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Is it possible to instantiate MLModel strictly from memory (Data) to support custom encryption?
We are trying to implement a custom encryption scheme for our Core ML models. Our goal is to bundle encrypted models, decrypt them into memory at runtime, and instantiate the MLModel without the unencrypted model file ever touching the disk. We have looked into the native apple encryption described here https://aninterestingwebsite.com/documentation/coreml/encrypting-a-model-in-your-app but it has limitations like not working on intel macs, without SIP, and doesn’t work loading from dylib. It seems like most of the Core ML APIs require a file path, there is MLModelAsset APIs but I think they just write a modelc back to disk when compiling but can’t find any information confirming that (also concerned that this seems to be an older API, and means we need to compile at runtime). I am aware that the native encryption will be much more secure but would like not to have the models in readable text on disk. Does anyone know if this is possible or any alternatives to try to obfuscate the Core ML models, thanks
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505
Feb ’26
“Accelerate Transformer Training on Apple Devices from Months to Hours!”
I am excited to share that I have developed a Metal kernel for Flash Attention that eliminates race conditions and fully leverages Apple Silicon’s shared memory and registers. This kernel can dramatically accelerate training of transformer-based models. Early benchmarks suggest that models which previously required months to train could see reductions to just a few hours on Apple hardware, while maintaining numerical stability and accuracy. I plan to make the code publicly available to enable the broader community to benefit. I would be happy to keep you updated on the latest developments and improvements as I continue testing and optimizing the kernel. I believe this work could provide valuable insights for Apple’s machine learning research and products.
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275
Nov ’25
Core Model Editor and Params
Optimal Precision • Current Precision: Mixed (Float32, int32) • Optimal Precision: Not specified in the image, but typically involves using the most efficient data type for the model's operations to balance speed and memory usage without significant loss of accuracy. Comparison: • Mixed Precision: Utilizes both Float32 and int32 to optimize performance. Float32 provides high precision, while int32 reduces memory usage and increases computational speed. • Optimal Precision: Aimed at achieving the best trade-off between performance and accuracy, potentially using other data types like Float16 (bfloat16) for even greater efficiency in certain hardware environments. Operation Distribution • Current Distribution: • iOS18.mul: 168 • iOS18.transpose: 126 • iOS18.linear: 98 • iOS18.add: 97 • iOS18.sliceByIndex: 96 • iOS18.expandDims: 74 • iOS18.concat: 72 • iOS18.squeeze: 72 • iOS18.reshape: 67 • iOS18.layerNorm: 49 • iOS18.matmul: 48 • iOS18.gelu: 26 • iOS18.softmax: 24 • Split: 24 • conv: 1 • iOS18.conv: 1 Comparison: • Operation Count: Indicates how frequently each operation is executed. High counts for operations like mul, transpose, and linear suggest these are computationally intensive parts of the model. • Optimization Opportunities: Reducing the count of high-frequency operations or optimizing their execution can improve performance. This might involve pruning unnecessary operations, optimizing algorithms, or leveraging hardware acceleration. General Recommendations • Precision Tuning: Experiment with different precision levels to find the best balance for your specific hardware and accuracy requirements. • Operation Optimization: Focus on optimizing the most frequent operations. Techniques include using more efficient algorithms, parallelizing computations, or utilizing specialized hardware like GPUs or TPUs. • Benchmarking: Regularly benchmark the model to assess the impact of changes and ensure that optimizations lead to meaningful performance improvements. By focusing on these areas, you can potentially enhance the efficiency and performance of your ML model.
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97
Feb ’26
How to Ensure Controlled and Contextual Responses Using Foundation Models ?
Hi everyone, I’m currently exploring the use of Foundation models on Apple platforms to build a chatbot-style assistant within an app. While the integration part is straightforward using the new FoundationModel APIs, I’m trying to figure out how to control the assistant’s responses more tightly — particularly: Ensuring the assistant adheres to a specific tone, context, or domain (e.g. hospitality, healthcare, etc.) Preventing hallucinations or unrelated outputs Constraining responses based on app-specific rules, structured data, or recent interactions I’ve experimented with prompt, systemMessage, and few-shot examples to steer outputs, but even with carefully generated prompts, the model occasionally produces incorrect or out-of-scope responses. Additionally, when using multiple tools, I'm unsure how best to structure the setup so the model can select the correct pathway/tool and respond appropriately. Is there a recommended approach to guiding the model's decision-making when several tools or structured contexts are involved? Looking forward to hearing your thoughts or being pointed toward related WWDC sessions, Apple docs, or sample projects.
