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

Why doesn't tensorflow-metal use AMD GPU memory?
From tensorflow-metal example: Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) I know that Apple silicon uses UMA, and that memory copies are typical of CUDA, but wouldn't the GPU memory still be faster overall? I have an iMac Pro with a Radeon Pro Vega 64 16 GB GPU and an Intel iMac with a Radeon Pro 5700 8 GB GPU. But using tensorflow-metal is still WAY faster than using the CPUs. Thanks for that. I am surprised the 5700 is twice as fast as the Vega though.
1
0
299
Apr ’25
The asset pack with the ID “testVideoAssetPack” couldn’t be looked up: Could not connect to the server.
On macOS Tahoe26.0, iOS 26.0 (23A5287g) not emulator, Xcode 26.0 beta 3 (17A5276g) Follow this tutorial Testing your asset packs locally The start the test server command I use this command line to start the test server:xcrun ba-serve --host 192.168.0.109 test.aar The terminal showThe content displayed on the terminal is: Loading asset packs… Loading the asset pack at “test.aar”… Listening on port 63125…… Choose an identity in the panel to continue. Listening on port 63125… running the project, Xcode reports an error:Download failed: Could not connect to the server. I use iPhone safari visit this website: https://192.168.0.109:63125, on the page display "Hello, world!" There are too few error messages in both of the above questions. I have no idea what the specific reasons are.I hope someone can offer some guidance. Best Regards. { "assetPackID": "testVideoAssetPack", "downloadPolicy": { "prefetch": { "installationEventTypes": ["firstInstallation", "subsequentUpdate"] } }, "fileSelectors": [ { "file": "video/test.mp4" } ], "platforms": [ "iOS" ] } this is my Manifest.json
1
0
398
Jul ’25
Image Playground Error: Unable to Generate Images Using externalProvider Style
I’m working on generating images using Image Playground. The code works fine for other styles but fails when using an external provider. I don’t see any other requirements mentioned in the documentation. Has anyone else encountered a similar issue? Here’s the relevant code snippet: https://developer.apple.com/documentation/imageplayground/imageplaygroundstyle/externalprovider?changes=_2 The error message is also not very helpful. It simply states that the creation failed. Note: I have enabled ChatGPT Plus, and the image generation using ChatGPT styles works fine when using the Playground app. do { let creator = try await ImageCreator() let concept = ImagePlaygroundConcept.text("Love") let images = creator.images(for: [concept], style: .externalProvider, limit: 1) for try await image in images { // Handle image break } } catch { // Handle error } I’m using the iOS 26 RC, and when I print creator.availableStyles, it doesn’t display the external Provider. [ImagePlayground.ImagePlaygroundStyle(id: "animation", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "emoji", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "illustration", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "sketch", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "messages-background", _representationInfo: nil)]
1
0
920
Sep ’25
Will Apple Intelligence Support Third-Party LLMs or Custom AI Agent Integrations?
Hi everyone, I’m an AI engineer working on autonomous AI agents and exploring ways to integrate them into the Apple ecosystem, especially via Siri and Apple Intelligence. I was impressed by Apple’s integration of ChatGPT and its privacy-first design, but I’m curious to know: • Are there plans to support third-party LLMs? • Could Siri or Apple Intelligence call external AI agents or allow extensions to plug in alternative models for reasoning, scheduling, or proactive suggestions? I’m particularly interested in building event-driven, voice-triggered workflows where Apple Intelligence could act as a front-end for more complex autonomous systems (possibly local or cloud-based). This kind of extensibility would open up incredible opportunities for personalized, privacy-friendly use cases — while aligning with Apple’s system architecture. Is anything like this on the roadmap? Or is there a suggested way to prototype such integrations today? Thanks in advance for any thoughts or pointers!
4
0
530
May ’25
Crash inside of Vision predictWithCVPixelBuffer - Crashed: com.apple.VN.detectorSyncTasksQueue.VNCoreMLTransformer
Hello, We have been encountering a persistent crash in our application, which is deployed exclusively on iPad devices. The crash occurs in the following code block: let requestHandler = ImageRequestHandler(paddedImage) var request = CoreMLRequest(model: model) request.cropAndScaleAction = .scaleToFit let results = try await requestHandler.perform(request) The client using this code is wrapped inside an actor, following Swift concurrency principles. The issue has been consistently reproduced across multiple iPadOS versions, including: iPad OS - 18.4.0 iPad OS - 18.4.1 iPad OS - 18.5.0 This is the crash log - Crashed: com.apple.VN.detectorSyncTasksQueue.VNCoreMLTransformer 0 libobjc.A.dylib 0x7b98 objc_retain + 16 1 libobjc.A.dylib 0x7b98 objc_retain_x0 + 16 2 libobjc.A.dylib 0xbf18 objc_getProperty + 100 3 Vision 0x326300 -[VNCoreMLModel predictWithCVPixelBuffer:options:error:] + 148 4 Vision 0x3273b0 -[VNCoreMLTransformer processRegionOfInterest:croppedPixelBuffer:options:qosClass:warningRecorder:error:progressHandler:] + 748 5 Vision 0x2ccdcc __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_5 + 132 6 Vision 0x14600 VNExecuteBlock + 80 7 Vision 0x14580 __76+[VNDetector runSuccessReportingBlockSynchronously:detector:qosClass:error:]_block_invoke + 56 8 libdispatch.dylib 