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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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342
Dec ’25
Data used for MLX fine-tuning
The WWDC25: Explore large language models on Apple silicon with MLX video talks about using your own data to fine-tune a large language model. But the video doesn't explain what kind of data can be used. The video just shows the command to use and how to point to the data folder. Can I use PDFs, Word documents, Markdown files to train the model? Are there any code examples on GitHub that demonstrate how to do this?
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476
Oct ’25
CoreML Unified Memory failure/silent exit on long video tasks (M1 Mac 32GB)
Hi Apple Engineers, I am experiencing a potential memory management bug with CoreML on M1 Mac (32GB Unified Memory). When processing long video files (approx. 12,000 frames) using a CoreML execution provider, the system often completes the 'Analysing' phase but fails to transition into 'Processing'. It simply exits silently or hits an import error (scipy). However, if I split the same task into small 20-frame segments, it works perfectly at high speeds (~40 FPS). This suggests the hardware is capable, but there is an issue with memory fragmentation or resource cleanup during long-running CoreML sessions. Is there a way to force a VRAM/Unified Memory flush via CLI, or is this a known limitation for large frame indexing?
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579
Dec ’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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221
Mar ’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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3.8k
Mar ’26
Provide spoken voice search string
Hello, My goal is to enable users to perform a freeform search request for any product I sell using a spoken phrase, for example, "Hey Siri, search GAMING CONSOLES on MyCatalogApp". The result would launch MyCatalogApp and navigate to a search results page displaying gaming consoles. I have defined a SearchIntent (using the .system.search schema) and a Shortcut to accomplish this. However, Siri doesn't seem to be able to correctly parse the spoken phrase, extract the search string, and provide it as the critiria term within SearchIntent. What am I doing wrong? Here is the SearchIntent. Note the print() statement outputs the search string--which in the scenario above would be "GAMING CONSOLES"--but it doesn't work. import AppIntents @available(iOS 17.2, *) @AppIntent(schema: .system.search) struct SearchIntent: ShowInAppSearchResultsIntent { static var searchScopes: [StringSearchScope] = [.general] @Parameter(title: "Criteria") var criteria: StringSearchCriteria static var title: LocalizedStringResource = "Search with MyCatalogApp" @MainActor func perform() async throws -> some IntentResult { let searchString = criteria.term print("**** Search String: \(searchString) ****") // tmp debugging try await MyCatalogSearchHelper.search(for: searchString) // fetch results via my async fetch API return .result() } } Here's the Shortcuts definition: import AppIntents @available(iOS 17.2, *) struct Shortcuts: AppShortcutsProvider { @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: SearchIntent(), phrases: ["Search for \(\.$criteria) on \(.applicationName)."], shortTitle: "Search", systemImageName: "magnifyingglass" ) } } Thanks for any help!
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845
Mar ’26
CreateML Training Object Detection Not using MPS
Hi everyone Im currently developing an object detection model that shall identify up to seven classes in an image. While im usually doing development with basic python and the ultralytics library, i thought i would like to give CreateML a shot. The experience is actually very nice, except for the fact that the model seem not to be using any ANE or GPU (MPS) for accelerated training. On https://developer.apple.com/machine-learning/create-ml/ it states: "On-device training Train models blazingly fast right on your Mac while taking advantage of CPU and GPU." Am I doing something wrong? Im running the training on Apple M1 Pro 16GB MacOS 26.1 (Tahoe) Xcode 26.1 (Build version 17B55) It would be super nice to get some feedback or instructions. Thank you in advance!