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136
Jul ’25
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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744
Nov ’25
Updated DetectHandPoseRequest revision from WWDC25 doesn't exist
I watched this year WWDC25 "Read Documents using the Vision framework". At the end of video there is mention of new DetectHandPoseRequest model for hand pose detection in Vision API. I looked Apple documentation and I don't see new revision. Moreover probably typo in video because there is only DetectHumanPoseRequst (swift based) and VNDetectHumanHandPoseRequest (obj-c based) (notice lack of Human prefix in WWDC video) First one have revision only added in iOS 18+: https://aninterestingwebsite.com/documentation/vision/detecthumanhandposerequest/revision-swift.enum/revision1 Second one have revision only added in iOS14+: https://aninterestingwebsite.com/documentation/vision/vndetecthumanhandposerequestrevision1 I don't see any new revision targeting iOS26+
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163
Oct ’25
RDMA API Documentation
With the release of the newest version of tahoe and MLX supporting RDMA. Is there a documentation link to how to utilizes the libdrma dylib as well as what functions are available? I am currently assuming it mostly follows the standard linux infiniband library but I would like the apple specific details.
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293
Dec ’25
Tensorflow metal: Issue using assign operation on MacBook M4
I get the following error when running this command in a Jupyter notebook: v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1))) v[0, 0].assign(3.) Environment: python == 3.11.14 tensorflow==2.19.1 tensorflow-metal==1.2.0 { "name": "InvalidArgumentError", "message": "Cannot assign a device for operation ResourceStridedSliceAssign: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.\nColocation Debug Info:\nColocation group had the following types and supported devices: \nRoot Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[]\nResourceStridedSliceAssign: CPU \n_Arg: GPU CPU \n\nColocation members, user-requested devices, and framework assigned devices, if any:\n ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0\n ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0\n\nOp: ResourceStridedSliceAssign\n [...] [[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign" } It seems like the ResourceStridedSliceAssign operation is not implemented for the GPU
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177
Feb ’26
How to create updatable models using Create ML app
I've built a model using Create ML, but I can't make it, for the love of God, updatable. I can't find any checkbox or anything related. It's an Activity Classifier, if it matters. I want to continue training it on-device using MLUpdateTask, but the model, as exported from Create ML, fails with error: Domain=com.apple.CoreML Code=6 "Failed to unarchive update parameters. Model should be re-compiled." UserInfo={NSLocalizedDescription=Failed to unarchive update parameters. Model should be re-compiled.}
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393
Nov ’25
Powermetrics GPU power vs system DC power discrepancy on M4 Max
While analyzing system power on an M4 Max under GPU-heavy compute workloads, I noticed that the the GPU power reported by powermetrics does not come anywhere close to total system DC power reported by the SMC counter PDTR (as used by utilities like mactop). For example, in a heavy GPU workload, powermetrics would report a 65W idle-load delta on the GPU, but at the same time system DC power would rise by 179W, leaving 114W or nearly 2/3 of total system DC power on a Mac Studio M4 Max unexplained. From measurements, the difference appears to correlate with the amount of on-chip data movement (for example, varying bytes-per-FLOP in the workload changes the observed gap). Using SMC and IOReport, I was able to reverse engineer an energy model for the GPU that explains almost all of the energy flow with less than 2% error on the workload I studied. The result is a simple two-term energy roofline model: P_GPU (GPU_combined term in the plot) ≈ a * bytes + b * FLOPs with: ~5 pJ/byte for SRAM movement ~2.7 pJ/FLOP for compute. Has anyone observed similar behavior, or is there guidance on how GPU power reported by IOReport/powermetrics should be interpreted relative to total system power? In particular, I’m interested in whether certain classes of GPU activity may not be attributed to the GPU component in IOReport. Full details with the methodology and results are available here: https://youtu.be/HKxIGgyeISM
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2w
Building Real-Time Voice Input on macOS 26 with SpeechAnalyzer + ScreenCaptureKit
We built an open-source macOS menu bar app that turns speech into text and pastes it into the active app — using SpeechAnalyzer for on-device transcription, ScreenCaptureKit + Vision for screen-aware context, and FluidAudio for speaker diarization in meeting mode. Here's what we learned shipping it on macOS 26. GitHub: github.com/Marvinngg/ambient-voice Architecture The app has two modes: hotkey dictation (press to talk, release to inject) and meeting recording (continuous transcription with a floating panel). Dictation Mode Audio capture uses AVCaptureSession (more on why below). The captured audio feeds into SpeechAnalyzer via an AsyncStream: let transcriber = SpeechTranscriber( locale: locale, transcriptionOptions: [], reportingOptions: [.volatileResults, .alternativeTranscriptions], attributeOptions: [.audioTimeRange, .transcriptionConfidence] ) let analyzer = SpeechAnalyzer(modules: [transcriber]) let (inputSequence, inputBuilder) = AsyncStream.makeStream() try await analyzer.start(inputSequence: inputSequence) While recording, we capture a screenshot of the focused window using ScreenCaptureKit, run Vision OCR (VNRecognizeTextRequest), extract keywords, and inject them into SpeechAnalyzer as contextual bias: let context = AnalysisContext() context.contextualStrings[.general] = ocrKeywords