In my quantization code, the line: compressed_model_a8 = cto.coreml.experimental.linear_quantize_activations( model, activation_config, [{'img':np.random.randn(1,13,1024,1024)}] ) has taken 90 minutes to run so far and is still not completed. From debugging, I can see that the line it's stuck on is line 261 in _model_debugger.py: model = ct.models.MLModel( cloned_spec, weights_dir=self.weights_dir, compute_units=compute_units, skip_model_load=False, # Don't skip model load as we need model prediction to get activations range. ) Is this expected behaviour? Would it be quicker to run on another computer with more RAM?

Is it possible to expose a custom VirtIO device to a Linux guest running inside a VM — likely using QEMU backed by Hypervisor.framework. The guest would see this device as something like /dev/npu0, and it would use a kernel driver + userspace library to submit inference requests. On the macOS host, these requests would be executed using CoreML, MPSGraph, or BNNS. The results would be passed back to the guest via IPC. Does the macOS allow this kind of "fake" NPU / GPU

I am working on a lung cancer scanning app in for iOS with a CoreML model and when I test my app on a physical device, the model results in the same prediction 100% of the time. I even changed the names around and still resulted in the same case. I have listed my labels in cases and when its just stuck on the same case (case 1) My code is below: https://github.com/ShivenKhurana1/Detect-to-Protect-App/blob/main/DetectToProtect/SecondView.swift I couldn't add the code as it was too long so I hope github link is fine!

Hello, I am developing an iOS app that uses machine learning models. To improve accuracy and user experience, I would like to download .mlmodel files (compiled and compressed as zip files) from our own server after the app is installed, and use them for inference within the app. No executable code, scripts, or dynamic libraries will be downloaded—only model data files are used. According to App Store Review Guideline 2.5.2, I understand that apps may not download or execute code which introduces or changes features or functionality. In this case, are compiled and zip-compressed .mlmodel files considered "data" rather than "code", and is it allowed to download and use them in the app? If there are any restrictions or best practices related to this, please let me know. Thank you.

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.

Whats to code to warm it up once? Saw this in a developer video but cannot find it. Prevent cold run within an application. Thank you in advance!

Things I did: created an Intents Extension target added "Supported Intents" to both my main app target and the intent extension, with "INAddTasksIntent" and "INCreateNoteIntent" created the AppIntentVocabulary in my main app target created the handlers in the code in the Intents Extension target class AddTaskIntentHandler: INExtension, INAddTasksIntentHandling { func resolveTaskTitles(for intent: INAddTasksIntent) async -> [INSpeakableStringResolutionResult] { if let taskTitles = intent.taskTitles { return taskTitles.map { INSpeakableStringResolutionResult.success(with: $0) } } else { return [INSpeakableStringResolutionResult.needsValue()] } } func handle(intent: INAddTasksIntent) async -> INAddTasksIntentResponse { // my code to handle this... let response = INAddTasksIntentResponse(code: .success, userActivity: nil) response.addedTasks = tasksCreated.map { INTask( title: INSpeakableString(spokenPhrase: $0.name), status: .notCompleted, taskType: .completable, spatialEventTrigger: nil, temporalEventTrigger: intent.temporalEventTrigger, createdDateComponents: DateHelper.localCalendar().dateComponents([.year, .month, .day, .minute, .hour], from: Date.now), modifiedDateComponents: nil, identifier: $0.id ) } return response } } class AddItemIntentHandler: INExtension, INCreateNoteIntentHandling { func resolveTitle(for intent: INCreateNoteIntent) async -> INSpeakableStringResolutionResult { if let title = intent.title { return INSpeakableStringResolutionResult.success(with: title) } else { return INSpeakableStringResolutionResult.needsValue() } } func resolveGroupName(for intent: INCreateNoteIntent) async -> INSpeakableStringResolutionResult { if let groupName = intent.groupName { return INSpeakableStringResolutionResult.success(with: groupName) } else { return INSpeakableStringResolutionResult.needsValue() } } func handle(intent: INCreateNoteIntent) async -> INCreateNoteIntentResponse { do { // my code for handling this... let response = INCreateNoteIntentResponse(code: .success, userActivity: nil) response.createdNote = INNote( title: INSpeakableString(spokenPhrase: itemName), contents: itemNote.map { [INTextNoteContent(text: $0)] } ?? [], groupName: INSpeakableString(spokenPhrase: list.name), createdDateComponents: DateHelper.localCalendar().dateComponents([.day, .month, .year, .hour, .minute], from: Date.now), modifiedDateComponents: nil, identifier: newItem.id ) return response } catch { return INCreateNoteIntentResponse(code: .failure, userActivity: nil) } } } uninstalled my app restarted my physical device and simulator Yet, when I say "Remind me to buy dog food in Index" (Index is the name of my app), as stated in the examples of INAddTasksIntent, Siri proceeds to say that a list named "Index" doesn't exist in apple Reminders app, instead of processing the request in my app. Am I missing something?