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392
Nov ’25
Massive CoreML latency spike on live AVFoundation camera feed vs. offline inference (CPU+ANE)
Hello, I’m experiencing a severe performance degradation when running CoreML models on a live AVFoundation video feed compared to offline or synthetic inference. This happens across multiple models I've converted (including SCI, RTMPose, and RTMW) and affects multiple devices. The Environment OS: macOS 26.3, iOS 26.3, iPadOS 26.3 Hardware: Mac14,6 (M2 Max), iPad Pro 11 M1, iPhone 13 mini Compute Units: cpuAndNeuralEngine The Numbers When testing my SCI_output_image_int8.mlpackage model, the inference timings are drastically different: Synthetic/Offline Inference: ~1.34 ms Live Camera Inference: ~15.96 ms Preprocessing is completely ruled out as the bottleneck. My profiling shows total preprocessing (nearest-neighbor resize + feature provider creation) takes only ~0.4 ms in camera mode. Furthermore, no frames are being dropped. What I've Tried I am building a latency-critical app and have implemented almost every recommended optimization to try and fix this, but the camera-feed penalty remains: Matched the AVFoundation camera output format exactly to the model input (640x480 at 30/60fps). Used IOSurface-backed pixel buffers for everything (camera output, synthetic buffer, and resize buffer). Enabled outputBackings. Loaded the model once and reused it for all predictions. Configured MLModelConfiguration with reshapeFrequency = .frequent and specializationStrategy = .fastPrediction. Wrapped inference in ProcessInfo.processInfo.beginActivity(options: .latencyCritical, reason: "CoreML_Inference"). Set DispatchQueue to qos: .userInteractive. Disabled the idle timer and enabled iOS Game Mode. Exported models using coremltools 9.0 (deployment target iOS 26) with ImageType inputs/outputs and INT8 quantization. Reproduction To completely rule out UI or rendering overhead, I wrote a standalone Swift CLI script that isolates the AVFoundation and CoreML pipeline. The script clearly demonstrates the ~15ms latency on live camera frames versus the ~1ms latency on synthetic buffers. (I have attached camera_coreml_benchmark.swift and coreml model (very light low light enghancement model) to this repo on github https://github.com/pzoltowski/apple-coreml-camera-latency-repro). My Question: Is this massive overhead expected behavior for AVFoundation + Core ML on live feeds, or is this a framework/runtime bug? If expected, what is the Apple-recommended pattern to bypass this camera-only inference slowdown? One think found interesting when running in debug model was faster (not as fast as in performance benchmark but faster than 16ms. Also somehow if I did some dummy calculation on on different DispatchQueue also seems like model got slightly faster. So maybe its related to ANE Power State issues (Jitter/SoC Wake) and going to fast to sleep and taking a long time to wakeup? Doing dummy calculation in background thought is probably not a solution. Thanks in advance for any insights!
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990
Mar ’26
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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312
Jul ’25
Creating powerful, efficient, and maintainable applications.
Recursive and Self-Referential Data Structures Combining recursive and self-referential data structures with frameworks like Accelerate, SwiftMacros, and utilizing SwiftUI hooks can offer significant benefits in terms of performance, maintainability, and expressiveness. Here is how Apple Intelligence breaks it down. Benefits: Natural Representation of Complex Data: Recursive structures, such as trees and graphs, are ideal for representing hierarchical or interconnected data, like file systems, social networks, and DOM trees. Simplified Algorithms: Many algorithms, such as traversals, sorting, and searching, are more straightforward and elegant when implemented using recursion. Dynamic Memory Management: Self-referential structures can dynamically grow and shrink, making them suitable for applications with unpredictable data sizes. Challenges: Performance Overhead: Recursive algorithms can lead to stack overflow if not properly optimized (e.g., using tail recursion). Self-referential structures can introduce memory management challenges, such as retain cycles. Accelerate Framework Benefits: High-Performance Computation: Accelerate provides optimized libraries for numerical and scientific computing, including linear algebra, FFT, and image processing. It can significantly speed up computations, especially for large datasets, by leveraging multi-core processors and GPU acceleration. Parallel Processing: Accelerate automatically parallelizes operations, making it easier to take advantage of modern hardware capabilities. Integration with Recursive Data: Matrix and Vector Operations: Use Accelerate for operations on matrices and vectors, which are common in recursive algorithms like those used in machine learning and physics simulations. FFT and Convolutions: Accelerate's FFT functions can be used in recursive algorithms