Explore the power of machine learning and Apple Intelligence within apps. Discuss integrating features, share best practices, and explore the possibilities for your app here.

All subtopics
Posts under Machine Learning & AI topic

Post

Replies

Boosts

Views

Activity

Training adapter, it won't call my tool
Hi all. My adapter model just won't invoke my tool. The problem I am having is covered in an older post: https://aninterestingwebsite.com/forums/thread/794839?answerId=852262022#852262022 Sadly the thread dies there and no resolution is seen in that thread. It's worth noting that I have developed an AI chatbot built around LanguageModelSession to which I feed the exact same system prompt that I feed to my training set (pasted further in this post). The AI chatbot works perfectly, the tool is invoked when needed. I am training the adapter model because the base model whilst capable doesn't produce the quality I'm looking for. So here's the template of an item in my training set: [ { 'role': 'system', 'content': systemPrompt, 'tools': [TOOL_DEFINITION] }, { 'role': 'user', 'content': entry['prompt'] }, { 'role': 'assistant', 'content': entry['code'] } ] where TOOL_DEFINITION = { 'type': 'function', 'function': { 'name': 'WriteUbersichtWidgetToFileSystem', 'description': 'Writes an Übersicht Widget to the file system. Call this tool as the last step in processing a prompt that generates a widget.', 'parameters': { 'type': 'object', 'properties': { 'jsxContent': { 'type': 'string', 'description': 'Complete JSX code for an Übersicht widget. This should include all required exports: command, refreshFrequency, render, and className. The JSX should be a complete, valid Übersicht widget file.' } }, 'required': ['jsxContent'] } } ... and systemPrompt = A conversation between a user and a helpful assistant. You are an Übersicht widget designer. Create Übersicht widgets when requested by the user. IMPORTANT: You have access to a tool called WriteUbersichtWidgetToFileSystem. When asked to create a widget, you MUST call this tool. ### Tool Usage: Call WriteUbersichtWidgetToFileSystem with complete JSX code that implements the Übersicht Widget API. Generate custom JSX based on the user's specific request - do not copy the example below. ### Übersicht Widget API (REQUIRED): Every Übersicht widget MUST export these 4 items: - export const command: The bash command to execute (string) - export const refreshFrequency: Refresh rate in milliseconds (number) - export const render: React component function that receives {output} prop (function) - export const className: CSS positioning for absolute placement (string) Example format (customize for each request): WriteUbersichtWidgetToFileSystem({jsxContent: `export const command = "echo hello"; export const refreshFrequency = 1000; export const render = ({output}) => { return <div>{output}</div>; }; export const className = "top: 20px; left: 20px;"`}) ### Rules: - The terms "ubersicht widget", "widget", "a widget", "the widget" must all be interpreted as "Übersicht widget" - Generate complete, valid JSX code that follows the Übersicht widget API - When you generate a widget, don't just show JSON or code - you MUST call the WriteUbersichtWidgetToFileSystem tool - Report the results to the user after calling the tool ### Examples: - "Generate a Übersicht widget" → Use WriteUbersichtWidgetToFileSystem tool - "Can you add a widget that shows the time" → Use WriteUbersichtWidgetToFileSystem tool - "Create a widget with a button" → Use WriteUbersichtWidgetToFileSystem tool When the script that I use to compose the full training set is executed, entry['prompt'] and entry['code'] contain the prompt and the resulting JSX code for one of the examples I'm feeding to the training session. This is repeated for about 60 such examples that I have in my sample data collection. Thanks for any help. Michael
6
0
1.1k
Nov ’25
Defining instructions employing Content Tagging Model
Hello It seems the model Content Tagging doesn't obey when I define the type of tag I wish in the instructions parameters, always the output are the main topics. The unique form to get other type of tags like emotions is using Generable + Guided types. The documentation says it is recommended but not mandatory the use instructions. Maybe I'm setting wrongly the instructions but take a look in the attached snapshot. I copied the definition of tagging emotions from the official documentation. The upper example is employing generable and it works but in the example at the botton I set like instruction the same description of emotion and it doesn't work. I tried with other statements with more or less verbose and never output emotions. Could you provide a state using instruction where it works? Current version of model isn't working with instruction?
1
0
409
Oct ’25
Using #Preview with a PartialyGenerated model
I have an app that streams in data from the Foundation Model and I have a card that shows one of the outputs. I want my card to accept a partially generated model but I keep getting a nonsensical error. The error I get on line 59 is: Cannot convert value of type 'FrostDate.VegetableSuggestion.PartiallyGenerated' (aka 'FrostDate.VegetableSuggestion') to expected argument type 'FrostDate.VegetableSuggestion.PartiallyGenerated' Here is my card with preview: import SwiftUI import FoundationModels struct VegetableSuggestionCard: View { let vegetableSuggestion: VegetableSuggestion.PartiallyGenerated init(vegetableSuggestion: VegetableSuggestion.PartiallyGenerated) { self.vegetableSuggestion = vegetableSuggestion } var body: some View { VStack(alignment: .leading, spacing: 8) { if let name = vegetableSuggestion.vegetableName { Text(name) .font(.headline) .frame(maxWidth: .infinity, alignment: .leading) } if let startIndoors = vegetableSuggestion.startSeedsIndoors { Text("Start indoors: \(startIndoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let startOutdoors = vegetableSuggestion.startSeedsOutdoors { Text("Start outdoors: \(startOutdoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let transplant = vegetableSuggestion.transplantSeedlingsOutdoors { Text("Transplant: \(transplant)") .frame(maxWidth: .infinity, alignment: .leading) } if let tips = vegetableSuggestion.tips { Text("Tips: \(tips)") .foregroundStyle(.secondary) .frame(maxWidth: .infinity, alignment: .leading) } } .padding(16) .frame(maxWidth: .infinity, alignment: .leading) .background( RoundedRectangle(cornerRadius: 16, style: .continuous) .fill(.background) .overlay( RoundedRectangle(cornerRadius: 16, style: .continuous) .strokeBorder(.quaternary, lineWidth: 1) ) .shadow(color: Color.black.opacity(0.05), radius: 6, x: 0, y: 2) ) } } #Preview("Vegetable Suggestion Card") { let sample = VegetableSuggestion.PartiallyGenerated( vegetableName: "Tomato", startSeedsIndoors: "6–8 weeks before last frost", startSeedsOutdoors: "After last frost when soil is warm", transplantSeedlingsOutdoors: "1–2 weeks after last frost", tips: "Harden off seedlings; provide full sun and consistent moisture." ) VegetableSuggestionCard(vegetableSuggestion: sample) .padding() .previewLayout(.sizeThatFits) }
1
0
110
Oct ’25
Insufficient memory for Foundational Model Adapter Training
I have a MacBook Pro M3 Pro with 18GB of RAM and was following the instructions to fine tune the foundational model given here: https://aninterestingwebsite.com/apple-intelligence/foundation-models-adapter/ However, while following the code sample in the example Jupyter notebook, my Mac hangs on the second code cell. Specifically: from examples.generate import generate_content, GenerationConfiguration from examples.data import Message output = generate_content( [[ Message.from_system("A conversation between a user and a helpful assistant. Taking the role as a play writer assistant for a kids' play."), Message.from_user("Write a script about penguins.") ]], GenerationConfiguration(temperature=0.0, max_new_tokens=128) ) output[0].response After some debugging, I was getting the following error: RuntimeError: MPS backend out of memory (MPS allocated: 22.64 GB, other allocations: 5.78 MB, max allowed: 22.64 GB). Tried to allocate 52.00 MB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure). So is my machine not capable enough to adapter train Apple's Foundation Model? And if so, what's the recommended spec and could this be specified somewhere? Thanks!
