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Plenty of LanguageModelSession.GenerationError.refusal errors after 26.4 update
Hello! After the 26.4 update I get a huge number of LanguageModelSession.GenerationError.refusal errors when using guided generation Generables for inexplicable reasons. Such errors also occur, if I want to cast a response to boolean by using 'generating: Bool.self'. The explanation generated on the grounds of the error always looks like this: Response(userPrompt: "", duration: 0.230917542, promptTokenCount: Optional(66), responseTokenCount: Optional(11), feedbackAttachment: nil, content: "I apologize, but I cannot fulfill this request.", rawContent: "I apologize, but I cannot fulfill this request.", transcriptEntries: ArraySlice([])) All the prompts and Generables I use are definitely not profane. Before 26.4 such errors on the same prompts and Generables never occurred. The 26.4 update rendered those features unusable to me. Is this a known bug or what am I doing wrong?
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Programmatic image creation using ImageCreator
Hello, Could you please provide details for maximum string length of the prompt and the title when using ImageCreator and the method extracted(from:title:)? static func extracted( from text: String, title: String? = nil ) -> ImagePlaygroundConcept Any additional details or example of prompt and title would help. Additionally, are ImagePlaygroundStyle.animation, ImagePlaygroundStyle.illustration and ImagePlaygroundStyle.sketch all available when using extracted(from:title:)? I am trying to generate images programmatically and would appreciate your guidance. Thank you.
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Getting CoreML to run inference on already allocated gpu buffers
I am running some experiments with WebGPU using the wgpu crate in rust. I have some Buffers already allocated in the GPU. Is it possible to use those already existing buffers directly as inputs to a predict call in CoreML? I want to prevent gpu to cpu download time as much as possible. Or are there any other ways to do something like this. Is this only possible using the latest Tensor object which came out with Metal 4 ?
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Nov ’25
Vision face landmarks shifted on iOS 26 but correct on iOS 18 with same code and image
I'm using Vision framework (DetectFaceLandmarksRequest) with the same code and the same test image to detect face landmarks. On iOS 18 everything works as expected: detected face landmarks align with the face correctly. But when I run the same code on devices with iOS 26, the landmark coordinates are outside the [0,1] range, which indicates they are out of face bounds. Fun fact: the old VNDetectFaceLandmarksRequest API works very well without encountering this issue How I get face landmarks: private let faceRectangleRequest = DetectFaceRectanglesRequest(.revision3) private var faceLandmarksRequest = DetectFaceLandmarksRequest(.revision3) func detectFaces(in ciImage: CIImage) async throws -> FaceTrackingResult { let faces = try await faceRectangleRequest.perform(on: ciImage) faceLandmarksRequest.inputFaceObservations = faces let landmarksResults = try await faceLandmarksRequest.perform(on: ciImage) ... } How I show face landmarks in SwiftUI View: private func convert( point: NormalizedPoint, faceBoundingBox: NormalizedRect, imageSize: CGSize ) -> CGPoint { let point = point.toImageCoordinates( from: faceBoundingBox, imageSize: imageSize, origin: .upperLeft ) return point } At the same time, it works as expected and gives me the correct results: region is FaceObservation.Landmarks2D.Region let points: [CGPoint] = region.pointsInImageCoordinates( imageSize, origin: .upperLeft ) After that, I found that the landmarks are normalized relative to the unalignedBoundingBox. However, I can’t access it in code. Still, using these values for the bounding box works correctly. Things I've already tried: Same image input Tested multiple devices on iOS 26.2 -> always wrong. Tested multiple devices on iOS 18.7.1 -> always correct. Environment: macOS 26.2 Xcode 26.2 (17C52) Real devices, not simulator Face Landmarks iOS 18 Face Landmarks iOS 26
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Dec ’25
CoreML multifunction model runtime memory cost
Recently, I'm trying to deploy some third-party LLM to Apple devices. The methodoloy is similar to https://github.com/Anemll/Anemll. The biggest issue I'm having now is the runtime memory usage. When there are multiple functions in a model (mlpackage or mlmodelc), the runtime memory usage for weights is somehow duplicated when I load all of them. Here's the detail: I created my multifunction mlpackage following https://apple.github.io/coremltools/docs-guides/source/multifunction-models.html I loaded each of the functions using the generated swift class: let config = MLModelConfiguration() config.computeUnits = MLComputeUnits.cpuAndNeuralEngine config.functionName = "infer_512"; let ffn1_infer_512 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_1024"; let ffn1_infer_1024 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_2048"; let ffn1_infer_2048 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) I observed that RAM usage increases linearly as I load each of the functions. Using instruments, I see that there are multiple HWX files generated and loaded, each of which contains all the weight data. My understanding of what's happening here: The CoreML framework did some MIL->MIL preprocessing before further compilation, which includes separating CPU workload from ANE workload. The ANE part of each function is moved into a separate MIL file then compile separately into a HWX file each. The problem is that the weight data of these HWX files are duplicated. Since that the weight data of LLMs is huge, it will cause out-of-memory issue on mobile devices. The improvement I'm hoping from Apple: I hope we can try to merge the processed MIL files back into one before calling ANECCompile(), so that the weights can be merged. I don't have control over that in user space and I'm not sure if that is feasible. So I'm asking for help here. Thanks.
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Apr ’25
Sharing a Swift port of Gemma 4 for mlx-swift-lm — feedback welcome
Hi all, I've been working on a pure-Swift port of Google's Gemma 4 text decoder that plugs into mlx-swift-lm as a sidecar model registration. Sharing it here in case anyone else hit the same wall I did, and to get feedback from the MLX team and the community before I propose anything upstream. Repo: https://github.com/yejingyang8963-byte/Swift-gemma4-core Why As of mlx-swift-lm 2.31.x, Gemma 4 isn't supported out of the box. The obvious workaround — reusing the Gemma 3 text implementation with a patched config — fails at weight load because Gemma 4 differs from Gemma 3 in several structural places. The chat-template path through swift-jinja 1.x also silently corrupts the prompt, so the model loads but generates incoherent text. What's in the package A from-scratch Swift implementation of the Gemma 4 decoder (Configuration, Layers, Attention, MLP, RoPE, DecoderLayer) Per-Layer Embedding (PLE) support — the shared embedding table that feeds every decoder layer through a gated MLP as a third residual KV sharing across the back half of the decoder, threaded through the forward pass via a donor table with a single global rope offset A custom Gemma4ProportionalRoPE class for the partial-rotation rope type that initializeRope doesn't currently recognize A chat-template bypass that builds the prompt as a literal string with the correct turn markers and encodes via tokenizer.encode(text:), matching Python mlx-lm's apply_chat_template byte-for-byte Measured on iPhone (A-series, 7.4 GB RAM) Model: mlx-community/gemma-4-e2b-it-4bit Warm load: ~6 s Memory after load: 341–392 MB Time to first token (end-to-end, 333-token system prompt): 2.82 s Generation throughput: 12–14 tok/s What I'd love feedback on Is the sidecar registration pattern the right way to extend mlx-swift-lm with new model families, or is there a more idiomatic path I missed? The chat-template bypass works but feels like a workaround. Is the right long-term fix in swift-jinja, in the tokenizer, or somewhere else entirely? Anyone running into the same PLE / KV-sharing issues on other Gemma-family checkpoints? I'd like to make sure the implementation generalizes beyond E2B before tagging a 0.2.0. Happy to open a PR against mlx-swift-lm if the maintainers think any of this belongs upstream. Thanks for reading.
