Hi everyone, I'm developing an iOS app using Foundation Models and I've hit a critical limitation that I believe affects many developers and millions of users. The Issue Foundation Models requires the device system language to be one of the supported languages. If a user has their device set to an unsupported language (Catalan, Dutch, Swedish, Polish, Danish, Norwegian, Finnish, Czech, Hungarian, Greek, Romanian, and many others), SystemLanguageModel.isSupported returns false and the framework is completely unavailable. Why This Is Problematic Scenario: A Catalan user has their iPhone in Catalan (native language). They want to use an AI chat app in Spanish or English (languages they speak fluently). Current situation: ❌ Foundation Models: Completely unavailable ✅ OpenAI GPT-4: Works perfectly ✅ Anthropic Claude: Works perfectly ✅ Any cloud-based AI: Works perfectly The user must choose between: Keep device in Catalan → Cannot use Foundation Models at all Change entire device to Spanish → Can use Foundation Models but terrible UX Impact This affects: Millions of users in regions where unsupported languages are official Multilingual users who prefer their device in their native language but can comfortably interact with AI in English/Spanish Developers who cannot deploy Foundation Models-based apps in these markets Privacy-conscious users who are ironically forced to use cloud AI instead of on-device AI What We Need One of these solutions would solve the problem: Option 1: Per-app language override (preferred) // Proposed API let session = try await LanguageModelSession(preferredLanguage: "es-ES") Option 2: Faster rollout of additional languages (particularly EU languages) Option 3: Allow fallback to user-selected supported language when system language is unsupported Technical Details Current behavior: // Device in Catalan let isAvailable = SystemLanguageModel.isSupported // Returns false // No way to override or specify alternative language Why This Matters Apple Intelligence and Foundation Models are amazing for privacy and performance. But this language restriction makes the most privacy-focused AI solution less accessible than cloud alternatives. This seems contrary to Apple's values of accessibility and user choice. Questions for the Community Has anyone else encountered this limitation? Are there any workarounds I'm missing? Has anyone successfully filed feedback about this?(Please share FB number so we can reference it) Are there any sessions or labs where this has been discussed? Thanks for reading. I'd love to hear if others are facing this and how you're handling it.

I've downloaded the Xcode-beta and run the sample project "FoundationModelsTripPlanner" but I got this error when trying generate the response. InferenceError::inferenceFailed::Error Domain=com.apple.UnifiedAssetFramework Code=5000 "There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog" UserInfo={NSLocalizedFailureReason=There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog} Device: M1 Pro Question: Is it because M1 not supporting this feature?

Hi Apple team, When using AppShortcutsProvider, I hit the hard limit: Each app may have at most 10 App Shortcuts. This feels limiting for apps that offer multiple workflows and would benefit from deeper Siri integration. Could this cap be raised — ideally to 30 — to support broader use of AppIntents, enhance Siri automation, and unlock more system-level capabilities? AppShortcuts are a fantastic tool. Increasing the limit would make them even more powerful. Thanks!

Introduced in the Keynote was the 3D Lock Screen images with the kangaroo: https://9to5mac.com/wp-content/uploads/sites/6/2025/06/3d-lock-screen-2.gif I can't see any mention on if this effect is available for developers with an API to convert flat 2D photos in to the same 3D feeling image. Does anyone know if there is an API?

I’m building an app that generates images based on text input from a specific text field. However, I’m encountering a problem: For short prompts like "a cat and a dog", the entire string is sent to the Image Playground, even when I use the extracted method. For longer inputs, the behavior is inconsistent. Sometimes it extracts keywords correctly, but other times it doesn’t extract anything at all. Since my app relies on generating images based on the extracted keywords, this inconsistency negatively impacts the user experience in my app. How can I make sure that keywords are always extracted from the input string? Button("Generate", systemImage: "apple.intelligence") { isPresented = true } .imagePlaygroundSheet(isPresented: $isPresented, concepts: [ImagePlaygroundConcept.extracted(from: text, title: textTitle)]) { url in imageURL = url }

I downloaded Xcode Beta 1 on my mac (did not upgrade the OS). The target OS level of iOS26 and the device simulator for iOS26 is downloaded and selected as the target. When I try a simple Playground in Xcode ( #Playground ) I get a session error. #Playground { let avail = SystemLanguageModel.default.availability if avail != .available { print("SystemLanguageModel not available") return } let session = LanguageModelSession() do { let response = try await session.respond(to: "Create a recipe for apple pie") } catch { print(error) } } The error I get is: Asset com.apple.gm.safety_deny_input.foundation_models.framework.api not found in Model Catalog Is there a way to test drive the FoundationModel code without upgrading to macos26?

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

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?

