.localizedStandardContains(searchText)
    }
    _trips = Query(filter: tripSearchPredicate, sort: \Trip.name)

    let activitySearchPredicate = #Predicate<Activity> {
        $0.trip != nil && $0.name.localizedStandardContains(searchText)
    }
    _activities = Query(filter: activitySearchPredicate, sort: \Activity.name)
}
```

## Autosave, errors, and model side effects

With the model container installed, automatic saving is enabled by default. The demo verifies the migration by adding a new trip, relaunching, and seeing the trip remain in the app.

Persistence introduces runtime failure modes such as storage capacity problems or unsupported predicates. The session updates ActivityItemView to capture thrown errors from updateGoalAchievements, report them to telemetry, and present an alert when recovery guidance is appropriate.

The earlier didSet behavior for dateEdited is restored using continuous observation. ActivityItemView observes edits to an Activity's name and completion state, updates dateEdited, and also updates the parent Trip's isComplete state when activity completion changes.

- Use SwiftUI error-presentation modifiers to surface recoverable persistence-related failures.
- Keep side effects near the UI interaction that performs the edit when property observers are not suitable for @Model properties.
- Updating dateEdited can trigger query-driven views sorted by edit date to refresh automatically.

### Capture and present update errors

A thrown update error is stored, reported, and bound to a SwiftUI alert.

```swift
var body: some View {
    HStack(alignment: .firstTextBaseline, spacing: 17) {
        Group {
            if isEditing {
                rowContentWhenEditing
            } else {
                rowContentWhenNotEditing
            }
        }
        .transition(.opacity.animation(.snappy))
        .animation(.snappy, value: isEditing)
    }
    .onDisappear {
        do {
            try updateGoalAchievements()
        } catch {
            updateError = error
            reportError(error)
        }
    }
    .alert(error: $updateError) {
        // Customize the presentation of the error
    }
}
```

### Restore dateEdited and trip completion side effects

Continuous observation reintroduces side effects that were formerly handled by property observers.

```text
init(activity: Activity, isLast: Bool, isEditing: Bool) {
    activity.token = withContinuousObservation(options: .didSet) { event in
        _ = activity.name
        _ = activity.isComplete

        if event.matches(\Activity.name) {
            activity.dateEdited = .now
        }
        if event.matches(\Activity.isComplete) {
            activity.dateEdited = .now
            activity.trip?.isComplete =
                activity.trip?.activities.isEmpty == false &&
                activity.trip?.activities.allSatisfy { $0.isComplete } == true
        }
    }
    self.activity = activity
    self.isLast = isLast
    self.isEditing = isEditing
}
```

Resources:
- Wishlist: Planning travel in a SwiftUI app: https://developer.apple.com/documentation/SwiftUI/wishlist-planning-travel-in-a-swiftui-app
- SwiftData: https://developer.apple.com/documentation/SwiftData

Chapters:
- 0:00 Introduction: Introduces the Wishlist SwiftUI sample app and the migration goal: connect its dynamic travel-planning data to SwiftData persistence. The existing app uses an in-memory DataSource, so newly added trips disappear after relaunch.
- 1:05 Identify relevant state: Identifies trip collections, goal statuses, trips, activities, and search results as state that should move from RAM-backed helpers into SwiftData. The target architecture uses models connected through a ModelContext.
- 3:17 Define your schemas: Converts Activity and Trip into @Model classes, adjusts persisted properties to meet SwiftData requirements, and makes TripCollection Codable. Refactors Goal from an enum into persistent model classes, using subclasses for trip and activity goals.
- 9:41 Define model relationships: Replaces dictionary and ID lookup patterns with a SwiftData to-many relationship from Trip to Activity, including a cascade delete rule. Updates photo storage, removes redundant DataSource and TripEditModel code, and installs the ModelContainer in the SwiftUI scene.
- 13:33 Update the view layer: Replaces environment DataSource reads with @Query properties using predicates, sort descriptors, fetch limits, and initializer-built queries for goals, recent trips, collections, and search. Also handles runtime errors and restores dateEdited and trip-completion side effects with continuous observation.
- 21:47 Next steps: Recaps the adoption guidance: design the schema around app state, write targeted queries to balance memory and storage I/O, and keep SwiftUI views interoperable with SwiftData as the app evolves.

### WidgetKit foundations

- Session ID: wwdc2026-277
- Page: https://wwdc.ai/2026/277
- Markdown: https://wwdc.ai/2026/277.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/277/
- Category: SwiftUI & UI Frameworks
- Description: Learn WidgetKit fundamentals: build SwiftUI widgets with timelines, reload policies, app integration, supported families, and adaptive styling.
- Duration: 20:20
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-277/eng_19e70907a3bc/wwdc2026-277-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/277/4/e9dd0c7d-3a2e-4cf3-9e65-c9cba19d3616/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/277/4/e9dd0c7d-3a2e-4cf3-9e65-c9cba19d3616/downloads/wwdc2026-277_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/277/4/e9dd0c7d-3a2e-4cf3-9e65-c9cba19d3616/downloads/wwdc2026-277_sd.mp4?dl=1

Learn WidgetKit fundamentals: build SwiftUI widgets with timelines, reload policies, app integration, supported families, and adaptive styling.

TLDR:
- Widgets are SwiftUI views supplied by a widget extension; the app and extension run separately, so shared data should use an app group container such as shared UserDefaults or a database.
- WidgetKit keeps widgets current with timelines made of TimelineEntry values; choose .atEnd, .afterDate, or .never reload policies based on how predictable your content updates are.
- Use deep links, AppIntent-based configuration, and interactive buttons or toggles to connect widgets back to app content and actions.
- Test widgets across supported families, full color/tinted/clear rendering modes, local devices, macOS remote widgets, SwiftUI previews, and WidgetKit developer mode.

## What makes a good widget

Widgets extend app content across iOS, iPadOS, watchOS, visionOS, macOS, CarPlay, and remote widget placements. The session frames effective widgets around three qualities: glanceable, relevant, and personalizable.

Glanceable widgets communicate useful information quickly. Relevant widgets update for time, personal patterns, or location. Personalizable widgets let people choose the content that matters to them.

- Build widgets with WidgetKit and SwiftUI, regardless of whether the containing app uses SwiftUI or UIKit.
- Use widgets for concise, high-value content rather than duplicating an entire app screen.
- Support multiple placements and sizes where the content still makes sense.

## Widget extension, configuration, and view structure

Apps expose widgets through a widget extension. The extension runs as a separate process from the app, so data shared between the app and widgets should be stored in a shared container configured with an app group.

A widget declares a WidgetConfiguration. Use StaticConfiguration when the widget configures itself from app state. Use AppIntentConfiguration when people should be able to customize the widget. The configuration supplies a unique kind, a timeline provider, and a SwiftUI view builder for each timeline entry.

Use containerBackground(for: .widget) to identify the widget background. This lets the system replace the background with an adaptive glass material in tinted or clear environments.

- The widget extension is focused on providing widget data and views to WidgetKit.
- Timeline entries contain the data needed to render a widget view at specific dates.
- The rendered widget views are archived and displayed by the system.

### Static widget configuration with a widget background

A simple widget uses StaticConfiguration, a TimelineProvider, and a SwiftUI view for each entry, with containerBackground marking the background for system adaptation.

```swift
struct DailyReadingGoalWidget: Widget {
    let kind = "DailyReadingGoalWidget"

    var body: some WidgetConfiguration {
        StaticConfiguration(
            kind: kind,
            provider: DailyReadingGoalProvider()
        ) { entry in
            DailyReadingGoalView(
                book: entry.book,
                message: entry.message,
                timeOfDay: entry.timeOfDay
            )
            .environment(\.colorScheme, .dark)
            .containerBackground(for: .widget) {
                Background()
            }
        }
    }
}
```

## Timelines and reload policies

WidgetKit asks the extension for a timeline: a series of timeline entries, each representing the data to display at a particular time. The timeline provider also supplies a snapshot for previews in the widget gallery and a placeholder for instant loading before content is available.

Placeholders should be synchronous and avoid disk or network work. The example uses a redacted version of the view. Snapshots should be realistic and make a strong first impression, even if the user has not yet created app data.

Reload policy is central to keeping a widget relevant while respecting system update budgets. WidgetKit may throttle frequent reloads, especially while the app is foregrounded, so provide multiple timeline entries whenever possible and consider a final reload when the app enters the background if data changed.

- Use .atEnd when the system should reload after all entries are exhausted.
- Use .afterDate when there is a known future time when the timeline should be recalculated, such as the end of the day.
- Use .never when automatic reloads do not make sense; then reload explicitly with WidgetCenter reload APIs or a push notification.
- For highly ephemeral, frequently updating, alert-capable content with a defined start and end, consider Live Activities instead of a widget.

## Families and placement

Widgets come in multiple families and placements. The recommendation is to support as many sizes as are appropriate, while starting with a few families can keep the initial implementation simple.

The same widget and timeline provider can often be reused across families while providing SwiftUI layouts that fit each family's shape and size. The session highlights systemExtraLargePortrait, introduced in visionOS 26 and newly available in macOS, iOS, and iPadOS 27.

- Use .supportedFamilies to declare which widget families a widget supports.
- Do not support families where the content does not work well.
- iOS widgets can also appear on CarPlay and as remote widgets on macOS, so interactions and layouts should be tested in those contexts.

### Restrict supported widget families

Declare the families a widget supports; add more families when the data and layout work for those sizes.

```swift
struct DailyReadingGoalWidget: Widget {
    let kind = "DailyReadingGoalWidget"

    var body: some WidgetConfiguration {
        StaticConfiguration(
            kind: kind,
            provider: DailyReadingGoalProvider()
        ) { entry in
            DailyReadingGoalView(
                book: entry.book,
                message: entry.message,
                timeOfDay: entry.timeOfDay
            )
            .environment(\.colorScheme, .dark)
            .containerBackground(for: .widget) {
                Background()
            }
        }
        .supportedFamilies([.systemMedium])
    }
}
```

## Integrating widgets with app content

WidgetKit provides three main integration paths: deep links, configurable widgets, and interactive elements. The default tap behavior opens the app, but widgets that display specific content should route directly to the corresponding destination.

Configurable widgets use App Intents so people can personalize widget content, such as selecting a location or a book. Keep configuration fast, limit it to one or two parameters when possible, and provide sensible defaults so configuration is not required up front.

Interactive widgets expose buttons or toggles. Because widget views are archived and app code is not running while the widget is onscreen, interactive controls execute an App Intent on behalf of the widget.

- Use widgetURL for whole-widget deep links into app-specific content.
- Use AppIntentConfiguration when the widget needs user-selected configuration.
- Use buttons and toggles for the most important lightweight actions from the app.

### Add a deep link to a widget

widgetURL lets a tap open the app directly to relevant content, such as a book detail page encoded by ID.

```swift
struct DailyReadingGoalWidget: Widget {
    let kind = "DailyReadingGoalWidget"

    var body: some WidgetConfiguration {
        StaticConfiguration(
            kind: kind,
            provider: DailyReadingGoalProvider()
        ) { entry in
            DailyReadingGoalView(
                book: entry.book,
                message: entry.message,
                timeOfDay: entry.timeOfDay
            )
            .environment(\.colorScheme, .dark)
            .containerBackground(for: .widget) {
                Background()
            }
            .widgetURL(URL(string: "bookclub://reading/\(book.bookID)"))
        }
        .supportedFamilies([.systemMedium])
    }
}
```

## Adapting and testing system appearance

Widgets adapt to full color, tinted, and clear system customizations. In tinted or clear modes, the system renders widget content through a glass material and replaces the declared background with an adaptive effect.

SwiftUI handles much of this automatically, but custom content may need explicit rendering behavior. The example book cover image rendered incorrectly in an accented environment until the image was marked to remain full color.

Testing should cover all environments where the widget appears. Use local devices, SwiftUI previews, WidgetKit developer mode, and macOS remote widget testing. Developer mode lifts constraints such as reload budgets to speed iteration.

- Test full color, tinted, and clear rendering modes.
- Check different families, color schemes, and rendering modes in the Xcode SwiftUI canvas.
- Verify widget interactions still feel right when an iOS widget appears as a remote widget on Mac.

### Preserve full-color image rendering in accented mode

Use widgetAccentedRenderingMode(.fullColor) for imagery, such as book covers, that should keep its original colors in accented widget rendering.

```swift
struct BookCoverImage: View {
    let imageName: String

    var body: some View {
        Image(imageName, bundle: .main)
            .widgetAccentedRenderingMode(.fullColor)
    }
}
```

Chapters:
- 0:01 Introduction: Introduces widgets as glanceable, relevant app content available across Apple platforms. Sets up the session topics: WidgetKit fundamentals, app integration, and adapting to system appearance changes.
- 1:03 Fundamentals: Explains the widget extension model, app group data sharing, WidgetConfiguration types, timelines, snapshots, placeholders, timeline entries, and reload policies. Also covers supported widget families, including systemExtraLargePortrait availability on more platforms.
- 13:15 Integrate with your app: Shows how widgets connect to apps with deep links, configurable App Intent-based widgets, and interactive buttons or toggles powered by App Intents. Emphasizes keeping configuration simple and providing defaults.
- 17:04 Adapt with the system: Covers how widgets adapt to full color, tinted, and clear rendering modes, including using widgetAccentedRenderingMode for images that should stay full color. Recommends testing on devices, as remote widgets on macOS, in SwiftUI previews, and with WidgetKit developer mode.

### Modernize your UIKit app

- Session ID: wwdc2026-278
- Page: https://wwdc.ai/2026/278
- Markdown: https://wwdc.ai/2026/278.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/278/
- Category: SwiftUI & UI Frameworks
- Description: Update UIKit apps for iOS 27 resizable iPhone environments, scene lifecycle, adaptive layout APIs, modern bars, Apple Intelligence, and Xcode agent skills.
- Duration: 16:03
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-278/eng_7a625a197261/wwdc2026-278-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/278/4/8c3f2e61-52d3-4915-9543-96e2f13adc8b/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/278/4/8c3f2e61-52d3-4915-9543-96e2f13adc8b/downloads/wwdc2026-278_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/278/4/8c3f2e61-52d3-4915-9543-96e2f13adc8b/downloads/wwdc2026-278_sd.mp4?dl=1

Update UIKit apps for iOS 27 resizable iPhone environments, scene lifecycle, adaptive layout APIs, modern bars, Apple Intelligence, and Xcode agent skills.

TLDR:
- iOS 27 makes iPhone apps fully resizable in iPhone Mirroring and on iPad, so UIKit apps must adapt to any scene size at runtime.
- UIScene lifecycle is required when building with the latest SDKs; audit legacy app lifecycle, UIScreen.main, userInterfaceIdiom, and interfaceOrientation usage.
- UIKit adds APIs for iPhone sidebar placement, prominent tabs, navigation bar minimization, menu image visibility, and view-based motion/location coordinate spaces.
- Apple Intelligence can use context menus, View Annotations, and drag handlers; Xcode 27 includes an app modernization skill that can automate many adaptivity migrations.

## Resizable iPhone apps are now a core UIKit requirement

In iOS and macOS 27, iPhone apps can be fully resized in iPhone Mirroring on Mac. iPhone-only apps running on iPad are also fully resizable like other iPad apps. UIKit apps should dynamically adjust to the available scene size instead of assuming a fixed device screen or orientation.

The session frames modernization around four common legacy areas: adopting UIScene lifecycle, removing main-screen references, replacing user-interface-idiom layout decisions, and replacing interface-orientation layout decisions.

- Build and test with the iOS 27 SDK to surface the new behavior.
- Adopt UIScene lifecycle; apps that still use only the legacy app lifecycle will no longer launch when built with the latest SDKs.
- Treat resizing as a runtime layout condition, not as a device-family or orientation switch.
- Use size classes and view sizes for layout, with finer-grained decisions based on local view bounds when needed.

## Replace main screen and screen-bounds assumptions

UIScreen.main always describes the device's main screen, which may not be the screen associated with a specific scene in iPhone Mirroring or on an external display. Code that needs screen information should use the local window scene, or better, avoid screen references where UIKit provides more adaptive alternatives.

For display scale, prefer traitCollection.displayScale. UIKit's automatic trait tracking can re-run common layout and drawing methods when tracked traits change. Where automatic tracking is not available, register for trait changes explicitly. For available space, prefer UIWindowScene effective geometry at the scene level and local view bounds inside views and view controllers.

- Use window?.windowScene?.screen instead of UIScreen.main when a screen reference is unavoidable.
- Use traitCollection.displayScale instead of screen.scale.
- Use windowScene.effectiveGeometry for scene-level available space.
- Use view.bounds or a containing view's bounds for local layout decisions.

### Use local screen and trait references

Use the scene's screen only when needed, and replace screen scale reads with the trait collection display scale.

```swift
// Access the correct screen through a window scene
let screen = window?.windowScene?.screen

func generateThumbnail(_ image: UIImage, screen: UIScreen) -> UIImage {
    // Existing code, replacing main screen with local screen reference
}

override func layoutSubviews() {
    super.layoutSubviews()
    let displayScale = traitCollection.displayScale
    // Update layout/drawing that depends on scale
}
```

### Track traits and effective geometry

Register for trait changes when automatic trait tracking is not enough, and use effective geometry or local bounds instead of screen bounds.

```swift
let displayScaleTrait: [UITrait] = [UITraitDisplayScale.self]
registerForTraitChanges(displayScaleTrait) { (view: GalleryView, previousTraitCollection: UITraitCollection) in
    view.cache.invalidate()
}

func windowScene(
    _ windowScene: UIWindowScene,
    didUpdateEffectiveGeometry previousEffectiveGeometry: UIWindowScene.Geometry
) {
    let geometry = windowScene.effectiveGeometry
    let availableSpace = geometry.coordinateSpace.bounds
    // React to scene geometry changes
}

override func viewDidLayoutSubviews() {
    super.viewDidLayoutSubviews()
    let availableSpace = view.bounds.size
    // Layout using the local view size
}
```

## Modern layout signals: size classes, fullscreen games, and body protocols

The user interface idiom trait is no longer a reliable layout signal. An iPhone app running on iPad or in iPhone Mirroring may remain in the phone idiom while still being fully resizable. Interface orientation is also not useful for layout in resizable environments; supported orientations are treated as system preferences and may be ignored.

For games, UIRequiresFullscreen is honored on iPhone in resizable environments starting in iOS 27, but it no longer opts the app fully out of resizing. Instead, it enables discrete resizing that respects supported orientations so rendering can remain full quality in the available space.

UIView now conforms to the new Body protocols from CoreMotion and CoreLocation, allowing motion and heading data to be connected to the view that visualizes it so data stays in the correct coordinate space.

- Replace userInterfaceIdiom layout checks with size-class checks or local size checks.
- Replace interfaceOrientation layout checks with size classes or bounds-based layout.
- Use UIRequiresFullscreen for games that need discrete resizing behavior.
- Attach motion and heading bodies to the relevant UIView for orientation-independent coordinate handling.

### Configure motion and heading bodies

Connect CoreMotion and CoreLocation managers to the UIView that visualizes the data.

```text
override func viewDidLoad() {
    super.viewDidLoad()
    motionManager.deviceMotionBody = view
    locationManager.headingBody = view
}
```

## New UIKit APIs for tabs, sidebars, navigation bars, and menus

iOS 27 lets iPhone apps opt into a sidebar representation for UITabBarController. Unlike iPad, this is an app choice, and the UI does not provide a user toggle between sidebar and tab bar. The system decides whether there is enough space to display the sidebar.

UITabBarController can also mark any tab as prominent so it remains visible when the tab bar collapses during scrolling. Navigation bars can now slide away interactively during scroll; apps can use the default system behavior or override it per navigation item.

Menu element images may not appear by default in some contexts such as menu bars on iPadOS and macOS. Use preferredImageVisibility only when an image must remain visible, and review the Human Interface Guidelines for menu imagery.

- Set tabBarController.sidebar.preferredPlacement = .sidebar to opt into sidebar layout on iPhone.
- Check tabBarController.sidebar.isAvailable before relying on sidebar presentation.
- Set prominentTabIdentifier to keep an important tab visible during tab bar collapse.
- Use navigationItem.barMinimizationBehavior and barMinimizationSafeAreaAdjustment to control navigation bar minimization.
- Re-evaluate custom scroll edge effect styles, especially previous overrides from .automatic to .soft.

### Opt into sidebar layout and check availability

An iPhone app can request sidebar placement, while the system determines whether the environment supports showing it.

```text
tabBarController.sidebar.preferredPlacement = .sidebar

if tabBarController.sidebar.isAvailable {
    // Sidebar representation can be shown
} else {
    // Surface nested-tab UI elsewhere
}
```

### Prominent tabs and navigation bar minimization

Pin an important tab and customize whether navigation bars minimize during scroll, including safe-area behavior when the app handles insets itself.

```swift
let tabs = [
    // ...
]
let tabBarController = UITabBarController(tabs: tabs)
tabBarController.prominentTabIdentifier = "cart"

navigationItem.barMinimizationBehavior = .always
navigationItem.barMinimizationSafeAreaAdjustment = .never
```

## Apple Intelligence integration and Xcode agentic modernization

Menus in iOS 27 can show an Ask Siri button when there is content relevant for Siri. Apps can provide more specific context using the View Annotations API by annotating views with AppEntities. If an app supports drag and drop, Siri can load resources from the app's drag handlers when Apple Intelligence is invoked from context menus.

Drag delegate code should account for sessions that start without a user gesture. Avoid starting animations or presenting modal UI from sessionWillBegin; move user-gesture-dependent stateful UI to sessionDidMove.

Xcode 27 includes an app modernization skill that understands the adaptivity migrations covered here. It can convert main-screen usage to trait-collection or scene-bounds checks, add invalidation logic, replace orientation checks with size-class checks, and help migrate to scene lifecycle. Skills can be exported as Markdown for other workflows.

- Use View Annotations and AppEntities to improve Siri context where appropriate.
- Review drag-and-drop delegates for Apple Intelligence-triggered loading paths.
- Use Device Hub and Xcode Previews resize mode to test arbitrary simulator sizes, then validate iPhone Mirroring and iPad on real devices.
- Try Xcode's app modernization skill for large UIKit adaptivity audits.

### Export Xcode skills for other tools

Exports Xcode skills as Markdown files that can be imported into other agentic coding workflows.

```text
xcrun agent skills export
```

Resources:
- TN3208: Preparing your app's launch screen to meet App Store requirements: https://developer.apple.com/documentation/Technotes/tn3208-preparing-your-apps-launch-screen-to-meet-app-store-requirements
- TN3210: Optimizing your app for iPhone Mirroring: https://developer.apple.com/documentation/Technotes/tn3210-optimizing-your-app-for-iphone-mirroring
- Make your UIKit app more flexible: https://developer.apple.com/videos/play/wwdc2025/282/
- Adapting your app when traits change: https://developer.apple.com/documentation/UIKit/adapting-your-app-when-traits-change
- Transitioning to the UIKit scene-based life cycle: https://developer.apple.com/documentation/UIKit/transitioning-to-the-uikit-scene-based-life-cycle
- Automatic trait tracking: https://developer.apple.com/documentation/UIKit/automatic-trait-tracking
- Human Interface Guidelines: Menus: https://developer.apple.com/design/human-interface-guidelines/menus

Chapters:
- 0:00 Introduction: Introduces UIKit modernization topics: new app adaptivity requirements, resizable iPhone apps, updated bars and menus, Apple Intelligence support, and an Xcode agent skill for modernization work.
- 0:34 App adaptivity: Explains that iPhone apps are fully resizable in iPhone Mirroring and on iPad, requiring runtime adaptation to any scene size. Identifies scene lifecycle, main screen references, user interface idiom checks, and interface orientation checks as key audit areas.
- 2:10 Legacy API: App lifecycle: States that UIScene lifecycle is required when building with the latest SDKs and apps without it will no longer launch. Recommends verifying use of UISceneDelegate and using migration resources if still on the legacy app lifecycle.
- 2:51 Legacy API: Main screen: Shows why UIScreen.main can be wrong in mirrored, external-display, or resizable environments. Recommends local window scene screen references, traitCollection.displayScale, trait-change registration, effective geometry, and view bounds.
- 5:46 Full-screen mode for games: Describes updated UIRequiresFullscreen behavior on iPhone in resizable environments. It enables discrete resizing that respects supported orientations rather than opting the app completely out of resizing.
- 6:17 Legacy API: User interface idiom: Explains that user interface idiom is no longer meaningful for layout because resizable iPhone apps may still report the phone idiom on iPad or Mac. Recommends size classes and local size checks instead.
- 7:06 Legacy API: Interface orientation: Explains that supported interface orientations are preferences and may be ignored in resizable environments. Recommends replacing orientation-based layout with size-class and bounds-based logic.
- 7:55 UIView Body protocols for motion & location: Introduces UIView conformance to new CoreMotion and CoreLocation Body protocols. Apps can attach motion and heading managers to the view that visualizes the data to keep coordinate spaces correct.
- 8:19 Test your resizable iPhone app: Shows testing with Xcode 27 Device Hub and Xcode Previews resize mode, allowing simulator windows to be freely resized. Recommends final validation on real devices for iPhone Mirroring and iPad.
- 9:18 Tab bars and sidebars: Covers iPhone opt-in to UITabBarController sidebar placement and checking sidebar availability. Also introduces prominentTabIdentifier for keeping a selected tab visible when the tab bar collapses during scrolling.
- 10:52 Navigation bars: Explains navigation bar minimization during scroll and how to override it with barMinimizationBehavior. Also calls out updated scroll edge effect visuals and the need to revisit custom appearance overrides.
- 12:37 Menus: Notes that menu element images may be hidden by default in some contexts with the refined Liquid Glass look. Apps can use preferredImageVisibility when visible images are still necessary.
- 13:01 Integrate with Apple Intelligence: Describes automatic Ask Siri menu integration and using View Annotations with AppEntities for more relevant app context. Advises reviewing drag-and-drop delegates because Siri can load resources through drag handlers without a user-initiated drag.
- 14:07 Agentic coding: Introduces Xcode 27's app modernization skill, which can automate many adaptivity migrations including main-screen replacement, orientation-to-size-class changes, and scene lifecycle migration. Skills can be exported with xcrun agent skills export.
- 15:32 Next steps: Recommends building with the iOS 27 SDK, testing resizable behavior in Device Hub and iPhone Mirroring on macOS 27, finding flexibility gaps, and trying the modernization skill.

### Explore advances in RealityKit

- Session ID: wwdc2026-279
- Page: https://wwdc.ai/2026/279
- Markdown: https://wwdc.ai/2026/279.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/279/
- Category: Spatial Computing
- Description: RealityKit gains lightmaps, soft shadows, NavMesh pathfinding, cloth simulation, LOD controls, Gaussian splats, and custom reverb meshes.
- Duration: 23:51
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-279/eng_66beed09e0a6/wwdc2026-279-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/279/4/ab575725-be7d-4348-a3ae-6595ef4070c4/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/279/4/ab575725-be7d-4348-a3ae-6595ef4070c4/downloads/wwdc2026-279_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/279/4/ab575725-be7d-4348-a3ae-6595ef4070c4/downloads/wwdc2026-279_sd.mp4?dl=1

RealityKit gains lightmaps, soft shadows, NavMesh pathfinding, cloth simulation, LOD controls, Gaussian splats, and custom reverb meshes.

TLDR:
- RealityKit adds higher-fidelity lighting workflows: lightmaps from Reality Composer Pro 3, soft shadows for dynamic lights, projective textures, and physical space lighting for spot and point lights.
- Navigation meshes now support pathfinding through NavigationMeshResource, NavigationComponent, and NavigationController, including traversal costs, area filtering, and off-mesh connections.
- Cloth simulation models cloth meshes as particles and springs, with ClothBodyComponent, ClothColliderComponent, ClothSimulationComponent, material properties, and kinematic pinned vertices.
- Performance guidance centers on LevelOfDetailComponent switching by camera distance or screen area, thermal-state adaptation, Gaussian splat rendering from buffers, and custom spatial-audio reverb meshes.

## Lighting and shadows

RealityKit's lighting updates focus on making virtual content blend more believably with virtual and physical environments. Lightmaps can represent complex static lighting effects such as indirect lighting, ambient occlusion, and beauty textures; the recommended workflow is to generate them with the Reality Composer Pro 3 light baker.

Dynamic lights gain soft shadows by configuring the shadow light size and quality. A larger light size produces a wider penumbra, but soft shadows require medium or high quality; low quality produces hard shadows regardless of light size.

Projective textures let spotlights project texture patterns, such as stars, caustics, or window patterns. Physical space lighting allows supported virtual lights to affect system environments or the real world through RealityKit scene understanding; the session states this is currently supported for spotlights and point lights.

- Use lightmaps for static indirect lighting, ambient occlusion, and precomputed beauty lighting.
- Use soft shadows on dynamic lights when the virtual light has a nonzero area and the cost is acceptable.
- Use SpotLightComponent.ProjectiveTexture to project patterns from a spotlight.
- Add SpotLightComponent.SurroundingsLight to enable physical space lighting for a spotlight.

### Enable soft shadows on a spotlight

Set a nonzero lightSize and use medium or high shadow quality to get soft shadow penumbra from a dynamic spotlight.

```text
guard var shadow = hearthSpotlight.components[SpotLightComponent.Shadow.self] else {
    // handle error
}

shadow.lightSize = 0.7      // meters
shadow.quality = .medium    // .medium or .high enables soft shadows
hearthSpotlight.components.set(shadow)
```

### Project a texture and enable physical space lighting

Attach a projective texture to a spotlight, then add SurroundingsLight so the light can interact with the physical environment.

```swift
let spotLightEntity = Entity()
spotLightEntity.components.set(SpotLightComponent(
    color: .white,
    intensity: intensity,
    innerAngleInDegrees: innerAngle,
    outerAngleInDegrees: outerAngle,
    attenuationRadius: attenuationRadius
))

let projectiveTexture: TextureResource = generateStarsAndNebulaeTexture()
spotLightEntity.components.set(SpotLightComponent.ProjectiveTexture(
    texture: projectiveTexture
))

spotLightEntity.components.set(SpotLightComponent.SurroundingsLight())
```

## Navigation mesh pathfinding

RealityKit navigation meshes define traversable regions for players and NPCs. A NavigationMeshResource stores the mesh geometry, labeled areas, custom flags, and connections between areas. Regions can be assigned traversal costs so pathfinding can prefer faster or easier terrain without necessarily excluding slower regions.

NavigationComponent references the mesh and provides a filter for included or excluded area flags and area costs. NavigationController computes paths from an entity with a navigation component, either synchronously or asynchronously. Path results are returned as nodes, including mesh points and off-mesh connections such as ladders or bridges.

- Define a NavigationMeshResource in Swift or in Reality Composer Pro 3.
- Use NavigationComponent filters to control allowed areas and traversal costs.
- Use NavigationController.computePath to query routes.
- Handle off-mesh connections separately when converting path nodes into character movement.

### Query a navigation mesh asynchronously

Use NavigationController to compute a path, then iterate path nodes and distinguish normal mesh points from off-mesh traversal.

```swift
extension Entity {
    public func navigate(/* ... */) async {
        let navigator = try! NavigationController(entity: self)

        guard let result = await navigator.computePath(
            from: fromPosition,
            to: toPosition
        ) else { return }

        if result.isEmpty { return }

        for node in result {
            switch node.category {
            case .meshPoint:
                finalPath.append(node.position)
            case .offMeshConnection:
                // handle ladders, bridges, or other special traversal
            }
        }
    }
}
```

## Cloth simulation

RealityKit cloth simulation represents cloth as a mesh whose vertices act as particles and whose edges act as springs. With enough mesh resolution, it can simulate flowing garments, bed covers, curtains, and other deformable surfaces in real time.

A cloth setup uses ClothBodyComponent for the cloth itself, ClothColliderComponent for rigid objects that cloth can collide with, and ClothSimulationComponent to run the simulation. The simulation component owns material references and global simulation settings such as solver choice, gravity, and timestep. Cloth and collider materials expose properties such as spring stiffness and friction.

Pinned cloth can be implemented by selecting mesh vertices near anchor positions and marking them kinematic. Kinematic vertices are moved by entity transforms rather than by the cloth solver, which keeps curtain hoops or other attachment points in place.

- Use ClothBodyComponent with a cloth mesh resource and cloth material properties.
- Use ClothColliderComponent for rigid collision geometry such as furniture or characters.
- Use ClothSimulationComponent on an ancestor to run the simulation for descendant cloth bodies and colliders.
- Pin vertices by selecting them from the cloth mesh and setting their motion type to .kinematic.

### Pin cloth vertices to anchor points

Select vertices near each pin entity and make them kinematic so the simulation cannot pull them away from the anchor.

```swift
for (pin, pinComponent) in pins {
    let position = pin.position(relativeTo: event.entity)
    let selectionSphere = ClothSphereShape(radius: pinComponent.radius)
    let vertices = clothMesh.vertices(
        in: .sphere(selectionSphere),
        center: position
    )
    clothBody.motionTypes.set(vertexIndices: vertices, value: .kinematic)
}
```

## Performance controls

The session emphasizes using advanced visual and simulation features carefully because they can add rendering or compute cost. Mesh level of detail is the primary optimization shown: render lower-detail geometry when the visual difference is negligible, such as when an object is far away or occupies little screen area.

RealityKit's LevelOfDetailComponent supports switching based on camera distance or screen area. Camera-distance LODs use a maximum distance per level, with the final level commonly using infinity. Screen-area LODs use a minimum fraction of screen area per level.

Apps and games should also monitor thermal state and adapt quality dynamically. When thermal state becomes serious or critical, suggested mitigations include more aggressive LOD switching and lower shadow quality.

- Use LevelOfDetailComponent.addByCameraDistance when distance is a good proxy for visible detail.
- Use LevelOfDetailComponent.addByScreenArea when projected size is a better proxy.
- Use ProcessInfo.thermalStateDidChange to respond to device heat.
- Adapt quality by adjusting LOD thresholds, shadow quality, or other expensive effects.

### Create distance-based LODs

Switch from high to lower mesh detail as the entity moves farther from the camera.

```swift
let lod0 = [ModelEntity(mesh: lodMesh0)]
let lod1 = [ModelEntity(mesh: lodMesh1)]
let lod2 = [ModelEntity(mesh: lodMesh2)]

let entity = Entity()
LevelOfDetailComponent.addByCameraDistance(to: entity, levels: [
    (entities: lod0, maxDistance: 1.0),
    (entities: lod1, maxDistance: 5.0),
    (entities: lod2, maxDistance: .infinity),
])
```

### React to thermal pressure

Observe thermal-state changes and reduce visual cost when the device reports serious or critical thermal pressure.

```text
NotificationCenter.default.addObserver(
    of: ProcessInfo.self,
    for: .thermalStateDidChange
) { _ in
    switch ProcessInfo.processInfo.thermalState {
    case .nominal, .fair:
        // Stay the course
    case .serious, .critical:
        // Improve performance, for example:
        // - More aggressive LOD switching
        // - Lower shadow quality
    }
}
```

## 3D Gaussian splats

RealityKit can render 3D Gaussian splats, a technique for high-quality rendering of volumetric captures from the real world. A capture is represented as many 3D Gaussians, described as ellipsoids with opacity and view-dependent color.

RealityKit does not require a specific Gaussian splat file format. Instead, developers provide buffers for position, scale, rotation, opacity, and spherical harmonics. The spherical harmonics degree controls color variation by view direction; degree 0 represents a solid color from all directions.

The workflow is to create a GaussianSplatResource.BufferResource from the buffers, wrap it in a GaussianSplatResource, create a GaussianSplatComponent, and attach the component to an entity.

- Use Gaussian splats for high-fidelity real-world object or scene captures.
- Provide explicit buffers rather than relying on a RealityKit-specific file format.
- Include spherical harmonics data and degree for view-dependent color.
- Consult the linked "Gaussian splats on visionOS" guide and sample for format conversion and end-to-end setup.

### Create and attach a Gaussian splat component

Build a Gaussian splat resource from property buffers, then attach it to an entity through GaussianSplatComponent.

```swift
let resource = try GaussianSplatResource.BufferResource(
    count: splatCount,
    position: positionBuffer,
    scale: scaleBuffer,
    rotation: rotationBuffer,
    opacity: opacityBuffer,
    sphericalHarmonics: (sphericalHarmonicsBuffer, degree)
)

let splatResource = GaussianSplatResource(resource)
let splatComponent = GaussianSplatComponent(splatResource)
splatEntity.components.set(splatComponent)
```

## Immersive audio and next steps

RealityKit's custom reverb mesh simulates reflections and reverberation using ray-traced geometrical acoustics. The geometry and material properties of an environment affect how audio sources sound, so a kitchen, museum, or small living room can produce different spatial audio results.

A custom reverb mesh is created with ReverbMeshResource, such as a mesh descriptor, mesh resource, or a simple shoebox. Combine that mesh with preset or custom Audio.Material values to create a simulated Reverb, then attach a ReverbComponent to an entity. Custom materials can define absorption and scattering across frequencies.

The custom reverb mesh feature only works in immersive spaces. In shared spaces, the system uses Apple Vision Pro room-sense reverb geometry built from the real-world surroundings. The session also mentions additional RealityKit updates, including coordinated multi-source audio, high-quality character rendering with subsurface scattering and advanced hair shaders, and portal customizations.

- Use ReverbMeshResource to describe the acoustic geometry of a virtual environment.
- Use preset materials such as .dryWall or define custom Audio.Material absorption and scattering.
- Attach ReverbComponent to activate the simulated reverb in the scene.
- Use custom reverb meshes in immersive spaces; shared spaces use system room-sense reverb geometry.

### Create a shoebox reverb mesh

Create a simple inward-facing box reverb mesh and apply a preset drywall acoustic material.

```swift
let mesh: ReverbMeshResource = .shoebox(size: [5, 4, 6])
let reverb: Reverb = .simulated(mesh: mesh, materials: [.dryWall])
entity.components.set(ReverbComponent(reverb: reverb))
```

### Define custom reverb materials

Scale a preset material's absorption or build a material from absorption and scattering coefficients.

```swift
let thickCarpet: Audio.Material = .carpet.scalingAbsorption { freq in
    0.1
}

let bookshelfAbsorption = Audio.Absorption([
    0.10, 0.15, 0.28, 0.20, 0.15,
    0.10, 0.10, 0.07, 0.07, 0.05
])
let bookshelfScattering = Audio.Scattering([
    500: 0.5,
    1000: 0.6,
    4000: 0.7
])
let bookshelf = Audio.Material(
    absorption: bookshelfAbsorption,
    scattering: bookshelfScattering
)
```

Resources:
- Gaussian splats on visionOS: https://developer.apple.com/documentation/visionOS/gaussian-splats-on-visionos

Chapters:
- 0:00 Introduction: Introduces RealityKit's 2026 updates across lighting, navigation, cloth, performance, Gaussian splats, and immersive audio. The session frames the features through a sample game, Chaparral Village, and points to Reality Composer Pro 3 sessions for authoring workflows.
- 2:00 Lighting and shadows: Covers lightmaps for static indirect lighting and ambient occlusion, soft shadows for dynamic lights, projective textures, and physical space lighting. Demonstrations show softening a hearth spotlight's shadows and projecting stars from a virtual planetarium onto physical surroundings.
- 7:44 Navigation mesh: Explains how navigation meshes define traversable areas, traversal costs, and off-mesh connections. Shows how NavigationMeshResource, NavigationComponent, and NavigationController work together to compute paths asynchronously and handle path nodes.
- 11:01 Cloth simulation: Describes RealityKit cloth as a particle-and-spring mesh and introduces ClothBodyComponent, ClothColliderComponent, and ClothSimulationComponent. Shows pinning curtain vertices by selecting vertices near anchor points and marking them kinematic.
- 13:42 Performance: Introduces mesh level of detail as a way to reduce rendering cost when detail is not visible. Demonstrates LOD switching by camera distance or screen area and recommends reacting to serious or critical thermal states with quality reductions.
- 17:09 3D Gaussian splats: Presents RealityKit support for rendering high-fidelity real-world captures as 3D Gaussian splats. The API accepts buffers for position, scale, rotation, opacity, and spherical harmonics, then renders them through GaussianSplatComponent.
- 19:08 Immersive audio: Explains custom reverb meshes for simulating spatial audio reflections and reverb based on environment geometry and materials. Shows shoebox reverb setup, preset materials, custom absorption and scattering data, and notes that custom reverb meshes work only in immersive spaces.
- 22:42 Next steps: Recaps the covered RealityKit advances and mentions additional updates such as coordinated multi-source audio, high-quality character rendering, and portal customizations. Recommends downloading samples and exploring Reality Composer Pro 3 resources.

### Iterate your spatial scenes faster with Reality Composer Pro 3

- Session ID: wwdc2026-280
- Page: https://wwdc.ai/2026/280
- Markdown: https://wwdc.ai/2026/280.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/280/
- Category: Spatial Computing
- Description: Use Reality Composer Pro 3 to import USD assets, build entity-component scenes, reuse prototypes, live preview on Apple Vision Pro, bake lightmaps, and generate 3D content with AI.
- Duration: 16:44
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-280/eng_1dfbbf420de2/wwdc2026-280-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/280/4/0f02d465-7874-4ac3-aac3-b1b792efecd3/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/280/4/0f02d465-7874-4ac3-aac3-b1b792efecd3/downloads/wwdc2026-280_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/280/4/0f02d465-7874-4ac3-aac3-b1b792efecd3/downloads/wwdc2026-280_sd.mp4?dl=1

Use Reality Composer Pro 3 to import USD assets, build entity-component scenes, reuse prototypes, live preview on Apple Vision Pro, bake lightmaps, and generate 3D content with AI.

TLDR:
- Reality Composer Pro 3 is a standalone app downloaded from developer.apple.com, designed for fast spatial-content iteration without needing to round-trip through Xcode.
- Scenes are built from entities and components; the session demonstrates importing USD assets, adding lights, physics/audio-style components, and using Compute Simulation with Compute Graphs for GPU-driven effects.
- New prototypes let developers turn entities into reusable assets, instantiate them throughout a scene, override instance properties, reset overrides, or propagate changes back to the source.
- Live Preview targets a connected Apple Vision Pro for instant in-headset updates, Lightmaps bake static indirect lighting/AO/beauty results, and Reality Composer Pro Assistant generates 3D objects and materials from prompts.

## Reality Composer Pro 3 workflow

Reality Composer Pro 3 is presented as a standalone tool rather than an Xcode developer tool. It can be downloaded from developer.apple.com and launched directly from the Applications folder.

The session uses the Chaparral Village sample project, specifically an Alchemy Area scene. Assets were modeled in Blender, imported as USD files, and arranged in Reality Composer Pro.

- Use the Project Browser import button to add a USD asset.
- Imported USD content is organized and optimized into an import bundle containing geometry, materials, textures, and related data.
- Drag an imported bundle into the viewport to add it to the scene as an entity.
- Use Focus Mode and the hierarchy/inspector panels to inspect and edit scene content.

## Entities, components, and simulation

Reality Composer Pro 3 uses an entity-component model. Entities appear in the hierarchy, can be reordered or nested, and are configured through components in the inspector.

The demo imports a cauldron, parents it under a fireplace entity, adjusts its Transform Component, then creates a Magic Effect entity with child content for lighting and simulation.

- Entities can have children, making it possible to organize scene structure around logical objects or effects.
- Components add behavior or data such as transforms, lights, physics, audio, and simulation.
- A Point Light component is used for a glow effect, with position, attenuation, color, and intensity adjusted in the inspector.
- A Compute Simulation component can reference a Compute Graph from the project using the Compute Graph picker.
- Compute Graphs are node-based GPU simulations suitable for effects such as particle systems and more complex simulations; they run during simulation.

## Iterating in the editor with prototypes

The simulation tab lets authored content run directly inside Reality Composer Pro, so developers can tune effects, physics, script graphs, and animations without a separate deployment step.

Prototypes add a reusable asset workflow. Any entity can be dragged from the hierarchy into the Project Browser to create a prototype asset, then dragged back into the scene to create instances.

- Instances can override selected properties without modifying the prototype source.
- Overrides can be reset back to the prototype's source values from the context menu.
- Overrides can also be propagated back to the source when the change should become part of the reusable asset.
- The demo creates a Magic Effect prototype, instantiates it as a Brewing Effect, switches its Compute Graph, and adjusts its glow settings.

## Live Preview and Lightmaps

Live Preview targets a simulation to a connected Apple Vision Pro and opens a companion app on visionOS. The editor continues to author content while updates appear immediately on device; the session notes this Live Preview capability ships later in the year.

Lightmaps address the cost of dynamic indirect lighting for static scenes. When lights do not move, Reality Composer Pro can pre-calculate lighting into texture lightmaps and use those baked results at runtime.

- Live Preview is useful for judging spatial effects, lighting, and physical-space lighting in headset instead of guessing from the Mac viewport.
- A Lightmap component controls which lighting terms are baked and exposes bake-quality settings.
- The Lightmap Preview tab shows the impact of baked lighting before committing to a full bake.
- Supported baked terms called out in the session include Indirect Lighting, Ambient Occlusion, and Beauty Lightmaps.
- The demo raises bake quality from low to high, previews the result, tweaks settings, then regenerates the scene lightmap.

## Reality Composer Pro Assistant and next steps

Reality Composer Pro Assistant is available from the right panel and accepts natural-language prompts. It uses generative models to create 3D objects and materials on demand inside the editor, and can also answer Reality Composer Pro questions.

The demo uses the assistant to add extra workbench items and candles to the scene. Apple recommends downloading Reality Composer Pro 3 from developer.apple.com, exploring sample projects, and watching related Reality Composer Pro sessions for deeper topics.

- Use the Assistant for fast ideation and asset generation directly in the scene-building workflow.
- Use related sessions for deeper coverage of no-code games, RealityKit advances, Xcode extension workflows, and advanced spatial workflows.
- The WWDC23 "Meet Reality Composer Pro" session remains relevant for editor basics.

Chapters:
- 0:00 Introduction: Introduces Reality Composer Pro 3 as a tool for faster spatial-content iteration and previews the session topics: entities and components, prototypes, Live Preview, Lightmaps, and the AI Assistant.
- 2:25 Overview: Explains that Reality Composer Pro 3 is now a standalone download from developer.apple.com, then introduces the Chaparral Village Alchemy Area sample scene and USD import workflow.
- 3:57 Entities and components: Shows how imported content becomes entities in the hierarchy and how components configure behavior and appearance. The demo adds nested entities, a Point Light, and a Compute Simulation component connected to a Compute Graph, then previews the running simulation in the editor.
- 8:45 Prototypes and instances: Demonstrates creating a prototype by dragging an entity into the Project Browser, instantiating it, overriding instance properties, resetting an override, and preserving the source unless changes are intentionally propagated.
- 11:06 Live preview: Shows Live Preview targeting a connected Apple Vision Pro through a visionOS companion app so edits made in Reality Composer Pro appear immediately in headset.
- 11:57 Lightmaps: Explains using a Lightmap component to bake static indirect lighting into textures for better visual fidelity without dynamic global illumination cost. The chapter covers bake quality, the Lightmap Preview tab, and supported terms including Indirect Lighting, Ambient Occlusion, and Beauty.
- 14:43 Reality Composer Pro Assistant: Introduces the AI Assistant in the right panel, which can generate 3D objects and materials from prompts and answer Reality Composer Pro questions. The demo uses it to add objects such as workbench items and candles.
- 16:07 Next steps: Recaps key capabilities and points developers to download Reality Composer Pro 3, explore sample projects, and watch related Reality Composer Pro and RealityKit sessions.

### Extend Reality Composer Pro 3 functionality with Xcode

- Session ID: wwdc2026-281
- Page: https://wwdc.ai/2026/281
- Markdown: https://wwdc.ai/2026/281.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/281/
- Category: Spatial Computing
- Description: Use Xcode-built Reality Composer Pro 3 plugins to expose custom RealityKit components, systems, animation actions, and Script Graph nodes to artists.
- Duration: 22:02
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-281/eng_02c5b36e3079/wwdc2026-281-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/281/6/1aef704f-ccc6-4c1d-b7b7-94da42d29609/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/281/6/1aef704f-ccc6-4c1d-b7b7-94da42d29609/downloads/wwdc2026-281_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/281/6/1aef704f-ccc6-4c1d-b7b7-94da42d29609/downloads/wwdc2026-281_sd.mp4?dl=1

Use Xcode-built Reality Composer Pro 3 plugins to expose custom RealityKit components, systems, animation actions, and Script Graph nodes to artists.

TLDR:
- Reality Composer Pro 3 can load project-specific plugin frameworks built in Xcode, letting editor content and app code share custom RealityKit types in one repository.
- Custom `Component` types must be `Codable` to appear in the editor and serialize to Reality Files; register components and systems from a `RealityComposerProPlugin`.
- Plugins can run RealityKit systems live in the editor, including driving child entities and `ShaderGraphMaterial` parameters from inspector-editable component properties.
- Custom `EntityAction` types can appear in the sequencer timeline, and `@Scriptable` can expose component schemas as custom Script Graph nodes for no-code workflows.

## Plugin architecture and team workflow

Reality Composer Pro 3 supports Xcode-built plugins so engineers can expose project-specific data and behavior directly inside the editor. Artists and designers can edit custom components, preview runtime behavior, and iterate without repeatedly building and deploying the app.

A typical project keeps the Reality Composer Pro project and Xcode project in the same git repository. The Xcode project builds both the final app and a plugin framework, while Reality Composer Pro authors scenes and exports Reality Files consumed by the app.

- Use the editor's simulation bar / Run With Xcode workflow to link the Reality Composer Pro project with the Xcode project.
- The Xcode project can have separate schemes for the app and the plugin framework, sharing custom RealityKit code between both.
- Imported assets are converted into Reality Composer Pro's internal JSON-backed data format; the editor also provides a custom merge tool intended to reduce content merge conflicts.
- When opening a project with plugins, Reality Composer Pro asks the user to trust the plugin before loading it.
- After rebuilding a plugin, restart Reality Composer Pro to load the updated dynamic library.

## Register custom components and systems

The cauldron example starts with a custom `Cauldron` component that stores a water level. Components exposed to Reality Composer Pro 3 must conform to `Codable` so they can be represented in the editor and serialized to Reality Files.

A custom RealityKit `System` queries entities with the component and runs inside the editor through the plugin. In the example, it finds a child water mesh and changes its position based on the component's inspector-editable `waterLevel` property.

- Implement custom data as a RealityKit `Component`.
- Implement editor/runtime behavior as a RealityKit `System`.
- Register both from a type conforming to `RealityComposerProPlugin`.
- Export `createRealityComposerProPlugin` with `@_cdecl` so the plugin loader can create the plugin instance.
- Debug plugin code by setting breakpoints in Xcode and attaching to the Reality Composer Pro application.

### Minimal custom component and system

Defines editor-serializable component state and a system that applies it live to the scene.

```swift
import RealityKit

public struct Cauldron: Component, Codable {
    public var waterLevel: Float
}

public struct CauldronSystem: System {
    let query = EntityComponentQuery(Cauldron.self)

    public init(scene: Scene) {}

    public func update(context: SceneUpdateContext) {
        for (entity, cauldron) in context.entities(matching: query) {
            guard let water = entity.findEntity(named: "Cauldron_Water_mesh") else { continue }
            water.setPosition(SIMD3<Float>(0, 1, 0) * cauldron.waterLevel,
                              relativeTo: entity)
        }
    }
}
```

### Reality Composer Pro plugin registration

Registers custom RealityKit types with the editor and exposes the plugin entry point.

```swift
import RealityComposerPro

final class RCPCustomComponentsPlugin: RealityComposerProPlugin {
    public func setup(context: any RealityComposerProContext) {
        context.registerComponent(Cauldron.self)
        context.registerSystem(CauldronSystem.self)
    }
}

@_cdecl("createRealityComposerProPlugin")
public func createRealityComposerProPlugin() -> UnsafeMutableRawPointer {
    return RCPCustomComponentsPlugin().passRetained()
}
```

## Drive Shader Graph materials from component data

The session extends the component with vortex-related properties, then updates the system so those properties drive a `ShaderGraphMaterial`. This lets a tech artist build the visual shader in Reality Composer Pro while engineers expose higher-level controls in Swift.

The system retrieves the water mesh's `ModelComponent`, casts the first material to `ShaderGraphMaterial`, computes derived surface parameters, sets named shader parameters, and writes the modified material back to the model.

- Use component properties such as `rotationSpeed`, `minWaterLevel`, `maxWaterLevel`, and `vortexCoeff` as editor-facing controls.
- Use `ShaderGraphMaterial.setParameter(name:value:)` to pass computed values into the shader graph.
- After changing the component's stored properties, Reality Composer Pro shows a change-acceptance dialog when the rebuilt plugin is loaded.

### Update Shader Graph parameters from a RealityKit system

Shows the core pattern for propagating component state into a Shader Graph material each update.

```swift
guard var model = water.components[ModelComponent.self] else { continue }
guard var mat = model.materials.first as? ShaderGraphMaterial else { continue }

let surface = computeSurface(cauldron: cauldron)
try? mat.setParameter(name: "Level Radius", value: .float(surface.levelRadius))
try? mat.setParameter(name: "Lowest Point", value: .float(cauldron.waterLevel - surface.lowestPoint))
try? mat.setParameter(name: "Height Change", value: .float(surface.heightChange))
try? mat.setParameter(name: "Level Coeff", value: .float(surface.levelCoeff))
try? mat.setParameter(name: "Is Level", value: .bool(surface.isLevel))

model.materials[0] = mat
water.components.set(model)
```

## Add custom animation actions to the sequencer

Reality Composer Pro 3's animation sequencer can use custom animation actions defined in a plugin. The cauldron example implements `SetWaterLevelAction`, which animates the component's `waterLevel` between start and end values on a sequencer timeline.

The action conforms to `EntityAction` and `Codable`. Execution is implemented by subscribing to `EntityAction` `.updated` events, computing normalized playback time, interpolating the water level, and writing the updated component back to the target entity.

- Return an `animatedValueType`; the example uses `Transform.self` to access the target entity in the animation executor.
- Register the action with `context.registerAction`.
- Call the action's subscription setup from plugin loading code so animation updates actually execute the action.
- In the editor, the custom action appears in the sequencer and can be dragged onto an animation track.

### Custom EntityAction for water-level animation

Defines the serializable action parameters that appear in the sequencer inspector.

```swift
import RealityKit

public struct SetWaterLevelAction: EntityAction, Codable {
    public let startWaterLevel: Float
    public let endWaterLevel: Float

    public var animatedValueType: (any AnimatableData.Type)? {
        Transform.self
    }
}
```

### Subscribe to animation updates and register the action

Executes the custom action during playback and makes it available to Reality Composer Pro.

```swift
extension SetWaterLevelAction {
    static func subscribe() {
        Task { @MainActor in
            SetWaterLevelAction.subscribe(to: .updated) { event in
                let normalizedTime = (event.playbackController.time - event.startTime) / event.duration
                let action = event.action
                let currentLevel = action.startWaterLevel +
                    Float(normalizedTime) * (action.endWaterLevel - action.startWaterLevel)

                guard let entity = event.targetEntity else { return }
                guard var cauldron = entity.components[Cauldron.self] else { return }
                cauldron.waterLevel = currentLevel
                entity.components.set(cauldron)
            }
        }
    }
}

public func setup(context: any RealityComposerProContext) {
    context.registerComponent(Cauldron.self)
    context.registerSystem(CauldronSystem.self)
    context.registerAction(SetWaterLevelAction.self)
    SetWaterLevelAction.subscribe()
}
```

## Expose custom types to Script Graph

Plugins can also add custom Script Graph nodes so designers can use project-specific Swift types in no-code graphs. The quickest path shown is annotating a custom component with `@Scriptable`, which generates a schema for scripting.

Register scripting modules on the main thread using `RealityKitScripting`. Once registered, the editor can show nodes for the custom component, such as setting the cauldron water level in response to keyboard input in a Script Graph.

- Import `RealityKitScripting` and `RealityKitScriptingMacros`.
- Add `@Scriptable` to the custom component type.
- Create an `RKS.Configuration` with one or more `Module` definitions.
- Add the generated schema, such as `Cauldron.SchemaProvider.schema`, to the module.
- Call `RKS.addConfiguration` from the plugin setup path on the main actor.

### Mark a component scriptable

Generates a schema that can be registered with the Script Graph system.

```swift
import RealityKit
import RealityKitScripting
import RealityKitScriptingMacros

@Scriptable
public struct Cauldron: Component, Codable {
    public var waterLevel: Float
    public var rotationSpeed: Float
    public var minWaterLevel: Float
    public var maxWaterLevel: Float
    public var vortexCoeff: Float
}
```

### Register a scripting module

Makes the generated custom component schema available as Script Graph nodes in the editor.

```swift
Task { @MainActor in
    let config = RKS.Configuration(id: "ChaparralVillage")
        .onInitialize { _ in
            [
                Module("ChaparralVillage") {
                    Cauldron.SchemaProvider.schema
                }
            ]
        }

    try! RKS.addConfiguration(config)
}
```

Chapters:
- 0:00 Introduction: Introduces Reality Composer Pro 3 plugins as a way to expose project-specific Swift code to artists and content creators inside the editor. The session previews custom components, systems, animation actions, and Script Graph nodes running live in the editor.
- 2:00 Extending the editor: Explains the editor/Xcode project relationship: the app and plugin framework are built from Xcode, while Reality Composer Pro authors scenes and exports Reality Files. It also covers shared git workflows, JSON-backed editor data, merge tooling, plugin trust, and separate app/plugin schemes.
- 4:51 Custom components and systems: Builds a `Cauldron` RealityKit component and `CauldronSystem` to control a water mesh from an inspector-editable `waterLevel`. The plugin registers the component and system so Reality Composer Pro can display the component and run the system in the editor.
- 10:32 Controlling the water surface: Adds vortex-related properties to the cauldron component and updates the system to drive a `ShaderGraphMaterial`. The editor reload flow shows accepting component changes and tuning shader-driven visual behavior from the inspector.
- 13:19 Custom animation actions: Implements `SetWaterLevelAction` as a custom `EntityAction` that interpolates component state during sequencer playback. The action is registered with the plugin and then used on a timeline track to animate the cauldron water level.
- 17:12 Custom Script Graph nodes: Uses `@Scriptable` plus `RealityKitScripting` registration to expose the custom component schema as Script Graph nodes. The example builds a graph that changes water level in response to keyboard input and tests it in simulation.
- 21:16 Next steps: Recaps the plugin capabilities: custom components, systems, animation actions, and Script Graph nodes. It points developers toward related sessions on RealityKit advances and Reality Composer Pro 3 workflow improvements.

### Discover the Spatial Preview framework

- Session ID: wwdc2026-282
- Page: https://wwdc.ai/2026/282
- Markdown: https://wwdc.ai/2026/282.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/282/
- Category: Spatial Computing
- Description: Use Spatial Preview to send Mac app documents and live USDKit stages to Quick Look on visionOS with device discovery, sync, editing, and review tools.
- Duration: 14:45
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-282/eng_96eecfd75eef/wwdc2026-282-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/282/5/958c34c9-f20e-4c6d-826a-eeed7ce7ba9e/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/282/5/958c34c9-f20e-4c6d-826a-eeed7ce7ba9e/downloads/wwdc2026-282_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/282/5/958c34c9-f20e-4c6d-826a-eeed7ce7ba9e/downloads/wwdc2026-282_sd.mp4?dl=1

Use Spatial Preview to send Mac app documents and live USDKit stages to Quick Look on visionOS with device discovery, sync, editing, and review tools.

TLDR:
- Spatial Preview lets macOS apps share content to Apple Vision Pro through Mac Virtual Display or a device picker; Quick Look on visionOS presents the content and no visionOS app code is required.
- DocumentPreviewSession is for files such as Apple Immersive Video frames, spatial photos, PDFs, images, and other documents; updateContents(url:) swaps content within an existing scene.
- USDPreviewSession shares a live USDKit USDStage for 3D review and editing, with built-in camera viewpoints, material overrides, immersive viewing, annotations, object manipulation, variants, export, and playback events.
- Spatial Preview optimizes USD assets by default for visionOS performance; apps can opt out with .unmodified but must handle unshareable complex assets.

## What Spatial Preview provides

Spatial Preview is a macOS framework for extending content from a Mac app into visionOS. A Mac app selects a SpatialPreviewEndpoint, creates a preview session, and starts it; Quick Look launches on Apple Vision Pro and displays the provided content.

The framework supports two main session types: DocumentPreviewSession for file-based content and USDPreviewSession for live 3D content backed by a USDKit stage. The session is driven from macOS, so adopting it does not require writing a visionOS companion app.

- Use the currently connected Mac Virtual Display endpoint when available.
- Use SpatialPreviewDevicePicker to let people choose a nearby Apple Vision Pro on the same iCloud account.
- Use DocumentPreviewSession for spatial media, PDFs, images, and other supported files.
- Use USDPreviewSession for 3D scenes represented as USD.

## Document Preview workflows

Document Preview is the lightweight path for sharing files from a Mac app to visionOS. The session starts on a chosen endpoint, then the app provides a content URL that Quick Look displays. The session example uses Apple Immersive Video frames, but the same pattern applies to spatial photos, PDFs, standard images, files, and supported 3D content.

For gallery-like experiences, reuse the same DocumentPreviewSession and call updateContents(url:) to replace the displayed file in place. Creating a new session and calling start again opens a new scene instead of updating the existing one.

- Create a ConnectedSpatialEndpointObserver to obtain an endpoint from Mac Virtual Display.
- Create DocumentPreviewSession with a display name and content type.
- Start the session, then call updateContents(url:) with the file URL.
- Observe session.state to detect invalidation when the scene is closed on visionOS.
- Call close() when finished to end the session and dismiss the scene.

### Start a document preview and show the device picker

Integrates endpoint selection into a SwiftUI macOS app, then starts a DocumentPreviewSession and sends a file to Quick Look on visionOS.

```swift
import SwiftUI
import SpatialPreview

let deviceObserver = ConnectedSpatialEndpointObserver()
let previewSession = DocumentPreviewSession(name: "Immersive.aivu", contentType: .aivu)

func startPreview(contentURL: URL, endpoint: SpatialPreviewEndpoint) async throws {
    try await previewSession.start(endpoint: endpoint)
    try await previewSession.updateContents(url: contentURL)
}

@State var showDevicePicker = false

var body: some View {
    // ...
    .sheet(isPresented: $showDevicePicker) {
        SpatialPreviewDevicePicker(isPresented: $showDevicePicker) { endpoint in
            showDevicePicker = false
            Task { try await startPreview(contentURL: contentURL, endpoint: endpoint) }
        }
    }
}
```

### Update an existing document preview session

Uses updateContents(url:) to swap files within the same launched scene, observes invalidation, and closes the session when done.

```text
ForEach(contentURLs, id: \.self) { url in
    Button {
        Task { try await previewSession?.updateContents(url: url) }
    }
}
.task(id: previewSession.map { ObjectIdentifier($0) }) {
    for await state in Observations({ session.state }) {
        if state.isInvalidated {
            previewSession = nil
            break
        }
    }
}

try await previewSession?.close()
```

## USD Preview for live 3D content

USD Preview shares a USDKit stage from a macOS app to visionOS. When the USDPreviewSession starts, the content appears in a bounded 3D view and can be opened immersively at full scale. Quick Look provides built-in 3D review features such as navigating scene cameras and applying material overrides like wireframe mode.

Spatial Preview automatically optimizes USD content before sharing to help it perform on Apple Vision Pro. Optimization can include mesh decimation, texture downsampling, and potentially full scene reconstruction. Reconstructed scenes may be viewable and annotatable but not editable.

- Open USD content with USDKit as a USDStage.
- Create USDPreviewSession(stage:) and start it on the chosen SpatialPreviewEndpoint.
- Use the default optimized path for complex assets unless exact unmodified sharing is required.
- If using .unmodified, handle USDPreviewSession.Error.assetUnshareable when the asset is too complex to share.

### Start a USD preview session

Loads a USD asset with USDKit, creates a live USDPreviewSession, and shares it to the selected Vision Pro endpoint.

```swift
import SpatialPreview
import USDKit

let deviceObserver = ConnectedSpatialEndpointObserver()
var usdSession: USDPreviewSession?

func shareStage(to endpoint: SpatialPreviewEndpoint) async throws {
    let stageURL = Bundle.main.url(forResource: "sampleScene", withExtension: "usdz")!
    let stage = try USDStage.open(stageURL)
    usdSession = USDPreviewSession(stage: stage)
    try await usdSession?.start(endpoint: endpoint)
}
```

### Request unmodified USD sharing

Opts out of Spatial Preview optimization and handles the error case for assets that cannot be shared as-is.

```text
do {
    try await usdSession.start(endpoint: endpoint, parameters: .unmodified)
} catch USDPreviewSession.Error.assetUnshareable {
    // Handle Asset Unshareable error
}
```

## Live USD editing and Quick Look features

A USDPreviewSession uses a live USD stage to synchronize edits between macOS and visionOS. Changes made with normal USDKit APIs on the Mac are reflected in Quick Look, and supported edits made in Quick Look are reflected back into the stage.

The session demonstrates variants for changing furniture layout, annotations represented as USD data, and object manipulation controlled by metadata. Annotations appear on visionOS when they are children of the document annotation group, and gesture manipulation requires the target prims to be marked spatially editable.

- Use USD variant sets to switch between alternative scene data such as layout positions and rotations.
- Observe UsdStage.ObjectsDidChange to react to edits arriving from visionOS.
- Use AppleTextAnnotation-style data for text, author, and a stable identifier when modeling annotations.
- Set apple.spatialEditable metadata on prims that should be moveable with gestures in Quick Look.
- Preview on macOS can help configure spatial-editable metadata on USD assets.

### Apply a layout variant with USDKit

Changes a USD variant selection in the macOS app; the live stage synchronizes the resulting layout to visionOS.

```swift
func applyLayoutVariant(named layoutVariantName: String) throws {
    let prim = stage.prim(at: SdfPath("/root/furniture"))
    try prim.variantSets?.setSelection("Layout", variantName: layoutVariantName)
}
```

### Observe USD changes and enable manipulation metadata

Listens for synchronized stage changes, such as annotations from Quick Look, and shows the metadata required for gesture-based object manipulation.

```swift
observerToken = stage.addObserver(for: UsdStage.ObjectsDidChange.self) { notice in
    for path in notice.resyncedPaths {
        let prim = notice.stage.prim(at: path)
        guard prim.isValid else { continue }
        if prim.isAnnotation {
            // Handle annotation change
            break
        }
    }
}

// Metadata required for object manipulation in Quick Look:
// customData = { dictionary apple = { bool spatialEditable = 1 } }
```

## Session options, events, progress, and next steps

USDPreviewSession exposes configurable Quick Look features and non-USD session events. By default, annotations, object manipulation, and USD export are enabled; apps can also pass explicit options at start time. Event streams can report animation playback changes, and observable progress can drive loading UI while large assets synchronize.

For new integrations, Apple recommends using the USDKit Swift APIs directly with Spatial Preview. Existing USD-based Mac apps can bridge their USD implementation to USDKit to transfer edits. Related follow-up topics include USDKit, OpenUSD fundamentals, asset rendering-cost reduction on visionOS, and collaborative structured 3D model workflows.

- Start USD sessions with options such as .annotations, .perObjectManipulation, and .export when customizing available Quick Look tools.
- Listen to session.events for timeChanged and playbackStateChanged to keep Mac-side playback UI synchronized.
- Observe session.progress.fractionCompleted to display transfer or synchronization progress.
- SharePlay support on visionOS allows collaborators to join the same live review and editing session.

### Configure USD session options and listen for playback events

Controls Quick Look editing features and synchronizes animation state back to the macOS app.

```swift
try await session.start(endpoint: endpoint, options: [.annotations, .perObjectManipulation, .export])

func listenForEvents(session: USDPreviewSession) async {
    for await event in session.events {
        if case .timeChanged(let time) = event {
            playbackModel.timeCode = time
        } else if case .playbackStateChanged(let isPlaying) = event {
            playbackModel.playbackStateChanged(isPlaying)
        }
    }
}
```

### Observe sharing progress

Displays progress in the Mac UI while USD content is synchronized to Apple Vision Pro.

```text
@State private var sessionProgress: Double = 0

.task(id: usdSession.map { ObjectIdentifier($0) }) {
    guard let session = usdSession else { return }
    for await fraction in Observations({ session.progress.fractionCompleted }) {
        sessionProgress = fraction
    }
}
.overlay(alignment: .bottom) {
    ProgressView(value: sessionProgress).padding()
}
```

Resources:
- Reducing the rendering cost of RealityKit content on visionOS: https://developer.apple.com/documentation/visionOS/reducing-the-rendering-cost-of-RealityKit-content-on-visionOS
- Spatial Preview: https://developer.apple.com/documentation/SpatialPreview

Chapters:
- 0:00 Introduction: Introduces Spatial Preview as a macOS and visionOS 27 framework for sending content from Mac apps to Apple Vision Pro, using Mac Virtual Display and Quick Look. The chapter frames the session around document preview and USD-based 3D workflows with live synchronization.
- 2:37 Learn about Spatial Preview: Explains the core model: select a spatial endpoint, create a preview session, and start it so Quick Look opens on visionOS. It distinguishes DocumentPreviewSession from USDPreviewSession and notes that no visionOS code is required.
- 3:30 Document Preview: Shows how a Mac app can send Apple Immersive Video frames and other documents to visionOS using ConnectedSpatialEndpointObserver, DocumentPreviewSession, and SpatialPreviewDevicePicker. It also covers updateContents(url:) for swapping gallery items within one scene, observing invalidation, and closing sessions.
- 6:36 USD Preview: Demonstrates sharing a USDKit stage through USDPreviewSession so 3D content appears in a volumetric view and can be inspected immersively. It covers built-in camera/material review features and the default USD optimization path, including the .unmodified opt-out and assetUnshareable handling.
- 9:16 Editing Features: Covers live bidirectional USD editing between macOS and visionOS, including layout variants, observing USD notices, annotations, and gesture-based object manipulation via spatialEditable metadata. It also introduces session options, playback events, progress observation, and SharePlay collaboration.
- 13:28 Next steps: Recommends adopting Spatial Preview with USDKit Swift APIs or bridging existing USD implementations to USDKit. The chapter encourages developers to explore both preview session types and Quick Look asset review tools such as material overrides, camera viewpoints, variants, annotations, object manipulation, and export.

### Explore enhancements to visionOS object tracking

- Session ID: wwdc2026-283
- Page: https://wwdc.ai/2026/283
- Markdown: https://wwdc.ai/2026/283.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/283/
- Category: Spatial Computing
- Description: Track moving objects at high frame rates in visionOS 27, bring reference-object tracking to iOS 27, and build custom spatial accessories for Vision Pro.
- Duration: 14:05
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-283/eng_0bf6e2863ea0/wwdc2026-283-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/283/4/22b92960-c65b-450f-b42c-6d6bff64a9b4/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/283/4/22b92960-c65b-450f-b42c-6d6bff64a9b4/downloads/wwdc2026-283_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/283/4/22b92960-c65b-450f-b42c-6d6bff64a9b4/downloads/wwdc2026-283_sd.mp4?dl=1

Track moving objects at high frame rates in visionOS 27, bring reference-object tracking to iOS 27, and build custom spatial accessories for Vision Pro.

TLDR:
- visionOS 27 adds high-frame-rate object tracking for moving and handheld objects, plus extended Create ML training for improved robustness.
- ARKit can now return object poses either with rendered display corrections or in metric space for measurement and physical-coordinate workflows.
- Object tracking comes to iOS 27 using the same trained reference object files, with separate APIs for mostly stationary detection and high-frame-rate moving-object tracking.
- Custom spatial accessories can combine LEDs, IMU, Bluetooth, inputs, and haptics; apps discover them with Game Controller and track them with ARKit's AccessoryTrackingProvider.

## What changed for object tracking

visionOS object tracking still starts from a USDZ model trained into a reference object, then uses ARKit anchors to give apps the physical object's position and orientation. visionOS 27 expands that model to support moving and handheld objects, including higher-frame-rate tracking for objects that move through space.

The session positions object tracking as the best fit when accuracy and precision matter, especially for measuring physical spaces or distances. If a photorealistic model of the object is unavailable, a developer can instead train and mount a marker object to the item being tracked.

- Enable high-frame-rate tracking per reference object with `ReferenceObject.Configuration`.
- Use the new extended training mode in Create ML for better accuracy and robustness, especially with handheld objects.
- Use metric-space poses when absolute physical coordinates matter, rather than visually corrected poses optimized for rendering.
- The same trained reference object can be used by both visionOS and iOS apps.

### Enable high-frame-rate tracking for a reference object

High-frame-rate tracking is configured when loading a reference object; it is not a training-time setting and can be applied based on the app's needs.

```swift
var configuration = ReferenceObject.Configuration()
configuration.highFrameRateTrackingEnabled = true

let refObjURL = Bundle.main.url(
    forResource: "flashlight",
    withExtension: ".referenceobject"
)!
let refObject = try await ReferenceObject(
    from: refObjURL,
    configuration: configuration
)
```

## Training and pose accuracy

Create ML gains an extended training mode for object tracking. It is recommended alongside high-frame-rate tracking when robustness is important, but it takes significantly longer than standard training.

ARKit's coordinate-space correction API lets apps choose between poses optimized for visual alignment and poses in metric space. Rendered poses keep virtual content aligned with the camera imagery, while uncorrected poses are intended for measurement and physical-coordinate calculations.

- Use standard or extended training mode in the Create ML Object Tracking template.
- Use the command-line workflow when training should run outside the Create ML app, including on a remote machine.
- Use `.rendered` for mixed-immersion rendering alignment.
- Use `.none` for metric-space measurements, distances between tracked objects, or physical placement calculations.

### Train a reference object with extended mode from the command line

Extended training can be selected from the command-line object tracker workflow as well as from the Create ML app.

```text
xrun createml objecttracker \
  --source flashlight.usdz \
  --output flashlight.referenceobject \
  --training-mode extended \
  --all-angles
```

### Request rendered or metric-space object poses

Use corrected poses for visual alignment and uncorrected metric poses for measurement workflows.

```swift
let renderingPose = myObjectAnchor.coordinateSpace(correction: .rendered)
let metricPose = myObjectAnchor.coordinateSpace(correction: .none)
```

## Object tracking on iOS 27

iOS 27 adds support for reference objects in ARKit APIs. Reference-object training is not platform-specific, so the same `.referenceobject` files can be shared between visionOS and iOS apps.

On iOS, apps load `ARReferenceObject` files, configure `ARWorldTrackingConfiguration`, and receive `ARObjectAnchor` updates through `ARSessionDelegate`. The API distinguishes mostly stationary objects from moving objects that should be tracked at high frame rate.

- Assign mostly stationary reference objects to `detectionObjects`.
- Assign moving reference objects to `trackingObjects`.
- Handle `didAdd`, `didUpdate`, and `didRemove` to create, update, hide, or remove associated RealityKit content.

### Configure object tracking on iOS

Use `detectionObjects` for low-frame-rate stationary detection and `trackingObjects` for high-frame-rate tracking of moving objects.

```swift
let stationaryObject = try ARReferenceObject(
    archiveURL: Bundle.main.url(
        forResource: "stationary",
        withExtension: "referenceobject"
    )!
)
let movingObject = try ARReferenceObject(
    archiveURL: Bundle.main.url(
        forResource: "moving",
        withExtension: "referenceobject"
    )!
)

let configuration = ARWorldTrackingConfiguration()
configuration.detectionObjects = [stationaryObject]
configuration.trackingObjects = [movingObject]

arView.session.delegate = self
arView.session.run(configuration)
```

## Custom spatial accessories

A spatial accessory is an electronic device that Vision Pro tracks in real time. It must include a visible LED constellation, an IMU, and Bluetooth communication; it can also include inputs such as buttons or a touchpad and outputs such as haptics.

Spatial accessories are aimed at faster, lower-latency, more robust tracking than vision-only object tracking, including temporary occlusion handling and lower-light scenarios. They are a better fit for fast-moving interactive objects or physical controls that need buttons and haptic feedback.

- Distribute LEDs to create a distinct pattern from expected viewing angles.
- Rigidly fix the LEDs and IMU to the board for accurate tracking.
- For handheld accessories, place LEDs where users are unlikely to cover them and consider battery size and ergonomics.
- For larger or distant accessories, account for LED count, size, and spacing so Vision Pro can track them accurately.
- Consult the Spatial Accessories chapter of the Accessory Design Guidelines for Apple Devices for detailed requirements and reference designs.

## Validate and prepare a spatial accessory

The validation workflow starts by pairing the accessory over Bluetooth with Vision Pro, then using the ARKit accessory tracking debug view on a device in Developer Mode. The debug view helps inspect the LEDs as seen by the headset's IR camera, validate IMU frequency, latency, axis values, scale, alignment, and motion response, and debug timing using the headset's IR illuminators as a sync reference.

To make an accessory trackable by apps, create an annotated USDZ that includes a photorealistic model plus the IMU and LED positions, then generate a reference accessory file. Accessory manufacturers bundle that file in an app and declare it as an exported UTType so it is registered system-wide. App developers using a third-party accessory can bundle the file and declare it as an imported type for app-local independence.

- Use the ARKit accessory tracking debug view from Settings while Vision Pro is in Developer Mode.
- Generate a `.referenceaccessory` bundle from an annotated USDZ of the accessory design.
- Export the UTType when distributing the accessory definition as the manufacturer.
- Import the UTType when bundling a third-party accessory definition only for your app.
- DFRobot and MIKROE reference hardware and development kits are called out as plug-and-play options for testing and app development.

## Connect accessories in an app

Apps discover generic spatial accessories through `GCSpatialAccessory` from Game Controller. ARKit resolves the `.referenceaccessory` bundle when creating an `Accessory`, then tracks it with `AccessoryTrackingProvider`.

visionOS 27 also adds an `updateAccessories` method so an app can switch or hot-swap tracked accessories without restarting the ARKit session.

- Use `GCSpatialAccessory.spatialAccessories` for discovery.
- Create an ARKit `Accessory` from the discovered device.
- Run an `AccessoryTrackingProvider` in an ARKit session.
- Call `updateAccessories` to change tracked accessories while the session continues running.

### Discover and track a spatial accessory

`Accessory(device:)` resolves the reference accessory bundle automatically, and `updateAccessories` supports changing accessories without interrupting the running ARKit session.