for signal processing and image analysis. SwiftMacros Benefits: Code Generation and Transformation: SwiftMacros allow you to generate and transform code at compile time, enabling the creation of DSLs, boilerplate reduction, and optimization. Improved Compile-Time Checks: Macros can perform complex compile-time checks, ensuring code correctness and reducing runtime errors. Integration with Recursive Data: DSL for Data Structures: Create a DSL using SwiftMacros to define recursive data structures concisely and safely. Optimization: Use macros to generate optimized code for recursive algorithms, such as memoization or iterative transformations. SwiftUI Hooks Benefits: State Management: Hooks like @State, @Binding, and @Effect simplify state management in SwiftUI, making it easier to handle dynamic data. Side Effects: @Effect allows you to perform side effects in a declarative manner, integrating seamlessly with asynchronous operations. Reusable Logic: Custom hooks enable the reuse of stateful logic across multiple views, promoting code maintainability. Integration with Recursive Data: Dynamic Data Binding: Use SwiftUI's data binding to manage the state of recursive data structures, ensuring that UI updates reflect changes in the underlying data. Efficient Rendering: SwiftUI's diffing algorithm efficiently updates the UI only for the parts of the recursive structure that have changed, improving performance. Asynchronous Data Loading: Combine @Effect with recursive data structures to fetch and process data asynchronously, such as loading a tree structure from a remote server. Example: Combining All Components Imagine you're building an app that visualizes a hierarchical file system using a recursive tree structure. Here's how you might combine these components: Define the Recursive Data Structure: Use SwiftMacros to create a DSL for defining tree nodes. @macro struct TreeNode { var value: T var children: [TreeNode] } Optimize with Accelerate: Use Accelerate for operations like computing the size of the tree or performing transformations on node values. func computeTreeSize(_ node: TreeNode) -> Int { return node.children.reduce(1) { $0 + computeTreeSize($1) } } Manage State with SwiftUI Hooks: Use SwiftUI hooks to load and display the tree structure dynamically. struct FileSystemView: View { @State private var rootNode: TreeNode = loadTree() var body: some View { TreeView(node: rootNode) } private func loadTree() -> TreeNode<String> { // Load or generate the tree structure } } struct TreeView: View { let node: TreeNode var body: some View { List(node.children, id: \.value) { Text($0.value) TreeView(node: $0) } } } Perform Side Effects with @Effect: Use @Effect to fetch data asynchronously and update the tree structure. struct FileSystemView: View { @State private var rootNode: TreeNode = TreeNode(value: "/") @Effect private var loadTreeEffect: () -> Void = { // Fetch data from a server or database } var body: some View { TreeView(node: rootNode) .onAppear { loadTreeEffect() } } } By combining recursive data structures with Accelerate, SwiftMacros, and SwiftUI hooks, you can create powerful, efficient, and maintainable applications that handle complex data with ease.
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601
Mar ’26
App stuck “In Review” for several days after AI-policy rejection — need clarification
Hello everyone, I’m looking for guidance regarding my app review timeline, as things seem unusually delayed compared to previous submissions. My iOS app was rejected on November 19th due to AI-related policy questions. I immediately responded to the reviewer with detailed explanations covering: Model used (Gemini Flash 2.0 / 2.5 Lite) How the AI only generates neutral, non-directive reflective questions How the system prevents any diagnosis, therapy-like behavior or recommendations Crisis-handling limitations Safety safeguards at generation and UI level Internal red-team testing and results Data retention, privacy, and non-use of data for model training After sending the requested information, I resubmitted the build on November 19th at 14:40. Since then: November 20th (7:30) → Status changed to In Review. November 21st, 22nd, 23rd, 24th, 25th → No movement, still In Review. My open case on App Store Connect is still pending without updates. Because of the previous rejection, I expected a short delay, but this is now 5 days total and 3 business days with no progress, which feels longer than usual for my past submissions. I’m not sure whether: My app is in a secondary review queue due to the AI-related rejection, The reviewer is waiting for internal clarification, Or if something is stuck and needs to be escalated. I don’t want to resubmit a new build unless necessary, since that would restart the queue. Could someone from the community (or Apple, if possible) confirm whether this waiting time is normal after an AI-policy rejection? And is there anything I should do besides waiting — for example, contacting Developer Support again or requesting a follow-up? Thank you very much for your help. I appreciate any insight from others who have experienced similar delays.