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Does ExecuTorch support VisionOS?
Does anyone know if ExecuTorch is officially supported or has been successfully used on visionOS? If so, are there any specific build instructions, example projects, or potential issues (like sandboxing or memory limitations) to be aware of when integrating it into an Xcode project for the Vision Pro? While ExecuTorch has support for iOS, I can't find any official documentation or community examples specifically mentioning visionOS. Thanks.
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Jul ’25
Building Real-Time Voice Input on macOS 26 with SpeechAnalyzer + ScreenCaptureKit
We built an open-source macOS menu bar app that turns speech into text and pastes it into the active app — using SpeechAnalyzer for on-device transcription, ScreenCaptureKit + Vision for screen-aware context, and FluidAudio for speaker diarization in meeting mode. Here's what we learned shipping it on macOS 26. GitHub: github.com/Marvinngg/ambient-voice Architecture The app has two modes: hotkey dictation (press to talk, release to inject) and meeting recording (continuous transcription with a floating panel). Dictation Mode Audio capture uses AVCaptureSession (more on why below). The captured audio feeds into SpeechAnalyzer via an AsyncStream: let transcriber = SpeechTranscriber( locale: locale, transcriptionOptions: [], reportingOptions: [.volatileResults, .alternativeTranscriptions], attributeOptions: [.audioTimeRange, .transcriptionConfidence] ) let analyzer = SpeechAnalyzer(modules: [transcriber]) let (inputSequence, inputBuilder) = AsyncStream.makeStream() try await analyzer.start(inputSequence: inputSequence) While recording, we capture a screenshot of the focused window using ScreenCaptureKit, run Vision OCR (VNRecognizeTextRequest), extract keywords, and inject them into SpeechAnalyzer as contextual bias: let context = AnalysisContext() context.contextualStrings[.general] = ocrKeywords try await analyzer.setContext(context) This improves accuracy for technical terms and proper nouns visible on screen. If your screen shows "SpeechAnalyzer", saying it out loud is more likely to be transcribed correctly. After transcription, an optional L2 step sends the text through a local LLM (ollama) for spoken-to-written cleanup, then CGEvent simulates Cmd+V to paste into the active app. Meeting Mode Meeting mode forks the same audio stream to two consumers: SpeechAnalyzer — real-time streaming transcription, displayed in a floating NSPanel FluidAudio buffer — accumulates 16kHz Float32 mono samples for batch speaker diarization after recording stops When the user ends the meeting, FluidAudio's performCompleteDiarization() runs on the accumulated audio. We align transcription segments with speaker segments using audioTimeRange overlap matching — each transcription segment gets assigned the speaker ID with the most time overlap. Results export to Markdown. Pitfalls We Hit on macOS 26 1. AVAudioEngine installTap doesn't fire with Bluetooth devices We started with AVAudioEngine.inputNode.installTap() for audio capture. It worked fine with built-in mics but the tap callback never fired with Bluetooth devices (tested with vivo TWS 4 Hi-Fi). Fix: switched to AVCaptureSession. The delegate callback captureOutput(_:didOutput:from:) fires reliably regardless of audio device. The tradeoff is you get CMSampleBuffer instead of AVAudioPCMBuffer, so you need a conversion step. 2. NSEvent addGlobalMonitorForEvents crashes Our global hotkey listener used NSEvent.addGlobalMonitorForEvents. On macOS 26, this crashes with a Bus error inside GlobalObserverHandler — appears to be a Swift actor runtime issue. Fix: switched to CGEventTap. Works reliably, but the callback runs on a CFRunLoop context, which Swift doesn't recognize as MainActor. 3. CGEventTap callbacks aren't on MainActor If your CGEventTap callback touches any @MainActor state, you'll get concurrency violations. The callback runs on whatever thread owns the CFRunLoop. Fix: bridge with DispatchQueue.main.async {} inside the tap callback before touching any MainActor state. 4. CGPreflightScreenCaptureAccess doesn't request permission We used CGPreflightScreenCaptureAccess() as a guard before calling ScreenCaptureKit. If it returned false, we'd bail out. The problem: this function only checks — it never triggers macOS to add your app to the Screen Recording permission list. Chicken-and-egg: you can't get permission because you never ask for it. Fix: call CGRequestScreenCaptureAccess() at app startup. This adds your app to System Settings → Screen Recording. Then let ScreenCaptureKit calls proceed without the preflight guard — SCShareableContent will also trigger the permission prompt on first use. 5. Ad-hoc signing breaks TCC permissions on every rebuild During development, codesign --sign - (ad-hoc) generates a different code directory hash on every build. macOS TCC tracks permissions by this hash, so every rebuild = new app identity = all permissions reset. Fix: sign with a stable certificate. If you have an Apple Development certificate, use that. The TeamIdentifier stays constant across rebuilds, so TCC permissions persist. We also discovered that launching via open WE.app (LaunchServices) instead of directly executing the binary is required — otherwise macOS attributes TCC permissions to Terminal, not your app. Benchmarks We ran end-to-end benchmarks on public datasets (Mac Mini M4 16GB, macOS 26): Transcription (SpeechAnalyzer, AliMeeting Chinese): • Near-field CER 34% (excluding outliers ~25%) • Far-field CER 40% (single channel, no beamforming, >30% overlap) • Processing speed 74-89x real-time Speaker diarization (FluidAudio offline): • AMI English 16 meetings: avg DER 23.2% (collar=0.25s, ignoreOverlap=True) • AliMeeting Chinese 8 meetings: DER 48.5% (including overlap regions) • Memory: RSS ~500MB, peak 730-930MB Full evaluation methodology, scripts, and raw results are in the repo. Open Source The project is MIT licensed: github.com/Marvinngg/ambient-voice It includes the macOS client (Swift 6.2, SPM), server-side distillation/training scripts (Python), and a complete evaluation framework with reproducible benchmarks. Feedback and contributions welcome.
