Hello Apple Developer Community, I'm exploring the integration of Apple Intelligence features into my mobile application and have a couple of questions regarding the current and upcoming API capabilities: Custom Prompt Support: Is there a way to pass custom prompts to Apple Intelligence to generate specific inferences? For instance, can we provide a unique prompt to the Writing Tools or Image Playground APIs to obtain tailored outputs? Direct Inference Capabilities: Beyond the predefined functionalities like text rewriting or image generation, does Apple Intelligence offer APIs that allow for more generalized inference tasks based on custom inputs? I understand that Apple has provided APIs such as Writing Tools, Image Playground, and Genmoji. However, I'm interested in understanding the extent of customization and flexibility these APIs offer, especially concerning custom prompts and generalized inference. Additionally, are there any plans or timelines for expanding these capabilities, perhaps with the introduction of new SDKs or frameworks that allow deeper integration and customization? Any insights, documentation links, or experiences shared would be greatly appreciated. Thank you in advance for your assistance!

Hello, I am currently developing an application that requires barcode scanning using Apple’s Vision framework (VNBarcodeSymbology). I noticed that the framework supports several GS1 DataBar symbologies, such as: VNBarcodeSymbology.gs1DataBar VNBarcodeSymbology.gs1DataBarExpanded VNBarcodeSymbology.gs1DataBarLimited However, I could not find any explicit reference to support for GS1 DataBar Stacked (both regular and expanded variants). Could you confirm whether GS1 DataBar Stacked is currently supported in VisionKit's DataScannerViewController or VNBarcodeObservation? If not, are there any plans to include support for this symbology in a future iOS update? This functionality is critical for my use case, as GS1 DataBar Stacked barcodes are widely used in retail, pharmaceuticals, and logistics, where space constraints prevent the use of standard GS1 DataBar formats. I appreciate any clarification on this matter and would be happy to provide additional details if needed.

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?

Hi everyone 👋 I'd like to use coremltools to see how well a model performs on a remote device as part of a CI/CD pipeline. According to the Core ML Tools "Debugging and Performance Utilities" guide, remote devices must be in a "connected" state in order for coremltools to install the ModelRunner application. The devices in our system have a "paired" state, and I'm unable to set the them as "connected." The only way I know how to connect a device is to physically plug it in to a computer and open Xcode. I don't have physical access to the devices in the CI/CD system, and the host computer that interacts with them doesn't have Xcode installed. Here are some questions I've been looking into and would love some help answering: Has anyone managed to use the coremltools performance utilities in a similar system? Can you put a device in a "connected" state if you don't have physical access to the device and if you only have access to Xcode command line tools and not the Xcode app? Is it at all possible to install the coremltools ModelRunner application on a "paired" device, for example, by manually building the app and installing it with devicectl? Would other utilities, such as the MLModelBenchmarker work as expected if the app is installed this way? Thank you!

When I use ChatGPT in Xcode, the following error is displayed： It was working fine before, but suddenly it became like this, without changing any configuration. Why?

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.

I am running some experiments with WebGPU using the wgpu crate in rust. I have some Buffers already allocated in the GPU. Is it possible to use those already existing buffers directly as inputs to a predict call in CoreML? I want to prevent gpu to cpu download time as much as possible. Or are there any other ways to do something like this. Is this only possible using the latest Tensor object which came out with Metal 4 ?

: Hello, I’m seeking clarification on whether Apple provides any framework or API that enables deep integration between Siri and advanced AI assistants (such as ChatGPT), including system-level functions like voice interaction, navigation, cross-platform syncing, and operational access similar to Siri’s own capabilities. If no such option exists today, I would appreciate guidance on the recommended path or approved third-party solutions for building a unified, voice-first experience across Apple’s ecosystem. Thank you for your time and insight.

Hi, I'm currently using Metal Performance Shaders Graph (MPSGraphExecutable) to run neural network inference operations as part of a metal rendering pipeline. I also tried to profile the usage of neural engine when running inference using MPSGraphExecutable but the graph shows no sign of neural engine usage. However, when I used the coreML model inspection tool in xcode and run performance report, it was able to use ANE. Does MPSGraphExecutable automatically utilize the Apple Neural Engine (ANE) when running inference operations, or does it only execute on GPU? My model (Core ML Package) was converted from a pytouch model using coremltools with ML program type and support iOS17.0+. Any insights or documentation references would be greatly appreciated!

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

I'm experimenting with the new SpeechTranscriber in macOS/iOS 26, transcribing speech from a prerecorded mp4 file. Speed and quality are amazing! I've told the transcriber to include time indexes. Each run is always exactly one word, which can be very useful. When I look at the indexes the end of one run is always identical to the start of the next run, even if there's a pause. I'd like to identify pauses, perhaps to generate something like phrases for subtitling. With each run of text going into the next I can't do this, other than using punctuation - which might be rather rough. Any suggestions on detecting pauses, or getting that kind of metadata from the transcriber? Here's a short sample, showing each run with the start, end, and characters in the run: 105.9 --> 107.04 I 107.04 --> 107.16 think 107.16 --> 108.0 more 108.0 --> 108.42 lighting 108.42 --> 108.6 is 108.6 --> 108.72 definitely 108.72 --> 109.2 needed, 109.2 --> 109.92 downtown. 109.98 --> 110.4 My 110.4 --> 110.52 only 110.52 --> 110.7 question 110.7 --> 111.06 is, 111.06 --> 111.48 poll 111.48 --> 111.78 five, 111.78 --> 111.84 that 111.84 --> 112.08 you're 112.08 --> 112.38 increasing 112.38 --> 112.5 the 112.5 --> 113.34 50,000? 113.4 --> 113.58 Where 113.58 --> 113.88 exactly

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!

I’m considering creating an ILMessageFilterExtension using a mini LLM/SLM to detect fraud and I’ve read it has strict memory limits yet I can’t find it in the documentation. What’s the set limit or any other constraints impacting the feasibility of running 100-500mb model?

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.}

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?

I'm trying to use Apple's new Visual Intelligence API for recommending content through screenshot image search. The problem I encountered is that the SemanticContentDescriptor labels are either completely empty or super misleading, making it impossible to query for similar content on my app. Even the closest matching example was inaccurate, returning a single label ["cardigan"] for a Supreme T-Shirt. I see other apps using this API like Etsy for example, and I'm wondering if they're using the input pixel buffer to query for similar content rather than using the labels? If anyone has a similar experience or something that wasn't called out in the documentation please lmk! Thanks.

I installed Xcode 26.0 beta and downloaded the generative models sample from here: https://developer.apple.com/documentation/foundationmodels/adding-intelligent-app-features-with-generative-models But when I run it in the iOS 26.0 simulator, I get the error shown here. What's going wrong?

Lookin for J - is this a safe place for discussing full apps ive built but not submitted or shared , I have maybe over 100 but had been unaware any assistance was provided.. is there a formal process to take to submit an app fro review to improve OS, other than during App Store review.

Is the face and body detection service in the Vision framework a local model or a cloud model? https://developer.apple.com/documentation/vision

I got 3203.23 GFLOPS (FP16) on the M3 Macbook Pro and only 2833.24 GFLOPS (FP16) on the M4 Macbook Air for 4096x4096 matrix multiplications for a PyTorch MPS FP16 Benchmark. Wasn't the performance supposed to be twice as high on the M4 compared to the M3 even with the termal throtling on the Macbook Air? What went wrong?