Hello, We find that models sometimes load very fast (<< 1 second) and sometimes encounter very long load times (>> 120 seconds). During such slow load times, the model is being compiled. We would greatly appreciate the ability to check cache validity via CoreML and determine that we are about to encounter long load times so that we can mitigate and provide a good user experience. A secondary issue: sometimes the cache is corrupted (typically .mpsgraphpackage yielding Metal cold asserts). This yields load failures and OS errors that persist between launches, and we have to manually nuke the cache (~/Library/..../my-app/...) for the CoreML assets. A CoreML API for clearing caches and hardening from asserts across the load paths would be appreciated

iOS26 is supported by a wider range of devices than are able to run AI, e.g iPhone 12 runs iOS26, but does not support AI. How do we determine in code if AI is supported on a device ? How do we determine what features use AI under the hood ? Thanks, Steve.

Hi all, I'm working on an app to classify dog breeds via CoreML, but when I try training a model using Image Feature Print v2, I get the following error: Failed to create CVPixelBufferPool. Width = 0, Height = 0, Format = 0x00000000 Strangely, when I switch back to Image Feature Print v1, the model trains perfectly fine. I've verified that there aren't any invalid or broken images in my dataset. Is there a fix for this? Thanks!

How reliable is the Models, to use as a comparison, such as a cholesterol test, to inform, for example, whether it is worth it to go see a doctor? I would like to use Tool to attach the simple blood test data to the session and with this the Model can analyse and made a simple suggestion if is necessary to see a doctor etc.. ? ps.: Local model

I didn't run benchmarks before update, but it seems at least 5x slower. Of course all the LLM work is on remote servers, so is non-intuitive to me this should be happening. Had updated MacOS and Xcode to 26.1RC at the same time, so can't even say I think it is MacOS or I think it is Xcode. Before the update the progress indicator for each piece of code might seem to get stuck at the very end (and toggling between Navigators and Coding Assistant) in Xcode UI seemed to refresh the UI and confirm coding complete... but now it seems progress races to 50%, then often is stuck at 75%... well earlier than used to get stuck. And it like something is legitimately processing not just a UI glitch. I'm wondering if this is somehow tied to visual rendering of the code in the little white window? CMD-TAB into Xcode seems laggy. Xcode is pinning a CPU. Why, this is all remote LLM work? MacBook Pro 2021 M1 64GB RAM. Went from 26.01 to 26.1RC. Didn't touch any of the betas until RC1.

I get the following dyld error on an iPad Pro with Xcode 26 beta 4: Symbol not found: _$s16FoundationModels20LanguageModelSessionC7prewarm12promptPrefixyAA6PromptVSg_tF Any advice?

I see the solution is simple "just change the language in the build settings" but the build settings are not a thing in an App Playground project. It also says duplicated tasks.

Documentation on adapter train is lacking any details related to training on dataset with tool calling. And page about tool calling itself only explain how to use it from Swift without any internal details useful in training. Question is how schema should looks like for including tool calling in dataset?

I'm developing a macOS application using the FoundationModels framework (LanguageModelSession) and encountering issues with the content sanitizer blocking legitimate text input. ** Issue Description:** The content sanitizer is flagging text strings that contain certain substrings, even when they represent legitimate technical content. For example: F_SEEL_SEX1S.wav (sE Electronics SEX1S microphone model) Technical product identifiers Serial numbers and version codes ** Broader Concern:** The content sanitizer appears to be applying restrictions that seem inappropriate for user-owned content. Even if a filename were something like "human sex.wav", users should have the right to process their own legitimate files on their own devices without content filtering interference. ** Error Messages:** SensitiveContentSettings: Sanitizer model found unsafe content in value FoundationModels.LanguageModelSession.GenerationError error 2 ** Questions:** Is there a way to disable content sanitization for processing user-owned content? 2. What's the recommended approach for applications that need to handle arbitrary user text? 3. Are there APIs to process personal content without filtering restrictions? ** Environment:** macOS 26.0 FoundationModels framework LanguageModelSession Any guidance would be appreciated.

Hi I'm having a problem with DataScannerViewController, I'm using the volume barcode scanning feature in my app, prior to that I was using an AVCaptureDevice with the UltraWideAngle set. After discovering DataScannerViewController, we planned to replace the previous obsolete code with DataScannerViewController, all together it was ok, when I want to set the ultra wide angle, I don't know how to start. I tried to get the minZoomFactor and I realized that I get 0.0 I tried to set zoomFactor to 1.0 and I found that he is not valid Note: func dataScannerDidZoom(_ dataScanner: DataScannerViewController), when I try to get the minZoomFactor, set the zoomFactor in this proxy method, I find that it is valid! What should I do next, I want to use only DataScannerViewController and implement ultra wide angle Thanks a lot.

Keep getting error : I have tried Picker for File, Photo Library , both same results . Debugging the resize for 360x360 but still facing this error. The model I'm trying to implement is created with CreateMLComponents The process is from example of WWDC 2022 Banana Ripeness , I have used index for each .jpg . Prediction Failed: The VNCoreMLTransform request failed Is there some possible way to solve it or is error somewhere in training of model ?