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What's the best way to load adapters to try?
I'm new to Swift and was hoping the Playground would support loading adaptors. When I tried, I got a permissions error - thinking it's because it's not in the project and Playgrounds don't like going outside the project? A tutorial and some sample code would be helpful. Also some benchmarks on how long it's expected to take. Selfishly I'm on an M2 Mac Mini.
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305
Jul ’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
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445
Aug ’25
tensorflow 2.20 broken support
Hi, testing latest tensorflow-metal plugin with tensorflow 2.20 doesn't work.. using python Python 3.12.11 (main, Jun 3 2025, 15:41:47) [Clang 17.0.0 (clang-1700.0.13.3)] on darwin simple testing shows error: import tensorflow as tf Traceback (most recent call last): File "", line 1, in File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file) tf.config.experimental.list_physical_devices('GPU') Traceback (most recent call last): File "", line 1, in NameError: name 'tf' is not defined I fixed this error by copying _pywrap_tensorflow_internal.so where it's searched.. 1)mkdir /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64 2)mkdir /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/ 3)cp /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/ then fails symbol not found: Symbol not found: __ZN10tensorflow28_AttrValue_default_instance_E in libmetal_plugin.dylib full log: with import tensorflow as tf Traceback (most recent call last): File "", line 1, in File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN10tensorflow28_AttrValue_default_instance_E Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: <2FF91C8B-0CB6-3E66-96B7-092FDF36772E> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so
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837
Oct ’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://aninterestingwebsite.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
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365
Jul ’25
LanguageModelSession with multiple tools and structured outpout
Hi, I'm using LanguageModelSession and giving it two different tools to query data from a local database. I'm wondering how I can have the session generate structured content as the response that includes data one or both tools (or no tool at all). Here is an example of what I'm trying to do: Let's say the app has access to a database that contains information about exercise and sleep data (this is just an analogy). There are two tools, GetExerciseData() and GetSleepData(). The user may then prompt something like, "how well did I sleep in November". I have this working so that it calls through to the right tool, which would return a SleepSummary. However, I can't figure out how to have the session return the right structured data. I can do this and get back good text data: let response = session.respond(to: userInput), but I believe I want to do something like: let response = session.respond(to: trimmed, generating: <SomeStructure?>) Sometimes the model I run one tool or the other, or both tools, or no tool at all. Any help of what the right way to go about this would be much appreciated. Most of the example I found have to do with 1 tool.
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723
Jan ’26
Image object detection with video sizing issue
I'm working on my first model that detects bowling score screens, and I have it working with pictures no problem. But when it comes to video, I have a sizing issue. I added my model to a small app I wrote for taking a picture of a Bowling Scoring Screen, where my model will frame the screens in the video feed from the camera. My model works, but my boxes are about 2/3 the size of the screens being detected. I don't understand the theory of the video stream the camera is feeding me. What I mean is that I don't want to make tweaks to the size of my rectangles by making them larger, and I'm not sure if the video feed is larger than what I'm detecting in code. Questions I have are like is the video feed a certain resolution like 1980x something, or a much higher resolution in the 12 megapixel range? On a static image of say 1920x something, My alignment is perfect. AI says that it's my model training, that I'm training on square images but video is 16:9. Or that I'm producing 4:3 images in a 16:9 environment. I'm missing something here but not sure what it is. I already wrote code to force it to fit, but reverted back to trying for a natural fit.
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382
Jan ’26
Xcode 26.1 RC ( RC1 ?) Apple Intelligence using GPT (with account or without) or Sonnet (via OpenRouter) much slower
I didn't run benchmarks before update, but it seems at least 5x slower. Of course all the LLM work is on remote servers, so is non-intuitive to me this should be happening. Had updated MacOS and Xcode to 26.1RC at the same time, so can't even say I think it is MacOS or I think it is Xcode. Before the update the progress indicator for each piece of code might seem to get stuck at the very end (and toggling between Navigators and Coding Assistant) in Xcode UI seemed to refresh the UI and confirm coding complete... but now it seems progress races to 50%, then often is stuck at 75%... well earlier than used to get stuck. And it like something is legitimately processing not just a UI glitch. I'm wondering if this is somehow tied to visual rendering of the code in the little white window? CMD-TAB into Xcode seems laggy. Xcode is pinning a CPU. Why, this is all remote LLM work? MacBook Pro 2021 M1 64GB RAM. Went from 26.01 to 26.1RC. Didn't touch any of the betas until RC1.