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Core-ml-on-device-llama Converting fails
I followed below url for converting Llama-3.1-8B-Instruct model but always fails even i have 64GB of free space after downloading model from huggingface. https://machinelearning.apple.com/research/core-ml-on-device-llama Also tried with other models Llama-3.1-1B-Instruct & Llama-3.1-3B-Instruct models those are converted but while doing performance test in xcode fails for all compunits. Is there any source code to run llama models in ios app.
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Apr ’25
Request: Official One-Click Local LLM Deployment for 2019 Mac Pro (7,1) Dual W6900X
I am a professional user of the 2019 Mac Pro (7,1) with dual AMD Radeon Pro W6900X MPX modules (32GB VRAM each). This hardware is designed for high-performance compute, but it is currently crippled for modern local LLM/AI workloads under Linux due to Apple's EFI/PCIe routing restrictions. Core Issue: rocminfo reports "No HIP GPUs available" when attempting to use ROCm/amdgpu on Linux Apple's custom EFI firmware blocks full initialization of professional GPU compute assets The dual W6900X GPUs have 64GB combined VRAM and high-bandwidth Infinity Fabric Link, but cannot be fully utilized for local AI inference/training My Specific Request: Apple should provide an official, one-click deployable application that enables full utilization of dual W6900X GPUs for local large language model (LLM) inference and training under Linux. This application must: Fully initialize both W6900X GPUs via HIP/ROCm, establishing valid compute contexts Bypass artificial EFI/PCIe routing restrictions that block access to professional GPU resources Provide a stable, user-friendly one-click deployment experience (similar to NVIDIA's AI Enterprise or AMD's ROCm Hub) Why This Matters: The 2019 Mac Pro is Apple's flagship professional workstation, marketed for compute-intensive workloads. Its high-cost W6900X GPUs should not be locked down for modern AI/LLM use cases. An official one-click deployment solution would demonstrate Apple's commitment to professional AI and unlock significant value for professional users. I look forward to Apple's response and a clear roadmap for enabling this critical capability. #MacPro #Linux #ROCm #LocalLLM #W6900X #CoreML
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Building a 4-agent autonomous coding pipeline on Apple Silicon — MLX backend questions
Hi, I'm building ANF (Autonomous Native Forge) — a cloud-free, 4-agent autonomous software production pipeline running on local hardware with local LLM inference. No middleware, pure Node.js native. Currently running on NVIDIA Blackwell GB10 with vLLM + DeepSeek-R1-32B. Now porting to Apple Silicon. Three technical questions: How production-ready is mlx-lm's OpenAI-compatible API server for long context generation (32K tokens)? What's the recommended approach for KV Cache management with Unified Memory architecture — any specific flags or configurations for M4 Ultra? MLX vs GGUF (llama.cpp) for a multi-agent pipeline where 4 agents call the inference endpoint concurrently — which handles parallel requests better on Apple Silicon? GitHub: github.com/trgysvc/AutonomousNativeForge Any guidance appreciated.
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Best approach for animating a speaking avatar in a macOS/iOS SwiftUI application
I am developing a macOS application using SwiftUI (with an iOS version as well). One feature we are exploring is displaying an avatar that reads or speaks dynamically generated text produced by an AI service. The basic flow would be: Text generated by an AI service Text converted to speech using a TTS engine An avatar (2D or 3D) rendered in the app that animates lip movement synchronized with the speech Ideally the avatar would render locally on the device. Questions: What Apple frameworks would be most appropriate for implementing a speaking avatar? SceneKit RealityKit SpriteKit (for 2D avatars) Is there any recommended way to drive lip-sync animation from speech audio using Apple frameworks? Does AVSpeechSynthesizer expose phoneme or viseme timing information that could be used for avatar animation? If such timing information is not available, what is the recommended approach for synchronizing character mouth animation with speech audio on macOS/iOS? Are there examples of real-time character animation synchronized with speech on macOS/iOS? Any architectural guidance or references would be greatly appreciated.
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Apple Intelligence Naughty Naughty
When doing some exploratory research into using Apple Intelligence in our aviation-focused application, I noticed that there were several times that key phases would be marked as inappropriate. I tried to stifle these using prompts and rules but couldn't get it to take hold. I was encouraged by an Apple employee to go ahead and post this so that the AI team can use the feedback. There were several terms that triggered this warning, but the two that were most prominent were: 'Tailwind' 'JFK' or 'KJFK' (NY airport ICAO/IATA codes)
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Mar ’26
`LanguageModelSession.respond()` never resolves in Beta 5
Hi all, I noticed on Friday that on the new Beta 5 using FoundationModels on a simulator LanguageModelSession.respond() neither resolves nor throws most of the time. The SwiftUI test app below was working perfectly in Xcode 16 Beta 4 and iOS 26 Beta 4 (simulator). import SwiftUI import FoundationModels struct ContentView: View { var body: some View { VStack { Image(systemName: "globe") .imageScale(.large) .foregroundStyle(.tint) Text("Hello, world!") } .padding() .onAppear { Task { do { let session = LanguageModelSession() let response = try await session.respond(to: "are cats better than dogs ???") print(response.content) } catch { print("error") } } } } } After updating to Xcode 16 Beta 5 and iOS 26 Beta 5 (simulator), the code now often hangs. Occasionally it will work if I toggle Apple Intelligence on and off in Settings, but it’s unreliable.