0x6c98 _dispatch_block_sync_invoke + 240 9 libdispatch.dylib 0x1b584 _dispatch_client_callout + 16 10 libdispatch.dylib 0x11728 _dispatch_lane_barrier_sync_invoke_and_complete + 56 11 libdispatch.dylib 0x7fac _dispatch_sync_block_with_privdata + 452 12 Vision 0x14110 -[VNControlledCapacityTasksQueue dispatchSyncByPreservingQueueCapacity:] + 60 13 Vision 0x13ffc +[VNDetector runSuccessReportingBlockSynchronously:detector:qosClass:error:] + 324 14 Vision 0x2ccc80 __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_4 + 336 15 Vision 0x14600 VNExecuteBlock + 80 16 Vision 0x2cc98c __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_3 + 256 17 libdispatch.dylib 0x1b584 _dispatch_client_callout + 16 18 libdispatch.dylib 0x6ab0 _dispatch_block_invoke_direct + 284 19 Vision 0x2cc454 -[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:] + 632 20 Vision 0x2cd14c __111-[VNDetector processUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke + 124 21 Vision 0x14600 VNExecuteBlock + 80 22 Vision 0x2ccfbc -[VNDetector processUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:] + 340 23 Vision 0x125410 __swift_memcpy112_8 + 4852 24 libswift_Concurrency.dylib 0x5c134 swift::runJobInEstablishedExecutorContext(swift::Job*) + 292 25 libswift_Concurrency.dylib 0x5d5c8 swift_job_runImpl(swift::Job*, swift::SerialExecutorRef) + 156 26 libdispatch.dylib 0x13db0 _dispatch_root_queue_drain + 364 27 libdispatch.dylib 0x1454c _dispatch_worker_thread2 + 156 28 libsystem_pthread.dylib 0x9d0 _pthread_wqthread + 232 29 libsystem_pthread.dylib 0xaac start_wqthread + 8 We found an issue similar to us - https://developer.apple.com/forums/thread/770771. But the crash logs are quite different, we believe this warrants further investigation to better understand the root cause and potential mitigation strategies. Please let us know if any additional information would help diagnose this issue.
3
0
420
Jul ’25
macOS 26 Beta 2 - Foundation Models - Symbol not found
It seems like there was an undocumented change that made Transcript.init(entries: [Transcript.Entry] initializer private, which broke my application, which relies on (manual) reconstruction of Transcript entries. Worked fine on beta 1, on beta 2 there's this error dyld[72381]: Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <44342398-591C-3850-9889-87C9458E1440> /Users/mika/experiments/apple-on-device-ai/fm Expected in: <66A793F6-CB22-3D1D-A560-D1BD5B109B0D> /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Is this a part of an API transition, if so - Apple, please update your documentation
3
0
386
Jun ’25
Real Time Text detection using iOS18 RecognizeTextRequest from video buffer returns gibberish
Hey Devs, I'm trying to create my own Real Time Text detection like this Apple project. https://developer.apple.com/documentation/vision/extracting-phone-numbers-from-text-in-images I want to use the new iOS18 RecognizeTextRequest instead of the old VNRecognizeTextRequest in my SwiftUI project. This is my delegate code with the camera setup. I removed region of interest for debugging but I'm trying to scan English words in books. The idea is to get one word in the ROI in the future. But I can't even get proper words so testing without ROI incase my math is wrong. @Observable class CameraManager: NSObject, AVCapturePhotoCaptureDelegate ... override init() { super.init() setUpVisionRequest() } private func setUpVisionRequest() { textRequest = RecognizeTextRequest(.revision3) } ... func setup() -> Bool { captureSession.beginConfiguration() guard let captureDevice = AVCaptureDevice.default( .builtInWideAngleCamera, for: .video, position: .back) else { return false } self.captureDevice = captureDevice guard let deviceInput = try? AVCaptureDeviceInput(device: captureDevice) else { return false } /// Check whether the session can add input. guard captureSession.canAddInput(deviceInput) else { print("Unable to add device input to the capture session.") return false } /// Add the input and output to session captureSession.addInput(deviceInput) /// Configure the video data output videoDataOutput.setSampleBufferDelegate( self, queue: videoDataOutputQueue) if captureSession.canAddOutput(videoDataOutput) { captureSession.addOutput(videoDataOutput) videoDataOutput.connection(with: .video)? .preferredVideoStabilizationMode = .off } else { return false } // Set zoom and autofocus to help focus on very small text do { try captureDevice.lockForConfiguration() captureDevice.videoZoomFactor = 2 captureDevice.autoFocusRangeRestriction = .near captureDevice.unlockForConfiguration() } catch { print("Could not set zoom level due to error: \(error)") return false } captureSession.commitConfiguration() // potential issue with background vs dispatchqueue ?? Task(priority: .background) { captureSession.startRunning() } return true } } // Issue here ??? extension CameraManager: AVCaptureVideoDataOutputSampleBufferDelegate { func captureOutput( _ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection ) { guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return } Task { textRequest.recognitionLevel = .fast textRequest.recognitionLanguages = [Locale.Language(identifier: "en-US")] do { let observations = try await textRequest.perform(on: pixelBuffer) for observation in observations { let recognizedText = observation.topCandidates(1).first print("recognized text \(recognizedText)") } } catch { print("Recognition error: \(error.localizedDescription)") } } } } The results I get look like this ( full page of English from a any book) recognized text Optional(RecognizedText(string: e bnUI W4, confidence: 0.5)) recognized text Optional(RecognizedText(string: ?'U, confidence: 0.3)) recognized text