try await analyzer.setContext(context) This improves accuracy for technical terms and proper nouns visible on screen. If your screen shows "SpeechAnalyzer", saying it out loud is more likely to be transcribed correctly. After transcription, an optional L2 step sends the text through a local LLM (ollama) for spoken-to-written cleanup, then CGEvent simulates Cmd+V to paste into the active app. Meeting Mode Meeting mode forks the same audio stream to two consumers: SpeechAnalyzer — real-time streaming transcription, displayed in a floating NSPanel FluidAudio buffer — accumulates 16kHz Float32 mono samples for batch speaker diarization after recording stops When the user ends the meeting, FluidAudio's performCompleteDiarization() runs on the accumulated audio. We align transcription segments with speaker segments using audioTimeRange overlap matching — each transcription segment gets assigned the speaker ID with the most time overlap. Results export to Markdown. Pitfalls We Hit on macOS 26 1. AVAudioEngine installTap doesn't fire with Bluetooth devices We started with AVAudioEngine.inputNode.installTap() for audio capture. It worked fine with built-in mics but the tap callback never fired with Bluetooth devices (tested with vivo TWS 4 Hi-Fi). Fix: switched to AVCaptureSession. The delegate callback captureOutput(_:didOutput:from:) fires reliably regardless of audio device. The tradeoff is you get CMSampleBuffer instead of AVAudioPCMBuffer, so you need a conversion step. 2. NSEvent addGlobalMonitorForEvents crashes Our global hotkey listener used NSEvent.addGlobalMonitorForEvents. On macOS 26, this crashes with a Bus error inside GlobalObserverHandler — appears to be a Swift actor runtime issue. Fix: switched to CGEventTap. Works reliably, but the callback runs on a CFRunLoop context, which Swift doesn't recognize as MainActor. 3. CGEventTap callbacks aren't on MainActor If your CGEventTap callback touches any @MainActor state, you'll get concurrency violations. The callback runs on whatever thread owns the CFRunLoop. Fix: bridge with DispatchQueue.main.async {} inside the tap callback before touching any MainActor state. 4. CGPreflightScreenCaptureAccess doesn't request permission We used CGPreflightScreenCaptureAccess() as a guard before calling ScreenCaptureKit. If it returned false, we'd bail out. The problem: this function only checks — it never triggers macOS to add your app to the Screen Recording permission list. Chicken-and-egg: you can't get permission because you never ask for it. Fix: call CGRequestScreenCaptureAccess() at app startup. This adds your app to System Settings → Screen Recording. Then let ScreenCaptureKit calls proceed without the preflight guard — SCShareableContent will also trigger the permission prompt on first use. 5. Ad-hoc signing breaks TCC permissions on every rebuild During development, codesign --sign - (ad-hoc) generates a different code directory hash on every build. macOS TCC tracks permissions by this hash, so every rebuild = new app identity = all permissions reset. Fix: sign with a stable certificate. If you have an Apple Development certificate, use that. The TeamIdentifier stays constant across rebuilds, so TCC permissions persist. We also discovered that launching via open WE.app (LaunchServices) instead of directly executing the binary is required — otherwise macOS attributes TCC permissions to Terminal, not your app. Benchmarks We ran end-to-end benchmarks on public datasets (Mac Mini M4 16GB, macOS 26): Transcription (SpeechAnalyzer, AliMeeting Chinese): • Near-field CER 34% (excluding outliers ~25%) • Far-field CER 40% (single channel, no beamforming, >30% overlap) • Processing speed 74-89x real-time Speaker diarization (FluidAudio offline): • AMI English 16 meetings: avg DER 23.2% (collar=0.25s, ignoreOverlap=True) • AliMeeting Chinese 8 meetings: DER 48.5% (including overlap regions) • Memory: RSS ~500MB, peak 730-930MB Full evaluation methodology, scripts, and raw results are in the repo. Open Source The project is MIT licensed: github.com/Marvinngg/ambient-voice It includes the macOS client (Swift 6.2, SPM), server-side distillation/training scripts (Python), and a complete evaluation framework with reproducible benchmarks. Feedback and contributions welcome.
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“Unleashing the MacBook Air M2: 673 TFLOPS Achieved with Highly Optimized Metal Shading Language”
Using highly optimized Metal Shading Language (MSL) code, I pushed the MacBook Air M2 to its performance limits with the deformable_attention_universal kernel. The results demonstrate both the efficiency of the code and the exceptional power of Apple Silicon. The total computational workload exceeded 8.455 quadrillion FLOPs, equivalent to processing 8,455 trillion operations. On average, the code sustained a throughput of 85.37 TFLOPS, showcasing the chip’s remarkable ability to handle massive workloads. Peak instantaneous performance reached approximately 673.73 TFLOPS, reflecting near-optimal utilization of the GPU cores. Despite this intensity, the cumulative GPU runtime remained under 100 seconds, highlighting the code’s efficiency and time optimization. The fastest iteration achieved a record processing time of only 0.051 ms, demonstrating minimal bottlenecks and excellent responsiveness. Memory management was equally impressive: peak GPU memory usage never exceeded 2 MB, reflecting efficient use of the M2’s Unified Memory. This minimizes data transfer overhead and ensures smooth performance across repeated workloads. Overall, these results confirm that a well-optimized Metal implementation can unlock the full potential of Apple Silicon, delivering exceptional computational density, processing speed, and memory efficiency. The MacBook Air M2, often considered an energy-efficient consumer laptop, is capable of handling highly intensive workloads at performance levels typically expected from much larger GPUs. This test validates both the robustness of the Metal code and the extraordinary capabilities of the M2 chip for high-performance computing tasks.