```swift
import ARKit
import GameController

if let device = GCSpatialAccessory.spatialAccessories.first {
    let accessory = try await Accessory(device: device)
    let provider = AccessoryTrackingProvider(accessories: [accessory])
    try await arkitSession.run([provider])
}

try await provider.updateAccessories([newAccessory])
```

Resources:
- Working with generic spatial accessories: https://developer.apple.com/documentation/visionOS/working-with-generic-spatial-accessories
- Preparing spatial accessories for tracking in your visionOS app: https://developer.apple.com/documentation/ARKit/preparing-spatial-accessories-for-tracking-in-your-visionos-app
- Spatial accessory design guidelines for Apple devices (section 20): https://at.apple.com/vzqbpy
- Exploring object tracking with ARKit: https://developer.apple.com/documentation/visionOS/exploring_object_tracking_with_arkit

Chapters:
- 0:00 Introduction: Introduces visionOS 27 enhancements for object tracking and the expansion of spatial accessories to custom third-party hardware. Examples include handheld measurement tools and a steering-wheel accessory that aligns a physical control with a digital vehicle interior.
- 2:20 Object tracking: Covers high-frame-rate tracking for moving objects, extended Create ML training, metric-space pose access, and object tracking support on iOS 27. Shows how these updates apply to handheld objects, marker-based tracking, measurement, and RealityKit relighting examples.
- 7:20 Spatial accessories: Defines spatial accessories as electronic devices with an LED constellation, IMU, and Bluetooth, optionally augmented with inputs and haptics. Explains that they provide high-frequency, low-latency tracking for fast motion, occlusion, and lower-light scenarios.
- 7:47 Creating a spatial accessory: Explains hardware and ergonomic design considerations for LED placement, rigid IMU/LED mounting, handheld use, larger accessories, and battery placement. Describes validation with the ARKit accessory tracking debug view and preparation of annotated USDZ and reference accessory bundles.
- 11:48 Plug-and-play accessories: Introduces off-the-shelf reference hardware and development kits from manufacturers including DFRobot and MIKROE. These accessories can be used for testing or integrated directly into visionOS apps without designing custom hardware first.
- 12:22 Implementing in your app: Shows how to discover spatial accessories with `GCSpatialAccessory`, create an ARKit `Accessory`, and track it with `AccessoryTrackingProvider`. Also introduces `updateAccessories` for switching accessories without restarting the ARKit session.
- 13:03 Next steps: Summarizes when to choose object tracking, marker-based tracking, spatial accessories, or custom accessories with buttons and haptics. Points developers toward related object tracking and spatial accessory input sessions and documentation.

### Collaborate on structured 3D models in visionOS

- Session ID: wwdc2026-284
- Page: https://wwdc.ai/2026/284
- Markdown: https://wwdc.ai/2026/284.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/284/
- Category: Spatial Computing
- Description: Prepare hierarchical USDZ assemblies for visionOS, then use RealityKit manipulation, clipping, and auto-expansion for collaborative design review.
- Duration: 25:16
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-284/eng_a69f98d8abf8/wwdc2026-284-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/284/4/fa1d15b1-3f28-415a-907a-8ae1bb344494/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/284/4/fa1d15b1-3f28-415a-907a-8ae1bb344494/downloads/wwdc2026-284_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/284/4/fa1d15b1-3f28-415a-907a-8ae1bb344494/downloads/wwdc2026-284_sd.mp4?dl=1

Prepare hierarchical USDZ assemblies for visionOS, then use RealityKit manipulation, clipping, and auto-expansion for collaborative design review.

TLDR:
- Preserve a deep, named USDZ hierarchy so code can find, isolate, hide, animate, and make individual parts interactive instead of treating the model as flattened geometry.
- Use RealityKit component placement to switch between manipulating a whole assembly and manipulating its child entities: move ManipulationComponent and InputTargetComponent between the root and children.
- ClippingComponent enables editable cross sections in visionOS 27; the sample models clipping as .off, .on, and .editing states with six draggable planes controlling an axis-aligned bounds box.
- Auto-expansion chooses a natural expansion axis by computing volume-weighted position variance across child assemblies, then animates parts apart with FromToBy animations.

## Goal: interactive structured assemblies for design review

The session focuses on building spatial design-review experiences for complex 3D assemblies on Apple Vision Pro. The core workflow is to prepare a structured model, let people manipulate either the whole assembly or its individual sub-assemblies, expose internal parts with clipping, and automatically expand the model so relationships between parts are easier to understand.

The collaboration example uses a shared 3D asset in a SharePlay-style design review, but the implementation topics apply to any complex multidimensional asset: mechanical assemblies, buildings, urban planning models, logistics layouts, production design, or real-estate models.

- Use structured 3D data rather than flattened geometry when the app needs selection, hiding, highlighting, animation, or per-part manipulation.
- Treat component placement in the RealityKit entity tree as a behavior switch: root-level components affect the assembly; child-level components affect parts.
- Use clipping and expansion to make internal structure understandable without replacing the model with screenshots or annotations.

## Prepare USDZ assets with a usable hierarchy

The model hierarchy is the foundation for the rest of the workflow. A flattened export can render correctly but still be hard to use because every part becomes an unorganized root-level item with names that are not meaningful to code or users. The session contrasts that with a deep, nested hierarchy where pistons, crankshafts, enclosures, earbuds, boards, and other sub-assemblies are named and grouped.

A good hierarchy lets the app find specific parts, isolate them, hide exterior shells, animate only one component, or allow a person to pull out a sub-assembly while other parts remain in place.

- Preserve part-whole relationships during USDZ export.
- Keep meaningful names and groups for assemblies and sub-assemblies.
- Avoid flattening everything to the root unless the model will only ever be viewed as static geometry.
- For broader asset optimization guidance, consult "Optimize your 3D assets for spatial computing" from WWDC24.

## Switch between whole-assembly and per-part manipulation

RealityKit's ManipulationComponent is used to make an entity movable, rotatable, and scalable with natural input on Apple Vision Pro. The important design pattern is not a new hierarchy: it is moving interactive components to the level of the tree that should respond.

When the assembly is closed, the root has ManipulationComponent and InputTargetComponent so the full model moves as one object. When it is opened, those components are removed from the root and added to each child so parts can be grabbed independently. The sample sets ManipulationComponent.releaseBehavior to .stay so released parts remain where the person placed them.

- Add CollisionComponent to interactive entities; the session calls this critical for event processing even though the code excerpt does not show it.
- Use InputTargetComponent together with ManipulationComponent so entities can receive input.
- Move components down to children to open an assembly; move them back to the root to close it.
- The hierarchy and geometry do not change-only component placement changes behavior.

### Opening an assembly by moving manipulation to children

The root stops being directly manipulable, and each child becomes an input target with its own ManipulationComponent.

```swift
func openAssembly() {
    components[ManipulationComponent.self] = nil
    components[InputTargetComponent.self] = nil

    for child in assemblyChildren {
        child.components.set(InputTargetComponent())

        var manipulation = ManipulationComponent()
        manipulation.releaseBehavior = .stay
        child.manipulationComponent = manipulation
    }
}
```

### Closing an assembly by restoring manipulation to the root

Children stop being individually manipulable, and the full assembly becomes manipulable as one entity again.

```swift
func closeAssembly() {
    for child in assemblyChildren {
        child.manipulationComponent = nil
        child.components[InputTargetComponent.self] = nil
    }

    components.set(InputTargetComponent())

    var manipulation = ManipulationComponent()
    manipulation.releaseBehavior = .stay
    manipulationComponent = manipulation
}
```

## Interactive clipping with editable bounds

Clipping is presented as a new RealityKit capability in visionOS 27 for seeing through complex assemblies. ClippingComponent clips rendered geometry against an axis-aligned bounding box in the entity's local space. The sample sets shouldClipChildren to true so a parent assembly's children are clipped, and relies on shouldClipSelf's default true behavior for the entity itself.

The sample models clipping with three states: .off, .on, and .editing. In .off, no ClippingComponent is active. In .on, ClippingComponent clips the model using cached bounds. In .editing, six visible plane entities appear-one for each face of the bounds box-and people drag those planes to update the clipping bounds.

- ClippingComponent.bounds is the main editable value: an axis-aligned bounding box in model-local space.
- shouldClipChildren defaults to false, so set it when clipping a parent assembly should affect children.
- A custom ClippingBoundsCache stores the last edited bounds for later reuse.
- A custom ClippingTransformSync keeps the clipping controls aligned when the assembly transform changes.
- A ClippingControl entity manages the visible, interactive clipping planes.

## Coordinate transforms for natural clipping-plane dragging

Dragging a clipping plane requires careful coordinate-frame handling. Gesture deltas arrive in the clipping plane's coordinate frame, but ClippingComponent bounds must be updated in the model coordinate frame. The sample transforms the drag delta from clipping-plane space to world space, then to model space, constrains it along the relevant plane normal, updates the bounds, and then converts a constrained delta back for moving the visual plane.

The key math operation is projection: measure how much of the drag delta lies along the axis normal for the selected bounds face, such as +x or -y. This keeps the plane movement constrained to the intended axis even if the person's hand motion is not perfectly aligned.

- The relevant coordinate frames are world, model, clipping control, and clipping plane.
- Each of the six planes controls one scalar value in the clipping bounds.
- Projection constrains the drag delta to the normal of the selected bounding-box face.
- The same idea is used twice: once to update model-space clipping bounds, and once to move the visual plane in its own frame.

## Auto-expansion with volume-weighted variance

Auto-expansion separates an assembly's children along a single axis so nested or overlapping parts become visible and individually grabbable. Rather than forcing the user or developer to choose an axis manually, the sample computes which axis is most meaningful from the children's positions and volumes.

The algorithm calculates volume-weighted position variance along x, y, and z, then expands along the axis with the largest value. In the AirPods Pro case example, the y axis wins because larger parts are farther apart along y. The sample then assembles FromToBy animations to move sub-assemblies into their expanded positions.

- Variance measures how spread out child positions are from their average along an axis.
- Weighting lets larger-volume parts contribute more strongly to the axis choice.
- The chosen axis is the one with the largest volume-weighted variance.
- Use this when a well-structured model loads in its real nested configuration but needs an exploratory exploded view.

Resources:
- Manipulating models with RealityKit: https://developer.apple.com/documentation/RealityKit/manipulating-models-with-realitykit

Chapters:
- 0:00 Introduction: Introduces collaborative spatial design review on Apple Vision Pro using a shared structured 3D model. The session frames the main techniques: asset hierarchy, assembly manipulation, clipping, and automatic expansion.
- 2:55 Asset preparation: Explains why preserving a deep, named hierarchy in a USDZ asset matters. Flattened geometry may render correctly, but it prevents code from reliably finding, isolating, hiding, animating, or manipulating individual parts.
- 5:05 Manipulating the hierarchy: Shows how RealityKit component placement controls whether a whole assembly or individual children are manipulable. The open and close patterns move ManipulationComponent and InputTargetComponent between the root and sub-entities, with releaseBehavior set to .stay.
- 8:15 Interactive clipping: Introduces ClippingComponent and the sample's .off, .on, and .editing clipping states. The chapter explains editable bounds, six draggable clipping planes, custom support components, and coordinate-frame transformations needed to update clipping bounds naturally.
- 18:16 Autoexpansion: Describes automatic exploded-view layout for sub-assemblies. The sample chooses an expansion axis by calculating volume-weighted position variance across child entities and then animates parts apart with FromToBy animations.
- 24:10 Next steps: Reviews the workflow and points developers to sample code and related resources. It also recommends background in statistics, vector math, and linear algebra, plus related sessions on Spatial Preview and object tracking.

### Discover USDKit and what's new in OpenUSD

- Session ID: wwdc2026-285
- Page: https://wwdc.ai/2026/285
- Markdown: https://wwdc.ai/2026/285.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/285/
- Category: Graphics & Games
- Description: Learn how USDKit brings first-class Swift APIs for OpenUSD scenes, compression, accessibility metadata, Spatial Preview, and USD web delivery.
- Duration: 14:33
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-285/eng_cba2f7f68489/wwdc2026-285-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/285/4/335150b6-c2b8-4711-a632-45a34d449eac/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/285/4/335150b6-c2b8-4711-a632-45a34d449eac/downloads/wwdc2026-285_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/285/4/335150b6-c2b8-4711-a632-45a34d449eac/downloads/wwdc2026-285_sd.mp4?dl=1

Learn how USDKit brings first-class Swift APIs for OpenUSD scenes, compression, accessibility metadata, Spatial Preview, and USD web delivery.

TLDR:
- USDKit is a new system framework for Swift apps that open, edit, compose, transform, and export USD stages with RealityKit and Spatial Preview integration.
- OpenUSD support on Apple platforms expands with updated OpenUSD, MaterialX, and OpenVDB, plus standards work for Particle Fields/Gaussian Splats and accessibility metadata.
- Preview on Mac adds 3D editing, asset conversion/compression, renderer selection across RealityKit, Storm, and a new Raytracer, with OpenPBR support.
- USD assets can be compressed through USDKit export options, Preview, or the usdcrush command-line tool using mesh compression and AVIF texture compression.

## OpenUSD foundation updates

USD remains the scene-description foundation for Apple spatial experiences. Apple highlights continued work with Pixar, the Academy Software Foundation, and the Alliance for OpenUSD to make USD a formal industry standard, including the first formal core USD specification and ongoing domain specifications for geometry, materials, and physics.

Apple platforms update OpenUSD, MaterialX, and OpenVDB together, positioning them as a composable foundation for 3D scenes, rich materials, and volumetric data.

- OpenUSD: industry-standard library for describing 3D scenes.
- MaterialX: material-description technology integrated with USD workflows.
- OpenVDB: newly called out for volumetric data in the USD platform stack.
- Particle Fields: a new USD primitive type for Gaussian Splats and related particle-based representations.

## USDKit for Swift apps

USDKit is a new system framework that brings first-class USD support to Swift apps. It is designed to feel familiar to developers who already know USD while using Swift patterns for developers who are new to 3D scene authoring.

The walkthrough frames the core USD model: a Layer is a data file, Composition combines layers, a Stage is the composed scene, and Prims are scene objects with Schemas, Attributes, and Metadata.

- Create an in-memory stage with `USDStage()`.
- Open a stage from disk with `USDStage.open(_:)`.
- Traverse `stage.descendants` to inspect prims.
- Define new prims by path and type, then add references to compose external assets without copying their data.
- Use transform operations to create the correct xform attributes and maintain transform order automatically.

### Open a USD stage

USDKit starts from `USDStage`, either newly created in memory or opened from an existing USD file.

```swift
import USDKit

// Create a new empty in-memory stage.
let stage = USDStage()

// Open a stage from a file on disk.
let url = URL(fileURLWithPath: "/ALab/entry.usda")
let openedStage = try USDStage.open(url)
```

### Reference an asset and move it

The example uses USD composition to reference an external asset, then adds a translate operation to place it in the scene.

```swift
for prim in stage.descendants {
    if prim.name == "scope" {
        // Found the oscilloscope.
    }
}

let scope = stage.definePrim(at: "/World/scope", type: "Xform")
try scope.references.add("/ALab/assets/scope.usda")

scope.addTransformOperation(type: .translate)
scope["xformOp:translate", as: USDValue.Vec3d.self] = [2.5, 0.0, -1.0]
```

## Preview, rendering, Spatial Preview, and web delivery

Preview on Mac adds essential 3D editing for USD assets: direct scene manipulation, property and lighting edits, hierarchy browsing, asset conversion, and compression. Preview and Quick Look on Mac can use RealityKit, Storm, or a new high-fidelity Raytracer; all three support OpenPBR materials.

Preview also integrates with the Spatial Preview framework on macOS 27, creating a live connection from Mac to Quick Look on Vision Pro. Edits made on Mac can appear live in spatial context, including SharePlay collaboration for group review.

Safari introduces the Model tag for embedding USD models in web pages. On macOS and iOS it provides interactive 3D in the browser; on visionOS the model can break out of the page into the user's space.

- Use RealityKit rendering for consistency across Apple platforms.
- Use Storm when existing production pipeline needs require it.
- Use the new Raytracer in Preview for higher-fidelity reflections, shadows, and lighting.
- Use Spatial Preview when a Mac app needs live review on Vision Pro.

## Accessibility metadata in USD

Apple has driven standardization of accessibility metadata directly in USD so 3D objects can carry assistive labels and descriptions. The metadata can be authored through USD APIs and has direct support in Blender and Maya.

In the USDKit walkthrough, schema-specific convenience APIs are not assumed. The example applies the multi-apply `AccessibilityAPI` schema, creates the required attributes by their specification-defined names, and sets a concise label plus a richer description.

- Apply `AccessibilityAPI` to the relevant prim.
- Create `accessibility:default:label` and `accessibility:default:description` attributes.
- Set values that help assistive technologies identify the object in context.

### Apply accessibility label and description

Accessibility metadata is authored as USD schema and attributes, making it portable across USD tools and APIs.

```text
try scope.applyAPISchema("AccessibilityAPI", instanceName: "default")

scope.makeAttribute(named: "accessibility:default:label", as: .string)
scope.makeAttribute(named: "accessibility:default:description", as: .string)

scope["accessibility:default:label", as: String.self] = "Oscilloscope"
scope["accessibility:default:description", as: String.self] = "Vintage signal analyzer with a 3D wireframe display, topped by a color bar test monitor"
```

## Compression and integration choices

USDKit can export compressed USDZ packages using export options for texture and mesh compression. The session cites a mesh compression codec developed with the Alliance for Open Media that can reduce mesh sizes by up to 90%, and AVIF texture compression; together, assets are described as seven times smaller on average.

The same compression workflow is available without writing code through Preview or the `usdcrush` command-line tool. Apple is working with Pixar to bring the compression support to OpenUSD.

For Apple-platform apps, USDKit is the recommended system-provided path. Advanced Swift or open-source workflows can use SwiftUSD through Swift Package Manager, and cross-platform C++ codebases can embed OpenUSD directly as a framework.

- USDKit: best starting point for Swift apps on Apple platforms.
- SwiftUSD: open-source Swift bindings for advanced or cross-platform Swift workflows.
- OpenUSD framework: C++ integration path for cross-platform codebases.
- The same USD file foundation is shared across these integration paths.

### Export a compressed USDZ package

`exportPackage` can request smaller texture files and smaller mesh files when producing a USDZ package.

```swift
let output = URL(fileURLWithPath: "/ALab/alab_compressed.usdz")

try stage.exportPackage(
    to: output,
    options: [
        .preferSmallTextureFiles(quality: .standard),
        .preferSmallMeshFiles
    ]
)
```

Chapters:
- 0:07 Introduction: Frames USD as the backbone for Apple spatial experiences and introduces platform USD updates plus the new USDKit framework for Mac and Vision Pro workflows.
- 0:53 OpenUSD: Industry Foundation and New Standards: Covers Apple's work in OpenUSD, MaterialX, and OpenVDB, along with participation in the Academy Software Foundation and Alliance for OpenUSD. It also notes the first formal USD core specification and ongoing domain specifications.
- 2:51 Gaussian Splats and Particle Fields: Introduces Particle Fields, a new USD primitive type developed with Alliance for OpenUSD partners to represent Gaussian Splats and related particle-based scene representations alongside traditional 3D data.
- 3:47 Introducing USDKit: Positions USDKit as the new system framework powering Apple's USD workflows and intended to handle common USD complexity for app developers.
- 4:06 3D Editing in Preview and New Renderers: Explains Preview's new 3D editing capabilities, including direct manipulation, property and lighting edits, hierarchy browsing, conversion, and compression. It also introduces renderer choices across RealityKit, Storm, and a new Raytracer, all supporting OpenPBR.
- 5:42 Spatial Preview: Live Collaboration Between Mac and Vision Pro: Describes Preview integration with the Spatial Preview framework on macOS 27, linking Mac editing to live Quick Look review on Vision Pro with SharePlay collaboration.
- 6:25 USD on the Web: The Safari Model Tag: Introduces Safari's Model tag for embedding interactive USD content in web pages on macOS and iOS, with spatial breakout behavior on visionOS.
- 6:57 USDKit: Key Concepts and Swift API Walkthrough: Defines core USD concepts such as Layers, Composition, Stages, Prims, Schemas, Attributes, and Metadata. Then it walks through opening a stage, traversing prims, adding a referenced asset, and moving it with a transform operation.
- 10:05 Accessibility Metadata in USD: Explains standardized accessibility metadata in USD and demonstrates applying `AccessibilityAPI` plus label and description attributes to a prim.
- 11:19 Asset Compression: Mesh and Texture: Presents mesh and texture compression for USD assets, including USDKit export options, Preview support, and the `usdcrush` command-line tool.
- 12:36 Integration Paths: USDKit, SwiftUSD, and OpenUSD: Compares integration options: USDKit for Apple-platform apps, SwiftUSD through Swift Package Manager for advanced Swift workflows, and embedded OpenUSD for cross-platform C++ codebases.
- 13:24 Next steps: Recaps the major announcements: 3D editing in Preview, Spatial Preview workflows, Safari Model tag support, and USDKit as the starting point for Swift USD development.

### Use foveated streaming to bring immersive content to visionOS

- Session ID: wwdc2026-286
- Page: https://wwdc.ai/2026/286
- Markdown: https://wwdc.ai/2026/286.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/286/
- Category: Spatial Computing
- Description: Learn how to stream OpenXR apps from a PC or cloud to Apple Vision Pro using FoveatedStreaming, CloudXR, SwiftUI, ARKit, and RealityKit.
- Duration: 14:22
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-286/eng_7cdccbd62e40/wwdc2026-286-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/286/4/fa302edd-f95a-49f4-b51c-3899d49c6dec/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/286/4/fa302edd-f95a-49f4-b51c-3899d49c6dec/downloads/wwdc2026-286_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/286/4/fa302edd-f95a-49f4-b51c-3899d49c6dec/downloads/wwdc2026-286_sd.mp4?dl=1

Learn how to stream OpenXR apps from a PC or cloud to Apple Vision Pro using FoveatedStreaming, CloudXR, SwiftUI, ARKit, and RealityKit.

TLDR:
- Foveated Streaming lets a visionOS receiver app connect wirelessly to an external streaming endpoint and display remotely rendered OpenXR content on Apple Vision Pro.
- The receiver app uses FoveatedStreamingSession, endpoint discovery/pairing, and ImmersiveSpace integration to present streamed content alongside native SwiftUI UI.
- The endpoint implements Apple's lightweight TCP-based Foveated Streaming Protocol for authentication, pairing, and session state, while NVIDIA CloudXR supplies the Windows OpenXR runtime and streaming transport.
- visionOS apps can enhance streamed experiences with message channels, ARKit alignment, alpha/depth compositing, RealityKit content, and Xcode's Foveated Streaming instrument for performance analysis.

## What Foveated Streaming provides

Foveated Streaming is a visionOS framework for bringing externally rendered immersive content, including OpenXR applications, to Apple Vision Pro. A visionOS receiver app connects to a streaming endpoint such as a Windows PC or cloud machine, sends input data such as hands, controller positions, and microphone, and receives video and audio from the OpenXR app.

The stream is optimized using Apple Vision Pro eye tracking: content near the person's focus is streamed in higher detail. visionOS includes NVIDIA CloudXR streaming support, allowing high-performance wireless streaming over Wi‑Fi from a local PC or from the cloud.

The intended architecture combines a native visionOS app with an OpenXR host app. The visionOS side can use SwiftUI, ARKit, and RealityKit, while the endpoint uses the Foveated Streaming Protocol and the NVIDIA CloudXR OpenXR runtime.

- Introduced in visionOS 26.4 as the FoveatedStreaming framework.
- Targets immersive OpenXR content rendered on an external endpoint.
- Supports native visionOS UI and spatial features around the streamed content.
- Uses foveated video processing while preserving the high-fidelity area around the viewer's gaze.

## Receiver app on visionOS

The visionOS receiver app is responsible for welcoming users, discovering and pairing with endpoints, and presenting the streamed content. The core API is FoveatedStreamingSession. Calling connect() presents available endpoints, handles pairing UI, and returns once the session is connected and ready to stream.

Pairing requires the endpoint to display a QR code containing pairing information. The framework presents scanning UI automatically, and the person scans the code by looking at it. After connection, pass the session to an ImmersiveSpace to include the streamed content in the spatial scene.

Because this is still a SwiftUI app, the receiver can include normal windows, volumetric windows, controls to pause/resume, widgets in the immersive space, spatial gestures, and immersion styles such as progressive immersion.

- Create one FoveatedStreamingSession for the app's streaming lifecycle.
- Use connect() from Swift concurrency to initiate endpoint selection and pairing.
- Use ImmersiveSpace(foveatedStreaming:) to display the stream.
- Compose additional SwiftUI content in windows or inside the immersive space.

### Connect to a streaming endpoint

A SwiftUI control can initiate discovery, pairing, and connection by calling connect() on the session.

```swift
import SwiftUI
import FoveatedStreaming

struct ConnectView: View {
    let session: FoveatedStreamingSession

    var body: some View {
        Button("Connect") {
            Task {
                try await session.connect()
            }
        }
    }
}
```

### Display a Foveated Streaming session in an ImmersiveSpace

Passing the session to ImmersiveSpace includes the streamed OpenXR content in the immersive scene.

```swift
import SwiftUI
import FoveatedStreaming

@main
struct FoveatedStreamingSampleApp: App {
    private let session = FoveatedStreamingSession()

    var body: some SwiftUI.Scene {
        ImmersiveSpace(foveatedStreaming: session)
    }
}
```

## Streaming endpoint and protocol integration

The streaming endpoint has two main responsibilities: implement Apple's Foveated Streaming Protocol and use the OpenXR runtime provided by the NVIDIA CloudXR SDK. Apple provides open-source Windows sample code on GitHub with a reference implementation of the protocol, an example OpenXR application, and setup guidance for the CloudXR runtime.

The Foveated Streaming Protocol is a lightweight TCP-based connection separate from the media stream. It authenticates the secure foveated stream and communicates session state between visionOS and the endpoint. Endpoints are made visible on the local network with Bonjour, visionOS connects after endpoint selection, pairing occurs if needed, and the stream begins when the endpoint reports that content is ready.

Protocol messages are JSON-encoded and follow a request/acknowledge pattern. During barcode pairing, the barcode is also JSON and contains a client token and a hash of the secure connection's certificate, both provided by the NVIDIA CloudXR SDK. Endpoint implementations should monitor session status, including pause behavior when Apple Vision Pro goes to sleep, and remain available so the user can reconnect.

- Use Bonjour for local endpoint visibility.
- Use Apple's Foveated Streaming Protocol for authentication, pairing, and session state.
- Use NVIDIA CloudXR's Windows OpenXR runtime for streaming transport.
- Support reconnection by keeping the endpoint available after sleep or connection loss.

## CloudXR OpenXR behavior and content composition

NVIDIA CloudXR provides the OpenXR runtime for Windows, so an OpenXR application connects to that runtime and CloudXR handles the streaming details. visionOS input data is provided to OpenXR automatically, including hand tracking via OpenXR extensions. PlayStation VR2 Sense Controllers can also pass through.

For better mixed immersion, the session recommends providing a depth buffer and an alpha channel. Depth enables streamed OpenXR content and native rendered content to occlude each other appropriately, while alpha allows the stream to blend with the user's surroundings.

Native content can be composed with the stream directly in the same immersive space. Adding SwiftUI content or RealityKit content lets the receiver app provide controls, widgets, overlays, and on-device 3D rendering around the remote application.

- Use the OpenXR hand tracking extension where appropriate.
- Provide a depth buffer for better occlusion behavior.
- Provide an alpha channel to mix streamed content with the physical environment.
- Add RealityKit content with RealityView inside the foveated streaming ImmersiveSpace.

### Compose SwiftUI content with Foveated Streaming

The receiver can combine streamed content with SwiftUI windows and spatial controls.

```swift
import SwiftUI
import FoveatedStreaming

@main
struct FoveatedStreamingSampleApp: App {
    private let session = FoveatedStreamingSession()
    private let appModel = AppModel()

    var body: some SwiftUI.Scene {
        Window("Main", id: appModel.mainWindowId) {
            ContentView(session: session)
                .environment(appModel)
                .environment(session)
        }

        ImmersiveSpace(foveatedStreaming: session) {
            SpatialContainer {
                ReopenMainWindowView().environment(appModel)
                TransformStreamWidgetView().environment(session)
            }
        }
    }
}
```

### Compose RealityKit content with Foveated Streaming

RealityKit content can be rendered natively alongside the streamed OpenXR scene.

```swift
import SwiftUI
import RealityKit
import FoveatedStreaming

@main
struct FoveatedStreamingSampleApp: App {
    private let session = FoveatedStreamingSession()
    private let appModel = AppModel()

    var body: some SwiftUI.Scene {
        ImmersiveSpace(foveatedStreaming: session) {
            RealityView { content in
                // Add native RealityKit content.
            }
        }
    }
}
```

## Enhancing and measuring a streamed experience

A foveated streaming experience is split across two apps: the visionOS receiver and the host app on the endpoint. FoveatedStreamingSession exposes message channels on visionOS, and CloudXR provides an OpenXR extension for message channels on the endpoint. Messages are opaque data blobs, so the apps define their own payloads.

Message channels can drive app-level coordination, such as a SwiftUI level picker sending commands to the OpenXR app and the endpoint reporting loading progress. They can also transmit ARKit-derived alignment data so the remote scene matches the user's physical space. The framework also provides API support for converting between OpenXR and ARKit coordinate frames.

For performance work, Xcode includes a Foveated Streaming instrument. It reports stream metrics such as bandwidth, pose latency, frame rate, and related statistics to help diagnose problems before shipping.

- Use message channels for app-specific commands and progress reporting.
- Use ARKit for physical-world alignment and send relevant data to the endpoint when needed.
- Use coordinate-frame conversion between OpenXR and ARKit through FoveatedStreamingSession.
- Profile bandwidth, pose latency, and frame rate with the Foveated Streaming instrument in Xcode.

Resources:
- Analyzing the performance of a foveated streaming session: https://developer.apple.com/documentation/FoveatedStreaming/analyzing-the-performance-of-a-foveated-streaming-session
- Establishing foveated streaming sessions with Apple Vision Pro: https://developer.apple.com/documentation/FoveatedStreaming/establishing-foveated-streaming-sessions-with-apple-vision-pro
- Streaming a CloudXR application to Apple Vision Pro with foveation: https://developer.apple.com/documentation/FoveatedStreaming/streaming-a-cloudxr-application-to-apple-vision-pro-with-foveation
- Creating a foveated streaming client on visionOS: https://developer.apple.com/documentation/FoveatedStreaming/creating-a-foveated-streaming-client-on-visionos
- Foveated Streaming: https://developer.apple.com/documentation/FoveatedStreaming
- StreamingSession: Streaming immersive content from a CloudXR™ application to visionOS and iOS: https://github.com/apple/StreamingSession

Chapters:
- 0:00 Introduction: Introduces Foveated Streaming as a way for Apple Vision Pro to connect to external devices and stream immersive OpenXR content. It explains eye-tracking-based video optimization, CloudXR wireless streaming, and examples such as X-Plane 12, iRacing, and Autodesk VRED.
- 4:08 How Foveated Streaming works: Outlines the architecture: the visionOS receiver app uses FoveatedStreaming, the OpenXR app implements Apple's Foveated Streaming Protocol, and the endpoint uses the NVIDIA CloudXR SDK runtime. Native visionOS frameworks such as SwiftUI, ARKit, and RealityKit can be layered into the experience.
- 4:46 Set up the streaming endpoint: Points developers to Apple's open-source Windows sample on GitHub. The sample includes a reference implementation of the protocol, an example OpenXR application, and guidance for configuring NVIDIA CloudXR.
- 5:18 Create a visionOS receiver app: Shows how a receiver app creates a FoveatedStreamingSession, calls connect(), pairs using a QR code, and presents streamed content in an ImmersiveSpace. It also covers adding SwiftUI windows, in-space controls, and progressive immersion.
- 8:02 Integrate with the streaming endpoint: Details endpoint responsibilities, including implementing the TCP/JSON Foveated Streaming Protocol for authentication, pairing, and session state. It also explains CloudXR's role as the Windows OpenXR runtime and recommends depth and alpha output for better composition.
- 11:28 Measure performance: Introduces the Foveated Streaming instrument in Xcode. Developers can inspect metrics such as stream bandwidth, pose latency, frame rate, and related stream statistics to diagnose performance issues.
- 11:56 Enhance with visionOS features: Explains message channels for exchanging opaque data between the visionOS app and OpenXR app. It also covers ARKit-based real-world alignment, OpenXR/ARKit coordinate conversion, alpha blending, and RealityKit composition with streamed content.
- 13:56 Next steps: Recommends downloading the sample code and guides, building a receiver app, and integrating an OpenXR client with Apple's protocol. The session closes by encouraging developers to use the provided resources to get streaming quickly.

### Build next-generation experiences with visionOS 27

- Session ID: wwdc2026-287
- Page: https://wwdc.ai/2026/287
- Markdown: https://wwdc.ai/2026/287.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/287/
- Category: Spatial Computing
- Description: Explore visionOS 27 development paths, from native RealityKit apps to Spatial Preview, Foveated Streaming, object tracking, and immersive media.
- Duration: 32:35
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-287/eng_520e7ca1a3d0/wwdc2026-287-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/287/4/979d9278-8250-46f9-ac82-79669ba7b479/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/287/4/979d9278-8250-46f9-ac82-79669ba7b479/downloads/wwdc2026-287_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/287/4/979d9278-8250-46f9-ac82-79669ba7b479/downloads/wwdc2026-287_sd.mp4?dl=1

Explore visionOS 27 development paths, from native RealityKit apps to Spatial Preview, Foveated Streaming, object tracking, and immersive media.

TLDR:
- visionOS 27 supports three main adoption paths: compatible or recompiled iOS/iPadOS apps, native spatial apps, and existing Mac/PC experiences via Spatial Preview or Foveated Streaming.
- RealityKit gains physical space lighting, Projective Textures, cloth simulation, Custom Reverb Mesh, and Gaussian Splatting; Reality Composer Pro 3 adds AI-assisted and node-based creation workflows.
- Unity, Unreal, Godot, and custom renderers continue to be supported, with plugins for spatial accessories, RealityKit rendering, CompositorServices, and PHASE audio.
- Object tracking expands with higher-frame-rate poses, stronger Create ML training, iOS support, and custom spatial accessories; Apple Immersive Video workflows gain IMS, preview, portal, foveation, and audio tooling updates.

## Choose the right path for visionOS

visionOS offers a spectrum of presentation options: windows and volumes in the Shared Space, or Immersive Spaces with Mixed, Progressive, or Full immersion. The session frames visionOS 27 around choosing the right path based on the source of an experience and how spatial it needs to become.

- Existing iOS or iPadOS apps can come to Apple Vision Pro through App Store Connect compatibility or by recompiling with visionOS as a deployment target in Xcode.
- Apps designed for spatial computing can use SwiftUI, RealityKit, Reality Composer Pro, CompositorServices, or third-party engines such as Unity, Unreal, and Godot.
- Existing Mac or PC spatial experiences can be extended to Apple Vision Pro with Spatial Preview or streamed with Foveated Streaming.
- Immersive Spaces can blend content with the physical world or fully replace it, depending on the selected immersion style.

## RealityKit and Reality Composer Pro 3

visionOS 27 adds several RealityKit features aimed at higher-fidelity spatial scenes. Physical space lighting and Projective Textures allow virtual light to conform to real-world surfaces, while cloth simulation, Custom Reverb Mesh, and Gaussian Splatting improve visual and audio realism.

Reality Composer Pro 3 receives a major workflow update focused on faster iteration, AI-assisted content creation, and node-based authoring without always returning to Xcode.

- RealityKit additions include physical space lighting, Projective Textures for spotlights, real-time cloth simulation, Custom Reverb Mesh for spatial audio reverb, and Gaussian Splatting for scanned real-world objects.
- Reality Composer Pro Assistant can generate textured 3D models and place them into a scene as placeholder or concept assets.
- Animation Graph supports runtime state transitions such as idle-to-walk behavior, with live visualization in the editor.
- Navigation Meshes, Script Graph, Shader Graph upgrades, Prototypes, Behavior Trees, Compute Graphs, and custom Script Graph nodes expand visual scene logic and material workflows.

## Engines, custom renderers, Spatial Preview, and Foveated Streaming

Native frameworks provide the deepest integration, but visionOS 27 also supports established engine and streaming workflows. Unity, Unreal Engine, Godot, and custom rendering engines can target Apple Vision Pro, while Mac and PC content can be previewed or streamed without rewriting everything as a native app.

Spatial Preview is a macOS framework for previewing spatial photos, Apple Immersive Video, and 3D/USD content directly on Apple Vision Pro. It supports real-time asset editing, material overrides, annotations, and SharePlay collaboration, and is integrated into Preview on macOS 27.

Foveated Streaming connects Apple Vision Pro to external devices such as PCs or cloud instances for OpenXR content. visionOS sends input such as hands, controller positions, and microphone data, while the remote device streams immersive video and audio using eye-tracked foveated compression.

- Unity supports windowed games rendered through RealityKit and immersive games rendered through RealityKit or CompositorServices, depending on rendering needs.
- Unreal Engine supports immersive mode, including use cases that apply static foveation for sharper visuals.
- Godot support includes CompositorServices rendering, a RealityKit rendering plugin, and a PHASE audio plugin available from Apple's GitHub page.
- Foveated Streaming is powered by NVIDIA CloudXR and is presented as suitable for local PC, Wi-Fi, or cloud-instance streaming of OpenXR applications.

## Interaction: object tracking and spatial accessories

Object tracking continues to turn physical objects into virtual anchors. The workflow starts from a USDZ model, trains a reference object in Create ML, and passes that reference object to the object tracking API so the app receives pose updates for the physical object.

visionOS 27 expands interaction hardware through custom spatial accessories. These are tracked electronic devices containing visible LEDs, an IMU, and Bluetooth, and can also include buttons, touchpads, and haptics.

- Object tracking now supports high-frame-rate tracking for more frequent pose updates as objects move.
- Create ML adds an extended training option intended to improve accuracy and robustness, especially for hand-held objects.
- A new API provides object pose in metric space without display corrections for high-precision spatial measurement use cases.
- The same reference object training can be used on both iOS and visionOS through a new ARKit API.
- Spatial accessories integrate through the Game Controller framework for input, and RealityKit or ARKit for movement and orientation tracking.

## Immersive media, Spatial Web, and platform updates

Apple Immersive Video remains the highest-fidelity immersive video format on visionOS, using high-resolution, high-frame-rate stereoscopic 180-degree capture and metadata-driven lens calibration. The Immersive Media Support framework enables reading and writing rich Apple Immersive Video metadata for production, post-production, and playback workflows.

visionOS 27 adds multiple media pipeline improvements, including camera presentation override commands, ImmersivePreviewRenderer, wide-aspect-ratio portals, static foveation sample code for dual-track QuickTime, and updates to the Apple Spatial Audio Format Production Suite.

- Wide-aspect-ratio portals can be configured in AVKit-based apps with AVPlayerViewController or RealityKit-based apps with VideoPlayerComponent.
- ImmersivePreviewRenderer supports real-time Apple Immersive Video preview on Apple Vision Pro from a Mac during editorial or live production workflows.
- Safari on visionOS 27 supports wider, naturally curved windows, and Web Environments are enabled by default.
- Other platform updates include redesigned Control Center, high-quality 4K capture directly on Apple Vision Pro, accessory widget support, Siri enhancements, the Iceland environment, Spatial Panoramas, Personal Environments, and Freeform updates.

Chapters:
- 0:00 Introduction: The session opens with examples of consumer, enterprise, creative, and simulation apps on Apple Vision Pro, then positions visionOS 27 as a release focused on new ways to build spatial experiences. It also highlights newer Apple Vision Pro hardware capabilities such as M5 compute, high-resolution displays, and 90 Hz hand tracking.
- 2:00 visionOS overview: A recap of the visionOS scene model covers windows and volumes in the Shared Space, plus Immersive Spaces with Mixed, Progressive, and Full immersion styles. These options define the range from multitasking productivity experiences to fully immersive worlds.
- 3:13 Paths to build a visionOS experience: The session lays out three development paths: bring existing iOS/iPadOS apps, build native spatial apps with Apple frameworks or engines, or bring existing Mac/PC experiences through Spatial Preview or Foveated Streaming. It previews the rest of the session's focus areas across native tools, engines, streaming, interaction, and immersive media.
- 6:39 RealityKit and Reality Composer Pro: RealityKit updates include physical space lighting, Projective Textures, cloth simulation, Custom Reverb Mesh, and Gaussian Splatting. Reality Composer Pro 3 adds AI-assisted creation and visual authoring features such as Animation Graph, Navigation Meshes, Script Graph, Shader Graph upgrades, Behavior Trees, and Compute Graphs.
- 13:42 Third-party game engines: Unity, Unreal Engine, and Godot are described as supported options for bringing games to Apple Vision Pro, with rendering paths through RealityKit or CompositorServices depending on the engine and mode. Apple also points to plugins for spatial accessories, Godot rendering, and PHASE audio, plus custom engine support through CompositorServices.
- 15:47 Spatial Preview: Spatial Preview is introduced as a macOS 27 framework for previewing spatial photos, Apple Immersive Video, and 3D/USD content on Apple Vision Pro without building a visionOS app. It supports live editing, material overrides, annotations, SharePlay collaboration, and integration with Preview on macOS.
- 17:28 Foveated Streaming: Foveated Streaming enables OpenXR content from a PC or cloud instance to appear on Apple Vision Pro with streamed immersive video and audio while visionOS sends input data back. The chapter explains eye-tracked foveated compression, NVIDIA CloudXR, and examples such as X-Plane, iRacing, and Autodesk VRED workflows.
- 20:36 Object tracking and spatial accessories: Object tracking gains high-frame-rate pose updates, extended Create ML training, metric-space pose access, and iOS support through ARKit. The chapter also explains custom spatial accessories, including their LED, IMU, Bluetooth, input, and haptics components, and how apps use Game Controller, RealityKit, or ARKit with them.
- 25:32 Immersive media: The immersive media section summarizes Apple Immersive Video requirements and production workflows, including IMS metadata support, video-on-demand and live streaming, and SMPTE 2110 production. visionOS 27 additions include camera presentation override commands, ImmersivePreviewRenderer, wide-aspect-ratio portals, static foveation sample code, and ASAF Production Suite updates.
- 30:46 Other visionOS 27 updates: Additional platform updates include wider curved Safari windows, Web Environments enabled by default, a redesigned Control Center, streamlined notifications, high-quality 4K capture directly on Apple Vision Pro, and accessory widgets. The chapter also mentions Siri enhancements, an Iceland environment, Spatial Panoramas, Personal Environments, and Freeform updates.
- 32:05 Next steps: The closing recap points developers toward the main visionOS 27 areas covered: Spatial Web enhancements, wider windows, accessory widgets, native spatial tooling, interaction updates, streaming, and immersive media. It also directs developers to related deep-dive sessions for implementation guidance.

### Modernize your AppKit app

- Session ID: wwdc2026-289
- Page: https://wwdc.ai/2026/289
- Markdown: https://wwdc.ai/2026/289.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/289/
- Category: SwiftUI & UI Frameworks
- Description: Modernize AppKit apps with gesture recognizers, control events, keyboard navigation, state restoration, and macOS 27 visual updates.
- Duration: 18:01
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-289/eng_eca10b668cd4/wwdc2026-289-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/289/5/6a2a7cfa-56a1-4cbb-ae54-1f229e1708ae/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/289/5/6a2a7cfa-56a1-4cbb-ae54-1f229e1708ae/downloads/wwdc2026-289_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/289/5/6a2a7cfa-56a1-4cbb-ae54-1f229e1708ae/downloads/wwdc2026-289_sd.mp4?dl=1

Modernize AppKit apps with gesture recognizers, control events, keyboard navigation, state restoration, and macOS 27 visual updates.

TLDR:
- Replace mouseDown overrides and tracking loops with view-based APIs, NSControl.Events, and NSGestureRecognizer for better cross-framework behavior.
- Improve keyboard access by enabling automatic key-view-loop recalculation and using NSStatusItem expanded interface sessions for custom menu bar UI.
- Make quit and relaunch seamless with preventsApplicationTerminationWhenModal, window identifiers, NSWindowRestoration, and explicit UI-state encoding.
- Adopt macOS 27 UI refinements such as Liquid Glass updates and NSViewCornerConfiguration for concentric rounded corners.

## Modern input: move beyond mouseDown tracking

AppKit apps should prefer modern event-handling APIs over mouseDown overrides and manual tracking loops. Gesture recognizers provide a common event model across AppKit, SwiftUI, and Mac Catalyst/UIKit, and allow AppKit to supply platform behaviors without custom low-level event code.

For common mouseDown use cases, AppKit usually has a dedicated higher-level API. Use selection properties and delegate callbacks for collection/table/outline selection, menu APIs for contextual menus, modern dragging delegates for drag and drop, and NSTextSelectionManager for text selection in custom views.

- Observe selected on NSCollectionViewItem and NSTableRowView, or use selection delegate callbacks such as NSTableViewDelegate and NSOutlineViewDelegate.
- Use NSView.defaultMenu when all instances share a menu, NSResponder.menu for per-responder menus, or NSView.menuForEvent(_:) for event-dependent menus.
- Use modern pasteboard writer delegate methods on NSTableView, NSCollectionView, NSOutlineView, and NSBrowser for dragging.
- Use NSTextSelectionManager in macOS 27 to bring macOS text-selection behavior to non-NSTextView custom views.

### Modern dragging delegate

Return an NSPasteboardWriting item from a table view dragging delegate instead of implementing drag behavior from low-level mouse tracking.

```swift
func tableView(_ tableView: NSTableView,
               pasteboardWriterForRow row: Int) -> (any NSPasteboardWriting)? {
    let pasteboardItem = NSPasteboardItem()
    pasteboardItem.setString(..., forType: .string)
    return pasteboardItem
}
```

## Control events and gesture recognizers

NSControl.Events brings a UIKit-like target/action model for user-driven tracking state changes to standard AppKit controls such as buttons and sliders. This avoids subclassing controls just to observe tracking transitions.

For interactions that are not covered by standard controls, attach AppKit gesture recognizers to views. If standard recognizers are not enough, create custom NSGestureRecognizer subclasses. Because gesture recognizers work through the view hierarchy, overlapping sibling views can block input; resize the overlay or override hit testing when clicks should pass through.

- Use NSControl.addTarget(_:action:for:) for control tracking state changes.
- Use NSGestureRecognizer for custom view interactions instead of manual tracking loops.
- If an overlay view should not intercept clicks, override hitTest(_:) and return nil.

### Register a control event

Registers target/action handling for a specific NSControl.Events value without subclassing NSButton.

```swift
let button = NSButton()
button.addTarget(
    self,
    action: #selector(trackingEndedOutsideHandler),
    for: .trackingEndedOutside
)
```

### Let hit testing fall through an overlay

Allows underlying content to receive mouse events when an overlay view should not participate in hit testing.

```text
override func hitTest(_ point: NSPoint) -> NSView? {
    return nil
}
```

## Keyboard navigation and status items

Keyboard navigation is central to macOS usability and accessibility. When full keyboard access is enabled, Tab and Shift-Tab move focus through the key view loop. If views are dynamically added or removed, let AppKit maintain that loop automatically.

Status items need explicit handling depending on their behavior. Menu-backed status items already behave like menu bar menus. Action-only status items should configure the NSStatusItem button with target/action, while status items that show custom transient UI should use the expanded interface session API so AppKit can manage keyboard focus correctly.

- Set window.autorecalculatesKeyViewLoop = true to update focus order as the hierarchy changes.
- For a status item action, configure NSStatusItem.button with target/action and optional image.
- For a custom status item view, set the view and add target/action to the status item.
- For status items that display a custom window, implement NSStatusItemExpandedInterfaceDelegate and cancel the expandedInterfaceSession when dismissing.

### Automatically maintain the key view loop

Lets AppKit recalculate Tab navigation order when the window's view hierarchy changes.

```text
window.autorecalculatesKeyViewLoop = true
```

### Status item expanded interface delegate

Uses AppKit's expanded interface lifecycle so custom menu bar UI participates correctly in keyboard focus behavior.

```swift
lightStatusItem.expandedInterfaceDelegate = self

extension LightAppDelegate: NSStatusItemExpandedInterfaceDelegate {
    func statusItem(_ statusItem: NSStatusItem,
                    didBegin session: NSStatusItemExpandedInterfaceSession) {
        // Show window
    }

    func statusItemDidEndExpandedInterfaceSession(_ statusItem: NSStatusItem,
                                                  animated: Bool) {
        // Hide window
    }

    func selectedAction() {
        // Take the action
        lightStatusItem.expandedInterfaceSession?.cancel()
    }
}
```

## Graceful termination and state restoration

A modern Mac app should quit without unnecessary blocking and relaunch into the same UI state. Blocking termination is appropriate for data-loss scenarios, but sheets and modals that do not require intervention should allow the app to terminate, especially during system restarts.

State restoration has three parts: opt windows into restoration, encode the UI state needed to reconstruct them, and decode that state after relaunch. The session emphasizes saving UI reconstruction identifiers rather than re-serializing document or database contents.

- Set preventsApplicationTerminationWhenModal to false for sheets or modals that do not strictly need user input before quit.
- Assign window.identifier and, for common non-document windows, setFrameAutosaveName(_:) so AppKit can restore frame and space placement.
- Set window.isRestorable = true and window.restorationClass to an NSWindowRestoration type.
- Override encodeRestorableState(with:) and restoreState(with:) for UI state, and call invalidateRestorableState() when relevant UI state changes.
- Always call the restoreWindow completionHandler, even on failure, because AppKit waits for every restorable window.

### Allow termination for noncritical modal UI

Prevents a noncritical sheet or modal from blocking app termination.

```text
window.preventsApplicationTerminationWhenModal = false
```

### Opt a window into restoration

Identifies a restorable window, enables frame autosave for a common window, and names the restoration class.

```text
window.identifier = NSUserInterfaceItemIdentifier(WindowIdentifiers.mainWindow)
window.setFrameAutosaveName(WindowIdentifiers.mainWindow)
window.isRestorable = true
window.restorationClass = WindowRestorationHandler.self
```

### Encode and invalidate restorable UI state

Stores only the identifiers needed to reconstruct UI state and invalidates the saved state when selection changes.

```text
override func encodeRestorableState(with coder: NSCoder) {
    super.encodeRestorableState(with: coder)
    coder.encode(selectedProduct?.identifier.uuid,
                 forKey: RestorationKeys.productIdentifier)
}

splitViewController.onProductSelected = { [weak self] product in
    self?.invalidateRestorableState()
}
```

### Restore windows and window UI

Recreates each restorable window by identifier, then decodes per-window UI state.

```swift
class WindowRestorationHandler: NSObject, NSWindowRestoration {
    static func restoreWindow(
        withIdentifier identifier: NSUserInterfaceItemIdentifier,
        state: NSCoder,
        completionHandler: @escaping (NSWindow?, Error?) -> Void
    ) {
        if identifier == .mainWindow,
           let window = appDelegate.mainWindowController?.window {
            completionHandler(window, nil)
        } else if identifier == .imageWindow {
            let controller = ImageWindowController()
            appDelegate.imageWindowControllers.append(controller)
            completionHandler(controller.window, nil)
        } else {
            completionHandler(nil, error)
        }
    }
}

override func restoreState(with coder: NSCoder) {
    super.restoreState(with: coder)
    if let productId = coder.decodeObject(of: [NSString.self],
                                          forKey: RestorationKeys.productIdentifier) as? String {
        splitViewController?.selectedProductId = productId
    }
}
```

## macOS 27 design updates and concentric corners

Apps that adopted Liquid Glass in macOS 26 receive several macOS 27 refinements automatically. The automatic NSScrollEdgeEffectStyle can resolve to a hard-edge effect for free-floating text such as window titles; sidebars extend to window edges; sidebar selections use semi-bold text; bordered toolbar items over sidebars adopt Liquid Glass.

macOS 27 also adds an interactive glass effect for controls and containers of interactive controls, intended to be used sparingly. For rounded content near container corners, NSViewCornerConfiguration lets a view compute corner radii that remain visually concentric with its container.

- Use the new glass interaction effect for controls, buttons, or glass containers of interactive controls-not every glass surface.
- Use NSView.cornerConfiguration to describe view corner behavior.
- Use NSViewCornerRadius.containerConcentric(_:) so corners adapt to the container shape, with a minimum radius for consistent rounding.
- Use .uniformCorners(radius:) when all four corners should share the same radius behavior.

### Concentric corner configuration

Makes a custom view's rounded corners follow the curve of the containing view while preserving a minimum corner radius.

```swift
class LocalWeatherView: NSView {
    override var cornerConfiguration: NSViewCornerConfiguration? {
        let radius: NSViewCornerRadius = .containerConcentric(minimumCornerRadius)
        return .uniformCorners(radius: radius)
    }
}
```

Resources:
- Use SwiftUI with AppKit: https://developer.apple.com/videos/play/wwdc2022/10075/
- Restoring your app's state with AppKit: https://developer.apple.com/documentation/AppKit/restoring-your-app-s-state-with-appkit
- Gestures: https://developer.apple.com/documentation/AppKit/gestures
- TN3212: Adopting gesture recognizers for Sidecar touch support: https://developer.apple.com/documentation/Technotes/tn3212-adopting-gesture-recognizers-for-sidecar-touch-support
- NSControl.Events: https://developer.apple.com/documentation/AppKit/NSControl/Events

Chapters:
- 0:00 Introduction: Introduces modernization themes for AppKit apps: input, system-managed continuity across launches, and visual integration with current macOS design.
- 1:06 Modern input: Frames precision input as a core Mac concern and introduces modern APIs for mouse, keyboard, and status item behavior.
- 1:27 Modern event handling with gesture recognizers: Explains that gesture recognizers are the modern AppKit event-handling model and a common language across AppKit, SwiftUI, and Mac Catalyst/UIKit.
- 2:25 Selection, context menus, and drag and drop: Maps common mouseDown overrides to higher-level AppKit APIs for selection observation, context menus, and drag-and-drop pasteboard writing.
- 3:52 Text selection in custom views: Introduces NSTextSelectionManager in macOS 27 for adding macOS text-selection behaviors to custom views outside NSTextView.
- 4:26 Control events and gesture recognizers: Shows how NSControl.Events handle standard control tracking states and how gesture recognizers or custom recognizers should be used for more specialized interactions.
- 5:51 Keyboard navigation and status items: Covers automatic key view loop recalculation and correct keyboard-focus handling for status items, including custom transient UI through expanded interface sessions.
- 8:57 Continuity across launches: Introduces the goal of making AppKit apps quit cleanly and restore quickly so relaunch feels uninterrupted.
- 9:08 Graceful app termination: Explains when modal UI blocks app termination and recommends disabling that behavior for modals that do not strictly require user intervention.
- 9:55 State restoration: Walks through NSWindowRestoration: opting windows in, encoding UI state, invalidating saved state when UI changes, restoring windows by identifier, and decoding UI state.
- 14:09 Design updates: Transitions to macOS visual updates, focusing on Liquid Glass evolution and new corner concentricity support.
- 14:24 Liquid Glass updates in macOS 27: Describes automatic macOS 27 Liquid Glass refinements for scroll edge effects, sidebars, toolbar items, and a new interactive glass effect for controls.
- 15:41 Concentricity: Introduces NSViewCornerConfiguration and container-concentric corner radii so views near container corners visually align with the container's curve.
- 16:59 Next steps: Recaps the modernization checklist: replace mouseDown patterns, support keyboard operation, restore gracefully across launches, and evaluate view hierarchies for concentricity.

### Craft clear names for features and labels in your app

- Session ID: wwdc2026-290
- Page: https://wwdc.ai/2026/290
- Markdown: https://wwdc.ai/2026/290.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/290/
- Category: Design
- Description: Learn a practical UX writing framework for naming app features, settings, labels, and plans so they feel clear, trustworthy, and on-brand.
- Duration: 15:04
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-290/eng_c16417b94aa4/wwdc2026-290-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/290/4/6a4ef6cd-2a95-432c-aac9-315cb3cb7ff6/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/290/4/6a4ef6cd-2a95-432c-aac9-315cb3cb7ff6/downloads/wwdc2026-290_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/290/4/6a4ef6cd-2a95-432c-aac9-315cb3cb7ff6/downloads/wwdc2026-290_sd.mp4?dl=1

Learn a practical UX writing framework for naming app features, settings, labels, and plans so they feel clear, trustworthy, and on-brand.

TLDR:
- Use three naming criteria as a guide: the name should belong in your app, set accurate expectations, and work across languages, markets, platforms, and contexts.
- Prioritize the criteria based on context: financial labels should favor neutral clarity, while some product areas can support more branded or evocative names if users still understand them.
- Generate names by starting with the audience and asking what people should think, feel, and do when they encounter the feature.
- Evaluate candidate names in real interface and conversation contexts; good names build a consistent language for your app over time.

## Naming is part of the user experience

Feature names, menu items, settings labels, tab labels, and product names all shape how people understand and navigate an app. A name can make a path feel obvious, reduce friction, and build trust; a poor name can make a feature feel confusing, judgmental, or out of place.

The session frames naming as a design decision on the same level as layout, interaction, and visual style. Names are not just descriptors; they establish the language of an app and influence how future features should be named.

## Three criteria for better names

Use three criteria to evaluate a name: whether it belongs, whether it sets the right expectation, and whether it works everywhere. These criteria are guides, not rigid rules; teams may also need to account for trademarks, regulations, localization, or industry-specific constraints.

- Belongs: the name fits the app's tone, domain, surrounding terminology, and the user's mental model.
- Sets expectations: the name accurately previews what people will find or what will happen, which supports clarity and trust.
- Works everywhere: the name holds up across languages, markets, platforms, surfaces, and contexts where the app appears.
- Trade-offs are allowed: some contexts require maximum clarity, while others can support stronger brand expression if comprehension is preserved.

## Use obvious language when trust matters

The Apple Cash example shows why neutral, concrete terminology is often best in high-trust contexts. A balance label needs to tell people how much money they have available, without adding ambiguity or emotional judgment.

Names like "Spending Power" can sound compelling but may imply a credit limit, score, or personal judgment. "Current Funds" is descriptive but unnatural in everyday speech. "Balance" works because it is familiar, neutral, industry-standard, and immediately understood.

- For financial, health, safety, or other trust-sensitive areas, prioritize clarity and neutrality over cleverness.
- Use a natural-language gut check: if people would not say the term out loud, it may not belong in the product.
- The most obvious word can be the strongest choice when it already matches user expectations.

## Generate names from the audience outward

Instead of naming a feature only by its function, implementation, or technology, start with the people using it and the value they need from it. The session recommends a simple exercise: identify the audience, then ask what they should think, feel, and do when they encounter the feature.

The Apple Maps "Visited Places" example demonstrates the process. A feature that remembers places someone has been should feel easy, exciting, and secure. Candidate names can then be grouped by themes and tested against the naming criteria.

- Write many ideas first; do not filter too early.
- Group repeated ideas into themes such as ease, excitement, privacy, security, or usefulness.
- Discard candidates that do not fit the app, feel vague, create the wrong tone, or may not translate well.
- Place candidate names into realistic UI strings or spoken sentences, such as "Search for ..." or "Check out ...", to see whether they read and sound natural.

## Different naming styles can all be clear

Clarity does not require every name to be plain or literal. The right style depends on the feature, context, audience, and the app's existing language.

Examples from Apple apps show several valid approaches: "Enhance Dialogue" is descriptive and action-oriented, "Memories" is emotional and user-centered, and "AutoMix" is an invented but understandable branded term whose parts explain its behavior.

- Use verbs when a feature is an action people control, such as turning on a playback enhancement.
- Avoid overly technical terms when they describe the implementation more than the user benefit.
- Evocative names can work when they match what people are trying to recover, feel, or accomplish.
- Invented names can work when their components are immediately understandable and the feature behavior confirms the promise.

## Evaluate names in context

A name does not need to satisfy every criterion equally, but it should be evaluated intentionally. The surrounding UI, the sensitivity of the feature, the app's tone, localization needs, and consistency with existing terminology all affect which criteria matter most.

Good names become reusable patterns. Over time, consistent naming choices create the vocabulary of an app, making later naming decisions easier and helping people feel at home in the product.

- Ask whether the name fits the app and neighboring labels.
- Ask whether people will correctly predict what happens before they tap or choose it.
- Ask whether the name remains understandable across surfaces, platforms, languages, and markets.
- Test names in real UI locations, menus, settings, onboarding copy, and spoken references.

Resources:
- Human Interface Guidelines: Writing: https://developer.apple.com/design/human-interface-guidelines/writing

Chapters:
- 0:00 Introduction: Introduces naming as a core design tool that affects how people understand and navigate an app. The session sets up three practical tools for naming decisions: criteria, process, and evaluation.
- 1:18 Criteria: Defines the three criteria for effective names: they should belong in the app, set the right expectation, and work everywhere. Examples such as Apple Cash and gym subscription plans show how clarity, trust, tone, and brand expression can create trade-offs.
- 5:25 Process: Presents an audience-centered naming exercise based on what people should think, feel, and do when they encounter a feature. The Apple Maps "Visited Places" example shows how to generate themes, narrow candidates, and test names in natural sentences.
- 10:34 Evaluation: Shows how to evaluate naming styles through examples including Photos "Memories," Apple Podcasts "Enhance Dialogue," and Apple Music "AutoMix." The chapter emphasizes that descriptive, emotional, and branded names can all work when they fit the context and meet user expectations.

### Design intuitive search experiences

- Session ID: wwdc2026-292
- Page: https://wwdc.ai/2026/292
- Markdown: https://wwdc.ai/2026/292.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/292/
- Category: Design
- Description: Design search that matches navigation, scope, and platform conventions, from ergonomic iOS placement to suggestions, filters, tokens, and empty states.
- Duration: 16:17
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-292/eng_6194e8484a27/wwdc2026-292-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/292/5/05adbfdf-d9ba-4a6d-8d2f-f43593907f55/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/292/5/05adbfdf-d9ba-4a6d-8d2f-f43593907f55/downloads/wwdc2026-292_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/292/5/05adbfdf-d9ba-4a6d-8d2f-f43593907f55/downloads/wwdc2026-292_sd.mp4?dl=1

Design search that matches navigation, scope, and platform conventions, from ergonomic iOS placement to suggestions, filters, tokens, and empty states.

TLDR:
- Use the system Search Field and preserve its core affordances: search icon, placeholder, clear button, and the iOS cancel behavior when focused.
- Choose search placement based on navigation model and search scope: bottom or top toolbars, inline fields, Search Tabs, sidebars, or trailing toolbar fields depending on platform and content.
- On iOS, bottom toolbar search improves reachability and animates above the keyboard; Search Tabs work well for global search in tabbed apps.
- Improve search quality with recent searches, predictive suggestions, scope bars, contextual filters, search tokens, and clear no-results states.

## Search is navigation, not just input

Search helps people find, navigate, and discover content, especially as apps contain more content and more destinations. The design goal is to let people get directly to what they need without forcing them to browse through hierarchy first.

Apple's search component includes expected behaviors and adapts its visual presentation to placement. In a toolbar it can use glass styling; in the scroll region it uses standard content styling.

- Keep the leading search icon recognizable; the magnifying glass is a universal search affordance.
- Use placeholder text to clarify what can be searched.
- Show a clear button after text entry.
- On iOS, focused search presents a Cancel button that exits search and dismisses the keyboard.
- If customizing for brand, keep the Search Field's core elements intact and make replacement symbols closely resemble the standard ones.

## Choose iOS placement from navigation and scope

On iOS, search can appear as a field or button in a toolbar, as a tab in the tab bar, or as an inline field under the top toolbar or in the content area. Placement affects both ergonomics and what people believe they are searching.

The two main design questions are: how people navigate the app, and what the search scope is. A global search affordance and a local search affordance should look and feel different enough that users understand the difference.

- Bottom toolbar search is preferred when it fits the app: it is ergonomic, adjacent to primary actions, and animates above the keyboard when active.
- Top toolbar search is appropriate when the bottom of the screen is occupied, such as by a sheet or other persistent UI.
- A Search Tab is recommended for tabbed apps that need a primary, app-wide search entry point.
- Use a standard Search Tab when the search area can include exploratory content or suggestions before typing.
- Use a prominent Search Tab when users usually know what they want and tapping search should immediately focus the field and show the keyboard.
- Use inline search for a clearly scoped local search, such as searching only within a current library section or list.

## Align iPad and Mac search patterns

iPad and Mac share wider displays and similar navigation models, so search placement should be kept as aligned as possible across both platforms. The main choices are the trailing position in the top toolbar, the top of a sidebar, or the top of a dedicated Search Tab or section.

- Place primary search in the trailing toolbar position for split-view apps that search across multiple columns, while keeping selected detail content visible.
- Use toolbar search when results are expected to appear in the detail view or directly filter the visible content below.
- Toolbar search can scale or collapse into a button when space is constrained; when activated, it expands to a text-entry width and can move overflow items into a menu.
- Place search in the sidebar when filtering sidebar content or navigation, especially when the detail view is rich and should not be confused with the search target.
- Use a dedicated sidebar item or Search Tab for rich, multi-section apps that benefit from a single global search surface and a larger results canvas.

## Use suggestions and recents to reduce typing

A good search experience starts helping before the user finishes a query. Recent searches help people return to prior results, and predictive suggestions reduce typing when they naturally complete what the user is entering.

- On iOS, show recent searches inline when the field becomes focused.
- On iPad and Mac, recent searches can appear in a menu when the field is in a toolbar or sidebar.
- In a Search Tab, recent searches can appear alongside other suggested content on the page.
- Be selective: in some apps, it is more useful to show specific results the user viewed or engaged with rather than every query.
- Allow deletion of individual recent searches, such as with swipe actions, and provide a clear-all action in the section header.
- Visually distinguish typed input from the predictive part of a suggestion, and limit suggestion count so results remain the focus.

## Help people refine results without losing context

Search should often start broad, especially for a primary Search Field, then let people narrow results as needed. The right refinement UI depends on whether users are narrowing by location, category, account, person, place, or content type.

- Use a scope bar for lightweight filtering, such as switching between all mailboxes and the current mailbox.
- Show contextual filters only when relevant to the query or result type to avoid overwhelming users.
- Use search tokens when specific keywords can be applied as inline filters within the Search Field.
- Tokens are useful for natural-language-style filtering, such as combining person, place, date, or content type constraints.
- Do not rely on tokens as the only filtering UI when discoverability matters; pair them with visible controls like a scope bar or filters.

## Fail gracefully when there are no results

A blank result area can make users wonder whether the search ran at all. A no-results state should confirm the search completed and help users recover, especially from typos or overly narrow filters.

- Use a well-considered empty state or no-results view when a query returns nothing.
- Apple provides a content unavailable view that can be configured for search with a search symbol, title, and subtitle.
- Consider including the current search text in the empty state so users can quickly spot mistakes.

Chapters:
- 0:00 Introduction: Introduces search as a core tool for finding, navigating, and discovering content. Frames the session around the Search Field, platform placement patterns, and best practices for input, filtering, and empty states.
- 1:39 Search field: Breaks down the standard Search Field affordances: search icon, placeholder text, clear button, and iOS Cancel behavior. Emphasizes preserving recognizable search semantics even when applying custom branding.
- 2:52 Patterns and placement: Explains search placement options across iOS, iPad, and Mac, including toolbars, inline fields, Search Tabs, and sidebars. Placement guidance is tied to navigation model, ergonomics, available space, and perceived search scope.
- 10:30 Best practices: Covers interaction refinements that make search feel faster and clearer: recent searches, predictive suggestions, scope bars, contextual filters, search tokens, and no-results states. The guidance emphasizes reducing typing, clarifying scope, and helping users recover from failed searches.
- 15:20 Next steps: Encourages reviewing existing apps for opportunities to improve reachability, add a Search Tab, and use suggestions or filters. Points developers to the Human Interface Guidelines, design resources, and related design-system sessions.

### Validate your App Intents adoption with AppIntentsTesting

- Session ID: wwdc2026-295
- Page: https://wwdc.ai/2026/295
- Markdown: https://wwdc.ai/2026/295.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/295/
- Category: AI & Machine Learning
- Description: Use AppIntentsTesting to run out-of-process App Intents tests for intents, entities, queries, Spotlight indexing, and view annotations.
- Duration: 25:57
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-295/eng_a763cb6c364a/wwdc2026-295-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/295/4/cdcee6d3-e3e9-4201-b1ef-cd33e2d10e6f/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/295/4/cdcee6d3-e3e9-4201-b1ef-cd33e2d10e6f/downloads/wwdc2026-295_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/295/4/cdcee6d3-e3e9-4201-b1ef-cd33e2d10e6f/downloads/wwdc2026-295_sd.mp4?dl=1

Use AppIntentsTesting to run out-of-process App Intents tests for intents, entities, queries, Spotlight indexing, and view annotations.

TLDR:
- AppIntentsTesting runs App Intents through the same on-device infrastructure used by Siri, Shortcuts, Spotlight, and Widgets, without importing app code into the test target.
- Tests live in a standard XCUITest bundle, address intents and entities by bundle identifier and string names, execute intents with makeIntent(...).run(), and inspect returned values with dynamic member lookup.
- The framework supports testing EntityStringQuery behavior, identifier lookups, suggested entities, chained intent workflows, Spotlight indexing, and current on-screen view annotations.
- Test-only intents, guarded with #if DEBUG and isDiscoverable = false, are recommended for seeding state, deep-linking to views, and keeping integration tests deterministic.

## What AppIntentsTesting is for

AppIntentsTesting is an integration testing framework for App Intents. It validates the actions, entities, enums, queries, and system integrations that power Siri, Shortcuts, Spotlight, and Widgets.

Tests run from a standard XCUITest bundle while the app runs in a separate process on device. The test target does not import the app module; it discovers App Intents definitions from the app bundle identifier and executes through the full App Intents stack.

- Use an existing UI testing target or create a new XCUITest bundle.
- The app and test runner must use the same development team for code signing.
- Tests use strings for intent names and parameter names, so the test target does not get app-type autocompletion.
- Because execution is out-of-process and not UI-driven, these tests are suitable for CI and release-to-release stability.

## Executing an intent and inspecting the result

The first test in the session creates a calendar in the CometCal sample app. The test creates an IntentDefinitions object from the app bundle identifier, looks up CreateCalendarIntent by name, builds a populated intent using makeIntent, and executes it with run().

AppIntentsTesting converts many parameter values automatically, including primitive values and AppEnum raw string values. For custom values, the session points to IntentValueConvertibleWrapper in the documentation. Returned entity values can be inspected through dynamic member lookup, such as result.value.title.

- IntentDefinitions exposes the app's intents, entities, and queries without importing app code.
- makeIntent accepts named parameters matching the intent definition.
- run() executes the intent in the app process and returns the intent result.
- Dynamic member lookup lets tests assert entity properties returned by the App Intents runtime.

### Execute CreateCalendarIntent

Creates and runs an App Intent on device, then asserts the returned CalendarEntity title.

```swift
import AppIntentsTesting

func testCreateCalendar() async throws {
    let definitions = IntentDefinitions(
        bundleIdentifier: "com.example.apple-samplecode.CometCal"
    )
    let createCalendar = definitions.intents["CreateCalendarIntent"]

    let result = try await createCalendar.makeIntent(
        name: "Occupy Saturn",
        color: "red"
    ).run()

    XCTAssertEqual(try result.value.title, "Occupy Saturn")
}
```

## Testing entity queries and chained workflows

Entity queries are responsible for resolving entities in Shortcuts, Siri, Spotlight, and other App Intents surfaces. AppIntentsTesting can call entity query behavior directly on the entity definition, including string queries, identifier lookups, and suggested entities.

The session demonstrates test-driven development for an EventEntity string query: write a failing test against entities(matching:), implement EntityStringQuery on EventEntityQuery, then rerun the test and verify the same behavior in Shortcuts. It also shows chaining intent results, where the EventEntity returned by CreateEventIntent is passed directly into UpdateEventIntent, mirroring how people compose Shortcuts.

- Use entities(matching:) on an entity definition to validate EntityStringQuery behavior on device.
- Use dynamic member lookup to assert properties on returned entity representations.
- When an intent parameter expects an entity, App Intents can resolve a string by calling that entity's string query and using the first matching value.
- Chained tests can cover create-update flows and other Shortcut-like automations in one test.

### Test and implement an EntityStringQuery

The test exercises the string query through AppIntentsTesting; the implementation filters events by title and returns EventEntity values.

```swift
func testEventStringQuery() async throws {
    let results = try await eventEntityDefinition
        .entities(matching: "Cosmic Ray")

    XCTAssertEqual(results.count, 1)
    XCTAssertEqual(try results[0].title, "Cosmic Ray Calibration")
}

struct EventEntityQuery: EntityStringQuery {
    func entities(matching string: String) async throws -> [EventEntity] {
        try calendarManager.fetchEvents()
            .filter { $0.title.localizedCaseInsensitiveContains(string) }
            .map(\.entity)
    }
}
```

### Chain create and update intents

Creates an event, passes the returned EventEntity into a second intent, and asserts the updated result.

```swift
func testCreateAndUpdateEvent() async throws {
    let createResult = try await createEventDefinition.makeIntent(
        title: "Asteroid Dodgeball Practice",
        startDate: Date(),
        isAllDay: false,
        calendar: "Deep Space"
    ).run()

    XCTAssertEqual(try createResult.value.title, "Asteroid Dodgeball Practice")

    let updateResult = try await updateEventDefinition.makeIntent(
        title: "Asteroid Dodgeball Rules Overview",
        event: createResult.value
    ).run()

    XCTAssertEqual(try updateResult.value.title, "Asteroid Dodgeball Rules Overview")
}
```

## Use test-only intents for deterministic setup

Reliable AppIntentsTesting tests should be self-contained. The session recommends creating focused test-only intents to seed known app data, reset state, navigate directly to a view, or wrap internal functionality that has not otherwise been exposed through App Intents.

A test-only intent should be hidden from system discovery and compiled only into debug/test builds. In CometCal, SeedSampleEventsIntent is used to create a known set of events before tests that depend on entity query behavior.

- Seed exact test data instead of relying on leftovers from prior runs.
- Jump directly to app screens without fragile UI navigation.
- Wrap internal navigation, data management, or state manipulation in a test-only App Intent when useful for testing.
- Keep these intents unavailable to users by combining isDiscoverable = false with #if DEBUG.

### Define a test-only App Intent

Hides the intent from system discovery and limits it to debug builds so tests can use it for setup.

```swift
#if DEBUG
struct SeedSampleEventsIntent: AppIntent {
    static let isDiscoverable = false

    func perform() async throws -> some IntentResult {
        // Create known list of events
        return .result()
    }
}
#endif
```

## Testing Spotlight indexing and view annotations

AppIntentsTesting can validate system-level integrations that are difficult to cover with unit tests. The session shows a regression test for Spotlight indexing: query Spotlight before creating an event, create the event with an App Intent, then query again and assert the indexed entity appears.

It also shows testing view annotations, which tell the system what entity is currently visible so Siri can act on what is on screen. The test opens an event through an intent, uses XCUI to confirm the expected page is visible, calls viewAnnotations() on the entity definition, and asserts the annotated entity is correct.

- spotlightQuery(_:) returns entity representations currently indexed in Spotlight for the query string.
- viewAnnotations() returns the entity view annotations the system reports as currently on screen.
- XCUITest and AppIntentsTesting can be combined in the same test when a screen needs to be opened and verified.
- These tests catch bugs such as forgotten indexing calls or incorrect EntityIdentifier values in annotations.

### Verify Spotlight indexing after intent execution

Asserts an entity is absent from Spotlight before creation and present after the app creates and indexes it.

```swift
func testNewEventIndexedInSpotlight() async throws {
    let before = try await eventEntityDefinition
        .spotlightQuery("Supernova Viewing Party")
    XCTAssertTrue(before.isEmpty)

    // Create "Supernova Viewing Party" with CreateEventIntent

    let after = try await eventEntityDefinition
        .spotlightQuery("Supernova Viewing Party")
    XCTAssertEqual(after.count, 1)
    XCTAssertEqual(try after[0].title, "Supernova Viewing Party")
}
```

### Verify the current view annotation

Combines an App Intent, XCUI verification, and viewAnnotations() to assert Siri would see the correct on-screen entity.

```swift
func testEventViewAnnotation() async throws {
    try await openEventDefinition
        .makeIntent(target: "Morning Launch Briefing")
        .run()

    let app = XCUIApplication()
    let title = app.staticTexts["Morning Launch Briefing"]
    XCTAssertTrue(title.waitForExistence(timeout: 5))

    let annotations = try await eventEntityDefinition.viewAnnotations()
    XCTAssertEqual(annotations.count, 1)
    XCTAssertEqual(try annotations[0].entity.title, "Morning Launch Briefing")
}
```

## Recommended testing workflow

Use AppIntentsTesting alongside, not instead of, existing App Intents development practices. Start by implementing the fundamental actions, data types, and queries, then validate them with automated tests. After that, cover deeper integrations such as view annotations, Spotlight donations, and cross-app data passing.

The session frames these as App Intents unit-style tests for foundational behavior and integration tests for system-facing behavior. Manual testing with Siri and Shortcuts remains important because it verifies the final user experience.

- Add AppIntentsTesting tests to the same CI path as XCUITest tests.
- Lift shared IntentDefinitions and entity definitions into reusable test setup code.
- Use setup or test-only intents to reset app state before each scenario.
- Still manually exercise important Siri and Shortcuts flows before shipping.

Resources:
- Testing your App Intents code: https://developer.apple.com/documentation/AppIntentsTesting/testing-your-app-intents-code
- App Intents Testing: https://developer.apple.com/documentation/AppIntentsTesting

Chapters:
- 0:16 Introduction: Introduces AppIntentsTesting as a new framework for testing App Intents that power Siri, Shortcuts, Spotlight, and Widgets. The agenda covers first tests, framework architecture, entity queries, combining intents, Spotlight, and view annotations.
- 2:01 Meet CometCal: your first test: Introduces the CometCal SwiftUI calendar sample app and sets up a UI testing bundle for AppIntentsTesting. The first scenario targets the app's CreateCalendarIntent.
- 2:29 How AppIntentsTesting works: Shows how to create IntentDefinitions from the app bundle identifier, build an intent with makeIntent, run it on device, and assert the returned entity. Explains that tests run out-of-process in an XCUITest bundle through the full App Intents stack with no app-code imports, mocks, or UI dependency.
- 9:39 Testing entity queries: Demonstrates testing an EventEntity string query by calling entities(matching:) on the entity definition, then implementing EntityStringQuery to make the failing test pass. The workflow verifies query behavior used by Shortcuts and other App Intents surfaces.
- 13:49 Combining multiple intents: Shows a multi-step test that creates an event and then updates it, passing the returned EventEntity from one intent into another. The example also notes that string parameters can resolve entity values through the associated EntityStringQuery.
- 16:27 Test-only intents: Explains how test-only intents help create deterministic, self-contained tests by seeding data, jumping to views, or wrapping internal functionality. These intents should be hidden with isDiscoverable = false and compiled only under #if DEBUG.
- 18:22 Testing Spotlight indexing: Builds a regression test for Spotlight indexing by querying before and after creating an event and asserting the indexed result appears. The test catches bugs such as a missing or commented-out indexing call.
- 20:56 Testing view annotations: Tests the entity annotation currently visible on screen by opening an event with an intent, confirming the UI with XCUI, then calling viewAnnotations() and asserting the annotated EventEntity. The example catches an incorrect EntityIdentifier bug.
- 24:00 The App Intents testing workflow: Positions AppIntentsTesting in the development workflow: validate foundational actions, data, and queries first, then cover system integrations such as view annotations and Spotlight. Manual testing with Siri and Shortcuts is still recommended for the final experience.
- 25:19 Next steps: Recommends downloading the CometCal sample project and reviewing the AppIntentsTesting documentation for the full API. Also points developers toward additional App Intents material for building Siri experiences.

### Best practices for integrating visual intelligence in your app

- Session ID: wwdc2026-297
- Page: https://wwdc.ai/2026/297
- Markdown: https://wwdc.ai/2026/297.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/297/
- Category: AI & Machine Learning
- Description: Integrate Visual Intelligence Image Search with App Intents, Vision feature prints, UnionValue results, deep links, and system store data.
- Duration: 17:45
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-297/eng_3ee717bddbaf/wwdc2026-297-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/297/5/25343020-b502-4808-967a-6f6460789dc2/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/297/5/25343020-b502-4808-967a-6f6460789dc2/downloads/wwdc2026-297_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/297/5/25343020-b502-4808-967a-6f6460789dc2/downloads/wwdc2026-297_sd.mp4?dl=1

Integrate Visual Intelligence Image Search with App Intents, Vision feature prints, UnionValue results, deep links, and system store data.

TLDR:
- Model searchable app content as AppEntity values with concise DisplayRepresentation metadata for Visual Intelligence result cards.
- Implement IntentValueQuery over SemanticContentDescriptor, use fast ranked image matching, and return an empty array when no relevant match exists.
- Use OpenIntent and semanticContentSearch to deep-link selected results and continue the captured visual context inside your app.
- Expand beyond single-result image matching with @UnionValue, iPadOS/macOS support, and system store integrations via EventKit, Contacts, and HealthKit.

## Integration model

Visual Intelligence integration has two main paths: provide results to Visual Intelligence through Image Search, and receive information that Visual Intelligence writes into shared system stores. Image Search uses App Intents plus the Visual Intelligence framework so the system can pass captured visual context to your app and display your app's results alongside other providers.

The sample app searches album artwork and surfaces both matching albums and related upcoming concerts. The same entity, query, and open-intent approach works across iOS, iPadOS, and macOS, with platform-specific input differences to account for.

- Use AppEntity for content that can appear in Visual Intelligence results.
- Use IntentValueQuery with SemanticContentDescriptor input to return visual search results.
- Use OpenIntent to navigate directly to the selected content.
- Use system stores such as EventKit, Contacts, and HealthKit when Visual Intelligence creates data that your app already reads.

## Define result content as AppEntity

Visual Intelligence displays your app's results using AppEntity values from the App Intents framework. The entity's DisplayRepresentation is especially important because the result UI has limited space: roughly a title, subtitle, and thumbnail.

Choose identifying text that helps users recognize the result quickly. If using image URLs, serve thumbnail-sized images when returning multiple results; full-resolution assets are unnecessary for small cards and can slow loading. If your integration typically returns one result, remember the image may occupy the full width of the results sheet.

- Include a stable identifier and properties needed to display and open the content.
- Provide typeDisplayRepresentation and defaultQuery as with other AppEntity adoption.
- Keep title/subtitle concise; for the sample album entity, the key fields are album name and artist name.

### Album AppEntity for Visual Intelligence results

Defines album content as an AppEntity and controls how each result appears in the Visual Intelligence sheet.

```swift
import AppIntents

struct AlbumEntity: AppEntity {
    var id: String
    @Property var name: String
    @Property var artistName: String
    var coverArtData: Data

    var displayRepresentation: DisplayRepresentation {
        DisplayRepresentation(
            title: "\(name)",
            subtitle: "\(artistName)",
            image: .init(data: coverArtData)
        )
    }

    static let defaultQuery = AlbumEntityQuery()
    static var typeDisplayRepresentation: TypeDisplayRepresentation { "Album" }
}
```

## Implement fast, ranked image search

An IntentValueQuery is the lightweight query entry point Visual Intelligence uses to ask your app for results. For visual search, the system passes a SemanticContentDescriptor containing information about the captured image, including a pixel buffer when available.

The sample performs on-device image similarity with Vision feature prints. It precomputes feature prints for catalog entries, generates a feature print for the captured image at query time, filters by a maximum distance threshold, sorts by similarity, and returns a limited set of top results.

The main best practices are independent of whether matching runs locally or on a server: keep queries fast, rank the most relevant results first, limit result count, and return an empty array if there are no good matches.

- Guard for missing pixelBuffer and return no results rather than failing the experience.
- Precompute expensive catalog representations such as feature prints outside the query path.
- Apply a distance or relevance threshold so weak matches do not clutter results.
- Consider other Vision APIs for app-specific search, such as text extraction, barcode scanning, face detection, and image classification.

### IntentValueQuery using SemanticContentDescriptor

Receives captured visual context and returns app-specific Visual Intelligence results.