Optional(RecognizedText(string: traQt4, confidence: 0.3)) recognized text Optional(RecognizedText(string: li, confidence: 0.3)) recognized text Optional(RecognizedText(string: 15,1,#, confidence: 0.3)) recognized text Optional(RecognizedText(string: jllÈ, confidence: 0.3)) recognized text Optional(RecognizedText(string: vtrll, confidence: 0.3)) recognized text Optional(RecognizedText(string: 5,1,: 11, confidence: 0.5)) recognized text Optional(RecognizedText(string: 1141, confidence: 0.3)) recognized text Optional(RecognizedText(string: jllll ljiiilij41, confidence: 0.3)) recognized text Optional(RecognizedText(string: 2f4, confidence: 0.3)) recognized text Optional(RecognizedText(string: ktril, confidence: 0.3)) recognized text Optional(RecognizedText(string: ¥LLI, confidence: 0.3)) recognized text Optional(RecognizedText(string: 11[Itl,, confidence: 0.3)) recognized text Optional(RecognizedText(string: 'rtlÈ131, confidence: 0.3)) Even with ROI set to a specific rectangle Normalized to Vision, I get the same results with single characters returning gibberish. Any help would be amazing thank you. Am I using the buffer right ? Am I using the new perform(on: CVPixelBuffer) right ? Maybe I didn't set up my camera properly? I can provide code
1
0
361
Jul ’25
LanguageModelStream and collecting the final output
I have a Generable type with many elements. I am using a stream() to incrementally process the output (Generable.PartiallyGenerated?) content. At the end, I want to pass the final version (not partially generated) to another function. I cannot seem to find a good way to convert from a MyGenerable.PartiallyGenerated to a MyGenerable. Am I missing some functionality in the APIs?
4
0
599
Jul ’25
Looking for a prebuilt TensorFlow Lite C++ library (libtensorflowlite) for macOS M1/M2
Hi everyone! 👋 I'm working on a C++ project using TensorFlow Lite and was wondering if anyone has a prebuilt TensorFlow Lite C++ library (libtensorflowlite) for macOS (Apple Silicon M1/M2) that they’d be willing to share. I’m looking specifically for the TensorFlow Lite C++ API — something that lets me use tflite::Interpreter, tflite::FlatBufferModel, etc. Building it from source using Bazel on macOS has been quite challenging and time-consuming, so a ready-to-use .dylib or .a build along with the required headers would be incredibly helpful. TensorFlow Lite version: v2.18.0 preferred Target: macOS arm64 (Apple Silicon) What I need: libtensorflowlite.dylib or .a Corresponding headers (ideally organized in a clean include/ folder) If you have one available or know where I can find a reliable prebuilt version, I’d be super grateful. Thanks in advance! 🙏
2
0
216
Apr ’25
Foundation model sandbox restriction error
I'm seeing this error a lot in my console log of my iPhone 15 Pro (Apple Intelligence enabled): com.apple.modelcatalog.catalog sync: connection error during call: Error Domain=NSCocoaErrorDomain Code=4099 "The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction." UserInfo={NSDebugDescription=The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction.} reached max num connection attempts: 1 Are there entitlements / permissions I need to enable in Xcode that I forgot to do? Code example Here's how I'm initializing the language model session: private func setupLanguageModelSession() { if #available(iOS 26.0, *) { let instructions = """ my instructions """ do { languageModelSession = try LanguageModelSession(instructions: instructions) print("Foundation Models language model session initialized") } catch { print("Error creating language model session: \(error)") languageModelSession = nil } } else { print("Device does not support Foundation Models (requires iOS 26.0+)") languageModelSession = nil } }
2
0
247
Jun ’25
Memory Attribution for Foundation Models in iOS 26
Hi, I’m developing an app targeting iOS 26, using the new FoundationModels framework to perform on-device LLM inference. I’m currently testing memory usage. Does the memory used by FoundationModels—including model weights, KV cache, and any inference-related buffers—count toward my app’s Jetsam memory limit, or is any of it managed separately by the system? I may need to run two concurrent inferences, each with a 4096-token context window. Is this explicitly supported or allowed by FoundationModels on iOS 26? Would this significantly increase the risk of memory-based termination? Thanks in advance for any clarification. Thanks.
1
0
424
Jul ’25
Is there an API to check if a Core ML compiled model is already cached?
Hello Apple Developer Community, I'm investigating Core ML model loading behavior and noticed that even when the compiled model path remains unchanged after an APP update, the first run still triggers an "uncached load" process. This seems to impact user experience with unnecessary delays. Question: Does Core ML provide any public API to check whether a compiled model (from a specific .mlmodelc path) is already cached in the system? If such API exists, we'd like to use it for pre-loading decision logic - only perform background pre-load when the model isn't cached. Has anyone encountered similar scenarios or found official solutions? Any insights would be greatly appreciated!
2
0
253
May ’25
Localizing prompts that has string interpolated generable objects
I'm working on localizing my prompts to support multiple languages, and in some cases my prompts has String interpolated Generable objects. for example: "Given the following workout routine: \(routine), suggest one additional exercise to complement it." In the Strings dictionary, I'm only able to select String, Int or Double parameters using %@ and %lld. Has anyone found a way to accomplish this?