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506
Nov ’25
ImagePlayground: Programmatic Creation Error
Hardware: Macbook Pro M4 Nov 2024 Software: macOS Tahoe 26.0 & xcode 26.0 Apple Intelligence is activated and the Image playground macOS app works Running the following on xcode throws ImagePlayground.ImageCreator.Error.creationFailed Any suggestions on how to make this work? import Foundation import ImagePlayground Task { let creator = try await ImageCreator() guard let style = creator.availableStyles.first else { print("No styles available") exit(1) } let images = creator.images( for: [.text("A cat wearing mittens.")], style: style, limit: 1) for try await image in images { print("Generated image: \(image)") } exit(0) } RunLoop.main.run()
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330
Sep ’25
Gazetteer encryption?
I have an app that uses a couple of mlmodels (word tagger and gazetteer) and I’m trying to encrypt them before publishing. The models are part of a package. I understand that Xcode can’t automatically handle the encryption for a model in a package the way it can within a traditional app structure. Given that, I’ve generated the Apple MLModel encryption key from Xcode and am encrypting via the command line with: xcrun coremlcompiler compile Gazetteer.mlmodel GazetteerENC.mlmodelc --encrypt Gazetteerkey.mlmodelkey In the package manifest, I’ve listed the encrypted models as .copy resources for my target and have verified the URL to that file is good. When I try to load the encrypted .mlmodelc file (on a physical device) with the line:
 gazetteer = try NLGazetteer(contentsOf: gazetteerURL!) I get the error: Failed to open file: /…/Scanner.bundle/GazetteerENC.mlmodelc/coremldata.bin. It is not a valid .mlmodelc file. So my questions are: Does the NLGazetteer class support encrypted MLModel files? Given that my models are in a package, do I have the right general approach? Thanks for any help or thoughts.
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162
May ’25
26.4 Foundation Model rejects most topics
I have an iOS app, "Spatial Agents" which ran great in 26.3. It creates dashboards around a topic. It can also decompose a topic into sub-topics, and explore those. All based on web articles and web article headlines. In iOS 26.4 almost every topic - even "MIT Innovation" are rejected with an apology of "I apologize I can not fulfill this request". I've tried softening all my prompts, and I can get only really benign very simple topics to respond, but not anything with any significance. It ran great on lots of topics in 26.3. My published App, is now useless, and all my users are unhappy. HELP!
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1d
Building a 4-agent autonomous coding pipeline on Apple Silicon — MLX backend questions
Hi, I'm building ANF (Autonomous Native Forge) — a cloud-free, 4-agent autonomous software production pipeline running on local hardware with local LLM inference. No middleware, pure Node.js native. Currently running on NVIDIA Blackwell GB10 with vLLM + DeepSeek-R1-32B. Now porting to Apple Silicon. Three technical questions: How production-ready is mlx-lm's OpenAI-compatible API server for long context generation (32K tokens)? What's the recommended approach for KV Cache management with Unified Memory architecture — any specific flags or configurations for M4 Ultra? MLX vs GGUF (llama.cpp) for a multi-agent pipeline where 4 agents call the inference endpoint concurrently — which handles parallel requests better on Apple Silicon? GitHub: github.com/trgysvc/AutonomousNativeForge Any guidance appreciated.
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3w
Full documentation of annotations file for Create ML
The documentation for the Create ML tool ("Building an object detector data source") mentions that there are options for using normalized values instead of pixels and also different anchor point origins ("MLBoundingBoxCoordinatesOrigin") instead of always using "center". However, the JSON format for these does not appear in any examples. Does anyone know the format for these options?
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263
May ’25
Mistral/LLaMa Core ML Conversion
Hi, I am new to developing on Apple’s platform yet I want to familiarize myself with Core ML and Core ML Tools. I was watching the WWDC24: Bring your machine learning and AI models to Apple Silicon video and was trying to follow along. After multiple attempts and much reading up on documentation, I am still unable to get a coherent script running that will convert the Mistral model that the host used and convert it to a valid Core ML model. here is a pastebin to what i have currently: https://pastebin.com/04cVjF1v if you require the output as well please let me know
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149
Apr ’25
Siri cut user's voice words in German version
My app used app intents. And when user said "Prüfung der Bluetooth Funktion", screen can show the whole words. But in my app, it only can get "Bluetooth Funktion". This behaviour only happened in German version. In English version, everything worked well. Is anyone can support me? Why German version siri cut my words?
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649
Activity
Nov ’25
Is it possible to instantiate MLModel strictly from memory (Data) to support custom encryption?