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764
Nov ’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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349
Feb ’26
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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455
Jul ’25
MLX/Ollama Benchmarking Suite - Open Source and Free
Hi all, I spent the last few months developing an MLX/Ollama local AI Benchmarking suite for Apple Silicon, written in pure Swift and signed with an Apple Developer Certificate, open source, GPL, and free. I would love some feedback to continue development. It is the only benchmarking suite I know of that supports live power metrics and MLX natively, as well as quick exports for benchmark results, and an arena mode, Model A vs B with history. I really want this project to succeed, and have widespread use, so getting 75 stars on the github repo makes it eligible for Homebrew/Cask distribution. Github Repo
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253
Feb ’26
Converting TF2 object detection to CoreML
I've spent way too long today trying to convert an Object Detection TensorFlow2 model to a CoreML object classifier (with bounding boxes, labels and probability score) The 'SSD MobileNet v2 320x320' is here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md And I've been following all sorts of posts and ChatGPT https://apple.github.io/coremltools/docs-guides/source/tensorflow-2.html#convert-a-tensorflow-concrete-function https://developer.apple.com/videos/play/wwdc2020/10153/?time=402 To convert it. I keep hitting the same errors though, mostly around: NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <ConcreteFunction signature_wrapper(input_tensor) at 0x366B87790> I've had varying success including missing output labels/predictions. But I simply want to create the CoreML model with all the right inputs and outputs (including correct names) as detailed in the docs here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md It goes without saying I don't have much (any) experience with this stuff including Python so the whole thing's been a bit of a headache. If anyone is able to help that would be great. FWIW I'm not attached to any one specific model, but what I do need at minimum is a CoreML model that can detect objects (has to at least include lights and lamps) within a live video image, detecting where in the image the object is. The simplest script I have looks like this: import coremltools as ct import tensorflow as tf model = tf.saved_model.load("~/tf_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model") concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] mlmodel = ct.convert( concrete_func, source="tensorflow", inputs=[ct.TensorType(shape=(1, 320, 320, 3))] ) mlmodel.save("YourModel.mlpackage", save_format="mlpackage")
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566
Jul ’25
AI and ML
Hello. I am willing to hire game developer for cards game called baloot. My question is Can the developer implement an AI when the computer is playing and the computer on the same time the conputer improves his rises level without any interaction? 🌹
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143
Jun ’25
Will mps support metal 4 new features for machine learning?
In WWDC25 Metal 4 released quite excited new features for machine learning optimization, but as we all know the pytorch based on metal shader performance (mps) is the one of most important tools for Mac machine learning area.but on mps introduced website we cannot see any support information for metal4.
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1
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187
Activity
Jul ’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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0
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0
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342
Activity
Dec ’25
Data used for MLX fine-tuning
The WWDC25: Explore large language models on Apple silicon with MLX video talks about using your own data to fine-tune a large language model. But the video doesn't explain what kind of data can be used. The video just shows the command to use and how to point to the data folder. Can I use PDFs, Word documents, Markdown files to train the model? Are there any code examples on GitHub that demonstrate how to do this?
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2
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0
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476
Activity
Oct ’25
CoreML Unified Memory failure/silent exit on long video tasks (M1 Mac 32GB)
Hi Apple Engineers, I am experiencing a potential memory management bug with CoreML on M1 Mac (32GB Unified Memory). When processing long video files (approx. 12,000 frames) using a CoreML execution provider, the system often completes the 'Analysing' phase but fails to transition into 'Processing'. It simply exits silently or hits an import error (scipy). However, if I split the same task into small 20-frame segments, it works perfectly at high speeds (~40 FPS). This suggests the hardware is capable, but there is an issue with memory fragmentation or resource cleanup during long-running CoreML sessions. Is there a way to force a VRAM/Unified Memory flush via CLI, or is this a known limitation for large frame indexing?
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0
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579
Activity
Dec ’25
Accessibility & Inclusion
When the system language and Siri language are not the same, Apple AI may not be usable. For example, if the system is in English and Siri is in Chinese, it may cause Apple AI to not work. May I ask if there are other reasons why the app still cannot be used internally even after enabling Apple AI?