1
0
385
Jul ’25
iOS 18 new RecognizedTextRequest DEADLOCKS if more than 2 are run in parallel
Following WWDC24 video "Discover Swift enhancements in the Vision framework" recommendations (cfr video at 10'41"), I used the following code to perform multiple new iOS 18 `RecognizedTextRequest' in parallel. Problem: if more than 2 request are run in parallel, the request will hang, leaving the app in a state where no more requests can be started. -> deadlock I tried other ways to run the requests, but no matter the method employed, or what device I use: no more than 2 requests can ever be run in parallel. func triggerDeadlock() {} try await withThrowingTaskGroup(of: Void.self) { group in // See: WWDC 2024 Discover Siwft enhancements in the Vision framework at 10:41 // ############## THIS IS KEY let maxOCRTasks = 5 // On a real-device, if more than 2 RecognizeTextRequest are launched in parallel using tasks, the request hangs // ############## THIS IS KEY for idx in 0..<maxOCRTasks { let url = ... // URL to some image group.addTask { // Perform OCR let _ = await performOCRRequest(on: url: url) } } var nextIndex = maxOCRTasks for try await _ in group { // Wait for the result of the next child task that finished if nextIndex < pageCount { group.addTask { let url = ... // URL to some image // Perform OCR let _ = await performOCRRequest(on: url: url) } nextIndex += 1 } } } } // MARK: - ASYNC/AWAIT version with iOS 18 @available(iOS 18, *) func performOCRRequest(on url: URL) async throws -> [RecognizedText] { // Create request var request = RecognizeTextRequest() // Single request: no need for ImageRequestHandler // Configure request request.recognitionLevel = .accurate request.automaticallyDetectsLanguage = true request.usesLanguageCorrection = true request.minimumTextHeightFraction = 0.016 // Perform request let textObservations: [RecognizedTextObservation] = try await request.perform(on: url) // Convert [RecognizedTextObservation] to [RecognizedText] return textObservations.compactMap { observation in observation.topCandidates(1).first } } I also found this Swift forums post mentioning something very similar. I also opened a feedback: FB17240843
7
0
422
Aug ’25
Failing to run SystemLanguageModel inference with custom adapter
Hi, I have trained a basic adapter using the adapter training toolkit. I am trying a very basic example of loading it and running inference with it, but am getting the following error: Passing along InferenceError::inferenceFailed::loadFailed::Error Domain=com.apple.TokenGenerationInference.E5Runner Code=0 "Failed to load model: ANE adapted model load failure: createProgramInstanceWithWeights:modelToken:qos:baseModelIdentifier:owningPid:numWeightFiles:error:: Program load new instance failure (0x170006)." UserInfo={NSLocalizedDescription=Failed to load model: ANE adapted model load failure: createProgramInstanceWithWeights:modelToken:qos:baseModelIdentifier:owningPid:numWeightFiles:error:: Program load new instance failure (0x170006).} in response to ExecuteRequest Any ideas / direction? For testing I am including the .fmadapter file inside the app bundle. This is where I load it: @State private var session: LanguageModelSession? // = LanguageModelSession() func loadAdapter() async throws { if let assetURL = Bundle.main.url(forResource: "qasc---afm---4-epochs-adapter", withExtension: "fmadapter") { print("Asset URL: \(assetURL)") let adapter = try SystemLanguageModel.Adapter(fileURL: assetURL) let adaptedModel = SystemLanguageModel(adapter: adapter) session = LanguageModelSession(model: adaptedModel) print("Loaded adapter and updated session") } else { print("Asset not found in the main bundle.") } } This seems to work fine as I get to the log Loaded adapter and updated session. However when the below inference code runs I get the aforementioned error: func sendMessage(_ msg: String) { self.loading = true if let session = session { Task { do { let modelResponse = try await session.respond(to: msg) DispatchQueue.main.async { self.response = modelResponse.content self.loading = false } } catch { print("Error: \(error)") DispatchQueue.main.async { self.loading = false } } } } }
3
0
239
Jun ’25
Is it possible to create a virtual NPU device on macOS using Hypervisor.framework + CoreML?
Is it possible to expose a custom VirtIO device to a Linux guest running inside a VM — likely using QEMU backed by Hypervisor.framework. The guest would see this device as something like /dev/npu0, and it would use a kernel driver + userspace library to submit inference requests. On the macOS host, these requests would be executed using CoreML, MPSGraph, or BNNS. The results would be passed back to the guest via IPC. Does the macOS allow this kind of "fake" NPU / GPU
1
0
441
Aug ’25
Why doesn't tensorflow-metal use AMD GPU memory?
From tensorflow-metal example: Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) I know that Apple silicon uses UMA, and that memory copies are typical of CUDA, but wouldn't the GPU memory still be faster overall? I have an iMac Pro with a Radeon Pro Vega 64 16 GB GPU and an Intel iMac with a Radeon Pro 5700 8 GB GPU. But using tensorflow-metal is still WAY faster than using the CPUs. Thanks for that. I am surprised the 5700 is twice as fast as the Vega though.
Replies
1
Boosts
0
Views
299
Activity
Apr ’25
The asset pack with the ID “testVideoAssetPack” couldn’t be looked up: Could not connect to the server.