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Aug ’25
ActivityClassifier doesn't classify movement
I'm using a custom create ML model to classify the movement of a user's hand in a game, The classifier has 3 different spell movements, but my code constantly predicts all of them at an equal 1/3 probability regardless of movement which leads me to believe my code isn't correct (as opposed to the model) which in CreateML at least gives me a heavily weighted prediction My code is below. On adding debug prints everywhere all the data looks good to me and matches similar to my test CSV data So I'm thinking my issue must be in the setup of my model code? /// Feeds samples into the model and keeps a sliding window of the last N frames. final class WandGestureStreamer { static let shared = WandGestureStreamer() private let model: SpellActivityClassifier private var samples: [Transform] = [] private let windowSize = 100 // number of frames the model expects /// RNN hidden state passed between inferences private var stateIn: MLMultiArray /// Last transform dropped from the window for continuity private var lastDropped: Transform? private init() { let config = MLModelConfiguration() self.model = try! SpellActivityClassifier(configuration: config) // Initialize stateIn to the model’s required shape let constraint = self.model.model.modelDescription .inputDescriptionsByName["stateIn"]! .multiArrayConstraint! self.stateIn = try! MLMultiArray(shape: constraint.shape, dataType: .double) } /// Call once per frame with the latest wand position (or any feature vector). func appendSample(_ sample: Transform) { samples.append(sample) // drop oldest frame if over capacity, retaining it for delta at window start if samples.count > windowSize { lastDropped = samples.removeFirst() } } func classifyIfReady(threshold: Double = 0.6) -> (label: String, confidence: Double)? { guard samples.count == windowSize else { return nil } do { let input = try makeInput(initialState: stateIn) let output = try model.prediction(input: input) // Save state for continuity stateIn = output.stateOut let best = output.label let conf = output.labelProbability[best] ?? 0 // If you’ve recognized a gesture with high confidence: if conf > threshold { return (best, conf) } else { return nil } } catch { print("Error", error.localizedDescription, error) return nil } } /// Constructs a SpellActivityClassifierInput from recorded wand transforms. func makeInput(initialState: MLMultiArray) throws -> SpellActivityClassifierInput { let count = samples.count as NSNumber let shape = [count] let timeArr = try MLMultiArray(shape: shape, dataType: .double) let dxArr = try MLMultiArray(shape: shape, dataType: .double) let dyArr = try MLMultiArray(shape: shape, dataType: .double) let dzArr = try MLMultiArray(shape: shape, dataType: .double) let rwArr = try MLMultiArray(shape: shape, dataType: .double) let rxArr = try MLMultiArray(shape: shape, dataType: .double) let ryArr = try MLMultiArray(shape: shape, dataType: .double) let rzArr = try MLMultiArray(shape: shape, dataType: .double) for (i, sample) in samples.enumerated() { let previousSample = i > 0 ? samples[i - 1] : lastDropped let model = WandMovementRecording.DataModel(transform: sample, previous: previousSample) // print("model", model) timeArr[i] = NSNumber(value: model.timestamp) dxArr[i] = NSNumber(value: model.dx) dyArr[i] = NSNumber(value: model.dy) dzArr[i] = NSNumber(value: model.dz) let rot = model.rotation rwArr[i] = NSNumber(value: rot.w) rxArr[i] = NSNumber(value: rot.x) ryArr[i] = NSNumber(value: rot.y) rzArr[i] = NSNumber(value: rot.z) } return SpellActivityClassifierInput( dx: dxArr, dy: dyArr, dz: dzArr, rotation_w: rwArr, rotation_x: rxArr, rotation_y: ryArr, rotation_z: rzArr, timestamp: timeArr, stateIn: initialState ) } }
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Jul ’25
Foundation Models framework dyld symbol errors after macOS 26 Beta 2 - LanguageModelSession constructor missing
Foundation Models framework worked perfectly on macOS 26 Beta 2, but starting from Beta 3 and continuing through Beta 6 (latest), I get dyld symbol errors even with the exact code from Apple's documentation. Environment: macOS 26.0 Beta 6 (25A5351b) Xcode 26 Beta 6 M4 Max MacBook Pro Apple Intelligence enabled and downloaded Error Details: dyld[Process]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC Referenced from: /path/to/app.debug.dylib Expected in: /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Code Used (Exact from Documentation): import FoundationModels // This worked on Beta 2, crashes on Beta 3+ let model = SystemLanguageModel.default let session = LanguageModelSession(model: model) let response = try await session.respond(to: "Hello") What I've Verified: FoundationModels.framework exists in /System/Library/Frameworks/ Framework is properly linked in Xcode project Apple Intelligence is enabled and working Same code works in older beta versions Issue persists even with completely fresh Xcode projects Analysis: The dyld error suggests the LanguageModelSession(model:) constructor is missing. The symbol shows it's looking for a constructor with parameters (model:guardrails:tools:instructions:), but the documentation still shows the simple (model:) constructor. Questions: Has the LanguageModelSession API changed since Beta 2? Should we now use the constructor with guardrails/tools/instructions parameters? Is this a known issue with recent betas? Are there updated code samples for the current API? Additional Context: This affects both basic SystemLanguageModel usage AND custom adapter loading. The same dyld symbol errors occur when trying to create SystemLanguageModel(adapter: adapter) as well. Any guidance on the correct API usage for current betas would be greatly appreciated. The documentation appears to be out of sync with the actual framework implementation.
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Sep ’25
Parallel/Steam processing of Apple Intelligence
I have built a MAC-OS machine intelligence application that uses Apple Intelligence. A part of the application is to preprocess text. For longer text content I have implemented chunking to get around the token limit. However the application performance is now limited by the fact that Apple Intelligence is sequential in operation. This has a large impact on the application performance. Is there any approach to operate Apple Intelligence in a parallel mode or even a streaming interface. As Apple Intelligence has Private Cloud Services I was hoping to be able to send multiple chunks in parallel as that would significantly improve performance. Any suggestions would be welcome. This could also be considered a request for a future enhancement.
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Feb ’26
Crashed: AXSpeech
Hello, My app is crashing a lot with this issue. I can't reproduce the problem but I can see it occurs at the user's devices. The Crashlytics report shows the following lines:Crashed: AXSpeech 0 libsystem_pthread.dylib 0x1824386bc pthread_mutex_lock$VARIANT$mp + 278 1 CoreFoundation 0x1826d3a34 CFRunLoopSourceSignal + 68 2 Foundation 0x18319ec90 performQueueDequeue + 468 3 Foundation 0x18325a020 __NSThreadPerformPerform + 136 4 CoreFoundation 0x1827b7404 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION__ + 24 5 CoreFoundation 0x1827b6ce0 __CFRunLoopDoSources0 + 456 6 CoreFoundation 0x1827b479c __CFRunLoopRun + 1204 7 CoreFoundation 0x1826d4da8 CFRunLoopRunSpecific + 552 8 Foundation 0x183149674 -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 304 9 libAXSpeechManager.dylib 0x192852830 -[AXSpeechThread main] + 284 10 Foundation 0x183259efc __NSThread__start__ + 1040 11 libsystem_pthread.dylib 0x182435220 _pthread_body + 272 12 libsystem_pthread.dylib 0x182435110 _pthread_body + 290 13 libsystem_pthread.dylib 0x182433b10 thread_start + 4The crash occurs in different threads (never at main thread)It is driving me crazy... Can anybody help me?Thanks a lot
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Siri UI returned to original design
Good morning all has anyone encountered the issue of Siri returning back to her original user interface on IOS-26? I’m trying to figure out the cause. I’ve sent feedback via the feedback app. Just seeing if anyone else has the same issue.