```swift
import AppIntents
import VisualIntelligence

struct SearchHandler: IntentValueQuery {
    @Dependency var catalog: AlbumCatalog

    func values(for input: SemanticContentDescriptor) async throws -> [VisualSearchResult] {
        guard let pixelBuffer = input.pixelBuffer else { return [] }
        let albums = try await catalog.search(matching: pixelBuffer)
        return albums.map { VisualSearchResult.album($0) }
    }
}
```

### Vision feature-print matching

Converts the captured pixel buffer, compares it to precomputed catalog feature prints, ranks by similarity, and limits results.

```swift
import Vision

private func generateFeaturePrint(for image: CGImage) async throws -> FeaturePrintObservation {
    let request = GenerateImageFeaturePrintRequest()
    return try await request.perform(on: image)
}

func search(matching pixelBuffer: CVReadOnlyPixelBuffer,
            limit: Int = 10,
            maxDistance: Double = 1.0) async throws -> [AlbumEntity] {
    var cgImage: CGImage?
    _ = pixelBuffer.withUnsafeBuffer {
        VTCreateCGImageFromCVPixelBuffer($0, options: nil, imageOut: &cgImage)
    }
    guard let cgImage else { return [] }

    let queryPrint = try await generateFeaturePrint(for: cgImage)
    return try entries.compactMap { entry -> (AlbumEntity, Double)? in
        let distance = try queryPrint.distance(to: entry.featurePrint)
        guard distance <= maxDistance else { return nil }
        return (entry.album, distance)
    }
    .sorted { $0.1 < $1.1 }
    .prefix(limit)
    .map { $0.0 }
}
```

## Open results and continue search in your app

When the user taps a Visual Intelligence result, the system invokes an OpenIntent for the selected entity. Reuse an existing OpenIntent if your app already defines one for App Intents or Siri; a Visual Intelligence-specific duplicate is not needed.

Keep the open intent lightweight because it runs while the app is foregrounding. Navigate to the right screen, then defer heavier loading until after the destination view appears.

For cases where the first result is not enough, adopt the semanticContentSearch schema. This gives users a "More results" path into your full in-app search experience, with the same SemanticContentDescriptor available so you can prepopulate filters, categories, and results from the captured context.

- OpenIntent should land users directly on the selected content, not a generic home screen.
- semanticContentSearch should preserve context from Visual Intelligence instead of starting a blank search.
- Your in-app search can show richer UI than the Visual Intelligence result sheet, including filters and deeper catalog navigation.

### OpenIntent for a selected album

Navigates directly to the selected album when a Visual Intelligence result is tapped.

```swift
import AppIntents

struct OpenAlbumIntent: OpenIntent {
    static let title: LocalizedStringResource = "Open Album"

    @Parameter(title: "Album") var target: AlbumEntity
    @Dependency var appState: AppState

    func perform() async throws -> some IntentResult {
        await appState.openAlbum(id: target.id)
        return .result()
    }
}
```

### Continue captured-context search in app

Uses the semantic content search schema to open a full in-app search seeded from the captured image.

```swift
@AppIntent(schema: .visualIntelligence.semanticContentSearch)
struct SemanticContentSearchIntent: AppIntent {
    static let title: LocalizedStringResource = "Search in app"
    static let openAppWhenRun: Bool = true

    var semanticContent: SemanticContentDescriptor
    @Dependency var catalog: AlbumCatalog
    @Dependency var concertFinder: ConcertFinder
    @Dependency var appState: AppState

    func perform() async throws -> some IntentResult {
        guard let pixelBuffer = semanticContent.pixelBuffer else { return .result() }
        let albums = try await catalog.search(matching: pixelBuffer)
        let artists = albums.map { $0.artistName }
        let concerts = await concertFinder.findNearby(byArtists: artists)
        await appState.openSearch(albums: albums, concerts: concerts)
        return .result()
    }
}
```

## Multiple result types and cross-platform input

An app can have only one IntentValueQuery that accepts SemanticContentDescriptor. To return more than one kind of result from that query, define a @UnionValue type with a case for each result entity. The sample combines visually matched albums with nearby concerts derived from the matched artists.

Visual Intelligence is available on iOS, iPadOS, and macOS. The same AppEntity, IntentValueQuery, and OpenIntent code can carry over with minimal changes, but the captured input may differ. iPhone usage often starts from the camera and physical objects, while iPad and Mac primarily use screenshots and digital media. On Mac, captured pixel buffers can be much larger, so resizing may be appropriate before processing.

- Use @UnionValue when one visual query should return heterogeneous entity types.
- Provide an OpenIntent for each entity type you return.
- Think beyond pixel matches: use visual context to derive related app content such as events, products, or recommendations.
- Test both camera-like captures and screenshot captures, especially for large macOS pixel buffers.

### UnionValue for albums and concerts

Returns multiple visual search result types from the single SemanticContentDescriptor query.

```swift
@UnionValue
enum VisualSearchResult {
    case album(AlbumEntity)
    case concert(ConcertEntity)
}

struct SearchHandler: IntentValueQuery {
    @Dependency var catalog: AlbumCatalog
    @Dependency var concertFinder: ConcertFinder

    func values(for input: SemanticContentDescriptor) async throws -> [VisualSearchResult] {
        guard let pixelBuffer = input.pixelBuffer else { return [] }
        let albums = try await catalog.search(matching: pixelBuffer)
        let artists = albums.map { $0.artistName }
        let concerts = await concertFinder.findNearby(byArtists: artists)
        return albums.map { .album($0) } + concerts.map { .concert($0) }
    }
}
```

## System store integrations

Visual Intelligence can also create data in shared system stores that apps may already read. Events created from detected event information are available through EventKit, contact information through Contacts, and medical-device readings through HealthKit.

The sample music app reads calendar events to show upcoming concerts. It requests calendar access, fetches events in the near future, filters for event titles that match known artists, and observes EventKit store changes so newly added Visual Intelligence events appear automatically.

- Use EKEventStore for events saved by Visual Intelligence.
- Use CNContactStore for contacts added from captured information such as business cards.
- Use HKHealthStore for supported medical-device readings, such as blood pressure monitor, glucose meter, or weight scale displays.
- Observe store-change notifications where available so app UI updates when Visual Intelligence adds new data.

### Fetch upcoming calendar events with EventKit

Reads calendar events that may have been created by Visual Intelligence and filters them into app-specific upcoming concerts.

```swift
import EventKit

@Observable
class UpcomingConcertManager {
    private let eventStore = EKEventStore()
    var upcomingConcerts: [EKEvent] = []
    var authorizationStatus: EKAuthorizationStatus = .notDetermined

    func requestAccessAndFetch() async throws {
        let granted = try await eventStore.requestFullAccessToEvents()
        guard granted else {
            authorizationStatus = .denied
            return
        }
        authorizationStatus = .fullAccess
        await fetchUpcomingConcerts()
    }

    func fetchUpcomingConcerts() async {
        let predicate = eventStore.predicateForEvents(
            withStart: .now,
            end: .now.addingTimeInterval(90 * 24 * 60 * 60),
            calendars: nil
        )
        let events = eventStore.events(matching: predicate)
        upcomingConcerts = events.filter { event in
            AlbumCatalog.shared.entries.contains { entry in
                event.title?.localizedCaseInsensitiveContains(entry.album.artistName) == true
            }
        }
    }
}
```

### Observe EventKit changes

Refreshes app data when the EventKit store changes, including events newly created through Visual Intelligence.

```text
for await _ in NotificationCenter.default.notifications(named: .EKEventStoreChanged) {
    await fetchUpcomingConcerts()
}
```

Resources:
- Integrating your app with visual intelligence: https://developer.apple.com/documentation/VisualIntelligence/integrating-your-app-with-visual-intelligence
- Visual Intelligence: https://developer.apple.com/documentation/VisualIntelligence

Chapters:
- 0:07 Introduction: Introduces Visual Intelligence integration and new capabilities, including iPad and macOS availability, contacts, multiple calendar events, and medical device logging. Sets up a music-discovery sample app that returns albums and concerts from captured images and reads calendar events created by Visual Intelligence.
- 2:02 Defining your content: Explains defining searchable app content as AppEntity values from App Intents. Covers DisplayRepresentation best practices for compact titles, subtitles, and thumbnail images in Visual Intelligence result cards.
- 5:03 Implementing a query: Shows how IntentValueQuery receives a SemanticContentDescriptor and returns results from a captured pixel buffer. Demonstrates on-device Vision feature-print matching with precomputed catalog entries, distance thresholds, similarity sorting, and limited result counts.
- 8:18 Opening results: Uses OpenIntent to navigate directly from a tapped Visual Intelligence result to the selected album in the app. Recommends reusing existing open intents and keeping foreground-time work lightweight.
- 10:03 Mac and iPad adoption: Explains that the same entities, query, and open intents work across iOS, iPadOS, and macOS. Notes input differences between camera captures and screenshots, and warns that macOS pixel buffers may be much larger and may need resizing.
- 12:27 Returning multiple result types: Introduces @UnionValue to return heterogeneous results from the single SemanticContentDescriptor query. The sample returns albums plus related nearby concerts derived from matched album artists.
- 12:56 Continuing search in your app: Shows semanticContentSearch adoption so users can continue from Visual Intelligence into the app's full search experience. The app uses the captured context to prepopulate albums and concerts rather than opening a blank search.
- 14:27 System store integrations: Describes how Visual Intelligence-created data can be read through existing system stores: EventKit for events, Contacts for contact information, and HealthKit for medical-device readings. Demonstrates requesting calendar access, fetching upcoming events, filtering by known artists, and observing EventKit changes.
- 17:16 Next steps: Recaps the two integration points: providing Image Search results and receiving data through system stores. Points developers to Visual Intelligence documentation and related App Intents and Vision material.

### Meet the Evaluations framework

- Session ID: wwdc2026-298
- Page: https://wwdc.ai/2026/298
- Markdown: https://wwdc.ai/2026/298.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/298/
- Category: AI & Machine Learning
- Description: Evaluate probabilistic AI features with Swift Testing, quantitative metrics, robust datasets, and model judges in the Evaluations framework.
- Duration: 25:46
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-298/eng_e02d8b74f23e/wwdc2026-298-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/298/5/0ffb7161-1edb-4e6f-872d-55be82c4402d/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/298/5/0ffb7161-1edb-4e6f-872d-55be82c4402d/downloads/wwdc2026-298_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/298/5/0ffb7161-1edb-4e6f-872d-55be82c4402d/downloads/wwdc2026-298_sd.mp4?dl=1

Evaluate probabilistic AI features with Swift Testing, quantitative metrics, robust datasets, and model judges in the Evaluations framework.

TLDR:
- The Evaluations framework lets you test stochastic model-driven features by measuring behavior across datasets instead of relying only on deterministic unit tests.
- An Evaluation defines the subject under test, a dataset of ModelSample values, Metrics and Evaluators, aggregate statistics, and a Swift Testing test with an optimization target.
- Robust datasets should cover varied inputs and expected outputs; SampleGenerator can synthesize more samples from seed ModelSamples.
- ModelJudgeEvaluator uses a capable language model, score dimensions, rationales, and ModelJudgePrompt context to measure qualitative traits that code heuristics cannot capture.

## Why evaluations are needed

Generative AI features break the usual testing assumption that the same input always produces the same output. Unit tests still matter, but they are insufficient for verifying probabilistic behavior such as unexpected outputs, unsafe responses, or agent paths that vary across runs.

The Evaluations framework provides protocols and types for measuring intelligent features at scale. The session focuses on language-model-backed features, but the framework can also evaluate other stochastic systems such as classifiers and linear regression models.

- Use evaluations to express expectations and measure how often a feature meets them.
- Use automated metrics and aggregate statistics instead of relying on one-off manual inspection.
- Use evaluation results to drive a hill-climbing loop: inspect failures, change the feature or prompt, rerun, and compare.

## Build an evaluation

The Book Tracker example evaluates a BookTaggingService that generates tags from a user's book review. A first manual pass identifies expectations: tag count should be in range, tags should not include titles, tags should be single-word or hyphenated, tags should include useful browsing categories, and tags should reflect the book rather than only the reader's opinion.

An evaluation has five parts: define the subject under test, define the dataset, define metrics and evaluators, aggregate measurements across samples, and run the evaluation from a test target.

- Implement the Evaluation protocol and import Evaluations.
- Use subject(from:) to call the feature being measured and return its output.
- Use ModelSample to pair prompts with expected values.
- Use Metric plus Evaluator for per-sample measurements such as pass/fail tag-count checks.

### Skeleton evaluation and tag-count evaluator

Defines a quantitative metric and an evaluator that records whether each generated tag list has the expected size.

```swift
import Evaluations

struct BookTaggingEvaluation: Evaluation {
    let tagCount = Metric("TagCount")

    var evaluators: Evaluators {
        Evaluator { _, subject in
            let count = subject.value.tags.count
            if count >= 3 && count <= 8 {
                return tagCount.passing(rationale: "\(count) tags")
            }
            return tagCount.failing(rationale: "Got \(count) tags, expected 3-8")
        }
    }
}
```

### Dataset with ModelSample

Wraps representative inputs and ideal outputs so the same evaluation can run repeatedly over known samples.

```swift
var dataset = ArrayLoader(samples: [
    ModelSample(
        prompt: "okay I am OBSESSED and I need everyone to read this RIGHT NOW...",
        expected: BookTags(tags: ["classic", "romance", "wit", "regency"])
    ),
    ModelSample(
        prompt: "Read this in one sitting between midnight and 4am and I cannot...",
        expected: BookTags(tags: ["classic", "gothic", "horror", "vampire", "suspense"])
    )
])
```

## Run with Swift Testing and inspect reports

Evaluations integrate with Swift Testing. A test uses the .evaluates trait to run an evaluation and can assert on aggregate values from the evaluation results bundle. In the example, the optimization target requires the tag-count metric to pass at least 80% of the time.

Xcode's evaluation test report shows aggregate metrics, per-sample rows, prompts, measurements, rationales, and the full model response. This is used to diagnose failures and decide what change to try next.

- Use aggregateValue(.mean(of: metric)) to assert an optimization target.
- Attach notes to an evaluation run so different configurations can be compared later.
- A failing optimization target is treated as signal for analysis, not just a binary test failure.

### Swift Testing optimization target

Runs the evaluation from a test target and fails the test if the mean pass rate for tag count falls below the chosen target.

```swift
@Test("Book Tag Evaluations", .evaluates(evaluation, info: evaluationInfo))
func evaluateBookTagging() async throws {
    let result = EvaluationContext.current.result
    let rangeMetric = BookTagEvaluationTests.evaluation.tagCount
    #expect(result.aggregateValue(.mean(of: rangeMetric)) >= 0.8)
}
```

### Constrain generated output with @Guide

The example hill-climbing change adds a count range to the generated BookTags shape, then reruns the evaluation to confirm the effect.

```swift
@Generable
struct BookTags: Codable {
    @Guide(
        description: "Descriptive tags capturing themes, genres, moods, and topics from the summary",
        .count(3...8)
    )
    var tags: [String]
}
```

## Scale datasets and refine quantitative metrics

Two samples are enough to prove the workflow, but not enough to characterize an intelligent feature. The session recommends datasets with broad variety: genres, review lengths, fiction and non-fiction, forms such as novels or essays, and personal opinions that the feature should ignore.

SampleGenerator can synthesize more samples from seed ModelSamples when manual data creation does not scale. After expanding the Book Tracker dataset, the evaluation revealed that a guide change made the service always generate eight tags, which led to more detailed metrics.

- Use scoring metrics when the measurement is not just pass/fail, such as recording total tag count.
- Aggregate means, standard deviations, and variances to identify distribution problems.
- Add rule-based evaluators for quantifiable expectations, such as no spaces in tags or at least one tag from a known genre list.

### Aggregate range and distribution metrics

Combines pass-rate and distribution statistics to detect behavior such as always returning the maximum number of tags.

```swift
let tagCount = Metric("TagCount")
let tagTotal = Metric("TagTotal")

func aggregateMetrics(using aggregator: inout MetricsAggregator) {
    aggregator.computeMean(of: tagCount)
    aggregator.group("Distribution of Tag Totals") { aggregator in
        aggregator.computeStandardDeviation(of: tagTotal)
        aggregator.computeMean(of: tagTotal)
        aggregator.computeVariance(of: tagTotal)
    }
}
```

### Rule-based evaluators for additional expectations

Shows how simple heuristics can measure traits that are easy to express in code.

```swift
let wordCount = Metric("WordCount")
Evaluator { _, subject in
    for tag in subject.value.tags {
        if tag.contains(" ") {
            return wordCount.failing(rationale: "Tag \(tag) contains multiple words")
        }
    }
    return wordCount.passing()
}

let hasGenreTag = Metric("HasGenreTag")
Evaluator { _, subject in
    let tags = subject.value.tags.map { $0.lowercased() }
    let knownGenres = await BookTaggingService.knownGenres
    for tag in tags where knownGenres.contains(tag) {
        return hasGenreTag.passing(rationale: "Matched \(tag)")
    }
    return hasGenreTag.failing()
}
```

## Use model judges for qualitative metrics

Some failures are qualitative: tags can satisfy count, word-count, and genre checks while still being unhelpful, irrelevant, or based on the reader's opinion instead of the book. A model judge uses a second language model to score feature output in a way similar to a human reviewer, consistently across the dataset.

The judge should be at least as capable as the model being evaluated. In the example, the app feature uses an on-device model, while the judge uses PrivateCloudComputeLanguageModel. ModelJudgeEvaluator is another Evaluator, so qualitative and quantitative metrics can coexist in the same evaluation.

- Define an explicit scale; the example uses four numeric levels to avoid a neutral middle score.
- Read rationales, because they explain why the judge scored a sample the way it did.
- If the judge's score does not match human judgment, refine the scoring guide rather than assuming the judge is wrong.

### Simple model judge

Adds a qualitative TagQuality metric scored by a more capable model judge.

```text
ModelJudgeEvaluator(
    "TagQuality",
    scale: .numeric([
        4: "Tags are relevant and helpful for browsing",
        3: "Mostly relevant, one tag too vague or generic",
        2: "Several tags are wrong or generic",
        1: "Unhelpful or irrelevant"
    ]),
    judge: PrivateCloudComputeLanguageModel()
)
```

## Refine judges with dimensions and app context

A broad model-judge question can hide disagreement. The session splits tag quality into ScoreDimensions such as Relevance and Usefulness, each with its own description and scale. Separate dimensions make rationales more diagnostic: one can explain what kind of tag is wrong, while another explains why it fails for browsing.

ModelJudgePrompt supplies application context, formats the evaluation target, and can pass reference information such as expected tags. In Book Tracker, the prompt tells the judge that the tags are for a personal library browsing and filtering experience, not a review platform.

- If all scores are the same, the judge question is probably too broad.
- If the problem cannot be isolated, split the scoring into dimensions.
- If the judge misunderstands the product intent, add context with ModelJudgePrompt.

### Score dimensions and contextual judge prompt

Breaks a qualitative judge into separate dimensions and gives the judge product-specific context.

```swift
ScoreDimension(
    "Relevance",
    description: """
    Whether each tag describes a quality, theme, or tone of the book itself rather than incidental details or the reader's personal reactions.
    """,
    scale: .numeric([
        4: "Every tag describes the book itself",
        3: "Most tags describe the book",
        2: "Some tags describe personal reactions",
        1: "Tags don't meaningfully describe the book"
    ])
)

ModelJudgeEvaluator(
    judge: PrivateCloudComputeLanguageModel(),
    dimensions: [relevance, usefulness],
    prompt: ModelJudgePrompt(
        instructions: """
        You are evaluating tags generated for a personal book-tracking app where users organize their library by browsing and filtering tags.
        """,
        evaluationTarget: { value in
            "\(value.tags.count) Generated tags: " + value.tags.joined(separator: ", ")
        },
        reference: { input, _ in
            let expectedTags = input.expected?.tags.joined(separator: ", ")
            return ["Expected Tags": expectedTags ?? "No expected tags defined"]
        }
    )
)
```

Resources:
- Book Tracker: Using Evaluations to evaluate an intelligent feature: https://developer.apple.com/documentation/Evaluations/book-tracker-using-evaluations-to-evaluate-an-intelligent-feature
- Designing datasets to test your feature: https://developer.apple.com/documentation/Evaluations/designing-evaluation-datasets
- Designing effective evaluations: https://developer.apple.com/documentation/Evaluations/designing-effective-evaluations
- Evaluating language model responses: https://developer.apple.com/documentation/Evaluations/evaluating-language-model-responses

Chapters:
- 0:00 Introduction: Introduces the Evaluations framework as a way to measure intelligent features whose probabilistic outputs cannot be fully verified by unit tests. It frames the framework around datasets, quantitative metrics, model judges, and score dimensions.
- 3:10 Demo app Book Tacker: a manual evaluation: Uses the Book Tracker app's BookTaggingService to manually inspect generated tags for Pride & Prejudice and Dracula. The manual pass surfaces expectations such as tag-count limits, no book titles, no multi-word tags, and useful genre-like categories.
- 4:31 Building your first evaluation: Builds the first Evaluation by defining the subject under test, wrapping review inputs and expected tags in ModelSample values, creating a TagCount Metric, and adding an Evaluator for the 3-to-8 tag range.
- 8:06 Running the evaluation and reading the report: Runs the evaluation from Swift Testing using the .evaluates trait and an #expect optimization target on the aggregate mean. The Xcode evaluation report is used to inspect per-sample results, prompts, measurements, and full model responses.
- 10:57 Building robust datasets: Explains why two samples are insufficient and describes dataset variety across genres, review lengths, fiction and non-fiction, forms, and personal opinions. SampleGenerator is introduced as a way to synthesize more samples from a seed set.
- 14:20 Refining metrics and evaluators: Adds more quantitative metrics, including total tag count distribution, single-word tag checks, and genre detection against knownGenres. Aggregate metrics provide deeper insight than a single pass/fail range check.
- 15:41 Evaluation-driven development and hill-climbing: Names the feedback loop of changing instructions, rerunning evaluations, and comparing results as hill-climbing. Centering development on that loop is described as evaluation-driven development.
- 16:12 Model judges: qualitative metrics: Shows that quantitative metrics can pass while outputs are still qualitatively wrong, such as tags based on reader opinions or incorrect genre inference. Introduces model judges as language models that score feature outputs at dataset scale.
- 18:42 Building a model judge: Defines a ModelJudgeEvaluator with a TagQuality metric on a 1-to-4 numeric scale and uses PrivateCloudComputeLanguageModel as the judge. The chapter emphasizes that model judges are regular evaluators and that rationales are essential for interpreting scores.
- 21:19 Refining with score dimensions: Refines an overly broad judge by splitting quality into ScoreDimensions such as Relevance and Usefulness. It explains that disagreement with a judge often means the scoring question or guide needs to be made more precise.
- 23:45 Reviewing dimension results: Reviews separate relevance and usefulness scores for the Alice in Wonderland sample. The separate rationales make the diagnosis clearer and feed back into the hill-climbing loop.
- 24:20 Best practices: Recommends starting with a focused 20-to-30-sample dataset, using heuristics for traits measurable in code, and using ModelJudgeEvaluator for traits describable only in words. It advises reading rationales, splitting broad questions, and adding app context when needed.
- 25:38 Next steps: Points developers to Evaluations documentation, the Book Tracker sample code, and companion material about hill-climbing prompts and robust evaluations for agentic apps.

### Create robust evaluations for agentic apps

- Session ID: wwdc2026-299
- Page: https://wwdc.ai/2026/299
- Markdown: https://wwdc.ai/2026/299.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/299/
- Category: AI & Machine Learning
- Description: Use Xcode 27's Evaluations framework to synthesize and validate datasets, compare results, and evaluate agentic tool-calling behavior.
- Duration: 21:28
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-299/eng_555a2468e54e/wwdc2026-299-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/299/4/ef9fbc06-fc78-4896-9848-0f0fe2e75fb9/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/299/4/ef9fbc06-fc78-4896-9848-0f0fe2e75fb9/downloads/wwdc2026-299_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/299/4/ef9fbc06-fc78-4896-9848-0f0fe2e75fb9/downloads/wwdc2026-299_sd.mp4?dl=1

Use Xcode 27's Evaluations framework to synthesize and validate datasets, compare results, and evaluate agentic tool-calling behavior.

TLDR:
- The session shows how to expand small evaluation datasets with synthetic samples using `makeSamples` and `SampleGenerator`, with coverage emphasized over raw sample count.
- `SampleGenerator` supports custom `LanguageModelSession` setup, including model selection, instructions, sampling strategy, and per-sample validation.
- Xcode 27's Evaluations Report can compare runs, helping reveal when a feature only looked good because the original dataset was too narrow.
- Tool evaluations use `TrajectoryExpectation` and `ToolCallEvaluator` to verify which tools an agent calls, with what arguments, in what order, and which tools must not be called.

## Why synthetic evaluation data matters

The Evaluations framework, new in Xcode 27, helps assess intelligence-powered Swift app features, track quality over time, and support production confidence across macOS, iOS, watchOS, and visionOS.

The BookTracker example starts with 13 hand-written `Book` samples for an auto-tagging feature. That is enough to begin development, but too narrow to represent the range of books, genres, review styles, vague summaries, and edge cases users may provide.

The session frames synthetic data as an iterative workflow: start with seed samples, generate more, validate the results, analyze representativeness, and repeat until the dataset covers meaningful real-world variation.

- Small datasets can produce misleadingly high evaluation scores.
- Synthetic data generation is defined in code, can run from command-line workflows, and supports structured data through the `Generable` macro.
- The useful question is not just "how many samples?" but whether the dataset covers the meaningful ways the feature will be used.

## Generate synthetic samples with makeSamples and SampleGenerator

`makeSamples` takes a generation prompt, an existing dataset, and a `targetCount`. The target count is the final dataset size including seed samples, so a target of 100 from 13 seeds generates 87 new samples.

For more control, `SampleGenerator` lets the app define a `sessionProvider` that returns a `LanguageModelSession`. This controls the model and system instructions used for generation. The session is reused across batches, but if the context window is exhausted, the provider may be called again, so instructions should be self-contained.

`samplingStrategy` controls which seed examples are shown in context. Random sampling is the default and works for unordered datasets; sliding-window sampling is useful when the seed dataset has meaningful order.

- Default generation uses the on-device model.
- A custom session can use `PrivateCloudComputeLanguageModel` when larger context is useful.
- Batch size is handled by the framework.
- Generation returns an async stream of new samples.

### Expand a dataset with makeSamples

Uses seed `ModelSample` values and a prompt to stream generated samples until the final dataset reaches the requested target count.

```swift
let prompt = Prompt("""
Generate diverse range of book reviews and corresponding tags.
Cover a wide range of genres, time periods, cultures, and reader personas.
Do not repeat books already in the dataset.
""")

let dataset = Book.sampleBooks.map { book in
    ModelSample(prompt: book.review, expected: BookTags(tags: book.tags))
}

let targetCount = 100
var expandedDataset = dataset

for try await sample in dataset.makeSamples(prompt, targetCount: targetCount) {
    expandedDataset.append(sample)
    print("Generated \(expandedDataset.count) samples so far.")
}
```

### Customize generation with SampleGenerator

Creates a generator with an explicit model and task instructions instead of relying on the default configuration.

```swift
let generator = SampleGenerator<ModelSample<BookTags>>(
    prompt,
    samples: dataset,
    targetCount: targetCount,
    sessionProvider: {
        LanguageModelSession(
            model: PrivateCloudComputeLanguageModel(),
            instructions: """
            You are a synthetic data generator for a book-tracking app's evaluation suite.
            Rules:
            - Review must be at least 100 characters long.
            - Review should cover a mix of genre, mood/tone, and themes.
            - Reviews should vary in length.
            - Create between 3 and 8 tags.
            - Tags must be lowercase.
            """
        )
    }
)
```

## Validate generated data before trusting it

Prompt instructions are not guarantees. The validator closure lets the evaluation pipeline accept or reject each generated sample using deterministic checks that can be applied to one sample at a time.

In BookTracker, the validator checks review length, tag count, and lowercase tags. Broader qualities like diversity across the entire dataset require separate review because the per-sample validator does not see other generated samples.

Valid generated samples are collected in `samples`; rejected outputs are collected in `invalidSamples`. Both are updated during the run and can be inspected while generation is running or after it completes.

- Use validators for systematic rules, not holistic judgments requiring cross-sample context.
- Save or reuse only the samples that satisfy the dataset contract.
- Inspect invalid samples to improve prompts, instructions, or validators.

### Validate synthetic BookTracker samples

Rejects generated samples that do not meet the app's structural requirements.

```text
validator: { sample in
    guard let book = sample.expected else { return false }

    guard sample.promptDescription.count >= 100 else { return false }
    guard (3...8).contains(book.tags.count) else { return false }
    guard book.tags.allSatisfy({ $0 == $0.lowercased() }) else { return false }

    return true
}
```

### Access valid and invalid results

Reads accepted and rejected generated samples from the generator after or during a run.

```swift
for try await sample in generator.run() {
    expandedDataset.append(sample)
}

let allSamples = await generator.samples
let invalidSamples = await generator.invalidSamples
print("Generated \(allSamples.count) new samples. Total: \(expandedDataset.count)")
```

## Use evaluation comparisons to find weak spots

The Xcode 27 Evaluations Report can compare evaluation runs. In the BookTracker example, the 13-sample run shows high tag relevance and usefulness scores, while the 100-sample run drops after adding broader synthetic coverage.

A score drop is useful signal, not automatically a failure of one component. It may indicate problems in the prompt, instructions, intelligence feature implementation, evaluation criteria, or the representativeness of the dataset itself.

- Compare small and expanded dataset runs to detect overconfidence from narrow coverage.
- Use score changes to decide whether to refine prompts, change feature logic, adjust evaluators, or generate more edge cases.
- Treat dataset expansion as part of the same develop-and-evaluate loop as model and prompt iteration.

## Evaluate agentic workflows with trajectory expectations

For agentic features, final output quality is not enough. A model can produce a plausible answer while using the wrong tool, skipping a required lookup, using bad arguments, or calling a tool the user explicitly asked it not to use.

The BookTracker library assistant uses tools such as `searchBooks`, `getBookDetails`, and `findSimilarBooks`. Tool evaluations check the path through these tools: correct tool names, argument values or intent, ordering, and disallowed calls.

`TrajectoryExpectation` describes expected tool calls in a language model session transcript. `ToolCallEvaluator` combines a `LanguageModelSession` with the app's tools, captures the structured transcript, and reports results alongside other evaluations in Xcode.

- Use unordered expectations when only the presence of a tool call matters.
- Use ordered expectations when one tool must precede another, such as searching before requesting details for a `bookId`.
- Use argument matchers such as `.exact`, `.naturalLanguage`, `.contains`, `.oneOf`, `.pattern`, and `.range` depending on how strict the expected argument should be.
- Use `disallowed` expectations for tools that must not appear in the transcript.

### Define a tool argument type

Uses a `Generable` struct so the model can populate optional tool arguments based on the user request.

```swift
@Generable
struct SearchBooksArguments {
    @Guide(description: "A freeform search term to match against titles, reviews, or tags")
    var query: String?

    @Guide(description: "Filter results to books with this specific tag")
    var tag: String?

    @Guide(description: "Filter results by mood")
    var mood: String?

    @Guide(description: "Filter results by genre")
    var genre: String?

    @Guide(description: "Maximum number of results to return. Defaults to 5.")
    var limit: Int?
}
```

### Check ordered, argument-aware tool calls

Requires the assistant to search for gothic books before requesting details for a returned book.

```text
TrajectoryExpectation(
    ordered: [
        ToolExpectation(
            "searchBooks",
            arguments: [
                .exact(argumentName: "tag", value: .string("gothic"))
            ]
        ),
        ToolExpectation(
            "getBookDetails",
            arguments: [
                .keyOnly(argumentName: "bookId")
            ]
        )
    ]
)
```

### Disallow an unwanted tool call

Verifies that a search happens while ensuring the model does not call `findSimilarBooks`.

```text
TrajectoryExpectation(
    unordered: [
        ToolExpectation(
            "searchBooks",
            arguments: [
                .naturalLanguage(
                    argumentName: "genre",
                    criteria: "Should refer to science fiction"
                )
            ]
        )
    ],
    disallowed: [
        ToolExpectation("findSimilarBooks")
    ]
)
```

## Synthesize datasets for tool-call evaluations

The same synthetic-data workflow can expand tool-evaluation datasets because `ModelSample` and `TrajectoryExpectation` are generable. The generation prompt and session instructions should explicitly describe available tools, their purposes, order constraints, and supported argument matchers.

Validation remains important for generated tool samples. The session validates that each sample has an expectation, includes at least one expected tool, and references only real tools from the app.

Running output evaluations and tool-call evaluations in one suite gives broader confidence: one evaluation checks what the model produces, while the other checks how it gets there.

- Tell the generator what tools exist; it does not infer your app's tool list automatically.
- Include order requirements, such as requiring `searchBooks` before `getBookDetails` or `findSimilarBooks`.
- Validate generated expectations before using them as test data.

### Validate generated tool-evaluation samples

Rejects generated trajectory samples that omit expectations, contain no tool calls, or reference tools the app does not define.

```swift
validator: { sample in
    guard sample.output.expectations != nil else { return false }
    let expectations = sample.output.expectations!

    let totalExpectations = expectations.ordered.count + expectations.unordered.count
    guard totalExpectations > 0 else { return false }

    let validTools: Set<String> = ["searchBooks", "getBookDetails", "findSimilarBooks"]
    let allExpectations = expectations.ordered + expectations.unordered + expectations.disallowed

    for expectation in allExpectations {
        guard validTools.contains(expectation.name) else { return false }
    }

    return true
}
```

Resources:
- Book Tracker: Using Evaluations to evaluate an intelligent feature: https://developer.apple.com/documentation/Evaluations/book-tracker-using-evaluations-to-evaluate-an-intelligent-feature
- Generating synthetic datasets: https://developer.apple.com/documentation/Evaluations/generating-synthetic-evaluation-datasets
- Evaluating tool-calling behavior: https://developer.apple.com/documentation/Evaluations/evaluating-tool-calling-behavior
- Scoring with model-as-judge evaluators: https://developer.apple.com/documentation/Evaluations/scoring-with-model-as-judge-evaluators

Chapters:
- 0:00 Introduction: Introduces advanced Evaluations framework features in Xcode 27 for scaling evaluation datasets and assessing agentic workflows. The session focuses on the develop-and-evaluate phase of building intelligence-powered features.
- 2:21 The dataset problem in BookTracker: BookTracker's auto-tagging feature starts with 13 hand-written samples, which are too narrow to represent real user reviews. The chapter motivates synthetic data as a way to expand coverage without manually writing every example.
- 3:46 Generating synthetic data with makeSamples: Shows `makeSamples` using a prompt, seed `ModelSample` dataset, and final `targetCount`. The generated samples arrive as an async stream and are appended to the expanded dataset.
- 6:27 Customizing generation with SampleGenerator: Introduces `SampleGenerator` for custom generation configuration, including a `sessionProvider` that returns a `LanguageModelSession` with a chosen model and instructions. It notes that context-window exhaustion can cause the provider to be called again, so instructions should be self-contained.
- 8:38 Sampling strategies: Explains how sampling strategy chooses seed examples for in-context generation. Random sampling is the default for unordered data, while sliding-window sampling is useful when sample order matters.
- 10:11 Validating synthetic samples: Adds a validator closure to accept or reject generated samples using systematic checks such as review length, tag count, and lowercase tags. Valid and invalid samples are tracked separately during generation.
- 13:04 Comparing evaluation results: Uses Xcode 27's Evaluations Report to compare the original 13-sample run against the expanded 100-sample run. The lower scores on the larger dataset reveal that the original evaluation was not comprehensive enough.
- 15:09 Tool calling and tool evaluations: Shifts from evaluating generated outputs to evaluating the behind-the-scenes tool calls used by agentic features. The BookTracker assistant uses tools such as `searchBooks`, `getBookDetails`, and `findSimilarBooks`.
- 18:54 Trajectory expectations: Defines `TrajectoryExpectation` as a way to check expected tool calls in a session transcript. It covers argument matching, ordered expectations, and disallowed tool calls.
- 21:26 Building a tool call evaluation: Combines prompts and trajectory expectations into a dataset scored by `ToolCallEvaluator`. The evaluator runs a language model session with tools, captures the structured transcript, and reports results in Xcode.
- 22:02 Synthetic data for tool evaluations: Shows that tool-evaluation samples can also be synthesized because `ModelSample` and `TrajectoryExpectation` are generable. The generated samples should be validated to ensure expectations exist, at least one tool is referenced, and only real tools are used.
- 23:49 Next steps: Recommends running output evaluations and tool evaluations together for end-to-end confidence. Developers are encouraged to create synthetic data, evaluate custom tools, and explore the sample app and documentation.

### Build a responsive camera app that launches quickly

- Session ID: wwdc2026-303
- Page: https://wwdc.ai/2026/303
- Markdown: https://wwdc.ai/2026/303.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/303/
- Category: Photos & Camera
- Description: Optimize AVFoundation camera startup by deferring non-preview outputs, rendering preview efficiently, adapting to pressure, and using AVProVideoStorage.
- Duration: 25:20
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-303/eng_2e5fb5f98e6f/wwdc2026-303-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/303/5/fb6dc55a-c026-4ce1-9902-7a744fef4c99/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/303/5/fb6dc55a-c026-4ce1-9902-7a744fef4c99/downloads/wwdc2026-303_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/303/5/fb6dc55a-c026-4ce1-9902-7a744fef4c99/downloads/wwdc2026-303_sd.mp4?dl=1

Optimize AVFoundation camera startup by deferring non-preview outputs, rendering preview efficiently, adapting to pressure, and using AVProVideoStorage.

TLDR:
- Make the first preview frame the primary launch metric: create only launch-critical UI up front, configure AVCaptureSession off the main thread, commit once, and never call startRunning/stopRunning on the main thread.
- Use deferred start in iOS 26 and later to initialize only the preview path before the first frame; defer photo/movie outputs and use automatic or manual runDeferredStartWhenNeeded depending on your preview pipeline.
- Pair deferred AVCapturePhotoOutput with responsive capture so the app can accept a photo capture before deferred photo initialization has fully completed.
- Maintain runtime quality by choosing AVCaptureVideoPreviewLayer when possible, monitoring hardwareCost and AVCaptureDevice.systemPressureState, and using AVProVideoStorage for deterministic high-data-rate video writes.

## Launch around the first preview frame

The session frames camera launch performance around how quickly the first preview frame appears. A camera app can feel ready before every control, picker, or background resource is initialized, so launch work should be split into preview-critical and post-preview phases.

For an AVCam-style interface, the preview and shutter button are launch-critical. Elements such as the image well, mode picker, preferences, and other non-essential resources should be created or faded in after preview is already streaming.

- Create AVCaptureSession as soon as the main thread finishes minimal UI setup, but create/configure it off the main thread so UI scene creation can proceed in parallel.
- Commit a single AVCaptureSession configuration during launch; avoid multiple reconfiguration passes before preview.
- Treat startRunning and stopRunning as blocking calls and keep them off the main thread.
- Only initialize the output needed to render preview before the first frame; other capture outputs can be deferred.

## Use deferred start for non-preview outputs

Deferred start, available in iOS 26 and later, lets an AVCaptureSession postpone initialization of outputs that are not needed to show preview. The preview path starts first, and deferred outputs are initialized later either automatically or when the app explicitly signals readiness.

Every AVCaptureOutput and AVCaptureVideoPreviewLayer has an isDeferredStartEnabled property. For fastest launch, disable deferral only for the output that renders preview, and enable it for outputs such as AVCapturePhotoOutput or movie recording outputs.

- Apps recompiled with the iOS 26 or later SDK use automatic deferred start by default through AVCaptureSession.automaticallyRunsDeferredStart.
- Automatic mode chooses a time shortly after preview appears and reports progress through AVCaptureSessionDeferredStartDelegate callbacks.
- Manual mode sets automaticallyRunsDeferredStart to false and calls runDeferredStartWhenNeeded after the app has presented the first frame or finished other startup work.
- Manual mode is especially useful when rendering preview with AVCaptureVideoDataOutput instead of AVCaptureVideoPreviewLayer.

### Automatic deferred start with a preview layer and deferred photo output

Only the preview layer is required for launch; photo output initialization is deferred until after preview begins.

```swift
import AVFoundation

final class DeferredStartDelegate: NSObject, AVCaptureSessionDeferredStartDelegate {
    func sessionWillRunDeferredStart(_ session: AVCaptureSession) {
        // Prepare background resources before deferred outputs initialize.
    }

    func sessionDidRunDeferredStart(_ session: AVCaptureSession) {
        // Deferred outputs are initialized and ready.
    }
}

let captureSession = AVCaptureSession()
captureSession.beginConfiguration()
captureSession.automaticallyRunsDeferredStart = true

let previewLayer = AVCaptureVideoPreviewLayer(session: captureSession)
previewLayer.isDeferredStartEnabled = false

let photoOutput = AVCapturePhotoOutput()
photoOutput.isDeferredStartEnabled = true
captureSession.addOutput(photoOutput)

captureSession.setDeferredStartDelegate(delegate,
    deferredStartDelegateCallbackQueue: sessionQueue)
captureSession.commitConfiguration()
sessionQueue.async { captureSession.startRunning() }
```

### Manual deferred start after first frame presentation

When the app owns preview rendering, wait until the first displayed frame before starting deferred output initialization.

```swift
import AVFoundation
import QuartzCore

captureSession.automaticallyRunsDeferredStart = false
videoOutput.isDeferredStartEnabled = false
photoOutput.isDeferredStartEnabled = true

private var firstFramePresented = false

if let drawable = layer.nextDrawable(), !firstFramePresented {
    drawable.addPresentedHandler { _ in
        // Create postponed UI, then allow deferred outputs to initialize.
        captureSession.runDeferredStartWhenNeeded()
    }
    firstFramePresented = true
}
```

## Keep capture responsive after deferring photo output

Deferring AVCapturePhotoOutput improves time to first preview frame, but it does not automatically reduce time to first completed photo capture. If the user taps the shutter before the photo output has finished initializing, the output must still finish deferred startup.

Responsive capture addresses this by adding buffering between starting a capture and processing. When supported, enable it alongside deferred photo output so a user can launch, see preview quickly, and still capture the moment.

- Set AVCapturePhotoOutput.maxPhotoQualityPrioritization as appropriate for your app's quality goals.
- Enable isResponsiveCaptureEnabled only when isResponsiveCaptureSupported is true.
- Use this combination for camera apps that need both fast preview launch and immediate shutter responsiveness.

### Enable responsive capture on photo output

Responsive capture lets the photo output accept captures immediately when the feature is supported.

```swift
import AVFoundation

func configurePhotoOutput(for session: AVCaptureSession, device: AVCaptureDevice) {
    let photoOutput = AVCapturePhotoOutput()
    guard session.canAddOutput(photoOutput) else { return }

    session.addOutput(photoOutput)
    photoOutput.maxPhotoQualityPrioritization = .quality
    photoOutput.isResponsiveCaptureEnabled = photoOutput.isResponsiveCaptureSupported
}
```

## Render preview with the right output

AVCaptureVideoPreviewLayer is the preferred preview renderer when the app only needs to show the camera feed. It is optimized for low latency, low CPU/GPU overhead, HDR tone mapping, and power-efficient preview rendering without app-side per-frame processing.

Use AVCaptureVideoDataOutput when the app needs per-frame access, custom processing, Metal integration, analysis, or per-frame overlays. This flexibility comes with more responsibility: keep per-frame work short to avoid dropped frames, and adopt manual deferred start to recover launch gains.

- Choose AVCaptureVideoPreviewLayer for simple, efficient preview and automatic deferred-start behavior when built with the iOS 26 or later SDK.
- Choose AVCaptureVideoDataOutput when frame processing or custom rendering is required.
- With AVCaptureVideoDataOutput, avoid expensive per-frame CPU, GPU, or Apple Neural Engine work during launch and while the device is under pressure.

## Sustain performance under cost, pressure, and storage constraints

As capture configurations become more complex, the app should evaluate whether the hardware can support them and adapt when conditions change. AVCaptureSession.hardwareCost reports active hardware usage; values above 1 indicate an unsupported configuration. AVCaptureDevice.systemPressureState reports pressure changes that may require reducing workload.

High data-rate video capture, such as ProRes, also depends on consistent file I/O. AVProVideoStorage, new in iOS 27, manages system-wide pre-allocated storage so supported movie recording and AVAssetWriter workflows can get deterministic write performance.

- Hardware cost is affected by the number of cameras, active formats such as 1080p or 4K, maximum frame rates, frame-rate overrides, and binned formats.
- After committing the session configuration, check hardwareCost and reconfigure if it exceeds 1.0 before starting the session.
- Observe AVCaptureDevice.systemPressureState and respond by reducing frame rate, throttling GPU or Apple Neural Engine work, or minimizing UI updates.
- For ProRes or other high-bandwidth recording, check AVProVideoStorage support and remaining capacity before starting capture.

### Check hardware cost and observe system pressure

Validate the configuration before running, then adapt when system pressure changes.

```swift
import AVFoundation

captureSession.beginConfiguration()
// Configure inputs, outputs, formats, and connections.
captureSession.commitConfiguration()

guard captureSession.hardwareCost <= 1.0 else {
    print("hardwareCost \(captureSession.hardwareCost) - cannot start session. Reconfiguring.")
    setupLowCostConfiguration()
    return
}

captureSession.startRunning()

let observer = activeVideoInput?.device.observe(\.systemPressureState,
    options: [.initial, .new]) { device, change in
        // Reduce frame rate, throttle processing, or simplify UI.
    }
```

### Use AVProVideoStorage for deterministic high-data-rate recording

When supported and not busy, AVProVideoStorage writes to pre-allocated storage during capture and moves the recording to the requested URL when finished.

```swift
import AVFoundation

guard AVProVideoStorage.isSupported,
      let pvs = AVProVideoStorage.shared else { return }

guard pvs.remainingCapacity != 0 else {
    pvs.openSettings()
    return
}

let movieOutput = AVCaptureMovieFileOutput()
guard movieOutput.isProVideoStorageSupported else { return }
guard !pvs.isBusy else { return }

let url = FileManager.default.temporaryDirectory
    .appendingPathComponent(UUID().uuidString)
    .appendingPathExtension("mov")

movieOutput.usesProVideoStorage = true
movieOutput.startRecording(to: url, recordingDelegate: delegate)
```

Resources:
- Performance and metrics: https://developer.apple.com/documentation/Xcode/performance-and-metrics
- AVCam: Building a camera app: https://developer.apple.com/documentation/AVFoundation/avcam-building-a-camera-app

Chapters:
- 0:00 Introduction: The session establishes that the first visible preview frame is the key factor in perceived camera launch speed. It outlines four areas: faster launch, steady preview rendering, sustained performance, and deterministic high-data-rate file writing.
- 2:02 Fast Launch: Camera launch is broken into app launch, session configuration/start, output initialization, and preview streaming. The chapter recommends creating only preview-critical UI up front, configuring AVCaptureSession off the main thread, committing once, and avoiding blocking session calls on the main thread.
- 6:52 Adopt deferred start: Deferred start in iOS 26 lets apps postpone initialization of non-preview outputs until after preview begins. The chapter covers automatic deferred start, manual runDeferredStartWhenNeeded, delegate callbacks, and pairing deferred photo output with responsive capture.
- 15:06 Steady preview: AVCaptureVideoPreviewLayer is recommended for simple, efficient, low-latency preview rendering with minimal CPU/GPU overhead. AVCaptureVideoDataOutput is appropriate when per-frame processing, overlays, Metal integration, or frame analysis are required, but apps must manage frame work carefully and use manual deferred start for launch gains.
- 18:04 Sustained performance: The chapter explains how AVCaptureSession.hardwareCost and AVCaptureDevice.systemPressureState help assess and adapt capture configurations. Apps should reconfigure unsupported hardware-cost setups and reduce workload as pressure increases.
- 21:14 Deterministic file writing: AVProVideoStorage is introduced for deterministic, sustained file write performance for high-data-rate video such as ProRes. The chapter shows checking support and capacity, ensuring the storage is not busy, enabling usesProVideoStorage, and recording with AVCaptureMovieFileOutput or AVAssetWriter.

### Implement high resolution photo capture

- Session ID: wwdc2026-304
- Page: https://wwdc.ai/2026/304
- Markdown: https://wwdc.ai/2026/304.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/304/
- Category: Photos & Camera
- Description: Use AVFoundation to capture 24MP and 48MP photos, choose RAW/bracketed/processed outputs, and keep camera apps responsive with prepared and deferred processing.
- Duration: 17:58
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-304/eng_71c721a6ab9a/wwdc2026-304-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/304/4/7a18d6ee-a63d-4402-bfb6-85a21dfac7dd/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/304/4/7a18d6ee-a63d-4402-bfb6-85a21dfac7dd/downloads/wwdc2026-304_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/304/4/7a18d6ee-a63d-4402-bfb6-85a21dfac7dd/downloads/wwdc2026-304_sd.mp4?dl=1

Use AVFoundation to capture 24MP and 48MP photos, choose RAW/bracketed/processed outputs, and keep camera apps responsive with prepared and deferred processing.

TLDR:
- High-resolution still capture requires the `.photo` session preset; 24MP and 48MP support depends on device camera, active format, requested dimensions, and quality prioritization.
- AVFoundation supports fully processed photos, exposure brackets, Bayer RAW, and Apple ProRAW for different capture and editing workflows.
- Configure `AVCapturePhotoOutput.maxPhotoQualityPrioritization`, choose `maxPhotoDimensions` from `activeFormat.supportedMaxPhotoDimensions`, and set matching per-capture `AVCapturePhotoSettings`.
- Use prepared photo settings, responsive capture, deferred photo processing, and fast capture prioritization to reduce shot-to-shot delay during high-quality captures.

## What "high resolution" means for iPhone capture

High-resolution photos provide more detail than the preview stream, which is only intended for on-screen camera display. They are useful for cropping, zooming, and image analysis, but require more memory and processing time.

The session distinguishes typical 12MP high-resolution capture from 24MP and 48MP modes. 48MP capture uses a full-resolution frame from the Quad sensor for maximum detail. 24MP capture combines a 12MP multi-frame fused HDR image with a 48MP full-resolution image through the Photonic Engine, producing more detail than 12MP while keeping file size more manageable than full 48MP.

- 48MP Quad sensor support starts with iPhone 14 Pro and iPhone 14 Pro Max.
- 24MP photo capture starts with iPhone 15 and is the default in the Camera app on supported devices.
- 24MP and 48MP support extends to the Tele camera on iPhone 16 Pro and the Ultra Wide camera on iPhone 17.
- Higher resolutions improve detail and flexibility, but increase processing cost and capture latency.

## Choose the capture type for the workflow

AVFoundation can request several high-resolution capture types. The right choice depends on whether the app wants a finished computational photo, inputs for HDR selection, or editable sensor data.

- Fully processed photos are multi-frame fused and processed by the Photonic Engine to improve dynamic range and fine detail.
- Exposure brackets capture multiple exposures of the same scene for HDR workflows or selection among different exposures.
- Bayer RAW provides minimally processed sensor data for custom post-processing and editing workflows.
- Apple ProRAW combines RAW flexibility with iPhone image processing for more control over exposure, color, and detail during editing.

## Configure the capture session and photo output

Use an `AVCaptureSession` with the `.photo` preset for 24MP and 48MP capture. Other session presets do not support these resolutions.

Configure `AVCapturePhotoOutput` before committing the session configuration. Changing key photo-output settings after commit can trigger a lengthy pipeline reconfiguration.

- Set `maxPhotoQualityPrioritization` according to the highest quality level the session should support: speed, balanced, or quality.
- Query `device.activeFormat.supportedMaxPhotoDimensions` on iOS 16 and later, then choose dimensions that fit the app's use case rather than always choosing the largest size.
- Set `photoOutput.maxPhotoDimensions` before `commitConfiguration()`.
- High-resolution availability can depend on `maxPhotoQualityPrioritization`.

### Configure a photo session

Use the photo preset, configure output quality prioritization, choose supported dimensions from the active format, and commit once configuration is complete.

```swift
import AVFoundation

private let session = AVCaptureSession()
private let photoOutput = AVCapturePhotoOutput()

private func configureSession() {
    session.beginConfiguration()
    session.sessionPreset = .photo

    photoOutput.maxPhotoQualityPrioritization = .quality // or .balanced

    let supported = device?.activeFormat.supportedMaxPhotoDimensions ?? []
    if let largest = supported.max(by: { lhs, rhs in
        Int(lhs.width) * Int(lhs.height) < Int(rhs.width) * Int(rhs.height)
    }) {
        photoOutput.maxPhotoDimensions = largest
    }

    session.commitConfiguration()
    session.startRunning()
}
```

## Request dimensions and quality per capture

For each photo request, set `maxPhotoDimensions` and `photoQualityPrioritization` on `AVCapturePhotoSettings`. This lets one session support multiple dimensions and quality levels without reconfiguring the capture pipeline between shots.

`maxPhotoDimensions` is a request, not a guarantee. The system considers light level, scene conditions, and available processing resources. The actual delivered dimensions are reported through `AVCaptureResolvedSettings`.

- 12MP capture is available across speed, balanced, and quality prioritization levels.
- 48MP capture is available with balanced or quality prioritization because it is a single-frame capture.
- 18MP and 24MP multi-frame fused captures require quality prioritization.
- The 18MP case discussed is for the Center Stage front camera on iPhone 17.

### Capture with requested dimensions and quality

Set the requested dimensions and quality on the capture settings for the current shot, then inspect resolved settings in the delegate for the actual result.

```swift
import AVFoundation

let settings = AVCapturePhotoSettings()
settings.maxPhotoDimensions = dimension.cmVideoDimensionsValue
settings.photoQualityPrioritization = .quality

if let delegate {
    photoOutput?.capturePhoto(with: settings, delegate: delegate)
}
```

## Prepare resources before high-resolution capture

High-resolution captures require resource allocation based on dimensions and quality prioritization. If those resources are not prepared ahead of time, allocation happens during capture and can slow down the shutter response.

Use `setPreparedPhotoSettingsArray` when entering a mode such as 48MP capture. The settings used later for the actual capture must be a new `AVCapturePhotoSettings` object whose configuration matches the prepared settings.

- Prepare as early as possible after the user activates a high-resolution mode.
- Do not reuse the prepared settings object for the actual capture.
- Match `maxPhotoDimensions` and `photoQualityPrioritization` between prepared and capture settings.

### Preallocate for a high-resolution mode

Prepare the expected high-resolution pipeline ahead of time, then create a separate matching settings object for capture.

```swift
import AVFoundation

let prepareSettings = AVCapturePhotoSettings()
prepareSettings.maxPhotoDimensions = photoOutput.maxPhotoDimensions
prepareSettings.photoQualityPrioritization = .quality

photoOutput.setPreparedPhotoSettingsArray([prepareSettings]) { prepared, error in
    if let error {
        print("Failed to prepare: \(error)")
        return
    }
    print("Pipeline prepared: \(prepared)")
}

// Later, create new settings with a matching configuration.
let captureSettings = AVCapturePhotoSettings()
captureSettings.maxPhotoDimensions = photoOutput.maxPhotoDimensions
captureSettings.photoQualityPrioritization = .quality
photoOutput.capturePhoto(with: captureSettings, delegate: self)
```

## Keep capture responsive

High-resolution photo processing can take several seconds. `AVCapturePhotoCaptureDelegate` callbacks report capture and processing progress, and `AVCaptureResolvedSettings.photoProcessingTimeRange` provides an estimate for when the photo will be delivered.

Without responsive capture, the next shot must wait for the previous photo to finish processing. Enabling responsive capture allows the next capture to begin after the previous capture stage completes, even while processing continues. Observe `AVCapturePhotoOutput.captureReadiness` to know when another photo can be captured.

Deferred photo processing improves responsiveness further by delivering a lightly processed proxy shortly after capture, then completing final processing later on demand through the photo library or in the background when system conditions are favorable. Fast capture prioritization can adapt rapid-fire quality captures down to balanced quality; starting with iOS 27 on iPhone 16 and iPhone 17, balanced fast captures can also be processed later using deferred processing.

- Use responsive capture to overlap the capture stage of a new photo with processing of the previous photo.
- Use deferred photo processing for high-quality captures so processing no longer blocks the next capture for as long.
- Use fast capture prioritization when the app should favor responsiveness during bursts of quick captures.
- Expect each photo's final processing work to still take time; these features reduce blocking and shot-to-shot delay, not the total work needed for final image quality.

Resources:
- Capturing photos in RAW and Apple ProRAW formats: https://developer.apple.com/documentation/AVFoundation/capturing-photos-in-raw-and-apple-proraw-formats
- AVCam: Building a camera app: https://developer.apple.com/documentation/AVFoundation/avcam-building-a-camera-app

Chapters:
- 0:00 Introduction: Introduces high-resolution photo capture tradeoffs, especially processing time versus final image quality. The session scope covers capture types, configuration, and keeping the camera app responsive.
- 0:52 High-resolution photos: Explains 12MP, 24MP, and 48MP capture and how Quad sensors and the Photonic Engine balance light, detail, and file size. It also identifies which iPhone generations and cameras gain 24MP and 48MP support.
- 4:07 Types of captures: Compares fully processed photos, exposure brackets, Bayer RAW, and Apple ProRAW. Each type serves a different workflow, from finished computational images to custom editing and HDR processing.
- 5:20 Configure a capture session: Shows how to configure `AVCaptureSession` with the `.photo` preset, set photo-output quality prioritization, choose supported maximum dimensions, and request dimensions per capture. It emphasizes configuring output before committing the session and preparing resources ahead of capture.
- 9:41 Responsive capture best practices: Describes why high-resolution processing can delay subsequent shots and how to reduce shot-to-shot delay. Recommended techniques include observing capture readiness, enabling responsive capture, adopting deferred photo processing, and using fast capture prioritization for rapid captures.

### Enhance RAW image processing with Core Image

- Session ID: wwdc2026-305
- Page: https://wwdc.ai/2026/305
- Markdown: https://wwdc.ai/2026/305.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/305/
- Category: Photos & Camera
- Description: Use Core Image RAW 9 and CIRAWFilter to improve RAW rendering quality, tune editing performance, and optimize custom CIImageProcessor kernels.
- Duration: 16:28
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-305/eng_c8611d274ce2/wwdc2026-305-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/305/5/d8d5f3ce-0ff1-45a3-a630-436743477c62/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/305/5/d8d5f3ce-0ff1-45a3-a630-436743477c62/downloads/wwdc2026-305_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/305/5/d8d5f3ce-0ff1-45a3-a630-436743477c62/downloads/wwdc2026-305_sd.mp4?dl=1

Use Core Image RAW 9 and CIRAWFilter to improve RAW rendering quality, tune editing performance, and optimize custom CIImageProcessor kernels.

TLDR:
- RAW 9 is a new Core Image RAW processing pipeline for iOS, iPadOS, macOS, and visionOS 27 that uses a tiled Core ML model on the Apple Neural Engine to improve demosaic and denoise quality.
- Apps must opt in to RAW 9 with CIRAWFilter by checking supportedDecoderVersions for version9 and setting decoderVersion; supportedCameraModels reports camera support for a decoder version.
- For interactive RAW editing, render at the needed scale, keep a CIContext per view with cacheIntermediates enabled, consider Extended Virtual Addressing, and render to Metal-backed views.
- For full-resolution export and custom processors, disable intermediate caching, tune CIContext memoryLimit, use heifRepresentation/jpegRepresentation, and adopt new CIImageProcessor tiling and temporary buffer APIs.

## Core Image RAW support and RAW 9

RAW files require a processing pipeline before display: metadata parsing and sensor unpacking, demosaic from a color-filter mosaic into RGB pixels, denoising, sharpening/local contrast convolutions, and final white balance, exposure, color, and tone adjustments. Core Image and Image IO provide system RAW support across Apple platforms, so many apps and frameworks get basic viewing automatically.

RAW 9 is a major update to the Core Image RAW pipeline in iOS, iPadOS, macOS, and visionOS 27. It uses a tiled Core ML model that combines demosaic and denoise, running on the Apple Neural Engine where available, to improve sharpness, color definition, and noise handling.

- System RAW support has grown from 21 camera models in 2006 to 784 models across major camera vendors.
- Older RAW pipeline versions remain available where supported so existing rendering choices can be preserved.
- RAW 9 supports hundreds of models at launch, can grow through operating-system updates, and is automatically supported for cameras that shoot DNG natively, including Apple iPhones.

## Enable RAW 9 and expose CIRAWFilter editing controls

RAW 9 is not enabled by default. Load RAW files with CIRAWFilter, check whether the filter's supportedDecoderVersions includes version9, then set decoderVersion to version9 when available. Use the supportedCameraModels class method to obtain the list of camera models supported by a decoder version.

CIRAWFilter's calibrated editing properties are the main app-facing way to expose RAW controls. The session highlights exposure, luminanceNoiseReductionAmount, sharpnessAmount, and contrastAmount as important controls for user-facing RAW editing.

- colorNoiseReductionAmount has no effect in RAW 9 because the Core ML model handles color noise reduction automatically.
- detailAmount and moireReductionAmount are no longer needed or supported in RAW 9.
- Use the filter's supported-property checks to determine whether a property is available for a given filter instance.

## Performance guidance for interactive editing

RAW 9 is more resource intensive than previous RAW versions, but Core Image can keep repeated edits responsive by caching intermediate work. This is especially important when a single RAW is rendered repeatedly at screen resolution while users adjust CIRAWFilter properties.

- Set CIRAWFilter scaleFactor when displaying a reduced-size image so Core Image does not render more pixels than needed.
- Use one CIContext per view and set cacheIntermediates to true for interactive editing.
- Add the Extended Virtual Addressing entitlement when appropriate so Core Image can use more memory for caching between renders.
- Render directly to Metal-backed views, such as MTKView, so repeated renders can overlap GPU work more effectively.

## Performance guidance for export

Exporting is a different workload: multiple RAW files are usually rendered once at full resolution to formats such as HEIF or JPEG. For this case, caching intermediate results is less useful and may waste memory.

- Create an export CIContext with cacheIntermediates set to false.
- Tune the CIContext memoryLimit option; iOS defaults to a conservative 256 MB, while 512 MB or 1024 MB can improve export performance when memory allows.
- Prefer Core Image's heifRepresentation and jpegRepresentation context methods for additional memory savings instead of calling Image IO directly.

### CIContext options for export

Creates an export-oriented CIContext that disables intermediate caching and raises Core Image's memory limit.

```swift
let exportCtx = CIContext(options: [
    .cacheIntermediate: false,
    .memoryLimit: 512
])
```

## New CIImageProcessor capabilities

RAW 9 uses CIImageProcessor because it can combine Core ML with other Core Image kernels. The session introduces two CIImageProcessor improvements useful for custom image processing: explicit output tile sizes and managed temporary pixel buffers.

Explicit tiling lets a processor choose output regions instead of relying entirely on Core Image's memory-driven tiling. Temporary buffers let processors request scratch CVPixelBuffers from Core Image's cache, which avoids repeatedly allocating and destroying buffers for each tile.

- In a CIImageProcessorKernel process callback, operate only on input.region and output.region.
- Use apply(withTiledExtent:inputs:arguments:) with an array of tile rectangles to control output tile layout.
- Use CIImageProcessorOutput temporaryPixelBuffer with an identifier when a process callback needs one or more scratch buffers.
- Core Image manages temporary buffer lifetime and can recycle buffers across tiles.

### CIImageProcessor with explicit output tile sizes

Builds 512×512 output tiles covering an image extent and applies a CIImageProcessorKernel using those explicit output regions.

```swift
import CoreImage

class MyProcessor: CIImageProcessorKernel {
    override class func roi(forInput input: Int32,
                            arguments: [String: Any]?,
                            outputRect: CGRect) -> CGRect {
        return outputRect
    }

    override class func process(with inputs: [CIImageProcessorInput]?,
                                arguments: [String: Any]?,
                                output: CIImageProcessorOutput) throws {
        guard let input = inputs?.first,
              let iBuffer = input.pixelBuffer,
              let oBuffer = output.pixelBuffer else { return }

        let iRegion = input.region
        let oRegion = output.region
        // MyCopyBuffer(iBuffer, iRegion, oBuffer, oRegion)
    }
}

let extent = inImg.extent
let tileSize = 512.0
var tiles: [CIVector] = []

for y in stride(from: extent.minY, to: extent.maxY, by: tileSize) {
    for x in stride(from: extent.minX, to: extent.maxX, by: tileSize) {
        let tile = CGRect(x: x, y: y,
                          width: min(tileSize, extent.maxX - x),
                          height: min(tileSize, extent.maxY - y))
        tiles.append(CIVector(cgRect: tile))
    }
}

let result = try MyProcessor.apply(withTiledExtent: tiles,
                                   inputs: [inImg],
                                   arguments: [:])
```

### CIImageProcessor temporary pixel buffer

Requests a Core Image-managed scratch CVPixelBuffer for tile processing, avoiding repeated manual allocation.

```swift
import CoreImage

class MyProcessor: CIImageProcessorKernel {
    override class func process(with inputs: [CIImageProcessorInput]?,
                                arguments: [String: Any]?,
                                output: CIImageProcessorOutput) throws {
        guard let input = inputs?.first,
              let srcPixelBuffer = input.pixelBuffer,
              let dstPixelBuffer = output.pixelBuffer else { return }

        guard let scratch = output.temporaryPixelBuffer(
            identifier: "myScratch",
            format: kCVPixelFormatType_64RGBAHalf,
            width: Int(output.region.width),
            height: Int(output.region.height),
            pixelBufferAttributes: nil
        ) else { return }

        // Step 1: copy input CVPixelBuffer → scratch
        // Step 2: process pixels in scratch
        // Step 3: copy scratch → output CVPixelBuffer
    }
}
```

Resources:
- Extended Virtual Addressing Entitlement: https://developer.apple.com/documentation/BundleResources/Entitlements/com.apple.developer.kernel.extended-virtual-addressing

Chapters:
- 0:00 Introduction: Introduces Core Image enhancements for RAW image processing, including RAW quality improvements, performance guidance, and CIImageProcessor additions for RAW-related workflows.
- 0:52 How Core Image supports RAW: Explains the RAW processing pipeline: metadata parsing, sensor unpacking, demosaic, denoise, sharpening/local contrast, and final color and tone adjustments. Notes that Image IO gets basic RAW support automatically, while CIRAWFilter enables advanced editing controls.
- 2:48 The evolution of RAW support: Reviews the growth of Apple RAW support from 21 calibrated camera models to 784, including Apple ProRAW. Emphasizes that older RAW photos can be reprocessed with newer algorithms while some previous versions remain available.
- 3:33 RAW 9 overview: Introduces RAW 9 as a major rendering update built on a tiled Core ML model that combines demosaic and denoise, using Apple Neural Engine cores for performance.
- 3:56 RAW 9 quality improvements: Compares RAW 8 and RAW 9 on low-noise, high-noise, and non-traditional sensor examples. RAW 9 improves apparent sharpness, color accuracy, noise reduction, artifact handling, and fine detail.
- 5:50 Enable and edit RAW 9 with CIRAWFilter API: Shows that apps must opt in by checking supportedDecoderVersions for version9 and setting decoderVersion. Covers supportedCameraModels and key CIRAWFilter editing properties, plus properties that no longer apply in RAW 9.
- 8:33 RAW 9 performance overview: Explains that RAW 9 is more compute- and resource-intensive than previous versions, but Core Image caches intermediate results so subsequent edits can remain responsive.
- 9:19 Interactive editing: Recommends using CIRAWFilter scaleFactor for reduced-size display, one CIContext per view with cacheIntermediates enabled, the Extended Virtual Addressing entitlement for more caching memory, and Metal-backed views for repeated renders.
- 10:52 Exporting to other formats: For full-resolution export to HEIF or JPEG, recommends disabling cacheIntermediates, increasing CIContext memoryLimit when appropriate, and using Core Image representation methods instead of calling Image IO directly.
- 11:50 New CIImageProcessor features: Introduces explicit output tile sizes and Core Image-managed temporary pixel buffers for CIImageProcessor. These features help custom processors, including Core ML-backed ones, control tiling and reduce repeated scratch-buffer allocation.

### Explore Retention Messaging in App Store Connect

- Session ID: wwdc2026-309
- Page: https://wwdc.ai/2026/309
- Markdown: https://wwdc.ai/2026/309.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/309/
- Category: App Store, Distribution & Marketing
- Description: Configure App Store subscription cancellation-flow retention messages, offers, and real-time server-driven responses with App Store Connect and the Retention Messaging API.
- Duration: 15:10
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-309/eng_66e3e48baab8/wwdc2026-309-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/309/4/afa0aec8-f216-43ed-bcb1-1a3742e49dac/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/309/4/afa0aec8-f216-43ed-bcb1-1a3742e49dac/downloads/wwdc2026-309_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/309/4/afa0aec8-f216-43ed-bcb1-1a3742e49dac/downloads/wwdc2026-309_sd.mp4?dl=1

Configure App Store subscription cancellation-flow retention messages, offers, and real-time server-driven responses with App Store Connect and the Retention Messaging API.

TLDR:
- Retention Messaging lets apps show a value proposition, image, or offer when a subscriber reaches the cancellation confirmation flow for an auto-renewable subscription.
- App Store Connect setup is no-server: create localized message text, optionally attach Asset Library images and retention offers, map the message to one or more subscriptions, and test in sandbox.
- Real-time Retention Messaging adds server-to-server decisioning through the Retention Messaging API, including message selection, promotional offers, and switch-plan alternatives within a subscription group.
- Use App Store Connect Retention Messaging even with real-time messaging as fallback; real-time production use requires sandbox setup, a passing performance test, production configuration, and requested access.

## What Retention Messaging does

Retention Messaging targets a specific subscription lifecycle moment: when a customer is about to cancel an auto-renewable subscription in the App Store manage-subscriptions flow. It lets developers present a reminder of subscription value or an incentive to stay subscribed before cancellation is confirmed.

The supported App Store Connect views are message-only, message with image, and message with offer. If a customer is eligible for an offer, the offer presentation replaces the image. Apple describes save rate as the percentage of subscribers who keep their subscription after reaching the cancel confirmation page, and notes that results vary across developers.

- Use message text to communicate upcoming features, benefits, or value proposition.
- Use Asset Library images for visual reinforcement.
- Use retention offers to provide an incentive such as a free period on the current subscription.
- A single retention message can be associated with multiple subscriptions.

## Configuring Retention Messaging in App Store Connect

App Store Connect adds a Retention Messaging area on the Subscriptions page. Developers create a named retention message, edit localized title and description text, optionally choose an image from Asset Library, select applicable subscriptions, and attach eligible retention offers. A live preview updates as the configuration changes.

The App Store automatically chooses the best eligible offer when multiple retention offers are selected for a subscription. Retention messages are testable in sandbox by canceling a sandbox subscription.

- Message text is required; image and offer are optional.
- Localizations can be configured for message content.
- Retention offers must already be configured before selecting them for the message.
- App Store Connect API support is mentioned for setting up retention messages and retention offers.

## Testing and transaction fields for retention offers

Retention offers are introduced as a new offer type for retention messages. When a retention offer is redeemed, signed transaction and renewal information include offer-related fields, including a new offerType value of 5. Sandbox testing can verify both the cancellation-flow presentation and the transaction or renewal-info fields.

- Check offerIdentifier, offerDiscountType, and offerPeriod when validating redemption behavior.
- Use sandbox cancellation flows before moving retention messages to production.
- Treat offerType = 5 as the signal that a redeemed offer was a retention offer.

### Signed transaction fields for a redeemed retention offer

A retention offer redemption appears in signed transaction or renewal info with offerType 5 and the usual offer metadata.

```text
{
  "bundleId": "com.example.app",
  "productId": "Yoga_summer_2026",
  "type": "Auto-Renewable Subscription",
  "transactionReason": "RENEWAL",
  "offerType": 5,
  "offerIdentifier": "Yoga_2026_cancel_free_3m",
  "offerDiscountType": "FREE_TRIAL",
  "offerPeriod": "P3M",
  "transactionId": "1000098916194",
  "originalTransactionId": "1000011859217"
}
```

## Real-time Retention Messaging with the Retention Messaging API

Real-time Retention Messaging uses a server-to-server HTTP request from the App Store when a customer is about to cancel. Your server responds with a preference for what the App Store should show. This enables per-customer decisioning instead of relying only on a static App Store Connect configuration.

The Retention Messaging API is used in sandbox and production to configure the app endpoint URL, create and manage messages, upload and manage images, and set default messages per subscription. Performance testing endpoints are available in sandbox only, and passing a sandbox performance test is required before using real-time Retention Messaging in production.

- The App Store request includes identifiers such as originalTransactionId, appAppleId, productId, userLocale, requestIdentifier, environment, and signedDate.
- Response options include message, alternateProduct, and promotionalOffer.
- alternateProduct supports switch-plan messaging for another product in the same subscription group.
- Promotional offers used in retention messages still require a promotional offer signature.
- Access to real-time Retention Messaging is requested through the linked interest form.

### Retention Messaging API endpoint categories

The API manages endpoint configuration, messages, defaults, images, and sandbox-only performance tests.

```text
// Base: https://api.storekit.apple.com/inApps/v1/messaging

// URL configuration
PUT /realtime/url
GET /realtime/url
DELETE /realtime/url

// Message configuration
PUT /message/{messageIdentifier}
DELETE /message/{messageIdentifier}
GET /message/list

// Defaults
PUT /default/{productId}/{locale}
DELETE /default/{productId}/{locale}
GET /default/{productId}/{locale}

// Images
PUT /image/{imageIdentifier}
DELETE /image/{imageIdentifier}
GET /image/list

// Sandbox performance testing
POST /performanceTest
GET /performanceTest/result/{requestId}
```

### Real-time request and message response

A basic real-time response selects a configured message, optionally paired with an image.

```text
// Request from the App Store
{
  "originalTransactionId": "123456789",
  "appAppleId": 6745974591,
  "productId": "Yoga_summer_2026",
  "userLocale": "en-US",
  "requestIdentifier": "c03248af-dd76-4e9b-9c1e-4489cd19a768",
  "environment": "Production",
  "signedDate": 1780920000000
}

// Your response
{
  "message": {
    "messageIdentifier": "551ee7c0-c097-418e-9dd5-2a98533a7390"
  }
}
```

## Choosing between App Store Connect and real-time messaging

The main difference is decisioning. App Store Connect Retention Messaging lets the App Store show the configured message and applicable offer without another server call. Real-time Retention Messaging lets your server choose what to show for each cancellation attempt.

Fallback behavior makes App Store Connect configuration valuable even when using real-time messaging. The App Store prioritizes a valid real-time response. If that response is unavailable or malformed, it falls back to the App Store Connect Retention Messaging preference, including eligible offers. If no App Store Connect message is configured, it falls back to default messaging configured with the Retention Messaging API.

- Choose App Store Connect Retention Messaging if you do not have a server or want App Store-managed selection.
- Choose real-time Retention Messaging if you have a fast server and need per-customer message, offer, or switch-plan decisions.
- Real-time messaging supports the same message/image/offer concepts plus a switch-plan view.
- For iOS 26.5 monthly subscriptions with a 12-month commitment, real-time alternateProduct responses can include billingPlanType to offer that plan type as a switch plan.
- Keep production messages and images up to date after enabling real-time production responses.

### Real-time alternate product and promotional offer responses

Real-time responses can offer a switch plan in the same subscription group or a signed promotional offer.

```text
// Switch plan response
{
  "alternateProduct": {
    "messageIdentifier": "ed7f25fc-5741-46a3-8502-062e0fb8afd0",
    "productId": "Yoga_summer_2026_annual"
  }
}

// Promotional offer response
{
  "promotionalOffer": {
    "messageIdentifier": "80135e2b-ae15-4ec4-8c5c-9ecc8045c0dc",
    "promotionalOfferSignatureV2": "eyJhbGciOiJFUzI..."
  }
}
```

Resources:
- Interest form: Real-time Retention Messaging: https://developer.apple.com/contact/request/retention-messaging-api/
- Supporting monthly subscriptions with a 12-month commitment: https://developer.apple.com/documentation/StoreKit/supporting-monthly-subscriptions-with-a-12-month-commitment
- Retention Messaging API: https://developer.apple.com/documentation/RetentionMessaging

Chapters:
- 0:00 Introduction: Introduces Retention Messaging as a way to reach subscribers in the App Store cancellation flow with a value proposition or offer. Shows the supported message-only, image, and offer presentations and explains the save-rate context.
- 2:38 Retention Messaging in App Store Connect: Walks through creating a retention message in App Store Connect, adding localized text, selecting an Asset Library image, mapping subscriptions, and attaching retention offers. Covers sandbox testing and the new offerType value of 5 for redeemed retention offers.
- 6:38 Real-time Retention Messaging: Explains the server-to-server model where the App Store calls a configured endpoint and the developer responds with a message, alternate product, or promotional offer. Covers API endpoint categories, request fields, performance-test requirements, fallback behavior, and setup order from sandbox to production.
- 11:46 Retention Messaging comparison: Compares App Store Connect Retention Messaging with real-time Retention Messaging, emphasizing static App Store-managed decisioning versus per-customer server decisioning. Recommends App Store Connect as a fallback even for real-time implementations and notes that real-time access requires an interest form.

### What's new in Shortcuts

- Session ID: wwdc2026-310
- Page: https://wwdc.ai/2026/310
- Markdown: https://wwdc.ai/2026/310.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/310/
- Category: System Services
- Description: Build better Shortcuts integrations with new automations, Use Model transcript debugging, and synced Storage for app data and App Entities.
- Duration: 11:02
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-310/eng_6ff105031402/wwdc2026-310-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/310/4/50ce70ab-88da-49ff-8c57-d9136d231e76/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/310/4/50ce70ab-88da-49ff-8c57-d9136d231e76/downloads/wwdc2026-310_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/310/4/50ce70ab-88da-49ff-8c57-d9136d231e76/downloads/wwdc2026-310_sd.mp4?dl=1

Build better Shortcuts integrations with new automations, Use Model transcript debugging, and synced Storage for app data and App Entities.

TLDR:
- Shortcuts automations are now configured inside the Shortcuts editor, with new triggers for screenshots, external keyboard connection changes, and app notifications.
- Notification automations can filter on notification content, so concise, specific, actionable notification text helps users build reliable automations around your app.
- The Use Model action can inspect the model transcript, showing the raw App Entity data passed to Apple Intelligence models so you can debug missing or misleading entity properties.
- Storage persists shortcut values between runs, supports global values shared across shortcuts, syncs via iCloud, and can store App Entities when their identifiers are stable across devices.

## Shortcuts integration focus

Shortcuts lets people combine app actions and run them from system surfaces such as Siri, Control Center, the Action Button, and automations. The session focuses on making app actions and app content more useful inside shortcuts through automations, the Use Model action, and persistent shortcut storage.

For app developers, the recurring theme is exposing structured, stable, and meaningful app data: notifications should be easy to interpret, App Entities should include the properties a model needs, and stored entities should have identifiers that remain valid on every device.

## New automation entry points

Automations now live directly in the Shortcuts editor alongside the actions that run in a shortcut. Users can browse and add automations from an Automation section rather than treating them as a separate setup flow.

There are three new automation types: a screenshot automation that runs when a screenshot is saved, a keyboard automation that runs when an external keyboard is connected or disconnected, and a notification automation that runs when a notification is received from a specific app.

- Notification automations can be filtered by keyword so they do not run for every notification from an app.
- The Soup Chef example uses a notification containing the driver name, the verb "arriving," and the estimated arrival time; the shortcut filters on "arriving" before turning on porch lights and announcing the delivery.
- Notification copy should follow the Human Interface Guidelines: make it concise, distinct, specific, and actionable so users can reliably automate from it.

## Use Model and App Entity debugging

The Use Model action can use more capable Apple Intelligence models, including models with web retrieval, and can operate on content exposed by apps. In the Soup Chef example, a shortcut finds soups available today, passes them to Use Model, and asks the model to choose a soup matching a spice preference before ordering it with an app intent.

When model output is surprising, the new transcript inspection workflow shows exactly what was passed to the model in raw form. The example adds a Show Content action after Use Model, selects the Transcript property from the Use Model output, and expands the App Entity data to inspect which properties the model saw.

The first soup entity only exposed name and availability, which was insufficient for judging spice level. Adding an ingredients property gave the model enough structured context to choose a spicier soup.

- Use EntityPropertyQuery and App Intents to expose app content that shortcuts can find and filter.
- Expose model-relevant App Entity properties; do not assume display text alone is enough for Use Model.
- Use the model transcript inspector to verify the exact structured representation sent to the model.

### App Entity properties visible to Use Model

Expose the properties the model needs, such as ingredients, not just the display name and availability.