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342
Oct ’25
Initializing session with transcript ignores tools
When I initialize a session with an existing transcript using this initializer: public convenience init(model: SystemLanguageModel = .default, guardrails: LanguageModelSession.Guardrails = .default, tools: [any Tool] = [], transcript: Transcript) The tools get ignored. I noticed that when doing that, the model never use the tools. When inspecting the transcript, I can see that the instruction entry does not have any tools available to it. I tried this for both transcripts that already include an instruction entry and ones that don't - both yielding the same result.. Is this the intended behavior / am I missing something here?
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225
Jul ’25
LLM size for fine-tuning using MLX in MacBook
Hi, recently i tried to fine-tune Gemma-2-2b mlx model on my macbook (24 GB UMA). The code started running, after few seconds i saw swap size reaching 50GB and ram around 23 GB and then it stopped. I ran the Gemma-2-2b (cuda) on colab, it ran and occupied 27 GB on A100 gpu and worked fine. Here i didn't experienced swap issue. Now my question is if my UMA was more than 27 GB, i also would not have experienced swap disk issue. Thanks.
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384
Oct ’25
NLTagger.requestAssets hangs indefinitely
When calling NLTagger.requestAssets with some languages, it hangs indefinitely both in the simulator and a device. This happens consistently for some languages like greek. An example call is NLTagger.requestAssets(for: .greek, tagScheme: .lemma). Other languages like french return immediately. I captured some logs from Console and found what looks like the repeated attempts to download the asset. I would expect the call to eventually terminate, either loading the asset or failing with an error.
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212
May ’25
Can not use Language Model in Xcode-beta
I've downloaded the Xcode-beta and run the sample project "FoundationModelsTripPlanner" but I got this error when trying generate the response. InferenceError::inferenceFailed::Error Domain=com.apple.UnifiedAssetFramework Code=5000 "There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog" UserInfo={NSLocalizedFailureReason=There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog} Device: M1 Pro Question: Is it because M1 not supporting this feature?
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318
Jun ’25
Image understanding to on-device model
I can’t seem to find a way to include an image when prompting the new on-device model in Xcode, even though Apple explicitly states that the model was trained and tested with image data (https://machinelearning.apple.com/research/apple-foundation-models-2025-updates). Has anyone managed to get this working, or are VLM-style capabilities simply not exposed yet?
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449
Jan ’26
MPS SDPA Attention Kernel Regression on A14-class (M1) in macOS 26.3.1 — Works on A15+ (M2+)
Summary Since macOS 26, our Core ML / MPS inference pipeline produces incorrect results on Mac mini M1 (Macmini9,1, A14-class SoC). The same model and code runs correctly on M2 and newer (A15-class and up). The regression appears to be in the Scaled Dot-Product Attention (SDPA) kernel path in the MPS backend. Environment Affected Mac mini M1 — Macmini9,1 (A14-class) Not affected M2 and newer (A15-class and up) Last known good macOS Sequoia First broken macOS 26 (Tahoe) ? Confirmed broken on macOS 26.3.1 Framework Core ML + MPS backend Language C++ (via CoreML C++ API) Description We ship an audio processing application (VoiceAssist by NoiseWorks) that runs a deep learning model (based on Demucs architecture) via Core ML with the MPS compute unit. On macOS Sequoia this works correctly on all Apple Silicon Macs including M1. After updating to macOS 26 (Tahoe), inference on M1 Macs fails — either producing garbage output or crashing. The same binary, same .mlpackage, same inputs work correctly on M2+. Our Apple contact has suggested the root cause is a regression in the A14-specific MPS SDPA attention kernel, which may have broken when the Metal/MPS stack was updated in macOS 26. The model makes heavy use of attention layers, and the failure correlates precisely with the SDPA path being exercised on A14 hardware. Steps to Reproduce Load a Core ML model that uses Scaled Dot-Product Attention (e.g. a transformer or attention-based audio model) Run inference with MLComputeUnits::cpuAndGPU (MPS active) Run on Mac mini M1 (Macmini9,1) with macOS 26.3.1 Compare output to the same model running on M2 / macOS Sequoia Expected: Correct inference output, consistent with M2+ and macOS Sequoia behavior Actual: Incorrect / corrupted output (or crash), only on A14-class hardware running macOS 26+ Workaround Forcing MLComputeUnits::cpuOnly bypasses MPS entirely and produces correct output on M1, confirming the issue is in the MPS compute path. This is not acceptable as a shipping workaround due to performance impact. Additional Notes The failure is hardware-specific (A14 only) and OS-specific (macOS 26+), pointing to a kernel-level regression rather than a model or app bug We first became aware of this through a customer report Happy to provide a symbolicated crash log if helpful this text was summarized by AI and human verified
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2w
What's the best way to load adapters to try?