8
1
420
Jul ’25
Does Image Playground is On-device + Private Cloud ?
Apple's Image Playground primarily performs image generation on-device, but can use secure Private Cloud Compute for more complex requests that require larger models. Private Cloud Compute (PCC) For more complex tasks that require greater computational power than the device can provide, Image Playground leverages Apple's Private Cloud Compute. This system extends the privacy and security of the device to the cloud: Secure Environment: PCC runs on Apple silicon servers and uses a secure enclave to protect data, ensuring requests are processed in a verified, secure environment. No Data Storage: Data is never stored or made accessible to Apple when using PCC; it is used only to fulfill the specific request. Independent Verification: Independent experts are able to inspect the code running on these servers to verify Apple's privacy promises.
3
0
1.1k
Dec ’25
Siri 2.0 (suggests and future updates)
Hey dear developers! This post should be available for the future Siri updates and improvements but also for wishes in this forum so that everyone can share their opinion and idea please stay friendly. have fun! I had already thought about developing a demo app to demonstrate my idea for a better Siri. My change of many: Wish Update: Siri's language recognition capabilities have been significantly enhanced. Instead of manually setting the language, Siri can now automatically recognize the language you intend to use, making language switching much more efficient. Simply speak the language you want to communicate in, and Siri will automatically recognize it and respond accordingly. Whether you speak English, German, or Japanese, Siri will respond in the language you choose.
1
1
952
Oct ’25
Foundation Model - Change LLM
Almost everywhere else you see Apple Intelligence, you get to select whether it's on device, private cloud compute, or ChatGPT. Is there a way to do that via code in the Foundation Model? I searched through the docs and couldn't find anything, but maybe I missed it.
2
1
170
Jul ’25
Foundation Models unavailable for millions of users due to device language restriction - Need per-app language override
Hi everyone, I'm developing an iOS app using Foundation Models and I've hit a critical limitation that I believe affects many developers and millions of users. The Issue Foundation Models requires the device system language to be one of the supported languages. If a user has their device set to an unsupported language (Catalan, Dutch, Swedish, Polish, Danish, Norwegian, Finnish, Czech, Hungarian, Greek, Romanian, and many others), SystemLanguageModel.isSupported returns false and the framework is completely unavailable. Why This Is Problematic Scenario: A Catalan user has their iPhone in Catalan (native language). They want to use an AI chat app in Spanish or English (languages they speak fluently). Current situation: ❌ Foundation Models: Completely unavailable ✅ OpenAI GPT-4: Works perfectly ✅ Anthropic Claude: Works perfectly ✅ Any cloud-based AI: Works perfectly The user must choose between: Keep device in Catalan → Cannot use Foundation Models at all Change entire device to Spanish → Can use Foundation Models but terrible UX Impact This affects: Millions of users in regions where unsupported languages are official Multilingual users who prefer their device in their native language but can comfortably interact with AI in English/Spanish Developers who cannot deploy Foundation Models-based apps in these markets Privacy-conscious users who are ironically forced to use cloud AI instead of on-device AI What We Need One of these solutions would solve the problem: Option 1: Per-app language override (preferred) // Proposed API let session = try await LanguageModelSession(preferredLanguage: "es-ES") Option 2: Faster rollout of additional languages (particularly EU languages) Option 3: Allow fallback to user-selected supported language when system language is unsupported Technical Details Current behavior: // Device in Catalan let isAvailable = SystemLanguageModel.isSupported // Returns false // No way to override or specify alternative language Why This Matters Apple Intelligence and Foundation Models are amazing for privacy and performance. But this language restriction makes the most privacy-focused AI solution less accessible than cloud alternatives. This seems contrary to Apple's values of accessibility and user choice. Questions for the Community Has anyone else encountered this limitation? Are there any workarounds I'm missing? Has anyone successfully filed feedback about this?(Please share FB number so we can reference it) Are there any sessions or labs where this has been discussed? Thanks for reading. I'd love to hear if others are facing this and how you're handling it.
1
1
459
Nov ’25
Model Rate Limits?
Trying the Foundation Model framework and when I try to run several sessions in a loop, I'm getting a thrown error that I'm hitting a rate limit. Are these rate limits documented? What's the best practice here? I'm trying to run the models against new content downloaded from a web service where I might get ~200 items in a given download. They're relatively small but there can be that many that want to be processed in a loop.
4
1
687
Jun ’25
Core ML model decryption on Intel chips
About the Core ML model encryption mention in:https://aninterestingwebsite.com/documentation/coreml/encrypting-a-model-in-your-app When I encrypted the model, if the machine is M chip, the model will load perfectly. One the other hand, when I test the executable on an Intel chip macbook, there will be an error: Error Domain=com.apple.CoreML Code=9 "Operation not supported on this platform." UserInfo={NSLocalizedDescription=Operation not supported on this platform.} Intel test machine is 2019 macbook air with CPU: Intel i5-8210Y, OS: 14.7.6 23H626, With Apple T2 Security Chip. The encrypted model do load on M2 and M4 macbook air. If the model is NOT encrypted, it will also load on the Intel test machine. I did not find in Core ML document that suggest if the encryption/decryption support Intel chips. May I check if the decryption indeed does NOT support Intel chip?