We are trying to implement a custom encryption scheme for our Core ML models. Our goal is to bundle encrypted models, decrypt them into memory at runtime, and instantiate the MLModel without the unencrypted model file ever touching the disk. We have looked into the native apple encryption described here https://aninterestingwebsite.com/documentation/coreml/encrypting-a-model-in-your-app but it has limitations like not working on intel macs, without SIP, and doesn’t work loading from dylib. It seems like most of the Core ML APIs require a file path, there is MLModelAsset APIs but I think they just write a modelc back to disk when compiling but can’t find any information confirming that (also concerned that this seems to be an older API, and means we need to compile at runtime). I am aware that the native encryption will be much more secure but would like not to have the models in readable text on disk. Does anyone know if this is possible or any alternatives to try to obfuscate the Core ML models, thanks
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505
Activity
Feb ’26
“Accelerate Transformer Training on Apple Devices from Months to Hours!”
I am excited to share that I have developed a Metal kernel for Flash Attention that eliminates race conditions and fully leverages Apple Silicon’s shared memory and registers. This kernel can dramatically accelerate training of transformer-based models. Early benchmarks suggest that models which previously required months to train could see reductions to just a few hours on Apple hardware, while maintaining numerical stability and accuracy. I plan to make the code publicly available to enable the broader community to benefit. I would be happy to keep you updated on the latest developments and improvements as I continue testing and optimizing the kernel. I believe this work could provide valuable insights for Apple’s machine learning research and products.
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275
Activity
Nov ’25
Core Model Editor and Params
Optimal Precision • Current Precision: Mixed (Float32, int32) • Optimal Precision: Not specified in the image, but typically involves using the most efficient data type for the model's operations to balance speed and memory usage without significant loss of accuracy. Comparison: • Mixed Precision: Utilizes both Float32 and int32 to optimize performance. Float32 provides high precision, while int32 reduces memory usage and increases computational speed. • Optimal Precision: Aimed at achieving the best trade-off between performance and accuracy, potentially using other data types like Float16 (bfloat16) for even greater efficiency in certain hardware environments. Operation Distribution • Current Distribution: • iOS18.mul: 168 • iOS18.transpose: 126 • iOS18.linear: 98 • iOS18.add: 97 • iOS18.sliceByIndex: 96 • iOS18.expandDims: 74 • iOS18.concat: 72 • iOS18.squeeze: 72 • iOS18.reshape: 67 • iOS18.layerNorm: 49 • iOS18.matmul: 48 • iOS18.gelu: 26 • iOS18.softmax: 24 • Split: 24 • conv: 1 • iOS18.conv: 1 Comparison: • Operation Count: Indicates how frequently each operation is executed. High counts for operations like mul, transpose, and linear suggest these are computationally intensive parts of the model. • Optimization Opportunities: Reducing the count of high-frequency operations or optimizing their execution can improve performance. This might involve pruning unnecessary operations, optimizing algorithms, or leveraging hardware acceleration. General Recommendations • Precision Tuning: Experiment with different precision levels to find the best balance for your specific hardware and accuracy requirements. • Operation Optimization: Focus on optimizing the most frequent operations. Techniques include using more efficient algorithms, parallelizing computations, or utilizing specialized hardware like GPUs or TPUs. • Benchmarking: Regularly benchmark the model to assess the impact of changes and ensure that optimizations lead to meaningful performance improvements. By focusing on these areas, you can potentially enhance the efficiency and performance of your ML model.
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97
Activity
Feb ’26
How to Ensure Controlled and Contextual Responses Using Foundation Models ?
Hi everyone, I’m currently exploring the use of Foundation models on Apple platforms to build a chatbot-style assistant within an app. While the integration part is straightforward using the new FoundationModel APIs, I’m trying to figure out how to control the assistant’s responses more tightly — particularly: Ensuring the assistant adheres to a specific tone, context, or domain (e.g. hospitality, healthcare, etc.) Preventing hallucinations or unrelated outputs Constraining responses based on app-specific rules, structured data, or recent interactions I’ve experimented with prompt, systemMessage, and few-shot examples to steer outputs, but even with carefully generated prompts, the model occasionally produces incorrect or out-of-scope responses. Additionally, when using multiple tools, I'm unsure how best to structure the setup so the model can select the correct pathway/tool and respond appropriately. Is there a recommended approach to guiding the model's decision-making when several tools or structured contexts are involved? Looking forward to hearing your thoughts or being pointed toward related WWDC sessions, Apple docs, or sample projects.
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136
Activity
Jul ’25
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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744
Activity
Nov ’25
Updated DetectHandPoseRequest revision from WWDC25 doesn't exist
I watched this year WWDC25 "Read Documents using the Vision framework". At the end of video there is mention of new DetectHandPoseRequest model for hand pose detection in Vision API. I looked Apple documentation and I don't see new revision. Moreover probably typo in video because there is only DetectHumanPoseRequst (swift based) and VNDetectHumanHandPoseRequest (obj-c based) (notice lack of Human prefix in WWDC video) First one have revision only added in iOS 18+: https://aninterestingwebsite.com/documentation/vision/detecthumanhandposerequest/revision-swift.enum/revision1 Second one have revision only added in iOS14+: https://aninterestingwebsite.com/documentation/vision/vndetecthumanhandposerequestrevision1 I don't see any new revision targeting iOS26+
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163
Activity
Oct ’25
RDMA API Documentation
With the release of the newest version of tahoe and MLX supporting RDMA. Is there a documentation link to how to utilizes the libdrma dylib as well as what functions are available? I am currently assuming it mostly follows the standard linux infiniband library but I would like the apple specific details.