On macOS Tahoe26.0, iOS 26.0 (23A5287g) not emulator, Xcode 26.0 beta 3 (17A5276g) Follow this tutorial Testing your asset packs locally The start the test server command I use this command line to start the test server:xcrun ba-serve --host 192.168.0.109 test.aar The terminal showThe content displayed on the terminal is: Loading asset packs… Loading the asset pack at “test.aar”… Listening on port 63125…… Choose an identity in the panel to continue. Listening on port 63125… running the project, Xcode reports an error:Download failed: Could not connect to the server. I use iPhone safari visit this website: https://192.168.0.109:63125, on the page display "Hello, world!" There are too few error messages in both of the above questions. I have no idea what the specific reasons are.I hope someone can offer some guidance. Best Regards. { "assetPackID": "testVideoAssetPack", "downloadPolicy": { "prefetch": { "installationEventTypes": ["firstInstallation", "subsequentUpdate"] } }, "fileSelectors": [ { "file": "video/test.mp4" } ], "platforms": [ "iOS" ] } this is my Manifest.json
Replies
1
Boosts
0
Views
398
Activity
Jul ’25
Image Playground Error: Unable to Generate Images Using externalProvider Style
I’m working on generating images using Image Playground. The code works fine for other styles but fails when using an external provider. I don’t see any other requirements mentioned in the documentation. Has anyone else encountered a similar issue? Here’s the relevant code snippet: https://developer.apple.com/documentation/imageplayground/imageplaygroundstyle/externalprovider?changes=_2 The error message is also not very helpful. It simply states that the creation failed. Note: I have enabled ChatGPT Plus, and the image generation using ChatGPT styles works fine when using the Playground app. do { let creator = try await ImageCreator() let concept = ImagePlaygroundConcept.text("Love") let images = creator.images(for: [concept], style: .externalProvider, limit: 1) for try await image in images { // Handle image break } } catch { // Handle error } I’m using the iOS 26 RC, and when I print creator.availableStyles, it doesn’t display the external Provider. [ImagePlayground.ImagePlaygroundStyle(id: "animation", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "emoji", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "illustration", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "sketch", _representationInfo: nil), ImagePlayground.ImagePlaygroundStyle(id: "messages-background", _representationInfo: nil)]
Replies
1
Boosts
0
Views
920
Activity
Sep ’25
Album segmentation model
I have a question. In China, long pressing a picture in the album can segment the target. Is this model a local model? Is there any information? Can developers use it?
Replies
2
Boosts
0
Views
311
Activity
Jul ’25
Will Apple Intelligence Support Third-Party LLMs or Custom AI Agent Integrations?
Hi everyone, I’m an AI engineer working on autonomous AI agents and exploring ways to integrate them into the Apple ecosystem, especially via Siri and Apple Intelligence. I was impressed by Apple’s integration of ChatGPT and its privacy-first design, but I’m curious to know: • Are there plans to support third-party LLMs? • Could Siri or Apple Intelligence call external AI agents or allow extensions to plug in alternative models for reasoning, scheduling, or proactive suggestions? I’m particularly interested in building event-driven, voice-triggered workflows where Apple Intelligence could act as a front-end for more complex autonomous systems (possibly local or cloud-based). This kind of extensibility would open up incredible opportunities for personalized, privacy-friendly use cases — while aligning with Apple’s system architecture. Is anything like this on the roadmap? Or is there a suggested way to prototype such integrations today? Thanks in advance for any thoughts or pointers!
Replies
4
Boosts
0
Views
530
Activity
May ’25
Crash inside of Vision predictWithCVPixelBuffer - Crashed: com.apple.VN.detectorSyncTasksQueue.VNCoreMLTransformer
Hello, We have been encountering a persistent crash in our application, which is deployed exclusively on iPad devices. The crash occurs in the following code block: let requestHandler = ImageRequestHandler(paddedImage) var request = CoreMLRequest(model: model) request.cropAndScaleAction = .scaleToFit let results = try await requestHandler.perform(request) The client using this code is wrapped inside an actor, following Swift concurrency principles. The issue has been consistently reproduced across multiple iPadOS versions, including: iPad OS - 18.4.0 iPad OS - 18.4.1 iPad OS - 18.5.0 This is the crash log - Crashed: com.apple.VN.detectorSyncTasksQueue.VNCoreMLTransformer 0 libobjc.A.dylib 0x7b98 objc_retain + 16 1 libobjc.A.dylib 0x7b98 objc_retain_x0 + 16 2 libobjc.A.dylib 0xbf18 objc_getProperty + 100 3 Vision 0x326300 -[VNCoreMLModel predictWithCVPixelBuffer:options:error:] + 148 4 Vision 0x3273b0 -[VNCoreMLTransformer processRegionOfInterest:croppedPixelBuffer:options:qosClass:warningRecorder:error:progressHandler:] + 748 5 Vision 0x2ccdcc __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_5 + 132 6 Vision 0x14600 VNExecuteBlock + 80 7 Vision 0x14580 __76+[VNDetector runSuccessReportingBlockSynchronously:detector:qosClass:error:]_block_invoke + 56 8 libdispatch.dylib 0x6c98 _dispatch_block_sync_invoke + 240 9 libdispatch.dylib 0x1b584 _dispatch_client_callout + 16 10 libdispatch.dylib 0x11728 _dispatch_lane_barrier_sync_invoke_and_complete + 56 11 libdispatch.dylib 0x7fac _dispatch_sync_block_with_privdata + 452 12 Vision 0x14110 -[VNControlledCapacityTasksQueue dispatchSyncByPreservingQueueCapacity:] + 60 13 Vision 0x13ffc +[VNDetector runSuccessReportingBlockSynchronously:detector:qosClass:error:] + 324 14 Vision 0x2ccc80 __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_4 + 336 15 Vision 0x14600 VNExecuteBlock + 80 16 Vision 0x2cc98c __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_3 + 256 17 libdispatch.dylib 0x1b584 _dispatch_client_callout + 16 18 libdispatch.dylib 0x6ab0 _dispatch_block_invoke_direct + 284 19 Vision 0x2cc454 -[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:] + 632 20 Vision 0x2cd14c __111-[VNDetector processUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke + 124 21 Vision 0x14600 VNExecuteBlock + 80 22 Vision 0x2ccfbc -[VNDetector processUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:] + 340 23 Vision 0x125410 __swift_memcpy112_8 + 4852 24 libswift_Concurrency.dylib 0x5c134 swift::runJobInEstablishedExecutorContext(swift::Job*) + 292 25 libswift_Concurrency.dylib 0x5d5c8 swift_job_runImpl(swift::Job*, swift::SerialExecutorRef) + 156 26 libdispatch.dylib 0x13db0 _dispatch_root_queue_drain + 364 27 libdispatch.dylib 0x1454c _dispatch_worker_thread2 + 156 28 libsystem_pthread.dylib 0x9d0 _pthread_wqthread + 232 29 libsystem_pthread.dylib 0xaac start_wqthread + 8 We found an issue similar to us - https://developer.apple.com/forums/thread/770771. But the crash logs are quite different, we believe this warrants further investigation to better understand the root cause and potential mitigation strategies. Please let us know if any additional information would help diagnose this issue.