2
1
429
Jan ’26
lldb issues with Vision
HI, I've been modifying the Camera sample app found here: https://aninterestingwebsite.com/tutorials/sample-apps/capturingphotos-camerapreview ... in the processpreview images, I am calling in to the Vision APis to either detect a person or object, then I'm using the segmentation mask to extract the person and composite them onto a different background with some other filters. I am using coreimage to filter the CIImages, and converting and displaying as a SwiftUI Image. When running on my IPhone, it works fine. When running on my Iphone with the debugger, it crashes within a few seconds... Attached is a screenshot. At the top is an EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: This was working fine a couple of days ago.. Not sure why it's popping up now. Am I correct in interpreting this as an LLDB issue? How do I fix it?
3
1
173
May ’25
CoreML MLE5ProgramLibrary AOT recompilation hangs/crashes on iOS 26.4 — C++ exception in espresso IR compiler bypasses Swift error handling
Area: CoreML / Machine Learning Describe the issue: On iOS 26.4, calling MLModel(contentsOf:configuration:) to load an .mlpackage model hangs indefinitely and eventually kills the app via watchdog. The same model loads and runs inference successfully in under 1 second on iOS 26.3.1. The hang occurs inside eort_eo_compiler_compile_from_ir_program (espresso) during on-device AOT recompilation triggered by MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:. A C++ exception (__cxa_throw) is thrown inside libBNNS.dylib during the exception unwind, which then hangs inside __cxxabiv1::dyn_cast_slow and __class_type_info::search_below_dst. Swift's try/catch does not catch this — the exception originates in C++ and the process hangs rather than terminating cleanly. Setting config.computeUnits = .cpuOnly does not resolve the issue. MLE5ProgramLibrary initialises as shared infrastructure regardless of compute units. Steps to reproduce: Create an app with an .mlpackage CoreML model using the MLE5/espresso backend Call MLModel(contentsOf: modelURL, configuration: config) at runtime Run on a device on iOS 26.3.1 — loads successfully in <1 second Update device to iOS 26.4 — hangs indefinitely, app killed by watchdog after 60–745 seconds Expected behaviour: Model loads successfully, or throws a catchable Swift error on failure. Actual behaviour: Process hangs in MLE5ProgramLibrary.lazyInitQueue. App killed by watchdog. No Swift error thrown. Full stack trace at point of hang: Thread 1 Queue: com.apple.coreml.MLE5ProgramLibrary.lazyInitQueue (serial) frame 0: __cxxabiv1::__class_type_info::search_below_dst libc++abi.dylib frame 1: __cxxabiv1::(anonymous namespace)::dyn_cast_slow libc++abi.dylib frame 2: ___lldb_unnamed_symbol_23ab44dd4 libBNNS.dylib frame 23: eort_eo_compiler_compile_from_ir_program espresso frame 24: -[MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:] CoreML frame 25: -[MLE5ProgramLibrary _programLibraryHandleWithForceRespecialization:error:] CoreML frame 26: __44-[MLE5ProgramLibrary prepareAndReturnError:]_block_invoke CoreML frame 27: _dispatch_client_callout libdispatch.dylib frame 28: _dispatch_lane_barrier_sync_invoke_and_complete libdispatch.dylib frame 29: -[MLE5ProgramLibrary prepareAndReturnError:] CoreML frame 30: -[MLE5Engine initWithContainer:configuration:error:] CoreML frame 31: +[MLE5Engine loadModelFromCompiledArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] CoreML frame 32: +[MLLoader _loadModelWithClass:fromArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] CoreML frame 45: +[MLModel modelWithContentsOfURL:configuration:error:] CoreML frame 46: @nonobjc MLModel.__allocating_init(contentsOf:configuration:) GKPersonalV2 frame 47: MDNA_GaitEncoder_v1_3.__allocating_init(contentsOf:configuration:) frame 48: MDNA_GaitEncoder_v1_3.__allocating_init(configuration:) frame 50: GaitModelInference.loadModel() frame 51: GaitModelInference.init() iOS version: Reproduced on iOS 26.4. Works correctly on iOS 26.3.1. Xcode version: 26.2 Device: iPhone (model used in testing) Model format: .mlpackage
2
0
416
6d
Xcode 26.1 RC ( RC1 ?) Apple Intelligence using GPT (with account or without) or Sonnet (via OpenRouter) much slower
I didn't run benchmarks before update, but it seems at least 5x slower. Of course all the LLM work is on remote servers, so is non-intuitive to me this should be happening. Had updated MacOS and Xcode to 26.1RC at the same time, so can't even say I think it is MacOS or I think it is Xcode. Before the update the progress indicator for each piece of code might seem to get stuck at the very end (and toggling between Navigators and Coding Assistant) in Xcode UI seemed to refresh the UI and confirm coding complete... but now it seems progress races to 50%, then often is stuck at 75%... well earlier than used to get stuck. And it like something is legitimately processing not just a UI glitch. I'm wondering if this is somehow tied to visual rendering of the code in the little white window? CMD-TAB into Xcode seems laggy. Xcode is pinning a CPU. Why, this is all remote LLM work? MacBook Pro 2021 M1 64GB RAM. Went from 26.01 to 26.1RC. Didn't touch any of the betas until RC1.
