I've built an iOS app with a novel approach to AI safety: a deterministic, pre-inference validation layer called Newton Engine. Instead of relying on the LLM to self-moderate, Newton validates every prompt BEFORE it reaches the model. It uses shape theory and semantic analysis to detect: • Corrosive frames (self-harm language patterns) • Logical contradictions (requests that undermine themselves) • Delegation attempts (asking AI to make human decisions) • Jailbreak patterns (prompt injection, role-play escapes) • Hallucination triggers (requests for fabricated citations) The system achieves a 96% adversarial catch rate across 847 test cases, with zero false positives on benign prompts. Key technical details: • Pure Swift/SwiftUI, no external dependencies • Runs entirely on-device (no server calls for validation) • Deterministic (same input always produces same output) • Auditable (full trace logging for every validation) I'm preparing to submit to the App Store and wanted to ask: Are there specific App Review guidelines I should reference for AI safety claims? Is there interest from Apple in deterministic governance layers for Apple Intelligence integration? Any recommendations for demonstrating safety compliance during review? The app is called Ada, and the engine is open source at: github.com/jaredlewiswechs/ada-newton Happy to share technical documentation or discuss the architecture with anyone interested. See: parcri.net

We are developing Apple AI for foreign markets and adapting it for iPhone models 17 and above. When the system language and Siri language are not the same—for example, if the system is in English and Siri is in Chinese—it can cause a situation where Apple AI cannot be used. So, may I ask if there are any other reasons that could cause Apple AI to be unavailable within the app, even if it has been enabled?

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?

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!

I’d like to submit a feature request regarding the availability of Foundation Models in MessageFilter extensions. Background MessageFilter extensions play a critical role in protecting users from spam, phishing, and unwanted messages. With the introduction of Foundation Models and Apple Intelligence, Apple has provided powerful on-device natural language understanding capabilities that are highly aligned with the goals of MessageFilter. However, Foundation Models are currently unavailable in MessageFilter extensions. Why Foundation Models Are a Great Fit for MessageFilter Message filtering is fundamentally a natural language classification problem. Foundation Models would significantly improve: Detection of phishing and scam messages Classification of promotional vs transactional content Understanding intent, tone, and semantic context beyond keyword matching Adaptation to evolving scam patterns without server-side processing All of this can be done fully on-device, preserving user privacy and aligning with Apple’s privacy-first design principles. Current Limitations Today, MessageFilter extensions are limited to relatively simple heuristics or lightweight models. This often results in: Higher false positives Lower recall for sophisticated scam messages Increased development complexity to compensate for limited NLP capabilities Request Could Apple consider one of the following: Allowing Foundation Models to be used directly within MessageFilter extensions Providing a constrained or optimized Foundation Model API specifically designed for MessageFilter Enabling a supported mechanism for MessageFilter extensions to delegate inference to the containing app using Foundation Models Even limited access (e.g. short text only, strict execution limits) would be extremely valuable. Closing Foundation Models have the potential to significantly raise the quality and effectiveness of message filtering on Apple platforms while maintaining strong privacy guarantees. Supporting them in MessageFilter extensions would be a major improvement for both developers and users. Thank you for your consideration and for continuing to invest in on-device intelligence.

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

v3 was released 2 years ago but developers are unable to convert models created with Keras v3 to CoreML

I'm using python 3.9.6, tensorflow 2.20.0, tensorflow-metal 1.2.0, and when I try to run import tensorflow as tf It gives Traceback (most recent call last): File "/Users/haoduoyu/Code/demo.py", line 1, in <module> import tensorflow as tf File "/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow/__init__.py", line 438, in <module> _ll.load_library(_plugin_dir) File "/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file) As long as I uninstall tensorflow-metal, nothing goes wrong. How can I fix this problem?

Hi, I am developing an iOS application that utilizes Apple’s Foundation Models to perform certain summarization tasks. I would like to understand whether user data is transferred to Private Cloud Compute (PCC) in cases where the computation cannot be performed entirely on-device. This information is critical for our internal security and compliance reviews. I would appreciate your clarification on this matter. Thank you.