Pretty much as per the title and I suspect I know the answer. Given that Foundation Models run on device, is it possible to use Foundation Models framework inside of a DeviceActivityReport? I've been tinkering with it, and all I get is errors and "Sandbox restrictions". Am I missing something? Seems like a missed trick to utilise on device AI/ML with other frameworks.

In the play ground I'm trying to bias my LanguageModel to use a tool I registered, but I don't see it actually calling the tool. I'm following the developer video on landmarks itinerary generation tutorial almost verbatim. Is this a prompt engineering thing I'm missing? Or is it possible that I'm injecting my tool wrong?

Hi, I'm looking for the best way to use MLX models, particularly those I've fine-tuned, within a React Native application on iOS devices. Is there a recommended integration path or specific API for bridging MLX's capabilities to React Native for deployment on iPhones and iPads?

When trying to open an encrypted CoreML model file on a system with SIP disabled, the error message is Failed to generate key request for <...> with error: -42187 This should state that SIP is disabled and needs to be enabled.

When the @Generable is applied toward a Swift struct declared within another struct, and when said nested struct is defined as the type of one of the properties of another @Generable type, which is in turn defined as the output format of Foundation Model session, Foundation Model can stuck in a loop trying to create a infinitely nested response, until the context window limit exceeded error is triggered. I have filed feedback FB19987191 with a demo project. Is this expected behavior?

Hi everyone, I've been building an on-device AI safety layer called Newton Engine, designed to validate prompts before they reach FoundationModels (or any LLM). Wanted to share v1.3 and get feedback from the community. The Problem Current AI safety is post-training — baked into the model, probabilistic, not auditable. When Apple Intelligence ships with FoundationModels, developers will need a way to catch unsafe prompts before inference, with deterministic results they can log and explain. What Newton Does Newton validates every prompt pre-inference and returns: Phase (0/1/7/8/9) Shape classification Confidence score Full audit trace If validation fails, generation is blocked. If it passes (Phase 9), the prompt proceeds to the model. v1.3 Detection Categories (14 total) Jailbreak / prompt injection Corrosive self-negation ("I hate myself") Hedged corrosive ("Not saying I'm worthless, but...") Emotional dependency ("You're the only one who understands") Third-person manipulation ("If you refuse, you're proving nobody cares") Logical contradictions ("Prove truth doesn't exist") Self-referential paradox ("Prove that proof is impossible") Semantic inversion ("Explain how truth can be false") Definitional impossibility ("Square circle") Delegated agency ("Decide for me") Hallucination-risk prompts ("Cite the 2025 CDC report") Unbounded recursion ("Repeat forever") Conditional unbounded ("Until you can't") Nonsense / low semantic density Test Results 94.3% catch rate on 35 adversarial test cases (33/35 passed). Architecture User Input ↓ [ Newton ] → Validates prompt, assigns Phase ↓ Phase 9? → [ FoundationModels ] → Response Phase 1/7/8? → Blocked with explanation Key Properties Deterministic (same input → same output) Fully auditable (ValidationTrace on every prompt) On-device (no network required) Native Swift / SwiftUI String Catalog localization (EN/ES/FR) FoundationModels-ready (#if canImport) Code Sample — Validation let governor = NewtonGovernor() let result = governor.validate(prompt: userInput) if result.permitted { // Proceed to FoundationModels let session = LanguageModelSession() let response = try await session.respond(to: userInput) } else { // Handle block print("Blocked: Phase \(result.phase.rawValue) — \(result.reasoning)") print(result.trace.summary) // Full audit trace } Questions for the Community Anyone else building pre-inference validation for FoundationModels? Thoughts on the Phase system (0/1/7/8/9) vs. simple pass/fail? Interest in Shape Theory classification for prompt complexity? Best practices for integrating with LanguageModelSession? Links GitHub: https://github.com/jaredlewiswechs/ada-newton Technical overview: parcri.net Happy to share more implementation details. Looking for feedback, collaborators, and anyone else thinking about deterministic AI safety on-device.