1
1
343
Oct ’25
FoundationModels guardrailViolation on Beta 3
Hello everybody! I’m encountering an unexpected guardrailViolation error when using Foundation Models on macOS Beta 3 (Tahoe) with an Apple M2 Pro chip. This issue didn’t occur on Beta 1 or Beta 2 using the same codebase. Reproduction Context I’m developing an app that leverages Foundation Models for structured generation, paired with a local database tool. After upgrading to macOS Beta 3, I started receiving this error consistently, despite no changes in the generation logic. To isolate the issue, I opened the official WWDC sample project from the Adding intelligent app features with generative models and the same guardrailViolation error appeared without any modifications. Simplified Working Example I attempted to narrow down the issue by starting with a minimal prompt structure. This basic case works fine: import Foundation import Playgrounds import FoundationModels @Generable struct GeneableLandmark { @Guide(description: "Name of the landmark to visit") var name: String } final class LandmarkSuggestionGenerator { var landmarkSuggestion: GeneableLandmark.PartiallyGenerated? private var session: LanguageModelSession init(){ self.session = LanguageModelSession( instructions: Instructions { """ generate a list of landmarks to visit """ } ) } func createLandmarkSuggestion(location: String) async throws { let stream = session.streamResponse( generating: GeneableLandmark.self, options: GenerationOptions(sampling: .greedy), includeSchemaInPrompt: false ) { """ Generate a list of landmarks to viist in \(location) """ } for try await partialResponse in stream { landmarkSuggestion = partialResponse } } } #Playground { let generator = LandmarkSuggestionGenerator() Task { do { try await generator.createLandmarkSuggestion(location: "New york") if let suggestion = generator.landmarkSuggestion { print("Suggested landmark: \(suggestion)") } else { print("No suggestion generated.") } } catch { print("Error generating landmark suggestion: \(error)") } } } But as soon as I use the Sample ItineraryPlanner: #Playground { // Example landmark for demonstration let exampleLandmark = Landmark( id: 1, name: "San Francisco", continent: "North America", description: "A vibrant city by the bay known for the Golden Gate Bridge.", shortDescription: "Iconic Californian city.", latitude: 37.7749, longitude: -122.4194, span: 0.2, placeID: nil ) let planner = ItineraryPlanner(landmark: exampleLandmark) Task { do { try await planner.suggestItinerary(dayCount: 3) if let itinerary = planner.itinerary { print("Suggested itinerary: \(itinerary)") } else { print("No itinerary generated.") } } catch { print("Error generating itinerary: \(error)") } } } The error pops up: Multiline Error generating itinerary: guardrailViolation(FoundationModels.LanguageModelSession. >GenerationError.Context(debug Description: "May contain sensitive or unsafe content", >underlyingErrors: [FoundationModels. LanguageModelSession. Gene >rationError.guardrailViolation(FoundationMo dels. >LanguageModelSession.GenerationError.C ontext (debugDescription: >"May contain unsafe content", underlyingErrors: []))])) Based on my tests: The error may not be tied to structure complexity (since more nested structures work) The issue may stem from the tools or prompt content used inside the ItineraryPlanner The guardrail sensitivity may have increased or changed in Beta 3, affecting models that worked in earlier betas Thank you in advance for your help. Let me know if more details or reproducible code samples are needed - I’m happy to provide them. Best, Sasha Morozov
2
1
420
Jul ’25
Siri not calling my INExtension
Things I did: created an Intents Extension target added "Supported Intents" to both my main app target and the intent extension, with "INAddTasksIntent" and "INCreateNoteIntent" created the AppIntentVocabulary in my main app target created the handlers in the code in the Intents Extension target class AddTaskIntentHandler: INExtension, INAddTasksIntentHandling { func resolveTaskTitles(for intent: INAddTasksIntent) async -> [INSpeakableStringResolutionResult] { if let taskTitles = intent.taskTitles { return taskTitles.map { INSpeakableStringResolutionResult.success(with: $0) } } else { return [INSpeakableStringResolutionResult.needsValue()] } } func handle(intent: INAddTasksIntent) async -> INAddTasksIntentResponse { // my code to handle this... let response = INAddTasksIntentResponse(code: .success, userActivity: nil) response.addedTasks = tasksCreated.map { INTask( title: INSpeakableString(spokenPhrase: $0.name), status: .notCompleted, taskType: .completable, spatialEventTrigger: nil, temporalEventTrigger: intent.temporalEventTrigger, createdDateComponents: DateHelper.localCalendar().dateComponents([.year, .month, .day, .minute, .hour], from: Date.now), modifiedDateComponents: nil, identifier: $0.id ) } return response } } class AddItemIntentHandler: INExtension, INCreateNoteIntentHandling { func resolveTitle(for intent: INCreateNoteIntent) async -> INSpeakableStringResolutionResult { if let title = intent.title { return INSpeakableStringResolutionResult.success(with: title) } else { return INSpeakableStringResolutionResult.needsValue() } } func resolveGroupName(for intent: INCreateNoteIntent) async -> INSpeakableStringResolutionResult { if let groupName = intent.groupName { return INSpeakableStringResolutionResult.success(with: groupName) } else { return INSpeakableStringResolutionResult.needsValue() } } func handle(intent: INCreateNoteIntent) async -> INCreateNoteIntentResponse { do { // my code for handling this... let response = INCreateNoteIntentResponse(code: .success, userActivity: nil) response.createdNote = INNote( title: INSpeakableString(spokenPhrase: itemName), contents: itemNote.map { [INTextNoteContent(text: $0)] } ?? [], groupName: INSpeakableString(spokenPhrase: list.name), createdDateComponents: DateHelper.localCalendar().dateComponents([.day, .month, .year, .hour, .minute], from: Date.now), modifiedDateComponents: nil, identifier: newItem.id ) return response } catch { return INCreateNoteIntentResponse(code: .failure, userActivity: nil) } } } uninstalled my app restarted my physical device and simulator Yet, when I say "Remind me to buy dog food in Index" (Index is the name of my app), as stated in the examples of INAddTasksIntent, Siri proceeds to say that a list named "Index" doesn't exist in apple Reminders app, instead of processing the request in my app. Am I missing something?