I'm new to Swift and was hoping the Playground would support loading adaptors. When I tried, I got a permissions error - thinking it's because it's not in the project and Playgrounds don't like going outside the project? A tutorial and some sample code would be helpful. Also some benchmarks on how long it's expected to take. Selfishly I'm on an M2 Mac Mini.
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305
Activity
Jul ’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
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445
Activity
Aug ’25
tensorflow 2.20 broken support
Hi, testing latest tensorflow-metal plugin with tensorflow 2.20 doesn't work.. using python Python 3.12.11 (main, Jun 3 2025, 15:41:47) [Clang 17.0.0 (clang-1700.0.13.3)] on darwin simple testing shows error: import tensorflow as tf Traceback (most recent call last): File "", line 1, in File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/homebrew/lib/_pywrap_tensorflow_internal.so' (no such file) tf.config.experimental.list_physical_devices('GPU') Traceback (most recent call last): File "", line 1, in NameError: name 'tf' is not defined I fixed this error by copying _pywrap_tensorflow_internal.so where it's searched.. 1)mkdir /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64 2)mkdir /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/ 3)cp /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/ then fails symbol not found: Symbol not found: __ZN10tensorflow28_AttrValue_default_instance_E in libmetal_plugin.dylib full log: with import tensorflow as tf Traceback (most recent call last): File "", line 1, in File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/init.py", line 438, in _ll.load_library(_plugin_dir) File "/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Symbol not found: __ZN10tensorflow28_AttrValue_default_instance_E Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/tensorflow-plugins/libmetal_plugin.dylib Expected in: <2FF91C8B-0CB6-3E66-96B7-092FDF36772E> /Users/obg/npu/venv-tf/lib/python3.12/site-packages/_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so
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837
Activity
Oct ’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://aninterestingwebsite.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
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365
Activity
Jul ’25
LanguageModelSession with multiple tools and structured outpout
Hi, I'm using LanguageModelSession and giving it two different tools to query data from a local database. I'm wondering how I can have the session generate structured content as the response that includes data one or both tools (or no tool at all). Here is an example of what I'm trying to do: Let's say the app has access to a database that contains information about exercise and sleep data (this is just an analogy). There are two tools, GetExerciseData() and GetSleepData(). The user may then prompt something like, "how well did I sleep in November". I have this working so that it calls through to the right tool, which would return a SleepSummary. However, I can't figure out how to have the session return the right structured data. I can do this and get back good text data: let response = session.respond(to: userInput), but I believe I want to do something like: let response = session.respond(to: trimmed, generating: <SomeStructure?>) Sometimes the model I run one tool or the other, or both tools, or no tool at all. Any help of what the right way to go about this would be much appreciated. Most of the example I found have to do with 1 tool.