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152
Activity
Jun ’25
Plenty of LanguageModelSession.GenerationError.refusal errors after 26.4 update
Hello! After the 26.4 update I get a huge number of LanguageModelSession.GenerationError.refusal errors when using guided generation Generables for inexplicable reasons. Such errors also occur, if I want to cast a response to boolean by using 'generating: Bool.self'. The explanation generated on the grounds of the error always looks like this: Response(userPrompt: "", duration: 0.230917542, promptTokenCount: Optional(66), responseTokenCount: Optional(11), feedbackAttachment: nil, content: "I apologize, but I cannot fulfill this request.", rawContent: "I apologize, but I cannot fulfill this request.", transcriptEntries: ArraySlice([])) All the prompts and Generables I use are definitely not profane. Before 26.4 such errors on the same prompts and Generables never occurred. The 26.4 update rendered those features unusable to me. Is this a known bug or what am I doing wrong?
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494
Activity
2w
Programmatic image creation using ImageCreator
Hello, Could you please provide details for maximum string length of the prompt and the title when using ImageCreator and the method extracted(from:title:)? static func extracted( from text: String, title: String? = nil ) -> ImagePlaygroundConcept Any additional details or example of prompt and title would help. Additionally, are ImagePlaygroundStyle.animation, ImagePlaygroundStyle.illustration and ImagePlaygroundStyle.sketch all available when using extracted(from:title:)? I am trying to generate images programmatically and would appreciate your guidance. Thank you.
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413
Activity
2w
Gemini2.5Flash with Json
I am using gemini2.5-flash with SwiftUI. How can I receive a response in JSON?
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210
Activity
Jul ’25
Getting CoreML to run inference on already allocated gpu buffers
I am running some experiments with WebGPU using the wgpu crate in rust. I have some Buffers already allocated in the GPU. Is it possible to use those already existing buffers directly as inputs to a predict call in CoreML? I want to prevent gpu to cpu download time as much as possible. Or are there any other ways to do something like this. Is this only possible using the latest Tensor object which came out with Metal 4 ?
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736
Activity
Nov ’25
Vision face landmarks shifted on iOS 26 but correct on iOS 18 with same code and image
I'm using Vision framework (DetectFaceLandmarksRequest) with the same code and the same test image to detect face landmarks. On iOS 18 everything works as expected: detected face landmarks align with the face correctly. But when I run the same code on devices with iOS 26, the landmark coordinates are outside the [0,1] range, which indicates they are out of face bounds. Fun fact: the old VNDetectFaceLandmarksRequest API works very well without encountering this issue How I get face landmarks: private let faceRectangleRequest = DetectFaceRectanglesRequest(.revision3) private var faceLandmarksRequest = DetectFaceLandmarksRequest(.revision3) func detectFaces(in ciImage: CIImage) async throws -> FaceTrackingResult { let faces = try await faceRectangleRequest.perform(on: ciImage) faceLandmarksRequest.inputFaceObservations = faces let landmarksResults = try await faceLandmarksRequest.perform(on: ciImage) ... } How I show face landmarks in SwiftUI View: private func convert( point: NormalizedPoint, faceBoundingBox: NormalizedRect, imageSize: CGSize ) -> CGPoint { let point = point.toImageCoordinates( from: faceBoundingBox, imageSize: imageSize, origin: .upperLeft ) return point } At the same time, it works as expected and gives me the correct results: region is FaceObservation.Landmarks2D.Region let points: [CGPoint] = region.pointsInImageCoordinates( imageSize, origin: .upperLeft ) After that, I found that the landmarks are normalized relative to the unalignedBoundingBox. However, I can’t access it in code. Still, using these values for the bounding box works correctly. Things I've already tried: Same image input Tested multiple devices on iOS 26.2 -> always wrong. Tested multiple devices on iOS 18.7.1 -> always correct. Environment: macOS 26.2 Xcode 26.2 (17C52) Real devices, not simulator Face Landmarks iOS 18 Face Landmarks iOS 26
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Activity
Dec ’25
CoreML multifunction model runtime memory cost
Recently, I'm trying to deploy some third-party LLM to Apple devices. The methodoloy is similar to https://github.com/Anemll/Anemll. The biggest issue I'm having now is the runtime memory usage. When there are multiple functions in a model (mlpackage or mlmodelc), the runtime memory usage for weights is somehow duplicated when I load all of them. Here's the detail: I created my multifunction mlpackage following https://apple.github.io/coremltools/docs-guides/source/multifunction-models.html I loaded each of the functions using the generated swift class: let config = MLModelConfiguration() config.computeUnits = MLComputeUnits.cpuAndNeuralEngine config.functionName = "infer_512"; let ffn1_infer_512 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_1024"; let ffn1_infer_1024 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_2048"; let ffn1_infer_2048 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) I observed that RAM usage increases linearly as I load each of the functions. Using instruments, I see that there are multiple HWX files generated and loaded, each of which contains all the weight data. My understanding of what's happening here: The CoreML framework did some MIL->MIL preprocessing before further compilation, which includes separating CPU workload from ANE workload. The ANE part of each function is moved into a separate MIL file then compile separately into a HWX file each. The problem is that the weight data of these HWX files are duplicated. Since that the weight data of LLMs is huge, it will cause out-of-memory issue on mobile devices. The improvement I'm hoping from Apple: I hope we can try to merge the processed MIL files back into one before calling ANECCompile(), so that the weights can be merged. I don't have control over that in user space and I'm not sure if that is feasible. So I'm asking for help here. Thanks.
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211
Activity
Apr ’25
Sharing a Swift port of Gemma 4 for mlx-swift-lm — feedback welcome
Hi all, I've been working on a pure-Swift port of Google's Gemma 4 text decoder that plugs into mlx-swift-lm as a sidecar model registration. Sharing it here in case anyone else hit the same wall I did, and to get feedback from the MLX team and the community before I propose anything upstream. Repo: https://github.com/yejingyang8963-byte/Swift-gemma4-core Why As of mlx-swift-lm 2.31.x, Gemma 4 isn't supported out of the box. The obvious workaround — reusing the Gemma 3 text implementation with a patched config — fails at weight load because Gemma 4 differs from Gemma 3 in several structural places. The chat-template path through swift-jinja 1.x also silently corrupts the prompt, so the model loads but generates incoherent text. What's in the package A from-scratch Swift implementation of the Gemma 4 decoder (Configuration, Layers, Attention, MLP, RoPE, DecoderLayer) Per-Layer Embedding (PLE) support — the shared embedding table that feeds every decoder layer through a gated MLP as a third residual KV sharing across the back half of the decoder, threaded through the forward pass via a donor table with a single global rope offset A custom Gemma4ProportionalRoPE class for the partial-rotation rope type that initializeRope doesn't currently recognize A chat-template bypass that builds the prompt as a literal string with the correct turn markers and encodes via tokenizer.encode(text:), matching Python mlx-lm's apply_chat_template byte-for-byte Measured on iPhone (A-series, 7.4 GB RAM) Model: mlx-community/gemma-4-e2b-it-4bit Warm load: ~6 s Memory after load: 341–392 MB Time to first token (end-to-end, 333-token system prompt): 2.82 s Generation throughput: 12–14 tok/s What I'd love feedback on Is the sidecar registration pattern the right way to extend mlx-swift-lm with new model families, or is there a more idiomatic path I missed? The chat-template bypass works but feels like a workaround. Is the right long-term fix in swift-jinja, in the tokenizer, or somewhere else entirely? Anyone running into the same PLE / KV-sharing issues on other Gemma-family checkpoints? I'd like to make sure the implementation generalizes beyond E2B before tagging a 0.2.0. Happy to open a PR against mlx-swift-lm if the maintainers think any of this belongs upstream. Thanks for reading.