```swift
// MARK: - Soup Entity
import AppIntents

struct SoupEntity: AppEntity, Identifiable {
    static var typeDisplayRepresentation = TypeDisplayRepresentation(
        name: "Soup",
        numericFormat: "\(placeholder: .int) soups"
    )

    static var defaultQuery = SoupEntityQuery()

    var id: Soup.ID

    @Property var name: String
    @Property(title: "Available Today") var isAvailableToday: Bool
    @Property(title: "Ingredients") var ingredients: String

    var displayRepresentation: DisplayRepresentation {
        DisplayRepresentation(title: "\(name)", subtitle: SoupStore.description(for: id))
    }
}
```

## Storage for stateful shortcuts

Storage lets a shortcut save values and retrieve them in later runs. The editor includes a storage view for creating, viewing, and editing stored values. Developers and users can also create global values shared across multiple shortcuts, such as an API key.

The storage actions support retrieving and updating stored data. The examples include simple counters or logs, a list of previously shown motor-racing facts, and a list of recent soup selections used as memory for Use Model so it avoids repeating prior results.

Storage works with any Shortcuts data type, including App Entities, and stored values sync across devices using iCloud. That makes shortcuts more consistent across iPhone, iPad, and Mac, but it also imposes requirements on entity identity.

- Get a stored value, pass it to Use Model as prior context, then update the stored value after the model returns a result.
- Use Add to List to append a new fact or selected entity, then use the setter action to persist the updated list.
- Use global storage for values that must be shared by multiple shortcuts.

## Stable App Entity identifiers across devices

Because stored values sync across devices, an App Entity saved on one device must be recognized as the same entity on another. A shortcut might retrieve a stored soup on iPad and pass it to an Order Soup intent even though the entity was originally stored on iPhone.

Use an identifier derived from a source that is stable across devices, not a device-local value. In the Soup Chef example, the app is backed by an online database, so each soup uses its database row ID as the stable entity identifier.

- Do not use identifiers that can vary per install or per device for entities that may be stored in Shortcuts.
- Test App Entities with both Use Model and Storage so they preserve meaning and identity across shortcut runs and devices.
- If an entity can be stored by Shortcuts, its App Intents query and intents should be able to resolve it consistently wherever the app runs.

## Recommended developer workflow

- Build shortcuts that use your app's actions and content to understand what users may want to automate.
- Refine notifications so users can trigger precise notification automations from your app.
- Inspect Use Model transcripts to confirm your App Entities expose the right properties for model reasoning.
- Validate stored App Entities across devices by using stable, device-consistent identifiers.

Resources:
- Shortcuts: https://developer.apple.com/shortcuts/
- Notifications: https://developer.apple.com/design/Human-Interface-Guidelines/notifications

Chapters:
- 0:01 Introduction: Introduces Shortcuts as a way to run app actions from system surfaces such as Siri, Control Center, and the Action Button. The session previews updates to automations, Use Model debugging, and persistent Storage.
- 0:57 Automations: Shows automations living directly in the Shortcuts editor and introduces new screenshot, keyboard, and notification automation types. The notification example demonstrates filtering on notification content and explains why concise, specific, actionable notifications make better automation triggers.
- 3:25 Use Model: Demonstrates using Use Model with App Intents and App Entities to choose a soup from app data, then debugging an unexpected result with the model transcript. The chapter shows that adding relevant entity properties, such as ingredients, improves the model's ability to reason about app content.
- 6:58 Storage: Explains shortcut Storage for persisting values between runs, including global values shared across shortcuts and iCloud-synced values across devices. Examples use storage as memory for Use Model and highlight the need for stable App Entity identifiers that resolve consistently on every device.

### Meet the Now Playing framework

- Session ID: wwdc2026-312
- Page: https://wwdc.ai/2026/312
- Markdown: https://wwdc.ai/2026/312.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/312/
- Category: Audio & Video
- Description: Learn how NowPlaying publishes local and remote media sessions to Lock Screen, Control Center, Dynamic Island, CarPlay, and device routing UI.
- Duration: 12:36
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-312/eng_6f3411d2a450/wwdc2026-312-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/312/5/3f128d25-f1c6-49d3-a9c0-0bdc22af5f95/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/312/5/3f128d25-f1c6-49d3-a9c0-0bdc22af5f95/downloads/wwdc2026-312_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/312/5/3f128d25-f1c6-49d3-a9c0-0bdc22af5f95/downloads/wwdc2026-312_sd.mp4?dl=1

Learn how NowPlaying publishes local and remote media sessions to Lock Screen, Control Center, Dynamic Island, CarPlay, and device routing UI.

TLDR:
- NowPlaying provides a Swift, observable media-session model for publishing app playback state, content metadata, artwork, and supported commands to system Now Playing surfaces.
- Adopt MediaSessionRepresentable for local playback, then create a MediaSession with your observable model so system UI stays synchronized automatically.
- Remote media sessions use an app extension plus APNs-delivered state to represent playback on external devices such as smart speakers and to route system commands back through your server.
- Media Sharing Extensions let apps use the system device picker for supported media protocols without embedding each protocol SDK in the app bundle.

## NowPlaying and system Now Playing surfaces

The NowPlaying framework connects an app's media playback to the system Now Playing experience across Apple platforms, including Lock Screen, Control Center, Dynamic Island, StandBy, CarPlay, Apple Watch, Apple Vision Pro, and Apple TV.

The session's sample app plays ambient sounds locally and can also control smart speakers. The same NowPlaying model concepts are used to describe current content, playback state, artwork, and user actions to the system.

## Publish local playback with MediaSessionRepresentable

For local audio or video, make an observable model conform to MediaSessionRepresentable. The representation provides a unique session identifier, current content, a playback snapshot, and the commands the app supports.

Content can use specialized types such as Music, Podcast, or MovieContent, or GenericContent for simpler cases. Artwork is supplied with an async closure so the system can request an image at the size it needs.

MediaPlaybackSnapshot tells the system whether playback is playing or paused. For indefinite ambient audio, the session uses a continuous duration and only needs the play/pause state; for media with a defined duration, include elapsedTime in the snapshot.

- Each session representation needs a unique id.
- The content property describes title, subtitle, media type, duration, and artwork.
- The commands array exposes actions such as play, pause, previous, and next as closures invoked by system UI.
- Command handlers should update the underlying player; observation then lets NowPlaying refresh system surfaces.

### Adopt MediaSessionRepresentable

A local playback model describes content, artwork, playback state, and the command closures the system can invoke.

```swift
import NowPlaying

extension PlayerModel: MediaSessionRepresentable {
    var id: String { "ambient-sound-session" }

    var content: (any MediaContentRepresentable)? {
        GenericContent(
            id: sound.id,
            title: sound.name,
            subtitle: sound.description,
            type: .audio,
            duration: .continuous,
            artwork: Artwork(id: sound.id) { size in
                let data = try await self.artworkData(size: size)
                return try ArtworkRepresentation(data: data)
            }
        )
    }

    var playbackSnapshot: MediaPlaybackSnapshot? {
        MediaPlaybackSnapshot(state: player.isPlaying ? .playing() : .paused)
    }

    var commands: [MediaCommand] {
        [
            .play { self.player.play() },
            .pause { self.player.pause() },
            .previous { self.player.previous() },
            .next { self.player.next() }
        ]
    }
}
```

## Connect the model with MediaSession

After the model conforms to MediaSessionRepresentable, create a MediaSession with that model where the app sets up its playback engine. MediaSession observes the model and keeps system Now Playing surfaces current as content or playback state changes.

- Initialize MediaSession once the player model exists.
- Keep the MediaSession alive for as long as the playback session should be represented to the system.

### Initialize MediaSession with the player model

MediaSession is the bridge between the app's observable representation and system Now Playing UI.

```swift
import NowPlaying

struct PlayerController {
    let player: SoundPlayer
    let model: PlayerModel
    let session: MediaSession<PlayerModel>

    init() {
        self.player = SoundPlayer()
        self.model = PlayerModel(player: player)
        self.session = MediaSession(model)
    }
}
```

## Represent playback on external devices with remote media sessions

Remote media sessions cover content playing outside the iPhone, such as on a smart speaker controlled by the app. The architecture uses an app extension and push notifications: the external device reports state to a server, the server sends an APNs notification to iPhone, and the system launches the extension with the updated state from the notification payload.

When a user acts from system UI on iPhone, the system calls the extension's command handler. The extension sends the command to the server, and the server forwards it to the external device.

- Create an app extension conforming to RemoteMediaSessionExtension.
- Use RemoteMediaSessionExtensionConfiguration and the com.apple.nowplaying remote-media extension point.
- Return a model from session(_:) using the RemotePlayerState supplied by the system.
- The remote state type represents the server state and push notification payload, and conforms to RemoteMediaSessionAttributes.

### Remote media session app extension entry point

The extension creates the model the system uses to update UI or handle interactions for a remote session.

```swift
import ExtensionFoundation
import NowPlaying

@main
final class SampleAppExtension: @MainActor RemoteMediaSessionExtension {
    var configuration: some AppExtensionConfiguration {
        RemoteMediaSessionExtensionConfiguration(extension: self)
    }

    var extensionPoint: AppExtensionPoint {
        AppExtensionPoint.Identifier(host: "com.apple.nowplaying", name: "remote-media")
    }

    func session(_ state: RemotePlayerState) async throws -> RemotePlayerModel {
        RemotePlayerModel(state: state)
    }
}
```

## Implement RemoteMediaSessionRepresentable

RemoteMediaSessionRepresentable looks similar to MediaSessionRepresentable for content, artwork, playback snapshot, and commands. The key difference is that command closures send requests to the server instead of directly controlling a local player.

Remote sessions also publish devices. Each MediaDevice needs a stable unique identifier, a display name, a type such as .speaker, and capabilities such as absolute volume. When system volume UI changes, the capability closure receives the new volume so the extension can send a server request.

The update(_:) method is called when a push notification arrives with new state. Updating an observable model lets NowPlaying detect the change and refresh system surfaces automatically.

### Remote session representation with commands, devices, and update

Remote sessions route system commands and volume changes through the app's server, while push-driven updates refresh the observable state.

```swift
import NowPlaying

extension RemotePlayerModel: @MainActor RemoteMediaSessionRepresentable {
    var id: String { state.sessionID }

    var playbackSnapshot: MediaPlaybackSnapshot? {
        MediaPlaybackSnapshot(state: state.isPlaying ? .playing() : .paused)
    }

    var commands: [MediaCommand] {
        [
            .play { try await self.client.send(.play) },
            .pause { try await self.client.send(.pause) },
            .previous { try await self.client.send(.previous) },
            .next { try await self.client.send(.next) }
        ]
    }

    var devices: [MediaDevice] {
        state.devices.map { device in
            MediaDevice(
                id: device.id,
                name: device.name,
                type: .speaker,
                capabilities: [
                    .absoluteVolume(device.volume) { volume in
                        // Send volume change to server.
                    }
                ]
            )
        }
    }

    func update(_ state: RemotePlayerState) {
        self.state = state
    }
}
```

## Media Sharing Extensions for routing

Media Sharing Extensions provide APIs for playing media from iPhone to other speakers and TVs through a unified system interface. Apps can use the system device picker for the media protocols they support, and the selection is reflected on system surfaces such as Control Center.

Instead of embedding each protocol SDK in the app bundle, protocol implementations live outside the app and are managed by the system. This lets the app focus on media content and take advantage of additional protocols as they become available.

- Use Media Sharing Extensions when the app needs system-managed media device selection and routing.
- See Apple's "Routing media to third-party devices" documentation for implementation details.

Resources:
- Routing media to third-party devices: https://developer.apple.com/documentation/AVSystemRouting/routing-media-to-third-party-devices
- Publishing remote media sessions: https://developer.apple.com/documentation/NowPlaying/publishing-remote-media-sessions
- Publishing media sessions: https://developer.apple.com/documentation/NowPlaying/publishing-media-sessions
- Setting up a remote notification server: https://developer.apple.com/documentation/UserNotifications/setting-up-a-remote-notification-server

Chapters:
- 0:00 Introduction: Introduces the system Now Playing experience across Apple platforms and the NowPlaying framework's role in surfacing app media on Lock Screen, Control Center, Dynamic Island, CarPlay, and other surfaces.
- 1:08 Media sessions: Shows how a local playback model adopts MediaSessionRepresentable to publish content, artwork, playback state, and media commands, then connects it to the system by initializing MediaSession.
- 5:03 Remote media sessions: Explains how remote media sessions represent playback on external devices using an app extension, APNs-delivered state, server-backed command handlers, device descriptions, volume capabilities, and update(_:).
- 10:31 Media sharing extensions: Describes Media Sharing Extensions as a system-managed way to use the device picker for supported media protocols, avoiding protocol SDKs embedded directly in the app bundle.

### Learn CSS Grid Lanes

- Session ID: wwdc2026-314
- Page: https://wwdc.ai/2026/314
- Markdown: https://wwdc.ai/2026/314.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/314/
- Category: Safari & Web
- Description: Use CSS Grid Lanes to build masonry and brick-wall layouts in Safari with familiar Grid syntax, item spanning, subgrid, and flow-tolerance.
- Duration: 10:25
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-314/eng_637bced641e2/wwdc2026-314-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/314/4/72928edd-5728-4010-b8f0-27f1a7bdec8c/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/314/4/72928edd-5728-4010-b8f0-27f1a7bdec8c/downloads/wwdc2026-314_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/314/4/72928edd-5728-4010-b8f0-27f1a7bdec8c/downloads/wwdc2026-314_sd.mp4?dl=1

Use CSS Grid Lanes to build masonry and brick-wall layouts in Safari with familiar Grid syntax, item spanning, subgrid, and flow-tolerance.

TLDR:
- CSS Grid Lanes is a new layout mode for masonry-style waterfall layouts and horizontal brick-wall layouts, available in Safari 26.4 and behind a flag in other browsers.
- A Grid Lanes container structures one axis with `grid-template-columns` or `grid-template-rows` and leaves the other axis free so mixed-size content packs tightly without stretching, zooming, or cropping.
- Grid Lanes reuses familiar Grid concepts: `fr` units, `gap`, `auto-fill`, `minmax()`, item spanning with `grid-column`, explicit column placement, and `subgrid` for nested alignment.
- `flow-tolerance` helps balance visual packing with accessibility by reducing surprising DOM-order versus visual-order mismatches; Safari Web Inspector can overlay lanes, gaps, and placement order.

## What Grid Lanes solves

CSS Grid Lanes targets layouts commonly called masonry, waterfall, or brick-wall layouts. These are useful when items have mixed dimensions or aspect ratios and should keep their natural proportions while packing into the available space.

Flexbox flows items along one axis and wraps, while Grid creates two-dimensional cells that can leave empty areas for uneven content. Grid Lanes sits between them: it defines lanes in one direction and lets the other direction size naturally.

- Use column lanes for vertical waterfall or masonry layouts.
- Use row lanes for horizontal brick-wall layouts.
- Use it for images, text, cards, or mixed content; items are not required to share the same size or aspect ratio.
- The placement algorithm normally puts each item into the lane whose current end is closest to the start of the container.

## Create a Grid Lanes container

A basic Grid Lanes layout starts with `display: grid-lanes`, a track definition for one axis, and an optional `gap`. The example shown divides the container into three equal columns using `fr` units.

- `grid-template-columns` creates vertical lanes for a waterfall layout.
- `grid-template-rows` creates horizontal lanes for a brick-wall variation.
- Grid Lanes structures one direction at a time; choose columns or rows, not both.

### Three-column waterfall layout

Creates three equal column lanes and spaces items with the same `gap` syntax used by CSS Grid.

```text
.container {
  display: grid-lanes;
  grid-template-columns: repeat(3, 1fr);
  gap: 10px;
}
```

### Brick-wall variation

Swaps columns for rows so items flow horizontally into row lanes.

```text
.container {
  display: grid-lanes;
  grid-template-rows: repeat(3, 1fr);
  gap: 10px;
}
```

## Adapt lane sizing to the container

Grid Lanes supports familiar CSS track sizing. You can use equal fractions, unequal fractions, `auto-fill`, `minmax()`, and repeating track patterns to adapt to different viewport widths and content needs.

- Use unequal `fr` values when one lane should be wider than others.
- Use `repeat(auto-fill, minmax(...))` when the browser should choose how many lanes fit.
- Use repeating patterns to mix narrow and wide lanes without manually enumerating every column.

### Unequal columns

Makes the center lane twice as wide as the side lanes.

```text
.container {
  display: grid-lanes;
  grid-template-columns: 1fr 2fr 1fr;
  gap: 10px;
}
```

### Responsive auto-filled lanes

Creates as many lanes as fit, with each lane at least 200px and able to grow to share available space.

```text
.container {
  display: grid-lanes;
  grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
  gap: 10px;
}
```

## Control individual items and nested layouts

Grid Lanes lets individual items use existing Grid placement syntax. Items can span multiple column lanes or be placed into a specific column range, while Grid Lanes still chooses their row position.

Nested layouts can combine Grid Lanes, regular Grid, and `subgrid`. In the session's recipe-card example, a card spans two columns and then uses `subgrid` so its internal image and text align with the parent lanes.

- Use `grid-column: span 2` to make an item span two column lanes.
- Use explicit column placement such as `grid-column: 2 / span 2` to choose the starting column and span.
- Rows remain automatic in a column-lane layout; Grid Lanes decides the row position.
- Nested regular Grid inside Grid Lanes, or Grid Lanes inside Grid, can be combined with familiar Grid syntax.

### Span an item across two columns

Gives a selected item more horizontal space while the surrounding layout adjusts.

```text
.container {
  display: grid-lanes;
  grid-template-columns: 1fr 1fr 1fr;
  gap: 10px;
}

.item {
  grid-column: span 2;
}
```

### Use subgrid inside a spanning item

Lets nested content participate in the parent column structure while the item spans two lanes.

```text
.container {
  display: grid-lanes;
  grid-template-columns: 1fr 1fr 1fr;
  gap: 10px;
}

.item {
  display: grid-lanes;
  grid-template-columns: subgrid;
  grid-column: span 2;
}
```

## Tune ordering with flow-tolerance and debug in Web Inspector

Because Grid Lanes normally places each item in the shortest lane, the visual order can diverge from DOM and keyboard navigation order when lane heights differ only slightly. That can create confusing accessibility behavior.

`flow-tolerance` adjusts how strictly the browser follows the shortest-lane rule. With tolerance, a later item may prefer an earlier lane when lane heights are close enough, improving left-to-right or top-to-bottom visual continuity for some layouts.

Safari Web Inspector supports Grid Lanes overlays that show lane lines, row and column lines, gaps, and order numbers over items, which is useful when tuning `flow-tolerance` or investigating unexpected placement.

- Default `flow-tolerance` is `1em`.
- Increase or tune the value for content where small height differences cause undesirable visual ordering.
- Use the Web Inspector overlay to inspect placement order and lane geometry.

### Tune flow tolerance

Loosens the shortest-column rule so lanes with similar heights can preserve a more intuitive visual flow.

```text
.container {
  display: grid-lanes;
  grid-template-columns: 1fr 1fr;
  gap: 10px;
  flow-tolerance: 2.1em;
}
```

Resources:
- WebKit.org - CSS Grid Lanes Field Guide: https://gridlanes.webkit.org/
- WebKit.org - Report issues to the WebKit open-source project: https://bugs.webkit.org/
- Submit feedback: http://feedbackassistant.apple.com/

Chapters:
- 0:00 Introduction: Introduces CSS Grid Lanes as a native layout mode for masonry-style waterfall and brick-wall patterns. The chapter positions it as a way to let varied content flow naturally with only a few CSS declarations.
- 1:35 CSS Flexbox and Grid: Explains how layout modes answer where items go and how much space they get. Flexbox and Grid are contrasted with the problems they create for mixed-aspect-ratio content, such as empty grid cells or distorted media.
- 2:45 CSS Grid Lanes: Defines Grid Lanes as a layout model between Grid and Flexbox: it structures one axis and leaves the other free. Items are distributed across multiple lanes and placed into the lane that keeps them closest to the start.
- 3:55 Build a Grid Lanes container: Shows the basic setup using `display: grid-lanes`, `grid-template-columns`, `fr` units, and `gap`. The result is a simple three-column masonry layout.
- 4:31 Implement brick variation: Demonstrates flipping the layout direction by replacing `grid-template-columns` with `grid-template-rows`. It notes that Grid Lanes uses one structured direction at a time.
- 4:49 Experiment with different layouts: Explores track sizing options including equal and unequal columns, `auto-fill`, `minmax()`, and repeating patterns. These options let the browser adapt the number and width of lanes to available space.
- 5:40 Control individual items: Shows how individual items can span lanes or be placed in explicit column ranges using familiar Grid properties. It also demonstrates nested layouts with `subgrid` and regular Grid or Grid Lanes combinations.
- 7:05 Flow Tolerance: Explains how the shortest-lane placement rule can cause visual order to differ from DOM and tab order. `flow-tolerance` is introduced as a tunable property that can prefer earlier lanes when lane-height differences are within tolerance.
- 8:46 Web Inspector: Covers Safari Web Inspector support for Grid Lanes overlays. The overlay can show lane lines, gaps, and item order numbers to help debug placement and tune layouts.
- 9:20 Next steps: Points developers to the WebKit Grid Lanes Field Guide, encourages trying Grid Lanes in Safari 26.4, and asks for feedback. It also recommends the Safari 27 WebKit session for broader platform updates.

### Rediscover the HTML select element

- Session ID: wwdc2026-315
- Page: https://wwdc.ai/2026/315
- Markdown: https://wwdc.ai/2026/315.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/315/
- Category: Safari & Web
- Description: Use Customizable Select in Safari 27 to style native HTML select controls with CSS, rich option content, and accessible progressive enhancement.
- Duration: 9:30
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-315/eng_badc1e86504d/wwdc2026-315-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/315/4/f3bd9835-9ced-4f6a-a0f1-655000972674/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/315/4/f3bd9835-9ced-4f6a-a0f1-655000972674/downloads/wwdc2026-315_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/315/4/f3bd9835-9ced-4f6a-a0f1-655000972674/downloads/wwdc2026-315_sd.mp4?dl=1

Use Customizable Select in Safari 27 to style native HTML select controls with CSS, rich option content, and accessible progressive enhancement.

TLDR:
- Customizable Select lets developers restyle native HTML `select` controls with CSS while retaining semantic HTML, keyboard behavior, screen reader support, and native fallback.
- Opt in with `appearance: base-select`, then style the button, open state, picker icon, drop-down picker, selected option, and option checkmark using new selectors and pseudo-elements.
- Options can contain rich content such as SVGs, images, spans, emojis, or video, and the picker can use layouts such as CSS Grid instead of a vertical text-only list.
- A custom first-child `button` plus the new `selectedcontent` element lets the select button mirror the rich content of the currently selected option; unsupported browsers fall back to the native popup.

## Why Customizable Select matters

Historically, styling a drop-down beyond the platform-native `select` often pushed web developers toward JavaScript widgets or `div`-based replacements, which can make keyboard interaction, screen reader behavior, and form semantics harder to preserve. Customizable Select keeps the existing HTML `select` element and exposes new CSS hooks for styling it.

The feature is presented as available in Safari 27 and Chrome 135. Because the markup remains semantic, browsers that do not support the new customization model continue to show a usable native popup.

- Use `select` and `option` instead of replacing the control with custom non-semantic markup.
- Keep built-in keyboard navigation and screen reader integration from the native element.
- Treat the feature as progressive enhancement: enhanced styling where supported, native control where not supported.

### Basic semantic select markup

Start from ordinary labeled `select` markup so the control remains accessible and has a native fallback.

```text
<label for="sort-select">Sort by</label>
<select id="sort-select">
  <option>Newest</option>
  <option>Oldest</option>
</select>
```

## Style the select button

The new styling model starts by applying `appearance: base-select` to the `select`. This opts out of the fully native look while retaining a base control that can inherit page fonts and accept familiar CSS such as background, border, and padding.

The select button can also react to the open state. Use `:open` to change button styles while the menu is displayed, and use `::picker-icon` to customize the drop-down arrow glyph.

- Apply `appearance: base-select` to the `select` to begin customization.
- Style the button with normal CSS properties such as `background-color`, `border`, and `padding`.
- Use `select:open` for the expanded state.
- Use `select::picker-icon` or `select:open::picker-icon` to replace or restyle the arrow.

### Opt in and style the button

`appearance: base-select` enables the customizable select styling model and allows page typography and button styles to apply.

```text
body {
  font-family: Gill Sans, sans-serif;
}

select {
  appearance: base-select;
  background-color: var(--green-10);
  border: none;
  padding: 0.6em 1em;
}
```

### Style the open state and picker icon

The `:open` pseudo-class and `::picker-icon` pseudo-element customize the button while the picker is visible.

```text
select:open {
  background-color: var(--green-100);
  color: white;
}

select:open::picker-icon {
  content: url(icons/arrow-white.svg);
}
```

## Customize the drop-down picker and options

The drop-down menu itself is exposed through `::picker(select)`. As with the button, the picker is first opted into the base styling model with `appearance: base-select`, then styled with spacing, borders, radius, and shadows.

Options can be styled with existing state selectors such as `:checked`, and the default checkmark can be replaced through `option::checkmark`.

- Use `::picker(select)` to target the menu surface.
- Set `appearance: base-select` on `::picker(select)` before styling the picker.
- Use `option:checked` to emphasize the selected option.
- Use `option:not(:checked)` for non-selected options.
- Use `option::checkmark` to replace or hide the selection indicator.

### Style the picker surface

The picker can be styled like other UI surfaces once it opts into `base-select`.

```text
::picker(select) {
  appearance: base-select;
  padding: 4px;
  margin-top: 0.5em;
  border: 1px solid rgba(0,0,0,0.2);
  border-radius: 9px;
  box-shadow: 0 4px 20px rgba(0,0,0,0.2);
}
```

### Style selected and unselected options

Use option states and `::checkmark` to customize how the active option is communicated visually.

```text
option:checked {
  font-weight: 600;
}

option:not(:checked) {
  color: #777;
}

option::checkmark {
  content: url(checkmark.svg);
  width: 0.65em;
}
```

## Use rich option content and custom layouts

Customizable Select allows option contents to go beyond plain text. The session demonstrates SVG icons and labels inside `option` elements for a category picker. Empty `alt` text is used on decorative images so screen readers do not announce both the image and the adjacent label.

The picker is not limited to a vertical list. Because the picker is styleable, it can use CSS layout features such as Grid to arrange visual options in rows and columns.

- Place content such as images, SVGs, spans, emojis, or video inside `option` elements.
- Use decorative image `alt=""` when the visible text label already names the option.
- If the default checkmark is removed, provide another clear selected state such as color or background.
- Use CSS Grid on `::picker(select)` when a visual menu would be too tall as a single column.

### Option with image and label

Rich option markup can include an icon and text while avoiding duplicate screen reader announcements for decorative images.

```text
<option value="flower">
  <img src="flowers.svg" alt="">
  <span class="text">Flowers</span>
</option>
```

### Grid layout for the picker

The picker can use CSS Grid to present options in a compact visual layout.

```text
::picker(select) {
  display: grid;
  grid-template: 1fr 1fr / 1fr 1fr 1fr;
  gap: 1rem;
}
```

## Show rich selected content in the button

By default, the select button displays text. To show the rich content from the selected option-such as the selected SVG icon plus label-Customizable Select allows a `button` element as the first child of the `select`. Inside that button, the new `selectedcontent` element mirrors the rich content of the currently selected option.

This enables the closed select button to visually match the selected option without duplicating option-specific UI manually.

- Place a `button` as the first child of the `select` to replace the built-in select button.
- Put `selectedcontent` inside that button to display the selected option's rich content.
- Keep the actual choices in `option` elements so form semantics and fallback remain intact.

### Custom select button with selectedcontent

The custom button displays the rich content of the currently selected option through `selectedcontent`.

```text
<select>
  <button>
    <selectedcontent></selectedcontent>
  </button>
  <option value="anywhere">
    <img src="icons/all.svg" alt="">
    <span class="text">Everything</span>
  </option>
  <option value="buildings">
    <img src="icons/buildings.svg" alt="">
    <span class="text">Buildings</span>
  </option>
  <option value="flowers">
    <img src="icons/flower.svg" alt="">
    <span class="text">Flowers</span>
  </option>
</select>
```

## Fallback, testing, and next steps

Unsupported browsers fall back to the native popup because the implementation still uses the standard `select` element. The recommended workflow is to test both enhanced and non-enhanced experiences, and to validate behavior with assistive tools.

To try the feature before broad Safari 27 availability, use Safari Technology Preview or Safari Beta. The session points to the WebKit customizable select demo and recommends the related Grid Lanes session for the photo layout shown around the select examples.

- Test in browsers that support Customizable Select and browsers that do not.
- Test with assistive technologies, especially when using rich option content and custom selected states.
- Use the WebKit demo as a reference implementation.
- Consult "Learn CSS Grid Lanes" for the layout method used elsewhere in the demo site.

Resources:
- WebKit.org - Example website demonstrating Customizable Select: https://webkit.org/demos/customizable-select/
- WebKit.org - CSS Grid Lanes Field Guide: https://gridlanes.webkit.org/
- WebKit.org - Report issues to the WebKit open-source project: https://bugs.webkit.org/
- Submit feedback: http://feedbackassistant.apple.com/

Chapters:
- 0:00 Introduction: Introduces Customizable Select as a way to style the native HTML `select` with HTML and CSS while keeping semantic behavior and accessibility. The demo begins with a photographer portfolio site that needs sort and filter controls.
- 2:32 Style the select button: Shows how to opt in with `appearance: base-select`, inherit site typography, and style the select button with background, border, and padding. The chapter also uses `::picker-icon` and `:open` to customize the arrow and open state.
- 3:47 Customize the drop-down: Targets the drop-down menu with `::picker(select)` and applies `appearance: base-select` before adding spacing, border, radius, and shadow. It then styles checked and unchecked options and replaces the default checkmark with `option::checkmark`.
- 5:00 Go beyond text options: Demonstrates rich option content by placing SVGs and labels inside `option` elements while using empty image alt text for decorative icons. The picker is then converted from a long vertical list to a CSS Grid layout.
- 6:50 The selectedcontent element: Explains how to replace the built-in select button by placing a `button` as the first child of the `select`. The new `selectedcontent` element inside that button mirrors the rich content of the selected option.
- 7:46 Fallback for unsupported browsers: Shows that unsupported browsers still present a native popup because the underlying control remains a semantic `select`. This preserves usability and built-in accessibility as a progressive enhancement fallback.
- 8:49 Next steps: Recommends trying the feature in Safari Technology Preview or Safari Beta, exploring the WebKit demo, and testing with unsupported browsers and assistive tools. It also points to "Learn CSS Grid Lanes" for the layout technique used around the demo.

### Build with the new Apple Foundation Model on Private Cloud Compute

- Session ID: wwdc2026-319
- Page: https://wwdc.ai/2026/319
- Markdown: https://wwdc.ai/2026/319.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/319/
- Category: AI & Machine Learning
- Description: Use Foundation Models to call Apple's Private Cloud Compute server LLM, choose between on-device and PCC models, and handle availability and quotas.
- Duration: 10:58
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-319/eng_f2e6542b4e45/wwdc2026-319-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/319/4/1a3ac4f6-73d2-4a24-9e5d-0cfd56564f42/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/319/4/1a3ac4f6-73d2-4a24-9e5d-0cfd56564f42/downloads/wwdc2026-319_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/319/4/1a3ac4f6-73d2-4a24-9e5d-0cfd56564f42/downloads/wwdc2026-319_sd.mp4?dl=1

Use Foundation Models to call Apple's Private Cloud Compute server LLM, choose between on-device and PCC models, and handle availability and quotas.

TLDR:
- Private Cloud Compute exposes a larger server LLM through the Foundation Models framework with the same Swift session, structured output, and tool-calling APIs used for the on-device model.
- PCC is privacy-preserving and OS/iCloud-integrated: no API keys, no developer token costs, but it requires Apple Intelligence support, an internet connection, eligibility, and a daily per-user quota.
- Choose between the on-device System model and PCC based on offline needs, quotas, context size, and reasoning; PCC offers 32K context and light/moderate/deep reasoning levels.
- Apps should check model availability and quota state, provide persistent actionable UI for limit states, and use Xcode's simulation options to test availability and quota behavior.

## What Private Cloud Compute adds

Private Cloud Compute lets apps access a new server LLM through Apple's privacy-preserving cloud architecture. It is intended for AI features that exceed what the on-device model can comfortably handle, such as reasoning over large inputs, making many tool calls with large outputs, or using the model from watchOS.

The on-device Foundation Models model is also improved with image input, better instruction following, and better custom tool calling, so PCC is not automatically the right choice for every feature.

- User data sent to PCC is used only for the request and is not stored, according to the session.
- PCC is integrated with the OS and iCloud, so apps do not manage authentication, accounts, API keys, or token billing.
- Each user has a daily limit; iCloud+ can provide higher limits.
- The PCC server model is available for apps with fewer than 2M downloads, and developers must apply on the developer website.

## Integrating PCC with Foundation Models

The Foundation Models framework provides a unified Swift API across the on-device and PCC models. If an app already uses `LanguageModelSession`, switching from the default on-device model to PCC is a one-line model selection change.

Structured output with `@Generable` and tool calling with `Tool` work the same way against the PCC model, allowing shared feature code across model backends.

- Use `LanguageModelSession()` for the default on-device model.
- Use `LanguageModelSession(model: PrivateCloudComputeLanguageModel())` for the PCC server model.
- Check availability before exposing model-backed UI, because PCC, like the on-device model, requires Apple Intelligence device support.

### Switch from on-device to PCC

Select `PrivateCloudComputeLanguageModel` when creating the session to route requests to the PCC server model.

```swift
import FoundationModels

let session = LanguageModelSession(
    model: PrivateCloudComputeLanguageModel()
)

let response = try await session.respond(
    to: "Summarize this article: \(article)"
)
```

### Use structured output and tools with PCC

The same `@Generable` structured output and `Tool` integration patterns apply to the PCC model.

```swift
import FoundationModels

@Generable
struct ArticleSummary {
    let oneLineSummary: String
    let keyPoints: [String]
}

struct FindRelatedArticlesTool: Tool { }

let session = LanguageModelSession(
    model: PrivateCloudComputeLanguageModel(),
    tools: [FindRelatedArticlesTool.self]
)

let response = try await session.respond(
    to: "Summarize this article: \(article)",
    generating: ArticleSummary.self
)
```

## Choosing on-device or PCC

The session frames model choice as a product and engineering tradeoff rather than a default upgrade path. Both models are privacy-oriented, but they differ in availability, limits, context size, and capabilities.

Apple recommends evaluating the quality of a specific feature instead of choosing based on assumptions. The updated on-device model may be sufficient for some tasks, while PCC is better suited to larger-context and reasoning-heavy workflows.

- On-device System model: works offline, has no request limits, and has a 4K context size in the comparison discussed in the session.
- PCC model: requires an internet connection, has a daily per-user quota, provides a 32K context size, and supports reasoning.
- Use the Evaluations framework to compare model quality and reasoning levels for the app's actual prompts and outputs.
- Apps can combine on-device and server models for agentic workflows when appropriate.

### Check availability before showing PCC UI

Use the model availability API to gracefully handle devices or states where Apple Intelligence-backed model access is unavailable.

```swift
import FoundationModels

struct ArticleSummarizationView: View {
    private var model = PrivateCloudComputeLanguageModel()

    var body: some View {
        if model.isAvailable {
            // Show UI for making request
        } else {
            // Fall back
        }
    }
}
```

## Reasoning levels and context size

PCC supports reasoning, where the model generates an additional reasoning segment before producing the final response. The session describes three reasoning levels: light, moderate, and deep.

Reasoning can improve complex responses but consumes tokens because the reasoning segment is generated text. That token use counts toward the model's context size, so apps should choose reasoning levels deliberately and can observe the transcript to show progress for longer requests.

- `.light` gathers some extra context.
- `.moderate` lets the model reason more deeply.
- `.deep` may produce a reasoning segment longer than the final response and can take more time.
- Use `contextSize` on model instances to programmatically adapt to available context.

### Set a reasoning level

Set the reasoning level per `respond` call using `ContextOptions`.

```swift
let response = try await session.respond(
    to: prompt,
    contextOptions: ContextOptions(reasoningLevel: .light)
)

// Reasoning levels: .light, .moderate, .deep
```

### Read model context size

Use `contextSize` to adapt prompts, document inputs, and tool outputs to the selected model.

```text
SystemLanguageModel().contextSize
// 4096 on 26.0
// 8192 on 27.0 (newer devices)

PrivateCloudComputeLanguageModel().contextSize
// 32768
```

## Handling PCC usage limits

PCC requests count against the user's iCloud account quota. When the limit is reached, requests throw an error, but the recommended experience is to proactively reflect quota state in persistent, actionable UI rather than surfacing a generic error or dismissible alert.

The session demonstrates disabling or annotating the request UI, showing limit status, and offering a button that lets the user manage or increase their limit when a `limitIncreaseSuggestion` is available.

- Check `model.quotaUsage.isLimitReached` to handle an exceeded daily limit.
- Check the `.belowLimit` status and `info.isApproachingLimit` to warn when the user is nearing the limit.
- Prefer persistent inline UI over alerts because the user may need the state and action to remain visible.
- Use Xcode's scheme Debug options, under Simulate Apple Foundation Models Availability, to test quota states such as Quota Usage Limit Reached and Nearing Usage Limit.

### Show quota-aware UI

Read quota state from `PrivateCloudComputeLanguageModel.quotaUsage` and surface actionable limit-management UI.

```swift
struct ArticleSummarizationView: View {
    private var model = PrivateCloudComputeLanguageModel()

    var body: some View {
        if case .belowLimit(let info) = model.quotaUsage.status {
            if info.isApproachingLimit {
                Text("Nearing usage limit.")
                    .foregroundStyle(Color.orange)
            }
        }

        if model.quotaUsage.isLimitReached {
            Text("Usage limit exceeded.")
                .foregroundStyle(Color.red)
        }

        if let suggestion = model.quotaUsage.limitIncreaseSuggestion {
            Button("Show options") {
                suggestion.show()
            }
        }
    }
}
```

Resources:
- Adding server-side intelligence with Private Cloud Compute: https://developer.apple.com/documentation/FoundationModels/adding-server-side-intelligence-with-private-cloud-compute

Chapters:
- 0:00 Introduction: Introduces the new PCC server LLM for apps and contrasts it with the improved on-device Foundation Models model. PCC is positioned for complex AI features such as large-input reasoning, extensive tool calling, and watchOS access.
- 1:23 What is Private Cloud Compute: Explains PCC as Apple's privacy-preserving server compute architecture for complex model requests. The chapter covers OS/iCloud integration, no app-managed API keys or token costs, daily per-user limits, iCloud+ higher limits, and the app eligibility/application requirement.
- 2:43 Integrating PCC with Foundation Models: Shows that existing Foundation Models code can switch to PCC by selecting `PrivateCloudComputeLanguageModel` in `LanguageModelSession`. Structured output, tools, and availability checks use the same unified Swift API patterns.
- 4:00 Deciding between on-device and PCC: Compares the on-device System model and PCC model across privacy, offline support, request limits, context size, and reasoning. PCC requires connectivity and quota handling, while the on-device model works offline without request limits.
- 4:32 Reasoning levels and context size: Defines PCC reasoning as extra generated text in a reasoning transcript segment and introduces light, moderate, and deep levels. The chapter also explains that reasoning consumes context tokens and shows the `contextSize` API.
- 6:15 Evaluating and combining models: Recommends choosing models and reasoning levels through evaluation rather than assumptions. Points developers to the Evaluations framework and notes that on-device and server models can be combined in agentic app experiences.
- 7:10 Handling usage limits: Demonstrates quota-aware UI for an article summarization app using PCC. Developers should handle `isLimitReached`, approaching-limit status, and `limitIncreaseSuggestion`, and test states with Xcode's Apple Foundation Models availability simulation.
- 10:15 Next steps: Directs developers to apply for PCC server model access on the developer website. Suggests related Foundation Models content for framework updates and Instruments-based debugging/profiling of agentic model behavior.

### Explore immersive website environments in visionOS

- Session ID: wwdc2026-320
- Page: https://wwdc.ai/2026/320
- Markdown: https://wwdc.ai/2026/320.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/320/
- Category: Safari & Web
- Description: Use Safari's new Immersive API on visionOS to turn HTML model elements into real-world-scale website environments with video, animation, and optimized USDZ assets.
- Duration: 19:09
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-320/eng_b433d9ba5ad3/wwdc2026-320-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/320/4/e1844891-477b-4612-ad8d-10e55bf395ba/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/320/4/e1844891-477b-4612-ad8d-10e55bf395ba/downloads/wwdc2026-320_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/320/4/e1844891-477b-4612-ad8d-10e55bf395ba/downloads/wwdc2026-320_sd.mp4?dl=1

Use Safari's new Immersive API on visionOS to turn HTML model elements into real-world-scale website environments with video, animation, and optimized USDZ assets.

TLDR:
- The Immersive API lets a website request an immersive presentation from an HTML `&lt;model&gt;` element using `requestImmersive()`, while keeping the webpage visible in Safari.
- Inline previews can reuse the same USDZ environment by controlling `entityTransform` with `DOMMatrix` to show a seat- or location-specific point of view before entering immersion.
- Immersive environments can combine with the Fullscreen API for docked video, model animations, RealityKit annotations, and Safari window shadows.
- Performance depends heavily on asset preparation: reduce vertices and entities, use low-poly meshes for shadow receiving, bake lighting where possible, and compress USDZ files with `usdcrush`.

## Immersive API basics

The session introduces website environments for Apple Vision Pro using Safari on visionOS. The core building block is the HTML `&lt;model&gt;` element, which displays a USDZ model on a webpage and can optionally use an environment map for lighting and reflections.

The new JavaScript Immersive API follows the same general pattern as the Fullscreen API: feature detection, a request method, state inspection, change and error events, and CSS support via an immersive pseudo-class. Unlike fullscreen, immersive presentation moves the model beyond the browser bounds while the webpage remains visible.

- Use `&lt;model src="..."&gt;` for the 3D asset and `environmentmap="..."` for lighting.
- Call `requestImmersive()` on a model element in response to a user interaction.
- Use `document.immersiveEnabled` to decide whether to show immersive UI.
- Use `document.immersiveElement` and `immersivechange` to keep page state synchronized.

### Basic model element with environment lighting

Loads a USDZ model and an HDR environment map for scene lighting.

```text
<model src="teapot.usdz" environmentmap="kitchen.hdr"> </model>
```

### Request immersive presentation

Requests the immersive transition from a user-initiated button click.

```text
immersiveButton.addEventListener("click", async () => {
  await model.requestImmersive();
});
```

## Inline environment previews

The ticket sales example starts with an inline preview of a theater environment. The same theater USDZ is embedded in the page, then transformed so the preview shows the view from a selected seat rather than the default fitted exterior view.

By waiting for the model to be ready and assigning `entityTransform`, the page can control the model's position, rotation, and scale. Seat data can be stored separately, with each seat mapped to a translation and orientation in the web's right-handed, Y-up coordinate system.

- The default inline model behavior scales the model to fit the element bounds, which is often not appropriate for an interior environment preview.
- Setting an identity `DOMMatrix` removes the default fitting transform.
- Translating the environment downward can place the virtual camera at seated eye level for inline preview.
- Applying the inverse of a seat's position and orientation makes the inline preview match the selected seat.

### Add the environment model inline

Embeds the theater USDZ and lighting map inside the ticket page for an inline preview.

```text
<div class="seat-preview">
  <model id="theater" src="theater-model.usdz" environmentmap="theater-lighting.hdr"> </model>
</div>
```

### Build a seat-specific inline transform

Constructs a transform that shows the model from the selected seat at seated eye level.

```text
function buildTransform(seat) {
  const transform = new DOMMatrix();
  const { x, y, z, ry } = seat;

  // Rotate and translate the model to match
  // the seat's origin and orientation
  transform.rotateSelf(0, -ry, 0);
  transform.translateSelf(-x, -y, -z);

  // Translate the model down, for eye level preview
  transform.translateSelf(0, -1.0, 0);
  return transform;
}
```

## Entering and leaving immersion

Inline and immersive presentations use different reference frames. Inline, the model element's origin is at the center of the inline layer and scale follows CSS conventions. Immersive, the origin is at the person's feet on the floor and the model is rendered at real-world scale.

Because the environment opens behind Safari's window, the model may need a different transform when immersive so the main focus is not hidden by the page. UI should also provide a clear exit affordance, while still responding to dismissal via the Digital Crown by listening for state changes.

The escape-room example shows that an inline preview is optional. A hidden `&lt;model&gt;` can still be requested as immersive; when hidden with `display: none`, the heavy model is not downloaded or decoded until the immersive request occurs.

- Show immersive controls only when `document.immersiveEnabled` is true.
- Request immersion only from a user interaction.
- Update transforms and layout in response to `immersivechange`, not just button clicks.
- Use loading UI around `requestImmersive()` when the model is not preloaded inline.

### Detect support and update on immersive changes

Shows immersive UI only when supported and recomputes page/model state whenever immersion changes.

```text
if (document.immersiveEnabled) {
  immersiveButton.hidden = false;
}

theater.addEventListener("immersivechange", () => {
  const isImmersive = !!document.immersiveElement;
  const transform = buildTransform(currentSeat, isImmersive);
  theater.entityTransform = transform;
  document.body.classList.toggle("immersive", isImmersive);
});
```

### Hidden model requested as immersive

Keeps the model out of the inline page, defers loading until entry, and displays progress while the immersive request completes.

```text
<model id="escapeRoom" src="escape-room.usdz" environmentmap="room-lighting.hdr" style="display: none"> </model>

enterButton.addEventListener("click", async () => {
  showLoadingAnimation();
  try {
    await escapeRoom.requestImmersive();
  } catch (error) {
    console.log(error);
  } finally {
    hideLoadingAnimation();
  }
});
```

## Video, animation, and shadows inside environments

The escape-room demo uses custom RealityKit annotations in the USDZ to mark a video docking region, such as a TV screen, and to support baked light spill around the docked video. The annotations can be authored with tools such as Reality Composer Pro or the provided Blender add-on.

Once the model contains a video docking region, requesting fullscreen on a video places it into the corresponding surface inside the immersive environment. Because the Immersive API and Fullscreen API can both be active, a website can show a fullscreen video while the user remains inside the environment.

Model animations exported with the USDZ can be triggered from JavaScript. The example waits for the video to end, exits fullscreen to undock the video, then plays the model animation that opens a door. Safari window shadows can be enabled by tagging receiving meshes with the Scene Understanding component; the session recommends using a dedicated low-poly mesh for that purpose.

- Use RealityKit annotations for nonstandard environment behaviors such as video docking and Scene Understanding shadow receivers.
- Use `requestFullscreen()` on the video to dock it into the environment when the USDZ is annotated appropriately.
- Use model animation playback APIs such as `play()` and timeline control with `currentTime` for staged environment changes.
- Prefer low-poly shadow-receiving meshes because complex geometry can make shadow computation expensive.

### Dock video with the Fullscreen API

Requests fullscreen video; with the right model annotations, Safari docks the video onto the tagged surface in the environment.

```text
const trailerVideo = document.getElementById("trailerVideo");
const demoButton = document.getElementById("demoButton");

demoButton.addEventListener("click", async () => {
  await trailerVideo.requestFullscreen();
});
```

### Play a model animation after video ends

Undocks the video and starts the exported model animation when playback finishes.

```text
trailerVideo.addEventListener("ended", async () => {
  await document.exitFullscreen();
  escapeRoom.play();
});
```

## Performance and image controls

Immersive environment USDZ files are often larger and more complex than object models, so asset optimization directly affects download time, memory use, and rendering performance. The session emphasizes removing invisible geometry, merging entities where appropriate, using low-poly support meshes, simplifying shaders, and baking lighting into textures for unlit materials where possible.

The session also briefly highlights image controls as another small markup feature that can add spatial behavior. Adding `controls` to an image lets the browser provide platform-appropriate controls; on visionOS, panoramas and spatial photos can be viewed immersively.

- Reduce vertex count by not exporting geometry that cannot be seen from the intended origin.
- Reduce entity count by merging static objects that do not need to be separate.
- Bake lighting and shadows into textures when that lets you avoid expensive runtime shading.
- Compress USDZ textures with `usdcrush` to reduce model size and improve loading time.

### Compress a USDZ with usdcrush

Uses the command-line tool available on Mac to produce a smaller optimized USDZ.

```text
usdcrush model.usdz -o optimized.usdz
```

### Enable browser image controls

Adds native image controls so visionOS can offer immersive viewing affordances for supported images.

```text
<img src="panorama.jpg" controls>
```

Resources:
- Download - Immersive model add-on for Blender: https://developer.apple.com/download/files/web-env-blender-plugin.zip
- WebKit.org - Theater Ticket Sales immersive website environment demo for Apple Vision Pro: https://webkit.org/demos/model-demos/ticket-sales.html
- WebKit.org - Escape Game immersive website demo for Apple Vision Pro: https://webkit.org/demos/model-demos/escape-room.html
- GitHub: Spatial Backdrop explainer: https://github.com/WebKit/explainers/tree/main/spatial-backdrop
- WebKit.org - Report issues to the WebKit open-source project: https://bugs.webkit.org/
- Submit feedback: http://feedbackassistant.apple.com/

Chapters:
- 0:00 Introduction: Introduces immersive website environments in Safari on Apple Vision Pro through a theater ticketing site and an escape-room marketing site. The session outlines inline previews, immersive entry, and optimization topics.
- 1:46 Meet the immersive API: Explains the HTML `&lt;model&gt;` element, USDZ assets, environment maps, and the Immersive API's similarity to the Fullscreen API. The key difference is that immersive presentation places the model beyond the browser bounds while the webpage remains visible.
- 4:16 Preview environments inline: Builds an inline theater preview by adding a model element and overriding its default fitted transform. Seat data drives `DOMMatrix` transforms so the preview matches the selected seat's point of view.
- 7:01 Go immersive: Shows feature detection, user-initiated `requestImmersive()`, and state handling with `immersivechange`. Covers coordinate differences between inline and immersive modes, exit affordances, Digital Crown dismissal, and entering a hidden escape-room model without an inline preview.
- 12:04 Optimize the experience: Adds richer environment behavior with RealityKit annotations for video docking, video light spill, model animations, and Safari window shadows via Scene Understanding. Ends with asset optimization guidance for vertex count, entity count, low-poly meshes, simple shaders, baked lighting, and USDZ compression.
- 17:17 Image controls: Highlights the image `controls` attribute as another lightweight way to expose platform-specific spatial behavior. On visionOS, this can enable immersive viewing for panoramas and spatial photos.
- 18:09 Next steps: Recommends trying the WebKit immersive demos on Apple Vision Pro, using the attached API/spec resources, filing WebKit feedback, and watching the related design session for photorealistic environment principles.

### Dive into lazy stacks and scrolling with SwiftUI

- Session ID: wwdc2026-321
- Page: https://wwdc.ai/2026/321
- Markdown: https://wwdc.ai/2026/321.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/321/
- Category: SwiftUI & UI Frameworks
- Description: Understand how SwiftUI LazyVStack and LazyHStack estimate layout, load subviews, prefetch work, and support smoother programmatic scrolling.
- Duration: 21:10
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-321/eng_d44b3ecca82d/wwdc2026-321-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/321/5/78830752-d07d-4d89-aeab-94405c084de9/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/321/5/78830752-d07d-4d89-aeab-94405c084de9/downloads/wwdc2026-321_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/321/5/78830752-d07d-4d89-aeab-94405c084de9/downloads/wwdc2026-321_sd.mp4?dl=1

Understand how SwiftUI LazyVStack and LazyHStack estimate layout, load subviews, prefetch work, and support smoother programmatic scrolling.

TLDR:
- Lazy stacks only lay out visible content and estimate off-screen sizes, so absolute content size and content offset are not stable values to build logic on.
- A SwiftUI view in a ForEach does not always map to one lazy-stack subview; dynamic or conditional subview counts can keep views alive longer and hurt scrolling performance.
- Lazy stacks prefetch subviews before they appear, so initialize subview state early and avoid doing essential setup only in onAppear.
- Programmatic scrolling with ScrollPosition works for off-screen targets, but is fastest when each ForEach element resolves to one stable subview and layout does not change after appearance.

## How lazy stacks lay out scrolling content

LazyVStack and LazyHStack improve performance by evaluating, laying out, and rendering only the subviews needed for the visible region, plus nearby prefetched content. As views scroll out, the lazy stack eventually removes them instead of keeping the entire content tree alive like a regular stack would.

Because off-screen subviews are not loaded, their sizes are estimated. A LazyVStack estimates its full height from the average size of already placed views and the number of remaining subviews. Its ideal width comes from the first subview, because it cannot inspect every off-screen child. A LazyHStack similarly uses the first subview to determine ideal height.

These estimates can change as new content becomes visible. The embedding ScrollView and lazy stack coordinate content offset corrections so visible content stays visually anchored, but code that depends on an exact absolute offset or total content size can become unreliable.

- Use lazy stacks for long or custom scrolling content where loading all children up front would be expensive.
- Avoid business logic tied to exact contentOffset or total content size; these are estimated and may shift as the lazy stack learns more.
- For horizontally scrolling LazyHStack content in a vertical ScrollView, use fixed or predictable heights when later children might otherwise be taller than the first child.

### Basic lazy vertical scrolling

A ScrollView with a LazyVStack loads step views as they approach the visible region instead of building all steps immediately.

```swift
struct ContentView: View {
    var body: some View {
        ScrollView {
            LazyVStack {
                ForEach(steps) { step in
                    StepView(step: step)
                }
            }
        }
    }
}
```

### Nested horizontal lazy stack

A LazyHStack can be nested inside a LazyVStack so the horizontal showcase is also loaded lazily.

```swift
struct Showcase: View {
    var body: some View {
        ScrollView(.horizontal) {
            LazyHStack {
                ForEach(photos) { photo in
                    PhotoView(photo: photo)
                }
            }
        }
    }
}
```

## Composition, pinned sections, and scroll effects

Lazy stacks can contain mixed content, nested scroll views, and sections. A LazyVStack can pin section headers by using its pinnedViews parameter, which is useful for a section such as a user-photo showcase after a list of instruction steps.

Scroll transitions can be applied to lazy-stack children, but transforms must respect the lazy stack's visibility assumptions. The lazy stack decides what to load based on original frames; if a transition pushes an otherwise off-screen view into the visible rect, the lazy stack may not load it, and if it pushes a visible view out of its original frame, it may disappear earlier than expected.

- Use LazyVStack(pinnedViews: [.sectionHeaders]) for pinned section headers.
- Keep scrollTransition transforms within the normal visibility region; avoid effects that move off-screen content into view.
- Prefer relative visibility APIs over absolute scroll offsets when showing or hiding overlay controls.

### Pinned section header

Pinned section headers work inside lazy vertical stacks.

```text
ScrollView {
    LazyVStack(pinnedViews: [.sectionHeaders]) {
        ForEach(steps) { step in
            StepView(step: step)
        }
        Showcase()
    }
}
```

### Visibility-based overlay state

Visibility of scroll targets is a better trigger for UI state than comparing an estimated absolute content offset.

```text
.onScrollTargetVisibilityChange(
    idType: Step.ID.self,
    threshold: 0.8
) { visibleIDs in
    isScrollToShowcaseVisible = shouldShowScrollButton(visibleIDs: visibleIDs)
}
```

## Subview resolution and dynamic child counts

The subviews a lazy stack manages are the resolved subviews of SwiftUI view bodies, not necessarily the view structs written in source. A ForEach generally resolves to one child per data element, but a child view whose body has multiple top-level views can resolve to multiple lazy-stack subviews.

Dynamic subview counts are especially important. If a leaf view inside a ForEach conditionally returns zero or one subviews based on environment or optional state, the lazy stack may need to keep earlier view structs alive so it can preserve indices if the condition changes. This can lead to unexpected body evaluations for off-screen views and state that is retained longer than expected.

Filter at the data level instead of hiding elements with conditional content inside each repeated leaf view. With SwiftData, use a Query predicate so the lazy stack can count and address subviews without constructing every view.

- Prefer one stable subview per ForEach element for best lazy-stack behavior.
- Avoid using conditional leaf-view bodies to filter large repeated data sets.
- Avoid optional unwrapping in a repeated view body when it controls whether the body returns content; handle authentication or availability higher in the hierarchy when possible.
- If an element has multiple visual parts, wrap them in a container layout such as VStack or a custom Layout when the lazy stack should treat them as one item.

### Avoid filtering in the repeated view body

This creates a dynamic number of resolved subviews per ForEach element, which can keep views alive and hurt lazy-stack performance.

```swift
struct StepView: View {
    let step: Step
    @Environment(\.detailLevel) var detailLevel

    var body: some View {
        if step.isVisible(in: detailLevel) {
            VStack { /* ... */ }
        }
    }
}
```

### Filter at the data level

Filtering the data source gives the lazy stack a stable, countable set of subviews.

```swift
struct ContentView: View {
    @Query var steps: [Step]

    init(detailLevel: DetailLevel) {
        _steps = Query(filter: #Predicate<Step> { step in
            step.detailLevel >= detailLevel
        })
    }

    var body: some View { /* ... */ }
}
```

## Prefetching, setup, and state lifetime

Lazy stacks prefetch content ahead of the visible region to avoid scroll hitches. Prefetching can evaluate a body and perform layout before a view appears, spreading work across multiple frames. If the user reverses direction, a prefetched view may never receive onAppear.

onAppear is still appropriate for some tasks, such as triggering infinite scrolling when a progress indicator reaches the screen. But essential per-row setup should not be deferred to onAppear, because the prefetched layout may be invalidated when the view appears, forcing extra work during scrolling.

Lazy-stack subviews are retained for a short time after scrolling off-screen, but they are eventually removed and their @State is deleted. State that must survive scrolling should live in a model object or an outer view and be passed down with a binding.

- Use onAppear for end-of-list pagination triggers, not for rebuilding the basic shape and size of every row.
- Initialize view models or loaders in init when the view can be made ready before it appears.
- Use caches or observable loader objects when work can begin during prefetching.
- Do not store durable item state only in a lazy-stack child's @State.

### Pagination with onAppear

Using onAppear on a trailing ProgressView is a reasonable way to fetch another page when the user reaches the end.

```swift
struct Showcase: View {
    @State var pager = ShowcasePager()

    var body: some View {
        ForEach(pager.pages) { page in
            PageView(page: page)
        }
        if !pager.atEnd {
            ProgressView()
                .progressViewStyle(.circular)
                .onAppear { pager.fetchPage() }
        }
    }
}
```

### Initialize lazy-stack child state before appearance

Initializing state in init lets prefetching work with a reasonably complete view before onAppear would be called.

```swift
struct StepView: View {
    @State var viewModel: StepViewModel

    init(id: Step.ID) {
        _viewModel = State(initialValue: StepViewModel(id: id))
    }

    var body: some View { /* ... */ }
}
```

### Move persistent item state outward

Highlight state stored outside the lazy-stack child survives when the child view scrolls off-screen and is later destroyed.

```swift
struct ContentView: View {
    @State var highlighted: Set<Step.ID> = []

    var body: some View { /* pass binding to StepView */ }
}

struct StepView: View {
    let step: Step
    @Binding var highlighted: Set<Step.ID>

    var body: some View { /* ... */ }
}
```

## Programmatic scrolling and layout stability

ScrollPosition can programmatically scroll to a target inside a lazy stack even when the target is off-screen. The lazy stack estimates the target's position and updates that estimate during animated scrolling.

Programmatic scrolling is most efficient when each ForEach element always resolves to one subview. Then the lazy stack can query the ForEach for the target ID and count subviews without constructing many views. Filtering in child bodies or returning multiple/dynamic subviews makes this harder.

Avoid layout changes after a lazy-stack child appears. Patterns such as measuring with onGeometryChange, writing the result into @State, and using that state to change another view's frame create a second layout pass that can push content away from the estimated target. When built-in layout primitives are not enough, use a custom Layout instead.

- Use .scrollPosition with a ScrollPosition binding for target-based scrolling.
- Keep target IDs attached to stable subviews.
- Do not use onAppear or onGeometryChange to substantially change row height after placement.
- Use a custom Layout to compute dependent sizes within layout instead of measuring after the fact.

### Scroll to an off-screen target

A ScrollPosition binding lets code scroll to a target by ID, including a target that has not been loaded yet.

```swift
struct ContentView: View {
    @State var scrollPosition = ScrollPosition()

    var body: some View {
        ScrollView { /* ... */ }
            .scrollPosition($scrollPosition)
            .overlay(alignment: .bottom) {
                Button { scrollToShowcase() } label: { /* ... */ }
            }
    }

    func scrollToShowcase() {
        withAnimation {
            scrollPosition.scrollTo(id: "showcase-header")
        }
    }
}
```

### Prefer custom layout over post-appearance measurement

A custom Layout avoids measuring a child after appearance and then changing the row's height in a later pass.

```swift
struct StepView: View {
    let step: Step

    var body: some View {
        StepLayout {
            StepDiagram(diagram: step.diagram)
            Title(step.title)
            Subtitle(step.subtitle)
        }
    }
}

struct StepLayout: Layout {
    /* compute related sizes during layout */
}
```

Resources:
- Grouping data with lazy stack views: https://developer.apple.com/documentation/SwiftUI/Grouping-Data-with-Lazy-Stack-Views

Chapters:
- 0:00 Introduction: Introduces lazy stacks as the SwiftUI tool for long, custom scrolling content and frames the session around understanding their internals and pitfalls. The sample Origami app starts with a ScrollView containing a LazyVStack of step views.
- 1:24 Layout: Explains that lazy stacks lay out only the visible region, estimate off-screen sizes, and coordinate changing estimates with the embedding ScrollView. It also covers nested lazy stacks, pinned section headers, scroll transitions, and why relative visibility is safer than absolute content offsets.
- 9:13 Subview loading: Shows that source-level view structs do not always correspond one-to-one with the subviews managed by a lazy stack. Dynamic subview counts from conditionals or optional unwrapping can keep repeated views alive and should be replaced with data-level filtering where possible.
- 13:15 Prefetching: Describes how lazy stacks prefetch body evaluation, layout, and rendering work before views appear to avoid dropped frames. It recommends initializing child views before onAppear and moving durable state out of lazy-stack children because off-screen children are eventually destroyed.
- 17:40 Programmatic scrolling: Covers ScrollPosition-based scrolling to off-screen targets and how lazy stacks estimate target positions during animated scrolling. It warns that dynamic child counts and post-appearance layout changes, such as state updates from onGeometryChange, reduce smoothness and reliability.
- 19:55 Next steps: Recaps the main best practices: avoid absolute size and offset assumptions, avoid conditional filtering inside repeated leaf views, set up children before onAppear when possible, and keep important state outside views that may scroll off-screen.

### Compose advanced graphics effects with SwiftUI

- Session ID: wwdc2026-322
- Page: https://wwdc.ai/2026/322
- Markdown: https://wwdc.ai/2026/322.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/322/
- Category: SwiftUI & UI Frameworks
- Description: Compose SwiftUI shader, timeline, scrolling, and alignment APIs into advanced animated visual effects from simple pipeline stages.
- Duration: 17:55
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-322/eng_f95a491015d5/wwdc2026-322-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/322/4/db4c622a-2091-45ef-a024-df317a5b55a5/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/322/4/db4c622a-2091-45ef-a024-df317a5b55a5/downloads/wwdc2026-322_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/322/4/db4c622a-2091-45ef-a024-df317a5b55a5/downloads/wwdc2026-322_sd.mp4?dl=1

Compose SwiftUI shader, timeline, scrolling, and alignment APIs into advanced animated visual effects from simple pipeline stages.

TLDR:
- Treat advanced SwiftUI graphics as a composable pipeline: start with existing data, transform it through small APIs, then connect stages together.
- Use SwiftUI shader effects-especially `layerEffect`-with Metal stitchable functions to sample a view layer and create image-based warps driven by noise textures.
- Drive stateless shader animation by passing changing time values from `TimelineView(.animation)` into shader parameters.
- Build synchronized foreground UI with `ScrollViewReader`, playback-state changes, overlays, and `alignmentGuide` instead of manual offsets.

## Creative pipelines for SwiftUI effects

The session frames advanced SwiftUI visuals as a pipeline: data enters one stage, gets transformed, and becomes input for the next stage. The example starts from a podcast app that already has cover art, playback state, and transcript text, then composes standard SwiftUI layout and graphics APIs into a richer experience.

The finished design has two parallel flows: a background flow that turns cover art into an animated visualizer, and a foreground flow that turns transcript data plus playback time into a synchronized scrolling transcript with a floating timestamp.

- Break down the design into data sources: image, playback time, transcript lines, and line timestamps.
- Choose a transformation for each stage: blur, shader warp, time-driven animation, current-line highlighting, scrolling, and overlay alignment.
- Compose stages rather than building one monolithic custom renderer.

## Cover art, blur, and shader effects

The background begins with a regular SwiftUI `Image`, softened with `blur(radius:)` so it can sit behind transcript text without competing for attention. The visualizer is then built with a SwiftUI shader effect.

SwiftUI exposes shader effects through `colorEffect`, `distortionEffect`, and `layerEffect`. `colorEffect` transforms each pixel color, `distortionEffect` maps output positions to source positions, and `layerEffect` receives the whole rendered layer so the shader can sample from nearby pixels or the broader region. The example chooses `layerEffect` for flexibility.

- Shaders run per pixel on the GPU; each pixel invocation is independent.
- A `[[stitchable]]` Metal function can be called from SwiftUI via `ShaderLibrary`.
- `layerEffect` receives `SwiftUI::Layer`, which can be sampled at arbitrary positions.
- Pass SwiftUI-side values, such as size, offsets, images, or time, as shader parameters.

### Start from cover art and soften it

The raw image becomes a blurred background layer before shader processing.

```text
Image("CoverArt")
    .blur(radius: 30)
```

### Minimal SwiftUI layer effect and Metal shader

The first shader simply samples the original layer at the current pixel position, producing the same image while establishing the shader pipeline.

```text
GeometryReader { proxy in
    CoverArtView()
        .layerEffect(
            ShaderLibrary.backgroundWarp(),
            maxSampleOffset: .zero
        )
}
.ignoresSafeArea()

[[stitchable]] half4 backgroundWarp(
    float2 position,
    SwiftUI::Layer layer
) {
    return layer.sample(position);
}
```

## Organic warping with noise and domain warping

A uniform offset only shifts every pixel in the same way. To get organic variation, the example passes a precomputed `NoiseTexture` into the shader and samples its red and green channels as a two-dimensional per-pixel offset.

The richer version uses domain warping: sample noise once to get an intermediate coordinate offset, then sample noise again at the shifted coordinate. That layered noise produces flowing blob-like distortions.

- Pass the view size so the shader can compute UV coordinates from pixel positions.
- Pass the noise image with `.image(Image("NoiseTexture"))`; Metal receives it as `texture2d&lt;half&gt;`.
- Use a repeating, linear sampler so the noise texture tiles smoothly.
- Scale the sampled red/green values around zero before adding them to the source sample position.

### SwiftUI passes size and noise texture to the shader

The layer effect receives both geometry-derived size and a noise texture.

```text
GeometryReader { proxy in
    CoverArtView()
        .layerEffect(
            ShaderLibrary.backgroundWarp(
                .float2(proxy.size),
                .image(Image("NoiseTexture"))
            ),
            maxSampleOffset: .zero
        )
}
.ignoresSafeArea()
```

### Metal layer shader with domain warping

The shader uses a second noise sample at a noise-shifted coordinate to create a more complex warp.

```text
[[stitchable]] half4 backgroundWarp(
    float2 position,
    SwiftUI::Layer layer,
    float2 size,
    texture2d<half> noiseTex
) {
    constexpr sampler s(address::repeat, filter::linear);
    float2 uv = position / size;
    half4 n = noiseTex.sample(s, uv);
    float2 q = float2(n.r, n.g);
    n = noiseTex.sample(s, uv + q);
    float2 offset = (float2(n.r, n.g) - 0.5) * 200.0;
    return layer.sample(position + offset);
}
```

## Animating shaders with time

SwiftUI transaction-based animation is not enough by itself for shader state, because shaders are stateless and compute output only from their inputs. To animate a shader, pass in a parameter that changes over time.

`TimelineView(.animation)` supplies a timestamp each frame. The example records a start date, computes elapsed time, and passes that value into the shader so the noise sampling position can change as playback continues.

- Use `TimelineView(.animation)` for frame-by-frame time values.
- Compute elapsed time relative to a stored `startDate`.
- Forward elapsed time as a `.float` shader argument.
- Use the time value in Metal to shift noise sampling and make the warp flow.

### Pass elapsed time into a layer shader

The timeline acts as the time pipe that drives an otherwise stateless shader.

```swift
@State private var startDate = Date.now

TimelineView(.animation) { timeline in
    let elapsed = timeline.date.timeIntervalSince(startDate)
    CoverArtView()
        .layerEffect(
            ShaderLibrary.backgroundWarp(
                .float2(proxy.size),
                .image(Image("NoiseTexture")),
                .float(elapsed)
            ),
            maxSampleOffset: .zero
        )
}
```

## Time-synced transcript and floating timestamps

The foreground transcript remains ordinary SwiftUI: `Text` views in a `LazyVStack` inside a `ScrollView`. Playback state determines the current line, which is styled differently from the rest.

`ScrollViewReader` and `onChange` keep the current line centered as playback progresses. Timestamp labels are attached with overlays, and `alignmentGuide` semantically changes how the overlay aligns so the timestamp floats outside the row edge without manual size-dependent offsets.

- Use playback timestamp to derive the current transcript line.
- Use `scrollProxy.scrollTo(_:anchor:)` when the current line changes.
- Keep timestamp overlays present but show only the active one, avoiding layout changes.
- Prefer `alignmentGuide` for semantic attachment over hard-coded `offset` values.

### Scroll transcript to the current line

The transcript highlights the current line and scrolls it to the center when playback advances.

```text
@State private var playback = PlaybackState()

ScrollViewReader { scrollProxy in
    ScrollView {
        LazyVStack(alignment: .leading, spacing: 12) {
            ForEach(sampleTranscript) { line in
                Text(line.text)
                    .transcriptLineStyle(
                        isCurrent: line.id == playback.currentLineIndex
                    )
            }
        }
    }
    .onChange(of: playback.currentLineIndex, { _, i in
        scrollProxy.scrollTo(i, anchor: .center)
    })
}
```

### Float a timestamp with an alignment guide

The overlay's bottom alignment is overridden to use the timestamp's top edge, placing it just outside the text row.

```text
Text(line.text)
    .overlay(alignment: .bottomLeading) {
        Text(line.formattedTimestamp)
            .alignmentGuide(.bottom) { $0[.top] }
    }
```

Resources:
- Alignment: https://developer.apple.com/documentation/SwiftUI/Alignment
- Composing advanced graphics effects with SwiftUI: https://developer.apple.com/documentation/SwiftUI/Composing-advanced-graphics-effects-with-SwiftUI
- Shader: https://developer.apple.com/documentation/SwiftUI/Shader

Chapters:
- 0:00 Introduction: Introduces advanced SwiftUI graphics and layout as composable pipelines made from standard APIs. The example goal is a richer podcast transcript UI with animated background visuals and synchronized text.
- 1:40 Design breakdown: Breaks the finished design into inputs and transformations: cover art becomes a shader-driven visualizer, time drives animation, and playback time synchronizes transcript scrolling. The foreground and background flows are later combined.
- 4:11 Cover art and shader effects: Starts with a cover art image, blurs it, and applies a `layerEffect` backed by a Metal stitchable shader. The chapter compares `colorEffect`, `distortionEffect`, and `layerEffect`, then builds a noise-based domain-warping shader.
- 11:07 Driving animation with time: Explains that shaders are stateless and need changing input to animate. `TimelineView(.animation)` supplies elapsed time, which is passed into the shader to make the warp pattern move.
- 12:00 Time-synced transcript view: Builds the transcript from `Text` views in a `LazyVStack` inside a `ScrollView`. Playback state highlights the current line and `ScrollViewReader` scrolls it to the center when the current line changes.
- 13:18 Floating timestamps with alignment guides: Adds timestamp overlays to transcript lines and explains SwiftUI alignment as matching alignment points between views. An `alignmentGuide` override moves the overlay's bottom guide to its top edge so the timestamp floats outside the row without manual offsets.
- 16:16 Creative pipelines: Generalizes the approach: each stage's output feeds the next stage's input. Similar pipelines could use other inputs, shaders, or foreground views while relying on the same SwiftUI APIs.
- 17:13 Next steps: Encourages experimenting with the sample shader by changing noise, speed, and images. The closing guidance is to look for places where small composed visual effects can improve an app.

### Meet Core AI

- Session ID: wwdc2026-324
- Page: https://wwdc.ai/2026/324
- Markdown: https://wwdc.ai/2026/324.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/324/
- Category: AI & Machine Learning
- Description: Core AI brings Apple's on-device inference stack to apps, with PyTorch conversion, Swift runtime APIs, Xcode tooling, profiling, states, and specialization control.
- Duration: 20:43
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-324/eng_3971542db8fe/wwdc2026-324-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/324/4/3b67b624-4060-495f-9ba7-659805ee6b88/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/324/4/3b67b624-4060-495f-9ba7-659805ee6b88/downloads/wwdc2026-324_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/324/4/3b67b624-4060-495f-9ba7-659805ee6b88/downloads/wwdc2026-324_sd.mp4?dl=1

Core AI brings Apple's on-device inference stack to apps, with PyTorch conversion, Swift runtime APIs, Xcode tooling, profiling, states, and specialization control.

TLDR:
- Core AI is Apple's on-device model deployment stack for Apple Silicon, spanning Python/PyTorch conversion and optimization, Swift inference APIs, Xcode inspection, debugging, profiling, and ahead-of-time compilation.
- The basic workflow is: export a PyTorch model with `torch.export`, convert it with `coreai_torch`, save a `.aimodel`, inspect it in Xcode, then load an `AIModel` and run an `InferenceFunction` with `NDArray` inputs.
- For iterative or decoder-style workloads, Core AI supports mutable model states; the session demonstrates a transformer key/value cache that avoids recomputing full history and reduces growing inference latency.
- Large models require device-specific specialization before running; Core AI exposes `AIModelCache`, explicit specialization, `SpecializationOptions`, cache management, app-group cache sharing, and ahead-of-time compilation to improve first-use experience.

## What Core AI provides

Core AI is the inference framework used for on-device Apple Intelligence, now exposed for developers building AI features in their own apps. It is designed for modern on-device workloads across Apple platforms and can execute across Apple Silicon CPU, GPU, and Neural Engine.

The framework is not just a runtime API. It covers the deployment lifecycle: model authoring, optimization, conversion, debugging, app integration, profiling, model specialization, ahead-of-time compilation, and lower-level customization when needed.

- Swift framework for loading `.aimodel` assets and running inference with memory-safe APIs.
- Python tooling built around familiar PyTorch workflows for conversion, authoring, optimization, and validation.
- Xcode integration for model inspection, Core AI Instruments profiling, a Core AI debug gauge, and a visual Core AI Debugger.
- Support for advanced needs such as stateful inference, custom GPU kernels with Metal 4, model specialization control, and optimized tight inference loops.

## Convert and validate a PyTorch model

The demo converts a PyTorch snake-action transformer into Core AI format using the Core AI Torch Python package. The model is exported with `torch.export`, including a dynamic sequence-length dimension so the converted model is not fixed to the sample input length.

After conversion, the session validates numerics by running the same input through the original PyTorch model and the Core AI model using the Python runtime bindings, then checking that logits are close enough for the app's use case.

- Use `torch.export.Dim` and `dynamic_shapes` for dynamic dimensions.
- Run Core AI's decomposition table before conversion.
- Use `TorchConverter().add_exported_program(...)` to name model inputs and outputs.
- Save the result as a `.aimodel` asset for Xcode and the Swift runtime.

### Convert a PyTorch model to Core AI

Exports a PyTorch model with a dynamic sequence dimension, converts it to a Core AI graph, and writes a `.aimodel` asset.

```swift
import torch
import coreai_torch

pt_model = SnakeTransformer().load_checkpoint("snake.pt")
example = torch.randn(1, 5, 16)

seq_len = torch.export.Dim("seq_len", min=1, max=256)
exported = torch.export.export(
    pt_model,
    args=(example,),
    dynamic_shapes={"features": {1: seq_len}},
)
exported = exported.run_decompositions(coreai_torch.get_decomp_table())

ai_program = coreai_torch.TorchConverter().add_exported_program(
    exported,
    input_names=["features"],
    output_names=["logits"],
).to_coreai()

ai_program.save_asset("SnakeTransformer.aimodel")
```

### Validate converted model numerics

Compares Core AI output against PyTorch output before leaving the Python environment.

```swift
import numpy as np
from coreai.runtime import AIModel, NDArray

ai_model = await AIModel.load("SnakeTransformer.aimodel")
function = ai_model.load_function("main")

features = make_snake_features(game).astype(np.float32)[np.newaxis]

with torch.no_grad():
    pytorch_logits = pt_model(torch.from_numpy(features)).numpy()[0, -1]

result = await function({"features": NDArray(data=features)})
coreai_logits = result["logits"].numpy()[0, -1]

assert np.max(np.abs(pytorch_logits - coreai_logits)) < 0.01
```

## Integrate a `.aimodel` in a Swift app

Xcode can open a `.aimodel` file and show metadata such as model size, operation distribution, and function signatures. In the demo, the model has one function that takes game-board features and outputs logits for the next movement direction.

The CoreAI Swift framework centers on `AIModel`, `InferenceFunction`, and `NDArray`. Apps typically create the `AIModel` and load the main function when preparing the feature, then construct `NDArray` inputs, run inference, and process output arrays.

- `AIModel` loads and inspects a `.aimodel` asset.
- `InferenceFunction` represents a runnable compute graph, commonly the model's `main` function.
- `NDArray` holds multidimensional input and output data.
- `NDArray.MutableView` and `NDArray.View` provide efficient, memory-safe access to array storage.

### Load and run an inference function

Shows the core Swift runtime flow: load a model, load a function, pass `NDArray` inputs, and read `NDArray` outputs.

```swift
import CoreAI

let model = try await AIModel(contentsOf: modelURL)
let mainFunction = try model.loadFunction(named: "main")!

let inputNDArray: NDArray = nextInput()
var outputs = try await mainFunction.run(inputs: ["input": inputNDArray])

guard let outputNDArray = outputs.remove("output")?.ndArray else {
    throw ModelError.missingOutput
}
```

### Prepare dynamic game features and run inference

The demo's model player builds a float32 `NDArray`, writes game features into it, runs inference, and chooses the direction with the highest logit.

```swift
extension ModelPlayer: SnakePlayer {
    mutating func chooseAction(game: SnakeGame) async throws -> Direction {
        var inputFeatures = NDArray(
            shape: [game.stepCount, hiddenDim],
            scalarType: .float32
        )
        writeFeatures(of: game, into: inputFeatures.mutableView())

        var outputs = try await nextActionFunction.run(
            inputs: ["features": inputFeatures]
        )
        guard let logits = outputs.remove("logits")?.ndArray else {
            throw ModelError.missingOutput
        }
        return predictedDirection(from: logits.view())
    }
}
```

## Profile and optimize with states

The first implementation feeds the full game history into a transformer on every move. Instruments shows inference intervals growing over time, which matches transformer quadratic complexity as sequence length increases.

The optimization uses Core AI states for transformer key/value caches. States are model inputs that are read and updated in-place during inference. After the first step, the app can pass only the newest features while the cached state carries the relevant history.

- In PyTorch, key and value caches are registered as buffers so the exported program treats them as mutable buffers.
- During conversion, `state_names` maps those mutable buffers to Core AI state arguments.
- In Swift, the app owns `NDArray` cache buffers and passes mutable views through `InferenceFunction.MutableViews`.
- The updated implementation keeps game speed steady and reduces the growth rate of inference latency.

### Convert a stateful model with key/value cache states

Adds state names during conversion so mutable key/value cache buffers become Core AI states.

```text
exported = torch.export.export(
    stateful_model,
    args=(example_features, example_position_ids),
    dynamic_shapes={"position_ids": {1: seq_len}},
)
exported = exported.run_decompositions(coreai_torch.get_decomp_table())

ai_program = coreai_torch.TorchConverter().add_exported_program(
    exported,
    input_names=["features", "position_ids"],
    state_names=["keyCache", "valueCache"],
    output_names=["logits"],
).to_coreai()

ai_program.save_asset("SnakeTransformer.aimodel")
```

### Pass mutable state views from Swift

Passes cache buffers as mutable state views so inference reads and updates them in-place.