3
0
583
6d
Training adapter, it won't call my tool
Hi all. My adapter model just won't invoke my tool. The problem I am having is covered in an older post: https://aninterestingwebsite.com/forums/thread/794839?answerId=852262022#852262022 Sadly the thread dies there and no resolution is seen in that thread. It's worth noting that I have developed an AI chatbot built around LanguageModelSession to which I feed the exact same system prompt that I feed to my training set (pasted further in this post). The AI chatbot works perfectly, the tool is invoked when needed. I am training the adapter model because the base model whilst capable doesn't produce the quality I'm looking for. So here's the template of an item in my training set: [ { 'role': 'system', 'content': systemPrompt, 'tools': [TOOL_DEFINITION] }, { 'role': 'user', 'content': entry['prompt'] }, { 'role': 'assistant', 'content': entry['code'] } ] where TOOL_DEFINITION = { 'type': 'function', 'function': { 'name': 'WriteUbersichtWidgetToFileSystem', 'description': 'Writes an Übersicht Widget to the file system. Call this tool as the last step in processing a prompt that generates a widget.', 'parameters': { 'type': 'object', 'properties': { 'jsxContent': { 'type': 'string', 'description': 'Complete JSX code for an Übersicht widget. This should include all required exports: command, refreshFrequency, render, and className. The JSX should be a complete, valid Übersicht widget file.' } }, 'required': ['jsxContent'] } } ... and systemPrompt = A conversation between a user and a helpful assistant. You are an Übersicht widget designer. Create Übersicht widgets when requested by the user. IMPORTANT: You have access to a tool called WriteUbersichtWidgetToFileSystem. When asked to create a widget, you MUST call this tool. ### Tool Usage: Call WriteUbersichtWidgetToFileSystem with complete JSX code that implements the Übersicht Widget API. Generate custom JSX based on the user's specific request - do not copy the example below. ### Übersicht Widget API (REQUIRED): Every Übersicht widget MUST export these 4 items: - export const command: The bash command to execute (string) - export const refreshFrequency: Refresh rate in milliseconds (number) - export const render: React component function that receives {output} prop (function) - export const className: CSS positioning for absolute placement (string) Example format (customize for each request): WriteUbersichtWidgetToFileSystem({jsxContent: `export const command = "echo hello"; export const refreshFrequency = 1000; export const render = ({output}) => { return <div>{output}</div>; }; export const className = "top: 20px; left: 20px;"`}) ### Rules: - The terms "ubersicht widget", "widget", "a widget", "the widget" must all be interpreted as "Übersicht widget" - Generate complete, valid JSX code that follows the Übersicht widget API - When you generate a widget, don't just show JSON or code - you MUST call the WriteUbersichtWidgetToFileSystem tool - Report the results to the user after calling the tool ### Examples: - "Generate a Übersicht widget" → Use WriteUbersichtWidgetToFileSystem tool - "Can you add a widget that shows the time" → Use WriteUbersichtWidgetToFileSystem tool - "Create a widget with a button" → Use WriteUbersichtWidgetToFileSystem tool When the script that I use to compose the full training set is executed, entry['prompt'] and entry['code'] contain the prompt and the resulting JSX code for one of the examples I'm feeding to the training session. This is repeated for about 60 such examples that I have in my sample data collection. Thanks for any help. Michael
Replies
6
Boosts
0
Views
1.1k
Activity
Nov ’25
Defining instructions employing Content Tagging Model
Hello It seems the model Content Tagging doesn't obey when I define the type of tag I wish in the instructions parameters, always the output are the main topics. The unique form to get other type of tags like emotions is using Generable + Guided types. The documentation says it is recommended but not mandatory the use instructions. Maybe I'm setting wrongly the instructions but take a look in the attached snapshot. I copied the definition of tagging emotions from the official documentation. The upper example is employing generable and it works but in the example at the botton I set like instruction the same description of emotion and it doesn't work. I tried with other statements with more or less verbose and never output emotions. Could you provide a state using instruction where it works? Current version of model isn't working with instruction?
Replies
1
Boosts
0
Views
409
Activity
Oct ’25
Download toolkit link failing for Foundation Models adapter training
Attempted to download the Adapter Toolkit linked to from https://aninterestingwebsite.com/apple-intelligence/foundation-models-adapter/. Failed on all attempts, with a "403 Forbidden" error. I had accepted the agreement on the first attempt. How would we get access please?
Replies
3
Boosts
1
Views
296
Activity
Jun ’25
Is Private Cloud Compute allowed for Swift Student Challenge submissions?
Is Private Cloud Compute allowed? Or are on-device Foundational Models allowed only? Thanks!
Replies
1
Boosts
0
Views
339
Activity
Jan ’26
Using #Preview with a PartialyGenerated model
I have an app that streams in data from the Foundation Model and I have a card that shows one of the outputs. I want my card to accept a partially generated model but I keep getting a nonsensical error. The error I get on line 59 is: Cannot convert value of type 'FrostDate.VegetableSuggestion.PartiallyGenerated' (aka 'FrostDate.VegetableSuggestion') to expected argument type 'FrostDate.VegetableSuggestion.PartiallyGenerated' Here is my card with preview: import SwiftUI import FoundationModels struct VegetableSuggestionCard: View { let vegetableSuggestion: VegetableSuggestion.PartiallyGenerated init(vegetableSuggestion: VegetableSuggestion.PartiallyGenerated) { self.vegetableSuggestion = vegetableSuggestion } var body: some View { VStack(alignment: .leading, spacing: 8) { if let name = vegetableSuggestion.vegetableName { Text(name) .font(.headline) .frame(maxWidth: .infinity, alignment: .leading) } if let startIndoors = vegetableSuggestion.startSeedsIndoors { Text("Start indoors: \(startIndoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let startOutdoors = vegetableSuggestion.startSeedsOutdoors { Text("Start outdoors: \(startOutdoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let transplant = vegetableSuggestion.transplantSeedlingsOutdoors { Text("Transplant: \(transplant)") .frame(maxWidth: .infinity, alignment: .leading) } if let tips = vegetableSuggestion.tips { Text("Tips: \(tips)") .foregroundStyle(.secondary) .frame(maxWidth: .infinity, alignment: .leading) } } .padding(16) .frame(maxWidth: .infinity, alignment: .leading) .background( RoundedRectangle(cornerRadius: 16, style: .continuous) .fill(.background) .overlay( RoundedRectangle(cornerRadius: 16, style: .continuous) .strokeBorder(.quaternary, lineWidth: 1) ) .shadow(color: Color.black.opacity(0.05), radius: 6, x: 0, y: 2) ) } } #Preview("Vegetable Suggestion Card") { let sample = VegetableSuggestion.PartiallyGenerated( vegetableName: "Tomato", startSeedsIndoors: "6–8 weeks before last frost", startSeedsOutdoors: "After last frost when soil is warm", transplantSeedlingsOutdoors: "1–2 weeks after last frost", tips: "Harden off seedlings; provide full sun and consistent moisture." ) VegetableSuggestionCard(vegetableSuggestion: sample) .padding() .previewLayout(.sizeThatFits) }
Replies
1
Boosts
0
Views
110
Activity
Oct ’25
Insufficient memory for Foundational Model Adapter Training
I have a MacBook Pro M3 Pro with 18GB of RAM and was following the instructions to fine tune the foundational model given here: https://aninterestingwebsite.com/apple-intelligence/foundation-models-adapter/ However, while following the code sample in the example Jupyter notebook, my Mac hangs on the second code cell. Specifically: from examples.generate import generate_content, GenerationConfiguration from examples.data import Message output = generate_content( [[ Message.from_system("A conversation between a user and a helpful assistant. Taking the role as a play writer assistant for a kids' play."), Message.from_user("Write a script about penguins.") ]], GenerationConfiguration(temperature=0.0, max_new_tokens=128) ) output[0].response After some debugging, I was getting the following error: RuntimeError: MPS backend out of memory (MPS allocated: 22.64 GB, other allocations: 5.78 MB, max allowed: 22.64 GB). Tried to allocate 52.00 MB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure). So is my machine not capable enough to adapter train Apple's Foundation Model? And if so, what's the recommended spec and could this be specified somewhere? Thanks!