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1
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293
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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493
Activity
Dec ’25
Tensorflow metal: Issue using assign operation on MacBook M4
I get the following error when running this command in a Jupyter notebook: v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1))) v[0, 0].assign(3.) Environment: python == 3.11.14 tensorflow==2.19.1 tensorflow-metal==1.2.0 { "name": "InvalidArgumentError", "message": "Cannot assign a device for operation ResourceStridedSliceAssign: Could not satisfy explicit device specification '/job:localhost/replica:0/task:0/device:GPU:0' because no supported kernel for GPU devices is available.\nColocation Debug Info:\nColocation group had the following types and supported devices: \nRoot Member(assigned_device_name_index_=1 requested_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' assigned_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' resource_device_name_='/job:localhost/replica:0/task:0/device:GPU:0' supported_device_types_=[CPU] possible_devices_=[]\nResourceStridedSliceAssign: CPU \n_Arg: GPU CPU \n\nColocation members, user-requested devices, and framework assigned devices, if any:\n ref (_Arg) framework assigned device=/job:localhost/replica:0/task:0/device:GPU:0\n ResourceStridedSliceAssign (ResourceStridedSliceAssign) /job:localhost/replica:0/task:0/device:GPU:0\n\nOp: ResourceStridedSliceAssign\n [...] [[{{node ResourceStridedSliceAssign}}]] [Op:ResourceStridedSliceAssign] name: strided_slice/_assign" } It seems like the ResourceStridedSliceAssign operation is not implemented for the GPU
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177
Activity
Feb ’26
How to create updatable models using Create ML app
I've built a model using Create ML, but I can't make it, for the love of God, updatable. I can't find any checkbox or anything related. It's an Activity Classifier, if it matters. I want to continue training it on-device using MLUpdateTask, but the model, as exported from Create ML, fails with error: Domain=com.apple.CoreML Code=6 "Failed to unarchive update parameters. Model should be re-compiled." UserInfo={NSLocalizedDescription=Failed to unarchive update parameters. Model should be re-compiled.}
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393
Activity
Nov ’25
Powermetrics GPU power vs system DC power discrepancy on M4 Max
While analyzing system power on an M4 Max under GPU-heavy compute workloads, I noticed that the the GPU power reported by powermetrics does not come anywhere close to total system DC power reported by the SMC counter PDTR (as used by utilities like mactop). For example, in a heavy GPU workload, powermetrics would report a 65W idle-load delta on the GPU, but at the same time system DC power would rise by 179W, leaving 114W or nearly 2/3 of total system DC power on a Mac Studio M4 Max unexplained. From measurements, the difference appears to correlate with the amount of on-chip data movement (for example, varying bytes-per-FLOP in the workload changes the observed gap). Using SMC and IOReport, I was able to reverse engineer an energy model for the GPU that explains almost all of the energy flow with less than 2% error on the workload I studied. The result is a simple two-term energy roofline model: P_GPU (GPU_combined term in the plot) ≈ a * bytes + b * FLOPs with: ~5 pJ/byte for SRAM movement ~2.7 pJ/FLOP for compute. Has anyone observed similar behavior, or is there guidance on how GPU power reported by IOReport/powermetrics should be interpreted relative to total system power? In particular, I’m interested in whether certain classes of GPU activity may not be attributed to the GPU component in IOReport. Full details with the methodology and results are available here: https://youtu.be/HKxIGgyeISM
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68
Activity
2w
Building Real-Time Voice Input on macOS 26 with SpeechAnalyzer + ScreenCaptureKit
We built an open-source macOS menu bar app that turns speech into text and pastes it into the active app — using SpeechAnalyzer for on-device transcription, ScreenCaptureKit + Vision for screen-aware context, and FluidAudio for speaker diarization in meeting mode. Here's what we learned shipping it on macOS 26. GitHub: github.com/Marvinngg/ambient-voice Architecture The app has two modes: hotkey dictation (press to talk, release to inject) and meeting recording (continuous transcription with a floating panel). Dictation Mode Audio capture uses AVCaptureSession (more on why below). The captured audio feeds into SpeechAnalyzer via an AsyncStream: let transcriber = SpeechTranscriber( locale: locale, transcriptionOptions: [], reportingOptions: [.volatileResults, .alternativeTranscriptions], attributeOptions: [.audioTimeRange, .transcriptionConfidence] ) let analyzer = SpeechAnalyzer(modules: [transcriber]) let (inputSequence, inputBuilder) = AsyncStream.makeStream() try await analyzer.start(inputSequence: inputSequence) While recording, we capture a screenshot of the focused window using ScreenCaptureKit, run Vision OCR (VNRecognizeTextRequest), extract keywords, and inject them into SpeechAnalyzer as contextual bias: let context = AnalysisContext() context.contextualStrings[.general] = ocrKeywords try await analyzer.setContext(context) This improves accuracy for technical terms and proper nouns visible on screen. If your screen shows "SpeechAnalyzer", saying it out loud is more likely to be transcribed correctly. After transcription, an optional L2 step sends the text through a local LLM (ollama) for spoken-to-written cleanup, then CGEvent simulates Cmd+V to paste into the active app. Meeting Mode Meeting mode forks the same audio stream to two consumers: SpeechAnalyzer — real-time streaming transcription, displayed in a floating NSPanel FluidAudio buffer — accumulates 16kHz Float32 mono samples for batch speaker diarization after recording stops When the user ends the meeting, FluidAudio's performCompleteDiarization() runs on the accumulated audio. We align transcription segments with speaker segments using audioTimeRange overlap matching — each transcription segment gets assigned the speaker ID with the most time overlap. Results export to Markdown. Pitfalls We Hit on macOS 26 1. AVAudioEngine installTap doesn't fire with Bluetooth devices We started with AVAudioEngine.inputNode.installTap() for audio capture. It worked fine with built-in mics but the tap callback never fired with Bluetooth devices (tested with vivo TWS 4 Hi-Fi). Fix: switched to AVCaptureSession. The delegate callback captureOutput(_:didOutput:from:) fires reliably regardless of audio device. The tradeoff is you get CMSampleBuffer instead of AVAudioPCMBuffer, so you need a conversion step. 2. NSEvent addGlobalMonitorForEvents crashes Our global hotkey listener used NSEvent.addGlobalMonitorForEvents. On macOS 26, this crashes with a Bus error inside GlobalObserverHandler — appears to be a Swift actor runtime issue. Fix: switched to CGEventTap. Works reliably, but the callback runs on a CFRunLoop context, which Swift doesn't recognize as MainActor. 3. CGEventTap callbacks aren't on MainActor If your CGEventTap callback touches any @MainActor state, you'll get concurrency violations. The callback runs on whatever thread owns the CFRunLoop. Fix: bridge with DispatchQueue.main.async {} inside the tap callback before touching any MainActor state. 4. CGPreflightScreenCaptureAccess doesn't request permission We used CGPreflightScreenCaptureAccess() as a guard before calling ScreenCaptureKit. If it returned false, we'd bail out. The problem: this function only checks — it never triggers macOS to add your app to the Screen Recording permission list. Chicken-and-egg: you can't get permission because you never ask for it. Fix: call CGRequestScreenCaptureAccess() at app startup. This adds your app to System Settings → Screen Recording. Then let ScreenCaptureKit calls proceed without the preflight guard — SCShareableContent will also trigger the permission prompt on first use. 5. Ad-hoc signing breaks TCC permissions on every rebuild During development, codesign --sign - (ad-hoc) generates a different code directory hash on every build. macOS TCC tracks permissions by this hash, so every rebuild = new app identity = all permissions reset. Fix: sign with a stable certificate. If you have an Apple Development certificate, use that. The TeamIdentifier stays constant across rebuilds, so TCC permissions persist. We also discovered that launching via open WE.app (LaunchServices) instead of directly executing the binary is required — otherwise macOS attributes TCC permissions to Terminal, not your app. Benchmarks We ran end-to-end benchmarks on public datasets (Mac Mini M4 16GB, macOS 26): Transcription (SpeechAnalyzer, AliMeeting Chinese): • Near-field CER 34% (excluding outliers ~25%) • Far-field CER 40% (single channel, no beamforming, >30% overlap) • Processing speed 74-89x real-time Speaker diarization (FluidAudio offline): • AMI English 16 meetings: avg DER 23.2% (collar=0.25s, ignoreOverlap=True) • AliMeeting Chinese 8 meetings: DER 48.5% (including overlap regions) • Memory: RSS ~500MB, peak 730-930MB Full evaluation methodology, scripts, and raw results are in the repo. Open Source The project is MIT licensed: github.com/Marvinngg/ambient-voice It includes the macOS client (Swift 6.2, SPM), server-side distillation/training scripts (Python), and a complete evaluation framework with reproducible benchmarks. Feedback and contributions welcome.