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Activity
3d
Does ExecuTorch support VisionOS?
Does anyone know if ExecuTorch is officially supported or has been successfully used on visionOS? If so, are there any specific build instructions, example projects, or potential issues (like sandboxing or memory limitations) to be aware of when integrating it into an Xcode project for the Vision Pro? While ExecuTorch has support for iOS, I can't find any official documentation or community examples specifically mentioning visionOS. Thanks.
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295
Activity
Jul ’25
Building Real-Time Voice Input on macOS 26 with SpeechAnalyzer + ScreenCaptureKit
We built an open-source macOS menu bar app that turns speech into text and pastes it into the active app — using SpeechAnalyzer for on-device transcription, ScreenCaptureKit + Vision for screen-aware context, and FluidAudio for speaker diarization in meeting mode. Here's what we learned shipping it on macOS 26. GitHub: github.com/Marvinngg/ambient-voice Architecture The app has two modes: hotkey dictation (press to talk, release to inject) and meeting recording (continuous transcription with a floating panel). Dictation Mode Audio capture uses AVCaptureSession (more on why below). The captured audio feeds into SpeechAnalyzer via an AsyncStream: let transcriber = SpeechTranscriber( locale: locale, transcriptionOptions: [], reportingOptions: [.volatileResults, .alternativeTranscriptions], attributeOptions: [.audioTimeRange, .transcriptionConfidence] ) let analyzer = SpeechAnalyzer(modules: [transcriber]) let (inputSequence, inputBuilder) = AsyncStream.makeStream() try await analyzer.start(inputSequence: inputSequence) While recording, we capture a screenshot of the focused window using ScreenCaptureKit, run Vision OCR (VNRecognizeTextRequest), extract keywords, and inject them into SpeechAnalyzer as contextual bias: let context = AnalysisContext() context.contextualStrings[.general] = ocrKeywords try await analyzer.setContext(context) This improves accuracy for technical terms and proper nouns visible on screen. If your screen shows "SpeechAnalyzer", saying it out loud is more likely to be transcribed correctly. After transcription, an optional L2 step sends the text through a local LLM (ollama) for spoken-to-written cleanup, then CGEvent simulates Cmd+V to paste into the active app. Meeting Mode Meeting mode forks the same audio stream to two consumers: SpeechAnalyzer — real-time streaming transcription, displayed in a floating NSPanel FluidAudio buffer — accumulates 16kHz Float32 mono samples for batch speaker diarization after recording stops When the user ends the meeting, FluidAudio's performCompleteDiarization() runs on the accumulated audio. We align transcription segments with speaker segments using audioTimeRange overlap matching — each transcription segment gets assigned the speaker ID with the most time overlap. Results export to Markdown. Pitfalls We Hit on macOS 26 1. AVAudioEngine installTap doesn't fire with Bluetooth devices We started with AVAudioEngine.inputNode.installTap() for audio capture. It worked fine with built-in mics but the tap callback never fired with Bluetooth devices (tested with vivo TWS 4 Hi-Fi). Fix: switched to AVCaptureSession. The delegate callback captureOutput(_:didOutput:from:) fires reliably regardless of audio device. The tradeoff is you get CMSampleBuffer instead of AVAudioPCMBuffer, so you need a conversion step. 2. NSEvent addGlobalMonitorForEvents crashes Our global hotkey listener used NSEvent.addGlobalMonitorForEvents. On macOS 26, this crashes with a Bus error inside GlobalObserverHandler — appears to be a Swift actor runtime issue. Fix: switched to CGEventTap. Works reliably, but the callback runs on a CFRunLoop context, which Swift doesn't recognize as MainActor. 3. CGEventTap callbacks aren't on MainActor If your CGEventTap callback touches any @MainActor state, you'll get concurrency violations. The callback runs on whatever thread owns the CFRunLoop. Fix: bridge with DispatchQueue.main.async {} inside the tap callback before touching any MainActor state. 4. CGPreflightScreenCaptureAccess doesn't request permission We used CGPreflightScreenCaptureAccess() as a guard before calling ScreenCaptureKit. If it returned false, we'd bail out. The problem: this function only checks — it never triggers macOS to add your app to the Screen Recording permission list. Chicken-and-egg: you can't get permission because you never ask for it. Fix: call CGRequestScreenCaptureAccess() at app startup. This adds your app to System Settings → Screen Recording. Then let ScreenCaptureKit calls proceed without the preflight guard — SCShareableContent will also trigger the permission prompt on first use. 5. Ad-hoc signing breaks TCC permissions on every rebuild During development, codesign --sign - (ad-hoc) generates a different code directory hash on every build. macOS TCC tracks permissions by this hash, so every rebuild = new app identity = all permissions reset. Fix: sign with a stable certificate. If you have an Apple Development certificate, use that. The TeamIdentifier stays constant across rebuilds, so TCC permissions persist. We also discovered that launching via open WE.app (LaunchServices) instead of directly executing the binary is required — otherwise macOS attributes TCC permissions to Terminal, not your app. Benchmarks We ran end-to-end benchmarks on public datasets (Mac Mini M4 16GB, macOS 26): Transcription (SpeechAnalyzer, AliMeeting Chinese): • Near-field CER 34% (excluding outliers ~25%) • Far-field CER 40% (single channel, no beamforming, >30% overlap) • Processing speed 74-89x real-time Speaker diarization (FluidAudio offline): • AMI English 16 meetings: avg DER 23.2% (collar=0.25s, ignoreOverlap=True) • AliMeeting Chinese 8 meetings: DER 48.5% (including overlap regions) • Memory: RSS ~500MB, peak 730-930MB Full evaluation methodology, scripts, and raw results are in the repo. Open Source The project is MIT licensed: github.com/Marvinngg/ambient-voice It includes the macOS client (Swift 6.2, SPM), server-side distillation/training scripts (Python), and a complete evaluation framework with reproducible benchmarks. Feedback and contributions welcome.