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635
Activity
Dec ’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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221
Activity
Mar ’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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Activity
Mar ’26
Provide spoken voice search string
Hello, My goal is to enable users to perform a freeform search request for any product I sell using a spoken phrase, for example, "Hey Siri, search GAMING CONSOLES on MyCatalogApp". The result would launch MyCatalogApp and navigate to a search results page displaying gaming consoles. I have defined a SearchIntent (using the .system.search schema) and a Shortcut to accomplish this. However, Siri doesn't seem to be able to correctly parse the spoken phrase, extract the search string, and provide it as the critiria term within SearchIntent. What am I doing wrong? Here is the SearchIntent. Note the print() statement outputs the search string--which in the scenario above would be "GAMING CONSOLES"--but it doesn't work. import AppIntents @available(iOS 17.2, *) @AppIntent(schema: .system.search) struct SearchIntent: ShowInAppSearchResultsIntent { static var searchScopes: [StringSearchScope] = [.general] @Parameter(title: "Criteria") var criteria: StringSearchCriteria static var title: LocalizedStringResource = "Search with MyCatalogApp" @MainActor func perform() async throws -> some IntentResult { let searchString = criteria.term print("**** Search String: \(searchString) ****") // tmp debugging try await MyCatalogSearchHelper.search(for: searchString) // fetch results via my async fetch API return .result() } } Here's the Shortcuts definition: import AppIntents @available(iOS 17.2, *) struct Shortcuts: AppShortcutsProvider { @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: SearchIntent(), phrases: ["Search for \(\.$criteria) on \(.applicationName)."], shortTitle: "Search", systemImageName: "magnifyingglass" ) } } Thanks for any help!
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Activity
Mar ’26
CreateML Training Object Detection Not using MPS
Hi everyone Im currently developing an object detection model that shall identify up to seven classes in an image. While im usually doing development with basic python and the ultralytics library, i thought i would like to give CreateML a shot. The experience is actually very nice, except for the fact that the model seem not to be using any ANE or GPU (MPS) for accelerated training. On https://developer.apple.com/machine-learning/create-ml/ it states: "On-device training Train models blazingly fast right on your Mac while taking advantage of CPU and GPU." Am I doing something wrong? Im running the training on Apple M1 Pro 16GB MacOS 26.1 (Tahoe) Xcode 26.1 (Build version 17B55) It would be super nice to get some feedback or instructions. Thank you in advance!
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392
Activity
Nov ’25
Massive CoreML latency spike on live AVFoundation camera feed vs. offline inference (CPU+ANE)
Hello, I’m experiencing a severe performance degradation when running CoreML models on a live AVFoundation video feed compared to offline or synthetic inference. This happens across multiple models I've converted (including SCI, RTMPose, and RTMW) and affects multiple devices. The Environment OS: macOS 26.3, iOS 26.3, iPadOS 26.3 Hardware: Mac14,6 (M2 Max), iPad Pro 11 M1, iPhone 13 mini Compute Units: cpuAndNeuralEngine The Numbers When testing my SCI_output_image_int8.mlpackage model, the inference timings are drastically different: Synthetic/Offline Inference: ~1.34 ms Live Camera Inference: ~15.96 ms Preprocessing is completely ruled out as the bottleneck. My profiling shows total preprocessing (nearest-neighbor resize + feature provider creation) takes only ~0.4 ms in camera mode. Furthermore, no frames are being dropped. What I've Tried I am building a latency-critical app and have implemented almost every recommended optimization to try and fix this, but the camera-feed penalty remains: Matched the AVFoundation camera output format exactly to the model input (640x480 at 30/60fps). Used IOSurface-backed pixel buffers for everything (camera output, synthetic buffer, and resize buffer). Enabled outputBackings. Loaded the model once and reused it for all predictions. Configured MLModelConfiguration with reshapeFrequency = .frequent and specializationStrategy = .fastPrediction. Wrapped inference in ProcessInfo.processInfo.beginActivity(options: .latencyCritical, reason: "CoreML_Inference"). Set DispatchQueue to qos: .userInteractive. Disabled the idle timer and enabled iOS Game Mode. Exported models using coremltools 9.0 (deployment target iOS 26) with ImageType inputs/outputs and INT8 quantization. Reproduction To completely rule out UI or rendering overhead, I wrote a standalone Swift CLI script that isolates the AVFoundation and CoreML pipeline. The script clearly demonstrates the ~15ms latency on live camera frames versus the ~1ms latency on synthetic buffers. (I have attached camera_coreml_benchmark.swift and coreml model (very light low light enghancement model) to this repo on github https://github.com/pzoltowski/apple-coreml-camera-latency-repro). My Question: Is this massive overhead expected behavior for AVFoundation + Core ML on live feeds, or is this a framework/runtime bug? If expected, what is the Apple-recommended pattern to bypass this camera-only inference slowdown? One think found interesting when running in debug model was faster (not as fast as in performance benchmark but faster than 16ms. Also somehow if I did some dummy calculation on on different DispatchQueue also seems like model got slightly faster. So maybe its related to ANE Power State issues (Jitter/SoC Wake) and going to fast to sleep and taking a long time to wakeup? Doing dummy calculation in background thought is probably not a solution. Thanks in advance for any insights!
