I am writing to inquire about content exclusion capabilities within Apple Intelligence, particularly regarding the use of configuration files such as .aiignore or .aiexclude—similar to what exists in other AI-assisted coding tools. These mechanisms are highly valuable in managing what content AI systems can access, especially in environments that involve sensitive code or proprietary frameworks. I would appreciate it if anyone could clarify whether Apple Intelligence currently supports any exclusion configuration for AI-assisted features. If so, could you kindly provide documentation or guidance on how developers can implement these controls? If not, Is there any plan to include such feature in future updates?

Hello, is it allowed to use Foundation Model Framework in submission app for WWDC26? The thing is that Apple Intelligence needs to be enabled in the settings. So, does that mean the jury won't be able to fully utilize the app's AI functionality?

I am excited to try Foundation Models during WWDC, but it doesn't work at all for me. When running on my iPad Pro M4 with iPadOS 26 seed 1, I get the following error even when running the simplest query: let prompt = "How are you?" let stream = session.streamResponse(to: prompt) for try await partial in stream { self.answer = partial self.resultString = partial } In the Xcode console, I see the following error: assetsUnavailable(FoundationModels.LanguageModelSession.GenerationError.Context(debugDescription: "Model is unavailable", underlyingErrors: [])) I have verified that Apple Intelligence is enabled on my iPad. Any tips on how can I get it working? I have also submitted this feedback: FB17896752

In the name of God, please allow initializing GeneratedContent from an array of key-value pairs. It’s literally the same thing KeyValuePairs uses internally, but it would let us initialize structure-like GeneratedContent from dynamic data without resorting to unsafeBitCast hacks.

I'm attempting to run a basic Foundation Model prototype in Xcode 26, but I'm getting the error below, using the iPhone 16 simulator with iOS 26. Should these models be working yet? Do I need to be running macOS 26 for these to work? (I hope that's not it) Error: Passing along Model Catalog error: 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.MobileAsset.UAF.FM.Overrides" UserInfo={NSLocalizedFailureReason=There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.MobileAsset.UAF.FM.Overrides} in response to ExecuteRequest Playground to reproduce: #Playground { let session = LanguageModelSession() do { let response = try await session.respond(to: "What's happening?") } catch { let error = error } }

In macOS Tahoe 26.2 an RDMA capability was added for Thunderbolt-5 interfaces. This has been demonstrated to significantly decrease the latency and maintain bandwidth for "clustered" Apple Silicon devices with TB5. What is the ideal and the maximum RDMA burst width for transfers over RDMA-enabled Thunderbolt-5 interfaces?

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?

Is there any way to ensure iOS apps we develop using Foundation Models can only be purchasable/downloadable on App Store by folks with capable devices? I would've thought there would be a Required Capabilities that App Store would hook into, but I don't seem to see it in the documentation here: https://developer.apple.com/documentation/bundleresources/information-property-list/uirequireddevicecapabilities The closest seems to be iphone-performance-gaming-tier as that seems to target all M1 and above chips on iPhone & iPad. There is an ipad-minimum-performance-m1 that would more reasonably seem to ensure Foundation Models is likely available, but that doesn't help with iPhone. So far, it seems the only path would be to set Minimum Deployment to iOS 26 and add iphone-performance-gaming-tier as a required capability, but I'm a bit worried that capability might diverge in the future from what's Foundation Model / Apple Intelligence capable. While I understand for the majority of apps they'll want to just selectively add in Apple Intelligence features and so can be usable by folks whose devices don't support it, the app experience I'm building doesn't make sense without the Foundation Models being available and I'd rather not have a large number of users downloading the app to be told "Sorry, you're not Apple Intelligence capable"

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 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!

Is anything configurable for LanguageModelSession.Guardrails besides the default? I'm prototyping a camping app, and it's constantly slamming into guardrail errors when I use the new foundation model interface. Any subjects relating to fishing, survival, etc. won't generate. For example the prompt "How can I kill deer ticks using a clothing treatment?" returns a generation error. The results that I get are great when it works, but so far the local model sessions are extremely unreliable.

Context I trained a LoRA adapter for Apple’s on-device language model using the Foundation Models Adapter Training Toolkit v0.2.0 on macOS 26 beta 4. Although training completes successfully, loading the resulting .fmadapter package fails with: Adapter is not compatible with the current system base model. What I’ve Observed, Hard-coded Signature: In export/constants.py, the toolkit sets, BASE_SIGNATURE = "9799725ff8e851184037110b422d891ad3b92ec1" Metadata Injection: The export_fmadapter.py script writes this value into the adapter’s metadata: self_dict[MetadataKeys.BASE_SIGNATURE] = BASE_SIGNATURE Compatibility Check: At runtime, the Foundation Models framework compares the adapter’s baseModelSignature against the OS’s system model signature, and reports compatibleAdapterNotFound if they don’t match—without revealing the expected signature. Questions Signature Generation - What exactly does the toolkit hash to derive BASE_SIGNATURE? Is it a straight SHA-1 of base-model.pt, or is there an additional transformation? Recomputing for Beta 4 - Is there a way to locally compute the correct signature for the macOS 26 beta 4 system model? Toolkit Updates - Will Apple release Adapter Training Toolkit v0.3.0 with an updated BASE_SIGNATURE for beta 4, or is there an alternative workaround to generate it myself? Any guidance on how the Foundation Models framework derives and verifies the base model signature—or how to regenerate it for beta 4—would be greatly appreciated.