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Activity
3w
Core-ml-on-device-llama Converting fails
I followed below url for converting Llama-3.1-8B-Instruct model but always fails even i have 64GB of free space after downloading model from huggingface. https://machinelearning.apple.com/research/core-ml-on-device-llama Also tried with other models Llama-3.1-1B-Instruct & Llama-3.1-3B-Instruct models those are converted but while doing performance test in xcode fails for all compunits. Is there any source code to run llama models in ios app.
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238
Activity
Apr ’25
Request: Official One-Click Local LLM Deployment for 2019 Mac Pro (7,1) Dual W6900X
I am a professional user of the 2019 Mac Pro (7,1) with dual AMD Radeon Pro W6900X MPX modules (32GB VRAM each). This hardware is designed for high-performance compute, but it is currently crippled for modern local LLM/AI workloads under Linux due to Apple's EFI/PCIe routing restrictions. Core Issue: rocminfo reports "No HIP GPUs available" when attempting to use ROCm/amdgpu on Linux Apple's custom EFI firmware blocks full initialization of professional GPU compute assets The dual W6900X GPUs have 64GB combined VRAM and high-bandwidth Infinity Fabric Link, but cannot be fully utilized for local AI inference/training My Specific Request: Apple should provide an official, one-click deployable application that enables full utilization of dual W6900X GPUs for local large language model (LLM) inference and training under Linux. This application must: Fully initialize both W6900X GPUs via HIP/ROCm, establishing valid compute contexts Bypass artificial EFI/PCIe routing restrictions that block access to professional GPU resources Provide a stable, user-friendly one-click deployment experience (similar to NVIDIA's AI Enterprise or AMD's ROCm Hub) Why This Matters: The 2019 Mac Pro is Apple's flagship professional workstation, marketed for compute-intensive workloads. Its high-cost W6900X GPUs should not be locked down for modern AI/LLM use cases. An official one-click deployment solution would demonstrate Apple's commitment to professional AI and unlock significant value for professional users. I look forward to Apple's response and a clear roadmap for enabling this critical capability. #MacPro #Linux #ROCm #LocalLLM #W6900X #CoreML
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Activity
2w
Building a 4-agent autonomous coding pipeline on Apple Silicon — MLX backend questions
Hi, I'm building ANF (Autonomous Native Forge) — a cloud-free, 4-agent autonomous software production pipeline running on local hardware with local LLM inference. No middleware, pure Node.js native. Currently running on NVIDIA Blackwell GB10 with vLLM + DeepSeek-R1-32B. Now porting to Apple Silicon. Three technical questions: How production-ready is mlx-lm's OpenAI-compatible API server for long context generation (32K tokens)? What's the recommended approach for KV Cache management with Unified Memory architecture — any specific flags or configurations for M4 Ultra? MLX vs GGUF (llama.cpp) for a multi-agent pipeline where 4 agents call the inference endpoint concurrently — which handles parallel requests better on Apple Silicon? GitHub: github.com/trgysvc/AutonomousNativeForge Any guidance appreciated.
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279
Activity
4w
Best approach for animating a speaking avatar in a macOS/iOS SwiftUI application
I am developing a macOS application using SwiftUI (with an iOS version as well). One feature we are exploring is displaying an avatar that reads or speaks dynamically generated text produced by an AI service. The basic flow would be: Text generated by an AI service Text converted to speech using a TTS engine An avatar (2D or 3D) rendered in the app that animates lip movement synchronized with the speech Ideally the avatar would render locally on the device. Questions: What Apple frameworks would be most appropriate for implementing a speaking avatar? SceneKit RealityKit SpriteKit (for 2D avatars) Is there any recommended way to drive lip-sync animation from speech audio using Apple frameworks? Does AVSpeechSynthesizer expose phoneme or viseme timing information that could be used for avatar animation? If such timing information is not available, what is the recommended approach for synchronizing character mouth animation with speech audio on macOS/iOS? Are there examples of real-time character animation synchronized with speech on macOS/iOS? Any architectural guidance or references would be greatly appreciated.
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583
Activity
4w
Apple Intelligence Naughty Naughty
When doing some exploratory research into using Apple Intelligence in our aviation-focused application, I noticed that there were several times that key phases would be marked as inappropriate. I tried to stifle these using prompts and rules but couldn't get it to take hold. I was encouraged by an Apple employee to go ahead and post this so that the AI team can use the feedback. There were several terms that triggered this warning, but the two that were most prominent were: 'Tailwind' 'JFK' or 'KJFK' (NY airport ICAO/IATA codes)
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Activity
Mar ’26
`LanguageModelSession.respond()` never resolves in Beta 5
Hi all, I noticed on Friday that on the new Beta 5 using FoundationModels on a simulator LanguageModelSession.respond() neither resolves nor throws most of the time. The SwiftUI test app below was working perfectly in Xcode 16 Beta 4 and iOS 26 Beta 4 (simulator). import SwiftUI import FoundationModels struct ContentView: View { var body: some View { VStack { Image(systemName: "globe") .imageScale(.large) .foregroundStyle(.tint) Text("Hello, world!") } .padding() .onAppear { Task { do { let session = LanguageModelSession() let response = try await session.respond(to: "are cats better than dogs ???") print(response.content) } catch { print("error") } } } } } After updating to Xcode 16 Beta 5 and iOS 26 Beta 5 (simulator), the code now often hangs. Occasionally it will work if I toggle Apple Intelligence on and off in Settings, but it’s unreliable.