```swift
var stateViews = InferenceFunction.MutableViews()
stateViews.insert(&keyCache, for: "keyCache")
stateViews.insert(&valueCache, for: "valueCache")

var outputs = try await nextActionFunction.run(
    inputs: ["features": inputFeatures],
    states: stateViews
)
```

## Specialization, caching, and ahead-of-time compilation

A shipped `.aimodel` is a source representation that can run on Apple devices, but it must be specialized for the user's specific device before loading and inference. First specialization can take significant time for very large models, while later loads can be fast from cache.

Core AI exposes APIs to check and manage the default model cache, request specialization explicitly, configure specialization behavior, delete unused entries, control persistence, and share a cache between apps in the same app group. The session recommends avoiding specialization inside user-interactive flows.

Ahead-of-time compilation can move part of the compilation work to the development machine. The compiled model still needs device-specific specialization, but there is less work left on the user's device.

- Use cache checks to gate features or inform users that AI features are being prepared.
- Request specialization after downloading assets or when a user opts into a feature.
- Consult the Core AI documentation for `SpecializationOptions`, cache policies, app-group cache sharing, and ahead-of-time compilation.
- For tight inference loops, lower-level APIs can query optimal `NDArray` memory layouts, preallocate outputs, and pipeline functions with asynchronous values.

### Check the model cache before loading

Checks whether a specialized model is already available before entering a user-visible flow.

```swift
let cache = AIModelCache.default
guard let model = try cache.model(for: modelURL, options: .default) else {
    Task { @MainActor in
        informUser("Preparing AI features. This may take a while...")
    }
    return
}
```

### Request specialization explicitly

Starts specialization independently of model loading so the model can be prepared ahead of use.

```text
try await AIModel.specialize(contentsOf: modelURL)
```

Resources:
- Core AI PyTorch Extensions: https://apple.github.io/coreai-torch
- Core AI Python: https://apple.github.io/coreai-torch/main/coreai-core
- Core AI Optimization: https://apple.github.io/coreai-optimization
- Core AI: https://developer.apple.com/documentation/CoreAI
- Compiling Core AI models ahead of time: https://developer.apple.com/documentation/CoreAI/compiling-core-ai-models-ahead-of-time
- Managing model specialization and caching: https://developer.apple.com/documentation/CoreAI/managing-model-specialization-and-caching

Chapters:
- 0:00 Introduction: Introduces Core AI as a way to add on-device AI features to apps, then outlines the session flow: conversion, app integration, performance optimization, and additional tooling.
- 0:33 What is Core AI: Defines Core AI as Apple's on-device inference framework for modern workloads across CPU, GPU, and Neural Engine. It covers the deployment lifecycle with Python tooling, Swift APIs, customization, Xcode tooling, debugging, Instruments profiling, and ahead-of-time compilation.
- 4:57 Model conversion: Demonstrates converting a PyTorch snake-action model with Core AI Torch by exporting with `torch.export`, preserving dynamic sequence length, running decompositions, naming inputs and outputs, saving a `.aimodel`, and validating outputs against PyTorch.
- 6:16 App integration: Shows how to inspect the `.aimodel` in Xcode and integrate it with the CoreAI Swift framework. The demo loads an `AIModel`, retrieves an `InferenceFunction`, builds `NDArray` inputs from game state, runs inference, and reads logits to choose the next action.
- 10:48 Profiling with Instruments: Uses the Core AI instrument in Xcode to identify increasing inference intervals in the snake app. The slowdown is tied to transformer inference cost growing as the input sequence length increases each move.
- 11:15 Optimizing performance: Optimizes the transformer by adding key/value cache states so each inference reads and updates cached history instead of recomputing all previous steps. The model is updated in PyTorch, reconverted with `state_names`, and the app passes mutable cache views to `InferenceFunction.run`.
- 14:13 Additional features: Surveys Core AI capabilities beyond the demo: direct model authoring with Core AI Python APIs, Apple Silicon optimization, custom Metal 4 kernels, the Core AI Debugger for numeric debugging, and the Core AI debug gauge in Xcode.
- 15:34 Specialization: Explains that shipped models must be specialized for the target device before running and that large-model first use should be planned outside interactive flows. Covers `AIModelCache`, explicit specialization, specialization options, cache management, app-group cache sharing, ahead-of-time compilation, and lower-level inference-loop optimizations.
- 20:07 Next steps: Wraps up Core AI's role across Apple Silicon, Python workflows, Swift app integration, and debugging tools. Points developers to the Core AI Models repository for ready-to-convert models, model-family Swift packages, and Foundation Models integration for custom language models.

### Dive into Core AI model authoring and optimization

- Session ID: wwdc2026-325
- Page: https://wwdc.ai/2026/325
- Markdown: https://wwdc.ai/2026/325.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/325/
- Category: AI & Machine Learning
- Description: Author, compress, debug, and re-author PyTorch models for efficient on-device execution with Core AI, coreai-torch, coreai-opt, and Core AI Debugger.
- Duration: 29:21
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-325/eng_2712362ef920/wwdc2026-325-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/325/5/8d08c9d4-3c64-49e1-8590-8b76bd9ad4cb/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/325/5/8d08c9d4-3c64-49e1-8590-8b76bd9ad4cb/downloads/wwdc2026-325_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/325/5/8d08c9d4-3c64-49e1-8590-8b76bd9ad4cb/downloads/wwdc2026-325_sd.mp4?dl=1

Author, compress, debug, and re-author PyTorch models for efficient on-device execution with Core AI, coreai-torch, coreai-opt, and Core AI Debugger.

TLDR:
- Core AI's Python workflow starts with `torch.export`, converts with `coreai_torch.TorchConverter`, optimizes, saves an `.aimodel`, and can run inference from Python on Apple silicon.
- `coreai-opt` supports config-driven compression including int4/int8/FP4/FP8 weights, calibration-based quantization, quantization-aware training, and presets such as 4-bit per-channel symmetric quantization.
- Core AI Debugger provides graph, source, inspector, device-run, intermediate-output, and comparison workflows to diagnose conversion and compression issues against PyTorch reference results.
- Advanced authoring can fuse operations, use prepackaged fast kernels such as Scaled Dot Product Attention, embed custom Metal kernels, or re-author models into multiple functions and iOS-friendly layouts.

## Core AI Python deployment workflow

Core AI is presented as a deployment lifecycle for Apple silicon: convert, optimize, debug, specialize, and integrate model assets. This session focuses on the Python ecosystem, especially PyTorch-based authoring and conversion.

The basic workflow uses a PyTorch `ExportedProgram` as the input to Core AI. `coreai-torch` converts that graph into a Core AI program, optimization produces a deployable `.aimodel` asset, and Python can load a specialized function and execute it with NumPy-backed tensors.

- Install the Python entry point with `pip install coreai-torch`; this installs both `coreai` and `coreai-torch`.
- Use `torch.export.export` to capture weights, operations, and shapes in a graph that Core AI can convert.
- Use `TorchConverter` with explicit input and output names, then call `to_coreai()`, `optimize()`, and `save_asset()`.
- Core AI conversion can also assemble multiple models into one artifact, register custom lowerings, and inline Metal kernels.

### Export a PyTorch model for Core AI conversion

`torch.export` produces the graph representation consumed by `coreai-torch`.

```swift
import torch
import torch.nn as nn

class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(256, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        return self.fc2(torch.relu(self.fc1(x)))

model = MLP().eval()
example_input = (torch.randn(1, 256),)
exported_program = torch.export.export(model, example_input)
```

### Convert, optimize, save, and run a Core AI model

The converted Core AI program becomes an `.aimodel` asset and can be specialized and executed from Python.

```swift
import coreai
import coreai_torch
from coreai.runtime import NDArray

converter = coreai_torch.TorchConverter()
converter.add_exported_program(
    exported_program,
    input_names=["features"],
    output_names=["logits"],
)
core_ai_program = converter.to_coreai()

core_ai_program.optimize()
asset = core_ai_program.save_asset("mlp.aimodel")

specialized_model = await AIModel.load("mlp.aimodel")
specialized_function = specialized_model.load_function("main")
result = await specialized_function({"features": NDArray(example[0].numpy())})
```

## Model repositories, reusable components, and Core AI Skills

The `coreai-models` repository is described as both a source of ready-to-go model architectures and reusable components for bringing custom models to Core AI. It also includes a Swift package for running LLMs in apps.

Core AI Skills are agent skills that can be installed into a coding assistant. They are intended to help an agent translate high-level deployment goals into concrete Core AI plans, including PyTorch changes, conversion steps, optimization choices, and target-hardware considerations.

- Use `coreai-models` for examples engineered around different use cases and constraints.
- Agent skills may ask for model type, target hardware families, and application constraints before suggesting a workflow.
- Use these examples and skills as starting points for authoring PyTorch that maps well to Apple silicon.

## Compression with coreai-opt

`coreai-opt` provides config-driven model compression. The session demonstrates it with SAM3, an 850-million-parameter prompt-based image segmentation model with image encoder, text encoder, and detector components.

The first compression pass uses the `presets.w4` configuration for 4-bit per-channel symmetric quantization. This reduces the converted SAM3 asset from over 3 GB to about 430 MB, but applying the same aggressive compression to every layer causes a missed detection in the output.

The key guidance is to compress selectively based on the model's structure and quality sensitivity. In the SAM3 example, the image and text encoders contain most parameters, while the detector is only about 4% of parameters and is more sensitive to compression.

- `coreai-opt` supports int4, int8, FP4, and FP8 weight compression with flexible granularity.
- Quantization APIs can use calibration data or quantization-aware training.
- `ExecutionMode.EAGER` is called out as a good fit for weight compression; activations use graph mode.
- Preserving high-level semantics such as attention during export may require Core AI's custom decomposition table.
- Casting exported programs to 16-bit floating point is supported through a `coreai-opt` helper when needed.

## Core AI Debugger for structure, runtime, and comparison analysis

Core AI Debugger is a standalone app for inspecting Core AI models on Apple platforms. It combines a PyTorch-module navigator, a structure graph, a source viewer tied to original Python code, and an inspector for operation inputs, outputs, and tensor details.

The debugger can run a model on a selected device, specialize it for that target, and expose intermediate output tensors without modifying the model. In the SAM3 example, this confirms that the quantized model's final mask is missing one flower.

For correctness analysis, the workflow saves PyTorch intermediate tensors using a new save-intermediates API, loads that file into Core AI Debugger, and compares specialized Core AI outputs against the PyTorch reference. The debugger creates sync points and reports similarity metrics such as PSNR, making it easier to find where results diverge.

- Navigator groups operations by PyTorch module hierarchy, which is useful for large models.
- Structure viewer shows operation connectivity, execution order, and data dependencies.
- Inspector can preview tensors after running on-device.
- Comparison mode pairs specialized-model operations with reference PyTorch operations as sync points.
- In the SAM3 case, low-PSNR sync points concentrated in the detector decoder, motivating a revised quantization scheme that skips the detector.

## Custom Metal kernels and fused operations

Advanced Core AI authoring can tune the computational graph rather than converting it end-to-end as-is. One approach is operation fusion: replacing several graph operations with one kernel dispatch.

Core AI includes prepackaged fast kernels and primitives for heavy operations such as Scaled Dot Product Attention. For lower-level customization, Core AI can embed custom Metal Shading Language source directly into an `.aimodel` asset. The PyTorch model calls a Python-side reference function for tracing, while the Core AI converter bundles the matching Metal implementation into the asset.

- Define a PyTorch reference implementation that `torch.export` can trace.
- Define the MSL kernel source and bind parameters such as `thread_position_in_grid`.
- Create a `TorchMetalKernel` with input names, result names, Metal source, reference function, and dtype templates.
- Call the custom kernel from PyTorch like a function, specifying grid sizes and result shapes.
- Register custom kernels with `TorchConverter` before adding the exported program.

### Define a SiLU custom Metal kernel with a PyTorch reference

The reference function is visible to PyTorch export; the MSL implementation is embedded in the Core AI asset.

```swift
import torch
from coreai_torch.dsl import TorchMetalKernel, MetalParameter

def silu_torch(x):
    return x * torch.sigmoid(x)

SILU_MSL = """
float val = float(x[gid]);
float sig = 1.0f / (1.0f + exp(-val));
y[gid] = TYPE(val * sig);
"""

silu_kernel = TorchMetalKernel(
    name="fused_silu",
    input_names=["x"],
    result_names=["y"],
    src=SILU_MSL,
    torch_defn=silu_torch,
    metal_params=[MetalParameter("gid", "uint", "thread_position_in_grid")],
    template_dtypes={"x": "TYPE"},
)
```

### Register and use a custom kernel during conversion

Registering the kernel lets the converter integrate the Metal implementation into the resulting `.aimodel`.

```swift
class MyModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(256, 256)

    def forward(self, x):
        h = self.linear(x)
        n = h.numel()
        return silu_kernel(
            h,
            threads_per_grid_size=(n, 1, 1),
            threads_per_thread_group=(min(n, 256), 1, 1),
            result_shapes=[h.shape],
        )

exported_program = torch.export.export(MyModel(), (torch.randn(1, 256),))
converter = coreai_torch.TorchConverter()
converter.register_custom_kernels([silu_kernel])
converter.add_exported_program(exported_program, input_names=["x"], output_names=["y"])
deployable = converter.to_coreai()
```

## Model re-authoring for target-specific performance

For more demanding constraints, especially on iOS, the session recommends model re-authoring: rewriting the PyTorch implementation with the target platform and usage pattern in mind. This can involve different operations, tensor layouts, static shapes, new interfaces, and reusable Core AI-friendly patterns.

The SAM3 re-authoring example splits a single end-to-end model into three Core AI functions: `image_encode`, `text_encode`, and `detect`. This lets an app run parts of the model at different cadences, cache intermediate results, and compress each function independently.

The re-authored SAM3 uses convolutional projections and channels-first/static-shape patterns for the encoders, applies 4-bit palettization with per-channel scales to the encoders, keeps the compression-sensitive detector uncompressed, casts exported programs to half precision, and packages all three functions into one asset.

- Use semantic PyTorch patterns that Core AI can map to optimized runtime implementations, such as in-place key-value cache updates for LLMs.
- Prefer static tensor shapes, channels-first layouts, and convolutional operation patterns when targeting iOS constraints.
- Test re-authored models at both module and model level with unit or integration tests.
- In the SAM3 example, changing the prompt from flowers to butterfly reuses cached image embeddings and only reruns the text encoder and detector, making the second inference 76% faster after warmup.

Resources:
- Core AI PyTorch Extensions: https://apple.github.io/coreai-torch
- Core AI Python: https://apple.github.io/coreai-torch/main/coreai-core
- Core AI Optimization: https://apple.github.io/coreai-optimization
- Inspecting, debugging, and profiling Core AI models: https://developer.apple.com/documentation/CoreAI/inspecting-debugging-and-profiling-core-ai-models
- Inspecting Core AI models with Core AI Debugger: https://developer.apple.com/documentation/CoreAI/inspecting-core-ai-models-with-core-ai-debugger
- Core AI: https://developer.apple.com/documentation/CoreAI

Chapters:
- 0:00 Introduction: Introduces Core AI as a deployment lifecycle for Apple silicon and frames the session around the Python ecosystem, conversion, optimization, debugging, and advanced authoring.
- 1:49 Models and skills: Covers the `coreai-models` repository, reusable model components, a Swift package for LLMs, and Core AI Skills for coding assistants that help plan deployment workflows.
- 3:27 Python workflow: Shows the basic PyTorch-to-Core AI pipeline: export with `torch.export`, convert with `TorchConverter`, optimize and save an `.aimodel`, then load and run inference from Python.
- 5:54 Model optimization: Demonstrates `coreai-opt` compression on SAM3, including 4-bit per-channel symmetric quantization, the resulting size reduction, and the quality issue caused by compressing all layers uniformly.
- 10:40 Core AI Debugger: Introduces Core AI Debugger's navigator, graph, source viewer, inspector, device execution, tensor previews, and comparison workflow using PyTorch intermediate outputs and PSNR sync points.
- 19:27 Advanced authoring: Explains why some models need graph-level tuning beyond end-to-end conversion, including fusing multiple operations and using Core AI's optimized primitives such as Scaled Dot Product Attention.
- 20:43 Custom Metal kernels: Shows how to define a PyTorch reference, write matching Metal Shading Language, wrap it in `TorchMetalKernel`, register it with `TorchConverter`, and embed the kernel in the model asset.
- 23:01 Model re-authoring: Describes re-authoring SAM3 for iOS-oriented efficiency by splitting it into image, text, and detector functions; using convolutional and layout-friendly patterns; selectively palettizing encoders; and caching work across prompts.
- 28:46 Next steps: Summarizes the recommended workflow: convert with Core AI Python libraries, optimize with `coreai-opt`, inspect with Core AI Debugger, build from `coreai-models`, and use Core AI Skills in coding agents.

### Integrate on-device AI models into your app using Core AI

- Session ID: wwdc2026-326
- Page: https://wwdc.ai/2026/326
- Markdown: https://wwdc.ai/2026/326.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/326/
- Category: AI & Machine Learning
- Description: Use Core AI to find, export, load, deploy, and optimize open-source on-device models such as SAM3 and Qwen in Swift apps.
- Duration: 23:44
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-326/eng_04f8d3020c02/wwdc2026-326-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/326/5/7ff038e2-12cb-4b92-9f49-1d051db7ce5d/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/326/5/7ff038e2-12cb-4b92-9f49-1d051db7ce5d/downloads/wwdc2026-326_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/326/5/7ff038e2-12cb-4b92-9f49-1d051db7ce5d/downloads/wwdc2026-326_sd.mp4?dl=1

Use Core AI to find, export, load, deploy, and optimize open-source on-device models such as SAM3 and Qwen in Swift apps.

TLDR:
- Core AI supports bringing optimized open-source models into Apple-platform apps, including task-specific vision models and language models running fully on device.
- The demo decomposes a camera-based vocabulary app into SAM3 for text-prompted image segmentation and Qwen for multilingual structured vocab-card generation.
- The Core AI Models repository provides export recipes, optimized .aimodel assets, and Swift runtime libraries that hide model-specific preprocessing and postprocessing.
- First-load specialization can be diagnosed with Core AI Instruments and reduced with Background Assets plus ahead-of-time compilation using coreai-build.

## Core AI app architecture

The session builds a camera-based vocabulary-learning feature that runs entirely on device. A student captures an object, prompts for what to learn, and the app creates a vocabulary card with a segmented image, translation, example sentence, and meaning.

The design starts by defining model requirements: real-world image content, multilingual output for Mandarin Chinese initially, and a small enough storage and memory footprint for iPhone. Rather than using one large general model, the app uses two smaller task-specific models that can be updated independently.

- Use SAM3, a promptable vision-transformer segmentation model, to isolate the requested object from the image.
- Use Qwen 0.6B as a compact multilingual reasoning model for vocabulary-card generation.
- Prefer variants under one billion parameters for the iPhone experience when combining multiple models.
- Use larger model variants on Mac when memory and processing headroom allow higher-quality output.

## Finding and exporting models

Core AI can work with models converted directly from PyTorch using Core AI PyTorch Extensions, and model compression can be incorporated with Core AI Optimization. For popular models, the Core AI Models repository provides a faster path with ready-made export recipes.

The repository includes a model catalog, Python export utilities, and Swift runtime libraries. The workflow shown is to browse the catalog, choose SAM3 and Qwen family models, run the provided export recipes, and produce optimized .aimodel files for use in Xcode.

- Inspect exported .aimodel files in Xcode for size, metadata, platform targets, function signatures, tensor shapes, and data types.
- The SAM3 model shown exposes multiple functions, including image encoding and detection, whose raw tensor interfaces would otherwise require custom preprocessing and postprocessing.
- Add the coreai-models Swift package to the app and select libraries such as CoreAILM and CoreAISegmentation for the target.

## Swift integration

The Core AI Models Swift libraries wrap model-specific details such as text encoding, mask extraction, labeling, tokenizer setup, and engine creation. This lets app code use concise Swift APIs instead of directly managing tensor shapes and raw outputs.

For language models, CoreAILanguageModel integrates with Foundation Models. You create a LanguageModelSession with your own model and use familiar APIs such as respond(to:), streaming, and structured output.

- Load SAM3 resources from disk and call text-prompted segmentation for an input image and label.
- Load Qwen with CoreAILanguageModel and pass it into LanguageModelSession.
- Use @Generable to guide structured generation into typed Swift fields instead of parsing free-form text.

### Load and run SAM3 image segmentation

Loads a SAM3 model bundle and asks it to segment the object matching a text prompt.

```swift
import CoreAIImageSegmenter

let segmenter = try await ImageSegmenter(resourcesAt: sam3ModelURL)
let response = try await segmenter.segment(image: inputImage, prompt: "flower")
let mask = response.segments.first?.mask
```

### Generate structured vocabulary-card output

Uses a custom Core AI language model through Foundation Models and requests typed structured output.

```swift
import FoundationModels
import CoreAILanguageModels

@Generable
struct VocabCard {
    let chineseWord: String
    let englishMeaning: String
    let exampleSentence: String
}

let model = try await CoreAILanguageModel(resourcesAt: modelURL)
let session = LanguageModelSession(model: model)
let response = try await session.respond(
    to: "Create a vocab card for flower",
    generating: VocabCard.self
)
let card: VocabCard = response.content
```

## First-run latency, specialization, and deployment

The first attempt to run segmentation exposed a delay. A trace with the Core AI Instruments template showed model loading dominated by specialization, the process that prepares a Core AI model for execution on the current device. Future loads can use the cache and are fast, but the first load needs to be designed into the product experience.

The recommended deployment strategy is deliberate: do not force large model downloads on every existing app user. Instead, introduce the feature, let interested users opt in, download model assets on demand, and perform preparation before the interactive workflow begins.

- Use Core AI Instruments to identify model load and specialization cost.
- Avoid doing first-time specialization in the middle of the primary user action.
- Use a first-run experience to explain the feature and prepare models.
- Keep large models out of the app bundle when the feature is optional; the demo models added over 1 GB when bundled.
- Use Background Assets to request model downloads only after the user opts in.

## Ahead-of-time compilation and multiplatform scaling

Core AI specialization includes expensive compilation work plus generation of executable artifacts tied to a specific device and OS version. The Core AI toolchain can perform part of that compilation ahead of time on a development machine, producing compiled model assets that still specialize on the user device but much faster.

The same Swift integration code can be reused on macOS. The Mac version adds batch processing for folders of photos, parallelizes segmentation work, and uses a larger Qwen3 8B model for richer reasoning, pinyin generation, multiple example sentences, and curriculum-style lesson plans.

- Use coreai-build to compile .aimodel assets ahead of time for target platforms or architectures.
- Create separate Background Assets for compiled model variants and choose the appropriate asset for the running device architecture.
- Use smaller models for interactive iPhone features and larger models on Mac when quality and longer context matter more than footprint.

### Compile a Core AI model ahead of time

Runs Core AI ahead-of-time compilation for an iOS-targeted model asset.

```text
xcrun coreai-build compile MyModel.aimodel --platform iOS
```

Resources:
- Core AI PyTorch Extensions: https://apple.github.io/coreai-torch
- Core AI Python: https://apple.github.io/coreai-torch/main/coreai-core
- Core AI Optimization: https://apple.github.io/coreai-optimization
- Core AI: https://developer.apple.com/documentation/CoreAI
- Compiling Core AI models ahead of time: https://developer.apple.com/documentation/CoreAI/compiling-core-ai-models-ahead-of-time

Chapters:
- 0:00 Introduction: Introduces Core AI as a way to bring advanced on-device AI into apps without server infrastructure, per-token cost, or cloud latency. The session frames a language-learning app using a vision model and language model together on device.
- 1:16 App concept: camera-based vocab learning: Presents an iOS vocabulary app where students point the camera at real-world objects and generate personalized vocab cards. The feature creates cards from the user's environment instead of relying only on static curated decks.
- 2:52 Model discovery: Defines the app requirements around real-world content, multilingual support, and iPhone storage and memory constraints. The solution uses SAM3 for promptable segmentation and Qwen 0.6B for multilingual reasoning and vocabulary generation.
- 7:40 Getting models with the Core AI models repository: Shows the Core AI Models repository as a source of popular models with export recipes, Python utilities, and optimized Core AI outputs. The workflow produces .aimodel files for SAM3 and Qwen.
- 8:37 Integration: Inspects exported .aimodel files in Xcode, including size, platform targets, functions, tensor shapes, and metadata. It then adds the coreai-models Swift package and selects runtime libraries for language models and segmentation.
- 10:55 Writing the Swift integration code: Demonstrates concise Swift integration for SAM3 segmentation and Qwen language generation. CoreAILanguageModel is used with Foundation Models' LanguageModelSession, including structured @Generable output for typed vocabulary cards.
- 13:05 Diagnosing model specialization latency: Uses the Core AI Instruments template to investigate a slow first segmentation request. The trace identifies first-load model specialization as the cause, while later cached loads are fast.
- 14:40 Deployment: Discusses how to deploy large optional model assets without bloating app updates for all users. The proposed flow uses a first-run feature introduction and Background Assets to download models only when the user opts in.
- 17:00 Ahead-of-time (AOT) compilation: Explains that Core AI specialization includes expensive compilation steps and device-specific executable artifact generation. The coreai-build command can precompile models on a development machine to reduce first-run preparation time on device.
- 18:03 iOS demo: Demonstrates the iOS app after adding AOT compilation, with faster preparation and on-device segmentation of real objects such as rocks, wood, and a sunflower. Subsequent inferences use cached model assets for a smoother experience.
- 19:57 Multiplatform: Extends the same Core AI code to macOS and adds batch processing for folders of photos. The Mac version uses a larger Qwen3 8B model for better reasoning, pinyin checking, multiple images per card, and curriculum generation.
- 23:06 Next steps: Wraps up by positioning Core AI as a toolkit for private, on-device, multiplatform AI experiences. Developers are encouraged to use the ready models and tools to build local intelligence into their apps.

### Explore numerical computing in Swift with MLX

- Session ID: wwdc2026-328
- Page: https://wwdc.ai/2026/328
- Markdown: https://wwdc.ai/2026/328.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/328/
- Category: Swift
- Description: Use MLX Swift for NumPy-style n-dimensional arrays, lazy GPU execution, convolutions, and automatic differentiation in Swift numerical code.
- Duration: 14:31
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-328/eng_f26799bf38b9/wwdc2026-328-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/328/5/51d0ab0a-f401-4514-9f04-6b211897d3e8/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/328/5/51d0ab0a-f401-4514-9f04-6b211897d3e8/downloads/wwdc2026-328_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/328/5/51d0ab0a-f401-4514-9f04-6b211897d3e8/downloads/wwdc2026-328_sd.mp4?dl=1

Use MLX Swift for NumPy-style n-dimensional arrays, lazy GPU execution, convolutions, and automatic differentiation in Swift numerical code.

TLDR:
- MLX Swift is positioned for mathematical Swift code that operates on n-dimensional arrays, with lazy evaluation enabling automatic GPU execution and function transformations such as `grad`.
- The session compares scalar Swift with MLX Swift for Mandelbrot computation, showing how array operations express the math directly and run across the grid on the GPU.
- Heat distribution is modeled with Jacobi iteration using `conv2d`, then accelerated with Successive Over-Relaxation using an omega parameter and red/black checkerboard masks.
- A curve-fitting example uses automatic differentiation to compute gradients for a mean-squared-error loss and run a gradient descent optimization loop without hand-written derivatives.

## Where MLX Swift fits

MLX Swift is for numerical computing code where the primary goal is expressing mathematical operations clearly while still getting performance. Apple's broader numerical ecosystem still matters: Accelerate provides tuned CPU vector primitives, BNNS provides neural-network building blocks, Metal Performance Shaders provides direct GPU kernels, and Swift Numerics provides types such as `Complex` and generic numeric protocols.

MLX Swift's central abstraction is the n-dimensional array, similar to NumPy. The framework is open source under the MIT license, and the same MLX concepts are available across Swift, Python, C++, and C front ends.

- Use MLX Swift when array-oriented mathematical code is more important than low-level control over kernels or memory bookkeeping.
- Most NumPy-style code patterns translate to MLX Swift with minimal conceptual changes.
- Lazy evaluation builds a compute graph and executes when a value is read or `eval` is called.
- The lazy model also supports automatic GPU execution and automatic differentiation.

## Core array model and lazy evaluation

The power-iteration example demonstrates how matrix-vector algorithms map directly into MLX Swift operations. `MLXArray` operations such as transpose, matrix multiplication, vector normalization, and addition look close to the mathematical notation.

Because operations are lazy, loops should call `eval` when needed to keep the graph from growing without bound. Reading a scalar result also forces computation.

- `.T` gives a transpose.
- `matmul` performs matrix-vector or matrix-matrix multiplication.
- `norm` computes the vector norm used for normalization.
- Use MLX's linear algebra package when full eigensystem routines are needed rather than implementing an iterative method manually.

### Power iteration with MLX Swift arrays

Shows array operations, transposition, matrix multiplication, normalization, and explicit evaluation inside an iterative loop.

```swift
import MLX

let n = 100
let steps = 10
let B = MLXRandom.normal([n, n])
var v = MLXRandom.normal([n])

// Symmetric matrix A = Bᵀ + B
let A = B.T + B

// v ← A v / ‖A v‖
for _ in 0 ..< steps {
    let Av = matmul(A, v)
    v = Av / norm(Av)
    eval(v)
}

// λ = vᵀ A v
let lambda = matmul(matmul(v.T, A), v)
print(lambda)
```

## Array computing with Mandelbrot

The Mandelbrot set is a useful example because the same recurrence, `z = z² + c`, is applied independently across a large grid of complex values. A scalar Swift implementation loops over pixels and iterations manually; the MLX Swift version constructs the whole grid and applies the recurrence to every point at once.

The MLX version removes per-pixel bookkeeping and uses the GPU by default. The exact speedup depends on the algorithm and workload, but the session notes that a 10x improvement is possible for this kind of array-parallel computation.

- Plain Swift is expressive but scalar-at-a-time for this implementation.
- MLX Swift expresses the grid as arrays and applies elementwise complex operations.
- The escape count is accumulated with array comparisons rather than nested pixel management.

### Mandelbrot set in MLX Swift

Builds a complex grid and applies the Mandelbrot recurrence across the full array instead of iterating scalar pixels manually.

```swift
import MLX

let x = linspace(-2.0, 0.5, count: w)
let y = linspace(-1.25, 1.25, count: h).reshaped(h, 1)
let c = x + y.asImaginary()

var z = MLXArray.zeros(like: c)
var counts = MLXArray.zeros(c.shape, dtype: .int16)

for _ in 0 ..< maxIterations {
    z = z * z + c
    counts = counts + (abs(z) .< 2)
}
```

## Stencil computation with convolutions

The heat-distribution example models a room as a 2D temperature grid. Jacobi iteration updates each cell by averaging its four neighbors, which is exactly a stencil operation and can be implemented as a convolution.

Boundary conditions are handled with `which`, an elementwise ternary operation. Heat sources and walls keep fixed values; other cells use the next value computed by `conv2d`.

- Represent the four-neighbor averaging stencil as a 3×3 convolution kernel.
- Use `conv2d(temperature, kernel, padding: 1)` to apply the update across the grid.
- Use a mask with `which` to preserve fixed heat sources or walls.

### Jacobi iteration with conv2d

Implements the four-neighbor heat-update rule as a convolution and applies fixed boundary values with an elementwise conditional.

```swift
let kernel = MLXArray(converting: [
    0, 0.25, 0,
    0.25, 0, 0.25,
    0, 0.25, 0,
]).reshaped(1, 3, 3, 1)

var temperature = heatSources

let next = conv2d(temperature, kernel, padding: 1)
temperature = which(heatMask, heatSources, next)
```

## Faster convergence and automatic differentiation

Jacobi iteration is easy to compute but can require about N² iterations for a grid with side length N. Successive Over-Relaxation uses the same convolution kernel, an omega parameter, and in-place-style updates to converge faster; MLX Swift simulates the in-place effect by updating red and black checkerboard cells in alternating phases.

The curve-fitting example demonstrates MLX Swift's `grad` transformation. A quadratic function and mean-squared-error loss are written normally, then `grad(loss)` produces a function that computes the gradient with respect to the parameter vector. The optimization loop updates parameters with gradient descent and calls `eval` each step.

- SOR uses `ω` to overshoot updates and correct over iterations.
- Red/black checkerboard masks let newly updated neighbors participate in the next half-step.
- `grad` derives gradients automatically, avoiding hand-written derivative code.
- MLX also includes optimizers such as SGD, Adam, and RMSprop for more general optimization workflows.

### Successive Over-Relaxation update

Uses omega and checkerboard masks to approximate in-place SOR updates in an array-oriented MLX computation.

```swift
let ω: Float = 2.0 / (1.0 + sin(Float.pi / Float(max(M, N))))
let redMask = checkerboard(rows: M, cols: N, phase: 0)
let blackMask = checkerboard(rows: M, cols: N, phase: 1)

let sorRed = ω * conv2d(temperature, kernel, padding: 1) + (1 - ω) * temperature
temperature = which(redMask, sorRed, temperature)
temperature = which(heatMask, heatSources, temperature)

let sorBlack = ω * conv2d(temperature, kernel, padding: 1) + (1 - ω) * temperature
temperature = which(blackMask, sorBlack, temperature)
temperature = which(heatMask, heatSources, temperature)
```

### Curve fitting with automatic differentiation

Defines a model and loss, transforms the loss into a gradient function, and runs gradient descent over the parameter array.

```swift
func f(_ θ: MLXArray) -> MLXArray {
    θ[0] + θ[1] * x + θ[2] * x ** 2
}

func loss(_ θ: MLXArray) -> MLXArray {
    mean((f(θ) - y) ** 2)
}

var θ = zeros([numParams])
let gradLoss = grad(loss)

for _ in 0 ..< steps {
    let g = gradLoss(θ)
    θ = θ - learningRate * g
    eval(θ)
}
```

## Toolkit and ecosystem

Beyond the examples, MLX includes linear algebra, FFTs, n-dimensional convolutions, reductions, scans, indexing, random number generation, and more. The session also points to packages built on MLX Swift, including the core `mlx-swift` framework, `mlx-swift-lm` for Swift language-model implementations, and `mlx-swift-examples` for example applications.

Example areas mentioned include LLM integration, stable diffusion, model training and fine-tuning, and the session's numerical examples. MLX's shared concepts across Swift, Python, C++, and C make it practical to prototype in one front end and transfer patterns to another.

- Start with the `mlx-swift` repository for framework documentation and tests.
- Use `mlx-swift-examples` for complete example applications.
- Look at `mlx-swift-lm` for Swift language-model implementations.
- Python-side projects such as `mlx-lm` and `mlx-vlm` show additional ecosystem work.

Resources:
- MLX Swift LM on GitHub: https://github.com/ml-explore/mlx-swift-lm
- MLX Swift Examples: https://github.com/ml-explore/mlx-swift-examples
- MLX Examples: https://github.com/ml-explore/mlx-examples
- MLX Swift: https://github.com/ml-explore/mlx-swift
- MLX LM - Python API: https://github.com/ml-explore/mlx-lm
- MLX Explore - Python API: https://github.com/ml-explore/mlx
- MLX Framework: https://mlx-framework.org/
- MLX: https://ml-explore.github.io/mlx/

Chapters:
- 0:00 Introduction: Defines numerical computing as computational techniques for solving mathematical problems that are impractical to solve by hand. Examples include simulations, signal processing, rendering, fractals, and gradient-descent-based machine learning training.
- 0:57 MLX Swift and the Apple ecosystem: Positions MLX Swift among Accelerate, BNNS, Metal Performance Shaders, and Swift Numerics. MLX Swift is recommended when developers want mathematical array code with performance, lazy evaluation, GPU execution, and automatic differentiation.
- 3:04 MLX Swift: Introduces n-dimensional arrays, NumPy-like APIs, and lazy evaluation through a power-iteration example. The chapter shows operations such as transpose, matrix multiplication, normalization, and `eval` in an iterative algorithm.
- 4:28 Mandelbrot: Compares scalar Swift and MLX Swift implementations of the Mandelbrot set. The MLX version applies the recurrence across an entire complex grid, reducing bookkeeping and using GPU execution by default.
- 6:34 Heat distribution: Models steady-state room temperature as a 2D grid solved with Jacobi iteration. The four-neighbor stencil is implemented as `conv2d`, and boundary conditions are applied with `which`.
- 8:12 Faster convergence with SOR: Explains Successive Over-Relaxation as a faster alternative to Jacobi iteration. The implementation uses an omega parameter and red/black checkerboard masks to simulate in-place updates while retaining MLX's array style.
- 10:17 Curve fitting: Uses automatic differentiation for fitting a quadratic curve to data. The loss is mean squared error, `grad(loss)` computes gradients with respect to parameters, and a gradient descent loop updates the coefficients.
- 12:17 The full MLX toolkit and ecosystem: Surveys MLX capabilities including linear algebra, FFTs, n-dimensional convolutions, reductions, scans, indexing, random generation, and optimizers. It also identifies the core Swift repositories and language-model/example packages.
- 13:47 Next steps: Encourages developers to explore `mlx-swift` documentation, tests, and examples, including LLM integration, stable diffusion, model training, fine-tuning, and the session examples. Notes that MLX concepts carry across Swift, Python, C++, and C front ends.

### Optimize custom machine learning operations with Metal tensors

- Session ID: wwdc2026-330
- Page: https://wwdc.ai/2026/330
- Markdown: https://wwdc.ai/2026/330.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/330/
- Category: AI & Machine Learning
- Description: Use Metal TensorOps to build optimized custom ML kernels with quantized tensors, cooperative tensors, FlashAttention, and Core AI integration.
- Duration: 16:13
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-330/eng_284acfdef789/wwdc2026-330-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/330/4/0ff2c290-e47b-4d88-8a8f-0634e11506a4/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/330/4/0ff2c290-e47b-4d88-8a8f-0634e11506a4/downloads/wwdc2026-330_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/330/4/0ff2c290-e47b-4d88-8a8f-0634e11506a4/downloads/wwdc2026-330_sd.mp4?dl=1

Use Metal TensorOps to build optimized custom ML kernels with quantized tensors, cooperative tensors, FlashAttention, and Core AI integration.

TLDR:
- TensorOps is a Metal Shading Language API for tensor operations such as matrix multiplication and convolution, automatically using available Apple Silicon GPU acceleration including the M5 neural accelerator.
- Metal tensors support quantized data types, with newer OS support for 4- and 8-bit floating point types, 2-bit integer types, and MX-style scale factors using auxiliary tensor planes.
- Quantized matmul can bind multi-plane tensors directly, slice data and scale planes together, and let TensorOps handle dequantization; custom formats can use cooperative tensors to avoid threadgroup-memory round trips.
- Advanced fused ops such as FlashAttention can use SIMD-group-scoped TensorOps, cooperative tensors, row reductions, iterator mapping, and direct cooperative-tensor matmul inputs, then integrate as custom Metal kernels in Core AI workflows.

## Where Metal TensorOps fits

Apple's ML stack ranges from high-level frameworks such as Core AI and MLX to lower-level Metal Performance Shaders, Metal Performance Primitives, and the TensorOps library. Working at the Metal level is useful when implementing fast custom operations for higher-level frameworks, contributing to ML runtimes such as MLX or llama.cpp, or building Metal-based applications.

TensorOps is a Metal Shading Language API for accelerated tensor operations, including matrix multiplication and convolution. It is designed to use available hardware acceleration across Apple Silicon GPU generations and specifically takes advantage of the neural accelerator in the M5 chip family.

- Use Core AI or MLX when high-level deployment is enough.
- Use Metal TensorOps when a model needs custom kernels, lower-level control, or framework/runtime integration.
- The M5 neural accelerator is located in each shader core and targets dense compute-bound work such as LLM prefill.

## Quantized tensors and scale planes

Quantization reduces model memory footprint and bandwidth by storing weights in lower-precision formats paired with scale factors. TensorOps can consume quantized tensors directly and use available hardware acceleration instead of requiring manual dequantization for standard formats.

Metal tensor support includes 4- and 8-bit integer types in macOS and iOS 26, with additional support in macOS and iOS 27 for 4- and 8-bit floating point types and 2-bit integer types. macOS and iOS 27 also allow one MTLTensor to represent quantized element data plus an auxiliary scale plane using the FP8 E8M0 block-wise scale factor format.

- Create a quantized tensor by setting `MTLTensorDescriptor.dataType` to a quantized `MTLTensorDataType`.
- Attach scale factors with `MTLTensorAuxiliaryPlaneDescriptorMap` and `MTLTensorPlaneTypeScales`.
- Each scale-plane element applies to a block of data-plane elements according to `blockFactors`.
- New smaller data types have additional alignment requirements; consult the Metal documentation for exact constraints.

### Create a quantized MTLTensor

A quantized tensor is allocated like a regular MTLTensor, but with a quantized Metal tensor data type.

```text
#define RANK 2

MTLTensorDescriptor *tensorDesc = [MTLTensorDescriptor new];
tensorDesc.dataType = MTLTensorDataTypeMetalFloat8E4M3;
tensorDesc.usage = MTLTensorUsageCompute;

NSInteger dimensions[RANK] = { NumCols, NumRows };
tensorDesc.dimensions = [[MTLTensorExtents alloc] initWithRank:RANK values:dimensions];

NSError *err = nil;
id<MTLTensor> tensor = [device newTensorWithDescriptor:tensorDesc error:&err];
```

### Declare an auxiliary scales plane

A multi-plane tensor can pack quantized element data, block-wise scales, and metadata into one tensor object.

```text
#define RANK 2

MTLTensorAuxiliaryPlaneDescriptor *planeDesc = [MTLTensorAuxiliaryPlaneDescriptor new];
planeDesc.dataType = MTLTensorDataTypeMetalFloat8UE8M0;

NSInteger blockFactors[RANK] = { 32, 1 };
planeDesc.blockFactors = [[MTLTensorExtents alloc] initWithRank:RANK values:blockFactors];

MTLTensorAuxiliaryPlaneDescriptorMap *auxiliaryPlanes = [MTLTensorAuxiliaryPlaneDescriptorMap new];
[auxiliaryPlanes setDescriptor:planeDesc forPlane:MTLTensorPlaneTypeScales];

tensorDesc.auxiliaryPlanes = auxiliaryPlanes;
```

## Quantized matrix multiplication

The session extends a tiled TensorOps matrix multiplication kernel to quantized inputs. The host can allocate full `MTLTensor` objects and bind them as tensor handles, or a shader can construct inline tensor views from raw buffers when a full host-side tensor object is not needed.

When slicing multi-plane tensors, the data plane and scale plane are sliced together according to the declared block size. Once the tiles are prepared, the `matmul2d` setup is the same as for non-quantized tensors, and TensorOps handles dequantization for supported formats.

- Define MSL tensor aliases for the quantized element format and scale plane.
- Use `tensor_handle` when binding host-created tensors to buffer slots.
- Use `tensor_inline` to construct temporary tensor views on the shader stack from buffers and metadata.
- Prefer feeding quantized tensors directly into TensorOps; use custom dequantization only when the format is not supported directly.

### MSL aliases for an MXFP8 tensor handle

The quantized element type and auxiliary scales plane are encoded in the MSL tensor type.

```text
#include <metal_tensor>
using namespace metal;

using scales_plane = tensor_blockwise<tensor_plane_scales,
                                      device metal_fp8_ue8m0_format,
                                      32, 1>;

using mxfp8_tensor = tensor<device metal_fp8_e4m3_format,
                            dextents<int, 2>,
                            tensor_handle,
                            scales_plane>;

kernel void matmul(mxfp8_tensor matrixA [[buffer(0)]],
                   mxfp8_tensor matrixB [[buffer(1)]],
                   tensor<device half, dextents<int, 2>> matrixC [[buffer(2)]]) {
    // ...
}
```

### Slice tiles and run quantized matmul

TensorOps accepts quantized tensor tiles directly and performs the needed dequantization for supported formats.

```text
auto tA = matrixA.slice(0, tgid.y * TILEM);
auto tB = matrixB.slice(tgid.x * TILEN, 0);
auto tC = matrixC.slice(tgid.x * TILEN, tgid.y * TILEM);

constexpr auto descriptor = matmul2d_descriptor(TILEM,
                                                TILEN,
                                                dynamic_length_v<int>,
                                                false,
                                                false);

matmul2d<descriptor, execution_simdgroups<4>> op;
op.run(tA, tB, tC);
```

## Custom dequantization and cooperative tensors

For unsupported or custom quantization formats, one option is to have each thread load quantized data, dequantize to f16, store to threadgroup memory, and pass an inline threadgroup tensor to TensorOps. That is simple but adds extra loads and stores.

A faster approach is to dequantize into a cooperative tensor. Cooperative tensors distribute storage across the private memory of participating threads and can now be passed as inputs to `matmul2d`, avoiding a round trip through threadgroup memory when layouts are compatible.

- Use cooperative tensors when intermediate data should remain in registers/private storage.
- Check compatibility before reusing a cooperative tensor as a matmul input.
- Fallback to store/reload through threadgroup memory if the cooperative tensor layout is not compatible.

### Reuse a cooperative tensor as matmul input

The new direct cooperative-tensor input path avoids threadgroup memory when the layout can be reused safely.

```text
constexpr auto mul_sv_op_desc = matmul2d_descriptor(/* ... */);
matmul2d<mul_sv_op_desc, metal::execution_simdgroup> mul_sv_op;

if (mul_sv_op.is_compatible_as_left_input<float, half, float>(ctQK)) {
    auto ctQKIn = mul_sv_op.get_left_input_cooperative_tensor<float, half, float>(ctQK);
    mul_sv_op.run(ctQKIn, tVSlice, ctO);
} else {
    ctQK.store(tgTensor);
    simdgroup_barrier(mem_flags::mem_threadgroup);

    auto ctQKIn = mul_sv_op.get_left_input_cooperative_tensor<float, half, float>();
    ctQKIn.load(tgTensor);
    mul_sv_op.run(ctQKIn, tVSlice, ctO);
}
```

## Building FlashAttention with TensorOps

FlashAttention fuses the Q×K multiplication, row-wise SoftMax, and multiplication by V into one kernel. The TensorOps implementation uses SIMD-group-scoped execution so each SIMD group owns complete rows of the intermediate matrix, allowing SoftMax to be computed without exchanging data between SIMD groups.

The intermediate Q×K tile is held in a cooperative tensor. TensorOps row reductions compute per-row values such as the maximum, and `map_iterator` maps each element of the 2D cooperative tensor to its corresponding row-reduction element for element-wise SoftMax work.

- Use `execution_simdgroup` for independent SIMD-group matrix multiplications.
- Use `get_destination_cooperative_tensor` for the Q×K intermediate.
- Use `reduce_rows` for row-wise reductions needed by SoftMax.
- Use `map_iterator` to relate 2D elements to row-reduction results.

### Compute Q×K into a cooperative tensor

The Q×K tile is produced into cooperative storage rather than written to memory.

```text
constexpr auto mul_qk_op_desc = matmul2d_descriptor(/* ... */);
matmul2d<mul_qk_op_desc, execution_simdgroups> mul_qk_op;

auto tQSlice = tQ.slice<D, ROWS_PER_SIMD>(0, sgid * ROWS_PER_SIMD);
auto tKSlice = tK.slice<D, BK>(0, k);
auto tVSlice = tV.slice<D, BK>(0, k);

auto ctQK = mul_qk_op.get_destination_cooperative_tensor<decltype(tQSlice),
                                                          decltype(tKSlice),
                                                          float>();

mul_qk_op.run(tQSlice, tKSlice, ctQK);
```

### Row reduction and element-wise SoftMax step

`reduce_rows` and `map_iterator` support the row-wise SoftMax portion of the fused attention kernel.

```text
auto ctTileRowMax = mul_qk_op.get_row_reduction_destination_cooperative_tensor<
    decltype(tQSlice), decltype(tKSlice), float>();

reduce_rows(ctQK, ctTileRowMax, reduction_operation::max, -INFINITY);

#pragma clang loop unroll(full)
for (auto it = ctQK.begin(); it != ctQK.end(); it++) {
    auto row_it = ctRowMax.map_iterator(it);
    *it = exp(*it - *row_it);
}
```

## Core AI integration path

Custom Metal TensorOps kernels can be integrated into Core AI applications. The session demonstrates the workflow conceptually with a custom FlashAttention kernel registered from Python, replacement of a default Hugging Face attention implementation, and export from PyTorch to an optimized Core AI asset.

The example uses a SAM3 image segmentation model and validates that the custom attention kernel is part of the Core AI inference path. For details of the conversion and authoring workflow, the session points to the Core AI model authoring and optimization material.

- Core AI Python tools can convert PyTorch models to Core AI models.
- Custom Metal kernels can be registered and called from model code before export.
- Use this path when a high-level model needs a custom fused Metal operation rather than a default framework implementation.

Resources:
- Running inline ML operations in a shader with Metal 4: https://developer.apple.com/documentation/Metal/running-inline-ml-operations-in-a-shader-with-metal-4
- Machine learning passes: https://developer.apple.com/documentation/Metal/machine-learning-passes
- Download the Metal Performance Primitives (MPP) Programming Guide: https://developer.apple.com/download/files/Metal-Performance-Primitives-Programming-Guide.pdf
- Metal Performance Shaders: https://developer.apple.com/documentation/MetalPerformanceShaders

Chapters:
- 0:00 Introduction: Introduces Metal tensors and TensorOps as tools for writing optimized custom ML kernels on Apple Silicon.
- 0:21 Apple's ML software stack: Places TensorOps within Apple's ML stack, from Core AI and MLX down to Metal Performance Shaders and Metal Performance Primitives. Explains why developers may work at the Metal level and notes hardware acceleration including the M5 neural accelerator.
- 2:25 Managing quantized data: Explains how quantization reduces model memory and bandwidth needs by pairing lower-precision weights with scale factors. Covers TensorOps support for quantized integer and floating-point formats across recent macOS and iOS releases.
- 4:23 Multi-plane tensors: Shows how one MTLTensor can represent quantized data and scale factors using an auxiliary scale plane. Describes configuring the scale plane data type, block factors, and auxiliary plane map.
- 5:17 Quantized matrix multiplication: Extends a tiled TensorOps matmul kernel to quantized tensors using MSL tensor aliases, tensor handles or inline tensors, tile slicing, and `matmul2d`. Also discusses custom dequantization and cooperative tensors as a way to avoid unnecessary threadgroup-memory traffic.
- 9:31 Building advanced ops: Builds the pieces of a FlashAttention-style fused kernel with SIMD-group-scoped matmuls, cooperative tensor intermediates, row reductions, SoftMax mapping, and direct cooperative tensor reuse in a second matmul when compatible.
- 13:35 Integrating custom ops into Core AI: Describes integrating a custom TensorOps FlashAttention kernel into a Core AI workflow using Python tools, PyTorch model conversion, and a SAM3 segmentation example.
- 15:25 Next steps: Recaps quantized data types, multi-plane tensors, cooperative tensors, reductions, FlashAttention, and Core AI integration. Points developers to Metal Performance Primitives documentation, the programming guide, sample code, and related Core AI and Metal sessions.

### Build AI-powered scripts with the fm CLI and Python SDK

- Session ID: wwdc2026-334
- Page: https://wwdc.ai/2026/334
- Markdown: https://wwdc.ai/2026/334.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/334/
- Category: AI & Machine Learning
- Description: Use macOS 27's fm command and the Foundation Models SDK for Python to prototype prompts, automate model workflows, and evaluate outputs.
- Duration: 16:36
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-334/eng_3a972e9b018e/wwdc2026-334-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/334/4/65b71eea-f323-4f86-9096-889b6da91bdd/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/334/4/65b71eea-f323-4f86-9096-889b6da91bdd/downloads/wwdc2026-334_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/334/4/65b71eea-f323-4f86-9096-889b6da91bdd/downloads/wwdc2026-334_sd.mp4?dl=1

Use macOS 27's fm command and the Foundation Models SDK for Python to prototype prompts, automate model workflows, and evaluate outputs.

TLDR:
- macOS 27 adds the preinstalled `fm` command line tool for prompting Apple Foundation Models from Terminal, including `fm chat`, `fm respond`, `fm schema`, image inputs, and model selection.
- `fm respond` is designed for scripting: it can return inline text or schema-constrained JSON that shell scripts can pipe into tools like `jq`.
- The Foundation Models SDK for Python exposes familiar Foundation Models concepts such as `LanguageModelSession`, tool calling, guided generation, text/image inputs, and streaming.
- Python integration is positioned for rapid prototyping and evaluation pipelines with tools such as Jupyter, Pandas, and matplotlib before implementing app features in Swift.

## New macOS access paths for Apple Foundation Models

The session introduces two macOS 27 ways to use Apple Foundation Models outside Swift apps: the preinstalled `fm` command line tool and the Foundation Models SDK for Python. Both are intended to make prompt development, automation, and evaluation faster without rebuilding an Xcode project for every iteration.

The tools build on Foundation Models framework concepts from Swift, including guided generation for structured output and tool calling for letting the model interact with app or script context. macOS 27 and iOS 27 also add framework support for image inputs and access to server models through the same Swift API.

- `fm` is available from Terminal on macOS 27.
- By default, `fm` uses the on-device model available with macOS.
- `fm` can also target the Apple Foundation Model on Private Cloud Compute, which is larger and intended for more complex problems but has usage limits.
- The Python SDK requires Python 3.10+, Xcode, and an Apple Silicon Mac.

## Using the fm command line tool

Running `fm` shows available commands. `fm chat` starts an interactive terminal conversation with the on-device model, supports follow-up turns, and includes slash commands such as `/model` to switch to Private Cloud Compute and `/save` to save a conversation for later.

For inline terminal use and automation, `fm respond` takes a prompt and writes the model response to stdout. It supports options such as `--model`, `--image`, `--schema`, and `--help`.

- Use `fm chat` for exploratory prompt testing.
- Use `fm respond` when a script needs a single response as command output.
- Use `fm schema object` to define structured JSON output for downstream shell processing.
- Use `--model pcc` when the task needs the Private Cloud Compute model.

### Prompt the model, use images, and request structured output

`fm respond` can target different models, include an image, and constrain output with a schema generated by `fm schema object`.

```text
$ fm respond "Provide a basic regex in Swift to parse an email address"

$ fm respond "Provide a comprehensive regex in Swift to parse an email address" --model pcc

$ fm respond "What app is the user using in this screenshot?" --model pcc --image Screenshot.png

$ fm schema object --name AppsIdentified --string app_names --array > schema.json
$ fm respond "What apps are the user actively using in this screenshot?" \
  --image Screenshot.png --model pcc --schema schema.json
# {"app_names": ["Messages", "Mail", "Calendar"]}

$ fm respond --help
```

## Automating file management with structured fm output

The automation demo uses `fm` inside a shell script to classify files in a messy project folder as final files or drafts based on their names. The model returns schema-constrained JSON, and the script uses that JSON to copy final files to backup storage and move drafts to an archive.

The important pattern is to keep the model responsible for fuzzy classification while the shell script remains responsible for deterministic file operations. Structured output makes the model result machine-readable enough to drive the rest of the script.

- Create a schema with two array fields: `final_files` and `draft_files`.
- Prompt the model with clear instructions and the full file list.
- Pipe JSON output through `jq` to iterate over each returned filename.
- Use standard shell commands such as `cp` and `mv` for the actual file changes.

### Classify files with fm and route them with jq

The model produces structured classification data; the script consumes that data to perform safe, explicit file operations.

```text
fm schema object --name "TriagedFileList" \
  --string 'final_files' --array \
  --string 'draft_files' --array > /tmp/schema.json

output=$(fm respond \
  --instructions "I just completed a project, and I need help triaging the latest version of the files from the previous versions. I will give you a list of files. Return a list of the latest files (i.e., all files that, you can infer from their name in the list, are the latest versions), and then return separately a list of all draft files (i.e., all files that weren't considered final)." \
  "This is the list of all files:\n\n${files_list}" \
  --schema /tmp/schema.json)

echo "${output}" | jq -r '.final_files[]' | while read -r file; do
  cp "${DIRECTORY_TO_TRIAGE}/${file}" "${FINAL_FILES_STORAGE_DIRECTORY}"
done

echo "${output}" | jq -r '.draft_files[]' | while read -r file; do
  mv "${DIRECTORY_TO_TRIAGE}/${file}" "${DRAFT_FILES_STORAGE_DIRECTORY}"
done
```

## Foundation Models SDK for Python basics

The Python SDK gives Python code access to Apple Foundation Models on macOS. It mirrors core Foundation Models framework features: text and image prompts, streaming responses, guided generation, and tool calling.

The session uses a grocery-ordering app prototype to show how a developer can iterate on prompts in Python before moving an implementation to Swift. A `LanguageModelSession` is created with instructions, and `session.respond` returns the model output.

- Install with `pip install apple_fm_sdk` or another Python package manager.
- Import the SDK as `apple_fm_sdk`.
- Use `LanguageModelSession` to hold instructions and tools.
- Call `session.respond(...)` asynchronously to prompt the model.

### Install the SDK

Installs the Foundation Models SDK for Python in a local Python environment.

```text
pip install apple_fm_sdk
```

### Create a session and respond to a prompt

Creates a language model session with app-specific instructions and returns a response for a user prompt.

```swift
import apple_fm_sdk as fm

INSTRUCTIONS = "You're an AI assistant for Cupertino Mart, a grocery store with in-app ordering."

async def answer_question(prompt: str) -> str:
    session = fm.LanguageModelSession(instructions=INSTRUCTIONS)
    return await session.respond(prompt)
```

## Tool calling and guided generation in Python

The SDK lets Python developers expose tools that the model can call. In the grocery example, a tool fetches recent orders so the model can provide personalized suggestions based on user context.

Guided generation uses the `fm.generable` decorator to define structured output types. The model can then generate directly into a typed object, such as an item suggestion object containing a list of item names.

- Subclass `fm.Tool` to define a callable tool.
- Expose argument structure with `@fm.generable` and `generation_schema()`.
- Use `fm.guide(...)` to describe fields for generation.
- Pass a structured type as the `generating` argument to `session.respond`.

### Define a model-callable tool

A Python tool exposes past-order lookup to the model with a generated argument schema.

```swift
class GetPastOrdersTool(fm.Tool):
    name = "get_past_orders"
    description = "Retrieves information about this user's past orders."

    @fm.generable("Past orders query parameter")
    class Arguments:
        number_orders: str = fm.guide("How many of the last orders to retrieve")

    @property
    def arguments_schema(self) -> fm.GenerationSchema:
        return self.Arguments.generation_schema()

    async def call(self, args: fm.GeneratedContent) -> str:
        number_orders = args.value(int, for_property="number_orders")
        return await Orders.load_last_orders(user_id=user_id, amount=number_orders)
```

### Generate a structured item suggestion object

`@fm.generable` constrains the response to an `ItemsSuggestion` structure instead of free-form text.

```swift
@fm.generable("Suggested items")
class ItemsSuggestion:
    item_names: list[str] = fm.guide("Names of the suggested items")

INSTRUCTIONS = "You're an AI assistant tasked with returning potential grocery items that the user might be interested in."

async def generate_suggested_cart_items(user_input: Optional[str]) -> ItemsSuggestion:
    session = fm.LanguageModelSession(instructions=INSTRUCTIONS, tools=load_tools())
    prompt = """Using the tools to load the user's previous orders, \
return a list of items the user has already ordered \
and that they might be interested in again \
as they're getting ready to place a new grocery order."""
    if user_input is not None:
        prompt += f"\nAccount for the following request from the user: {user_input}"
    return await session.respond(prompt, generating=ItemsSuggestion)
```

## Evaluation pipelines with Python tooling

A major motivation for the Python SDK is integration with the Python data science ecosystem. The session demonstrates an evaluation workflow in a Jupyter Notebook for a cart-completion feature: generate evaluation inputs, run several prompt implementations, store results in a Pandas DataFrame, grade outputs with a server judge model, and visualize metrics with matplotlib.

The example compares three prompt styles: minimal, more descriptive, and comprehensive with rules. The resulting charts show tradeoffs such as generation errors, excess items, missing expected items, and hallucinated items. Those measurements guide prompt iteration without rebuilding the app.

- Use the on-device model to generate outputs for each prompt implementation.
- Use a server model as a judge to score outputs against criteria selected for the feature.
- Track inputs, outputs, and grades in a Pandas DataFrame.
- Use matplotlib charts to compare implementations and identify prompt regressions.
- Swift developers can also use the Evaluations framework in Xcode 27 for similar measurement workflows.

Resources:
- Foundation Models SDK for Python on GitHub: https://github.com/apple/python-apple-fm-sdk
- Foundation Models SDK for Python Documentation on GitHub: https://apple.github.io/python-apple-fm-sdk/

Chapters:
- 0:00 Introduction: Introduces new macOS ways to access Apple Foundation Models beyond Swift, building on guided generation and tool calling. Also notes macOS 27 and iOS 27 framework additions such as image inputs and server model access.
- 1:22 Introducing the fm CLI and Python SDK: Presents the two new access paths: the preinstalled macOS 27 `fm` command line tool and the Foundation Models SDK for Python. The CLI targets terminal prompting and automation, while the Python SDK supports Python-based prototyping and evaluation.
- 3:23 Command line tool: Shows how to discover `fm` commands and use `fm chat` for an interactive terminal conversation. The chapter covers switching to the Private Cloud Compute model with `/model` and saving conversations with `/save`.
- 5:02 fm respond and structured output: Explains `fm respond` for inline prompts and script-friendly output. It covers options for model selection, image input, schema-constrained structured JSON, and help discovery.
- 6:11 Automating file management with fm: Demonstrates a shell automation that classifies project files into final and draft groups using `fm respond` with a generated schema. The JSON result is consumed by the script to copy final files to backup and move drafts to an archive.
- 8:52 Python SDK: Introduces installation and platform requirements for the Python SDK and lists supported Foundation Models features. The SDK supports text and image inputs, streaming, guided generation, and tool calling.
- 9:42 Prompting, tool calling and guided generation: Shows Python code for creating a `LanguageModelSession`, responding to prompts, exposing a past-orders tool, and generating typed structured output. The example is a grocery app prototype that suggests items from order history.
- 10:44 Building an evaluation pipeline in Python: Walks through a Jupyter-based evaluation pipeline using the Python SDK, Pandas, a server judge model, and matplotlib. Three prompt implementations are compared on metrics such as generation errors, excess items, missing items, and hallucinations.
- 15:20 Next steps: Recommends exploring `fm` in Terminal, using the Python SDK GitHub repository and documentation, and building evaluation datasets to quantify prompt quality. The tools can be used alongside Xcode projects or independently for automation and prototyping.

### Improve your prompts by hill-climbing with Evaluations

- Session ID: wwdc2026-335
- Page: https://wwdc.ai/2026/335
- Markdown: https://wwdc.ai/2026/335.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/335/
- Category: AI & Machine Learning
- Description: Use Apple's Evaluations framework to iteratively improve AI prompts, align model judges with expert ratings, and compare feature changes in Xcode.
- Duration: 26:41
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-335/eng_bd56c26e46d1/wwdc2026-335-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/335/4/a464d330-6aa2-456d-9a07-eae997aef08c/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/335/4/a464d330-6aa2-456d-9a07-eae997aef08c/downloads/wwdc2026-335_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/335/4/a464d330-6aa2-456d-9a07-eae997aef08c/downloads/wwdc2026-335_sd.mp4?dl=1

Use Apple's Evaluations framework to iteratively improve AI prompts, align model judges with expert ratings, and compare feature changes in Xcode.

TLDR:
- Hill-climbing is presented as a disciplined develop-run-analyze loop for improving intelligence-powered features using evaluation scores as feedback.
- Model-judge drift is measured by comparing judge ratings with expert human ratings; Cohen's kappa is recommended over simple accuracy when score distributions are uneven.
- Xcode 27 evaluation reports support side-by-side comparative evaluation, helping isolate one prompt, score-dimension, tool, model, dataset, or evaluator change at a time.
- The Book Tracker example improves a tag generator by first aligning the judge prompt and scoring dimensions, then comparing the feature with and without a book lookup tool.

## Hill-climbing with Evaluations

The session frames prompt engineering as hill-climbing: make a targeted change, run an evaluation, analyze the results, then repeat. The approach assumes an existing evaluation pipeline with datasets, subjects, evaluators, metrics, and expectations already in place.

The running example is Book Tracker, a book cataloging app whose tagging service generates tags from reader reviews. The initial output is plausible but misses important themes and sometimes includes reader reactions or overly specific phrases that are not useful search tags.

- Use evaluation scores as a guide, not as a substitute for inspection.
- Define pass/fail expectations with Swift Testing so evaluation runs can fail when aggregate metrics fall below target.
- Use Xcode's evaluation report and assistant editor to inspect aggregate charts and per-sample model outputs against expected data.

### Evaluation expectations with Swift Testing

Evaluation tests can encode acceptance criteria so hill-climbing iterations produce concrete pass/fail signal.

```swift
@Test("Book Tag Evaluations", .evaluates(evaluation))
func evaluateBookTagging() async throws {
    let result = EvaluationContext.current.result
    #expect(result.aggregateValue(.mean(of: evaluation.tagCount)) >= 0.8)
    #expect(result.aggregateValue(.mean(of: evaluation.noDuplicates)) == 1)
}
```

## Evaluate both heuristics and qualitative quality

The BookTaggingEvaluation combines deterministic checks with model-judge scoring. Heuristics verify requirements such as 3-8 tags, at least one genre tag, and no duplicates. Qualitative score dimensions measure whether tags are relevant to the book and useful for browsing a library.

The model judge uses ScoreDimension definitions and a ModelJudgePrompt to rate generated tags. The session emphasizes that the definitions must be specific enough for the judge to apply the same standards as the developer.

- Relevance: tags should describe the book itself, such as genre, themes, tone, setting, or form.
- Usefulness: tags should work as shelf labels, broad enough to group books but specific enough to narrow search.
- Bad tags include reader reactions, meta-commentary about the review, author facts, genre contradictions, character names, made-up phrases, and hyper-specific descriptors.

### Qualitative score dimensions

ScoreDimension descriptions are a high-leverage part of a model-judge evaluator because they define the rubric the judge applies.

```swift
let relevance = ScoreDimension(
    "Relevance",
    description: "Whether each tag describes the book itself - its genre, themes, tone, or setting - rather than the reader's reactions, meta-commentary, or facts about the author.",
    scale: .numeric([
        4: "Every tag describes the book itself",
        1: "Tags don't meaningfully describe the book"
    ])
)

let usefulness = ScoreDimension(
    "Usefulness",
    description: "Whether tags work as library shelf labels - broad enough that several books could share the tag, specific enough to meaningfully narrow a search.",
    scale: .numeric([
        4: "Every tag could group multiple books while still narrowing a search",
        1: "Tags would not help with browsing"
    ])
)
```

## Detect and reduce model-judge drift

During analysis, the developer and model judge disagree on usefulness scores even when relevance scores match. This divergence is called drift: the model judge's ratings increasingly differ from expert human ratings as the dataset grows.

The session recommends aligning the judge to expert opinion before trusting it to score feature changes. Simple accuracy can be misleading when most examples are high quality or score distributions are uneven, because a judge can appear aligned by guessing common high scores. Cohen's kappa accounts for chance agreement and is used as the alignment metric.

- Build a judge-alignment evaluation from the same review/tag pairs that both the human expert and model judge rate.
- Add expert ratings to the extracted dataset, then have the model judge rate the same generated tags.
- Aggregate mean, standard deviation, and a custom Cohen's kappa score for each dimension.
- Use an alignment target of 0.6 as a meaningful agreement threshold in this example.

### Custom Cohen's kappa aggregation

A custom aggregation compares expert scores with judge scores to quantify alignment instead of relying on raw average scores.

```swift
func aggregateMetrics(using aggregator: inout MetricsAggregator) {
    let expertRelevance = Self.samples.map { Double($0.expected?.expertRelevanceScore ?? 0) }

    aggregator.group("Relevance") { group in
        group.computeMean(of: relevance.metric)
        group.computeStandardDeviation(of: relevance.metric)
        group.custom(of: relevance.metric, label: "Relevance Alignment Score") { judge in
            cohensKappa(ratings1: expertRelevance, ratings2: judge) ?? 0
        }
    }
}
```

### Judge calibration expectation

The calibration test fails until the judge prompt and scoring dimensions are refined enough to match the expert's rubric.

```swift
@Test("Judge Calibration", .evaluates(evaluation))
func evaluateJudgeCalibration() async throws {
    let result = EvaluationContext.current.result
    #expect(result.aggregateValue(.custom(label: "Relevance: Judge vs Expert")) > 0.6)
    #expect(result.aggregateValue(.custom(label: "Usefulness: Judge vs Expert")) > 0.6)
}
```

## Use comparative evaluation like a controlled experiment

Xcode 27 can compare two evaluations side by side. The session treats this like a controlled experiment: the baseline evaluation is the control, and a second evaluation with one intentional change is the experimental group.

The first experimental judge prompt adds more app context and examples of good and bad tags. Relevance alignment improves, but usefulness alignment drops, illustrating that prompt changes can introduce tradeoffs. Subsequent iterations isolate one variable at a time by applying the new prompt to the baseline before changing only the ScoreDimension descriptions.

Few-shot worked examples are added only after prompt and rubric refinements still fall short. The examples show how the expert scores several tag sets, but the session cautions to keep the example set small to avoid overfitting the alignment score.

- Compare baseline and experimental evaluations in one test suite.
- Inspect per-sample differences in Xcode's comparison view to find failure patterns.
- When changing scoring dimensions, keep the prompt constant so the effect of the rubric change is isolated.
- Use worked examples to calibrate the judge, but do not simply train it to memorize the calibration dataset.

### Experimental judge prompt structure

The improved prompt gives the judge app context, separate dimensions, positive examples, and known failure modes.

```text
ModelJudgeEvaluator(
    judge: .default,
    dimensions: [relevance, usefulness],
    prompt: ModelJudgePrompt(
        instructions: """
        You are an experienced reader and librarian evaluating tags automatically generated for Book Tracker.
        Score the tag set on two independent dimensions: Relevance and Usefulness.
        ## What a good tag looks like
        - Genre/form, theme/subject, tone/atmosphere, setting/era
        ## Common failure modes
        - Reader reactions, meta-commentary, author facts, genre contradictions
        """,
        evaluationTarget: { output in output.tags.joined(separator: ", ") },
        reference: { input, _ in ["Book Review": input.promptDescription] }
    )
)
```

## Hill-climb beyond prompts with tools

After aligning the judge, the session applies it to the actual Book Tracker tag generator. The next feature change is not a prompt change: the on-device model receives a BookLookupTool that can provide title and author from distinguishing review details.

BookTaggingService gains a tools parameter with an empty-array default so the existing evaluation path remains unchanged. A second evaluation passes the lookup tool and is compared against the no-tool baseline. The tool-enabled version meets expectations and performs better in the small dataset, but the session notes that 13 samples is not enough coverage and that tool calls should be evaluated directly.

- Tools, models, instructions, datasets, aggregations, and evaluators are all valid hill-climbing variables.
- Keep existing tests stable by defaulting new tool parameters to an empty array.
- For agentic behavior, evaluate whether the tool was called in the right situations, not just whether final output improved.
- The session points to tool call evaluators and the Sample Generator API for broader agentic app coverage.

### BookTaggingService with tools

Adding a tools parameter lets the same service run both the baseline and tool-enabled variants.

```swift
struct BookTaggingService {
    static func generateTags(for review: String, tools: [any Tool] = []) async throws -> BookTags {
        let session = LanguageModelSession(
            model: SystemLanguageModel(guardrails: .permissiveContentTransformations),
            tools: tools,
            instructions: instructions
        )
        let response = try await session.respond(to: tagsPrompt(review: review), generating: BookTags.self)
        return response.content
    }
}
```

### Evaluation variant with lookup tool

The experimental evaluation differs from the baseline only by passing the BookLookupTool.

```swift
struct BookTaggingWithLookupEvaluation: Evaluation {
    func subject(from sample: ModelSample<BookTags>) async throws -> ModelSubject<BookTags> {
        let result = try await BookTaggingService.generateTags(
            for: sample.promptDescription,
            tools: [BookLookupTool()]
        )
        return ModelSubject(value: result)
    }
}
```

Resources:
- Book Tracker: Using Evaluations to evaluate an intelligent feature: https://developer.apple.com/documentation/Evaluations/book-tracker-using-evaluations-to-evaluate-an-intelligent-feature
- Designing effective model-as-judge evaluators: https://developer.apple.com/documentation/Evaluations/designing-effective-model-judges
- Designing specific, measurable criteria in an evaluation suite: https://developer.apple.com/documentation/Evaluations/designing-evaluation-criteria

Chapters:
- 0:00 Introduction: Introduces hill-climbing as an iterative process for improving intelligence-powered features using evaluation scores. The session assumes an existing evaluation pipeline and focuses on scientific iteration, comparative evaluation, and changes beyond prompts.
- 2:42 BookTracker's tagging problem: Revisits Book Tracker's tag generator, which produces tags that may miss important themes or reflect reader reactions instead of book properties. The existing evaluation uses score dimensions such as Relevance and Usefulness with a ModelJudgeEvaluator.
- 5:27 Analyzing the evaluation results: Adds Treasure Island and Little Women reviews to the dataset, runs the evaluation, and inspects Xcode's evaluation report. Manual inspection shows the developer disagrees with the model judge's usefulness ratings.
- 8:26 Drift between judge and human: Defines drift as the discrepancy between human expert ratings and model-judge ratings. As datasets grow, drift can make evaluation results untrustworthy unless the judge is aligned to expert opinion.
- 9:37 Measuring drift with Cohen's kappa: Explains why simple accuracy can be misleading when score distributions are uneven. Cohen's kappa is introduced as an alignment metric that accounts for agreement that could happen by chance.
- 12:26 Building a judge alignment evaluation: Builds a separate evaluation where both the expert and model judge rate the same generated tag sets. The evaluation loads extracted review/tag pairs, adds expert ratings, reuses the model judge, and aggregates Cohen's kappa, mean, and standard deviation.
- 15:16 Analyzing alignment failures: The calibration test fails, so the evaluation report is used to inspect cases such as Frankenstein and The Ramakien. Failures show the judge overrating off-theme or overly specific tags because the prompt lacks enough context.
- 17:16 Comparative evaluation: control vs experimental: Introduces Xcode 27 side-by-side comparison of two evaluations as a controlled experiment. A richer experimental prompt improves relevance alignment but hurts usefulness, showing the need to evaluate tradeoffs.
- 19:12 Refining the scoring dimensions: Uses the comparison view to isolate usefulness failures and refine the ScoreDimension descriptions. The baseline is updated to hold the prompt constant, leaving the scoring-dimension changes as the only variable.
- 21:23 Adding few-shot examples to the judge: Adds a small number of worked examples to the judge prompt to calibrate it to the expert's scoring style. The examples help the alignment scores exceed the target, but the session warns against too many examples because of overfitting.
- 23:38 Going beyond prompts: adding a tool: Adds a BookLookupTool to provide the on-device tag model with title and author context. A new evaluation compares the tagging service with and without the tool, while noting the dataset is small and tool-call behavior needs direct evaluation.
- 27:17 Next steps: Summarizes the practice: make one change at a time, expect failed experiments, consider many possible variables, and watch for judge drift. Points developers to the Book Tracker sample, Evaluations documentation, and related guidance for robust agentic evaluations.

### Build live production tools for Apple Immersive Video

- Session ID: wwdc2026-338
- Page: https://wwdc.ai/2026/338
- Markdown: https://wwdc.ai/2026/338.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/338/
- Category: Audio & Video
- Description: Build Apple Immersive Video live production tools using ProRes, ASAF audio, per-frame metadata, SMPTE 2110, AVFoundation, VideoToolbox, and IMS.
- Duration: 16:23
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-338/eng_b7593ce7e1a1/wwdc2026-338-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/338/5/4549be24-44c7-4214-ab9b-f21f9ed04691/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/338/5/4549be24-44c7-4214-ab9b-f21f9ed04691/downloads/wwdc2026-338_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/338/5/4549be24-44c7-4214-ab9b-f21f9ed04691/downloads/wwdc2026-338_sd.mp4?dl=1

Build Apple Immersive Video live production tools using ProRes, ASAF audio, per-frame metadata, SMPTE 2110, AVFoundation, VideoToolbox, and IMS.

TLDR:
- Apple Immersive Video live production uses streamed ProRes video, uncompressed PCM ASAF audio, and per-frame JSON metadata as the common interchange format between tools.
- Real-time transport is based on SMPTE 2110 over IP: ProRes over 2110-22, ASAF audio over 2110-30, and user-defined metadata over 2110-41.
- Recording and replay workflows should preserve quality by copying the native ProRes payload into QuickTime MOV files instead of decode/re-encode cycles.
- Use AVAssetWriter, VideoToolbox projection metadata, and Immersive Media Support to write video, audio, and synchronized immersive metadata tracks for editorial, replay, and playout.

## Live production context

Live Apple Immersive Video production follows the same broad structure as traditional broadcast: a production domain captures and creatively combines media, then a delivery domain encodes and streams the result to viewers. Production tools can range from large broadcast trucks and studios to smaller venues, but they share core concepts: cameras, graphics, replay, switching, microphones, audio mixing, and routing.

The session focuses on the production domain: how devices exchange live immersive media and how developers can build tools that participate in that workflow.

- Live cameras capture multiple viewpoints or angles.
- Graphics systems generate overlays such as lower thirds, scoreboards, and animations.
- Replay systems record media for instant replay, archive, editorial, or post-production use.
- Video switchers assemble cameras and graphics into the final creative output.
- Audio consoles combine microphones, commentary, venue sound, and other sources into the final mix.
- A media router connects devices so signals can be exchanged across the production workflow.

## What changes for immersive live

Immersive live production must preserve fidelity and presence throughout the pipeline because the viewer is placed inside the event rather than watching a flat broadcast. That changes the scale of the media and the requirements for each production device.

Compared with typical 2D broadcast production, Apple Immersive Video uses much larger video resolution and higher frame rate, and the audio mix is far richer than stereo or 5.1 surround. These requirements exceed what many traditional broadcast formats, transport methods, and tools were designed to handle.

- Video resolution is described as 32 times larger than typical 2D broadcast production.
- The format is produced at twice the frame rate referenced for typical 2D broadcast production.
- Apple Spatial Audio Format (ASAF) mixes can contain 64 or more channels.
- Tools need to preserve high-fidelity video, spatial audio, and synchronized metadata through capture, routing, recording, editing, replay, and playout.

## Immersive live media format

The live immersive production format combines three existing media approaches into a shared language that production devices can exchange: streamed ProRes frames for video, uncompressed PCM tracks for spatial audio, and per-frame JSON metadata. Devices that output, ingest, or both output and ingest media need to comply with these media types to interoperate in the wider ecosystem.

ProRes is used instead of uncompressed video frames because it provides a practical bandwidth and processing profile while preserving the image quality required for immersive production. Apple Silicon is optimized for ProRes processing, making it a strong platform for production tools.

- Apple Immersive Live Video is composed of streamed ProRes frames.
- ASAF audio is carried as standard uncompressed PCM audio tracks containing high-order ambisonic beds and spatial audio objects.
- Metadata is delivered as per-frame JSON objects describing related video and audio attributes such as lens calibrations, creative events, spatial audio behavior, and motion-related data.
- The format is intended for real-time tool interoperability across cameras, graphics systems, monitors, switchers, encoders, recorders, and replay systems.

## Real-time transport over SMPTE 2110

Live immersive feeds are transported between production devices as individual SMPTE 2110 media streams over IP. SMPTE 2110 uses multicast RTP to carry timing information, user flags, metadata, and the primary media payload, and is widely used in professional broadcast facilities.

Each stream carries one media class, with the transport behavior defined by a sub-standard within the broader SMPTE 2110 family.

- Immersive ProRes video uses SMPTE 2110-22, the standard for compressed media over IP.
- A 2110-22 immersive video flow contains both left-eye and right-eye essences inside a single stream, avoiding side-by-side frame packing and avoiding separate IP streams per eye.
- ASAF audio uses SMPTE 2110-30 streams carrying the ambisonic and object channels that make up the spatial audio mix.
- Per-frame JSON metadata uses SMPTE 2110-41, the user-defined metadata transport over IP.
- Using separate 2110 flows for video, audio, and metadata keeps the production architecture aligned with professional IP media workflows.

## Recording, MOV files, and playback

Recording and replay are central to live production, but immersive workflows cannot tolerate unnecessary generational loss from repeated encode/decode/re-encode cycles. The live immersive format avoids this because the video payload is already ProRes and file-friendly: the same ProRes frames can be copied directly into QuickTime MOV files and later read back for playout into SMPTE 2110 streams.

AVFoundation's AVAssetWriter is used to write MOV tracks. Video goes into QuickTime MOV video tracks, uncompressed PCM audio goes into audio tracks, and JSON-derived immersive metadata is written into MEBX tracks after parsing and conversion with Immersive Media Support.

- Use AVAssetWriter to save ProRes video feeds into MOV without additional encoding or decoding.
- Set the VideoToolbox projection kind so the MOV gets the correct `vexu` static metadata that identifies it as Apple Immersive Video.
- Write ASAF audio as uncompressed PCM audio tracks in the MOV container.
- Deserialize and parse streamed JSON metadata, then use Immersive Media Support to create lens calibration objects, camera IDs, and other synchronized metadata objects.
- For playback, read video, audio, and metadata tracks from MOV and retransmit them into SMPTE 2110 output streams for use in the live production workflow.

### Set compression properties for Apple Immersive Video `vexu` metadata

When writing the MOV video track, set `kVTProjectionKind_AppleImmersiveVideo` in the compression properties so the file is signaled as Apple Immersive Video to other applications.