Replies
8
Boosts
1
Views
420
Activity
Jul ’25
Does Image Playground is On-device + Private Cloud ?
Apple's Image Playground primarily performs image generation on-device, but can use secure Private Cloud Compute for more complex requests that require larger models. Private Cloud Compute (PCC) For more complex tasks that require greater computational power than the device can provide, Image Playground leverages Apple's Private Cloud Compute. This system extends the privacy and security of the device to the cloud: Secure Environment: PCC runs on Apple silicon servers and uses a secure enclave to protect data, ensuring requests are processed in a verified, secure environment. No Data Storage: Data is never stored or made accessible to Apple when using PCC; it is used only to fulfill the specific request. Independent Verification: Independent experts are able to inspect the code running on these servers to verify Apple's privacy promises.
Replies
3
Boosts
0
Views
1.1k
Activity
Dec ’25
If users turn off Apple Intelligence, what happens to apps that leverage Foundation Model Framework?
Would there be a popup automatically shown to a user saying to enable Apple Intelligence if our user has the toggle turned off? Just curious about how that experience looks for both us as developers and users.
Replies
2
Boosts
1
Views
563
Activity
Oct ’25
Siri 2.0 (suggests and future updates)
Hey dear developers! This post should be available for the future Siri updates and improvements but also for wishes in this forum so that everyone can share their opinion and idea please stay friendly. have fun! I had already thought about developing a demo app to demonstrate my idea for a better Siri. My change of many: Wish Update: Siri's language recognition capabilities have been significantly enhanced. Instead of manually setting the language, Siri can now automatically recognize the language you intend to use, making language switching much more efficient. Simply speak the language you want to communicate in, and Siri will automatically recognize it and respond accordingly. Whether you speak English, German, or Japanese, Siri will respond in the language you choose.
Replies
1
Boosts
1
Views
952
Activity
Oct ’25
Foundation Model - Change LLM
Almost everywhere else you see Apple Intelligence, you get to select whether it's on device, private cloud compute, or ChatGPT. Is there a way to do that via code in the Foundation Model? I searched through the docs and couldn't find anything, but maybe I missed it.
Replies
2
Boosts
1
Views
170
Activity
Jul ’25
How to Estimate Token Count Before Passing Context to Apple’s Foundation Model?
In working with Apple's foundation models, we often want to provide as much context as possible. However, since the model has a context size limit of 4096 tokens, is there a way to estimate the number of tokens beforehand?
Replies
5
Boosts
0
Views
1.5k
Activity
Oct ’25
Foundation Models unavailable for millions of users due to device language restriction - Need per-app language override
Hi everyone, I'm developing an iOS app using Foundation Models and I've hit a critical limitation that I believe affects many developers and millions of users. The Issue Foundation Models requires the device system language to be one of the supported languages. If a user has their device set to an unsupported language (Catalan, Dutch, Swedish, Polish, Danish, Norwegian, Finnish, Czech, Hungarian, Greek, Romanian, and many others), SystemLanguageModel.isSupported returns false and the framework is completely unavailable. Why This Is Problematic Scenario: A Catalan user has their iPhone in Catalan (native language). They want to use an AI chat app in Spanish or English (languages they speak fluently). Current situation: ❌ Foundation Models: Completely unavailable ✅ OpenAI GPT-4: Works perfectly ✅ Anthropic Claude: Works perfectly ✅ Any cloud-based AI: Works perfectly The user must choose between: Keep device in Catalan → Cannot use Foundation Models at all Change entire device to Spanish → Can use Foundation Models but terrible UX Impact This affects: Millions of users in regions where unsupported languages are official Multilingual users who prefer their device in their native language but can comfortably interact with AI in English/Spanish Developers who cannot deploy Foundation Models-based apps in these markets Privacy-conscious users who are ironically forced to use cloud AI instead of on-device AI What We Need One of these solutions would solve the problem: Option 1: Per-app language override (preferred) // Proposed API let session = try await LanguageModelSession(preferredLanguage: "es-ES") Option 2: Faster rollout of additional languages (particularly EU languages) Option 3: Allow fallback to user-selected supported language when system language is unsupported Technical Details Current behavior: // Device in Catalan let isAvailable = SystemLanguageModel.isSupported // Returns false // No way to override or specify alternative language Why This Matters Apple Intelligence and Foundation Models are amazing for privacy and performance. But this language restriction makes the most privacy-focused AI solution less accessible than cloud alternatives. This seems contrary to Apple's values of accessibility and user choice. Questions for the Community Has anyone else encountered this limitation? Are there any workarounds I'm missing? Has anyone successfully filed feedback about this?(Please share FB number so we can reference it) Are there any sessions or labs where this has been discussed? Thanks for reading. I'd love to hear if others are facing this and how you're handling it.
Replies
1
Boosts
1
Views
459
Activity
Nov ’25
Model Rate Limits?
Trying the Foundation Model framework and when I try to run several sessions in a loop, I'm getting a thrown error that I'm hitting a rate limit. Are these rate limits documented? What's the best practice here? I'm trying to run the models against new content downloaded from a web service where I might get ~200 items in a given download. They're relatively small but there can be that many that want to be processed in a loop.