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723
Activity
Jan ’26
Image object detection with video sizing issue
I'm working on my first model that detects bowling score screens, and I have it working with pictures no problem. But when it comes to video, I have a sizing issue. I added my model to a small app I wrote for taking a picture of a Bowling Scoring Screen, where my model will frame the screens in the video feed from the camera. My model works, but my boxes are about 2/3 the size of the screens being detected. I don't understand the theory of the video stream the camera is feeding me. What I mean is that I don't want to make tweaks to the size of my rectangles by making them larger, and I'm not sure if the video feed is larger than what I'm detecting in code. Questions I have are like is the video feed a certain resolution like 1980x something, or a much higher resolution in the 12 megapixel range? On a static image of say 1920x something, My alignment is perfect. AI says that it's my model training, that I'm training on square images but video is 16:9. Or that I'm producing 4:3 images in a 16:9 environment. I'm missing something here but not sure what it is. I already wrote code to force it to fit, but reverted back to trying for a natural fit.
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382
Activity
Jan ’26
Xcode 26.1 RC ( RC1 ?) Apple Intelligence using GPT (with account or without) or Sonnet (via OpenRouter) much slower
I didn't run benchmarks before update, but it seems at least 5x slower. Of course all the LLM work is on remote servers, so is non-intuitive to me this should be happening. Had updated MacOS and Xcode to 26.1RC at the same time, so can't even say I think it is MacOS or I think it is Xcode. Before the update the progress indicator for each piece of code might seem to get stuck at the very end (and toggling between Navigators and Coding Assistant) in Xcode UI seemed to refresh the UI and confirm coding complete... but now it seems progress races to 50%, then often is stuck at 75%... well earlier than used to get stuck. And it like something is legitimately processing not just a UI glitch. I'm wondering if this is somehow tied to visual rendering of the code in the little white window? CMD-TAB into Xcode seems laggy. Xcode is pinning a CPU. Why, this is all remote LLM work? MacBook Pro 2021 M1 64GB RAM. Went from 26.01 to 26.1RC. Didn't touch any of the betas until RC1.
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342
Activity
Oct ’25
Foundation Models reliable for medicine purposes?
How reliable is the Models, to use as a comparison, such as a cholesterol test, to inform, for example, whether it is worth it to go see a doctor? I would like to use Tool to attach the simple blood test data to the session and with this the Model can analyse and made a simple suggestion if is necessary to see a doctor etc.. ? ps.: Local model
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219
Activity
Jun ’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.
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647
Activity
Sep ’25
Initializing session with transcript ignores tools
When I initialize a session with an existing transcript using this initializer: public convenience init(model: SystemLanguageModel = .default, guardrails: LanguageModelSession.Guardrails = .default, tools: [any Tool] = [], transcript: Transcript) The tools get ignored. I noticed that when doing that, the model never use the tools. When inspecting the transcript, I can see that the instruction entry does not have any tools available to it. I tried this for both transcripts that already include an instruction entry and ones that don't - both yielding the same result.. Is this the intended behavior / am I missing something here?
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225
Activity
Jul ’25
LLM size for fine-tuning using MLX in MacBook
Hi, recently i tried to fine-tune Gemma-2-2b mlx model on my macbook (24 GB UMA). The code started running, after few seconds i saw swap size reaching 50GB and ram around 23 GB and then it stopped. I ran the Gemma-2-2b (cuda) on colab, it ran and occupied 27 GB on A100 gpu and worked fine. Here i didn't experienced swap issue. Now my question is if my UMA was more than 27 GB, i also would not have experienced swap disk issue. Thanks.
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384
Activity
Oct ’25
NLTagger.requestAssets hangs indefinitely
When calling NLTagger.requestAssets with some languages, it hangs indefinitely both in the simulator and a device. This happens consistently for some languages like greek. An example call is NLTagger.requestAssets(for: .greek, tagScheme: .lemma). Other languages like french return immediately. I captured some logs from Console and found what looks like the repeated attempts to download the asset. I would expect the call to eventually terminate, either loading the asset or failing with an error.
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212
Activity
May ’25
face and body detection is local model or a cloud model?
Is the face and body detection service in the Vision framework a local model or a cloud model? https://aninterestingwebsite.com/documentation/vision
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746
Activity
Sep ’25
Can not use Language Model in Xcode-beta
I've downloaded the Xcode-beta and run the sample project "FoundationModelsTripPlanner" but I got this error when trying generate the response. InferenceError::inferenceFailed::Error Domain=com.apple.UnifiedAssetFramework Code=5000 "There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog" UserInfo={NSLocalizedFailureReason=There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog} Device: M1 Pro Question: Is it because M1 not supporting this feature?