```swift
import VideoToolbox

let compressionProperties: [String: Any] = [
    // ...
    kVTCompressionPropertyKey_ProjectionKind as String: kVTProjectionKind_AppleImmersiveVideo
    // ...
]
```

Resources:
- kVTCompressionPropertyKey_ProjectionKind: https://developer.apple.com/documentation/VideoToolbox/kVTCompressionPropertyKey_ProjectionKind
- CMVideoCodecType: https://developer.apple.com/documentation/CoreMedia/CMVideoCodecType
- Apple ProRes RAW White Paper: https://www.apple.com/final-cut-pro/docs/Apple_ProRes_RAW.pdf
- Apple ProRes White Paper: https://www.apple.com/final-cut-pro/docs/Apple_ProRes.pdf
- Immersive Media Support: https://developer.apple.com/documentation/ImmersiveMediaSupport

Chapters:
- 0:00 Introduction: Introduces Apple Immersive Video live streaming through examples such as live courtside LA Lakers games on Apple Vision Pro. Frames the session around building production tools and understanding the live broadcast workflow behind immersive events.
- 2:08 Live production overview: Explains the production and delivery domains of a live pipeline and surveys common production tools: cameras, graphics, replay, video switchers, microphones, audio consoles, and media routers. Establishes the traditional broadcast foundation needed to understand immersive workflows.
- 5:16 What makes immersive live different: Describes the scale and fidelity requirements that separate immersive live from traditional 2D broadcast, including much larger video resolution, higher frame rate, and ASAF mixes with many spatial audio channels. Explains why these requirements force changes to formats, tools, and transport.
- 7:05 Immersive live format: Defines the common immersive production format as streamed ProRes video, uncompressed PCM ASAF audio, and per-frame JSON metadata. Emphasizes that devices must agree on these media types to exchange content reliably in real time.
- 9:09 Real-time media transport: Shows how immersive media moves between devices over IP using SMPTE 2110. ProRes video is transported over 2110-22, ASAF audio over 2110-30, and per-frame JSON metadata over 2110-41.
- 11:25 Recording and playback: Explains how to record live immersive streams into MOV files without quality loss by copying native ProRes frames and PCM audio directly, while converting JSON metadata into MEBX tracks using Immersive Media Support. Playback reverses the process by reading MOV tracks and transmitting them back into SMPTE 2110 streams.

### Bring an LLM provider to the Foundation Models framework

- Session ID: wwdc2026-339
- Page: https://wwdc.ai/2026/339
- Markdown: https://wwdc.ai/2026/339.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/339/
- Category: AI & Machine Learning
- Description: Implement a Foundation Models LanguageModelExecutor to package custom local or server-backed LLMs behind LanguageModelSession.
- Duration: 20:41
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-339/eng_fee4c1f401ae/wwdc2026-339-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/339/4/334f1ee9-4263-4c86-9b10-632f0f2edab1/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/339/4/334f1ee9-4263-4c86-9b10-632f0f2edab1/downloads/wwdc2026-339_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/339/4/334f1ee9-4263-4c86-9b10-632f0f2edab1/downloads/wwdc2026-339_sd.mp4?dl=1

Implement a Foundation Models LanguageModelExecutor to package custom local or server-backed LLMs behind LanguageModelSession.

TLDR:
- Foundation Models now supports interchangeable LLM providers through the public LanguageModel and LanguageModelExecutor protocols, covering Apple models, Core AI, MLX, and custom packages.
- A provider package should be a Swift package with deliberate platform support and dependencies; each unique executor Configuration is the cache key for a session's executor store.
- Executors translate Transcript entries to the model's native message format, apply ContextOptions and GenerationOptions, stream metadata/usage/text/tool events, and manage state such as loaded weights or KV cache reuse.
- Server-backed providers should guide developers toward secure authentication, use Keychain for persisted tokens, consider App Attest, and expose custom metadata, custom segments, or server-side tools when needed.

## Model choices through one session API

Foundation Models is extended so local and server-backed LLMs can be used through the same LanguageModelSession API. The session can be constructed with Apple's on-device SystemLanguageModel, Private Cloud Compute, Core AI resources, MLX models, or a provider's own LanguageModel implementation.

The same framework features remain available to app developers when the model conforms to LanguageModel. The session emphasizes swapping models without changing the surrounding app code.

- SystemLanguageModel is the on-device Apple Foundation Model.
- PrivateCloudComputeLanguageModel provides server-scale inference through Private Cloud Compute.
- CoreAILanguageModel runs local model resources efficiently, including ANE support.
- MLXLanguageModel can load open-source MLX models by model ID.

### Swap models behind LanguageModelSession

App code talks to LanguageModelSession while the concrete model can be swapped.

```swift
import FoundationModels
import MLXFoundationModels

// On-device Apple Foundation Model
let model = SystemLanguageModel()

// Other options use the same session shape:
// let model = PrivateCloudComputeLanguageModel()
// let model = try await CoreAILanguageModel(resourcesAt: modelURL)
// let model = MLXLanguageModel(modelID: "mlx-community/my-model")

let session = LanguageModelSession(model: model)
let response = try await session.respond(to: "...")
print(response.content)
```

## Package the provider as a Swift package

Provider integrations should be distributed as Swift packages so app developers can add the repository URL directly in Xcode. Choose supported platforms intentionally: Foundation Models supports iOS, macOS, visionOS, and watchOS, and the open-source framework can also make Linux support useful for server-side Swift.

Dependencies matter because each dependency can add shipped bytes to the developer's app. Publishing is done through normal Swift Package Manager distribution: create a git tag for a release.

- Expose the public LanguageModel conformance from the package target developers import.
- Keep runtime implementation details in separate targets when useful.
- Minimize dependency size and surface area.
- Use semantic releases that developers can consume from a repository URL.

### Package.swift shape for a model package

The package exposes a model library and can isolate runtime code in an implementation target.

```swift
// Package.swift
let package = Package(
    name: "MyModel",
    platforms: [
        .macOS(.v27), .iOS(.v27), .visionOS(.v27), .watchOS(.v27)
    ],
    products: [
        .library(name: "MyModel", targets: ["MyModel"])
    ],
    dependencies: [
        .package(url: "...", .upToNextMinor(from: "1.0.0"))
    ],
    targets: [
        .target(name: "MyModelRuntime"),
        .target(name: "MyModel", dependencies: ["MyModelRuntime"]),
        .testTarget(name: "MyModelTests", dependencies: ["MyModel"])
    ]
)
```

## Implement LanguageModel and LanguageModelExecutor

The integration has two core protocol types. LanguageModel describes the model to the framework by declaring capabilities and providing an executor configuration. LanguageModelExecutor performs the work: initialization, optional prewarming, request handling, and streaming generation back to the session.

Executor instances are cached inside each LanguageModelSession by Configuration, not by model value. If two model values produce the same Hashable configuration, they resolve to the same executor. When the session deallocates, its executor store is released, allowing provider resources such as weights or connections to be torn down naturally.

- Declare only capabilities the model can support, such as tool calling, guided generation, or reasoning.
- Keep the model value lightweight; put resource loading and inference behavior in the executor.
- Use prewarm for expensive setup like loading weights or opening connections, but make respond work even if prewarm was never called.
- For stateful integrations, compare each new full Transcript with saved prior state and preserve KV cache or session state only for the unchanged prefix.

### Core protocol shape

LanguageModel supplies description and configuration; LanguageModelExecutor performs inference.

```swift
public protocol LanguageModel: Sendable {
    var capabilities: LanguageModelCapabilities { get }
    var executorConfiguration: Executor.Configuration { get }
}

public protocol LanguageModelExecutor: Sendable {
    init(configuration: Configuration) throws
    func prewarm(model: Model, transcript: Transcript)
    func respond(
        to request: LanguageModelExecutorGenerationRequest,
        model: Model,
        streamingInto channel: LanguageModelExecutorGenerationChannel
    ) async throws
}
```

### Provider conformance sketch

A provider declares capabilities on the model and implements request execution in the executor.

```text
public struct MyLanguageModel: LanguageModel {
    typealias Executor = MyLanguageModelExecutor

    public var capabilities: LanguageModelCapabilities {
        LanguageModelCapabilities(capabilities: [
            .toolCalling, .guidedGeneration, .reasoning
        ])
    }

    public var executorConfiguration: Executor.Configuration {
        Executor.Configuration(/* ... */)
    }
}

public struct MyLanguageModelExecutor: LanguageModelExecutor {
    public typealias Model = MyLanguageModel

    public struct Configuration: Hashable, Sendable { /* ... */ }

    public init(configuration: Configuration) throws { /* ... */ }

    public func respond(
        to request: LanguageModelExecutorGenerationRequest,
        model: MyLanguageModel,
        streamingInto channel: LanguageModelExecutorGenerationChannel
    ) async throws {
        /* ... */
    }
}
```

## Translate transcripts, options, and streaming responses

The executor receives the full conversation as a Transcript and must translate entries into the provider's native message format. Foundation Models transcript entries cover instructions, prompts, tool calls, tool outputs, responses, and reasoning; the provider decides how these map to its roles, such as system, user, assistant, or a dedicated tool role.

Each request also carries developer intent through ContextOptions and GenerationOptions. Context options affect prompt/context construction, including reasoning level and response schema. Generation options affect decoding, including sampling strategy, temperature, and maximum response length.

Responses are always implemented as streaming events, even when the developer uses a one-shot API. The executor should send useful metadata first, then usage information, then token/tool/reasoning deltas as they arrive.

- Map transcript entries carefully; tool calls, tool outputs, and reasoning may all map to assistant for models without dedicated roles.
- Read contextOptions and generationOptions from LanguageModelExecutorGenerationRequest before calling the model.
- Send metadata such as modelID and requestID early for logging and debugging.
- Send prompt token usage before generation when available so developers can account for cost without waiting for the full stream.

### Read request options

Executors are responsible for applying both context and decoding options.

```swift
func respond(
    to request: LanguageModelExecutorGenerationRequest,
    model: MyLanguageModel,
    streamingInto channel: LanguageModelExecutorGenerationChannel
) async throws {
    let reasoningLevel = request.contextOptions.reasoningLevel
    let temperature = request.generationOptions.temperature
    let maxTokens = request.generationOptions.maximumResponseTokens

    // Pass these through to the provider's inference call.
}
```

### Stream metadata, usage, and text deltas

The recommended stream ordering gives developers identifiers and accounting before generated content.

```text
await channel.send(.response(action: .updateMetadata([
    "modelID": "my-model-2026-06-08",
    "requestID": request.id.uuidString
])))

await channel.send(.response(action: .updateUsage(
    input: .init(totalTokenCount: promptTokens, cachedTokenCount: cachedTokens),
    output: .init(totalTokenCount: 0, reasoningTokenCount: 0)
)))

for try await token in tokens {
    await channel.send(.response(action: .appendText(token)))
}
```

## Handle unsupported requests and provider errors

If a model cannot exactly satisfy a request, the executor can approximate when that still honors the developer's intent. For example, a service that accepts only temperature can map greedy sampling to temperature 0. If there is no honest approximation, throw an error instead.

Foundation Models provides built-in LanguageModelError cases for common cross-model failures. Custom errors should be reserved for service-specific failures such as subscription limits, unavailable account features, or account state problems.

- Use LanguageModelError.contextSizeExceeded when the transcript exceeds the context window.
- Use LanguageModelError.unsupportedCapability or unsupportedGenerationGuide when a requested feature cannot be honored.
- Use rateLimited, refusal, guardrailViolation, unsupportedTranscriptContent, unsupportedLanguageOrLocale, or timeout when those cases fit.
- Define custom LocalizedError cases only for failures unique to the provider service.

### Approximate or throw

Approximate compatible intent where possible; otherwise fail with a meaningful framework error.

```swift
if request.generationOptions.sampling?.kind == .greedy {
    serviceRequest.temperature = 0
}

if let schema = request.schema,
   let budget = request.generationOptions.maximumResponseTokens,
   budget < minimumTokens(for: schema) {
    throw LanguageModelError.unsupportedCapability(
        .init(
            capability: .guidedGeneration,
            debugDescription: "Token budget too small to satisfy this schema."
        )
    )
}
```

## Authentication, metadata, custom segments, and server-side tools

Server-backed model packages should steer developers toward secure credential flows instead of encouraging raw API key strings in initializers. If the package fetches or persists access tokens, store them securely with Keychain. Providers should also consider App Attest for device verification, tamper detection, signed payloads, and fraud signals for cloud traffic.

Customization lets a provider expose differentiating capabilities without forcing developers to leave LanguageModelSession. Providers can attach custom metadata, define custom Transcript segments for new modalities, and surface server-side tool results at different levels of detail.

- Custom response metadata can report service-specific data such as tokensPerSecond and timeToFirstToken.
- Custom segments are typed PromptRepresentable values that can appear in prompts and responses, supporting modalities such as audio or video.
- Server-side tools can run privately, annotate text with metadata such as citations, or emit their structured output as custom segments.
- Privacy characteristics differ significantly between on-device and cloud-backed packages; developers and users should understand which model type is in use.

### Custom segment for audio input and output

Custom segments carry typed non-text content through LanguageModelSession and the executor channel.

```swift
public struct AudioSegment: Transcript.CustomSegment {
    public var id: String
    public var content: URL
}

let recording = AudioSegment(
    id: UUID().uuidString,
    content: URL(filePath: "/path/to/recording.m4a")
)

let response = try await session.respond {
    "Where was Frank Lloyd Wright's original architecture school located?"
    recording
}

for try await event in stream {
    switch event {
    case .audioFileGenerated(let file):
        await channel.send(.response(action: .updateCustomSegment(
            AudioSegment(id: file.id, content: file.url)
        )))
    }
}
```

### Surface server-side tool output

A provider can expose server-side tools as typed model options and stream their structured results.

```swift
public struct MyLanguageModel: LanguageModel {
    public struct ServerTool: Sendable {
        public static let webSearch: ServerTool = ...
    }

    public init(serverTools: [ServerTool] = []) { }
}

let client = MyServerClient(serverTools: model.serverTools)
let response = try await client.send(prompt: .init(request))

for try await chunk in response {
    switch chunk {
    case .webSearch(let webSearch):
        await channel.send(.response(action: .updateCustomSegment(
            WebSearchSegment(url: webSearch.url, content: webSearch.html)
        )))
    case .textDelta(let textDelta):
        await channel.send(.response(action: .appendText(
            textDelta.text,
            tokenCount: textDelta.tokenCount
        )))
    }
}
```

Resources:
- Foundation Models: https://developer.apple.com/documentation/FoundationModels
- Core AI Models: https://github.com/apple/coreai-models
- MLX Swift LM on GitHub: https://github.com/ml-explore/mlx-swift-lm

Chapters:
- 0:00 Introduction: Foundation Models opens to nearly any local or server-backed LLM through a common LanguageModel protocol. The session previews swapping SystemLanguageModel, Private Cloud Compute, Core AI, and MLX models into the same LanguageModelSession API.
- 3:37 Packaging: Provider integrations should be packaged with Swift Package Manager, with deliberate platform support, minimized dependencies, and release distribution through git tags. The package is the developer-facing distribution mechanism for the model integration.
- 4:48 Protocol: LanguageModel declares capabilities and supplies executor configuration, while LanguageModelExecutor initializes resources, prewarms, translates Transcript data, applies request options, streams events, and handles state reuse. Executor caching by configuration enables resource sharing and KV cache preservation across calls when transcripts share a prefix.
- 14:50 Authentication: Server-backed packages should guide developers toward secure token providers or sign-in flows instead of raw API key strings. Persist fetched tokens with Keychain and consider App Attest to verify devices, detect tampered builds, sign payloads, and reduce abusive traffic.
- 15:51 Customization: Providers can attach custom metadata, define custom Transcript segments for new modalities, and expose server-side tools such as web search, code execution, or image generation. Tool work can remain private, annotate text with metadata, or be surfaced as structured custom segments.
- 19:47 Next steps: The closing guidance emphasizes privacy differences between on-device and cloud-backed model packages and points developers to related Foundation Models, Core AI, Private Cloud Compute, and agentic app experience sessions.

### Support the Center Stage front camera in your iOS app

- Session ID: wwdc2026-341
- Page: https://wwdc.ai/2026/341
- Markdown: https://wwdc.ai/2026/341.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/341/
- Category: Photos & Camera
- Description: Use AVCapture APIs on iOS 26 to adopt the Center Stage front camera for selfies, video recording, video calls, smart framing, and stabilization.
- Duration: 17:44
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-341/eng_3c009c0c4a3c/wwdc2026-341-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/341/4/fa1380a3-e2ab-4442-9302-817be212e991/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/341/4/fa1380a3-e2ab-4442-9302-817be212e991/downloads/wwdc2026-341_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/341/4/fa1380a3-e2ab-4442-9302-817be212e991/downloads/wwdc2026-341_sd.mp4?dl=1

Use AVCapture APIs on iOS 26 to adopt the Center Stage front camera for selfies, video recording, video calls, smart framing, and stabilization.

TLDR:
- The Center Stage front camera on iPhone 17, iPhone Air, and iPhone 17 Pro uses a square sensor and 95-degree field of view to support flexible front-camera framing without rotating the device.
- iOS 26 adds dynamic aspect ratio support on AVCaptureDevice, allowing seamless preview changes among supported ratios such as 3x4, 4x3, 9x16, 16x9, and 1x1 on compatible square formats.
- AVCaptureSmartFramingMonitor provides photo-oriented framing recommendations with an aspect ratio and zoom factor, enabling Auto Zoom and Auto Rotate based on face and gaze detection.
- Video apps can use Center Stage for calls via process-wide AVCaptureDevice settings and can enable low-latency stabilization on the capture connection for smoother real-time video.

## Center Stage front camera capabilities

The Center Stage front camera is available on iPhone 17, iPhone Air, and iPhone 17 Pro. Unlike earlier front cameras with 4x3 sensors, it uses a square image sensor paired with a 95-degree field-of-view lens.

The square sensor lets apps crop portrait, landscape, square, and widescreen framings without requiring the user to rotate the phone. The wider view also supports group selfies, video stabilization, and Center Stage behavior for video calls.

- The front camera is represented through AVCaptureDevice as the front-facing .builtInUltraWideCamera.
- The sensor supports framing flexibility for selfies and group shots while keeping a secure one-handed grip and more natural eye contact.
- For photos, the main building blocks are dynamic aspect ratio, smart framing monitor, and sensor orientation compensation.

## Capture setup and dynamic aspect ratio

A typical photo setup uses AVCaptureSession with an AVCaptureDeviceInput for the Center Stage front camera, AVCaptureVideoPreviewLayer for preview, and AVCapturePhotoOutput for still capture. AVCaptureSession creates compatible AVCaptureConnection objects between inputs and outputs.

Starting in iOS 26, AVCaptureDevice exposes dynamic aspect ratio. Setting it crops the requested aspect ratio from the square sensor without rebuilding the capture session or interrupting preview, and the API returns the timestamp of the first buffer where the change takes effect.

- Use .builtInUltraWideCamera with .front position to discover the Center Stage front camera.
- Choose an activeFormat whose supportedDynamicAspectRatios contains the target ratio.
- Supported ratios include 3x4, 4x3, 9x16, 16x9, and 1x1 on compatible square formats.
- The 4032 photo format supports only 3x4 and 4x3 because those provide the highest photo resolution.

### Select a Center Stage front camera format and set a dynamic aspect ratio

Finds the front ultra-wide Center Stage camera, selects a format that supports 4x3, and applies that dynamic aspect ratio without rebuilding the session.

```swift
import AVFoundation

let discovery = AVCaptureDevice.DiscoverySession(
    deviceTypes: [.builtInUltraWideCamera],
    mediaType: .video,
    position: .front
)

guard let camera = discovery.devices.first else { return }

for format in camera.formats where format.supportedDynamicAspectRatios.contains(.ratio4x3) {
    try camera.lockForConfiguration()
    camera.activeFormat = format
    camera.unlockForConfiguration()
    break
}

try camera.lockForConfiguration()
let timestamp = try await camera.setDynamicAspectRatio(.ratio4x3)
print("Applied at: \(timestamp)")
camera.unlockForConfiguration()
```

## Smart framing for Auto Zoom and Auto Rotate

AVCaptureSmartFramingMonitor works with dynamic aspect ratio to automate photo framing. It periodically recommends a framing based on automatic face and gaze detection, and each recommendation includes an aspect ratio and zoom factor that the app can apply or ignore.

The monitor is intended for photo capture and only provides recommendations when using the 4032 photo format. By default, it provides no recommendations until enabledFramings is configured.

- Select a camera format that supports smart framing and the desired dynamic aspect ratios.
- Set monitor.enabledFramings, either to all supported framings or a restricted subset.
- Observe recommendedFraming and apply the recommended aspect ratio before applying the zoom factor for a smooth preview transition.
- Start monitoring while the AVCaptureSession is running if needed, and invalidate observation plus stopMonitoring when automatic framing is disabled.

### Configure and observe AVCaptureSmartFramingMonitor

Enables supported smart framings, observes recommendations, and applies the recommended aspect ratio and zoom factor.

```swift
for format in camera.formats where format.isSmartFramingSupported {
    try camera.lockForConfiguration()
    camera.activeFormat = format
    camera.unlockForConfiguration()
    break
}

let monitor = camera.smartFramingMonitor!
try camera.lockForConfiguration()
monitor.enabledFramings = monitor.supportedFramings
camera.unlockForConfiguration()

observation = monitor.observe(\.recommendedFraming, options: [.new]) { monitor, _ in
    guard let framing = monitor.recommendedFraming else { return }
    Task {
        try camera.lockForConfiguration()
        try await camera.setDynamicAspectRatio(framing.aspectRatio)
        camera.videoZoomFactor = CGFloat(framing.zoomFactor)
        camera.unlockForConfiguration()
    }
}

try monitor.startMonitoring()

// Later, when disabling automatic framing:
observation?.invalidate()
observation = nil
monitor.stopMonitoring()
```

## Photo orientation and sensor orientation compensation

Earlier iPhone front camera sensors were mounted in Landscape Left orientation, while the Center Stage front camera sensor is mounted in Portrait orientation. Apps that assume older sensor rotation behavior may otherwise show photos sideways or upside down.

AVCapturePhotoOutput applies sensor orientation compensation by default: it physically rotates photos and updates EXIF metadata before delivery so resulting photos match the Landscape Left behavior apps have historically expected.

- Compensation applies to HEIC, JPEG, and uncompressed processed photos.
- Compensation is never applied to Bayer RAW or Apple ProRAW captures.
- Starting in iOS 26, apps can control this with cameraSensorOrientationCompensationEnabled.
- If using AVCapturePhotoOutput, test with compensation off for performance and verify that photo orientation remains correct.
- For broader rotation handling, the session points to AVCaptureRotationCoordinator and the WWDC 2023 external cameras session.

## Video recording behavior

Dynamic aspect ratio also applies to video recording, enabling experiences like tap-to-rotate for a wider view. However, QuickTime movie tracks require all samples in a track to have the same dimensions, so changing dynamic aspect ratio during recording requires a recording transition.

With AVCaptureMovieFileOutput, recording stops automatically when the aspect ratio changes. With AVCaptureVideoDataOutput and AVAssetWriter, the timestamp returned by setDynamicAspectRatio can be used to end the current recording and begin a new one with the updated aspect ratio.

- Use AVCaptureMovieFileOutput instead of AVCapturePhotoOutput for a movie-file recording setup.
- Use AVCaptureVideoDataOutput plus AVAssetWriter when the app needs app-layer recording control.
- Consider cinematicExtended and cinematicExtendedEnhanced stabilization modes for recordings; on the Center Stage front camera they are face-aware and prioritize keeping the subject stable over the background.

## Center Stage and stabilization for video calls

Video conferencing apps commonly use AVCaptureVideoDataOutput to receive video buffers and handle display, encoding, and transmission themselves. If the app uses the Voice over IP background mode to keep calls connected while locked, users can enable Center Stage from Control Center's Video Effects menu.

Apps that do not use the Voice over IP background mode can still adopt the Center Stage API on supported iPhone front cameras. Like other system-wide video effects such as Portrait, Studio Light, and Gestures, Center Stage is enabled per process and applies to supported cameras in the app once active.

For real-time calls, the Center Stage front camera also supports low-latency video stabilization starting in iOS 26. It is off by default and is enabled by setting the capture connection's preferredVideoStabilizationMode to lowLatency.

- Find and set an active format whose isCenterStageSupported property is true.
- Set AVCaptureDevice.centerStageControlMode to cooperative or app before enabling Center Stage.
- In cooperative mode, users can also control Center Stage from a button in the app.
- Set AVCaptureDevice.isCenterStageEnabled to true to activate automatic framing that keeps people centered.
- Set preferredVideoStabilizationMode on AVCaptureConnection to lowLatency for smoother real-time video calls.

### Enable Center Stage for video calls

Selects a Center Stage-capable format, chooses a control mode, and enables Center Stage process-wide.

```text
for format in camera.formats where format.isCenterStageSupported {
    try camera.lockForConfiguration()
    camera.activeFormat = format
    camera.unlockForConfiguration()
    break
}

AVCaptureDevice.centerStageControlMode = .cooperative
AVCaptureDevice.isCenterStageEnabled = true
```

### Enable low-latency stabilization on a capture connection

Turns on the iOS 26 low-latency stabilization mode for real-time video conferencing.

```text
connection.preferredVideoStabilizationMode = .lowLatency
```

Resources:
- Supporting Center Stage front camera in your iOS app: https://developer.apple.com/documentation/AVFoundation/supporting-center-stage-front-camera-in-your-ios-app
- AVCam: Building a camera app: https://developer.apple.com/documentation/AVFoundation/avcam-building-a-camera-app
- AVFoundation: https://developer.apple.com/documentation/AVFoundation
- Capture setup: https://developer.apple.com/documentation/AVFoundation/capture-setup

Chapters:
- 0:00 Introduction: Introduces Center Stage front camera support for iOS apps and frames the session around photos, video recordings, and video calls. The supported devices are iPhone 17, iPhone Air, and iPhone 17 Pro.
- 1:07 Center Stage front camera: Explains the square image sensor and 95-degree field of view. These hardware characteristics allow flexible portrait, landscape, square, and wide framing without rotating the device.
- 2:09 Center Stage for photos: Describes Auto Zoom and Auto Rotate for photo capture. The behavior combines the square sensor, wide lens, and face/gaze detection to adjust framing as people enter or leave the scene.
- 3:09 Capture session setup: Reviews the baseline AVCaptureSession setup: discover the front .builtInUltraWideCamera, create an AVCaptureDeviceInput, add preview via AVCaptureVideoPreviewLayer, and add AVCapturePhotoOutput.
- 3:56 Dynamic aspect ratio: Introduces the iOS 26 dynamic aspect ratio API on AVCaptureDevice. The chapter covers compatible formats, supported ratios, and how to switch aspect ratio without rebuilding the session or interrupting preview.
- 6:47 Smart framing monitor: Covers AVCaptureSmartFramingMonitor, which generates recommended aspect ratio and zoom factor values for photo framing. It explains selecting a supported format, enabling framings, observing recommendedFraming, and starting or stopping monitoring.
- 9:24 Sensor orientation compensation: Explains that the Center Stage front camera sensor is mounted in Portrait orientation, unlike earlier front cameras. AVCapturePhotoOutput compensates by default for processed photo outputs so existing rotation assumptions continue to work.
- 11:53 Center Stage for video recordings: Shows how dynamic aspect ratio applies to recordings and why recordings must stop or split when dimensions change. It also recommends cinematicExtended and cinematicExtendedEnhanced stabilization modes for smoother face-aware front-camera recordings.
- 13:16 Center Stage for video calls: Explains Center Stage adoption for video conferencing apps, including Control Center behavior, cooperative or app control modes, and process-wide enablement. It also introduces low-latency stabilization for smoother real-time calls.

### Explore advanced App Intents features for Siri and Apple Intelligence

- Session ID: wwdc2026-343
- Page: https://wwdc.ai/2026/343
- Markdown: https://wwdc.ai/2026/343.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/343/
- Category: AI & Machine Learning
- Description: Advanced App Intents techniques for polishing Siri responses, donating UI interactions, indexing/searching entities, and adding onscreen context for Apple Intelligence.
- Duration: 24:08
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-343/eng_7ad4d06208ae/wwdc2026-343-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/343/4/00190d1d-55b6-4eb2-9ee3-e09f3d8d1c7d/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/343/4/00190d1d-55b6-4eb2-9ee3-e09f3d8d1c7d/downloads/wwdc2026-343_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/343/4/00190d1d-55b6-4eb2-9ee3-e09f3d8d1c7d/downloads/wwdc2026-343_sd.mp4?dl=1

Advanced App Intents techniques for polishing Siri responses, donating UI interactions, indexing/searching entities, and adding onscreen context for Apple Intelligence.

TLDR:
- Customize Siri conversations with `ProvidesDialog`, dialog requests, entity `DisplayRepresentation`, and SwiftUI snippet views while keeping voice-only devices in mind.
- Donate real UI interactions with `IntentDonationManager` so Apple Intelligence can learn app preferences and understand ongoing activities started inside your app.
- Make app content discoverable through `IndexedEntity`, Core Spotlight indexing, `IntentValueQuery`, and the `.system.searchInApp` App Schema.
- Improve contextual Siri requests by annotating onscreen views, activities, notifications, Now Playing state, and AlarmKit alarms with persistent app entity identifiers.

## Shape Siri conversations and responses

Siri handles natural language understanding and can generate default responses, but App Intents can refine the conversation so it matches an app's terminology and tone. Returning an empty result lets Siri respond automatically; adopting `ProvidesDialog` lets the intent return an `IntentDialog` with both a full spoken response and a shorter supporting string for UI.

The full dialog string should stand alone on voice-only devices such as AirPods. Clarifying questions can be asked from inside `perform()` by requesting a missing parameter value, but they should be used sparingly to avoid adding friction.

- Use default Siri responses when they are accurate enough.
- Use `ProvidesDialog` for app-specific vocabulary and tone.
- Use dialog requests to collect missing values while an intent runs.
- Keep responses natural and correct across visual and voice-only contexts.

### Custom dialog response

Adopts `ProvidesDialog` and returns an `IntentDialog` with a full spoken response plus a shorter supporting string.

```swift
@AppIntent(schema: .audio.addToPlaylist)
struct AddToPlaylistIntent {
    func perform() async throws -> some IntentResult & ProvidesDialog {
        // Add song to playlist.
        return .result(
            dialog: IntentDialog(
                full: "Added \(song.title) to the \(playlist.title) mix tape.",
                supporting: "Added"
            )
        )
    }
}
```

### Ask a clarifying question inside an intent

Requests an optional parameter value only when additional information is needed to complete the action.

```swift
@AppIntent(schema: .clock.createTimer)
struct CreateTimerIntent {
    var duration: Duration
    var label: String?
    var isSleepTimer: Bool

    func perform() async throws -> some ReturnsValue<TimerEntity> {
        label = try await $label.requestValue(
            "You already have a timer running. What should we call this one?"
        )
        return .result(value: timerEntity)
    }
}
```

## Add visual identity to Siri results

An entity's `DisplayRepresentation` is reused throughout the system: Siri responses, disambiguation, Spotlight, Shortcuts, and questions about app content. A good representation should include enough text and imagery for users to recognize the entity quickly.

For action-specific presentation, intents can return SwiftUI snippet views with `ShowsSnippetView`. Snippets are useful when a particular action benefits from a custom layout, but they should complement rather than replace clear spoken dialog.

- Start with meaningful entity titles, then add subtitles and images where helpful.
- Use custom snippets for specific actions that need app-styled UI.
- Test how snippets scale across Siri surfaces and platforms.
- Do not rely on visuals for information needed on voice-only devices.

### Enhanced DisplayRepresentation

Provides a title, subtitle, and image so the entity is recognizable across Siri, Spotlight, and Shortcuts.

```swift
@AppEntity(schema: .audio.song)
struct SongEntity {
    var displayRepresentation: DisplayRepresentation {
        DisplayRepresentation(
            title: "\(title)",
            subtitle: "\(artistName)",
            image: artworkImage
        )
    }
}
```

### Return a custom snippet view

Adds `ShowsSnippetView` so the intent can return both dialog and a custom SwiftUI snippet.

```swift
@AppIntent(schema: .audio.addToPlaylist)
struct AddToPlaylistIntent {
    var audioEntity: AudioEntity
    var playlist: PlaylistEntity

    func perform() async throws -> some IntentResult & ProvidesDialog & ShowsSnippetView {
        let view = PlaylistSnippetView(
            playlist: updatedEntity,
            tracks: updated.tracks
        )
        return .result(dialog: dialog, view: view)
    }
}
```

## Donate UI interactions and model ownership

Siri and Shortcuts interactions are already visible to the system, but actions taken through an app's own UI are not. Interaction donations represent real UI actions as schema-conforming App Intents, giving Apple Intelligence context about user preferences and ongoing app activities.

Donations should be accurate and limited to real user behavior; excessive donation can be ignored. The session also covers confirmations: Siri may automatically confirm actions with meaningful side effects, especially when they affect public or shared content. `OwnershipProvidingEntity` lets an app tell Siri whether an entity is shared, public, unknown, or private enough to skip some confirmations.

- Donate UI actions, not actions already performed through Siri or Shortcuts.
- Populate intent parameters and results before donating.
- Use donations for preference learning and ongoing activities such as navigation sessions or stopwatches where supported by the domain.
- Adopt `OwnershipProvidingEntity` only for entities that can be shared or made public, and keep ownership state current.

### Donate a UI interaction

Donates the schema-conforming send-message action only when the action originated from the app UI.

```swift
@ModelActor
actor ModelManager {
    func sendMessage(_ /* ... */, donateIntent: Bool = false) async throws -> [Message.ID] {
        if donateIntent {
            let intent = SendMessageIntent()
            intent.destination = .recipients(conversation.recipients.map(\.entity))
            let result = messages.map(\.entity)

            Task {
                try await IntentDonationManager.shared.donate(
                    intent: intent,
                    result: .result(value: result)
                )
            }
        }
    }
}
```

### Declare entity ownership for confirmations

Supplies ownership metadata so Siri can decide when a side-effecting action should be confirmed.

```swift
@AppEntity(schema: .calendar.event)
struct EventEntity: OwnershipProvidingEntity {
    var ownership: EntityOwnership {
        attendees.isEmpty ? .unknown : .shared
    }
}
```

## Make content discoverable with indexing and search

For local app content, adopt `IndexedEntity` and index entities with Core Spotlight using `indexAppEntities`. Indexing app entities populates Spotlight's semantic index, making content discoverable by Siri, Apple Intelligence, and the Spotlight UI. The index must be kept fresh as entities are added, updated, or removed.

For large, server-side, or frequently changing datasets, use `IntentValueQuery` instead of indexing everything ahead of time. The system passes structured search input, such as `AudioSearch`, and the app returns matching entities. For user-facing searches, adopt the `.system.searchInApp` schema so Siri can re-run a search inside the app's own search UI.

- Use `IndexedEntity` for local content suitable for ahead-of-time indexing.
- Keep Spotlight entries current and support reindexing with `IndexedEntityQuery` unless existing Core Spotlight reindexing already covers it.
- Use `IntentValueQuery` when search must be resolved dynamically.
- Use `.system.searchInApp` to open the app and show results in the app's own search experience.

### Index entities with IndexedEntity

Indexes app entities into Spotlight so Siri and Apple Intelligence can find local content semantically.

```swift
struct EntityIndexingHelper {
    func indexPlaylist(_ playlist: Playlist) async throws {
        let entity = PlaylistEntity(playlist: playlist)
        try await CSSearchableIndex(name: indexName)
            .indexAppEntities([entity])
    }
}
```

### Structured search with IntentValueQuery

Handles structured search criteria dynamically and returns app entities without pre-indexing every item.

```swift
struct AudioIntentValueQuery: IntentValueQuery {
    func values(for input: AudioSearch) async throws -> [AudioEntity] {
        switch input.criteria {
        case .searchQuery(let query):
            return try await searchResults(for: query)
        case .unspecified:
            return try await likedSongResults()
        // Also handle URL criteria where appropriate.
        }
    }
}
```

### Re-run Siri search in app

Adopts the renamed system search schema so Siri can show search results in the app's own UI.

```swift
@AppIntent(schema: .system.searchInApp)
struct SearchAudioLibraryIntent {
    var criteria: StringSearchCriteria

    func perform() async throws -> some IntentResult {
        navigation.searchText = criteria.term
        navigation.selectedTab = .library
        return .result()
    }
}
```

## Provide onscreen awareness

Onscreen awareness connects visible UI to structured app entities so Siri can resolve references like "the third one" or "that conversation." Text visible in pixels is not enough for Siri to understand hidden attributes, entity identity, or possible actions.

Start with `NSUserActivity` for a screen dedicated to one primary entity, and use view entity annotations when an entity is one of several visible items. For large lists, collection annotations avoid per-row overhead and can preserve selection context for scrolled-off items. Custom canvas annotations cover non-standard views, and UIKit and AppKit have equivalent APIs.

Display-representation queries help Siri quickly resolve visible entities without fetching full model objects. Implement them to return only the requested display components when possible.

- Use `.userActivity` for the primary entity of a screen.
- Use `.appEntityIdentifier` on views representing individual entities.
- Use `.appEntityIdentifier(forSelectionType:)` on lists and collections.
- Use display-representation queries to make on-screen resolution faster.
- Consult platform-specific APIs such as `AppEntityAnnotatable`, `UICollectionViewAppIntentsDataSource`, and `appEntityUIElementProvider` for UIKit/AppKit.

### Onscreen awareness annotations

Shows the three common SwiftUI patterns: primary entity via `NSUserActivity`, individual view annotations, and collection annotations for list selection.

```swift
struct NowPlayingView: View {
    @Environment(PlaybackController.self) private var playback

    var body: some View {
        VStack { /* Player UI */ }
            .userActivity("cosmotunes.nowPlaying", isActive: playback.currentTrack) { activity in
                activity.title = playback.currentTrack?.title
                activity.appEntityIdentifier = EntityIdentifier(
                    for: SongEntity.self,
                    identifier: playback.currentTrack.id
                )
            }
    }
}

struct AlbumView: View {
    private var header: some View {
        VStack(alignment: .leading, spacing: 6) { /* ... */ }
            .appEntityIdentifier(
                EntityIdentifier(for: AlbumEntity.self, identifier: session.id.uuidString)
            )
    }
}

struct PlaylistDetailView: View {
    var body: some View {
        List {
            ForEach(playlist.tracks) { track in
                PlaylistTrackRow(track: track)
            }
        }
        .appEntityIdentifier(forSelectionType: GeneratedTrack.ID.self) { trackID in
            EntityIdentifier(for: SongEntity.self, identifier: trackID)
        }
    }
}
```

### Component-based display representation query

Lets Siri fetch lightweight display data for visible entities instead of loading full content.

```swift
extension PlaylistQuery {
    func displayRepresentations(
        for identifiers: [PlaylistEntity.ID],
        requestedComponents: DisplayRepresentation.Components = .text
    ) async throws -> [PlaylistEntity.ID: DisplayRepresentation] {
        let entities = try await model.playlistEntities(for: identifiers)
        var result: [PlaylistEntity.ID: DisplayRepresentation] = [:]
        for entity in entities {
            result[entity.id] = await entity.displayRepresentation(with: requestedComponents)
        }
        return result
    }
}
```

## Annotate existing system integrations

The same entity-identifier pattern can add Siri context to integrations an app may already use: user notifications, Now Playing, and AlarmKit. These annotations let Siri understand content encountered outside the app UI, such as announced notifications, currently playing media, or firing alarms.

These APIs require persistent app entities. `TransientAppEntity` is not appropriate because transient objects do not have stable identifiers.

- Set `UNMutableNotificationContent.appEntityIdentifiers` so Siri can act on notification-related content during announcements.
- Set Now Playing `appEntityIdentifiers` from most specific to least specific, such as song, artist, then playlist.
- Pass an `appEntityIdentifier` when creating an `AlarmKit` alarm or timer configuration.
- Do not use transient entities for these annotations.

### Entity annotations on system integrations

Adds persistent entity identifiers to notifications, Now Playing content, and AlarmKit configurations.

```swift
// User notifications
let content = UNMutableNotificationContent()
content.title = author.name
content.body = message.body
content.appEntityIdentifiers = [
    EntityIdentifier(for: MessageEntity.self, identifier: message.id)
]

// Now Playing
var content = MusicContent(id: track.id.uuidString, songTitle: track.title)
content.appEntityIdentifiers = [
    EntityIdentifier(for: SongEntity.self, identifier: track.id),
    EntityIdentifier(for: ArtistEntity.self, identifier: track.session.artistName),
    EntityIdentifier(for: PlaylistEntity.self, identifier: currentPlaylist.id)
]

// AlarmKit
let configuration = AlarmManager.AlarmConfiguration<CosmoTunesAlarmMetadata>.alarm(
    schedule: schedule,
    attributes: attributes,
    appEntityIdentifier: EntityIdentifier(for: AlarmEntity.self, identifier: alarm.id),
    stopIntent: DismissAlarmIntent(),
    secondaryIntent: SnoozeAlarmIntent(),
    sound: sound
)
```

Resources:
- App Intents Testing: https://developer.apple.com/documentation/AppIntentsTesting
- Donating your app's data and actions to the system: https://developer.apple.com/documentation/AppIntents/donating-your-apps-data-and-actions-to-the-system
- Donations and discovery: https://developer.apple.com/documentation/AppIntents/donations-and-discovery
- Making app entities available in Spotlight: https://developer.apple.com/documentation/AppIntents/making-app-entities-available-in-spotlight
- Making actions and content discoverable by Apple Intelligence: https://developer.apple.com/documentation/AppIntents/making-actions-and-content-discoverable-by-apple-intelligence
- Providing contextual cues to Apple Intelligence and Siri: https://developer.apple.com/documentation/AppIntents/providing-contextual-cues-to-apple-intelligence-and-siri
- Apple Intelligence and Siri AI: https://developer.apple.com/documentation/AppIntents/apple-intelligence-and-siri-ai

Chapters:
- 0:00 Introduction: Introduces advanced App Intents techniques for polished Siri and Apple Intelligence experiences. The session focuses on shaping Siri conversations, improving content discovery, adding onscreen context, and annotating existing system integrations.
- 1:59 Customize how Siri responds: Shows how to let Siri respond automatically or provide custom responses with `ProvidesDialog` and `IntentDialog`. Also demonstrates asking clarifying questions during `perform()` by requesting missing parameter values.
- 4:20 Visual responses: Explains how `DisplayRepresentation` defines entity visuals across Siri, Spotlight, Shortcuts, and disambiguation. Demonstrates returning a custom SwiftUI snippet view from an intent using `ShowsSnippetView`.
- 6:22 Interaction donations: Covers donating real UI actions with `IntentDonationManager` so Apple Intelligence can learn user preferences and maintain awareness of ongoing activities. Emphasizes that donations should accurately represent user behavior and should not duplicate Siri-originated interactions.
- 9:46 Confirmations and entity ownership: Explains Siri's automatic confirmations for actions with meaningful side effects, especially shared or public content. Introduces `OwnershipProvidingEntity` so apps can report entity ownership state for better confirmation decisions.
- 11:59 Semantic index with IndexedEntity: Shows how to adopt `IndexedEntity` and index entities with `CSSearchableIndex.indexAppEntities` so Siri, Apple Intelligence, and Spotlight can find local content. Notes the need to update, delete, and reindex entries as content changes.
- 13:32 Structured search with IntentValueQuery: Introduces `IntentValueQuery` for content that is too large, server-side, or dynamic to pre-index. The app receives structured search input such as `AudioSearch` and returns matching app entities.
- 15:27 In-app search: Describes adopting `.system.searchInApp`, the renamed system search schema, so Siri can hand off a search query to the app's own search UI. This works regardless of other adopted domains and does not require indexed entities.
- 16:22 Onscreen awareness: Explains how to connect visible UI to app entities so Siri can resolve contextual references. Covers `NSUserActivity`, view annotations, collection annotations, custom canvas annotations, UIKit/AppKit support, and display-representation queries for fast resolution.
- 20:51 Leverage existing integrations: Shows how to attach persistent entity identifiers to user notifications, Now Playing state, and AlarmKit configurations. These annotations give Siri context for announced notifications, current media, and firing alarms or timers.
- 23:30 Next steps: Recommends starting with entity display representations, then adding indexing, dynamic search, in-app search, onscreen annotations, integration annotations, and UI interaction donations. Points developers toward sample projects and the related App Schemas code-along.

### Code-along: Make your app available to Siri

- Session ID: wwdc2026-344
- Page: https://wwdc.ai/2026/344
- Markdown: https://wwdc.ai/2026/344.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/344/
- Category: AI & Machine Learning
- Description: Code along with a SwiftUI calendar app that adopts App Schemas, Spotlight donations, onscreen awareness, and App Intents so Siri can understand and manage events.
- Duration: 24:20
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-344/eng_117deff8f2f7/wwdc2026-344-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/344/4/ee45cb19-e252-41f4-a2e0-e9b59238c7aa/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/344/4/ee45cb19-e252-41f4-a2e0-e9b59238c7aa/downloads/wwdc2026-344_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/344/4/ee45cb19-e252-41f4-a2e0-e9b59238c7aa/downloads/wwdc2026-344_sd.mp4?dl=1

Code along with a SwiftUI calendar app that adopts App Schemas, Spotlight donations, onscreen awareness, and App Intents so Siri can understand and manage events.

TLDR:
- Adopt App Schemas from the App Intents Calendar domain to model calendars, attendees, and events in terms Siri already understands, without custom training phrases or app-side NLP.
- Use IndexedEntity plus Spotlight donations for persistent content such as calendars and events; use TransientAppEntity for content like event attendees that is only meaningful through a parent entity.
- Connect Siri and Spotlight results to in-app navigation with a system.open intent, and provide onscreen awareness with .appEntityIdentifier and .userActivity using EntityIdentifier.
- Implement schematized create, update, and delete event intents so Siri can clarify, confirm, disambiguate, and perform calendar actions, including optional-parameter clearing via IntentParameter.valueState.

## Goal and architecture

The session turns CometCal, a SwiftUI calendar sample app backed by SwiftData, into an app Siri can understand and operate through Apple Intelligence. The integration path is App Intents plus App Schemas: schemas describe the app's entities, intent parameters, and outputs using concepts Siri already knows.

The Calendar App Schema Domain supplies schemas for calendars, events, attendees, and calendar actions. The app does not implement custom natural-language parsing or training phrases; it exposes structured content and actions, and Siri handles language understanding, clarification, confirmation, and disambiguation.

- Primary content goals: expose calendars, events, notes, attendees, locations, recurrence, and related event metadata.
- Primary action goals: create, update, open, and delete events from Siri and Spotlight-driven experiences.
- The CometCal sample is the reference implementation for following the code-along.

## Model app content with schematized entities

The first implementation step is to create AppEntity types from Calendar-domain snippets in Xcode. CalendarEntity uses the calendar_calendar schema, maps its identifier to the app model's UUID, and conforms to IndexedEntity so it can be donated to the Spotlight semantic index.

The entity query uses @Dependency to access the app's CalendarManager instead of constructing a new data layer object. Because CalendarManager is main-actor isolated, the query is marked @MainActor. EntityQuery resolves known IDs; EnumerableEntityQuery exposes all calendars so Siri can offer them as choices when an action needs a calendar parameter.

AttendeeEntity uses the calendar_attendee schema but conforms to TransientAppEntity rather than IndexedEntity. In CometCal, an attendee represents a person's participation in one event, not a standalone person record, so it has no independent identifier, query, or Spotlight index entry.

- Use DisplayRepresentation to control how entities appear in Siri and Spotlight, such as a calendar title and system calendar image.
- Use IntentPerson for attendee person/contact information that may be shared across app actions, such as composing mail.
- Use schematized @AppEnum types for attendee status and attendee type, mapping existing app terminology to the schema's cases.

### CalendarEntity implementation pattern

The session emphasizes the required decisions rather than a standalone pasted listing: match the app model ID, provide query access through the app data layer, and make calendars enumerable for Siri parameter selection.

```text
// Schematized from the Calendar domain's calendar_calendar snippet
// Key choices shown in the session:
// - @AppEntity
// - id type: UUID
// - conform to IndexedEntity
// - query uses @Dependency CalendarManager
// - query is @MainActor
// - also conform to EnumerableEntityQuery for allEntities()
```

### AttendeeEntity implementation pattern

Attendees are intentionally transient because they are only accessed through their event and should not create duplicate standalone Spotlight results.

```text
// Schematized from calendar_attendee
// Conform to TransientAppEntity, not IndexedEntity
// Use IntentPerson for the person/contact data
// Add schema enums such as calendar_attendeeStatus and calendar_attendeeType
```

## Donate indexed entities to Spotlight

Conforming to IndexedEntity defines the shape of indexable content, but it does not index anything by itself. Persistent entities need to be donated whenever they are created or updated, and removed from the index when deleted.

CometCal keeps a CSSearchableIndex instance in CalendarManager, initialized with a unique name for the app. Calendar creation and update paths call indexAppEntities, while delete paths call deleteAppEntities with the entity ID and type. Once donated, Spotlight and Siri can resolve content by name, property, and semantic context.

- Donate CalendarEntity instances when calendars are created or updated.
- Delete calendar index entries when calendars are removed.
- Apply the same IndexedEntity and donation pattern to EventEntity so Siri can answer questions from event titles and notes.

### Spotlight donation lifecycle

The exact sample code lives in CometCal, but the workflow is explicit: update the Spotlight semantic index in the same data-layer operations that mutate indexed entities.

```text
// In the app's data layer after create/update:
// searchableIndex.indexAppEntities(...)

// In the delete path:
// searchableIndex.deleteAppEntities(..., type: CalendarEntity.self)
```

## Build EventEntity and connect it to navigation and context

EventEntity is the central indexed entity in the sample. It uses the calendar_event schema and composes the previously defined CalendarEntity and AttendeeEntity, allowing Siri to understand relationships such as which calendar an event belongs to and who is attending.

The event schema includes required and optional properties. CometCal wires essentials like title and start date, leaves unsupported optional schema properties unset, and can still include app-specific properties that are not part of the schema. Recurrence is represented using Foundation's Calendar.RecurrenceRule and mapped to and from the app's simpler frequency model.

The schema also uses union values for fields such as location and alarms. Location can be represented as a GeoToolbox PlaceDescriptor or a String, and alarms can be a Duration or a Date.

- Use IndexedEntity for EventEntity so Siri can answer questions from titles, notes, locations, and other indexed event content.
- Implement an OpenEventIntent conforming to the system.open schema so tapping or asking to open an event navigates directly to its detail view.
- Add onscreen awareness: use .appEntityIdentifier on event lists and .userActivity with an EntityIdentifier on an event detail view so Siri can resolve references like "this event" or "that third event".

### OpenIntent and onscreen awareness pattern

The session shows OpenIntent for entity deep linking and two SwiftUI modifiers for binding visible UI to AppEntity identifiers.

```text
// OpenEventIntent conforms to the system.open schema
// It takes an EventEntity target
// It asks the app's NavigationManager to navigate to that event

// In SwiftUI views:
// .appEntityIdentifier(EntityIdentifier(...))
// .userActivity(... EntityIdentifier(...))
```

## Expose Siri actions with create, update, delete, and snippets

Schematized intents are added from Calendar-domain snippets such as calendar_createEvent and calendar_updateEvent. The create intent fills in schema parameter types, injects CalendarManager with @Dependency, marks perform() @MainActor, resolves schema values into the app data model, calls the data layer, and returns an EventEntity.

UpdateEventIntent follows the same pattern but most parameters are optional because a user may change only one field. The important distinction is IntentParameter.valueState: .set with a value means change the field, .set with nil means explicitly clear it, and .unset means leave it unchanged.

DeleteEventIntent is the simplest action: it takes the target event and an optional span for recurring events, then deletes through the data layer. Siri handles confirmation before removal and disambiguates when more than one event matches. Result cards can be customized by adding ShowsSnippetView to an intent's return type and returning a lightweight SwiftUI snippet view.

- CreateEventIntent resolves fields such as title, date/time, calendar, location union values, and recurrence before creating the model object.
- UpdateEventIntent must not treat nil alone as "no change"; use valueState for optional fields where clearing is meaningful.
- DeleteEventIntent relies on Siri's built-in confirmation and disambiguation behavior for destructive actions.
- Custom snippet views let the app replace Siri's default result card while keeping the view simple and lightweight.

### Optional update parameter handling

This is the key update-intent subtlety from the session, especially for recurrence and other optional fields where removal is a valid request.

```text
// For optional update parameters:
// .set(value)  -> apply the new value
// .set(nil)    -> explicitly clear the value
// .unset       -> leave the existing value unchanged
```

### Custom Siri result snippet pattern

Snippet views replace the default Siri card with a custom SwiftUI result view, such as CometCal's event card with its app-specific visual style.

```text
// In an intent returning a result:
// add ShowsSnippetView to the return type
// return the updated EventEntity with EventSnippetView(entity)
```

Resources:
- Integrating your calendar app with Apple Intelligence: https://developer.apple.com/documentation/AppIntents/integrating-your-calendar-app-with-apple-intelligence
- Donating your app's data and actions to the system: https://developer.apple.com/documentation/AppIntents/donating-your-apps-data-and-actions-to-the-system
- Donations and discovery: https://developer.apple.com/documentation/AppIntents/donations-and-discovery
- Making app entities available in Spotlight: https://developer.apple.com/documentation/AppIntents/making-app-entities-available-in-spotlight
- Making actions and content discoverable by Apple Intelligence: https://developer.apple.com/documentation/AppIntents/making-actions-and-content-discoverable-by-apple-intelligence
- Providing contextual cues to Apple Intelligence and Siri: https://developer.apple.com/documentation/AppIntents/providing-contextual-cues-to-apple-intelligence-and-siri
- Apple Intelligence and Siri AI: https://developer.apple.com/documentation/AppIntents/apple-intelligence-and-siri-ai
- Calendar: https://developer.apple.com/documentation/AppIntents/app-schema-domain-calendar
- App schema domains: https://developer.apple.com/documentation/AppIntents/app-schema-domains

Chapters:
- 0:00 Introduction: A conversational Siri scenario demonstrates searching event data, updating event time with confirmation, messaging attendees, answering from event notes, and using location information. The goal is to make an existing SwiftUI calendar app available to Siri.
- 1:43 App Schemas and the plan: The session introduces App Intents and App Schemas as the way to expose app content and actions to Apple Intelligence. CometCal will be updated so Siri can understand calendar content and perform event actions.
- 3:44 Build the CalendarEntity: CalendarEntity is created from the calendar_calendar schema, uses a UUID ID, conforms to IndexedEntity, and provides queries through a @Dependency-injected CalendarManager. The entity is donated to and removed from the Spotlight semantic index through data-layer create, update, and delete paths.
- 8:00 Build the AttendeeEntity: AttendeeEntity is created from the calendar_attendee schema but uses TransientAppEntity because attendees are only meaningful through an event. The chapter introduces IntentPerson and schematized attendee status and type enums.
- 10:30 Build the EventEntity: EventEntity is built from the calendar_event schema as an IndexedEntity and composes CalendarEntity plus an array of AttendeeEntity. It covers recurrence with Calendar.RecurrenceRule, union values for locations and alarms, and event-related schema enums.
- 14:34 Open events with OpenIntent: OpenEventIntent conforms to the system.open schema, accepts an EventEntity, and asks the app's NavigationManager to navigate to that event. This lets Siri or Spotlight results open directly to the event detail view.
- 15:30 Onscreen awareness: The app connects visible UI to entities using .appEntityIdentifier on the event list and .userActivity with EntityIdentifier on the detail view. Siri can then resolve references such as "this event" or "that third event."
- 17:18 Create events with Siri: CreateEventIntent is generated from the calendar_createEvent schema, fills in parameter types, injects CalendarManager, resolves schema values into model values, creates the event, and returns an EventEntity. Siri handles natural-language interpretation, clarifications, and confirmations.
- 19:24 Update events: UpdateEventIntent mirrors create but most parameters are optional because updates may target only a few fields. The key implementation detail is using IntentParameter.valueState to distinguish changing a value, clearing a value, and leaving it unchanged.
- 21:30 Custom snippet views: The default Siri result card is replaced with a custom SwiftUI snippet by adding ShowsSnippetView to the intent return type and returning an EventSnippetView. The guidance is to keep snippets visually app-specific but lightweight.
- 22:30 Delete events: DeleteEventIntent takes an event and optional recurrence span, then deletes through the data layer. Siri automatically confirms destructive actions and disambiguates when multiple events match.
- 23:35 Next steps: Developers are directed to download the CometCal sample, review App Intents documentation for schemas and domains, test with AppIntentsTesting, and watch the advanced App Intents session for deeper refinements.

### Discover new capabilities in the App Intents framework

- Session ID: wwdc2026-345
- Page: https://wwdc.ai/2026/345
- Markdown: https://wwdc.ai/2026/345.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/345/
- Category: AI & Machine Learning
- Description: Advanced App Intents updates for structured entity transfer, relevance, large entity sets, syncable IDs, union parameters, and long-running execution.
- Duration: 18:02
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-345/eng_2f6db43e1243/wwdc2026-345-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/345/4/bc719e14-772a-4737-aceb-6e54cda6b511/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/345/4/bc719e14-772a-4737-aceb-6e54cda6b511/downloads/wwdc2026-345_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/345/4/bc719e14-772a-4737-aceb-6e54cda6b511/downloads/wwdc2026-345_sd.mp4?dl=1

Advanced App Intents updates for structured entity transfer, relevance, large entity sets, syncable IDs, union parameters, and long-running execution.

TLDR:
- ValueRepresentation lets AppEntity types share structured system-understood values, such as PlaceDescriptor, across apps instead of only file or data formats.
- RelevantEntities complements Spotlight indexing and interaction donation by registering entities that are relevant in a specific context even before users search for or interact with them.
- EntityCollection improves large-entity performance by passing identifiers to an intent instead of resolving every AppEntity before perform().
- SyncableEntity, @UnionValue, LongRunningIntent, CancellableIntent, and ExecutionTargets add cross-device entity identity, flexible parameters, extended execution, cancellation cleanup, and process targeting.

## Entity transfer and relevance

The session focuses on advanced App Intents capabilities in the 2027 releases: richer entities, more flexible parameters, and better execution control across Siri, Shortcuts, Spotlight, Widgets, and Apple Intelligence.

ValueRepresentation extends CoreTransferable beyond FileRepresentation and DataRepresentation. It lets an AppEntity export structured values that the system understands, such as a PlaceDescriptor from GeoToolbox, so another app can consume the semantic value directly. The example exports a landmark coordinate and name so Maps can open directions to the landmark.

RelevantEntities lets an app tell the system that specific entities matter in a particular context. It is intended for content that may not yet be discoverable through Spotlight search or learned through interaction donation, such as a new running playlist that should be suggested when a running workout starts.

- Use Spotlight when content should be searchable and retrievable by Siri.
- Use interaction donation through IntentDonationManager when user actions should teach the system repeatable patterns.
- Use RelevantEntities when content should be suggested in a specific situation even if the user has not searched for or used it yet.
- Registered relevant entities remain registered until explicitly removed by context, by entity, or globally.

### Export a structured place value from an entity

ValueRepresentation can export structured system values directly, avoiding ad hoc file or data formats for semantic data like places.

```swift
struct LandmarkEntity: AppEntity, Transferable {
    var id: Int
    var landmark: Landmark

    static var transferRepresentation: some TransferRepresentation {
        ValueRepresentation { entity in
            PlaceDescriptor(
                representations: [.coordinate(entity.landmark.locationCoordinate)],
                commonName: entity.landmark.name
            )
        }
    }
}

// If the entity already stores the value, use a key path.
struct LandmarkEntity: AppEntity, Transferable {
    var id: Int
    @Property var placeDescriptor: PlaceDescriptor

    static var transferRepresentation: some TransferRepresentation {
        ValueRepresentation(exporting: \.placeDescriptor)
    }
}
```

### Register and remove relevant entities

RelevantEntities associates AppEntity instances with a contextual signal so system surfaces can suggest them at the right moment.

```swift
let playlistEntities = [dailyRun, runningMix]
let workoutContext = AppEntityContext.audio(.workout(activityType: .running))

try await RelevantEntities.shared.updateEntities(
    playlistEntities,
    for: workoutContext
)

try await RelevantEntities.shared.removeAllEntities(for: workoutContext)
try await RelevantEntities.shared.removeEntities(playlistEntities, from: workoutContext)
try await RelevantEntities.shared.removeAllEntities()
```

## Efficient entity handling and cross-device identity

App Intents normally resolves every entity parameter before perform() runs, calling the entity query to populate properties. That is useful when the intent needs complete entity data, but it is wasteful for operations that only need identifiers, such as tagging thousands of photos.

EntityCollection stores entity identifiers instead of fully resolved entities. Changing a parameter from an array of entities to EntityCollection allows perform() to operate on identifiers directly and avoids large pre-resolution costs.

SyncableEntity addresses cross-device Siri conversations. Local identifiers can differ across devices, so entities that may be referenced on another device need a stable ID, such as a server UUID or CloudKit record ID. When an app uses local IDs internally, SyncableEntityIdentifier pairs a local ID for on-device code with a stable ID for the system.

- Adopt EntityCollection for intents that process large sets and only require IDs.
- Adopt SyncableEntity when an entity should be usable across devices in continuing Siri interactions.
- If the entity ID is already stable everywhere, conforming to SyncableEntity is enough.
- If the app uses local identifiers, use SyncableEntityIdentifier with both local and stable values.

### Use EntityCollection to avoid resolving thousands of entities

EntityCollection passes identifiers to perform(), which is much faster when the operation does not need fully populated entity properties.

```swift
struct TagPhotosIntent: AppIntent {
    static let title: LocalizedStringResource = "Tag Travel Photos"

    @Parameter var photos: EntityCollection<PhotoEntity>
    @Parameter var tag: String

    func perform() async throws -> some IntentResult {
        modelData.tagPhotos(ids: photos.identifiers, tag: tag)
        return .result()
    }
}
```

### Provide stable entity IDs across devices

SyncableEntity tells the system that the entity can be identified consistently across devices; SyncableEntityIdentifier bridges local and stable identifiers.

```swift
// Stable ID already available.
struct PhotoEntity: AppEntity, SyncableEntity {
    var id: Int
}

// Local ID plus stable cross-device ID.
struct PhotoEntity: AppEntity, SyncableEntity {
    var id: SyncableEntityIdentifier<String, String>

    init(localID: String, stableID: String) {
        self.id = SyncableEntityIdentifier(local: localID, stable: stableID)
    }
}
```

## Richer parameter types and union values

The system already provides pickers, Siri understanding, and localization for declared @Parameter values. The 2027 updates expand native parameter support to additional types, including Duration and PersonNameComponents, so apps can avoid custom pickers or plain-string substitutes for common structured input.

@UnionValue lets a single parameter accept one of several wrapped types. The example uses one widget configuration value that can be either a landmark collection or a photo album. The macro generates the type information, case metadata, and picker support needed by system surfaces, including Shortcuts.

- Use native parameter types where possible to get consistent UI and language understanding across Siri, Shortcuts, and Widgets.
- Use @UnionValue when one logical parameter can be backed by multiple entity or value types.
- Provide typeDisplayRepresentation for the overall union type and caseDisplayRepresentations for picker labels.

### Define a union value parameter type

A @UnionValue enum wraps multiple possible parameter types while still giving the system enough metadata for pickers and Shortcuts integration.

```text
@UnionValue
enum TravelGalleryContent {
    case landmarkCollection(LandmarkCollectionEntity)
    case photoAlbum(PhotoAlbumEntity)

    static let typeDisplayRepresentation: TypeDisplayRepresentation = "Travel Gallery"

    static let caseDisplayRepresentations: [Cases: DisplayRepresentation] = [
        .landmarkCollection: "Landmark Collection",
        .photoAlbum: "Photo Album"
    ]
}
```

## Long-running and cancellable intents

Standard intents have a 30-second execution limit. LongRunningIntent supports work that needs more time, such as uploading large photos from a widget-triggered intent, and manages the app's background task lifecycle.

LongRunningIntent requires progress reporting so the system can tell the task is active. Progress updates appear automatically as a Live Activity, including a stop control. CancellableIntent lets the intent clean up partial work when a user cancels, the system times out, or resources must be reclaimed.

LongRunningIntent also supports background GPU access on supported devices for work such as photo processing or on-device inference, provided the app has the required GPU access entitlement.

- Wrap extended work in performBackgroundTask.
- Update the built-in progress object as units complete.
- Check Task cancellation during loops or chunked work.
- Use CancellableIntent's cancellation handler to clean up partial uploads or cancel in-flight requests.

### Run beyond 30 seconds and clean up on cancellation

LongRunningIntent extends execution, ProgressReportingIntent behavior supplies progress, and CancellableIntent enables graceful cleanup.

```swift
struct UploadPhotoIntent: LongRunningIntent, CancellableIntent {
    static let title: LocalizedStringResource = "Upload Photo"

    @Parameter var photo: IntentFile

    func perform() async throws -> some IntentResult & ProvidesDialog {
        let result = try await performBackgroundTask {
            let chunks = calculateChunks(for: photo)
            progress.totalUnitCount = Int64(chunks)

            for chunk in 1...chunks {
                try Task.checkCancellation()
                try await uploadChunk(chunk)
                progress.completedUnitCount = Int64(chunk)
            }

            return "Upload complete!"
        } onCancel: { reason in
            cleanup(for: reason)
        }

        return .result(dialog: "\(result)")
    }
}
```

## Choosing the execution process

Apps commonly place intents and entities in a shared Swift package or framework imported by the main app and extensions. When the same intent is linked into multiple processes, the system normally chooses a target using heuristics, such as preferring the app if it is already running or launching an extension otherwise.

ExecutionTargets lets the intent override those heuristics. This matters when only one process should perform the work, such as a widget button that marks a photo as favorite while the app enforces main-app-only writes to avoid data-store conflicts.

- Use .main for work that must run in the main app, such as writes guarded by the app process.
- Use .appIntentsExtension for standalone intent work that does not need the app process.
- Use .widgetKitExtension for display-oriented or widget-owned work.
- Return a set of targets when more than one process is valid and the system can choose among them.

### Restrict intent execution to the correct process

ExecutionTargets gives each intent explicit process affinity when it is available from multiple app or extension targets.

```swift
struct UpdateFavoriteIntent: AppIntent {
    static var allowedExecutionTargets: ExecutionTargets { .main }
}

struct DownloadPhotoIntent: AppIntent {
    static var allowedExecutionTargets: ExecutionTargets { .appIntentsExtension }
}

struct GetLandmarkStatusIntent: AppIntent {
    static var allowedExecutionTargets: ExecutionTargets { .widgetKitExtension }
}

struct TagPhotosIntent: AppIntent {
    static var allowedExecutionTargets: ExecutionTargets { [.main, .appIntentsExtension] }
}
```

Resources:
- Adopting App Intents to support system experiences: https://developer.apple.com/documentation/AppIntents/adopting-app-intents-to-support-system-experiences
- App Intents: https://developer.apple.com/documentation/AppIntents

Chapters:
- 0:00 Introduction: Introduces App Intents updates for the 2027 releases, organized around entity enhancements, richer parameters, and execution control. The examples build on the Landmarks Travel Tracking sample app and assume familiarity with App Intents basics.
- 2:40 Share entities across apps with ValueRepresentation: Explains why file and data transfer representations are insufficient for structured values such as places. Shows ValueRepresentation exporting a landmark as a PlaceDescriptor so Maps can receive navigation-ready data.
- 3:45 Register relevant entities with RelevantEntities: Describes RelevantEntities as a way to suggest entities in a contextual moment even before users search for or interact with them. Contrasts it with Spotlight indexing and interaction donation and shows update and removal operations.
- 7:05 Handle entities efficiently with EntityCollection: Shows how resolving every entity before perform() can be slow for large batches. EntityCollection changes the parameter to pass identifiers only, making operations like tagging 1000 photos much faster when full entity data is unnecessary.
- 8:55 Use entities across devices with SyncableEntity: Explains that cross-device Siri conversations require stable entity IDs because local IDs can differ per device. SyncableEntity declares stable IDs, while SyncableEntityIdentifier pairs local and stable IDs when both are needed.
- 11:01 Richer parameter types: Covers expanded native @Parameter support so system UI, Siri understanding, and localization apply to more types. Examples include Duration and PersonNameComponents across Siri, Shortcuts, and Widgets.
- 12:38 Union value parameters: Introduces @UnionValue for parameters that can accept multiple wrapped types. The example creates one gallery content value that can be either a landmark collection or a photo album, with picker display metadata.
- 13:26 Extend execution with LongRunningIntent: Explains the 30-second limit for normal intents and shows LongRunningIntent for extended uploads with progress shown as a Live Activity. Adds CancellableIntent so the intent can clean up when cancelled, timed out, or stopped by the system.
- 15:27 Target the right process with ExecutionTargets: Describes the problem of shared intents linked into the main app and extensions, where heuristic process selection may be wrong. ExecutionTargets lets an intent require the main app, App Intents extension, WidgetKit extension, or a combination.
- 17:14 Next steps: Recaps recommended adoption steps: add ValueRepresentation, register relevant content, use EntityCollection for large sets, and adopt LongRunningIntent for work over 30 seconds. Points developers toward follow-up material on building Siri experiences and testing App Intents.

### Secure your app: mitigate risks to agentic features

- Session ID: wwdc2026-347
- Page: https://wwdc.ai/2026/347
- Markdown: https://wwdc.ai/2026/347.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/347/
- Category: Privacy & Security
- Description: Threat-model agentic app features and apply deterministic mitigations for indirect prompt injection in Foundation Models and App Intents.
- Duration: 25:12
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-347/eng_b3c4f52052fe/wwdc2026-347-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/347/4/07cdbfeb-280a-49e3-aeba-c18fbb0d32b4/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/347/4/07cdbfeb-280a-49e3-aeba-c18fbb0d32b4/downloads/wwdc2026-347_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/347/4/07cdbfeb-280a-49e3-aeba-c18fbb0d32b4/downloads/wwdc2026-347_sd.mp4?dl=1

Threat-model agentic app features and apply deterministic mitigations for indirect prompt injection in Foundation Models and App Intents.

TLDR:
- Indirect prompt injection is a key risk when LLM agents consume untrusted context and can call actions with side effects.
- Threat modeling should identify untrusted prompt sources, then classify each agent action by side effects such as financial loss, data exfiltration, data loss, or persistent poisoning.
- Foundation Models lifecycle modifiers such as `.onToolCall` and `.historyTransform` let apps enforce confirmations, spotlight untrusted tool output, and redact sensitive data.
- App Intents adds system guardrails including contextual risk-based confirmations and lock-screen authentication policies, with schema-adopted intents inheriting risk metadata and default authentication requirements.

## Risk model for agentic features

Agentic app features add an LLM-driven decision point that can consume user requests, app-provided context, tool outputs, and system data before choosing actions to run. That makes the LLM a powerful but probabilistic component that can be tricked by attacker-controlled instructions embedded in otherwise normal data.

The session focuses on external attackers compromising app behavior, not model safety, output safety, or guardrail circumvention. The central concern is whether the app still acts as intended when untrusted context reaches the model.

- Agentic experiences can be built with the Foundation Models framework or exposed to Siri and Apple Intelligence through App Intents.
- Attackers are most interested when an app has sensitive user data, financial capabilities, system-resource access, physical-device control, or destructive operations.
- Indirect prompt injection is instructions embedded in extra context or tool results with the intent to redirect model control flow.

## Indirect prompt injection and side effects

Indirect prompt injection becomes dangerous when untrusted content is combined with action calling. An injected instruction in a calendar event, public feed item, email, or tool result can influence what action the model chooses or what arguments it supplies.

The session distinguishes two effects: data poisoning, where the attacker influences parameters of an action, and action poisoning, where the attacker influences which action is executed. This maps to the broader "lethal trifecta" risk: private data, untrusted content, and the ability to communicate externally or perform side effects.

- Data exfiltration: a tool posts or sends sensitive information to an attacker-controlled destination.
- Financial harm: a purchasing action runs with attacker-influenced arguments.
- Data loss: a destructive action such as deletion runs without user intent or undo.
- Persistent poisoning: an apparently low-risk action, such as creating a timer label, stores attacker-controlled text that may later re-enter model context.

## Threat-modeling workflow

Start with data-flow analysis for the prompt and agent loop. List every source used to construct the prompt, including instructions, the user request, app data, system data, and tool outputs. Treat inputs from external entities as attack surface, then mark which sources are untrusted.

Next, enumerate every action the agent can call and classify its side effects. The point is to decide where prompt-level mitigations are appropriate and where execution-time mitigations must stop or gate tool execution.

- Identify untrusted context sources such as shared calendars, public feeds, messages, email, web content, or tool outputs from external data.
- Classify tools and intents by risk: financial impact, public communication, destructive behavior, data access, device control, or persistent storage.
- Prefer deterministic mitigations as a baseline because they are easier to audit and reason about; use probabilistic mitigations such as spotlighting as additional defense.
- Apply mitigations either before data reaches the model or immediately before an action executes.

## Foundation Models mitigations

Foundation Models `LanguageModelSession` profiles can use lifecycle event modifiers as deterministic checkpoints in an agent loop. The session highlights `.onToolCall`, which runs before tool execution and can block execution by throwing, and `.historyTransform`, which runs before transcript rendering for inference.

Use `.onToolCall` for confirmation of risky tools, such as a purchase tool. Use `.historyTransform` to mark untrusted tool output with spotlighting delimiters or redact sensitive data before it reaches the model. Transformed entries are scoped to the current inference iteration, so transformations must be applied again; expensive stateful transformations can use `@SessionProperty`.

- Define tools with names, descriptions, arguments, and implementations so the model can reason about calls.
- Gate high-risk tools before execution rather than relying on prompt instructions alone.
- Spotlight untrusted tool output by delimiting it with model-appropriate tags.
- Redact PII or other sensitive data before it is rendered into model context.

### Confirm a financially risky tool before execution

`.onToolCall` is guaranteed to run before the executor invokes the tool. Throwing from the callback prevents the tool from running.

```swift
var body: some DynamicProfile {
    Profile {
        Instructions("You are a helpful, tea-loving assistant ... ")
        OrderTeaTool() // Financial impact; risky tool.
        // Other Tools
    }
    .onToolCall { call in
        guard call.toolName == "orderTeaTool" else { return }
        guard ConfirmationAction.confirmWithUser() else {
            throw LooseLeafError.userConfirmationDenied
        }
    }
}
```

### Spotlight untrusted public-feed tool output

`.historyTransform` can rewrite transcript entries before inference, allowing untrusted tool results to be demarcated before the model sees them.

```swift
var body: some DynamicProfile {
    Profile {
        Instructions("You are a helpful, tea-loving assistant ... ")
        PostAndFetchPublicFeedTool() // Returns untrusted data.
        // Other Tools
    }
    .historyTransform { entries in
        entries.map { entry in
            guard case .toolOutput(var toolOutput) = entry,
                  toolOutput.toolName == "postAndFetchPublicFeedTool" else {
                return entry
            }
            toolOutput.segments = toolOutput.segments.map { segment in
                delimit(
                    segment: segment,
                    startDelimiter: "<<UNTRUSTED>>",
                    endDelimiter: "<</UNTRUSTED>>"
                )
            }
            return .toolOutput(toolOutput)
        }
    }
}
```

## App Intents mitigations

When an App Intent adopts an App Schema, it becomes available as a tool to the Siri model. Because a model chooses which intent to invoke, prompt injection can misuse app actions unless deterministic guardrails are present.

The App Intents system provides risk-based contextual confirmations and lock-screen authentication. Risk evaluation uses static intent risk metadata plus dynamic system state. Intent schemas carry internal risk metadata and default authentication policies, which schema-adopting intents inherit automatically.

- High-risk actions, such as destructive operations or data-exfiltrating actions, are more likely to trigger user confirmation before execution.
- If the user declines a system confirmation, execution is blocked and the intent is not invoked.
- Custom App Intents can set `authenticationPolicy` explicitly, for example to require unlock before destructive behavior.
- Schema-adopting intents inherit the schema's default authentication policy; developers may override it only with a stricter policy, not a weaker one.

### Require authentication for a destructive custom intent

Use `authenticationPolicy` to prevent risky custom intents from running from the lock screen without authentication.

```swift
struct DeletePhotoIntent: DeleteIntent {
    var entities: [LooseLeafPhoto]

    static var authenticationPolicy: IntentAuthenticationPolicy = .requiresAuthentication

    func perform() async throws -> some IntentResult {
        // Implementation
    }
}
```

### Schema-adopting intent inherits schema authentication policy

Intent schemas can define default authentication behavior based on the sensitivity of the schema and data it handles.

```swift
@AppIntent(schema: .photos.deleteAssets)
struct DeletePhotoIntent {
    var entities: [LooseLeafPhoto]

    // Example: schema default authentication policy is .requiresAuthentication
    func perform() async throws -> some IntentResult {
        // Implementation
    }
}
```

Resources:
- Security Overview: https://developer.apple.com/security/

Chapters:
- 0:00 Introduction: Introduces agentic app features built with Foundation Models or App Intents and frames the security problem as protecting app behavior against external attackers. Clarifies that the focus is not model safety or model guardrail circumvention.
- 2:06 Risks: Explains why attackers target agentic apps and defines indirect prompt injection as attacker-controlled instructions embedded in context or tool results. Describes how action-calling agents can suffer data exfiltration, financial loss, data loss, data poisoning, and action poisoning.
- 6:32 Threat modeling: Walks through identifying data sources used to construct prompts, marking externally controlled sources as untrusted, and classifying agent actions by side effects. Recommends deterministic mitigations as the baseline, with prompt-level controls and action-execution controls layered together.
- 11:56 Implementing mitigations: Transitions from threat modeling to concrete platform mechanisms for securing agentic features. The remainder covers Foundation Models lifecycle modifiers and App Intents guardrails.
- 12:03 Foundation Models: Shows how to build a simple Foundation Models agent with tools and a profile, then inject security policy with lifecycle event modifiers. Demonstrates `.onToolCall` for user confirmation and `.historyTransform` for spotlighting untrusted tool output or redacting sensitive data before inference.
- 17:55 App Intents: Explains how App Intents exposed to Siri can be selected by a model and therefore need guardrails against poisoned context. Covers contextual risk-based confirmations, schema-derived risk metadata, and authentication policies for lock-screen protection.

### Bringing Cyberpunk 2077 to Mac

- Session ID: wwdc2026-356
- Page: https://wwdc.ai/2026/356
- Markdown: https://wwdc.ai/2026/356.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/356/
- Category: Graphics & Games
- Description: CD PROJEKT RED explains how Cyberpunk 2077 reached macOS with native Apple silicon builds, Metal, MetalFX, platform features, and per-Mac presets.
- Duration: 27:54
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-356/eng_0a29cd5a7295/wwdc2026-356-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/356/5/d3ce460b-554d-4760-ae03-072c5acf42aa/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/356/5/d3ce460b-554d-4760-ae03-072c5acf42aa/downloads/wwdc2026-356_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/356/5/d3ce460b-554d-4760-ae03-072c5acf42aa/downloads/wwdc2026-356_sd.mp4?dl=1

CD PROJEKT RED explains how Cyberpunk 2077 reached macOS with native Apple silicon builds, Metal, MetalFX, platform features, and per-Mac presets.

TLDR:
- Use Game Porting Toolkit early to run the Windows build on macOS, gather feasibility and hotspot data, and turn CPU/GPU signals into a native-port roadmap.
- A production Mac target needs native Apple silicon builds, adapted data and shader pipelines, architecture validation, Metal rendering, and Metal Shader Converter integration.
- MetalFX Upscaling with Dynamic Resolution Scaling helped Cyberpunk 2077 maintain stable performance across Mac hardware without broadly lowering visual quality.
- The "For this Mac" preset and native macOS features-notifications, Game Mode, Game Controller, EDR HDR, spatial audio, and iCloud Drive saves-made the first-launch experience feel tuned for each device.

## Porting goals and quality bar

CD PROJEKT RED framed the Mac port around three requirements: preserve Cyberpunk 2077's visual identity, keep performance stable in demanding scenes, and make the game feel native on macOS. The game is a heavy benchmark because Night City combines dense streaming, crowds, traffic, AI, physics, animation, quests, mixed lighting, reflections, volumetrics, ray tracing, and path tracing.

Apple silicon maturity made it feasible to target a serious quality level rather than merely getting the game running. The team treated Mac as another supported platform in its production process, not as a one-off compatibility layer.

- Visual fidelity: lighting, materials, post effects, ray tracing, and path tracing needed to match the game's identity on other platforms.
- Stable performance: test dense driving, combat, crowds, screen-space reflections, streaming, and other high CPU/GPU scenes.
- Native feel: adopt macOS behaviors and Apple-platform features instead of shipping only a basic renderable build.

## Evaluate first with Game Porting Toolkit

Before building the native path, the team used Game Porting Toolkit to run the Windows build in a translated environment on macOS. The goal was not final performance numbers; it was to learn whether the port could meet the quality bar and where frame time pressure would likely appear.

They ran a fixed set of hotspot sequences and inspected data from multiple angles: in-engine statistical frame-time profiling, Metal HUD correlation, and engine thread breakdowns. Once native builds were available, Metal HUD became the primary frame-time capture tool because it could collect comparable data across many devices on builds without debug or profiling settings enabled.

- Use the evaluation environment before writing port-specific code to identify CPU, GPU, shader, audio, streaming, and scene-specific risks.
- Expect some artifacts from the translated environment; Cyberpunk saw live shader-translation frame-time oscillation and audio middleware overhead that were resolved with native binaries.
- Convert evaluation findings into a roadmap: create a real Mac target, bring up rendering and shaders, then optimize and polish.

## Native Mac target, shaders, Metal, and MetalFX

The production port started by making macOS a real build target: native Apple silicon game binaries, native development tools, macOS outputs in the existing build pipeline, platform-specific archives, shader cache generation, and architecture validation with unit testing.

Metal Shader Converter was integrated into the shader build to quickly reach broad shader coverage. The team iterated by generating Metal shader output in normal builds, validating repeatable scenes, comparing lighting/material/post-effect results, and refining advanced shaders or edge cases. In parallel, they built a native Metal rendering foundation from unit tests to stationary scenes and then dynamic scenes with camera movement, streaming, gameplay, ray tracing, and path tracing.

MetalFX Upscaling and Dynamic Resolution Scaling provided performance headroom across the Mac lineup by rendering at a lower internal resolution and reconstructing a higher-resolution output while adapting under load.

- Treat shader conversion as a build-and-validation loop, not a one-time conversion step.
- Validate image correctness progressively: unit tests, stationary scenes, dynamic traversal, then heavy gameplay.
- Use MetalFX with Dynamic Resolution Scaling to maintain stable frame time in CPU/GPU-heavy scenes while preserving motion quality.

## "For this Mac" graphics preset

The "For this Mac" preset is a device-based graphics preset system. It detects the hardware in the player's Mac and automatically chooses a stable, high-quality starting point for that specific device.

The team tuned each supported Mac by selecting a 30 or 60 FPS target, enabling MetalFX with Dynamic Resolution Scaling, setting minimum and maximum internal resolution bounds, configuring output resolution, V-Sync, and HDR, then iterating across scenes that stress CPU, GPU, and streaming systems.

- Choose an image-fidelity target per supported Mac before optimizing individual settings.
- Set a target FPS, then tune MetalFX Dynamic Resolution Scaling bounds to stay within the frame-time budget.
- Tune video settings as part of the preset: final output resolution, V-Sync/frame pacing, and HDR defaults based on display capability.
- Revalidate in consistent hotspot scenes across the lineup until the default experience is both performant and visually acceptable.

## macOS integration and native platform features

The Mac version responds to macOS windowing, app switching, display, and cursor events through AppKit notifications. For example, it reduces CPU/GPU activity when not visible, updates the game window when display parameters change, handles moving to a new display, and switches between the game cursor and system cursor when focus changes.

Game Mode is automatically enabled for apps categorized as games and gives the game higher priority access to CPU/GPU resources while reducing background-task impact. It also improves latency for wireless controllers and AirPods by increasing Bluetooth sampling rate.

Input support uses Game Controller framework for third-party controllers and advanced features such as touchpad and adaptive triggers. The game also adapts controls for Magic Mouse and trackpad, including toggle aiming and an alternative to middle mouse button behavior using a modifier key plus click.

- Use `NSWindowDidChangeOcclusionStateNotification` plus `NSWindow.occlusionState` to pause or resume rendering when the game is hidden or visible.
- Use `NSApplicationDidChangeScreenParametersNotification` to update window sizing after display configuration changes.
- Use `NSWindowDidChangeScreenNotification` to collect details about a newly assigned display, such as Display ID, resolution, mirror mode, and screen name.
- Use `NSWindowDidResignKeyNotification` and `NSWindowDidBecomeKeyNotification` to coordinate custom game cursor visibility with the system cursor.

### Representative AppKit notification handling

The session highlights these notifications as key hooks for background rendering, display changes, moving between displays, and cursor/focus behavior.