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Activity
Mar ’26
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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312
Activity
Jul ’25
Creating powerful, efficient, and maintainable applications.
Recursive and Self-Referential Data Structures Combining recursive and self-referential data structures with frameworks like Accelerate, SwiftMacros, and utilizing SwiftUI hooks can offer significant benefits in terms of performance, maintainability, and expressiveness. Here is how Apple Intelligence breaks it down. Benefits: Natural Representation of Complex Data: Recursive structures, such as trees and graphs, are ideal for representing hierarchical or interconnected data, like file systems, social networks, and DOM trees. Simplified Algorithms: Many algorithms, such as traversals, sorting, and searching, are more straightforward and elegant when implemented using recursion. Dynamic Memory Management: Self-referential structures can dynamically grow and shrink, making them suitable for applications with unpredictable data sizes. Challenges: Performance Overhead: Recursive algorithms can lead to stack overflow if not properly optimized (e.g., using tail recursion). Self-referential structures can introduce memory management challenges, such as retain cycles. Accelerate Framework Benefits: High-Performance Computation: Accelerate provides optimized libraries for numerical and scientific computing, including linear algebra, FFT, and image processing. It can significantly speed up computations, especially for large datasets, by leveraging multi-core processors and GPU acceleration. Parallel Processing: Accelerate automatically parallelizes operations, making it easier to take advantage of modern hardware capabilities. Integration with Recursive Data: Matrix and Vector Operations: Use Accelerate for operations on matrices and vectors, which are common in recursive algorithms like those used in machine learning and physics simulations. FFT and Convolutions: Accelerate's FFT functions can be used in recursive algorithms for signal processing and image analysis. SwiftMacros Benefits: Code Generation and Transformation: SwiftMacros allow you to generate and transform code at compile time, enabling the creation of DSLs, boilerplate reduction, and optimization. Improved Compile-Time Checks: Macros can perform complex compile-time checks, ensuring code correctness and reducing runtime errors. Integration with Recursive Data: DSL for Data Structures: Create a DSL using SwiftMacros to define recursive data structures concisely and safely. Optimization: Use macros to generate optimized code for recursive algorithms, such as memoization or iterative transformations. SwiftUI Hooks Benefits: State Management: Hooks like @State, @Binding, and @Effect simplify state management in SwiftUI, making it easier to handle dynamic data. Side Effects: @Effect allows you to perform side effects in a declarative manner, integrating seamlessly with asynchronous operations. Reusable Logic: Custom hooks enable the reuse of stateful logic across multiple views, promoting code maintainability. Integration with Recursive Data: Dynamic Data Binding: Use SwiftUI's data binding to manage the state of recursive data structures, ensuring that UI updates reflect changes in the underlying data. Efficient Rendering: SwiftUI's diffing algorithm efficiently updates the UI only for the parts of the recursive structure that have changed, improving performance. Asynchronous Data Loading: Combine @Effect with recursive data structures to fetch and process data asynchronously, such as loading a tree structure from a remote server. Example: Combining All Components Imagine you're building an app that visualizes a hierarchical file system using a recursive tree structure. Here's how you might combine these components: Define the Recursive Data Structure: Use SwiftMacros to create a DSL for defining tree nodes. @macro struct TreeNode { var value: T var children: [TreeNode] } Optimize with Accelerate: Use Accelerate for operations like computing the size of the tree or performing transformations on node values. func computeTreeSize(_ node: TreeNode) -> Int { return node.children.reduce(1) { $0 + computeTreeSize($1) } } Manage State with SwiftUI Hooks: Use SwiftUI hooks to load and display the tree structure dynamically. struct FileSystemView: View { @State private var rootNode: TreeNode = loadTree() var body: some View { TreeView(node: rootNode) } private func loadTree() -> TreeNode<String> { // Load or generate the tree structure } } struct TreeView: View { let node: TreeNode var body: some View { List(node.children, id: \.value) { Text($0.value) TreeView(node: $0) } } } Perform Side Effects with @Effect: Use @Effect to fetch data asynchronously and update the tree structure. struct FileSystemView: View { @State private var rootNode: TreeNode = TreeNode(value: "/") @Effect private var loadTreeEffect: () -> Void = { // Fetch data from a server or database } var body: some View { TreeView(node: rootNode) .onAppear { loadTreeEffect() } } } By combining recursive data structures with Accelerate, SwiftMacros, and SwiftUI hooks, you can create powerful, efficient, and maintainable applications that handle complex data with ease.