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318
Activity
Jun ’25
What Should the iOS Deployment Target Be Set to?
Originally, I set my iOS deployment target to 18.1, but now that I'm integrating Foundational Models, I set it to iOS 26.0. Is this ok?
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153
Activity
Feb ’26
Image understanding to on-device model
I can’t seem to find a way to include an image when prompting the new on-device model in Xcode, even though Apple explicitly states that the model was trained and tested with image data (https://machinelearning.apple.com/research/apple-foundation-models-2025-updates). Has anyone managed to get this working, or are VLM-style capabilities simply not exposed yet?
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449
Activity
Jan ’26
Symbol not found
I get the following dyld error on an iPad Pro with Xcode 26 beta 4: Symbol not found: _$s16FoundationModels20LanguageModelSessionC7prewarm12promptPrefixyAA6PromptVSg_tF Any advice?
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346
Activity
Jul ’25
Unpredictable performance when using structured output
Hey, When generating responses with structured output and non-streaming API, it sometimes takes 3s, sometimes 10-20s. I am firing that request subsequently while testing the app. Is this by design, or any place I can learn more about what contributes to such variation?
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223
Activity
Jul ’25
Download the Foundation Models Adaptor Training Toolkit
Download the Foundation Models Adaptor Training Toolkit Hi, after I clicked on the download button, I was redirected to this page https://aninterestingwebsite.com and did not download the toolkit.
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479
Activity
Jul ’25
MPS SDPA Attention Kernel Regression on A14-class (M1) in macOS 26.3.1 — Works on A15+ (M2+)
Summary Since macOS 26, our Core ML / MPS inference pipeline produces incorrect results on Mac mini M1 (Macmini9,1, A14-class SoC). The same model and code runs correctly on M2 and newer (A15-class and up). The regression appears to be in the Scaled Dot-Product Attention (SDPA) kernel path in the MPS backend. Environment Affected Mac mini M1 — Macmini9,1 (A14-class) Not affected M2 and newer (A15-class and up) Last known good macOS Sequoia First broken macOS 26 (Tahoe) ? Confirmed broken on macOS 26.3.1 Framework Core ML + MPS backend Language C++ (via CoreML C++ API) Description We ship an audio processing application (VoiceAssist by NoiseWorks) that runs a deep learning model (based on Demucs architecture) via Core ML with the MPS compute unit. On macOS Sequoia this works correctly on all Apple Silicon Macs including M1. After updating to macOS 26 (Tahoe), inference on M1 Macs fails — either producing garbage output or crashing. The same binary, same .mlpackage, same inputs work correctly on M2+. Our Apple contact has suggested the root cause is a regression in the A14-specific MPS SDPA attention kernel, which may have broken when the Metal/MPS stack was updated in macOS 26. The model makes heavy use of attention layers, and the failure correlates precisely with the SDPA path being exercised on A14 hardware. Steps to Reproduce Load a Core ML model that uses Scaled Dot-Product Attention (e.g. a transformer or attention-based audio model) Run inference with MLComputeUnits::cpuAndGPU (MPS active) Run on Mac mini M1 (Macmini9,1) with macOS 26.3.1 Compare output to the same model running on M2 / macOS Sequoia Expected: Correct inference output, consistent with M2+ and macOS Sequoia behavior Actual: Incorrect / corrupted output (or crash), only on A14-class hardware running macOS 26+ Workaround Forcing MLComputeUnits::cpuOnly bypasses MPS entirely and produces correct output on M1, confirming the issue is in the MPS compute path. This is not acceptable as a shipping workaround due to performance impact. Additional Notes The failure is hardware-specific (A14 only) and OS-specific (macOS 26+), pointing to a kernel-level regression rather than a model or app bug We first became aware of this through a customer report Happy to provide a symbolicated crash log if helpful this text was summarized by AI and human verified
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2w