```text
// Names used by Cyberpunk 2077's Mac integration
NSWindowDidChangeOcclusionStateNotification
NSApplicationDidChangeScreenParametersNotification
NSWindowDidChangeScreenNotification
NSWindowDidResignKeyNotification
NSWindowDidBecomeKeyNotification
```

## EDR HDR, spatial audio, and saves

Cyberpunk 2077 uses Apple's Extended Dynamic Range pipeline to calibrate HDR automatically on Apple displays. The game polls the display's current maximum EDR value and sends it to the tone mapper, and it enables HDR by default when the display has sufficient EDR headroom.

The game also enables head-tracked spatial audio for AirPods through audio middleware that implements Apple spatial audio APIs via AVAudioEngine. Saves are supported through iCloud Drive integration on Apple devices, alongside CD PROJEKT RED's cross-progression system for continuing across platforms.

- Poll `maximumExtendedDynamicRangeColorComponentValue` for the current display and drive the tone mapper's maximum HDR output from that value.
- Check `maximumPotentialExtendedDynamicRangeColorComponentValue`; Cyberpunk enables HDR by default when the display's maximum potential EDR value is greater than 2.0.
- Enable AirPods head tracking by setting `AVAudioEnvironmentNode.listenerHeadTrackingEnabled` to `true` when using AVAudioEngine-based spatial audio.
- Use iCloud Drive integration to reduce save-transfer friction between Apple devices.

### Automatic EDR-driven HDR calibration

The game uses the current EDR value for tone mapping and enables HDR by default on displays with enough potential EDR headroom.

```swift
let currentEDR = screen.maximumExtendedDynamicRangeColorComponentValue
toneMapper.maximumHDROutput = currentEDR

let potentialEDR = screen.maximumPotentialExtendedDynamicRangeColorComponentValue
let enableHDRByDefault = potentialEDR > 2.0
```

### Head-tracked spatial audio with AirPods

Cyberpunk's audio middleware uses AVAudioEngine spatial audio APIs and enables head tracking through `AVAudioEnvironmentNode`.

```text
audioEnvironmentNode.listenerHeadTrackingEnabled = true
```

Resources:
- Performing your own tone mapping: https://developer.apple.com/documentation/Metal/performing-your-own-tone-mapping
- Personalizing spatial audio in your app: https://developer.apple.com/documentation/PHASE/personalizing-spatial-audio-in-your-app
- Download the Game Porting Toolkit: https://developer.apple.com/games/game-porting-toolkit/

Chapters:
- 0:00 Introduction: Garrett Austin introduces Paweł Sasko from CD PROJEKT RED to discuss how Cyberpunk 2077: Ultimate Edition was brought to Mac with strong performance across the Mac lineup.
- 0:44 What is Cyberpunk 2077: The session summarizes Cyberpunk 2077 as a dense open-world RPG whose streaming, AI, physics, crowds, lighting, reflections, ray tracing, and path tracing make it a demanding benchmark.
- 2:57 Why bring it to Mac: CD PROJEKT RED chose Mac because of its history on Apple platforms, the maturity of Apple silicon, and the opportunity to reach more players while meeting the studio's quality bar.
- 3:45 How we brought it to Mac: The team defines a successful Mac port as preserving visual fidelity, maintaining stable performance, and adding native platform polish before outlining the production approach.
- 4:50 Game Porting Toolkit evaluation: Game Porting Toolkit was used to evaluate the Windows build before native work began, with in-engine profiling, Metal HUD, and thread breakdowns informing a roadmap for native builds, Metal rendering, shaders, optimization, and polish.
- 13:05 What we did to stand out: The focus shifts from a playable native build to a polished first-launch Mac experience with platform-specific features and optimized defaults.
- 13:16 "For this Mac" preset: The device-based preset detects the Mac and configures graphics, MetalFX Dynamic Resolution Scaling, FPS target, resolution bounds, V-Sync, and HDR; the chapter also covers AppKit notifications, Game Mode, controller support, EDR HDR, spatial audio, and save integration.
- 26:59 Next steps: Developers are encouraged to try Game Porting Toolkit's evaluation environment, inspect performance with Metal HUD, build optimized first-launch settings, and adopt native features such as EDR and AirPods head-tracked spatial audio.

### Speedrun your game port with agentic coding

- Session ID: wwdc2026-357
- Page: https://wwdc.ai/2026/357
- Markdown: https://wwdc.ai/2026/357.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/357/
- Category: Graphics & Games
- Description: Use Game Porting Toolkit 4 agentic skills to structure, implement, validate, debug, and tune native Apple-platform game ports with Metal 4 and MetalFX.
- Duration: 28:00
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-357/eng_4e4532d258a1/wwdc2026-357-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/357/5/5cfd0ceb-598f-4535-9abc-12e22a778326/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/357/5/5cfd0ceb-598f-4535-9abc-12e22a778326/downloads/wwdc2026-357_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/357/5/5cfd0ceb-598f-4535-9abc-12e22a778326/downloads/wwdc2026-357_sd.mp4?dl=1

Use Game Porting Toolkit 4 agentic skills to structure, implement, validate, debug, and tune native Apple-platform game ports with Metal 4 and MetalFX.

TLDR:
- Game Porting Toolkit 4 adds agentic expert skills and workflow skills that guide coding agents through game-porting tasks with Apple-platform best practices.
- The porting assistant uses a Discover, Plan, Execute-and-Validate workflow with milestone-specific skills, ground-truth captures, Metal validation, anti-pattern checks, and memory checks.
- Metal 4 porting guidance covers windowing and frame pacing, residency sets, shader converter reflection, argument buffer layout, and explicit producer-consumer synchronization.
- macOS 27 GPU command-line tools, `gpucapture` and `gpudebug`, let agents capture and inspect GPU traces autonomously; MetalFX skills and HUD overlays help validate upscaling and frame interpolation.

## Agentic porting workflow in Game Porting Toolkit 4

Game Porting Toolkit 4 provides agentic skills for coding assistants: expert skills encode Apple-platform porting knowledge, while workflow skills impose a structured process. The porting assistant coordinates those skills so milestone work loads the relevant expertise automatically instead of relying on the model to choose the right context.

The demonstrated workflow ports Microsoft MiniEngine, a D3D12 open-source engine, to macOS using Claude Code. The same approach is described as applicable to larger projects, including adding Metal 4 alongside an existing Metal 3 backend in Godot.

- Discover: scan the codebase, capture reference output from the evaluation environment, and gather developer preferences.
- Plan: define porting goals and break the work into milestones small enough for agent sessions.
- Execute and Validate: implement milestone changes, then verify app launch, Metal API and shader validation, visual correctness, ground-truth comparisons, anti-pattern review, and memory issues.
- Skills preserve learned context across milestones so decisions and findings are not lost between sessions.

### Install Game Porting Toolkit skills

Add the Game Porting Toolkit marketplace and install the skills plugin before invoking the porting assistant on a codebase.

```text
/plugin marketplace add apple/game-porting-toolkit
/plugin install game-porting-skills@game-porting-toolkit
```

## Windowing, presentation, and frame pacing

The first milestone brings up a native window and a stable render loop before renderer work begins. Several skills work together: window creation and lifecycle, translating D3D12 swap-chain concepts to Metal, presenting drawables, and `metal-cpp` object lifetime patterns.

The goal is not just to show pixels; it is to avoid common blank-output, stutter, and frame-pacing failures before more complex rendering is added.

- Use Metal Display Link to drive the render loop.
- Handle lifecycle events such as focus changes and fullscreen transitions.
- Configure layer resolution and color space appropriately for games.
- Use direct-to-display presentation where appropriate for lower latency.
- Manage drawable lifetimes and resource residency to avoid stuttering and invalid GPU access.

## Rendering with Metal 4

The Metal 4 renderer bring-up is split into GPU resources, shader pipelines, and command encoding. The skills bridge D3D12 engine assumptions to Metal 4's explicit memory management, shader binding model, and synchronization model.

For MiniEngine, the initial rendering goals include depth, shadow, and color passes, then SSAO and tone mapping, followed by dynamic lights using a compute-based light-culling pass. Validation compares GPU work against the evaluation environment before moving to later milestones.

- Register textures and other GPU resources in residency sets before use so the GPU can access them reliably.
- Use Metal Shader Converter runtime information instead of assuming D3D12-style descriptor layouts map directly to Metal argument buffers.
- Map D3D12 states to Metal 4 producer and consumer stages instead of relying on broad blanket barriers.
- Use shader reflection to detect parameter-count and layout mismatches that can silently shift bindings and break rendering.

### Register resources for residency

Metal 4 resource access requires explicit residency management; skipping registration can compile but produce incorrect GPU results.

```text
// With skill
residencySet->addAllocation(texture);
residencySet->commit();
// ...
argumentTable->setAddress(texture->gpuAddress(), bindPoint);

// Without skill
argumentTable->setAddress(texture->gpuAddress(), bindPoint);
```

### Map D3D12 states to Metal 4 stages

The synchronization skill teaches the agent to translate D3D12 resource-state transitions into Metal 4's explicit producer-consumer barrier model.

```text
// With skill
m_MtlPendingProducerStages |= MtlProducerStageFromD3D12(OldState);
m_MtlPendingConsumerStages |= MtlConsumerStageFromD3D12(NewState);
// ...
m_ComputeEncoder->barrierAfterStages(
    m_MtlPendingProducerStages,
    m_MtlPendingConsumerStages,
    MTL4::VisibilityOptionDevice);

// Without skill
m_ComputeEncoder->barrierAfterStages(
    MTL::StageDispatch,
    MTL::StageAll,
    MTL4::VisibilityOptionDevice);
```

## Shader layout and reflection pitfalls

Several rendering bugs come from assuming that MiniEngine's D3D12 root-signature layout can be reused directly. The shader converter skills guide the agent to ask the Metal Shader Converter runtime for actual argument buffer offsets and reflected shader parameters.

These checks matter because layout mismatches may not produce immediate API errors; they can instead appear as incorrect lighting, shifted sampler tables, stretched textures, or other visual defects.

- Query argument buffer offsets from the Metal Shader Converter runtime rather than computing `parameterIndex * descriptorSize`.
- Reflect compiled shader objects to determine the actual parameter count used by the converted shader.
- Use reflection results to keep descriptor tables, sampler tables, and root-signature translations aligned with the shader.

### Query argument buffer offsets

The Metal Shader Converter runtime owns the converted layout; hardcoded offset calculations can bind resources at the wrong locations.

```text
// With skill
IRRootSignatureGetResourceLocations(m_MtlCurIRRootSig, locations);
size_t offset = locations[i].topLevelOffset;

// Without skill
size_t offset = paramIndex * descriptorSize;
```

### Query shader reflection parameter count

Reflection avoids carrying over stale D3D12 root-parameter counts when the converted HLSL shader declares a different layout.

```text
// With skill
IRShaderReflection* refl = IRShaderReflectionCreate();
IRObjectGetReflection(compiledObj, IRShaderStageCompute, refl);
// ...
s_RootSignature.Reset(4, 2); // Reflection reveals: 4 params

// Without skill
s_RootSignature.Reset(5, 2);
```

## Autonomous GPU debugging with command-line tools

macOS 27 introduces command-line GPU debugging tools designed for agent workflows. `gpucapture` captures a GPU frame trace, and `gpudebug` inspects captures without requiring manual Xcode interaction.

In the MiniEngine port, the agent starts from visual symptoms, captures a trace, inspects resource bindings, constants, resource contents, pipeline data flow, dispatch calls, and dispatch dimensions, then compares against ground-truth captures to find and fix rendering issues.

- Use `gpucapture` while the app is running to collect a frame trace.
- Use `gpudebug` to inspect the trace similarly to GPU debugging in Xcode.
- Let validation compare pipelines, dispatch calls, dimensions, and other capture details against reference traces.
- Use the tools both for root-cause analysis and for milestone validation.

## Game controllers and MetalFX integration

The game controller skill ports Windows input assumptions to the Game Controller framework. Instead of hardcoding an XInput-style fixed layout, the agent is guided to discover connected controllers, query supported controls, and handle connect and disconnect events dynamically.

MetalFX work is split into temporal upscaling and frame interpolation skills. The upscaling skill covers jitter in pixel space, motion-vector scale and conventions, MIP bias, and history reprojection. The frame interpolation skill covers a dedicated present thread, precise frame timing, and correct presentation order.

- Use `GCController` discovery and dynamic button-layout queries rather than fixed XInput mappings.
- Use MetalFX HUD overlays in macOS 27 to verify exposure, jitter sequence, and motion-vector scale behavior.
- Use HUD overrides for jitter multipliers and motion-vector scales to diagnose artifacts while the app is running.
- If HUD overrides fix the image, trace the fix back to the app logic that computes jitter or motion-vector values.

Chapters:
- 0:00 Introduction: Game Porting Toolkit 4 introduces agentic expert and workflow skills that give coding agents Apple-platform porting knowledge. The session frames these skills as a way to reduce manual guidance and improve port quality.
- 1:06 Porting assistant workflow: The porting assistant workflow is organized into Discover, Plan, and Execute-and-Validate stages. It scans the codebase, captures reference output, defines milestones, loads relevant skills, and validates app launch, Metal correctness, visual results, anti-patterns, and memory behavior.
- 6:25 Windowing and frame pacing: The windowing milestone uses skills for native windows, Metal swap-chain translation, drawable presentation, frame pacing, and `metal-cpp` lifetime handling. The agent sets up Metal Display Link, lifecycle handling, layer configuration, direct-to-display presentation, and drawable/resource lifetime management.
- 8:28 Scene rendering with Metal 4: The rendering section covers Metal 4 resource management, shader pipeline setup, and command encoding for a D3D12-to-Metal port. Key details include residency sets, constant buffers, Metal Shader Converter runtime layout queries, shader reflection, and mapping D3D12 states to Metal 4 explicit barriers.
- 15:16 Debugging with GPU command-line tools: macOS 27's `gpucapture` and `gpudebug` tools enable agent-driven GPU frame capture and inspection without manual Xcode use. The agent uses them to trace rendering divergence, inspect bindings and dispatches, implement a fix, and validate against reference captures.
- 19:09 Game controllers and MetalFX: The controller skill ports from XInput to `GCController` by using discovery, dynamic layout queries, and connect/disconnect handling. MetalFX skills integrate temporal upscaling and frame interpolation, while macOS 27 Metal HUD overlays and overrides help validate exposure, jitter, motion vectors, and frame pacing.
- 25:11 Next steps: The recap recommends installing the Game Porting Toolkit 4 skills from the GitHub repository and invoking the porting assistant on a game codebase. Developers remain responsible for architectural decisions, review, and game-specific context while the skills supply platform guidance.

### Make your game great with touch

- Session ID: wwdc2026-358
- Page: https://wwdc.ai/2026/358
- Markdown: https://wwdc.ai/2026/358.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/358/
- Category: Graphics & Games
- Description: Design and implement adaptive, low-clutter iPhone and iPad game touch controls with Touch Controller, Game Controller, UIKit, and Metal.
- Duration: 24:23
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-358/eng_558d0fe802bc/wwdc2026-358-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/358/4/fdd21d54-a233-49d4-8d00-4dc51284515d/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/358/4/fdd21d54-a233-49d4-8d00-4dc51284515d/downloads/wwdc2026-358_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/358/4/fdd21d54-a233-49d4-8d00-4dc51284515d/downloads/wwdc2026-358_sd.mp4?dl=1

Design and implement adaptive, low-clutter iPhone and iPad game touch controls with Touch Controller, Game Controller, UIKit, and Metal.

TLDR:
- Touch Controller builds on Game Controller: a TCTouchController appears as a GCController, so existing controller polling and value-changed input logic can often be reused.
- Use flexible layouts with anchors, offsets, sections, and UIKit safeAreaInsets so controls stay reachable and avoid the Dynamic Island, home indicator, rounded corners, and core gameplay space.
- Do not ship a one-to-one controller overlay: use contextual icons, hide irrelevant controls, replace overlays with direct touch controls, and redesign multi-button actions for two-finger play.
- Improve movement and camera feel with large collider shapes, tilt-magnitude sprint, TCTouchpad relative input, and clear visual feedback such as custom TCControlContents layers.

## Touch Controller builds on existing controller input

The session starts from a game that already supports physical controllers through the Game Controller framework. Touch Controller extends that model to touch input: when enabled, the touch controller appears as a GCController, allowing existing polling or value-changed handler code to keep driving game logic.

Setup has three parts: create a TCTouchController from a descriptor, connect it, forward UIKit touch events into the touch controller, and render controls through the Metal renderer.

- Use TCTouchControllerDescriptor with the game's MTKView.
- Call connect() to enable the touch controller and the controller input path.
- Forward touchesBegan, touchesMoved, and touchesEnded from UIView.
- Call render(using:) from the Metal rendering path.
- Map controls with labels such as TCControlLabel.buttonB so existing controller actions continue to work.

### Create, connect, render, and forward touches

A TCTouchController integrates with UIKit touch delivery, Metal rendering, and GCController-style input handlers.

```swift
private(set) var touchController: TCTouchController?

let descriptor = TCTouchControllerDescriptor(mtkView: mtkView)
if TCTouchController.isSupported {
    touchController = TCTouchController(descriptor: descriptor)
}

touchController?.connect()
touchController?.render(using: renderEncoder)

override func touchesBegan(_ touches: Set<UITouch>, with event: UIEvent?) {
    for touch in touches {
        touchControls.handleTouchBegan(at: touch.location(in: view), index: touch.hash)
    }
}

buttonA?.valueChangedHandler = { button, value, pressed in
    // Existing game action logic
}
```

## Design adaptive layouts instead of fixed overlays

A good touch layout must work across iPhone and iPad screen sizes. Touch Controller provides nine layout anchors; controls can be placed with offsets relative to those anchors, and related controls can be grouped into sections that preserve size and distance as the device changes.

Fullscreen games must account for safe areas. UIKit safeAreaInsets should be applied to offsets so touch targets do not conflict with rounded corners, the home indicator, or the Dynamic Island. Frequently used controls should stay near the thumbs, less frequent controls can move toward the top, and the center should remain clear for gameplay.

- Anchor frequent actions near bottom-left and bottom-right thumb regions.
- Avoid expected movement and camera input regions unless a control is intentionally using those areas.
- Keep the player character and important play space unobscured.
- Use safeAreaInsets when calculating offsets for fullscreen placement.

### Add a safe-area-aware circular button

Controls are created through descriptors, mapped to controller labels, anchored, offset, given visual contents, and added to TCTouchController.

```swift
let buttonBDesc = TCButtonDescriptor()
buttonBDesc.label = TCControlLabel.buttonB
buttonBDesc.anchor = .bottomRight
buttonBDesc.offset = adjustedOffset(CGPoint(x: -35, y: -106), for: buttonBDesc.anchor)
buttonBDesc.contents = .buttonContents(
    forSystemImageNamed: "b.circle",
    size: buttonBDesc.size,
    shape: .circle,
    controller: touchController
)

touchController.addButton(descriptor: buttonBDesc)

func adjustedOffset(_ offset: CGPoint, for anchor: TCControlLayoutAnchor) -> CGPoint {
    var x = offset.x
    var y = offset.y
    switch anchor {
    case .bottomRight:
        x -= safeArea.right
        y -= safeArea.bottom
    default:
        break
    }
    return CGPoint(x: x, y: y)
}
```

## Make controls dynamic and contextual

A direct physical-controller mapping can clutter the screen and force players to interpret abstract button labels. Touch controls can communicate game state directly by changing their glyphs, appearing only when useful, and replacing non-touch-friendly overlays with actual touch targets.

The example changes Button B from a generic controller label to action-specific SF Symbols, updates that symbol when the selected power changes, hides thumbsticks when idle, shows a pickup button only near nearby items, and presents power choices as temporary touch controls instead of a separate overlay.

- Use action glyphs instead of physical-button names when that improves clarity.
- Update control contents when a button's meaning changes with game state.
- Set hidesWhenNotPressed for thumbsticks that should disappear when idle.
- Use isEnabled to show or hide controls with stable positions; add/remove controls when their position is dynamic.
- Auto-dismiss temporary controls such as a power picker if no selection is made.

### Update a button icon based on context

Dynamic contents let one touch button represent the currently available action instead of exposing multiple inactive buttons.

```swift
func setButtonBContents(symbolName: String) {
    for button in touchController.buttons where button.label == TCControlLabel.buttonB {
        button.contents = .buttonContents(
            forSystemImageNamed: symbolName,
            size: buttonSize,
            shape: .circle,
            controller: touchController
        )
    }
}

func cyclePower() {
    switch currentPower {
    case .strike:
        touchControls?.setButtonBContents(symbolName: "figure.fencing")
    case .fireball:
        touchControls?.setButtonBContents(symbolName: "flame.fill")
    case .waterBlaster:
        touchControls?.setButtonBContents(symbolName: "drop.fill")
    }
}
```

### Hide idle or unavailable controls

Remove or hide controls when they are not actionable to keep the play area clean.

```swift
let leftStickDesc = TCThumbstickDescriptor()
leftStickDesc.hidesWhenNotPressed = true

touchController.addThumbstick(descriptor: leftStickDesc)

func hidePickupButton() {
    for button in touchController.buttons where button.label == TCControlLabel.buttonY {
        touchController.removeControl(button)
    }
}
```

## Redesign interactions for touch, not for a controller diagram

The strongest guidance is to rethink interactions that require multiple simultaneous physical inputs. Touch players may only have two comfortable fingers available, so controller combinations should often become single, contextual actions.

For movement, the session expands hit areas beyond visible controls by using leftSide and rightSide collider shapes. Sprint no longer requires pressing a stick while moving; instead, the game checks left thumbstick tilt magnitude. Camera control moves from a virtual right thumbstick to a TCTouchpad that reports relative values across the right half of the screen.

- Use .leftSide or .rightSide colliderShape to make large, forgiving input areas.
- Trigger sprint from thumbstick magnitude instead of requiring a second input.
- Use TCTouchpad with reportsRelativeValues for camera gestures that feel direct and avoid over-rotation.
- Collapse quick-time-event button combinations into one event-specific touch button.
- For aim-and-release powers, hold one action button, drag to aim via raw touch delta, and release to fire.

### Use the left half of the screen for movement and magnitude for sprint

A large collider makes movement easier to start, while thumbstick tilt magnitude replaces a separate sprint button.

```swift
let leftStickDesc = TCThumbstickDescriptor()
leftStickDesc.colliderShape = .leftSide

touchController.addThumbstick(descriptor: leftStickDesc)

func pollInput() {
    if let gamePad = gameController.extendedGamepad {
        let gamePadLeft = gamePad.leftThumbstick
        let moveInput = simd_make_float2(gamePadLeft.xAxis.value, -gamePadLeft.yAxis.value)
        let magnitude = simd_length(moveInput)

        if magnitude > 0.8 {
            self.runModifier = 1.3
        }
        self.characterDirection = moveInput
    }
}
```

### Replace a virtual camera stick with a relative touchpad

A right-side TCTouchpad maps to existing right-thumbstick camera logic while using relative finger movement.

```swift
let touchpadDesc = TCTouchpadDescriptor()
touchpadDesc.label = TCControlLabel.rightThumbstick
touchpadDesc.colliderShape = .rightSide
touchpadDesc.reportsRelativeValues = true

touchController.addTouchpad(descriptor: touchpadDesc)
```

### Hold, drag, and release with one action button

Aiming is tracked from raw touch movement while the button is held; releasing the same button fires the power.

```swift
buttonB?.valueChangedHandler = { button, value, pressed in
    self.releasePower(pressed: pressed)
}

override func touchesMoved(_ touches: Set<UITouch>, with event: UIEvent?) {
    for touch in touches {
        let point = touch.location(in: metalView)
        if let gc = gameController, gc.isAiming {
            let prev = touch.previousLocation(in: metalView)
            gc.aimTouchDelta += simd_float2(Float(point.x - prev.x), Float(point.y - prev.y))
        }
    }
}
```

## Provide visible feedback for touch state

Touch controls need clear feedback because players cannot feel a physical button or stick. Touch Controller supplies default pressed states: buttons highlight when pressed and thumbsticks animate as they move.

Busy games may need stronger custom feedback. The session demonstrates adding a glowing halo around the left thumbstick when sprint is active by constructing TCControlContents from layered TCControlImage values, including a custom Metal texture, then swapping between normal and halo backgrounds.

- Every touch control should have an obvious pressed or active state.
- Use built-in highlighting and thumbstick animation where sufficient.
- Use custom TCControlContents when game state needs stronger feedback, such as sprint activation.

### Add a custom halo layer to thumbstick contents

TCControlContents can layer custom imagery with standard control art to reflect active gameplay states.

```swift
let haloLayer = TCControlImage(
    texture: haloTexture,
    size: haloSize,
    highlight: nil,
    offset: .zero,
    tintColor: tint
)

let normalBgImages = TCControlContents
    .thumbstickStickBackgroundContents(size: bgSize, controller: controller)
    .images

haloThumbstickBg = TCControlContents(images: [haloLayer] + normalBgImages)
thumbstick.backgroundContents = active ? haloThumbstickBg : normalThumbstickBg
```

Chapters:
- 0:00 Introduction: Introduces why iPhone and iPad games need high-quality touch controls even when controller support exists. The session frames the workflow around setup, flexible layouts, fluid interactions, and player feedback.
- 1:42 Set up a touch controller: Explains that Touch Controller extends Game Controller so touch input appears as a GCController. Shows creating and connecting a TCTouchController, forwarding UIView touches, rendering through Metal, and using existing polling or value-changed handlers.
- 4:52 Design flexible layouts: Covers adaptive placement with nine anchors, relative offsets, grouped sections, and safe area handling. Demonstrates adding a safe-area-aware Button B and choosing screen regions that are comfortable without obscuring gameplay.
- 10:17 Design fluid interactions: Reworks a cluttered one-to-one controller overlay into contextual touch controls. Topics include dynamic icons, hiding unavailable controls, direct power-selection controls, large movement hit areas, magnitude-based sprinting, TCTouchpad camera input, simplified QTEs, and hold-drag-release aiming.
- 21:16 Provide rich feedback: Describes built-in pressed states and custom visual feedback for busy games. Demonstrates a sprint halo by layering a custom Metal texture into TCControlContents for the thumbstick background.
- 23:49 Next steps: Recaps the improved design: cleaner controls, contextual buttons, right-side camera touchpad, one-button aim and release, and sprint feedback. Recommends designing touch controls with Touch Controller, testing across device sizes, and iterating based on player feedback.

### Build real-time neural rendering pipelines with Metal

- Session ID: wwdc2026-359
- Page: https://wwdc.ai/2026/359
- Markdown: https://wwdc.ai/2026/359.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/359/
- Category: Graphics & Games
- Description: Integrate ML into Metal 4 renderers with MetalFX denoising, ML command encoder deployment, and inline shader networks using TensorOps.
- Duration: 22:16
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-359/eng_d6bbe0a17747/wwdc2026-359-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/359/5/9da4a720-0dcb-4b8e-b61b-ba8310a61f29/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/359/5/9da4a720-0dcb-4b8e-b61b-ba8310a61f29/downloads/wwdc2026-359_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/359/5/9da4a720-0dcb-4b8e-b61b-ba8310a61f29/downloads/wwdc2026-359_sd.mp4?dl=1

Integrate ML into Metal 4 renderers with MetalFX denoising, ML command encoder deployment, and inline shader networks using TensorOps.

TLDR:
- Use MetalFX Denoising for low-sample real-time path tracing; quality depends heavily on clean auxiliary inputs, primary surface replacement, and dejittered motion vectors.
- Deploy offline-trained neural render stages, such as a neural tone mapper, by exporting to MTLPackage and running them with the Metal 4 ML command encoder inside the same command buffer as rendering work.
- Build tiny specialized neural networks directly in Metal shaders with TensorOps and cooperative tensors, including online-trained models that adapt over frames.
- The session frames ML adoption as three levels: platform-integrated MetalFX, custom pre-trained models with the ML command encoder, and inline neural evaluation/training in shaders.

## Three levels of ML integration in Metal renderers

Metal 4 supports a progression of machine-learning integration points for real-time rendering. At the highest level, MetalFX provides platform-integrated neural denoising and upscaling. For custom trained models, the Metal 4 ML command encoder runs model inference directly in a command buffer. For maximum flexibility, TensorOps lets shaders evaluate small neural networks inline, using hardware acceleration on supported Apple silicon GPUs.

The session focuses on practical rendering use cases: denoising a one-sample-per-pixel path tracer, replacing a multi-stage post-processing chain with a neural tone mapper, and evaluating/training tiny scene-specific networks such as a sky illumination probe.

- MetalFX: ready-to-use neural denoising and upscaling for interactive renderers.
- ML command encoder: run pre-trained models in the same frame and command buffer as graphics, compute, and render passes.
- TensorOps API: implement small networks directly in shaders, including inference and back propagation routines.
- Relevant workloads include real-time path tracing viewports, games, learned tone mapping, image-based lighting, and other learnable rendering signals.

## MetalFX Denoising for low-sample path tracing

A real-time path tracer often has a frame budget of only one or a few samples per pixel. MetalFX Denoising is designed for low-latency live viewports and combines neural denoising with upscaling to turn noisy input into a temporally stable output.

The Redshift Live example from Maxon demonstrates a one-sample-per-pixel path-traced viewport using hardware-accelerated ray tracing plus MetalFX neural denoising to produce cleaner interactive lighting, shadows, and global illumination.

- Provide high-quality auxiliary inputs such as diffuse albedo, depth, and motion vectors; denoiser output quality tracks input quality.
- Treat diffuse albedo as a strong denoising signal and make it as close as possible to a noise-free version of the desired final result.
- Build debug views for every MetalFX input and inspect textures frame-by-frame in a GPU capture.
- Use the transparency overlay for noise-free layers such as particles, fog, volumetrics, or sky that should be upscaled and composited rather than denoised.
- Use the denoiser strength mask to reduce or disable denoising per pixel, from 0 for no denoising to 1 for maximum strength.

## Inputs, reflections, glass, and motion vectors

MetalFX quality depends on feeding the model the signal it expects. For mirror-like and transmissive materials, the auxiliary buffers should represent what the viewer sees rather than only the primary hit surface. The session describes this as primary surface replacement.

For mirrors, store reflected geometry properties such as albedo, normal, and roughness in the mirror-like object's auxiliary data. For glass, blend reflected and refracted geometry properties using the Fresnel term to reduce noisy inputs and preserve sharp reflection and refraction results.

Motion vectors are also critical for temporal stability. MetalFX expects dejittered per-pixel screen-space displacements from the current frame to the previous frame. If subpixel jitter is left in the motion vectors, edges can shimmer because vectors may be nearly a pixel wrong.

- For static objects, compute camera-only motion vectors by projecting the current world position with current and previous view-projection matrices, then subtracting the jitter delta.
- For moving or deforming geometry, store previous-frame world positions or skin twice to compute actual motion.
- For unreliable fast motion, such as alpha-blended particles, use the reactive mask rather than trusting bad motion vectors.

### Compute camera-only motion vectors

Projects the same world position into current and previous clip space, computes screen-space displacement, then removes the jitter delta to produce dejittered motion vectors for MetalFX.

```text
#include <metal_stdlib>
using namespace metal;

// Compute camera-only motion vectors
float4 clipCurrent = viewProjCurrent * float4(worldPos, 1.0);
float2 ndcCurrent = clipCurrent.xy / clipCurrent.w;

float4 clipPrevious = viewProjPrevious * float4(worldPos, 1.0);
float2 ndcPrevious = clipPrevious.xy / clipPrevious.w;

float2 motion = ndcPrevious - ndcCurrent;

// Get subpixel offset for current and previous frames
float2 jitterCurrent = getJitter(frameIndex);
float2 jitterPrevious = getJitter(frameIndexPrevious);

motion -= jitterPrevious - jitterCurrent;
```

## Deploy a custom neural tone mapper with the ML command encoder

Metal 4 can run a custom trained neural network inline with the rest of a renderer's command-buffer work. The example use case is replacing a complex HDR post-processing pipeline - tone mapping, color grading, film emulation, and related stages - with a single learned color transformation.

The session uses HDRNet as an example architecture. HDRNet operates on a downsampled image, performs global and local analysis, creates localized color transforms for 16×16 tiles, and applies them with edge-aware techniques to produce the final tone-mapped image.

- Train the network offline in a framework such as PyTorch, using manually tone-mapped projects or generated tone-mapped images from the renderer as training data.
- Export the trained model to an MTLPackage.
- During setup, load the MTLPackage, specify the network function with a function descriptor, and create a machine learning pipeline descriptor.
- During execution, create an ML command encoder, bind inputs and outputs through an argument table, and dispatch the work in the command buffer.
- A representative frame pipeline becomes: path tracing samples, MetalFX denoising, neural tone mapping, then display/output work.

## Inline neural networks with TensorOps

TensorOps supports small neural networks directly inside Metal shaders, alongside ALU, texture sampling, compute, and render work. This is aimed at tiny, specialized models with a few thousand parameters or fewer, often trained on scene-specific data rather than large general-purpose datasets.

The sky illumination example models average sky lighting from visible directions. Instead of relying on an offline-precomputed signal that can become stale during a dynamic day-night cycle, the renderer can run training iterations over frames: sample a direction, run inference, compute an analytical target, calculate error, and run back propagation to improve the model.

The example network is a fully connected multilayer perceptron with a 3-4-4-3 shape: three floats encode an input direction, two hidden layers have four neurons each, and three output floats represent color.

- Batch inputs as 2D tensors, such as a matrix of input directions; outputs can likewise be a 2D matrix of colors.
- Evaluate a forward pass by multiplying the input tensor by layer weights with a 2D tensor matmul, then applying activation functions between layers.
- Use thread execution scope when one thread should own the operation, such as divergent work or stages without full threadgroup control.
- Use SIMD group execution scope in compute when participating threads can cooperate on the same matrix multiplication.
- Use cooperative tensors to keep intermediate results distributed in fast thread storage memory instead of round-tripping through main memory.

## Adoption guidance and next steps

The recommended entry point depends on how much control the renderer needs. MetalFX is the fastest path for real-time apps that need denoising and upscaling. The ML command encoder is appropriate when a renderer has an offline-trained model to deploy as a standalone stage. TensorOps is for highly specialized inline networks where shader-level integration and possibly online training are needed.

- Start with MetalFX Denoising and Upscaling for real-time viewports and games, especially low-sample path tracing.
- Use the Metal 4 sample code and Xcode to explore the core API patterns.
- Try training a neural tone mapper against an existing post-processing pipeline before replacing it in production.
- Use the Metal Performance Primitives Programming Guide for TensorOps and back propagation implementation details.

Resources:
- Training a neural network to render irradiance in real time: https://developer.apple.com/documentation/Metal/training-a-neural-network-to-render-irradiance-in-real-time
- Metal sample code library: https://developer.apple.com/documentation/Metal/metal-sample-code-library
- Download the Metal Performance Primitives (MPP) Programming Guide: https://developer.apple.com/download/files/Metal-Performance-Primitives-Programming-Guide.pdf
- Understanding the Metal 4 core API: https://developer.apple.com/documentation/Metal/understanding-the-metal-4-core-api

Chapters:
- 0:00 Introduction: Introduces machine learning as a production rendering tool in Metal 4 and lays out three integration levels: MetalFX, the ML command encoder, and TensorOps in shaders.
- 2:16 MetalFX Denoising: Explains how to adopt MetalFX Denoising for one-sample-per-pixel path tracing, using Redshift Live as an example. Covers clean auxiliary inputs, transparency overlay, denoiser strength mask, primary surface replacement, and dejittered motion vectors.
- 9:57 Deploy custom ML networks with Metal 4: Shows how a renderer can train a neural tone mapper offline, export it to MTLPackage, and run it through the Metal 4 ML command encoder in the same command buffer as rendering work.
- 13:40 Inline neural networks with tensorOps: Describes using TensorOps and cooperative tensors to implement small MLPs directly in shaders. The sky illumination example demonstrates online training over frames and inline inference for dynamic scene data.
- 20:55 Next steps: Recaps the three Metal ML integration levels and recommends starting with MetalFX for real-time apps, then exploring neural tone mapping and specialized TensorOps networks with the Metal 4 resources.

### Find your accessory with Bluetooth Channel Sounding

- Session ID: wwdc2026-369
- Page: https://wwdc.ai/2026/369
- Markdown: https://wwdc.ai/2026/369.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/369/
- Category: System Services
- Description: Use Bluetooth Channel Sounding on iOS 27 to measure accessory distance with Core Bluetooth, or distance and direction with Nearby Interaction.
- Duration: 8:12
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-369/eng_85999316b43d/wwdc2026-369-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/369/4/fea90204-fd38-4da4-b9e7-5dce37bc87d8/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/369/4/fea90204-fd38-4da4-b9e7-5dce37bc87d8/downloads/wwdc2026-369_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/369/4/fea90204-fd38-4da4-b9e7-5dce37bc87d8/downloads/wwdc2026-369_sd.mp4?dl=1

Use Bluetooth Channel Sounding on iOS 27 to measure accessory distance with Core Bluetooth, or distance and direction with Nearby Interaction.

TLDR:
- Bluetooth Channel Sounding measures distance to paired, connected Bluetooth accessories more accurately than RSSI when the accessory does not include Ultra Wideband.
- Use Core Bluetooth when the app only needs distance; start a channel sounding session on a connected CBPeripheral and receive per-procedure distance results.
- Use Nearby Interaction when the app needs distance and direction; pass the Core Bluetooth peripheral identifier into NINearbyAccessoryConfiguration and enable Camera Assistance for direction.
- Accessory hardware must support Bluetooth 6.3, inline PCT, phase-based ranging mode-0 and mode-2, and T_FCS of at least 100 µs.

## What Bluetooth Channel Sounding adds

Bluetooth Channel Sounding lets an iPhone measure distance to a nearby Bluetooth accessory by sending tones to a connected accessory and measuring reflected tones across the 2.4 GHz band. In the Channel Sounding roles, iPhone is the initiator and the accessory is the reflector.

This is positioned as the best option for distance-aware experiences when the accessory only has a Bluetooth chipset. For the highest accuracy, Apple still points developers toward Ultra Wideband accessories with Nearby Interaction.

- Use Channel Sounding instead of RSSI when the app benefits from measured distance rather than rough signal-strength estimation.
- A single distance measurement is called a procedure; iOS repeatedly performs procedures during a session.
- The accessory should already be paired and set up with AccessorySetupKit and connected through Core Bluetooth before starting.

## Distance with Core Bluetooth

Core Bluetooth is the direct path when the app only needs distance. Check support on the local iOS device, then start a Channel Sounding session on a connected CBPeripheral. iOS performs repeated procedures and reports measured distance in meters through the peripheral delegate.

Cancel the session when the app no longer needs measurements. A completion delegate callback reports that the session has ended.

- Check CBCentralManager.supportsFeatures(.channelSounding) on iOS 27 or later.
- Call startChannelSoundingSession(_:) on a connected CBPeripheral using CBChannelSoundingSessionConfiguration(role: .initiator).
- Read distance from CBChannelSoundingProcedureResults.distance in the delegate callback.
- Call cancelChannelSoundingSession when finished; observe didCompleteChannelSoundingSession for completion.

### Start and receive Core Bluetooth Channel Sounding results

Shows the Core Bluetooth flow: check feature support, start a session on a connected peripheral, and receive distance results.

```swift
import CoreBluetooth

func isChannelSoundingSupported() -> Bool {
    guard centralManager.state == .poweredOn else { return false }
    if #available(iOS 27.0, *) {
        return CBCentralManager.supportsFeatures(.channelSounding)
    }
    return false
}

func startChannelSounding(_ peripheral: CBPeripheral) {
    guard peripheral.isConnected else { return }
    if #available(iOS 27.0, *) {
        let config = CBChannelSoundingSessionConfiguration(role: .initiator)
        peripheral.startChannelSoundingSession(config)
    }
}

func peripheral(_ peripheral: CBPeripheral,
                didReceive results: CBChannelSoundingProcedureResults?,
                error: Error?) {
    guard let results else { return }
    let distance = results.distance
    // Use distance in meters.
}
```

### Cancel a Core Bluetooth Channel Sounding session

End the session explicitly and handle the completion callback.

```swift
func cancelChannelSounding(_ peripheral: CBPeripheral) {
    guard peripheral.isConnected else { return }
    if #available(iOS 27.0, *) {
        peripheral.cancelChannelSoundingSession(config)
    }
}

func peripheral(_ peripheral: CBPeripheral,
                didCompleteChannelSoundingSession error: Error?) {
    // Session is complete.
}
```

## Distance and direction with Nearby Interaction

Nearby Interaction is the recommended API when the app needs both distance and direction to a Bluetooth accessory. The configuration links the Nearby Interaction session to the Core Bluetooth peripheral by passing the peripheral.identifier as the bluetoothChannelSoundingIdentifier.

Direction output requires Camera Assistance. If the app knows whether the accessory is moving or stationary, pass that motion state to Nearby Interaction to improve direction estimates.

- Check NISession.deviceCapabilities.supportsBluetoothChannelSounding before creating the configuration.
- Create NINearbyAccessoryConfiguration with bluetoothChannelSoundingIdentifier: peripheral.identifier.
- Enable config.isCameraAssistanceEnabled when NISession.deviceCapabilities.supportsCameraAssistance and direction is needed.
- Run an NISession with the accessory configuration and receive NINearbyObject updates containing optional distance and horizontalAngle values.

### Configure a Nearby Interaction Channel Sounding session

Creates the Nearby Interaction accessory configuration using the Core Bluetooth peripheral identifier and enables Camera Assistance when available.

```swift
import CoreBluetooth
import NearbyInteraction

func makeChannelSoundingConfiguration(for peripheral: CBPeripheral) -> NINearbyAccessoryConfiguration? {
    if #available(iOS 27.0, *) {
        guard NISession.deviceCapabilities.supportsBluetoothChannelSounding else { return nil }

        let config = NINearbyAccessoryConfiguration(
            bluetoothChannelSoundingIdentifier: peripheral.identifier,
            previousChannelSoundingIdentifier: nil
        )

        if NISession.deviceCapabilities.supportsCameraAssistance {
            config.isCameraAssistanceEnabled = true
        }
        return config
    }
    return nil
}
```

### Run the session and consume Nearby Interaction updates

Runs the NISession, optionally supplies accessory motion state, and reads distance and direction from Nearby Interaction updates.

```swift
let session = NISession()
session.delegate = self
session.run(config)

func updateAccessoryMotionState(_ isMoving: Bool, object: NINearbyObject) {
    let motionState: NIMotionActivityState = isMoving ? .moving : .stationary
    session.updateMotionState(motionState, forObjectWithToken: object.discoveryToken)
}

func session(_ session: NISession, didUpdate nearbyObjects: [NINearbyObject]) {
    guard let object = nearbyObjects.first else { return }

    if let distance = object.distance {
        // Use distance.
    }
    if let direction = object.horizontalAngle {
        // Use horizontal angle.
    }
}
```

## Runtime behavior and platform constraints

Nearby Interaction fuses raw Bluetooth Channel Sounding measurements with camera inputs when Camera Assistance is enabled. iOS also filters outliers and smooths results to improve the user experience.

Both distance and direction should be treated as optional. A distance value can be nil if a Channel Sounding measurement fails, and direction also depends on the necessary inputs being available.

- Channel Sounding APIs are described for iOS 27.
- Channel Sounding is available on iPhones with the N1 chip.
- Use Channel Sounding while the app is in the foreground; iOS pauses the session when the app moves to the background.
- iOS may reduce Channel Sounding measurement frequency when other Bluetooth or Wi-Fi activity increases.

## Accessory hardware requirements

Accessory-side support is required for Channel Sounding to work well with iOS. The accessory acts as the reflector and must implement the Bluetooth features and timing needed by iOS phase-based ranging.

- Support Bluetooth 6.3.
- Support the inline PCT feature.
- Support phase-based ranging mode-0 and mode-2 as defined by the Bluetooth specification.
- Support T_FCS interspace timing between tones of at least 100 µs.

Resources:
- AccessorySetupKit: https://developer.apple.com/documentation/AccessorySetupKit
- Nearby Interaction: https://developer.apple.com/documentation/NearbyInteraction
- Core Bluetooth: https://developer.apple.com/documentation/CoreBluetooth

Chapters:
- 0:01 Introduction: Introduces Bluetooth Channel Sounding as a way to find nearby Bluetooth accessories, then frames the session around use cases, app APIs, accessory hardware requirements, and next steps.
- 0:50 Overview: Explains the value of measuring distance to Bluetooth accessories and contrasts Channel Sounding with RSSI and Ultra Wideband. It describes the initiator/reflector model and how iPhone estimates distance from reflected tones across the 2.4 GHz band.
- 3:17 Core Bluetooth API: Shows the Core Bluetooth flow for distance-only use cases: verify local support, start a session on a connected CBPeripheral, receive distance results from repeated procedures, and cancel the session when done.
- 4:34 Nearby Interaction API: Shows how to use Nearby Interaction for distance and direction by creating an NINearbyAccessoryConfiguration from the Core Bluetooth peripheral identifier, enabling Camera Assistance, running an NISession, and handling optional distance and direction updates.
- 7:05 Hardware tips: Lists accessory requirements for iOS Channel Sounding support, including Bluetooth 6.3, inline PCT, phase-based ranging mode-0 and mode-2, and T_FCS timing of at least 100 µs. It also notes foreground use, possible measurement-rate reductions, and availability on iPhones with the N1 chip.

### Elevate your app's text experience with TextKit

- Session ID: wwdc2026-370
- Page: https://wwdc.ai/2026/370
- Markdown: https://wwdc.ai/2026/370.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/370/
- Category: SwiftUI & UI Frameworks
- Description: Use new TextKit hooks to extend UITextView and NSTextView with viewport-aware rendering, collapsible layout, and reusable text attachments.
- Duration: 23:46
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-370/eng_dacf0f06e656/wwdc2026-370-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/370/5/f61dbe38-7302-451a-b3ab-9851d5746315/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/370/5/f61dbe38-7302-451a-b3ab-9851d5746315/downloads/wwdc2026-370_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/370/5/f61dbe38-7302-451a-b3ab-9851d5746315/downloads/wwdc2026-370_sd.mp4?dl=1

Use new TextKit hooks to extend UITextView and NSTextView with viewport-aware rendering, collapsible layout, and reusable text attachments.

TLDR:
- TextKit's four layers are storage, layout, viewport, and view; the viewport layout process is the key extension point for visible text rendering.
- UITextView and NSTextView now publicly conform to NSTextViewportLayoutControllerDelegate, enabling subclasses to override viewport layout callbacks while keeping built-in editing behavior.
- New rendering surface APIs let custom TextKit views associate layout fragments with drawable views or layers across layout cycles.
- UITextView can register text attachment view provider reuse policies to preserve attachment state during inline edits or viewport scrolling.

## Why TextKit matters for rich text experiences

TextKit powers text layout and rendering across SwiftUI, UIKit, and AppKit text controls. Developers typically choose between framework text views, which provide editing behavior such as input, selection, accessibility, undo, dictation, and inline predictions, or fully custom TextKit views, which provide low-level control but require much more implementation work.

The new APIs are aimed at reducing that tradeoff: start from UITextView or NSTextView when possible, then extend their TextKit viewport behavior where custom rendering or layout metadata is needed.

- Use UITextView on UIKit and NSTextView on AppKit for a full-featured editing base.
- Use SwiftUI TextEditor for the most convenient SwiftUI long-form editing path, or wrap UITextView/NSTextView with ViewRepresentable when TextKit hooks are needed.
- Build a fully custom TextKit view when the app needs direct control over storage, layout, viewport coordination, and rendering surfaces.

## TextKit architecture recap

TextKit is organized into four layers: text storage, layout, viewport, and view. The storage and layout layers break attributed text into text elements and produce layout fragments; the viewport layer determines which fragments are visible; the view layer renders those fragments into UI framework views or layers.

For an NSAttributedString-backed document, NSTextContentStorage creates NSTextParagraph elements, and NSTextLayoutManager creates immutable NSTextLayoutFragment objects with calculated layout information. Editing a paragraph recreates the paragraph and its corresponding layout fragment.

NSTextViewportLayoutController coordinates with the text layout manager and text view whenever the viewport changes due to scrolling, editing, or selection. It asks for layout fragments intersecting the viewport and sends them to its delegate for rendering.

- NSTextContentStorage and NSTextParagraph are concrete NSAttributedString-based storage types.
- NSTextContentManager and NSTextElement are the abstract types to subclass for nonstandard backing stores.
- Multiple NSTextLayoutManager instances can be connected to the same NSTextContentStorage to present synchronized views of the same document.

## New rendering surface APIs for custom TextKit views

NSTextViewportRenderingSurface is a new protocol for drawable visual elements inside a viewport. UIView, NSView, or CALayer types can conform so TextKit code can reason about the surfaces used to render layout fragments.

NSTextViewportRenderingSurfaceKey identifies a rendering surface across viewport layout cycles. NSTextLayoutFragment can be used as a key, making it practical to cache or look up rendering surfaces with map tables or dictionaries during viewport layout.

- Assign a rendering surface for a key during the viewport layout process with the delegate API.
- Mappings are cleared at the beginning of a viewport layout process.
- Query a rendering surface for a key during didLayout with the viewport controller's renderingSurfaceFor method.

### Rendering surface conformance

A UIView subclass can opt in as a TextKit viewport rendering surface.

```swift
class MyView: UIView, NSTextViewportRenderingSurface {}
```

### Caching surfaces by layout fragment

NSTextLayoutFragment can act as a rendering surface key for cache lookups across viewport layout work.

```swift
class MyView: UIView, NSTextViewportRenderingSurface {}
var cache: NSMapTable<NSTextLayoutFragment, MyView>
```

## Extending UITextView and NSTextView

UITextView and NSTextView now publicly conform to NSTextViewportLayoutControllerDelegate. Subclasses can override the viewport layout delegate callbacks to add behavior before layout, while configuring each layout fragment, and after layout completes.

The important pattern is to call super in each override so the framework text view keeps its default behavior, then add app-specific collection, rendering, or layout invalidation logic.

- Override willLayout for setup before a viewport layout pass.
- Override configureRenderingSurfaceFor to inspect or configure each visible NSTextLayoutFragment.
- Override didLayout to consume accumulated information after visible fragments have been processed.
- Use ViewRepresentable to embed UITextView or NSTextView in SwiftUI when TextEditor is not enough.

### Wrap framework text views for SwiftUI

SwiftUI apps can host NSTextView or UITextView directly when they need TextKit-specific hooks.

```swift
import SwiftUI

struct MyTextView: View {
    var body: some View {
        TextViewRepresentable()
    }
}

#if os(macOS)
struct TextViewRepresentable: NSViewRepresentable {
    func makeNSView(context: Context) -> NSTextView { NSTextView() }
    func updateNSView(_ nsView: NSTextView, context: Context) {}
}
#else
struct TextViewRepresentable: UIViewRepresentable {
    func makeUIView(context: Context) -> UITextView { UITextView() }
    func updateUIView(_ uiView: UITextView, context: Context) {}
}
#endif
```

### Viewport delegate override points

The three delegate methods provide the core extension points for framework text views.

```swift
class TextView: UITextView {
    override func textViewportLayoutControllerWillLayout(
        _ textViewportLayoutController: NSTextViewportLayoutController
    ) {
        super.textViewportLayoutControllerWillLayout(textViewportLayoutController)
        // Set up state for this layout pass.
    }

    override func textViewportLayoutController(
        _ textViewportLayoutController: NSTextViewportLayoutController,
        configureRenderingSurfaceFor textLayoutFragment: NSTextLayoutFragment
    ) {
        super.textViewportLayoutController(
            textViewportLayoutController,
            configureRenderingSurfaceFor: textLayoutFragment
        )
        // Inspect or collect information for this visible fragment.
    }

    override func textViewportLayoutControllerDidLayout(
        _ textViewportLayoutController: NSTextViewportLayoutController
    ) {
        super.textViewportLayoutControllerDidLayout(textViewportLayoutController)
        // Publish accumulated information.
    }
}
```

## Example patterns: line numbers and collapsible sections

For a code editor, a UITextView subclass can use viewport callbacks to collect paragraph frames for visible layout fragments, calculate the first visible line number, and send that information to a container view that draws a line-number gutter.

For collapsible sections, the text view can combine viewport delegate callbacks with NSTextContentStorageDelegate. The delegate method textContentManager(_:shouldEnumerate:options:) can skip layout for collapsed text elements, while a set of paragraph offsets tracks which sections are collapsed.

- Compute the starting line number by enumerating text elements from the document start until the viewport range.
- Collect each fragment's layoutFragmentFrame during configureRenderingSurfaceFor.
- Convert fragment frames from text container coordinates to viewport coordinates before drawing external decorations.
- After toggling collapse state, ask the viewport layout controller delegate to mark layout as needed.

### Compute the starting line number for the viewport

The sample counts paragraphs before the viewport; the session notes the sample code improves this with caching.

```swift
func startingLineNumber(for viewportRange: NSTextRange?) -> Int {
    guard let viewportRange,
          let storage = textLayoutManager?.textContentManager as? NSTextContentStorage
    else { return 0 }

    let startLocation = storage.documentRange.location
    var count = 1

    storage.enumerateTextElements(from: startLocation) { element in
        guard let range = element.elementRange else { return true }
        if range.location.compare(viewportRange.location) != .orderedAscending {
            return false
        }
        count += 1
        return true
    }

    return count
}
```

### Collapse state and layout invalidation

Collapsed sections are represented as paragraph offsets, and toggling a section forces the viewport to relayout.

```swift
class TextView: UITextView, NSTextContentStorageDelegate {
    var collapsedSections: Set<Int> = []

    func textContentManager(
        shouldEnumerate textElement: NSTextElement,
        options: NSTextContentManager.EnumerationOptions
    ) -> Bool {
        // Return false for collapsed paragraphs.
    }

    func toggleSection(headerOffset: Int) {
        if collapsedSections.contains(headerOffset) {
            collapsedSections.remove(headerOffset)
        } else {
            collapsedSections.insert(headerOffset)
        }

        guard let textLayoutManager else { return }
        let controller = textLayoutManager.textViewportLayoutController
        controller.delegate?.textViewportLayoutControllerReceivedSetNeedsLayout?(controller)
    }
}
```

## Text attachment view provider reuse

Text attachments are stored in text storage as NSTextAttachment objects. During layout, TextKit asks for an NSTextAttachmentViewProvider, which supplies the information needed to render the attachment inside the text view.

Because text layout objects are immutable, edits in a paragraph can otherwise recreate attachment view providers and reset state, such as an inline animation. UITextView now supports registering reuse policies for a particular NSTextAttachmentViewProvider subclass.

- Use onEditingInlineParagraphs to preserve a view provider across edits in the same paragraph.
- Use onScrollingOutOfViewport to cache an attachment rendering surface when it leaves the viewport and restore it later.
- Reuse policies can be combined depending on the attachment's behavior and state requirements.

### Register attachment view provider reuse policy

Registering a policy for the provider subclass lets UITextView preserve provider state for matching attachments.

```swift
class ViewController: UIViewController {
    var textView: UITextView!

    func setupTextView() {
        textView = UITextView()
        textView.register(
            [.onEditingInlineParagraphs],
            forTextAttachmentViewProviderType: AnimatedAttachmentViewProvider.self
        )
    }
}
```

Resources:
- Enriching your text in text views: https://developer.apple.com/documentation/UIKit/enriching-your-text-in-text-views
- TextKit: https://developer.apple.com/documentation/AppKit/textkit

Chapters:
- 0:00 Introduction: Introduces the tradeoff between framework text views that provide editing behavior and custom TextKit views that provide rendering control. Frames the session around combining convenience with deeper TextKit customization.
- 3:09 TextKit architecture: Explains TextKit's storage, layout, viewport, and view layers. Covers NSTextContentStorage, NSTextLayoutManager, immutable layout fragments, and how NSTextViewportLayoutController coordinates visible layout and rendering.
- 9:17 What's new in TextKit: Introduces NSTextViewportRenderingSurface and NSTextViewportRenderingSurfaceKey for identifying and managing drawable surfaces in the viewport. Describes assigning and querying rendering surfaces during viewport layout.
- 11:27 Extending framework text views: Shows that UITextView and NSTextView can now be extended through public NSTextViewportLayoutControllerDelegate conformance. Also demonstrates wrapping these views with SwiftUI ViewRepresentable.
- 12:58 Example: Code editor with line numbers: Builds a UITextView-based code editor gutter by overriding viewport layout callbacks. The example counts paragraphs before the viewport, collects visible layout fragment frames, converts them to viewport coordinates, and draws line numbers in a container view.
- 17:52 Example: Collapsible recipe sections: Adds collapsible multi-paragraph sections to a UITextView by combining viewport delegate methods with NSTextContentStorageDelegate. Collapsed paragraph offsets are tracked in state, skipped during enumeration, and relaid out when toggled.
- 19:56 Text attachments and view provider reuse: Explains how text attachments use NSTextAttachment and NSTextAttachmentViewProvider within the same TextKit architecture. Introduces UITextView reuse policies that preserve attachment provider state across edits or scrolling.
- 23:00 Next steps: Recommends starting with UITextView, NSTextView, or SwiftUI hosting when possible, and using custom TextKit rendering only when more control is needed. Points developers to sample code for line numbers, collapsible sections, and attachment reuse.

### Unwrap PaperKit

- Session ID: wwdc2026-372
- Page: https://wwdc.ai/2026/372
- Markdown: https://wwdc.ai/2026/372.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/372/
- Category: App Services
- Description: Use PaperKit's new data model, markup elements, and adornments to build editable canvas apps with locked templates and custom overlays.
- Duration: 7:39
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-372/eng_f8afe0b32ef5/wwdc2026-372-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/372/4/012a7de6-cf54-420f-aaf7-02ea568485bf/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/372/4/012a7de6-cf54-420f-aaf7-02ea568485bf/downloads/wwdc2026-372_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/372/4/012a7de6-cf54-420f-aaf7-02ea568485bf/downloads/wwdc2026-372_sd.mp4?dl=1

Use PaperKit's new data model, markup elements, and adornments to build editable canvas apps with locked templates and custom overlays.

TLDR:
- PaperMarkup now exposes `subelements` as a readable and writable `MarkupOrderedSet`, letting apps create, inspect, reorder, and update canvas contents programmatically.
- Every canvas item conforms to `Markup`; `allowedInteractions` lets apps selectively permit moving, resizing, rotating, deleting, styling, or selecting, with `.readOnly` for locked template content.
- Concrete markup element types include shapes, images, links, loupes, and PencilKit strokes, each with type-specific properties such as shape stroke and fill colors.
- `MarkupAdornment` adds non-persisted interactive overlays anchored to canvas coordinates, useful for buttons, annotations, collaboration UI, and integrations such as Image Playground.

## PaperKit as an app canvas

PaperKit is the canvas technology used across Apple apps such as Notes, Preview, and Freeform, and is available to apps on iOS, macOS, and visionOS 27. The session demonstrates a comic book editor that turns page templates into editable PaperKit canvases.

The main workflow is to create `PaperMarkup`, populate it with markup elements, configure how those elements can be interacted with, then layer non-document controls on top using adornments.

- Use `PaperMarkup` as the document-level canvas model.
- Use `PaperMarkupViewController.markup` to display and update canvas contents.
- Use PaperKit when the app needs a mixed canvas of Pencil input, shapes, text, images, and other markup elements.

## Build markup with the data model

`PaperMarkup` has a `subelements` property that exposes every canvas element as a `MarkupOrderedSet`. The ordered set can be read and written, so apps can generate markup from their own models and assign the result back to the controller.

In the comic editor example, each panel rectangle is converted into a `ShapeMarkup` and appended to the markup's subelements.

- Create `PaperMarkup` with bounds matching the page size.
- Read `markup.subelements` into a mutable `MarkupOrderedSet`.
- Append generated elements, then assign the ordered set back to `markup.subelements`.
- Assign the completed markup to the PaperKit view controller.

### Create shape subelements for a template

Generates a `PaperMarkup` page by appending one `ShapeMarkup` per comic panel.

```swift
import PaperKit

func generateMarkup(pageSize: CGSize,
                    panelFrames: [CGRect],
                    configuration: ShapeConfiguration) -> PaperMarkup {
    var markup = PaperMarkup(bounds: CGRect(origin: .zero, size: pageSize))
    var subelements: MarkupOrderedSet = markup.subelements

    for panelFrame in panelFrames {
        let shape = ShapeMarkup(frame: panelFrame, configuration: configuration)
        subelements.append(shape)
    }

    markup.subelements = subelements
    return markup
}
```

## Lock template elements with allowed interactions

Every element on the canvas conforms to the `Markup` protocol, which provides common properties such as `frame` and `rotation`. The new `allowedInteractions` property uses the `MarkupInteractions` option set to control what users can modify per element.

Interactions can be controlled independently for moving, resizing and rotating, deleting, styling, and selecting. The `.readOnly` convenience value locks down all of them, which is useful for template content such as comic panel borders while leaving other user-created elements editable.

- Use `allowedInteractions` on individual elements, not just the whole canvas.
- Set template elements to `.readOnly` when they should remain visible but not selectable, movable, stylable, or deletable.
- Keep user-added elements, such as speech bubbles or drawings, interactive if the app's editing model requires it.

### Make generated template shapes read-only

Locks the generated panel shapes so the user cannot select, move, style, or delete the template.

```swift
import PaperKit

func generateMarkup(pageSize: CGSize,
                    panelFrames: [CGRect],
                    configuration: ShapeConfiguration) -> PaperMarkup {
    var markup = PaperMarkup(bounds: CGRect(origin: .zero, size: pageSize))
    var subelements: MarkupOrderedSet = markup.subelements

    for panelFrame in panelFrames {
        var shape = ShapeMarkup(frame: panelFrame, configuration: configuration)
        shape.allowedInteractions = .readOnly
        subelements.append(shape)
    }

    markup.subelements = subelements
    return markup
}
```

## Work with concrete element types

Although all elements live in the same `MarkupOrderedSet` and conform to `Markup`, each element has a concrete type with its own properties. The session calls out shapes, images, links, loupes, and pencil strokes.

For shape-based templates, apps can cast elements to `ShapeMarkup` and update properties such as stroke and fill colors. The example also updates the markup background color to match the selected style.

- Iterate through `markup.subelements` to inspect and modify existing canvas elements.
- Cast to concrete types such as `ShapeMarkup` or `ImageMarkup` when type-specific properties are needed.
- Use `updateOrAppend(_:)` after changing an element in a `MarkupOrderedSet`.
- PaperKit builds on PencilKit; Apple Pencil strokes become markup elements and can use PencilKit model APIs.

### Apply a selected color to panel shapes

Finds shape elements, updates their style, writes the ordered set back to the markup, and refreshes the controller.

```swift
import PaperKit

func updatePanelColor(_ selectedColor: CGColor) {
    guard var markup: PaperMarkup = paperMarkupViewController.markup else { return }
    var subelements: MarkupOrderedSet = markup.subelements

    for element in subelements {
        guard var shape = element as? ShapeMarkup else { continue }
        shape.strokeColor = selectedColor
        shape.fillColor = selectedColor.copy(alpha: 0.15)
        subelements.updateOrAppend(shape)
    }

    markup.subelements = subelements
    markup.backgroundColor = selectedColor.copy(alpha: 0.15)
    paperMarkupViewController.markup = markup
}
```

## Add custom controls with adornments

`MarkupAdornment` provides a visual overlay anchored to canvas coordinates. Adornments track zoom and scroll, but remain separate from persisted markup, so they are not saved, printed, or exported as document content.

The comic editor adds an adornment button to each panel using an SF Symbol image configuration. Taps are handled through the PaperKit view controller delegate; the example launches Image Playground, then inserts the generated image as an `ImageMarkup` into the canvas.

- Use adornments for editing-only UI such as buttons, annotations, and collaboration controls.
- Anchor adornments to canvas positions, such as the center of each panel.
- Assign adornments through `paperMarkupViewController.adornments`.
- Maintain your own mapping from adornment IDs to app model objects, such as panel indexes.

### Create one adornment per panel

Adds non-persisted overlay controls anchored to panel centers.

```swift
import PaperKit

func addPanelAdornments(for page: Page) {
    var adornments: [MarkupAdornment] = []

    for (panelIndex, panel) in page.panels.enumerated() {
        let adornmentID = UUID()
        adornmentPanelMapping[adornmentID] = panelIndex

        let center = CGPoint(x: panel.midX, y: panel.midY)
        let adornment = MarkupAdornment(
            id: adornmentID,
            anchor: .canvas(location: center),
            imageConfiguration: .systemImage("photo.badge.plus"),
            dragRegion: .fixed,
            scalesWithZoom: false
        )
        adornments.append(adornment)
    }

    paperMarkupViewController.adornments = adornments
}
```

### Respond to an adornment and insert generated artwork

Uses an adornment tap to present Image Playground, then stores the generated image as persisted `ImageMarkup` content.

```swift
import ImagePlayground
import PaperKit

func paperMarkupViewController(_ paperMarkupViewController: PaperMarkupViewController,
                               didTapAdornmentWithID id: UUID) {
    guard let panelIndex = adornmentPanelMapping[id] else { return }
    activeImageGenerationPanelIndex = panelIndex

    let imagePlaygroundViewController = ImagePlaygroundViewController()
    imagePlaygroundViewController.delegate = self
    present(imagePlaygroundViewController, animated: true)
}

func imageViewController(_ imageViewController: ImagePlaygroundViewController,
                         didCreateImageAt imageURL: URL) {
    guard let panelFrame = activeGenerationPanelFrame,
          let paperMarkupViewController = pageViewController.paperViewController,
          var markup = paperMarkupViewController.markup,
          let image = UIImage(contentsOfFile: imageURL.path) else { return }

    let imageMarkup = ImageMarkup(frame: panelFrame, image: image)
    markup.subelements.append(imageMarkup)
    paperMarkupViewController.markup = markup
}
```

Resources:
- PaperKit: https://developer.apple.com/documentation/PaperKit

Chapters:
- 0:00 Introduction: Introduces PaperKit as the canvas system behind Apple apps such as Notes, Preview, and Freeform, now available for iOS, macOS, and visionOS 27 apps. The session frames the demo around a comic book editor and previews the data model, elements, and adornments.
- 1:22 Data model: Shows how `PaperMarkup.subelements` exposes canvas contents as a readable and writable `MarkupOrderedSet`. Demonstrates generating template panel shapes and using `allowedInteractions = .readOnly` to prevent template elements from being edited.
- 3:41 Elements: Explains that markup contents share the `Markup` protocol but have concrete types such as shapes, images, links, loupes, and pencil strokes. Demonstrates iterating through subelements, casting to `ShapeMarkup`, and updating stroke, fill, and background colors.
- 5:17 Adornments: Introduces `MarkupAdornment` as a non-persisted overlay anchored to canvas coordinates for controls, annotations, and collaboration UI. Demonstrates panel-centered adornment buttons that launch Image Playground and insert the generated result as `ImageMarkup`.
- 7:11 Next steps: Wraps up by emphasizing PaperKit's data model for reading and modifying canvas contents and adornments for app-specific interactive overlays. Points developers toward related Image Playground and PencilKit material for deeper integrations.

### Create high quality images using Image Playground

- Session ID: wwdc2026-375
- Page: https://wwdc.ai/2026/375
- Markdown: https://wwdc.ai/2026/375.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/375/
- Category: AI & Machine Learning
- Description: Use ImagePlayground.framework to present Apple's image creation UI, seed it with app context, configure style and size, and handle availability.
- Duration: 14:11
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-375/eng_46643fa08f51/wwdc2026-375-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/375/4/01104285-3253-4b2d-80c3-0d5cdf95c97e/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/375/4/01104285-3253-4b2d-80c3-0d5cdf95c97e/downloads/wwdc2026-375_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/375/4/01104285-3253-4b2d-80c3-0d5cdf95c97e/downloads/wwdc2026-375_sd.mp4?dl=1

Use ImagePlayground.framework to present Apple's image creation UI, seed it with app context, configure style and size, and handle availability.

TLDR:
- Image Playground now uses higher-quality generative models on Private Cloud Compute, supporting many styles including photorealistic, multiple aspect ratios, people, and personalization.
- Adoption is UI-driven: SwiftUI uses `.imagePlaygroundSheet`, while UIKit/AppKit use `ImagePlaygroundViewController`; `ImageCreator` is deprecated.
- Seed generation with `ImagePlaygroundConcept` values from text, extracted long-form content, source images, or PencilKit drawings, then save the temporary result URL your app receives.
- Configure `ImagePlaygroundOptions` for size and personalization, constrain or default styles with `imagePlaygroundGenerationStyle`, and gate UI with `supportsImageGeneration`.

## What Image Playground provides

ImagePlayground.framework brings the Image Playground creation experience into iOS, iPadOS, macOS, and visionOS apps on devices with Apple Intelligence support. The experience includes prompt entry, style selection, people/personalization features, previewing, and user confirmation.

The model can generate images from short or detailed descriptions, include one or more people, use preset or text-described styles, and target landscape, portrait, or square use cases. Generation runs on Private Cloud Compute; Apple handles the server-side model infrastructure and user usage limits.

- Supports high-quality images in many styles, including photorealistic results.
- Preset styles discussed include animation, illustration, sketch, and emoji/Genmoji-oriented output.
- The requested size is mapped to the closest supported resolution and aspect ratio.
- `ImageCreator`, the previous non-UI image generation API, is deprecated in favor of the new Image Playground experience.

## Present the Image Playground UI

In SwiftUI, adoption starts with the `.imagePlaygroundSheet` view modifier. The sheet is controlled by a binding and calls a completion handler with a file URL when the user accepts a generated image.

The returned URL points to a temporary location inside the app container, so apps should copy or persist the generated image before the session ends. Image Playground owns the UI, model interaction, style picker, and preview flow.

- No SDK initialization, API keys, server endpoints, or entitlements are described for basic adoption.
- The sheet can start empty or be prefilled with app-provided context.
- UIKit and AppKit apps can present `ImagePlaygroundViewController` and receive the result through its delegate.

### SwiftUI sheet adoption

Attach `.imagePlaygroundSheet` to a view, toggle presentation with state, and save the generated temporary file URL in the completion handler.

```swift
@State private var showingPlayground = false

var body: some View {
    Button("Create image") {
        showingPlayground = true
    }
    .imagePlaygroundSheet(
        isPresented: $showingPlayground,
        onCompletion: { url in
            var updated = currentCard
            store.saveImage(url, for: &updated)
        }
    )
}
```

### UIKit/AppKit presentation

Use `ImagePlaygroundViewController` for UIKit or AppKit-style integration and handle generated image URLs through the delegate.

```swift
func presentViewController() {
    let viewController = ImagePlaygroundViewController()
    viewController.concepts = [
        .text(card.theme),
        .extracted(from: card.message)
    ]
    viewController.delegate = self
    present(viewController, animated: true)
}

func imagePlaygroundViewController(
    _ viewController: ImagePlaygroundViewController,
    didCreateImageAt url: URL
) {
    var updated = card
    store.saveImage(url, for: &updated)
    dismiss(animated: true)
}
```

## Seed generation with app context

`ImagePlaygroundConcept` lets an app prime the sheet with relevant information so the user does not start from a blank prompt. Direct text can represent a concise theme, while extracted text lets the system pull the most relevant concepts from longer content such as a message.

The sheet can also take a SwiftUI `Image` as a source image and can include a `PKDrawing` from PencilKit as a visual suggestion. These inputs guide the composition but do not lock the result; users can replace or refine them in the sheet.

- Use `.text(...)` for direct descriptions such as a card theme.
- Use `.extracted(from:title:)` or `.extracted(from:)` for longer prose where the system should infer useful concepts.
- Pass `sourceImage:` to provide a reference photo or existing image.
- Append `.drawing(PKDrawing)` to concepts when strokes should guide generation.

### Text and extracted concepts

Seed the prompt with both an explicit theme and concepts extracted from longer app content.

```swift
var concepts: [ImagePlaygroundConcept] {
    [
        .text(card.theme),
        .extracted(from: card.message, title: card.theme)
    ]
}

var body: some View {
    Button("Create image") {
        showingPlayground = true
    }
    .imagePlaygroundSheet(
        isPresented: $showingPlayground,
        concepts: concepts,
        onCompletion: { url in
            var updated = card
            store.saveImage(url, for: &updated)
        }
    )
}
```

### PencilKit drawing as a concept

Include a non-empty `PKDrawing` so the model can use the user's strokes as visual guidance.

```swift
@State private var drawing = PKDrawing()

var concepts: [ImagePlaygroundConcept] {
    var result: [ImagePlaygroundConcept] = [
        .text(card.theme),
        .extracted(from: card.message)
    ]

    if !drawing.strokes.isEmpty {
        result.append(.drawing(drawing))
    }

    return result
}
```

## Configure size, style, external providers, and personalization

`ImagePlaygroundOptions` configures sheet behavior such as size and personalization. For size, the app can request the closest supported output to a `CGSize`, allowing the same code path to adapt to landscape cards, portrait cards, square thumbnails, banners, or wallpapers.

Generation style can be defaulted and constrained. Passing a single allowed style locks the picker to that style; passing multiple styles lets the picker reflect the current app context. The `externalProvider` style is opt-in and surfaces a third-party provider the user has configured in Settings, such as ChatGPT, with setup handled by the system when needed.

- Use `options.sizeSpecification = .closest(to: size)` to request the nearest supported aspect ratio and resolution.
- Use `.imagePlaygroundGenerationStyle(defaultStyle, in: allowedStyles)` to control the default and allowed style set.
- Use `.externalProvider` only by adding it to the allowed styles list.
- Set `options.personalization = .disabled` when people-based personalization does not fit the app context.

### Size and style configuration

Request an output size derived from app state, provide options to the sheet, and allow app-specific styles plus an external provider.

```swift
var options: ImagePlaygroundOptions {
    var options = ImagePlaygroundOptions()
    options.sizeSpecification = .closest(to: card.format.size)
    return options
}

var body: some View {
    Button("Create image") {
        showingPlayground = true
    }
    .imagePlaygroundSheet(
        isPresented: $showingPlayground,
        concepts: concepts,
        onCompletion: { url in
            var updated = card
            store.saveImage(url, for: &updated)
        }
    )
    .imagePlaygroundOptions(options)
    .imagePlaygroundGenerationStyle(
        pendingStylePreset.defaultStyle,
        in: pendingStylePreset.allowedStyles + [.externalProvider]
    )
}
```

### Disable personalization

Remove people picker and name-detection features when personalization is not appropriate.

```swift
var options: ImagePlaygroundOptions {
    var options = ImagePlaygroundOptions()
    options.sizeSpecification = .closest(to: card.format.size)
    options.personalization = .disabled
    return options
}
```

## Emoji-style output and availability fallback

When `ImagePlaygroundStyle.emoji` is active, the sheet can call `onAdaptiveImageGlyphCreation` with an `NSAdaptiveImageGlyph` instead of returning a generated image URL. Adaptive image glyphs can be embedded inline with text, like emoji, which makes them useful for small expressive icons such as recipient thumbnails.

Apps should use the `supportsImageGeneration` environment value to decide whether to show the full Image Playground path or a fallback. It is true only when image generation is fully available: device capability, supported language and region, and the user's Settings state are all satisfied.

- Use `.emoji` when the output should be an adaptive glyph rather than regular artwork.
- Save the `NSAdaptiveImageGlyph` through the glyph-specific completion path.
- Provide a non-generation fallback, such as a Photos picker, when `supportsImageGeneration` is false.
- Do not build custom usage-limit UI; the system manages Image Playground usage limits for users.

### Create an adaptive image glyph

Lock the sheet to emoji style and handle `NSAdaptiveImageGlyph` output for inline expressive icons.

```swift
@State private var showingIconPlayground = false

var body: some View {
    Button("Create icon") {
        showingIconPlayground = true
    }

    Color.clear
        .imagePlaygroundSheet(
            isPresented: $showingIconPlayground,
            concepts: concepts,
            onCompletion: { _ in },
            onAdaptiveImageGlyphCreation: { glyph in
                var updatedCard = card
                store.saveIcon(glyph, for: &updatedCard)
            }
        )
        .imagePlaygroundGenerationStyle(.emoji, in: [.emoji])
}
```

### Check image generation support

Branch to the Image Playground experience only when image generation is fully available, otherwise show a fallback UI.