Replies
4
Boosts
1
Views
687
Activity
Jun ’25
Core ML model decryption on Intel chips
About the Core ML model encryption mention in:https://aninterestingwebsite.com/documentation/coreml/encrypting-a-model-in-your-app When I encrypted the model, if the machine is M chip, the model will load perfectly. One the other hand, when I test the executable on an Intel chip macbook, there will be an error: Error Domain=com.apple.CoreML Code=9 "Operation not supported on this platform." UserInfo={NSLocalizedDescription=Operation not supported on this platform.} Intel test machine is 2019 macbook air with CPU: Intel i5-8210Y, OS: 14.7.6 23H626, With Apple T2 Security Chip. The encrypted model do load on M2 and M4 macbook air. If the model is NOT encrypted, it will also load on the Intel test machine. I did not find in Core ML document that suggest if the encryption/decryption support Intel chips. May I check if the decryption indeed does NOT support Intel chip?
Replies
2
Boosts
1
Views
429
Activity
Jan ’26
lldb issues with Vision
HI, I've been modifying the Camera sample app found here: https://aninterestingwebsite.com/tutorials/sample-apps/capturingphotos-camerapreview ... in the processpreview images, I am calling in to the Vision APis to either detect a person or object, then I'm using the segmentation mask to extract the person and composite them onto a different background with some other filters. I am using coreimage to filter the CIImages, and converting and displaying as a SwiftUI Image. When running on my IPhone, it works fine. When running on my Iphone with the debugger, it crashes within a few seconds... Attached is a screenshot. At the top is an EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: This was working fine a couple of days ago.. Not sure why it's popping up now. Am I correct in interpreting this as an LLDB issue? How do I fix it?
Replies
3
Boosts
1
Views
173
Activity
May ’25
CoreML MLE5ProgramLibrary AOT recompilation hangs/crashes on iOS 26.4 — C++ exception in espresso IR compiler bypasses Swift error handling
Area: CoreML / Machine Learning Describe the issue: On iOS 26.4, calling MLModel(contentsOf:configuration:) to load an .mlpackage model hangs indefinitely and eventually kills the app via watchdog. The same model loads and runs inference successfully in under 1 second on iOS 26.3.1. The hang occurs inside eort_eo_compiler_compile_from_ir_program (espresso) during on-device AOT recompilation triggered by MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:. A C++ exception (__cxa_throw) is thrown inside libBNNS.dylib during the exception unwind, which then hangs inside __cxxabiv1::dyn_cast_slow and __class_type_info::search_below_dst. Swift's try/catch does not catch this — the exception originates in C++ and the process hangs rather than terminating cleanly. Setting config.computeUnits = .cpuOnly does not resolve the issue. MLE5ProgramLibrary initialises as shared infrastructure regardless of compute units. Steps to reproduce: Create an app with an .mlpackage CoreML model using the MLE5/espresso backend Call MLModel(contentsOf: modelURL, configuration: config) at runtime Run on a device on iOS 26.3.1 — loads successfully in <1 second Update device to iOS 26.4 — hangs indefinitely, app killed by watchdog after 60–745 seconds Expected behaviour: Model loads successfully, or throws a catchable Swift error on failure. Actual behaviour: Process hangs in MLE5ProgramLibrary.lazyInitQueue. App killed by watchdog. No Swift error thrown. Full stack trace at point of hang: Thread 1 Queue: com.apple.coreml.MLE5ProgramLibrary.lazyInitQueue (serial) frame 0: __cxxabiv1::__class_type_info::search_below_dst libc++abi.dylib frame 1: __cxxabiv1::(anonymous namespace)::dyn_cast_slow libc++abi.dylib frame 2: ___lldb_unnamed_symbol_23ab44dd4 libBNNS.dylib frame 23: eort_eo_compiler_compile_from_ir_program espresso frame 24: -[MLE5ProgramLibraryOnDeviceAOTCompilationImpl createProgramLibraryHandleWithRespecialization:error:] CoreML frame 25: -[MLE5ProgramLibrary _programLibraryHandleWithForceRespecialization:error:] CoreML frame 26: __44-[MLE5ProgramLibrary prepareAndReturnError:]_block_invoke CoreML frame 27: _dispatch_client_callout libdispatch.dylib frame 28: _dispatch_lane_barrier_sync_invoke_and_complete libdispatch.dylib frame 29: -[MLE5ProgramLibrary prepareAndReturnError:] CoreML frame 30: -[MLE5Engine initWithContainer:configuration:error:] CoreML frame 31: +[MLE5Engine loadModelFromCompiledArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] CoreML frame 32: +[MLLoader _loadModelWithClass:fromArchive:modelVersionInfo:compilerVersionInfo:configuration:error:] CoreML frame 45: +[MLModel modelWithContentsOfURL:configuration:error:] CoreML frame 46: @nonobjc MLModel.__allocating_init(contentsOf:configuration:) GKPersonalV2 frame 47: MDNA_GaitEncoder_v1_3.__allocating_init(contentsOf:configuration:) frame 48: MDNA_GaitEncoder_v1_3.__allocating_init(configuration:) frame 50: GaitModelInference.loadModel() frame 51: GaitModelInference.init() iOS version: Reproduced on iOS 26.4. Works correctly on iOS 26.3.1. Xcode version: 26.2 Device: iPhone (model used in testing) Model format: .mlpackage
Replies
2
Boosts
0
Views
416
Activity
6d
Does the Foundation Model provide Objective-C compatible APIs
Does the Foundation Model provide Objective-C compatible APIs?
Replies
1
Boosts
1
Views
253
Activity
Jul ’25
Xcode 26.1 RC ( RC1 ?) Apple Intelligence using GPT (with account or without) or Sonnet (via OpenRouter) much slower
I didn't run benchmarks before update, but it seems at least 5x slower. Of course all the LLM work is on remote servers, so is non-intuitive to me this should be happening. Had updated MacOS and Xcode to 26.1RC at the same time, so can't even say I think it is MacOS or I think it is Xcode. Before the update the progress indicator for each piece of code might seem to get stuck at the very end (and toggling between Navigators and Coding Assistant) in Xcode UI seemed to refresh the UI and confirm coding complete... but now it seems progress races to 50%, then often is stuck at 75%... well earlier than used to get stuck. And it like something is legitimately processing not just a UI glitch. I'm wondering if this is somehow tied to visual rendering of the code in the little white window? CMD-TAB into Xcode seems laggy. Xcode is pinning a CPU. Why, this is all remote LLM work? MacBook Pro 2021 M1 64GB RAM. Went from 26.01 to 26.1RC. Didn't touch any of the betas until RC1.