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371
Activity
Aug ’25
ActivityClassifier doesn't classify movement
I'm using a custom create ML model to classify the movement of a user's hand in a game, The classifier has 3 different spell movements, but my code constantly predicts all of them at an equal 1/3 probability regardless of movement which leads me to believe my code isn't correct (as opposed to the model) which in CreateML at least gives me a heavily weighted prediction My code is below. On adding debug prints everywhere all the data looks good to me and matches similar to my test CSV data So I'm thinking my issue must be in the setup of my model code? /// Feeds samples into the model and keeps a sliding window of the last N frames. final class WandGestureStreamer { static let shared = WandGestureStreamer() private let model: SpellActivityClassifier private var samples: [Transform] = [] private let windowSize = 100 // number of frames the model expects /// RNN hidden state passed between inferences private var stateIn: MLMultiArray /// Last transform dropped from the window for continuity private var lastDropped: Transform? private init() { let config = MLModelConfiguration() self.model = try! SpellActivityClassifier(configuration: config) // Initialize stateIn to the model’s required shape let constraint = self.model.model.modelDescription .inputDescriptionsByName["stateIn"]! .multiArrayConstraint! self.stateIn = try! MLMultiArray(shape: constraint.shape, dataType: .double) } /// Call once per frame with the latest wand position (or any feature vector). func appendSample(_ sample: Transform) { samples.append(sample) // drop oldest frame if over capacity, retaining it for delta at window start if samples.count > windowSize { lastDropped = samples.removeFirst() } } func classifyIfReady(threshold: Double = 0.6) -> (label: String, confidence: Double)? { guard samples.count == windowSize else { return nil } do { let input = try makeInput(initialState: stateIn) let output = try model.prediction(input: input) // Save state for continuity stateIn = output.stateOut let best = output.label let conf = output.labelProbability[best] ?? 0 // If you’ve recognized a gesture with high confidence: if conf > threshold { return (best, conf) } else { return nil } } catch { print("Error", error.localizedDescription, error) return nil } } /// Constructs a SpellActivityClassifierInput from recorded wand transforms. func makeInput(initialState: MLMultiArray) throws -> SpellActivityClassifierInput { let count = samples.count as NSNumber let shape = [count] let timeArr = try MLMultiArray(shape: shape, dataType: .double) let dxArr = try MLMultiArray(shape: shape, dataType: .double) let dyArr = try MLMultiArray(shape: shape, dataType: .double) let dzArr = try MLMultiArray(shape: shape, dataType: .double) let rwArr = try MLMultiArray(shape: shape, dataType: .double) let rxArr = try MLMultiArray(shape: shape, dataType: .double) let ryArr = try MLMultiArray(shape: shape, dataType: .double) let rzArr = try MLMultiArray(shape: shape, dataType: .double) for (i, sample) in samples.enumerated() { let previousSample = i > 0 ? samples[i - 1] : lastDropped let model = WandMovementRecording.DataModel(transform: sample, previous: previousSample) // print("model", model) timeArr[i] = NSNumber(value: model.timestamp) dxArr[i] = NSNumber(value: model.dx) dyArr[i] = NSNumber(value: model.dy) dzArr[i] = NSNumber(value: model.dz) let rot = model.rotation rwArr[i] = NSNumber(value: rot.w) rxArr[i] = NSNumber(value: rot.x) ryArr[i] = NSNumber(value: rot.y) rzArr[i] = NSNumber(value: rot.z) } return SpellActivityClassifierInput( dx: dxArr, dy: dyArr, dz: dzArr, rotation_w: rwArr, rotation_x: rxArr, rotation_y: ryArr, rotation_z: rzArr, timestamp: timeArr, stateIn: initialState ) } }
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Activity
Jul ’25
Foundation Models framework dyld symbol errors after macOS 26 Beta 2 - LanguageModelSession constructor missing
Foundation Models framework worked perfectly on macOS 26 Beta 2, but starting from Beta 3 and continuing through Beta 6 (latest), I get dyld symbol errors even with the exact code from Apple's documentation. Environment: macOS 26.0 Beta 6 (25A5351b) Xcode 26 Beta 6 M4 Max MacBook Pro Apple Intelligence enabled and downloaded Error Details: dyld[Process]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC Referenced from: /path/to/app.debug.dylib Expected in: /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Code Used (Exact from Documentation): import FoundationModels // This worked on Beta 2, crashes on Beta 3+ let model = SystemLanguageModel.default let session = LanguageModelSession(model: model) let response = try await session.respond(to: "Hello") What I've Verified: FoundationModels.framework exists in /System/Library/Frameworks/ Framework is properly linked in Xcode project Apple Intelligence is enabled and working Same code works in older beta versions Issue persists even with completely fresh Xcode projects Analysis: The dyld error suggests the LanguageModelSession(model:) constructor is missing. The symbol shows it's looking for a constructor with parameters (model:guardrails:tools:instructions:), but the documentation still shows the simple (model:) constructor. Questions: Has the LanguageModelSession API changed since Beta 2? Should we now use the constructor with guardrails/tools/instructions parameters? Is this a known issue with recent betas? Are there updated code samples for the current API? Additional Context: This affects both basic SystemLanguageModel usage AND custom adapter loading. The same dyld symbol errors occur when trying to create SystemLanguageModel(adapter: adapter) as well. Any guidance on the correct API usage for current betas would be greatly appreciated. The documentation appears to be out of sync with the actual framework implementation.
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686
Activity
Sep ’25
Parallel/Steam processing of Apple Intelligence
I have built a MAC-OS machine intelligence application that uses Apple Intelligence. A part of the application is to preprocess text. For longer text content I have implemented chunking to get around the token limit. However the application performance is now limited by the fact that Apple Intelligence is sequential in operation. This has a large impact on the application performance. Is there any approach to operate Apple Intelligence in a parallel mode or even a streaming interface. As Apple Intelligence has Private Cloud Services I was hoping to be able to send multiple chunks in parallel as that would significantly improve performance. Any suggestions would be welcome. This could also be considered a request for a future enhancement.
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Activity
Feb ’26
Crashed: AXSpeech
Hello, My app is crashing a lot with this issue. I can't reproduce the problem but I can see it occurs at the user's devices. The Crashlytics report shows the following lines:Crashed: AXSpeech 0 libsystem_pthread.dylib 0x1824386bc pthread_mutex_lock$VARIANT$mp + 278 1 CoreFoundation 0x1826d3a34 CFRunLoopSourceSignal + 68 2 Foundation 0x18319ec90 performQueueDequeue + 468 3 Foundation 0x18325a020 __NSThreadPerformPerform + 136 4 CoreFoundation 0x1827b7404 __CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION__ + 24 5 CoreFoundation 0x1827b6ce0 __CFRunLoopDoSources0 + 456 6 CoreFoundation 0x1827b479c __CFRunLoopRun + 1204 7 CoreFoundation 0x1826d4da8 CFRunLoopRunSpecific + 552 8 Foundation 0x183149674 -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 304 9 libAXSpeechManager.dylib 0x192852830 -[AXSpeechThread main] + 284 10 Foundation 0x183259efc __NSThread__start__ + 1040 11 libsystem_pthread.dylib 0x182435220 _pthread_body + 272 12 libsystem_pthread.dylib 0x182435110 _pthread_body + 290 13 libsystem_pthread.dylib 0x182433b10 thread_start + 4The crash occurs in different threads (never at main thread)It is driving me crazy... Can anybody help me?Thanks a lot
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3.6k
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3w