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Activity
Mar ’26
App stuck “In Review” for several days after AI-policy rejection — need clarification
Hello everyone, I’m looking for guidance regarding my app review timeline, as things seem unusually delayed compared to previous submissions. My iOS app was rejected on November 19th due to AI-related policy questions. I immediately responded to the reviewer with detailed explanations covering: Model used (Gemini Flash 2.0 / 2.5 Lite) How the AI only generates neutral, non-directive reflective questions How the system prevents any diagnosis, therapy-like behavior or recommendations Crisis-handling limitations Safety safeguards at generation and UI level Internal red-team testing and results Data retention, privacy, and non-use of data for model training After sending the requested information, I resubmitted the build on November 19th at 14:40. Since then: November 20th (7:30) → Status changed to In Review. November 21st, 22nd, 23rd, 24th, 25th → No movement, still In Review. My open case on App Store Connect is still pending without updates. Because of the previous rejection, I expected a short delay, but this is now 5 days total and 3 business days with no progress, which feels longer than usual for my past submissions. I’m not sure whether: My app is in a secondary review queue due to the AI-related rejection, The reviewer is waiting for internal clarification, Or if something is stuck and needs to be escalated. I don’t want to resubmit a new build unless necessary, since that would restart the queue. Could someone from the community (or Apple, if possible) confirm whether this waiting time is normal after an AI-policy rejection? And is there anything I should do besides waiting — for example, contacting Developer Support again or requesting a follow-up? Thank you very much for your help. I appreciate any insight from others who have experienced similar delays.
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Activity
Nov ’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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349
Activity
Feb ’26
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
MLX/Ollama Benchmarking Suite - Open Source and Free
Hi all, I spent the last few months developing an MLX/Ollama local AI Benchmarking suite for Apple Silicon, written in pure Swift and signed with an Apple Developer Certificate, open source, GPL, and free. I would love some feedback to continue development. It is the only benchmarking suite I know of that supports live power metrics and MLX natively, as well as quick exports for benchmark results, and an arena mode, Model A vs B with history. I really want this project to succeed, and have widespread use, so getting 75 stars on the github repo makes it eligible for Homebrew/Cask distribution. Github Repo
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Activity
Feb ’26
Converting TF2 object detection to CoreML
I've spent way too long today trying to convert an Object Detection TensorFlow2 model to a CoreML object classifier (with bounding boxes, labels and probability score) The 'SSD MobileNet v2 320x320' is here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md And I've been following all sorts of posts and ChatGPT https://apple.github.io/coremltools/docs-guides/source/tensorflow-2.html#convert-a-tensorflow-concrete-function https://developer.apple.com/videos/play/wwdc2020/10153/?time=402 To convert it. I keep hitting the same errors though, mostly around: NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <ConcreteFunction signature_wrapper(input_tensor) at 0x366B87790> I've had varying success including missing output labels/predictions. But I simply want to create the CoreML model with all the right inputs and outputs (including correct names) as detailed in the docs here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md It goes without saying I don't have much (any) experience with this stuff including Python so the whole thing's been a bit of a headache. If anyone is able to help that would be great. FWIW I'm not attached to any one specific model, but what I do need at minimum is a CoreML model that can detect objects (has to at least include lights and lamps) within a live video image, detecting where in the image the object is. The simplest script I have looks like this: import coremltools as ct import tensorflow as tf model = tf.saved_model.load("~/tf_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model") concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] mlmodel = ct.convert( concrete_func, source="tensorflow", inputs=[ct.TensorType(shape=(1, 320, 320, 3))] ) mlmodel.save("YourModel.mlpackage", save_format="mlpackage")
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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://developer.apple.com and did not download the toolkit.
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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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Activity
Jul ’25
AI and ML
Hello. I am willing to hire game developer for cards game called baloot. My question is Can the developer implement an AI when the computer is playing and the computer on the same time the conputer improves his rises level without any interaction? 🌹
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Jun ’25