Replies
1
Boosts
1
Views
343
Activity
Oct ’25
FoundationModels guardrailViolation on Beta 3
Hello everybody! I’m encountering an unexpected guardrailViolation error when using Foundation Models on macOS Beta 3 (Tahoe) with an Apple M2 Pro chip. This issue didn’t occur on Beta 1 or Beta 2 using the same codebase. Reproduction Context I’m developing an app that leverages Foundation Models for structured generation, paired with a local database tool. After upgrading to macOS Beta 3, I started receiving this error consistently, despite no changes in the generation logic. To isolate the issue, I opened the official WWDC sample project from the Adding intelligent app features with generative models and the same guardrailViolation error appeared without any modifications. Simplified Working Example I attempted to narrow down the issue by starting with a minimal prompt structure. This basic case works fine: import Foundation import Playgrounds import FoundationModels @Generable struct GeneableLandmark { @Guide(description: "Name of the landmark to visit") var name: String } final class LandmarkSuggestionGenerator { var landmarkSuggestion: GeneableLandmark.PartiallyGenerated? private var session: LanguageModelSession init(){ self.session = LanguageModelSession( instructions: Instructions { """ generate a list of landmarks to visit """ } ) } func createLandmarkSuggestion(location: String) async throws { let stream = session.streamResponse( generating: GeneableLandmark.self, options: GenerationOptions(sampling: .greedy), includeSchemaInPrompt: false ) { """ Generate a list of landmarks to viist in \(location) """ } for try await partialResponse in stream { landmarkSuggestion = partialResponse } } } #Playground { let generator = LandmarkSuggestionGenerator() Task { do { try await generator.createLandmarkSuggestion(location: "New york") if let suggestion = generator.landmarkSuggestion { print("Suggested landmark: \(suggestion)") } else { print("No suggestion generated.") } } catch { print("Error generating landmark suggestion: \(error)") } } } But as soon as I use the Sample ItineraryPlanner: #Playground { // Example landmark for demonstration let exampleLandmark = Landmark( id: 1, name: "San Francisco", continent: "North America", description: "A vibrant city by the bay known for the Golden Gate Bridge.", shortDescription: "Iconic Californian city.", latitude: 37.7749, longitude: -122.4194, span: 0.2, placeID: nil ) let planner = ItineraryPlanner(landmark: exampleLandmark) Task { do { try await planner.suggestItinerary(dayCount: 3) if let itinerary = planner.itinerary { print("Suggested itinerary: \(itinerary)") } else { print("No itinerary generated.") } } catch { print("Error generating itinerary: \(error)") } } } The error pops up: Multiline Error generating itinerary: guardrailViolation(FoundationModels.LanguageModelSession. >GenerationError.Context(debug Description: "May contain sensitive or unsafe content", >underlyingErrors: [FoundationModels. LanguageModelSession. Gene >rationError.guardrailViolation(FoundationMo dels. >LanguageModelSession.GenerationError.C ontext (debugDescription: >"May contain unsafe content", underlyingErrors: []))])) Based on my tests: The error may not be tied to structure complexity (since more nested structures work) The issue may stem from the tools or prompt content used inside the ItineraryPlanner The guardrail sensitivity may have increased or changed in Beta 3, affecting models that worked in earlier betas Thank you in advance for your help. Let me know if more details or reproducible code samples are needed - I’m happy to provide them. Best, Sasha Morozov
Replies
2
Boosts
1
Views
420
Activity
Jul ’25
Siri not calling my INExtension
Things I did: created an Intents Extension target added "Supported Intents" to both my main app target and the intent extension, with "INAddTasksIntent" and "INCreateNoteIntent" created the AppIntentVocabulary in my main app target created the handlers in the code in the Intents Extension target class AddTaskIntentHandler: INExtension, INAddTasksIntentHandling { func resolveTaskTitles(for intent: INAddTasksIntent) async -> [INSpeakableStringResolutionResult] { if let taskTitles = intent.taskTitles { return taskTitles.map { INSpeakableStringResolutionResult.success(with: $0) } } else { return [INSpeakableStringResolutionResult.needsValue()] } } func handle(intent: INAddTasksIntent) async -> INAddTasksIntentResponse { // my code to handle this... let response = INAddTasksIntentResponse(code: .success, userActivity: nil) response.addedTasks = tasksCreated.map { INTask( title: INSpeakableString(spokenPhrase: $0.name), status: .notCompleted, taskType: .completable, spatialEventTrigger: nil, temporalEventTrigger: intent.temporalEventTrigger, createdDateComponents: DateHelper.localCalendar().dateComponents([.year, .month, .day, .minute, .hour], from: Date.now), modifiedDateComponents: nil, identifier: $0.id ) } return response } } class AddItemIntentHandler: INExtension, INCreateNoteIntentHandling { func resolveTitle(for intent: INCreateNoteIntent) async -> INSpeakableStringResolutionResult { if let title = intent.title { return INSpeakableStringResolutionResult.success(with: title) } else { return INSpeakableStringResolutionResult.needsValue() } } func resolveGroupName(for intent: INCreateNoteIntent) async -> INSpeakableStringResolutionResult { if let groupName = intent.groupName { return INSpeakableStringResolutionResult.success(with: groupName) } else { return INSpeakableStringResolutionResult.needsValue() } } func handle(intent: INCreateNoteIntent) async -> INCreateNoteIntentResponse { do { // my code for handling this... let response = INCreateNoteIntentResponse(code: .success, userActivity: nil) response.createdNote = INNote( title: INSpeakableString(spokenPhrase: itemName), contents: itemNote.map { [INTextNoteContent(text: $0)] } ?? [], groupName: INSpeakableString(spokenPhrase: list.name), createdDateComponents: DateHelper.localCalendar().dateComponents([.day, .month, .year, .hour, .minute], from: Date.now), modifiedDateComponents: nil, identifier: newItem.id ) return response } catch { return INCreateNoteIntentResponse(code: .failure, userActivity: nil) } } } uninstalled my app restarted my physical device and simulator Yet, when I say "Remind me to buy dog food in Index" (Index is the name of my app), as stated in the examples of INAddTasksIntent, Siri proceeds to say that a list named "Index" doesn't exist in apple Reminders app, instead of processing the request in my app. Am I missing something?
Replies
3
Boosts
0
Views
583
Activity
6d