Replies
3
Boosts
0
Views
420
Activity
Jul ’25
Create ML training data gives unreadable error message
Can't import data in create ML word tagging project training data is 100% correct I guarantee it: I mean look this one has one entry in it. [ { "tokens": [ "a", "august", "gruters" ], "labels": [ "BUILDER", "BUILDER", "BUILDER" ] } ]
Replies
1
Boosts
0
Views
186
Activity
Mar ’25
macOS 26 Beta 2 - Foundation Models - Symbol not found
It seems like there was an undocumented change that made Transcript.init(entries: [Transcript.Entry] initializer private, which broke my application, which relies on (manual) reconstruction of Transcript entries. Worked fine on beta 1, on beta 2 there's this error dyld[72381]: Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <44342398-591C-3850-9889-87C9458E1440> /Users/mika/experiments/apple-on-device-ai/fm Expected in: <66A793F6-CB22-3D1D-A560-D1BD5B109B0D> /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Is this a part of an API transition, if so - Apple, please update your documentation
Replies
3
Boosts
0
Views
386
Activity
Jun ’25
Provide unique identifier for tool calls and responses
Hey, Would be great to have an equivalent of toolCallId for both toolCall and toolResult in the transcript. Otherwise, it is hard to connect tool calls with their respective responses, when there were multiple parallel calls to the same tool. Thanks!
Replies
3
Boosts
0
Views
419
Activity
Jul ’25
Real Time Text detection using iOS18 RecognizeTextRequest from video buffer returns gibberish
Hey Devs, I'm trying to create my own Real Time Text detection like this Apple project. https://developer.apple.com/documentation/vision/extracting-phone-numbers-from-text-in-images I want to use the new iOS18 RecognizeTextRequest instead of the old VNRecognizeTextRequest in my SwiftUI project. This is my delegate code with the camera setup. I removed region of interest for debugging but I'm trying to scan English words in books. The idea is to get one word in the ROI in the future. But I can't even get proper words so testing without ROI incase my math is wrong. @Observable class CameraManager: NSObject, AVCapturePhotoCaptureDelegate ... override init() { super.init() setUpVisionRequest() } private func setUpVisionRequest() { textRequest = RecognizeTextRequest(.revision3) } ... func setup() -> Bool { captureSession.beginConfiguration() guard let captureDevice = AVCaptureDevice.default( .builtInWideAngleCamera, for: .video, position: .back) else { return false } self.captureDevice = captureDevice guard let deviceInput = try? AVCaptureDeviceInput(device: captureDevice) else { return false } /// Check whether the session can add input. guard captureSession.canAddInput(deviceInput) else { print("Unable to add device input to the capture session.") return false } /// Add the input and output to session captureSession.addInput(deviceInput) /// Configure the video data output videoDataOutput.setSampleBufferDelegate( self, queue: videoDataOutputQueue) if captureSession.canAddOutput(videoDataOutput) { captureSession.addOutput(videoDataOutput) videoDataOutput.connection(with: .video)? .preferredVideoStabilizationMode = .off } else { return false } // Set zoom and autofocus to help focus on very small text do { try captureDevice.lockForConfiguration() captureDevice.videoZoomFactor = 2 captureDevice.autoFocusRangeRestriction = .near captureDevice.unlockForConfiguration() } catch { print("Could not set zoom level due to error: \(error)") return false } captureSession.commitConfiguration() // potential issue with background vs dispatchqueue ?? Task(priority: .background) { captureSession.startRunning() } return true } } // Issue here ??? extension CameraManager: AVCaptureVideoDataOutputSampleBufferDelegate { func captureOutput( _ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection ) { guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return } Task { textRequest.recognitionLevel = .fast textRequest.recognitionLanguages = [Locale.Language(identifier: "en-US")] do { let observations = try await textRequest.perform(on: pixelBuffer) for observation in observations { let recognizedText = observation.topCandidates(1).first print("recognized text \(recognizedText)") } } catch { print("Recognition error: \(error.localizedDescription)") } } } } The results I get look like this ( full page of English from a any book) recognized text Optional(RecognizedText(string: e bnUI W4, confidence: 0.5)) recognized text Optional(RecognizedText(string: ?'U, confidence: 0.3)) recognized text Optional(RecognizedText(string: traQt4, confidence: 0.3)) recognized text Optional(RecognizedText(string: li, confidence: 0.3)) recognized text Optional(RecognizedText(string: 15,1,#, confidence: 0.3)) recognized text Optional(RecognizedText(string: jllÈ, confidence: 0.3)) recognized text Optional(RecognizedText(string: vtrll, confidence: 0.3)) recognized text Optional(RecognizedText(string: 5,1,: 11, confidence: 