```swift
@Environment(\.supportsImageGeneration) private var supportsImageGeneration

var body: some View {
    NavigationLink(card.recipient) {
        if supportsImageGeneration {
            CardEditorView(card: card)
        } else {
            CardPickerView(card: card)
        }
    }
}
```

Chapters:
- 0:00 Introduction: Introduces ImagePlayground.framework as a way to bring the full Image Playground image creation experience into apps on Apple Intelligence-capable platforms. The session outlines capabilities, adoption, configuration, and availability handling.
- 2:03 Capabilities: Explains that Image Playground can create high-quality images from text, include people with personalization, use multiple styles, and target several aspect ratios. Generation runs on Private Cloud Compute, usage limits are system-managed, and the older `ImageCreator` API is deprecated.
- 5:02 Adopt Image Playground: Shows SwiftUI adoption with `.imagePlaygroundSheet`, completion via a temporary result URL, and seeding the sheet with text, extracted concepts, source images, and PencilKit drawings. Also covers the UIKit/AppKit `ImagePlaygroundViewController` path.
- 8:29 Options: Covers `ImagePlaygroundOptions` and style configuration for size, aspect ratio, default and allowed styles, external provider support, emoji-style adaptive image glyphs, and disabling personalization when appropriate.
- 12:15 Availability: Describes using the `supportsImageGeneration` environment value to determine whether the full Image Playground experience is available. Apps should branch cleanly to a fallback experience when generation is unsupported or disabled.

### Unlock in-game content with StoreKit and Background Assets

- Session ID: wwdc2026-378
- Page: https://wwdc.ai/2026/378
- Markdown: https://wwdc.ai/2026/378.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/378/
- Category: App Store, Distribution & Marketing
- Description: Use new Apple Unity plug-ins for StoreKit and Background Assets to sell, unlock, localize, download, and test game content on Apple platforms.
- Duration: 9:59
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-378/eng_74139f6906ad/wwdc2026-378-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/378/6/16c93f95-21e8-4f7f-bb96-2b3c682fa6c7/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/378/6/16c93f95-21e8-4f7f-bb96-2b3c682fa6c7/downloads/wwdc2026-378_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/378/6/16c93f95-21e8-4f7f-bb96-2b3c682fa6c7/downloads/wwdc2026-378_sd.mp4?dl=1

Use new Apple Unity plug-ins for StoreKit and Background Assets to sell, unlock, localize, download, and test game content on Apple platforms.

TLDR:
- Managed Background Assets can reduce initial game size by downloading asset packs only when needed; Apple-hosted assets support up to 200 GB per App Store app and localized asset packs in iOS 27.
- Localized asset packs use language tags in asset pack manifests, deliver only the player's preferred language when available, and fall back to a closest match or the app's primary language.
- A new Steam Asset Converter in `xcrun ba-package` converts Steam depots into Background Assets manifests and asset pack archives for Apple-platform distribution.
- New Apple Unity plug-ins for StoreKit and Background Assets expose C# APIs for fetching products, purchasing, listening for transactions, ensuring asset pack availability, and testing with Xcode 27.

## Background Assets for game content delivery

Managed Background Assets lets games avoid bundling all audio, video, textures, machine learning models, and level content up front. The system downloads asset packs when needed, saving player download time and device storage.

For App Store apps, Apple can host up to 200 GB of assets per app as part of Developer Program membership. Apple-Hosted Background Assets is available starting with iOS, iPadOS, macOS, tvOS, and visionOS 26.

- Use Background Assets for content that is only needed at specific moments in the game.
- Use Apple-hosted asset packs to reduce app binary size and offload hosting for large assets.
- For deeper setup and integration details, the session points to "Discover Apple-Hosted Background Assets" from WWDC25.

## Localized asset packs

In iOS 27, Managed Background Assets adds localized asset packs. The system reads the player's preferred language from Settings and downloads only the asset pack for that language when available.

Fallback is automatic: if an exact regional language pack is not provided, the system can use a base-language match such as English-US for English-UK. If no suitable language variant exists, it falls back to the app's primary language.

- Add a language tag to each localized asset pack manifest JSON file.
- Provide localized packs only for languages you support; the system handles matching and fallback.
- This is most useful for language-heavy content such as voice-over, video, tutorials, and localized media.

### Asset pack manifest for a localized asset pack

Add a `language` tag such as `en-US` to the asset pack manifest so the system can select the appropriate localized pack.

```text
// Asset pack manifest
{
  "assetPackID": "voice-english",
  "downloadPolicy": { /* ... */ },
  "language": "en-US",
  "sourceRoot": ".",
  "fileSelectors": [ /* ... */ ],
  "platforms": [ /* ... */ ]
  // ...
}
```

## Convert Steam depots to Background Assets

Games already using Steam depots can migrate asset packaging workflows to Apple platforms with the new Steam Asset Converter. On macOS, install Xcode 27 and run `xcrun ba-package convert` to produce an asset pack manifest from a Steam manifest build script.

After generating the manifest, run `ba-package` again to create an asset pack archive that can be used by the game. The same conversion tool is described as coming soon to Linux and Windows.

- Pass an asset pack ID, an optional language ID, and the desired download policy.
- Use the Steam manifest build script as input.
- Package the resulting manifest into an asset pack archive before shipping or testing.

### Convert a Steam depot to an asset pack manifest

Creates a Background Assets manifest from a Steam depot manifest build script.

```text
xcrun ba-package convert \
  --asset-pack-id voice-english \
  --l en-US \
  --on-demand \
  voice-english.vdf \
  -o voice-english.json
```

### Convert an asset pack manifest to an archive

Packages the generated manifest into an asset pack archive.

```text
xcrun ba-package voice-english.json -o voice-english.aar
```

## Apple Unity plug-ins for StoreKit and Background Assets

Two new Apple Unity plug-ins join the Apple Unity plug-in portfolio: StoreKit and Background Assets. They are available on GitHub alongside the existing Apple Unity plug-ins and expose C# Unity APIs that bridge to the native Apple frameworks.

To build, package, and test the plug-ins, use Xcode 27, Python 3, and Unity 2022 LTS or later. The plug-ins are built with the same Python script used for other Apple Unity plug-ins.

- Use the StoreKit plug-in to fetch products, initiate purchases, verify transactions, and finish transactions.
- Use `Transaction.Updates` to handle purchases or transaction changes that happen outside the app or on other devices.
- For consumables, handle verified, non-revoked transactions inline; for non-consumables and subscriptions, use current entitlements as the source of truth.
- Use the Background Assets plug-in to ensure purchased asset packs are locally available and monitor download progress.

### Fetch and purchase products with the StoreKit plug-in

Fetches App Store products from Unity, starts the system purchase flow, verifies the transaction, unlocks content, and finishes the transaction.

```swift
using UnityEngine;
using Apple.StoreKit;

async void Start() {
    var products = await Product.FetchProducts(new[] { "com.thecoast.capecod" });
}

async void Purchase(Product product) {
    var result = await product.Purchase();
    if (result.Result == PurchaseResult.ResultEnum.Success &&
        result.TransactionVerification.IsVerified) {
        // Unlock access to purchased content
        result.TransactionVerification.SafePayload.Finish();
    }
}
```

### Download asset packs with the Background Assets plug-in

Finds an asset pack from the manifest, observes download status, ensures local availability, and starts content once the assets are present.

```text
using Apple.BackgroundAssets;
using UnityEngine;

async void LoadTutorial(string language) {
    try {
        string assetPackId = $"tutorial-{language}";
        AssetPackManifest manifest = await AssetPackManager.GetManifestAsync();
        AssetPack assetPack = manifest.GetAssetPack(assetPackId);

        CancellationTokenSource tokenSource = new CancellationTokenSource();
        _ = Task.Run(async () => {
            await foreach (AssetPackManager.DownloadStatusUpdate statusUpdate in
                AssetPackManager.DownloadStatusUpdatesAsync(assetPackId)) {
                // Update download progress in UI
            }
        }, tokenSource.Token);

        await AssetPackManager.EnsureLocalAvailabilityOfAssetPackAsync(assetPack);
        tokenSource.Cancel();
        // Start tutorial with the locally available assets
    } catch (Exception exception) {
        // Handle the exception
    }
}
```

## Testing and App Store presentation

After installing the Unity plug-ins, export the Unity project to Xcode. StoreKit Testing in Xcode can use a StoreKit configuration file with test products, while the Background Assets mock server can serve packaged asset packs during a debug session.

In the scheme's Run settings, choose the StoreKit configuration file and select the folder containing packaged asset packs. When running in Xcode 27, the Background Assets mock server starts and attaches to the debug session. Sandbox testing remains available for products configured in App Store Connect.

The session also highlights new App Store and Apple Games app presentation assets: product page header visuals and search-result images and videos. In iOS 27, players see a redesigned system payment sheet that works well in landscape mode for games.

- Use StoreKit configuration files for local purchase testing.
- Use the Background Assets mock server with packaged asset packs during Xcode debugging.
- Use App Store Connect for sandbox testing and for uploading localized asset packs.
- Consider new image and video assets for App Store search results and the Apple Games app.

Resources:
- Apple Unity Plug-Ins on GitHub: https://github.com/apple/unityplugins
- Background Assets: https://developer.apple.com/documentation/BackgroundAssets
- StoreKit: https://developer.apple.com/documentation/StoreKit

Chapters:
- 0:01 Introduction: Introduces new tools for Apple-platform games: Background Assets updates, Unity plug-ins for StoreKit and Background Assets, and new ways to improve App Store and Apple Games app presence.
- 0:33 Background Assets: Explains how Managed Background Assets downloads asset packs only when needed, reducing initial download size and storage use. Apple-hosted assets support up to 200 GB per App Store app starting with the 26 OS releases.
- 1:35 Localized asset packs: Describes iOS 27 localized asset packs, where the system installs only assets for the player's preferred language when available. It also covers regional and primary-language fallback behavior and adding a language tag to the manifest.
- 3:14 Convert Steam depots to asset packs: Shows how to use `xcrun ba-package convert` with Xcode 27 to convert Steam depot manifests into Background Assets manifests. The manifest can then be packaged into an asset pack archive for use in the game.
- 4:15 Unity plug-ins: Introduces the StoreKit and Background Assets Unity plug-ins, available from Apple's Unity plug-ins GitHub repository. The plug-ins provide C# APIs bridging to native frameworks and require Xcode 27, Python 3, and Unity 2022 LTS or later for build and test workflows.
- 5:52 StoreKit and Background Assets sample code: Walks through C# examples for fetching products, purchasing, verifying and finishing transactions, listening for transaction updates, and downloading Background Assets. It also explains local testing with StoreKit Testing in Xcode and the Background Assets mock server.
- 8:25 Game presence: Covers new visual assets for App Store product page headers and search results, with search-result images and videos also appearing in the Apple Games app. It notes the redesigned iOS 27 payment sheet for landscape game flows.
- 9:10 Next steps: Recommends uploading localized asset packs in App Store Connect, adopting the new Unity plug-ins, and preparing new image and video assets for App Store and Apple Games app presentation.

### Meet Trust Insights

- Session ID: wwdc2026-379
- Page: https://wwdc.ai/2026/379
- Markdown: https://wwdc.ai/2026/379.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/379/
- Category: Privacy & Security
- Description: Use the iOS 27 Trust Insights framework to detect possible social-engineering coaching, interpret risk signals, and add privacy-preserving interventions.
- Duration: 13:57
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-379/eng_cb79a597fca0/wwdc2026-379-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/379/4/e12c4703-5c00-44f7-a5f8-80f6e5b7ebd5/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/379/4/e12c4703-5c00-44f7-a5f8-80f6e5b7ebd5/downloads/wwdc2026-379_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/379/4/e12c4703-5c00-44f7-a5f8-80f6e5b7ebd5/downloads/wwdc2026-379_sd.mp4?dl=1

Use the iOS 27 Trust Insights framework to detect possible social-engineering coaching, interpret risk signals, and add privacy-preserving interventions.

TLDR:
- Trust Insights is an iOS 27 framework that provides a privacy-preserving behavioral signal for possible coercion or real-time scam coaching during sensitive user actions.
- Integration is client-side Swift: add the entitlement/capability, request one or more insights with an operation category, authorize, evaluate asynchronously, and handle results and errors.
- `IsLikelyBeingCoachedInsight` returns `unknown`, `medium`, or `high`; `unknown` is not low risk, and Trust Insights should feed existing risk logic rather than be the sole decision factor.
- Real-time consumption feedback is mandatory for every evaluation, and optional offline fraud labels through Apple Business Register help improve model performance.

## What Trust Insights is for

Trust Insights addresses social-engineering attacks where the authenticated user performs a legitimate action while being pressured or coached by an attacker. Traditional authentication can confirm who is acting, but not whether the person is acting freely.

The framework provides behavioral context for sensitive moments in an app, such as payments, account changes, costly resource use, communications, remote-access grants, personal data export, or other irreversible actions.

- Introduced as a new iOS 27 framework.
- Designed for scams involving coercion, authority impersonation, tech-support fraud, family emergency fraud, and real-time coaching.
- Uses privacy-preserving machine learning to produce a small risk signal rather than exposing raw behavioral inputs.
- Best used alongside existing fraud, risk, and decisioning systems.

## Generating an insight

Trust Insights requires an entitlement, configured by declaring the capability on the app target in Xcode. The app then imports `TrustInsights`, creates requested insight parameters, builds an `InsightEvaluator.InsightContext`, checks authorization, and asynchronously requests an evaluation.

The `operationCategory` tells the system what type of action is being evaluated and determines which model logic applies. Available categories are `payment`, `account`, `resourceUse`, `communication`, and `other`; Apple asks developers to file feedback if an important use case falls into `other`.

Evaluation can take a couple of seconds and requires Internet reachability, so apps should place the request at an appropriate point in the flow, such as during an existing interstitial, animation, or confirmation step.

- A schema is required when requesting an insight; `modelVersion` is optional.
- Requesting current and prior model versions can support model governance and validation.
- Development requests use a sandbox environment; App Store-distributed apps use production models and servers.
- Xcode build-scheme launch arguments can override insight values and errors for testing decision logic and UX variants.

### Request and evaluate an IsLikelyBeingCoachedInsight

Creates an insight request, evaluates it for a resource-use operation, handles the result, and reports consumption feedback.

```swift
import TrustInsights

let request = IsLikelyBeingCoachedInsight.request(
    schema: .version1,
    modelVersion: .current
)

let context = InsightEvaluator.InsightContext(
    operationCategory: .resourceUse,
    requestedEvaluations: request
)

let evaluator = InsightEvaluator()

guard try await evaluator.requestAuthorization(for: context) == .authorized else {
    return
}

let assessment = try await evaluator.requestEvaluation(context: context)

do {
    try handleAssessment(assessment)
} catch {
    // Handle error
}

assessment.reportConsumption(.usedIncreasedFriction)
```

## Interpreting results and errors

The response includes one result per requested insight evaluation. For `IsLikelyBeingCoachedInsight`, the outcome can be `unknown`, `medium`, or `high`. The session emphasizes that `unknown` means the system has no evidence of scam risk; it must not be treated as low risk.

A `medium` result indicates some evidence of coaching risk and may justify added friction, extra verification, or risk-score adjustment. A `high` result indicates significant evidence of coaching risk, and the user should be informed of the determined risks before proceeding.

Evaluation-level errors and insight-level errors have different meanings and should be handled independently.

- Do not block a user solely because of a Trust Insights signal.
- Do not treat missing values, errors, or `unknown` as safe outcomes.
- Use results as inputs to the app's broader risk and product logic.
- Handle `@unknown default` for future enum cases.

### Handle IsLikelyBeingCoachedInsight outcomes

Switches over the supported coaching-risk outcomes while leaving room for future cases.

```swift
func handleAssessment(_ assessment: InsightEvaluation<IsLikelyBeingCoachedInsight>) throws {
    switch try assessment.insight.outcome.get() {
    case .unknown:
        break
    case .medium:
        break
    case .high:
        break
    @unknown default:
        break
    }
}
```

## Feedback requirements

Trust Insights requires real-time consumption feedback for each insight evaluation. Apps call `reportConsumption` on the evaluation result to describe whether and how the insight affected the experience. Omitting this feedback may cause the app to be rate-limited.

Offline feedback is separate. If an operation that used Trust Insights is later confirmed as fraudulent, the app or business can submit an offline label through Apple Business Register using a server-to-server API and the insight identifier from the original evaluation.

- Consumption values include `usedReducedFriction`, `usedUnchangedFriction`, `usedIncreasedFriction`, `notUsedNotNeeded`, `notUsedError`, and `usedEvaluationOnly`.
- Offline labels may arrive days, weeks, or months after the original evaluation.
- Offline label submissions should not include surplus information such as PII.
- Apply privacy-preserving techniques to any remaining values that could be used for fingerprinting.
- Offline labels are optional for using Trust Insights, but help improve the ecosystem.

### Report real-time consumption feedback

Reports that the insight caused the app to add checks or friction; this feedback is mandatory for each evaluation request.

```text
assessment.reportConsumption(.usedIncreasedFriction)
```

## Privacy model and user control

Trust Insights is designed around data minimization. Device-sourced data is processed locally, inputs are discarded immediately after evaluation, and only a single output value leaves the user's device. The final output may also incorporate Apple Account signals and velocity checks.

The framework analyzes interaction patterns, timing, context, and basic sensor data. It does not inspect content from Photos, Messages, or Mail, and device-derived signals are not shared with Apple or third parties.

Users control whether Trust Insights is enabled and can disable it in Settings. A cooldown period may apply after disabling to protect users who may have been coached into turning it off.

- Apps should query authorization status before relying on Trust Insights.
- If not authorized, consider how to inform the user or fall back to other risk logic.
- Privacy constraints are part of the framework's threat model, not an optional implementation detail.

## Best practices and adoption

The highest-value adoption points are moments where coercion would cause meaningful harm: high-value financial transactions, irreversible operations, permission grants, and sensitive data sharing. A sample response to a `medium` signal is to warn the user and delay a large transfer, but other apps might route the case to server-side risk logic or manual review.

Trust Insights should be integrated into existing risk and decision pipelines. Use model-version sampling to understand how newer models affect decisions before changing production behavior.

- Identify sensitive flows where behavioral context adds value.
- Use increased friction, warnings, delays, additional verification, server-side review, or risk-score adjustments based on app context.
- Avoid using Trust Insights as the only determinant in a decision.
- Submit required real-time feedback and, where possible, offline fraud labels through Apple Business Register.
- Use Feedback Assistant for framework feedback, capability requests, or high-volume use cases.

Resources:
- TrustInsights: https://developer.apple.com/documentation/TrustInsights

Chapters:
- 0:00 Introduction: Introduces social-engineering and coercion scenarios where authenticated users are guided into risky actions. Trust Insights is positioned as an iOS 27 framework that provides behavioral context while preserving privacy.
- 2:35 Generating insights: Explains client-side Swift integration: add the entitlement, create insight requests, choose an operation category, authorize, call `requestEvaluation`, and handle results. Covers operation categories, sandbox versus production behavior, test overrides, and the `unknown`, `medium`, and `high` outcomes.
- 6:50 Feedback requirements: Describes mandatory real-time consumption feedback through `reportConsumption` and the available consumption values. Also introduces optional offline fraud labels submitted through Apple Business Register for confirmed fraud cases.
- 9:25 Privacy: Details the privacy architecture: device-derived data stays on device, inputs are discarded after evaluation, and only a single output value leaves the device. Users can control Trust Insights in Settings, with a possible cooldown after disabling.
- 10:34 Best practices: Shows how an app might respond to a `medium` result with a warning and delay, then generalizes to other interventions such as server-side handling or manual review. Recommends applying Trust Insights at high-impact moments and using it alongside existing risk logic.
- 12:48 Next steps: Encourages developers to identify sensitive app flows, adopt the framework using the documentation and best practices, register with Apple Business Register for Partner Data Services, and provide feedback through Feedback Assistant. Mentions App Attest as a related framework for verifying legitimate app instances.

### Find and fix performance issues in your Metal games

- Session ID: wwdc2026-388
- Page: https://wwdc.ai/2026/388
- Markdown: https://wwdc.ai/2026/388.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/388/
- Category: Graphics & Games
- Description: Use Metal Performance HUD, Instruments, metalperftrace, StateReporting, and MetricKit to diagnose long-session Metal game performance issues.
- Duration: 21:01
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-388/eng_b2bb1ac3ae12/wwdc2026-388-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/388/4/682e727f-75f9-441f-81d9-2d6f38bde4b0/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/388/4/682e727f-75f9-441f-81d9-2d6f38bde4b0/downloads/wwdc2026-388_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/388/4/682e727f-75f9-441f-81d9-2d6f38bde4b0/downloads/wwdc2026-388_sd.mp4?dl=1

Use Metal Performance HUD, Instruments, metalperftrace, StateReporting, and MetricKit to diagnose long-session Metal game performance issues.

TLDR:
- Metal performance metrics such as FPS, GPU time, frame interval, memory, layer size, composition mode, and MetalFX metrics provide a baseline for spotting rendering and pacing issues.
- Game Performance Overview in Instruments captures desk-test sessions, while system look-back collection stores Metal performance and resource metrics for hours or days on macOS and iOS.
- metalperftrace summarizes collected traces, exports JSON for scripts or agents, and can aggregate metrics by StateReporting domains and states.
- StateReporting adds game context such as level, graphics settings, network state, and metadata to HUD, Instruments, metalperftrace, and MetricKit reports.

## Long-session Metal performance workflow

Smooth game performance requires repeated play testing across devices, form factors, thermal conditions, graphics settings, levels, and gameplay situations. The session frames this as a loop: collect data, analyze it, identify issues, fix them, and repeat until the game meets its targets.

Metal tools expose performance metrics that help compare sessions and diagnose issues. Timing metrics such as FPS, GPU time, and frame interval show frame pacing and GPU utilization. Display and configuration metrics such as layer size, composition mode, and MetalFX-related metrics help verify that the rendering path is configured as expected.

- Use the Metal Performance HUD for an immediate in-game overlay with FPS, memory usage, frame interval, and configurable metric presets.
- Use Instruments when metrics need to be saved and analyzed across minutes or longer sessions.
- Use documentation resources for metric definitions: "Monitoring your Metal app's graphics performance" and "Understanding the Metal Performance HUD metrics".

## Collect traces at your desk and after the fact

For desk testing, the Game Performance Overview template in Instruments captures aggregated Metal performance metrics plus Time Profiler CPU samples. It can launch a game or attach to a running game on a connected device, then record the session.

The system also continuously records efficient Metal performance and resource usage metrics. Aggregated and optional per-frame CPU, GPU, FPS, and memory data are saved for days, allowing developers to collect a look-back trace after a long play session has completed.

- On macOS 27, use the new metalperftrace command-line tool to collect look-back traces without extra configuration.
- On iOS, enable Developer Mode, enable Performance Trace in Developer settings, choose Lookback Collection, configure the look-back duration, and add the Performance Trace button to Control Center.
- After an iOS play test, tap the Control Center Performance Trace button, wait for processing, then transfer the trace from Available Trace Files to a Mac for analysis.

### Collect a trace with metalperftrace

Collects a look-back Metal performance trace for a recent duration or explicit time window.

```text
# Collect the last 5 hours
metalperftrace collect /tmp --last 5h

# Output
# Metal performance traces collected to: /tmp
# /tmp/MetalPerfTrace_20260401_094100_to_144100.atrc

# Or collect an explicit time range
metalperftrace collect /tmp \
  --start 2026-04-01T09:41:00 \
  --end 2026-04-01T12:41:00
```

## Analyze traces with metalperftrace and Instruments

metalperftrace overview prints a high-level report for each process in a trace. The report includes resource usage statistics such as memory, CPU time, and disk activity, plus Metal performance statistics for each layer, including FPS, frame time, on-GPU time, next drawable wait, and shader compilation information.

For automation, metalperftrace can filter to a specific process and emit JSON output, making traces suitable for regression scripts or agent-based triage. For deeper inspection, Instruments opens the trace visually, plots metrics on a timeline, highlights anomalous values, and recomputes min, max, average, and standard deviation for selected time ranges.

- Use metalperftrace for quick summary reports and structured output.
- Use Instruments to find time ranges with FPS drops, low GPU usage, high frame time, or other outliers.
- A metric-only trace may reveal when a drop occurred but not what the game was doing; StateReporting supplies that missing context.

### Print a trace overview

Prints an aggregated overview of resource usage and Metal performance metrics for a trace.

```text
metalperftrace overview /Data/MyGameTrace.atrc

# [Modern Renderer pid:13833]
# Mem: 2146.1 MiB (2343.9 Peak, 1199.4 Metal)
# Total CPU Time: 2417.601s (33.17% Sys, 66.83% User)
# Instructions: 9944836683668 (5.75% P, 94.25% E)
# Cycles: 5176430469224 (4.45% P, 95.55% E)
# Disk Reads / Writes: 317.37 / 0.04 MiB (Logical Write 0.04)
# Layer 0x729293000 (3456x2104) Interval 300.065s Active 300.065s
# 59.7 FPS 17735 Frames 188 Skipped
# Frame Time avg: 16.74ms min: 8.33 max: 125.00 stddev: 3.70
# CPU Begin-to-Present avg: 3.99ms min: 1.40 max: 94.37 stddev: 1.80
# On-GPU Time avg: 13.39ms min: 5.24 max: 37.57 stddev: 1.43
# Next Drawable Wait avg: 0.26ms min: 0.00 max: 91.08 stddev: 1.75
# Shader Compilation Time: 0.000s (Total: 0, Cached: 18)
```

## Add game context with StateReporting

StateReporting lets a game describe its runtime behavior over time. A domain is a finite state machine for one area of functionality, such as level progress, graphics settings, or network state. Each domain has one current state, identified by a label and optional metadata.

Stable metadata is immutable information associated with a state, such as a level ID or biome. Volatile metadata represents values that can change while remaining in the same state, such as player health or position. These reports appear in the Metal Performance HUD, metalperftrace output, and Instruments Points of Interest tracks.

- Choose conceptually orthogonal domains instead of packing too many dimensions into one domain.
- Keep transitions at the cadence of user actions or slower; StateReporting is not intended for high-frequency state changes and may throttle excessive transition rates.
- Validate state correctness in the Metal Performance HUD and Instruments so missing edge cases do not make traces misleading.

### Report state transitions and metadata

Creates a StateReporting domain, reports a state label with optional stable metadata, and updates volatile metadata without changing state.

```text
#import <StateReporting/StateReporting.h>

NSString *domain = @"com.mygame.level";
SRStateReporter *reporter = [SRStateReporter reporterForDomain:domain];

[reporter reportTransitionToStateLabel:@"Level 1"
                         stableMetadata:nil
                       volatileMetadata:nil];

[reporter reportTransitionToStateLabel:@"Level 1"
                         stableMetadata:@{ @"id": @1001 }
                       volatileMetadata:nil];

[reporter reportVolatileMetadataUpdate:@{ @"health": @100 }];
```

## Aggregate performance by state

When traces include StateReporting transitions, metalperftrace can print state information and aggregate metrics as a function of state. This makes questions like "what was the average FPS when graphics settings were High?" directly answerable from the trace.

In Instruments, each StateReporting domain appears as a track in the Points of Interest instrument. State transitions and volatile updates can be inspected alongside Metal metrics, making it easier to correlate an FPS drop with a level, setting, or other game state.

- Use --include-state-transitions to show detailed state transitions in overview output.
- Use --aggregate to group metrics across domains, within one domain, or for a specific state label.
- After narrowing the issue to a specific state or time range, use Metal System Trace in Instruments for detailed CPU/GPU scheduling data or Xcode's Metal debugger to capture and profile frames.

### Include full state transitions in overview

Shows StateReporting domains, labels, durations, and metadata in a trace overview.

```text
metalperftrace overview /Data/MyGameTrace.atrc --include-state-transitions

# [States]
# com.mygame.graphics
# High (30.59%, 14.996s) raytracing: 1 shadow: ultra
# Medium (69.38%, 34.012s) raytracing: 0 shadow: medium
# com.mygame.level
# Level 1 (20.47%, 10.033s) biome: forest id: 1001
# Level 2 (79.53%, 38.991s) biome: volcano id: 1002
```

### Aggregate metrics by state

Groups Metal performance metrics by all states, by one domain, or by a specific state label.

```text
# Aggregate across all domains / transitions
metalperftrace overview /Data/MyGameTrace.atrc --aggregate

# Aggregate one domain
metalperftrace overview /Data/MyGameTrace.atrc --aggregate \
  --domain com.mygame.graphics

# Aggregate a specific state label within a domain
metalperftrace overview /Data/MyGameTrace.atrc --aggregate \
  --domain com.mygame.graphics \
  --state-label "High"
```

## Monitor shipped games with MetricKit

MetricKit provides in-process access to power and performance reports. It continuously collects data in the background and delivers daily reports to the game.

In macOS and iOS 27, MetricKit exposes Metal frame rate information along with other performance and power metrics. It can also report Metal frame rate grouped by StateReporting states, allowing shipped games to correlate field performance with levels or other reported domains.

- Use MetricKit metrics to monitor smoothness and resource usage after release.
- Use StateReporting domains to make field frame-rate reports more actionable.
- Use MetricKit diagnostics for issues such as memory exceptions, including cases where the game is terminated for exceeding its memory limit.

Resources:
- Understanding the Metal Performance HUD metrics: https://developer.apple.com/documentation/Xcode/Understanding-metal-performance-hud-metrics
- Monitoring your Metal app's graphics performance: https://developer.apple.com/documentation/Xcode/Monitoring-your-Metal-apps-graphics-performance
- Getting started with StateReporting: https://developer.apple.com/documentation/StateReporting/getting-started-with-statereporting
- Metal debugger: https://developer.apple.com/documentation/Xcode/Metal-debugger

Chapters:
- 0:00 Introduction: Introduces the challenge of maintaining smooth frame rates across long play sessions and changing device or gameplay conditions. The workflow covers collecting, analyzing, contextualizing, and later monitoring Metal performance data.
- 1:51 Metal performance metrics: Reviews metrics available through Metal tools, including FPS, GPU time, frame interval, memory, layer sizes, composition mode, and MetalFX-related data. The Metal Performance HUD is presented as a quick configurable overlay for development-time inspection.
- 3:32 Trace collection: Shows how to collect performance traces with the Game Performance Overview template in Instruments for desk testing. It also introduces always-on system look-back collection, using metalperftrace on macOS 27 and Control Center Performance Trace setup on iOS.
- 6:38 Analyze performance traces: Demonstrates metalperftrace overview for resource and Metal metric summaries, plus JSON output for scripts or agents. Instruments is used to visualize traces, select anomalous ranges, and inspect aggregated statistics over time.
- 10:08 Contextualize with StateReporting: Introduces StateReporting domains, state labels, stable metadata, and volatile metadata for describing game behavior over time. The chapter shows integration with the Metal Performance HUD, metalperftrace aggregation, and Instruments Points of Interest tracks, along with adoption best practices.
- 17:48 Collect field data with MetricKit: Explains how MetricKit delivers daily performance and power reports from player devices. In macOS and iOS 27, it includes Metal frame-rate metrics and can break them down by StateReporting states.
- 19:41 Next steps: Recaps always-recorded Metal metrics, look-back trace collection, metalperftrace and Instruments analysis, StateReporting context, and MetricKit field monitoring. Recommended next steps are to design useful domains, test long sessions, and collect daily reports from shipped games.

### Discover container machines

- Session ID: wwdc2026-389
- Page: https://wwdc.ai/2026/389
- Markdown: https://wwdc.ai/2026/389.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/389/
- Category: Developer Tools
- Description: Use Container machine in the container tool to create fast, persistent Linux environments on macOS for cross-platform development workflows.
- Duration: 11:07
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-389/eng_65c255f4da22/wwdc2026-389-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/389/4/8dd035e7-0481-4028-b4bd-e91ba3634198/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/389/4/8dd035e7-0481-4028-b4bd-e91ba3634198/downloads/wwdc2026-389_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/389/4/8dd035e7-0481-4028-b4bd-e91ba3634198/downloads/wwdc2026-389_sd.mp4?dl=1

Use Container machine in the container tool to create fast, persistent Linux environments on macOS for cross-platform development workflows.

TLDR:
- Container machine is a new feature of the open-source container tool built on the Containerization Swift framework for running persistent Linux environments on macOS.
- Each Container machine runs in its own lightweight VM, uses OCI images as starting points, and combines container-like speed with VM-like statefulness.
- The workflow is designed to feel native on macOS through automatic user mapping, shared filesystem support, consistent working directories, and interactive Linux shells from Terminal.
- The demo creates an Alpine-based default machine, runs Linux commands, then builds and tests a Vapor server in Linux while editing files with Xcode, Icon Composer, and Safari on macOS.

## What Container machine provides

Container machine is a new feature built on top of Containerization, Apple's Swift framework for running Linux containers on macOS. It provides a lightweight Linux environment that is fast like a container but persistent like a virtual machine.

The goal is cross-platform development with minimal context switching: developers can edit and use macOS tools while building, running, and testing inside Linux.

- Runs each Container machine inside its own lightweight virtual machine.
- Uses the same OCI image format as containers.
- Persists changes made inside the environment across sessions.
- Integrates with the existing `container` command-line tool.

## Containerization foundation

Containerization supplies APIs for storage, networking, execution, and a Linux init system. It is designed around VM-based isolation for each container while preserving lightweight behavior and sub-second startup characteristics.

The companion `container` tool provides commands for image creation, distribution, and lifecycle management. Container machine extends that tool with a Linux environment that can be created, run, stopped, and revisited.

- For architecture background, see the related WWDC 2025 session "Meet Containerization."
- The open-source resources are Apple's `container` and `containerization` GitHub projects.

## Design principles

Container machine was shaped around four practical requirements: fast and lightweight operation, simple management, persistence, and seamless integration with macOS.

The persistence model lets a project-specific Linux environment accumulate tools and dependencies over time, while quick creation makes it reasonable to use separate machines for separate projects or toolchains.

- Fast and lightweight enough to fit existing development workflows.
- Simple to create and operate from the CLI.
- Stateful, so installed tools and project changes remain available later.
- Integrated with macOS to reduce switching between host and Linux environments.

## Creating and running a machine

The `container machine` command exposes subcommands such as create, run, and stop. A new machine can be created from an OCI image and set as the default so subsequent commands do not need to repeat its name.

Running commands through the machine executes them in Linux. For example, `uname` prints `Linux` inside the machine, while macOS would report Darwin.

- Use `container machine create` with a name and image to create an environment.
- Use `container machine run &lt;command&gt;` to execute a command inside the default machine.
- Use `container machine run` with no command to start an interactive shell.

### Inspect Container machine commands

Shows available Container machine actions, including commands such as create, run, and stop.

```bash
container machine
```

### Create an Alpine-based default machine

Creates a machine named `demo`, sets it as the default machine on the Mac, and uses the Alpine OCI image as the starting point.

```bash
container machine create --name demo --set-default alpine
```

### Run commands inside Linux

Runs a one-off command, checks the Linux runtime with `uname`, and starts an interactive shell when no command is supplied.

```bash
container machine run echo hi
container machine run uname
container machine run
```

## macOS integration and project workflow

Container machine automatically mirrors the macOS username and current working directory inside the Linux environment. Combined with filesystem sharing, this makes project files edited on macOS immediately available inside the machine.

The demo uses a Vapor web server project: files are edited in Xcode, assets are edited in Icon Composer, the app is built and run in Linux with Swift, and the running server is tested from Safari on macOS.

Because the machine has an isolated network, the demo lists machines to find the machine IP address and configures Vapor to listen on that external interface so Safari can connect to the server.

- `container machine list` displays machine names, IP addresses, and resource information.
- Shared project files avoid manual copying between macOS and Linux.
- For host-browser testing, configure the server to listen on the Container machine IP and the expected port, such as 8080 in the demo.

### List machines and enter the environment

Lists Container machines to obtain details such as IP address, then opens an interactive Linux shell.

```bash
container machine list
container machine run
```

### Build and run the Swift server in Linux

Compiles and runs the Vapor application from inside the Container machine using the installed Swift toolchain.

```bash
swift run
```

Resources:
- Container: https://github.com/apple/container
- Containerization: https://github.com/apple/containerization

Chapters:
- 0:00 Introduction: Introduces Container machine as a feature built on Containerization that provides a fast, lightweight, persistent Linux environment integrated with macOS.
- 1:19 Containerization: Reviews Containerization as an open-source Swift framework for running Linux containers on macOS, including storage, networking, execution, a Linux init system, and VM-based isolation. It also references the companion `container` CLI and the WWDC 2025 "Meet Containerization" session for architecture details.
- 2:14 Design principles: Explains the goals behind Container machine: fast and lightweight operation, easy creation and management, persistence for installed tools and dependencies, and minimal context switching between macOS and Linux.
- 3:36 Container machine: Describes how Container machine builds on Containerization with lightweight VMs, OCI image support, first-class integration in the `container` tool, stateful environments, automatic user mapping, shared filesystems, and consistent terminal entry points.
- 4:36 Demo: Demonstrates creating an Alpine-based default machine, running commands and an interactive shell, then building and testing a Vapor app in Linux while editing source and assets with macOS tools. The workflow shows shared files, user and directory mirroring, listing the machine IP, configuring the server for external access, and testing from Safari.
- 10:33 Next steps: Recaps Container machine as combining container usability and speed with virtual-machine-like persistence. Developers are encouraged to download the latest `container` tool release from GitHub and try it in their workflows.

### Offer subscriptions to groups and organizations

- Session ID: wwdc2026-391
- Page: https://wwdc.ai/2026/391
- Markdown: https://wwdc.ai/2026/391.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/391/
- Category: App Store, Distribution & Marketing
- Description: Learn how to sell auto-renewable subscriptions to teams, schools, and businesses with group purchases, volume purchasing, and volume pricing.
- Duration: 7:56
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-391/eng_8b9261decf7e/wwdc2026-391-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/391/4/84af4bfe-b42d-4350-91d0-5581899a3e9d/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/391/4/84af4bfe-b42d-4350-91d0-5581899a3e9d/downloads/wwdc2026-391_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/391/4/84af4bfe-b42d-4350-91d0-5581899a3e9d/downloads/wwdc2026-391_sd.mp4?dl=1

Learn how to sell auto-renewable subscriptions to teams, schools, and businesses with group purchases, volume purchasing, and volume pricing.

TLDR:
- Subscriptions for groups and organizations are available for auto-renewable subscriptions that use StoreKit 2.
- There are two purchase paths: in-app group purchases for smaller teams and social groups, and volume purchasing through Apple Business and Apple School Manager for organizations.
- App Store Connect controls availability and pricing, including opt-out behavior, Apple School Manager-only availability, and up to five volume pricing bands.
- Seat assignment is handled through device management for volume purchasing, invitation links for group purchases, or custom flows using new App Store Server API endpoints.

## Two ways to sell subscriptions to groups

Subscriptions can now be offered to groups and organizations so one buyer can purchase multiple seats for a team, club, school, or company. The two supported paths target different buyer workflows.

Group purchases happen inside your app. A customer buys multiple seats for a plan, then shares an invitation link so other members can accept and receive access. Volume purchasing happens through Apple Business and Apple School Manager, where organizations can buy subscriptions through the App Store and assign seats using their existing device management workflows.

- Use group purchases for smaller teams, social groups, or app-led collaboration flows.
- Use volume purchasing for businesses and schools that need organization-owned purchasing, large-scale assignment, management, and identity workflows.
- Both paths ultimately assign transactions to members so the app can grant subscription access.

## Availability and App Store Connect configuration

These options are available for auto-renewable subscriptions that use StoreKit 2. For most new and existing StoreKit 2 subscriptions, selling to groups and organizations is enabled by default.

Subscriptions with Family Sharing enabled are opted out by default, so developers can decide how Family Sharing and group or organization purchases should coexist. App Store Connect provides controls to adjust availability per subscription.

- Make a subscription available for both in-app group purchases and volume purchasing.
- Limit availability to Apple School Manager to create plans and pricing for verified educational institutions.
- Turn off selling to groups and organizations entirely while continuing to sell the subscription to individual App Store customers.
- Adopt StoreKit 2 before offering subscriptions to groups and organizations.

## Volume pricing

By default, each seat is sold at the subscription's current App Store Connect price. To encourage larger purchases, App Store Connect supports volume pricing: up to five price bands with developer-controlled quantity thresholds and per-seat prices.

The session's example uses three bands: the base price through 20 seats, a lower price for seats 21 through 40, and a further reduced price for seat 41 and above. This makes larger consolidated purchases cheaper on average for the buyer.

- Configure volume pricing directly in App Store Connect.
- Set up to five price bands.
- Use quantity thresholds to offer reduced per-seat pricing for larger purchases.
- Volume pricing applies as an incentive for group and organization buyers without changing the individual subscription offer.

## Purchasing implementation

For volume purchasing, Apple Business and Apple School Manager display eligible subscriptions and handle the purchase process. The developer's main task is to ensure the subscription is available to organizations.

For group purchases, the app is responsible for merchandising and UI. After presenting the value of buying for a group, collect the requested seat count and pass it into the StoreKit 2 purchase request.

- Design in-app merchandising that explains why a customer should buy seats for a team or social group.
- Collect the number of seats before starting the purchase.
- Trigger the StoreKit 2 purchase flow with that requested seat count.
- Do not build a separate purchase flow for volume purchasing; that purchase happens in Apple Business and Apple School Manager.

### Group purchase flow concept

The session does not show exact code, but the required flow is: build in-app UI, ask for the number of seats, then start a StoreKit 2 purchase request that includes the requested seat count.

## Seat management and server-side group data

Seat assignment differs by purchase path. For volume purchasing, organizations assign seats through a device management service, the same way they assign apps. For group purchases, an invitation link is generated for the initial purchaser to share with members.

Developers can use Apple's included seat management system for group purchases, covering invitation link generation, member acceptance and assignment tracking, and seat lifecycle handling such as cancellations. Apps with existing invitation or member-management systems can integrate custom invitation flows using new App Store Server API endpoints.

For apps with collaboration or shared resources, App Store Server API Group management endpoints can provide information about the groups a customer belongs to and the members in a group. These endpoints support volume purchasing and group purchases that use the included seat management flows.

- After assignment, the App Store creates a transaction for each member so the app can grant access.
- Included seat management minimizes infrastructure needed for group purchases.
- Custom invitation flows are intended for apps that already manage members or invitations.
- Group management endpoints help apps map subscription groups to collaborative product experiences.

Chapters:
- 0:00 Introduction: Introduces selling subscriptions to groups and organizations through two paths: in-app group purchases and volume purchasing in Apple Business and Apple School Manager. It explains which kinds of customers each path is intended for, from small social groups to managed businesses and schools.
- 2:17 Availability: Explains that the feature is available for auto-renewable subscriptions using StoreKit 2. Most subscriptions are enabled by default, Family Sharing subscriptions are opted out by default, and App Store Connect can limit or disable availability.
- 3:24 Pricing: Describes default per-seat pricing and the new volume pricing configuration in App Store Connect. Developers can define up to five price bands to reduce per-seat costs at higher quantities.
- 4:43 Purchasing: Contrasts the purchase flows: Apple Business and Apple School Manager handle volume purchases, while apps implement their own UI for group purchases. For group purchases, the app collects the seat count and passes it into the StoreKit 2 purchase request.
- 5:25 Seat Management: Covers how seats are assigned after purchase: organizations use device management services for volume purchases, and group purchasers share invitation links. It also introduces included seat management and custom or group-management support through App Store Server API endpoints.
- 5:26 Next steps: Recommends adopting StoreKit 2, reviewing availability and pricing strategies, and considering collaborative app experiences that benefit from group or organization subscriptions.

### Supercharge your spatial workflows with Reality Composer Pro 3

- Session ID: wwdc2026-393
- Page: https://wwdc.ai/2026/393
- Markdown: https://wwdc.ai/2026/393.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/393/
- Category: Spatial Computing
- Description: Use Reality Composer Pro 3's visual graph tools to author character animation, behaviors, interactivity, navigation, particles, and materials for spatial scenes.
- Duration: 21:52
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-393/eng_d15f31717315/wwdc2026-393-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/393/4/e68b947f-f7f3-49b5-b959-7a70fd9899c3/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/393/4/e68b947f-f7f3-49b5-b959-7a70fd9899c3/downloads/wwdc2026-393_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/393/4/e68b947f-f7f3-49b5-b959-7a70fd9899c3/downloads/wwdc2026-393_sd.mp4?dl=1

Use Reality Composer Pro 3's visual graph tools to author character animation, behaviors, interactivity, navigation, particles, and materials for spatial scenes.

TLDR:
- Reality Composer Pro 3 adds visual, node-based workflows for authoring runtime animation, autonomous character behavior, scene interactivity, navigation, GPU particles, and materials in the editor.
- Animation Graph can blend character states with State Machines, Animation Clip nodes, transition conditions, and runtime parameters such as an `isWalking` Boolean.
- Behavior Tree and Script Graph work together: Behavior Tree defines ordered routines and preconditions, while Script Graph responds to events like initialization and taps to set entity parameters.
- Navigation Mesh supports editor-authored pathfinding bounds, off-mesh connections, and generation tuning; Compute Graph builds Metal-backed particle simulations with Shader Graph materials.

## Reality Composer Pro 3 graph workflows

Reality Composer Pro 3 is positioned as a visual editor for building spatial scenes with animation, interactivity, visual effects, lighting, and materials. The session's example scene is an alchemy area from the Chaparral Village game, where an alchemist character walks through a routine and a cauldron emits smoke.

The core workflow combines multiple graph systems: Animation Graph for runtime animation blending, Behavior Tree for autonomous character routines, Script Graph for event-driven interactivity, Navigation Mesh for pathfinding, Compute Graph for particles, and Shader Graph for material rendering.

- Use Animation Graph when a character needs runtime animation state changes such as idle-to-walk transitions.
- Use Behavior Tree to define multi-step autonomous routines such as patrols, reactions, or scripted character tasks.
- Use Script Graph to connect scene or entity events to behavior changes without writing code.
- Use Compute Graph and Shader Graph together for GPU-driven effects and custom particle rendering.

## Animation Graph: blend idle and walk states

Animation Graph is a visual, node-based editor for controlling character animation at runtime. The demo builds a simple State Machine that chooses between idle and walk animation clips based on a runtime Boolean input.

The graph starts from a Final Pose node, connects to a State Machine, and defines two states: Idle and Walk. Transitions use Bool Conditions tied to an `isWalking` input: `true` transitions from Idle to Walk, and `false` transitions back to Idle. Each state input is then wired to an Animation Clip node for the corresponding animation.

- Final Pose displays the pose flowing into it.
- State Machine determines which animation state is active.
- Animation State nodes represent Idle and Walk states.
- Transition conditions reference runtime parameters from the Inputs Inspector.
- The editor highlights active states during playback, which helps debug larger animation graphs.

## Behavior Tree: author the character routine

Behavior Trees in Reality Composer Pro define behavior as a hierarchy evaluated from top to bottom and left to right. Composite nodes control flow, while Action nodes perform work. The alchemist routine uses a Sequence so steps happen in order: turn toward a destination, move to it, and optionally wait or continue based on parameters.

The session uses built-in Action nodes including Move To, Rotate To Face, Wait, and Parameter Setter. Parameter Setter nodes update the Animation Graph's `isWalking` input before and after movement so the walk animation only plays while the character is moving.

- Sequence runs children one by one and stops if one fails.
- Selector evaluates children until one succeeds.
- Parallel runs children at the same time.
- Inputs such as `tablePosition`, `cauldronPosition`, `rotationRate`, and `movementRate` feed movement and rotation nodes.
- A `readyToBrew` Boolean precondition on the cauldron sub-sequence makes the character wait at the table until an interaction enables the next step.

## Script Graph: connect events to behavior

Script Graph is Reality Composer Pro's visual scripting system for event-driven scene and entity behavior. It can run in response to scene-level or entity-specific events, making it useful for prototyping interactivity in the editor without a build cycle.

The demo uses an On Initialize node for setup, connected to a reusable subgraph that finds the table and cauldron entities and writes their world positions to entity parameters. It then adds an On Tap node and a Set Entity Parameter node to set `readyToBrew` to `true`, allowing the Behavior Tree to continue from the table to the cauldron.

The session also shows testing the interaction with Live Preview on Apple Vision Pro through the Reality Composer Pro Companion App.

- On Initialize is used for setup when the Scripting component initializes.
- Subgraphs package reusable visual logic, similar to functions.
- On Tap listens for tap gesture events on an entity.
- Set Entity Parameter bridges Script Graph events into Behavior Tree preconditions and other entity-driven logic.

## Navigation Mesh: pathfinding through spatial scenes

The Navigation Mesh component defines walkable surfaces so navigation can route characters between points while avoiding obstacles. In the village example, the character can navigate to a tapped location while avoiding trees and water.

The component includes Shapes, Off-Mesh Connections, and Generation Parameters. Shapes define the bounding box used to select scene geometry for mesh generation. Off-mesh connections link areas that would not otherwise be connected, such as a ladder from the ground to a rooftop. Generation parameters tune how geometry is sampled, including cell size: smaller values capture finer detail, while larger values create a more approximate mesh.

- Use the bounding box to control which scene geometry contributes to the Navigation Mesh resource.
- Use off-mesh connections for ladders, bridges, or other non-contiguous traversal links.
- Adjust generation parameters such as cell size to balance detail and approximation.
- Once configured, the Navigation Mesh can be used with Behavior Tree, Animation Graph, or a custom Swift system through the navigation component.

## Compute Graph and Shader Graph enhancements

Compute Graph is a visual, node-based tool for Metal-backed GPU particle simulations. A Compute Graph is organized into four phases: Emitter, Initialize, Simulate, and Output. The cauldron smoke effect uses continuous emission, randomized size and lifetime, a custom Spawn in Sphere node from a Compute Graph bundle, upward motion from negative gravity, and output rules for fade, scale, and color changes over time.

Compute Graph renders particles using a Shader Graph material; the demo material draws a circular billboard for rounder smoke particles. Reality Composer Pro 3 also enhances Shader Graph with RealityKit PBR Surface 2, Hair Surface, Portal Surface, and Portal Geometry Modifier.

- Emitter Phase controls when particles are born, including continuous, burst, or single-shot emission.
- Initialize Phase runs once at birth to set values such as velocity, lifetime, size, and initial position.
- Simulate Phase runs every frame to apply forces such as gravity and turbulence.
- Output Phase controls particle appearance as particles age and move.
- RealityKit PBR Surface 2 adds properties such as sheen and subsurface scattering, plus more accurate diffuse and occlusion shading.

Chapters:
- 0:00 Introduction: Introduces Reality Composer Pro 3's visual graph tools and the alchemy scene used throughout the session. The planned workflow combines Animation Graph, Behavior Tree, Script Graph, Navigation Mesh, Compute Graph, and Shader Graph to create an interactive character and cauldron effect.
- 2:10 Animation Graph: Shows how to build an Animation Graph State Machine with Idle and Walk states, transition conditions, and an `isWalking` runtime Boolean. Animation Clip nodes feed the states, and playback demonstrates smooth blending and active-state highlighting for debugging.
- 6:21 Behavior Tree: Explains Behavior Tree evaluation, Composite nodes, and built-in Action nodes. Builds the alchemist's routine with sequences for rotating, moving, waiting, and setting animation parameters around table and cauldron destinations.
- 11:22 Script Graph: Uses Script Graph for event-driven setup and interaction. An On Initialize flow sets destination parameters, while an On Tap flow sets `readyToBrew` to `true`, allowing the Behavior Tree to continue to the cauldron and enabling live testing on Apple Vision Pro.
- 14:46 Navigation Mesh: Introduces the Navigation Mesh component for defining walkable surfaces and routing characters around obstacles. Covers bounding boxes, off-mesh connections such as ladders, and generation parameters including cell size.
- 17:08 Compute Graph: Builds a Metal-backed cauldron smoke particle effect using Compute Graph's Emitter, Initialize, Simulate, and Output phases. The effect uses continuous emission, randomized particle properties, a custom spawn node, upward force, lifetime-based output changes, and a Shader Graph material.
- 19:43 Shader Graph enhancements: Summarizes new Shader Graph surface capabilities in Reality Composer Pro 3. Highlights RealityKit PBR Surface 2 with sheen and subsurface scattering, Hair Surface for strand lighting, and portal surface and geometry modifier support.
- 21:12 Next steps: Recaps the visual tools used to build animation, behavior, interactivity, navigation, particles, and materials in Reality Composer Pro 3. Points developers to related sessions on no-code games, RealityKit advances, and materials.

### Get ready for WWDC26

- Session ID: wwdc2026-394
- Page: https://wwdc.ai/2026/394
- Markdown: https://wwdc.ai/2026/394.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/394/
- Category: Essentials
- Description: Plan WWDC26 participation with Apple Developer app setup, session reminders, Group Labs, Forums, community activities, and event dates.
- Duration: 1:24
- Transcript source: https://devimages-cdn.apple.com/wwdc-services/transcripts/individual/wwdc2026/wwdc2026-394/eng_0a2df1aa951c/wwdc2026-394-transcript-eng.json
- Apple HLS stream: https://devstreaming-cdn.apple.com/videos/wwdc/2026/394/3/957ab100-9008-44f0-804b-37ad25ee524c/cmaf.m3u8
- HD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/394/3/957ab100-9008-44f0-804b-37ad25ee524c/downloads/wwdc2026-394_hd.mp4?dl=1
- SD video: https://devstreaming-cdn.apple.com/videos/wwdc/2026/394/3/957ab100-9008-44f0-804b-37ad25ee524c/downloads/wwdc2026-394_sd.mp4?dl=1

Plan WWDC26 participation with Apple Developer app setup, session reminders, Group Labs, Forums, community activities, and event dates.

TLDR:
- WWDC26 runs online and free from June 8 through June 12, 2026.
- Developers should install the Apple Developer app and use a free Apple Developer account for personalized recommendations across activities and 100+ sessions on developer.apple.com.
- Key schedule items are the Keynote on Monday, June 8 at 10 a.m. Pacific and Platforms State of the Union at 1 p.m. Pacific.
- Group Labs provide live streaming Q&A with Apple engineers and designers; Apple Developer Forums are available for questions before and during WWDC.

## What to do before WWDC26

WWDC26 is positioned as a full online week for developers, with sessions, labs, and community activities available free of charge. The immediate preparation steps are to use the Apple Developer app and ensure access to a free Apple Developer account.

- Download the Apple Developer app to follow the conference experience.
- Create or sign in with a free Apple Developer account to receive personalized recommendations.
- Browse activities and more than 100 sessions on developer.apple.com.

## Key dates and live programming

The session calls out the main opening-day events and the overall conference window so developers can plan their week.

- WWDC26 takes place June 8-12, 2026.
- Keynote: Monday, June 8 at 10 a.m. Pacific.
- Platforms State of the Union: Monday, June 8 at 1 p.m. Pacific.
- Set reminders for live events and sessions you plan to attend.

## Labs, Q&A, and support channels

Developers can sign up for Group Labs to participate in live streaming Q&A sessions with Apple engineers and designers who work on Apple technologies. The Apple Developer Forums are also recommended as a place to get answers before and during WWDC.

- Use the Group Labs schedule to sign up for hosted Q&A opportunities.
- Use Apple Developer Forums for technical questions around WWDC announcements and existing platform work.
- Prefer labs and forums for implementation questions that benefit from Apple engineer guidance.

## Community and extras

WWDC26 includes community activities online and around the world, alongside conference extras such as an Apple Music playlist, WWDC26 wallpaper, stickers, and the 2026 Apple Design Award finalists.

- Look for community activities throughout the week.
- Check out the 2026 Apple Design Award finalists for notable apps and games.
- Use the WWDC26 wallpaper and Apple Developer app stickers, including Clarus the dogcow.

Resources:
- Sign up for Group Labs: https://developer.apple.com/wwdc26/schedule/group-labs

### Swift Group Lab

- Session ID: wwdc2026-8001
- Page: https://wwdc.ai/2026/8001
- Markdown: https://wwdc.ai/2026/8001.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8001/
- Category: Swift
- Description: Online WWDC26 Swift group lab for asking Apple engineers and designers questions about the week's major Swift announcements.

Online WWDC26 Swift group lab for asking Apple engineers and designers questions about the week's major Swift announcements.

TLDR:
- Metadata-only lab entry; no transcript, chapters, resources, or code snippets are available for this Apple Developer page.
- The lab is an online Q&A and discussion focused on WWDC26 Swift announcements.
- Apple engineers and designers were available to answer questions, provide advice, and discuss Swift-related topics.
- Use this as a pointer to the live lab format, not as a source for specific API guidance or implementation details.

## What this lab covers

Swift Group Lab is an online WWDC26 lab centered on the week's major Swift announcements. The published description frames it as a deep dive with Apple engineers and designers, with time for attendee questions, advice, and discussion.

## Format and scope

This is a lab listing rather than a transcript-backed technical session. No chapters, sample code, resources, related videos, or Apple summary bullets are provided in the available metadata.

- Conducted online.
- Conducted in English.
- Focused on Swift announcements from WWDC26.
- Intended for interactive Q&A and discussion rather than a scripted API walkthrough.

## How to use this entry

Treat this page as a reference that a Swift-focused group lab existed at WWDC26. Because no transcript or supporting materials are available here, do not use it as evidence for specific Swift language features, migration steps, compiler behavior, or API recommendations.

- For concrete Swift guidance, consult the relevant WWDC26 Swift sessions, official Swift documentation, release notes, and sample code.
- When answering developer questions, cite this lab only for its stated purpose: Q&A and discussion about Swift announcements.

### SwiftUI for Beginners Group Lab

- Session ID: wwdc2026-8002
- Page: https://wwdc.ai/2026/8002
- Markdown: https://wwdc.ai/2026/8002.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8002/
- Category: SwiftUI & UI Frameworks
- Description: Online group lab for developers getting started with SwiftUI, focused on beginner questions, advice, and discussion.

Online group lab for developers getting started with SwiftUI, focused on beginner questions, advice, and discussion.

TLDR:
- Metadata-only lab entry; no transcript, chapters, resources, code snippets, or related videos are available.
- The session is an online SwiftUI beginners group lab conducted in English.
- Use it as a pointer to live Q&A-style guidance for starting with SwiftUI rather than as a source of specific API instruction.
- For concrete implementation details, consult SwiftUI documentation or transcript-backed WWDC sessions.

## Overview

SwiftUI for Beginners Group Lab is an online group lab intended for developers who are getting started with SwiftUI. The available metadata describes it as a place to ask questions, get advice, and follow discussion.

- Format: online group lab
- Topic: beginning SwiftUI development
- Language: English

## What can be inferred

Because no transcript, chapters, code snippets, Apple summary bullets, resources, or related videos are provided, the session should be treated as a metadata-only reference. It does not provide enough information to summarize specific demos, APIs, workflows, or recommendations.

- Relevant for SwiftUI onboarding and beginner-level questions
- Not enough metadata to identify specific SwiftUI APIs or sample code
- Use other SwiftUI resources for concrete implementation guidance

### Power and Performance Group Lab

- Session ID: wwdc2026-8003
- Page: https://wwdc.ai/2026/8003
- Markdown: https://wwdc.ai/2026/8003.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8003/
- Category: System Services
- Description: Online WWDC26 group lab for discussing power and performance announcements with Apple engineers and designers.

Online WWDC26 group lab for discussing power and performance announcements with Apple engineers and designers.

TLDR:
- Metadata-only summary: this was an online WWDC26 group lab focused on power and performance topics.
- The lab offered Q&A and discussion with Apple engineers and designers about the week's related announcements.
- No transcript, chapters, resources, code snippets, or related videos are available in the provided metadata.
- Use this page as a pointer to a live discussion format, not as standalone technical guidance.

## Overview

Power and Performance Group Lab was an online WWDC26 lab conducted in English. Its stated purpose was to let developers ask questions, get advice, and follow discussion about the week's biggest power and performance announcements.

## What developers could use it for

- Discuss app power usage and performance considerations with Apple engineers and designers.
- Ask follow-up questions related to WWDC26 power and performance announcements.
- Get advice in a group setting rather than from a prepared technical session.

## Available materials

The provided metadata does not include a transcript, chapters, resources, code snippets, Apple summary items, or related videos. No implementation details or specific APIs can be inferred from this page alone.

### visionOS Group Lab

- Session ID: wwdc2026-8004
- Page: https://wwdc.ai/2026/8004
- Markdown: https://wwdc.ai/2026/8004.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8004/
- Category: Spatial Computing
- Description: Online WWDC26 group lab for discussing visionOS announcements with Apple engineers and designers and getting high-level guidance.

Online WWDC26 group lab for discussing visionOS announcements with Apple engineers and designers and getting high-level guidance.

TLDR:
- Metadata-only lab listing; no transcript, chapters, resources, code snippets, or related videos are available.
- The session is an online group lab focused on WWDC26 visionOS announcements and developer questions.
- Apple engineers and designers are listed as participants for discussion, advice, and Q&A.
- Use this entry as a pointer to a live/interactive lab format, not as a source of specific API guidance.

## Overview

This WWDC26 session is an online visionOS group lab. The listed purpose is to let developers discuss the week's major visionOS announcements with Apple engineers and designers, ask questions, and get advice.

No transcript, chapters, sample code, resources, or related videos are provided in the metadata, so there are no specific APIs, workflows, or implementation recommendations to summarize.

## What Developers Could Use It For

- Ask clarifying questions about WWDC26 visionOS announcements.
- Discuss visionOS app design and engineering considerations with Apple engineers and designers.
- Get advice in a group setting rather than from a prerecorded technical session.
- Use alongside the relevant WWDC26 visionOS announcement and technical sessions for concrete implementation details.

## Limitations of Available Material

Because this is a metadata-only lab entry, it should not be treated as documentation for specific APIs or migration steps. For implementation guidance, consult the official visionOS session videos, documentation, and sample code associated with the WWDC26 announcements being discussed.

### Accessibility Technologies Group Lab

- Session ID: wwdc2026-8005
- Page: https://wwdc.ai/2026/8005
- Markdown: https://wwdc.ai/2026/8005.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8005/
- Category: Accessibility & Inclusion
- Description: Online WWDC26 lab for discussing accessibility technology announcements with Apple engineers and designers.

Online WWDC26 lab for discussing accessibility technology announcements with Apple engineers and designers.

TLDR:
- This is a metadata-only listing for an online WWDC26 Accessibility Technologies Group Lab.
- The lab focuses on Q&A, advice, and discussion around the week's accessibility technology announcements.
- No transcript, chapters, code snippets, resources, or related videos are available in the provided metadata.
- Use this entry as a pointer to a live or recorded lab context, not as a source for specific API guidance.

## Overview

Accessibility Technologies Group Lab is an online WWDC26 lab conducted in English. The session is described as a deep dive with Apple engineers and designers focused on accessibility technologies announced during the week.

## Intended Developer Value

- Ask questions about WWDC26 accessibility technology announcements.
- Get advice from Apple engineers and designers on accessibility-related implementation topics.
- Follow discussion around the week's major accessibility and inclusion updates.

## Available Material

The provided metadata does not include a transcript, chapters, code snippets, resources, Apple summary bullets, or related videos. Because of that, no specific APIs, tools, demos, or implementation recommendations can be inferred from this listing alone.

### SwiftUI Group Lab

- Session ID: wwdc2026-8006
- Page: https://wwdc.ai/2026/8006
- Markdown: https://wwdc.ai/2026/8006.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8006/
- Category: SwiftUI & UI Frameworks
- Description: Online SwiftUI group lab for discussing WWDC26 SwiftUI announcements and getting engineering and design guidance from Apple.

Online SwiftUI group lab for discussing WWDC26 SwiftUI announcements and getting engineering and design guidance from Apple.

TLDR:
- Metadata-only lab listing; no transcript, chapters, code snippets, resources, or related videos are available.
- The lab focuses on WWDC26 SwiftUI announcements and provides a forum for questions and discussion with Apple engineers and designers.
- Useful for identifying that Apple offered live SwiftUI guidance, but not for extracting concrete API details or implementation steps.

## Purpose

SwiftUI Group Lab is an online WWDC26 lab focused on the week's SwiftUI announcements. The session is described as a live opportunity to ask questions, get advice, and follow discussion with Apple engineers and designers.

## What the metadata supports

- Format: online group lab conducted in English.
- Topic area: SwiftUI and UI frameworks.
- Primary value: interactive discussion and guidance around WWDC26 SwiftUI announcements.
- No resources, chapters, transcript, code snippets, or related videos are listed in the provided metadata.

## How to use this entry

Use this page as a pointer to the existence of a WWDC26 SwiftUI lab rather than as a source for specific API behavior. For implementation guidance, consult the original Apple page or other WWDC26 SwiftUI sessions that include transcripts, sample code, or documentation links.

### Coding Intelligence for Beginners Group Lab

- Session ID: wwdc2026-8007
- Page: https://wwdc.ai/2026/8007
- Markdown: https://wwdc.ai/2026/8007.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8007/
- Category: Developer Tools
- Description: Online beginner group lab for asking questions and getting guidance on starting with coding intelligence in developer workflows.

Online beginner group lab for asking questions and getting guidance on starting with coding intelligence in developer workflows.

TLDR:
- Metadata-only lab entry; no transcript, chapters, resources, code snippets, or related videos are available.
- The lab is aimed at beginners getting started with coding intelligence.
- The format is an online English-language group discussion with Q&A and advice.
- Use this entry as a pointer to the live lab context rather than as technical implementation guidance.

## Overview

This WWDC 2026 developer tools lab is an online group session about getting started with coding intelligence. The provided metadata describes it as a place to ask questions, get advice, and follow discussion in English.

## Intended audience and format

- Audience: developers new to coding intelligence concepts or workflows.
- Format: online group lab with discussion and Q&A.
- Language: English.
- No technical transcript, sample code, resources, or chapter breakdown is available in the provided metadata.

## What can be inferred

The session should be treated as a beginner-oriented support and discussion opportunity rather than a reference session with documented APIs or step-by-step implementation details. The metadata does not identify specific Apple frameworks, Xcode features, commands, or coding intelligence APIs.

### Privacy and Security Group Lab

- Session ID: wwdc2026-8009
- Page: https://wwdc.ai/2026/8009
- Markdown: https://wwdc.ai/2026/8009.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8009/
- Category: Privacy & Security
- Description: Online WWDC26 group lab for discussing Apple privacy and security announcements with Apple engineers and designers.

Online WWDC26 group lab for discussing Apple privacy and security announcements with Apple engineers and designers.

TLDR:
- Metadata-only entry: no transcript, chapters, resources, code snippets, or related videos are available for this lab page.
- The lab is an online English-language discussion focused on WWDC26 privacy and security announcements.
- Developers could ask Apple engineers and designers questions and get advice about the week's privacy and security topics.

## Overview

Privacy and Security Group Lab was an online WWDC26 lab conducted in English. The page describes it as a deep dive with Apple engineers and designers focused on the week's major privacy and security announcements.

## Intended Use

Use this entry as a pointer to a live discussion format rather than a technical session with documented APIs or implementation steps. No transcript, chapters, downloads, resources, code snippets, or related videos are provided in the available metadata.

- Format: online group lab
- Topic area: Privacy & Security
- Audience activity: ask questions, get advice, and follow discussion with Apple engineers and designers

### App Store Connect Group Lab

- Session ID: wwdc2026-8010
- Page: https://wwdc.ai/2026/8010
- Markdown: https://wwdc.ai/2026/8010.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8010/
- Category: App Store, Distribution & Marketing
- Description: Online WWDC26 group lab for App Store Connect questions, advice, and discussion of the week's App Store Connect announcements.

Online WWDC26 group lab for App Store Connect questions, advice, and discussion of the week's App Store Connect announcements.

TLDR:
- Metadata-only summary: no transcript, chapters, resources, code snippets, or related videos are available for this page.
- The lab is an online Q&A/discussion with Apple engineers and designers focused on App Store Connect at WWDC26.
- Use it as a pointer to official Apple guidance and discussion around App Store Connect announcements from the week.
- The session is conducted in English and is categorized under App Store, Distribution & Marketing.

## Overview

App Store Connect Group Lab is an online WWDC26 lab for developers to ask questions, get advice, and follow discussion about the week's App Store Connect announcements with Apple engineers and designers.

The available metadata does not include a transcript, chapters, resources, code snippets, or related sessions, so no specific APIs, workflows, or announced features can be summarized from this page.

## Developer relevance

- Relevant for teams that manage app metadata, TestFlight, App Store distribution, review, pricing, analytics, or other App Store Connect workflows.
- Best used as a signpost to WWDC26 App Store Connect announcements and official Apple Developer/App Store Connect documentation.
- Because no detailed content is available here, consult the Apple Developer page or associated WWDC26 App Store Connect announcements for concrete implementation or process changes.

### Apple Intelligence Group Lab

- Session ID: wwdc2026-8011
- Page: https://wwdc.ai/2026/8011
- Markdown: https://wwdc.ai/2026/8011.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8011/
- Category: AI & Machine Learning
- Description: Online WWDC26 group lab for discussing Apple Intelligence announcements with Apple engineers and designers.

Online WWDC26 group lab for discussing Apple Intelligence announcements with Apple engineers and designers.

TLDR:
- Metadata-only page for an online Apple Intelligence group lab conducted in English during WWDC26.
- The lab is intended for developer questions, advice, and discussion with Apple engineers and designers.
- No transcript, chapters, code snippets, resources, or related videos are available in the provided metadata.
- Use this entry as a pointer to the Apple Developer lab page rather than as technical implementation guidance.

## Overview

Apple Intelligence Group Lab is an online WWDC26 lab focused on the week's Apple Intelligence announcements. The session description positions it as a discussion and Q&A opportunity with Apple engineers and designers, conducted in English.

## What developers can use it for

- Ask questions about Apple Intelligence topics announced during WWDC26.
- Get advice from Apple engineers and designers in an online group-lab format.
- Follow broader discussion around the week's Apple Intelligence developer announcements.

## Available material

The provided metadata does not include a transcript, chapters, code snippets, downloadable resources, Apple summary bullets, or related videos. Treat this as a lab listing, not a technical session with reproducible implementation steps.

### Icon Composer for Beginners Group Lab

- Session ID: wwdc2026-8012
- Page: https://wwdc.ai/2026/8012
- Markdown: https://wwdc.ai/2026/8012.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8012/
- Category: Design
- Description: Online beginner group lab for asking questions and getting practical advice on starting with Icon Composer.

Online beginner group lab for asking questions and getting practical advice on starting with Icon Composer.

TLDR:
- Metadata-only summary: this was an online group lab focused on getting started with Icon Composer.
- The lab was conducted in English and centered on Q&A, advice, and discussion rather than a recorded technical presentation.
- No chapters, resources, code snippets, or related videos were provided in the available metadata.

## Session scope

Icon Composer for Beginners Group Lab was an online session for developers and designers who wanted help getting started with Icon Composer. The available metadata describes it as a Q&A-oriented group lab with advice and discussion, conducted in English.

## What to use it for

- Use this session as a pointer to beginner-level Icon Composer guidance.
- Expect discussion-style support rather than a structured API walkthrough or implementation tutorial.
- The provided metadata does not list downloadable resources, sample code, chapters, or related sessions.

### Xcode Tips and Tricks Group Lab

- Session ID: wwdc2026-8013
- Page: https://wwdc.ai/2026/8013
- Markdown: https://wwdc.ai/2026/8013.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8013/
- Category: Developer Tools
- Description: Online WWDC26 group lab for asking Apple engineers and designers questions about using Xcode more effectively.

Online WWDC26 group lab for asking Apple engineers and designers questions about using Xcode more effectively.

TLDR:
- Metadata-only summary: no transcript, chapters, resources, code snippets, or related videos are available for this lab page.
- The session is an online group lab focused on getting the most out of Xcode during WWDC26.
- Developers could ask Apple engineers and designers questions and follow discussion about Xcode workflows and advice.
- Use the Apple Developer page for the original lab context; no specific tips, APIs, or demos are documented in the provided metadata.

## Overview

This WWDC26 session is listed as an online group lab about Xcode tips and tricks. The stated purpose is to let developers ask questions, get advice, and follow discussion with Apple engineers and designers about using Xcode effectively.

## What is known from the metadata

- Format: online group lab.
- Language: English.
- Topic: getting the most out of Xcode.
- Audience: developers looking for Xcode workflow guidance from Apple engineers and designers.

## Limits of this summary

No transcript, chapters, resources, code snippets, Apple summary bullets, or related videos were provided. Specific Xcode features, shortcuts, troubleshooting steps, or recommendations should not be inferred from this metadata alone.

### watchOS Group Lab

- Session ID: wwdc2026-8014
- Page: https://wwdc.ai/2026/8014
- Markdown: https://wwdc.ai/2026/8014.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8014/
- Category: SwiftUI & UI Frameworks
- Description: Online WWDC26 group lab for discussing watchOS announcements with Apple engineers and designers and getting development guidance.

Online WWDC26 group lab for discussing watchOS announcements with Apple engineers and designers and getting development guidance.

TLDR:
- Metadata-only summary: no transcript, chapters, resources, code snippets, or related videos are available for this Apple Developer page.
- The session is an online WWDC26 group lab focused on watchOS announcements from the week.
- It provides an opportunity to ask Apple engineers and designers questions and follow discussion about watchOS development topics.
- The lab is conducted in English.

## Overview

watchOS Group Lab is an online WWDC26 lab session centered on the week's watchOS announcements. The format is described as a discussion with Apple engineers and designers, with time for developer questions and advice.

## What to expect

- A group-lab format rather than a presentation-style technical session.
- Discussion of major WWDC26 watchOS announcements.
- Opportunity to ask questions and get guidance from Apple engineers and designers.
- Conducted in English.

## Available materials

No transcript, chapters, downloadable resources, code snippets, Apple summary bullets, or related videos are provided in the supplied metadata. Treat this page as a pointer to the lab event rather than as a source of implementation details.

### Safari and Web Technologies Group Lab

- Session ID: wwdc2026-8015
- Page: https://wwdc.ai/2026/8015
- Markdown: https://wwdc.ai/2026/8015.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8015/
- Category: Safari & Web
- Description: Online WWDC26 group lab for asking Apple engineers about Safari and web technologies announcements and implementation guidance.

Online WWDC26 group lab for asking Apple engineers about Safari and web technologies announcements and implementation guidance.

TLDR:
- Metadata-only lab listing; no transcript, chapters, resources, code snippets, or related videos are available.
- The lab is an online English discussion with Apple engineers focused on WWDC26 Safari and web technologies announcements.
- Use it as a pointer to live Q&A and guidance, not as a source for specific API behavior or implementation steps.

## Overview

Safari and Web Technologies Group Lab is an online WWDC26 lab conducted in English. The listing describes a forum to ask Apple engineers questions, get advice, and follow discussion about Safari and web technologies announcements from the week.

## What developers could use this lab for

- Clarifying questions about WWDC26 Safari announcements.
- Getting implementation advice from Apple engineers for web technologies on Apple platforms.
- Following discussion around the week's major Safari and web platform topics.

## Available materials

No transcript, chapters, resources, code snippets, Apple summary entries, or related videos are provided in the metadata. Treat this page as a lab listing rather than a technical session summary.

### Machine Learning & AI Group Lab

- Session ID: wwdc2026-8016
- Page: https://wwdc.ai/2026/8016
- Markdown: https://wwdc.ai/2026/8016.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8016/
- Category: AI & Machine Learning
- Description: Online WWDC26 group lab for developer Q&A and discussion with Apple engineers and designers about machine learning and AI announcements.

Online WWDC26 group lab for developer Q&A and discussion with Apple engineers and designers about machine learning and AI announcements.

TLDR:
- Group lab focused on WWDC26 machine learning and AI announcements, conducted online in English.
- Intended for developers seeking discussion, advice, and Q&A with Apple engineers and designers.
- No transcript, chapters, code snippets, resources, or related videos are available in the provided metadata.
- Use this session as a pointer to the lab topic rather than as a source of concrete API guidance.

## Overview

Machine Learning & AI Group Lab was an online WWDC26 lab session for discussion with Apple engineers and designers. The metadata describes it as a deep dive into the week's machine learning and AI announcements, with opportunities to ask questions and get advice.

## Format and scope

- Online group lab conducted in English.
- Focused on machine learning and AI topics announced during WWDC26.
- Designed around Q&A, advice, and group discussion rather than a prepared technical transcript.
- No resources, code snippets, chapters, or related videos are listed in the provided metadata.

## How to use this entry

Because no transcript or supporting materials are available here, treat this entry as metadata for discovering the lab, not as authoritative implementation guidance. For concrete APIs, workflows, and migration advice, consult the relevant WWDC26 machine learning and AI sessions and Apple documentation.

### SwiftData Group Lab

- Session ID: wwdc2026-8017
- Page: https://wwdc.ai/2026/8017
- Markdown: https://wwdc.ai/2026/8017.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8017/
- Category: App Services
- Description: Online WWDC26 group lab for discussing SwiftData announcements and getting guidance from Apple engineers and designers.

Online WWDC26 group lab for discussing SwiftData announcements and getting guidance from Apple engineers and designers.

TLDR:
- Metadata-only entry for an online WWDC26 SwiftData group lab conducted in English.
- The lab focused on Q&A, discussion, and advice around the week's SwiftData announcements.
- No transcript, chapters, code snippets, resources, or related videos are available in the provided metadata.

## Overview

SwiftData Group Lab was an online WWDC26 lab for developers to discuss SwiftData with Apple engineers and designers. The session description frames it as a deep dive into the week's biggest SwiftData announcements, with time for questions, advice, and group discussion.

## What developers could use it for

- Ask implementation and design questions about SwiftData announcements from WWDC26.
- Get guidance from Apple engineers and designers in an online group setting.
- Follow broader developer discussion about SwiftData changes and best practices.

## Available materials

The provided metadata does not include a transcript, chapters, resources, code snippets, Apple summary bullets, or related videos. Treat this entry as a pointer to the Apple Developer lab page rather than a technical reference.

### Camera and Photo Technologies Group Lab

- Session ID: wwdc2026-8018
- Page: https://wwdc.ai/2026/8018
- Markdown: https://wwdc.ai/2026/8018.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8018/
- Category: Photos & Camera
- Description: Online WWDC26 group lab for discussing camera and photo technology announcements with Apple engineers and designers.

Online WWDC26 group lab for discussing camera and photo technology announcements with Apple engineers and designers.

TLDR:
- Metadata-only page for an English WWDC26 online group lab focused on camera and photo technologies.
- Intended for developer Q&A, advice, and discussion with Apple engineers and designers about the week's camera/photo announcements.
- No transcript, chapters, resources, code snippets, or related videos are listed, so there are no specific APIs or implementation details to summarize.

## Session format

This is an online WWDC26 group lab conducted in English. The lab is positioned as a live discussion and Q&A opportunity with Apple engineers and designers rather than a presentation with published technical walkthroughs.

## Topic scope

The lab focuses on camera and photo technologies and the related announcements from WWDC26. It is best treated as a place where developers could ask implementation questions, get advice, and follow discussion around those announcements.

## Available materials

- No transcript is available for this Apple Developer page.
- No chapters, resources, code snippets, Apple summary items, or related videos are listed in the provided metadata.
- Because the page is metadata-only, it does not establish specific API guidance, migration steps, or sample code.

### SwiftUI Group Lab

- Session ID: wwdc2026-8120
- Page: https://wwdc.ai/2026/8120
- Markdown: https://wwdc.ai/2026/8120.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8120/
- Category: SwiftUI & UI Frameworks
- Description: Online WWDC26 SwiftUI group lab for asking Apple engineers and designers about the week's major SwiftUI announcements.

Online WWDC26 SwiftUI group lab for asking Apple engineers and designers about the week's major SwiftUI announcements.

TLDR:
- Metadata-only entry: no transcript, chapters, resources, code snippets, or related videos are available for this lab page.
- The lab is an online SwiftUI Q&A and discussion with Apple engineers and designers during WWDC26.
- Use it as a pointer to live/interactive guidance around the week's biggest SwiftUI announcements, not as a source of specific API details.

## What this lab covers

SwiftUI Group Lab is an online WWDC26 discussion and Q&A session with Apple engineers and designers. The stated focus is helping developers ask questions, get advice, and follow discussion about the week's major SwiftUI announcements.

## Available material

The Apple Developer metadata for this page does not include a transcript, chapters, resources, code snippets, Apple summary bullets, or related videos. Treat it as a lab listing rather than a technical session with recorded implementation guidance.

- Format: online group lab
- Language: English
- Primary topic: SwiftUI announcements from WWDC26

## How to use this entry

Use this entry to identify that Apple offered a SwiftUI-focused group lab at WWDC26. For concrete APIs, migration steps, sample code, or design guidance, consult the specific WWDC26 SwiftUI announcement sessions and documentation associated with the topics discussed that week.

### Coding Intelligence, Machine Learning & AI Group Lab

- Session ID: wwdc2026-8121
- Page: https://wwdc.ai/2026/8121
- Markdown: https://wwdc.ai/2026/8121.md
- Apple session: https://developer.apple.com/videos/play/wwdc2026/8121/
- Category: AI & Machine Learning
- Description: Online WWDC26 group lab for developer Q&A and discussion with Apple engineers on coding intelligence, machine learning, and AI announcements.

Online WWDC26 group lab for developer Q&A and discussion with Apple engineers on coding intelligence, machine learning, and AI announcements.

TLDR:
- Metadata-only entry for an online WWDC26 group lab, not a recorded technical session with transcript-backed guidance.
- Focused on Q&A and discussion with Apple engineers and designers about coding intelligence, machine learning, and AI announcements from WWDC26.
- No resources, chapters, code snippets, related videos, or APIs are listed in the provided metadata.
- Use this entry as a pointer to the lab topic rather than as implementation documentation.

## What this lab covered

This was an online WWDC26 group lab conducted in English for discussion and Q&A with Apple engineers and designers. The listed focus areas were coding intelligence, machine learning, and AI announcements from the week.

## Developer relevance

The metadata indicates a discussion-oriented lab rather than a session with prepared technical chapters or sample code. It may have been useful for asking implementation questions, clarifying announcements, and getting advice directly from Apple staff.

- Relevant topics: coding intelligence, machine learning, AI, and WWDC26 announcements.
- No specific frameworks, APIs, workflows, or migration steps are named in the available metadata.
- No downloadable resources or related sessions are listed.

## Limitations of the available material

No transcript, chapters, Apple summary bullets, resources, code snippets, or related videos are available in the provided metadata. Treat this summary as a conservative catalog entry only.

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