Hi, I am modifying the sample camera app that is here: https://developer.apple.com/tutorials/sample-apps/capturingphotos-camerapreview ... In the processPreviewImages, I am using the Vision APIs to generate a segmentation mask for a person/object, then compositing that person onto a different background (with some other filtering). The filtering and compositing is done via CoreImage. At the end, I convert the CIImage to a CGImage then to a SwiftUI Image. When I run it on my iPhone, it works fine, and has not crashed. When I run it on the iPhone with the debugger, it crashes within a few seconds with: EXC_BAD_ACCESS in libRPAC.dylib`std::__1::__hash_table<std::__1::__hash_value_type<long, qos_info_t>, std::__1::__unordered_map_hasher<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::hash, std::__1::equal_to, true>, std::__1::__unordered_map_equal<long, std::__1::__hash_value_type<long, qos_info_t>, std::__1::equal_to, std::__1::hash, true>, std::__1::allocator<std::__1::__hash_value_type<long, qos_info_t>>>::__emplace_unique_key_args<long, std::__1::piecewise_construct_t const&, std::__1::tuple<long const&>, std::__1::tuple<>>: It had previously been working fine with the debugger, so I'm not sure what has changed. Is there a difference in how the Vision APIs are executed if the debugger is attached vs. not?

I have used mlx_lm.lora to fine tune a mistral-7b-v0.3-4bit model with my data. I fused the mistral model with my adapters and upload the fused model to my directory on huggingface. I was able to use mlx_lm.generate to use the fused model in Terminal. However, I don't know how to load the model in Swift. I've used Imports import SwiftUI import MLX import MLXLMCommon import MLXLLM let modelFactory = LLMModelFactory.shared let configuration = ModelConfiguration( id: "pharmpk/pk-mistral-7b-v0.3-4bit" ) // Load the model off the main actor, then assign on the main actor let loaded = try await modelFactory.loadContainer(configuration: configuration) { progress in print("Downloading progress: \(progress.fractionCompleted * 100)%") } await MainActor.run { self.model = loaded } I'm getting an error runModel error: downloadError("A server with the specified hostname could not be found.") Any suggestions? Thanks, David PS, I can load the model from the app bundle // directory: Bundle.main.resourceURL! but it's too big to upload for Testflight

Encountered a few times when the answer get "stuck" (I am now at beta 6). This is an example.

Is there an API that allows iOS app developers to leverage Apple Foundation Models to authorize a user's Apple Intelligence extension, chatGPT login account? I'm trying to provide a real-time question feature for chatGPT, a logged-in extension account, while leveraging Apple Intelligence's LLM. Is there an API that also affects the extension login account?

Hi all, I am interested in unlocking unique applications with the new foundational models. I have a few questions regarding the availability of the following features: Image Input: The update in June 2025 mentions "image" 44 times (https://machinelearning.apple.com/research/apple-foundation-models-2025-updates) - however I can't seem to find any information about having images as the input/prompt for the foundational models. When will this be available? I understand that there are existing Vision ML APIs, but I want image input into a multimodal on-device LLM (VLM) instead for features like "Which player is holding the ball in the image", etc (image understanding) Cloud Foundational Model - when will this be available? Thanks! Clement :)

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

I’ve been testing silent Siri engagement via typing on iOS 18 and also on iOS 26 beta 1 and beta 2. While normal typing works perfectly in type-to-Siri mode, I’ve noticed that swipe-to-type gestures don’t work within Siri’s input field. Interestingly, you still feel the usual haptic feedback associated with swipe typing, but no text appears in the Siri text box. Swipe-to-type continues to work flawlessly in other apps like Messages and Notes, so this seems to be an issue specific to Siri’s typing input handler in these betas. Hopefully, it will be fixed in the next release because swipe typing is essential to my silent Siri workflow.