0.5)) recognized text Optional(RecognizedText(string: 1141, confidence: 0.3)) recognized text Optional(RecognizedText(string: jllll ljiiilij41, confidence: 0.3)) recognized text Optional(RecognizedText(string: 2f4, confidence: 0.3)) recognized text Optional(RecognizedText(string: ktril, confidence: 0.3)) recognized text Optional(RecognizedText(string: ¥LLI, confidence: 0.3)) recognized text Optional(RecognizedText(string: 11[Itl,, confidence: 0.3)) recognized text Optional(RecognizedText(string: 'rtlÈ131, confidence: 0.3)) Even with ROI set to a specific rectangle Normalized to Vision, I get the same results with single characters returning gibberish. Any help would be amazing thank you. Am I using the buffer right ? Am I using the new perform(on: CVPixelBuffer) right ? Maybe I didn't set up my camera properly? I can provide code
Replies
1
Boosts
0
Views
361
Activity
Jul ’25
LanguageModelStream and collecting the final output
I have a Generable type with many elements. I am using a stream() to incrementally process the output (Generable.PartiallyGenerated?) content. At the end, I want to pass the final version (not partially generated) to another function. I cannot seem to find a good way to convert from a MyGenerable.PartiallyGenerated to a MyGenerable. Am I missing some functionality in the APIs?
Replies
4
Boosts
0
Views
599
Activity
Jul ’25
Looking for a prebuilt TensorFlow Lite C++ library (libtensorflowlite) for macOS M1/M2
Hi everyone! 👋 I'm working on a C++ project using TensorFlow Lite and was wondering if anyone has a prebuilt TensorFlow Lite C++ library (libtensorflowlite) for macOS (Apple Silicon M1/M2) that they’d be willing to share. I’m looking specifically for the TensorFlow Lite C++ API — something that lets me use tflite::Interpreter, tflite::FlatBufferModel, etc. Building it from source using Bazel on macOS has been quite challenging and time-consuming, so a ready-to-use .dylib or .a build along with the required headers would be incredibly helpful. TensorFlow Lite version: v2.18.0 preferred Target: macOS arm64 (Apple Silicon) What I need: libtensorflowlite.dylib or .a Corresponding headers (ideally organized in a clean include/ folder) If you have one available or know where I can find a reliable prebuilt version, I’d be super grateful. Thanks in advance! 🙏
Replies
2
Boosts
0
Views
216
Activity
Apr ’25
Foundation model sandbox restriction error
I'm seeing this error a lot in my console log of my iPhone 15 Pro (Apple Intelligence enabled): com.apple.modelcatalog.catalog sync: connection error during call: Error Domain=NSCocoaErrorDomain Code=4099 "The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction." UserInfo={NSDebugDescription=The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction.} reached max num connection attempts: 1 Are there entitlements / permissions I need to enable in Xcode that I forgot to do? Code example Here's how I'm initializing the language model session: private func setupLanguageModelSession() { if #available(iOS 26.0, *) { let instructions = """ my instructions """ do { languageModelSession = try LanguageModelSession(instructions: instructions) print("Foundation Models language model session initialized") } catch { print("Error creating language model session: \(error)") languageModelSession = nil } } else { print("Device does not support Foundation Models (requires iOS 26.0+)") languageModelSession = nil } }
Replies
2
Boosts
0
Views
247
Activity
Jun ’25
Memory Attribution for Foundation Models in iOS 26
Hi, I’m developing an app targeting iOS 26, using the new FoundationModels framework to perform on-device LLM inference. I’m currently testing memory usage. Does the memory used by FoundationModels—including model weights, KV cache, and any inference-related buffers—count toward my app’s Jetsam memory limit, or is any of it managed separately by the system? I may need to run two concurrent inferences, each with a 4096-token context window. Is this explicitly supported or allowed by FoundationModels on iOS 26? Would this significantly increase the risk of memory-based termination? Thanks in advance for any clarification. Thanks.
Replies
1
Boosts
0
Views
424
Activity
Jul ’25
Is there an API to check if a Core ML compiled model is already cached?
Hello Apple Developer Community, I'm investigating Core ML model loading behavior and noticed that even when the compiled model path remains unchanged after an APP update, the first run still triggers an "uncached load" process. This seems to impact user experience with unnecessary delays. Question: Does Core ML provide any public API to check whether a compiled model (from a specific .mlmodelc path) is already cached in the system? If such API exists, we'd like to use it for pre-loading decision logic - only perform background pre-load when the model isn't cached. Has anyone encountered similar scenarios or found official solutions? Any insights would be greatly appreciated!