Hi everyone, I’m working on an iOS app that uses a Core ML model to run live image recognition. I’ve run into a persistent issue with the mlpackage not being turned into a swift class. This following error is in the code, and in carDetection.mlpackage, it says that model class has not been generated yet. The error in the code is as follows: What I’ve tried: Verified Target Membership is checked for carDetectionModel.mlpackage Confirmed the file is listed under Copy Bundle Resources (and removed from Compile Sources) Cleaned the build folder (Shift + Cmd + K) and rebuilt Renamed and re-added the .mlpackage file Restarted Xcode and re-added the file Logged bundle contents at runtime, but the .mlpackage still doesn’t appear The mlpackage is in Copy bundle resources, and is not in the compile sources. I just don't know why a swift class is not being generated for the mlpackage. Could someone please give me some guidance on what to do to resolve this issue? Sorry if my error is a bit naive, I'm pretty new to iOS app development

Hi team, I’m exploring the Model Context Protocol (MCP), which is used to connect LLMs/AI agents to external tools in a structured way. It's becoming a common standard for automation and agent workflows. Before I go deeper, I want to confirm: Does Apple currently provide any official MCP server, API surface, or SDK on iOS/macOS? From what I see, only third-party MCP servers exist for iOS simulators/devices, and Apple’s own frameworks (Foundation Models, Apple Intelligence) don’t expose MCP endpoints. Is there any chance Apple might introduce MCP support—or publish recommended patterns for safely integrating MCP inside apps or developer tools? I would like to see if I can share my app's data to the MCP server to enable other third-party apps/services to integrate easily

Apple's Image Playground primarily performs image generation on-device, but can use secure Private Cloud Compute for more complex requests that require larger models. Private Cloud Compute (PCC) For more complex tasks that require greater computational power than the device can provide, Image Playground leverages Apple's Private Cloud Compute. This system extends the privacy and security of the device to the cloud: Secure Environment: PCC runs on Apple silicon servers and uses a secure enclave to protect data, ensuring requests are processed in a verified, secure environment. No Data Storage: Data is never stored or made accessible to Apple when using PCC; it is used only to fulfill the specific request. Independent Verification: Independent experts are able to inspect the code running on these servers to verify Apple's privacy promises.

Hello, I am interested in using jax-metal to train ML models using Apple Silicon. I understand this is experimental. After installing jax-metal according to https://aninterestingwebsite.com/metal/jax/, my python code fails with the following error JaxRuntimeError: UNKNOWN: -:0:0: error: unknown attribute code: 22 -:0:0: note: in bytecode version 6 produced by: StableHLO_v1.12.1 My issue is identical to the one reported here https://github.com/jax-ml/jax/issues/26968#issuecomment-2733120325, and is fixed by pinning to jax-metal 0.1.1., jax 0.5.0 and jaxlib 0.5.0. Thank you!

I have an app that stores lots of data that is of interest to the user. Analogies would be the Photos apps or the Health app. I'm trying to use the Foundation Models framework to allow users to surface information they find interesting using natural language, for example, "Tell me about the widgets from yesterday" or "Tell me about the widgets for the last 3 days". Specifically, I'm trying to get a date range passed down to the Tool so that I can pull the relevant widgets from the database in the call function. What is the right way to set up the Arguments to get at a date range?

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.

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.

I am follwing this tutorial: https://apple.github.io/coremltools/docs-guides/source/convert-a-torchvision-model-from-pytorch.html I have obtained simialr result using the python code. However when I view it in Xcode, the preview prediction percentage confidence is way off I suspect it is due the the output of the model, which is in percentage already and in Xcode it multiply 100 again leading to this result. Please give me any feedback to fix this, thank you.

Hi, guys. I'm writing about Apple Intelligence and I reached the point I have to explain App Intent Domains https://aninterestingwebsite.com/documentation/AppIntents/app-intent-domains but I noticed that there is a note explaining that these services are not available with Siri. I tried the example provided by Apple at https://aninterestingwebsite.com/documentation/AppIntents/making-your-app-s-functionality-available-to-siri and I can only make the intents work from the Shortcuts App, but not from Siri. Is this correct. App Intent Domains are still not available with Siri? Thanks

Is foundation models matured enough to take input from the Apple Vision framework to generate responses? Something similar to what google's gemini does although in a much smaller scale and for a very specific niche.