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396
Activity
2w
“Unleashing the MacBook Air M2: 673 TFLOPS Achieved with Highly Optimized Metal Shading Language”
Using highly optimized Metal Shading Language (MSL) code, I pushed the MacBook Air M2 to its performance limits with the deformable_attention_universal kernel. The results demonstrate both the efficiency of the code and the exceptional power of Apple Silicon. The total computational workload exceeded 8.455 quadrillion FLOPs, equivalent to processing 8,455 trillion operations. On average, the code sustained a throughput of 85.37 TFLOPS, showcasing the chip’s remarkable ability to handle massive workloads. Peak instantaneous performance reached approximately 673.73 TFLOPS, reflecting near-optimal utilization of the GPU cores. Despite this intensity, the cumulative GPU runtime remained under 100 seconds, highlighting the code’s efficiency and time optimization. The fastest iteration achieved a record processing time of only 0.051 ms, demonstrating minimal bottlenecks and excellent responsiveness. Memory management was equally impressive: peak GPU memory usage never exceeded 2 MB, reflecting efficient use of the M2’s Unified Memory. This minimizes data transfer overhead and ensures smooth performance across repeated workloads. Overall, these results confirm that a well-optimized Metal implementation can unlock the full potential of Apple Silicon, delivering exceptional computational density, processing speed, and memory efficiency. The MacBook Air M2, often considered an energy-efficient consumer laptop, is capable of handling highly intensive workloads at performance levels typically expected from much larger GPUs. This test validates both the robustness of the Metal code and the extraordinary capabilities of the M2 chip for high-performance computing tasks.
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506
Activity
Nov ’25
ImagePlayground: Programmatic Creation Error
Hardware: Macbook Pro M4 Nov 2024 Software: macOS Tahoe 26.0 & xcode 26.0 Apple Intelligence is activated and the Image playground macOS app works Running the following on xcode throws ImagePlayground.ImageCreator.Error.creationFailed Any suggestions on how to make this work? import Foundation import ImagePlayground Task { let creator = try await ImageCreator() guard let style = creator.availableStyles.first else { print("No styles available") exit(1) } let images = creator.images( for: [.text("A cat wearing mittens.")], style: style, limit: 1) for try await image in images { print("Generated image: \(image)") } exit(0) } RunLoop.main.run()
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330
Activity
Sep ’25
Gazetteer encryption?
I have an app that uses a couple of mlmodels (word tagger and gazetteer) and I’m trying to encrypt them before publishing. The models are part of a package. I understand that Xcode can’t automatically handle the encryption for a model in a package the way it can within a traditional app structure. Given that, I’ve generated the Apple MLModel encryption key from Xcode and am encrypting via the command line with: xcrun coremlcompiler compile Gazetteer.mlmodel GazetteerENC.mlmodelc --encrypt Gazetteerkey.mlmodelkey In the package manifest, I’ve listed the encrypted models as .copy resources for my target and have verified the URL to that file is good. When I try to load the encrypted .mlmodelc file (on a physical device) with the line:
 gazetteer = try NLGazetteer(contentsOf: gazetteerURL!) I get the error: Failed to open file: /…/Scanner.bundle/GazetteerENC.mlmodelc/coremldata.bin. It is not a valid .mlmodelc file. So my questions are: Does the NLGazetteer class support encrypted MLModel files? Given that my models are in a package, do I have the right general approach? Thanks for any help or thoughts.
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162
Activity
May ’25
26.4 Foundation Model rejects most topics
I have an iOS app, "Spatial Agents" which ran great in 26.3. It creates dashboards around a topic. It can also decompose a topic into sub-topics, and explore those. All based on web articles and web article headlines. In iOS 26.4 almost every topic - even "MIT Innovation" are rejected with an apology of "I apologize I can not fulfill this request". I've tried softening all my prompts, and I can get only really benign very simple topics to respond, but not anything with any significance. It ran great on lots of topics in 26.3. My published App, is now useless, and all my users are unhappy. HELP!
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106
Activity
1d
Building a 4-agent autonomous coding pipeline on Apple Silicon — MLX backend questions
Hi, I'm building ANF (Autonomous Native Forge) — a cloud-free, 4-agent autonomous software production pipeline running on local hardware with local LLM inference. No middleware, pure Node.js native. Currently running on NVIDIA Blackwell GB10 with vLLM + DeepSeek-R1-32B. Now porting to Apple Silicon. Three technical questions: How production-ready is mlx-lm's OpenAI-compatible API server for long context generation (32K tokens)? What's the recommended approach for KV Cache management with Unified Memory architecture — any specific flags or configurations for M4 Ultra? MLX vs GGUF (llama.cpp) for a multi-agent pipeline where 4 agents call the inference endpoint concurrently — which handles parallel requests better on Apple Silicon? GitHub: github.com/trgysvc/AutonomousNativeForge Any guidance appreciated.
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251
Activity
3w
Full documentation of annotations file for Create ML
The documentation for the Create ML tool ("Building an object detector data source") mentions that there are options for using normalized values instead of pixels and also different anchor point origins ("MLBoundingBoxCoordinatesOrigin") instead of always using "center". However, the JSON format for these does not appear in any examples. Does anyone know the format for these options?
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263
Activity
May ’25
Mistral/LLaMa Core ML Conversion
Hi, I am new to developing on Apple’s platform yet I want to familiarize myself with Core ML and Core ML Tools. I was watching the WWDC24: Bring your machine learning and AI models to Apple Silicon video and was trying to follow along. After multiple attempts and much reading up on documentation, I am still unable to get a coherent script running that will convert the Mistral model that the host used and convert it to a valid Core ML model. here is a pastebin to what i have currently: https://pastebin.com/04cVjF1v if you require the output as well please let me know
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149
Activity
Apr ’25