I am watching a few WWDC sessions on Foundation Model and its usage and it looks pretty cool. I was wondering if it is possible to perform RAG on the user documents on the devices and entuallly on iCloud... Let's say I have a lot of pages documents about me and I want the Foundation model to access those information on the documents to answer questions about me that can be retrieved from the documents. How can this be done ? Thanks

Hi everyone, I’m exploring ideas around on-device analysis of user typing behavior on iPhone, and I’d love input from others who’ve worked in this area or thought about similar problems. Conceptually, I’m interested in things like: High-level sentiment or tone inferred from what a user types over time using ML-models Identifying a user’s most important or frequent topics over a recent window (e.g., “last week”) Aggregated insights rather than raw text (privacy-preserving summaries: e.g., your typo-rate by hour to infer highly efficient time slots or "take-a-break" warning typing errors increase) I understand the significant privacy restrictions around keyboard input on iOS, especially for third-party keyboards and system text fields. I’m not trying to bypass those constraints—rather, I’m curious about what’s realistically possible within Apple’s frameworks and policies. (For instance, Grammarly as a correction tool includes some information about tone) Questions I’m thinking through: Are there any recommended approaches for on-device text analysis that don’t rely on capturing raw keystrokes? Has anyone used NLP / Core ML / Natural Language successfully for similar summarization or sentiment tasks, scoped only to user-explicit input? For custom keyboards, what kinds of derived or transient signals (if any) are acceptable to process and summarize locally? Any design patterns that balance usefulness with Apple’s privacy expectations? If you’ve built something adjacent—journaling, writing analytics, well-being apps, etc.—I’d appreciate hearing what worked, what didn’t, and what Apple reviewers were comfortable with. Thanks in advance for any ideas or references 🙏

I am using macOS Tahoe on Xcode 26.

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

I want to use Foundation Models in a project, but I know my users will want to avoid environmentally intensive AI work in data centers. Does Foundation Models ever use Private Compute Cloud or any other kind of cloud-based AI system? I'd like to be able to assure my users that the LLM usage is relatively environmentally friendly. It would be great to be able to cite a specific Apple page explaining that Foundation Models work is always done locally. If there's any chance that work can be done in the cloud, is there a way to opt out of that?

Using Tensorflow for Silicon gives inaccurate results when compared to Google Colab GPU (9-15% differences). Here are my install versions for 4 anaconda env's. I understand the Floating point precision can be an issue, batch size, activation functions but how do you rectify this issue for the past 3 years? 1.) Version TF: 2.12.0, Python 3.10.13, tensorflow-deps: 2.9.0, tensorflow-metal: 1.2.0, h5py: 3.6.0, keras: 2.12.0 2.) Version TF: 2.19.0, Python 3.11.0, tensorflow-metal: 1.2.0, h5py: 3.13.0, keras: 3.9.2, jax: 0.6.0, jax-metal: 0.1.1,jaxlib: 0.6.0, ml_dtypes: 0.5.1 3.) python: 3.10.13,tensorflow: 2.19.0,tensorflow-metal: 1.2.0, h5py: 3.13.0, keras: 3.9.2, ml_dtypes: 0.5.1 4.) Version TF: 2.16.2, tensorflow-deps:2.9.0,Python: 3.10.16, tensorflow-macos 2.16.2, tensorflow-metal: 1.2.0, h5py:3.13.0, keras: 3.9.2, ml_dtypes: 0.3.2 Install of Each ENV with common example: Create ENV: conda create --name TF_Env_V2 --no-default-packages start env: source TF_Env_Name ENV_1.) conda install -c apple tensorflow-deps , conda install tensorflow,pip install tensorflow-metal,conda install ipykernel ENV_2.) conda install pip python==3.11, pip install tensorflow,pip install tensorflow-metal,conda install ipykernel ENV_3) conda install pip python 3.10.13,pip install tensorflow, pip install tensorflow-metal,conda install ipykernel ENV_4) conda install -c apple tensorflow-deps, pip install tensorflow-macos, pip install tensor-metal, conda install ipykernel Example used on all 4 env: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64)

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 working on an app using FoundationModels to process web pages. I am looking to find ways to filter the input to fit within the token limits. I have unit tests, UI tests and the app running on an iPad in the simulator. It appears that the different configurations of the test environment seems to affect the token limits. That is, the same input in a unit test and UI test will hit different token limits. Is this correct? Or is this an artifact of my test tooling?