Replies
2
Boosts
0
Views
253
Activity
May ’25
Localizing prompts that has string interpolated generable objects
I'm working on localizing my prompts to support multiple languages, and in some cases my prompts has String interpolated Generable objects. for example: "Given the following workout routine: \(routine), suggest one additional exercise to complement it." In the Strings dictionary, I'm only able to select String, Int or Double parameters using %@ and %lld. Has anyone found a way to accomplish this?
Replies
1
Boosts
0
Views
385
Activity
Jul ’25
iOS 18 new RecognizedTextRequest DEADLOCKS if more than 2 are run in parallel
Following WWDC24 video "Discover Swift enhancements in the Vision framework" recommendations (cfr video at 10'41"), I used the following code to perform multiple new iOS 18 `RecognizedTextRequest' in parallel. Problem: if more than 2 request are run in parallel, the request will hang, leaving the app in a state where no more requests can be started. -> deadlock I tried other ways to run the requests, but no matter the method employed, or what device I use: no more than 2 requests can ever be run in parallel. func triggerDeadlock() {} try await withThrowingTaskGroup(of: Void.self) { group in // See: WWDC 2024 Discover Siwft enhancements in the Vision framework at 10:41 // ############## THIS IS KEY let maxOCRTasks = 5 // On a real-device, if more than 2 RecognizeTextRequest are launched in parallel using tasks, the request hangs // ############## THIS IS KEY for idx in 0..<maxOCRTasks { let url = ... // URL to some image group.addTask { // Perform OCR let _ = await performOCRRequest(on: url: url) } } var nextIndex = maxOCRTasks for try await _ in group { // Wait for the result of the next child task that finished if nextIndex < pageCount { group.addTask { let url = ... // URL to some image // Perform OCR let _ = await performOCRRequest(on: url: url) } nextIndex += 1 } } } } // MARK: - ASYNC/AWAIT version with iOS 18 @available(iOS 18, *) func performOCRRequest(on url: URL) async throws -> [RecognizedText] { // Create request var request = RecognizeTextRequest() // Single request: no need for ImageRequestHandler // Configure request request.recognitionLevel = .accurate request.automaticallyDetectsLanguage = true request.usesLanguageCorrection = true request.minimumTextHeightFraction = 0.016 // Perform request let textObservations: [RecognizedTextObservation] = try await request.perform(on: url) // Convert [RecognizedTextObservation] to [RecognizedText] return textObservations.compactMap { observation in observation.topCandidates(1).first } } I also found this Swift forums post mentioning something very similar. I also opened a feedback: FB17240843
Replies
7
Boosts
0
Views
422
Activity
Aug ’25
IamNew_here
Lookin for J - is this a safe place for discussing full apps ive built but not submitted or shared , I have maybe over 100 but had been unaware any assistance was provided.. is there a formal process to take to submit an app fro review to improve OS, other than during App Store review.
Replies
1
Boosts
0
Views
643
Activity
Sep ’25
Failing to run SystemLanguageModel inference with custom adapter
Hi, I have trained a basic adapter using the adapter training toolkit. I am trying a very basic example of loading it and running inference with it, but am getting the following error: Passing along InferenceError::inferenceFailed::loadFailed::Error Domain=com.apple.TokenGenerationInference.E5Runner Code=0 "Failed to load model: ANE adapted model load failure: createProgramInstanceWithWeights:modelToken:qos:baseModelIdentifier:owningPid:numWeightFiles:error:: Program load new instance failure (0x170006)." UserInfo={NSLocalizedDescription=Failed to load model: ANE adapted model load failure: createProgramInstanceWithWeights:modelToken:qos:baseModelIdentifier:owningPid:numWeightFiles:error:: Program load new instance failure (0x170006).} in response to ExecuteRequest Any ideas / direction? For testing I am including the .fmadapter file inside the app bundle. This is where I load it: @State private var session: LanguageModelSession? // = LanguageModelSession() func loadAdapter() async throws { if let assetURL = Bundle.main.url(forResource: "qasc---afm---4-epochs-adapter", withExtension: "fmadapter") { print("Asset URL: \(assetURL)") let adapter = try SystemLanguageModel.Adapter(fileURL: assetURL) let adaptedModel = SystemLanguageModel(adapter: adapter) session = LanguageModelSession(model: adaptedModel) print("Loaded adapter and updated session") } else { print("Asset not found in the main bundle.") } } This seems to work fine as I get to the log Loaded adapter and updated session. However when the below inference code runs I get the aforementioned error: func sendMessage(_ msg: String) { self.loading = true if let session = session { Task { do { let modelResponse = try await session.respond(to: msg) DispatchQueue.main.async { self.response = modelResponse.content self.loading = false } } catch { print("Error: \(error)") DispatchQueue.main.async { self.loading = false } } } } }
Replies
3
Boosts
0
Views
239
Activity
Jun ’25
Is it possible to create a virtual NPU device on macOS using Hypervisor.framework + CoreML?
Is it possible to expose a custom VirtIO device to a Linux guest running inside a VM — likely using QEMU backed by Hypervisor.framework. The guest would see this device as something like /dev/npu0, and it would use a kernel driver + userspace library to submit inference requests. On the macOS host, these requests would be executed using CoreML, MPSGraph, or BNNS. The results would be passed back to the guest via IPC. Does the macOS allow this kind of "fake" NPU / GPU
Replies
1
Boosts
0
Views
441
Activity
Aug ’25