Hi We're on tensorflow 2.20 that has support now for python 3.13 (finally!). tensorflow-metal is still only supporting 2.18 which is over a year old. When can we expect to see support in tensorflow-metal for tf 2.20 (or later!) ? I bought a mac thinking I would be able to get great performance from the M processors but here I am using my CPU for my ML projects. If it's taking so long to release it, why not open source it so the community can keep it more up to date? cheers Matt

At WWDC25 we launched a new type of Lab event for the developer community - Group Labs. A Group Lab is a panel Q&A designed for a large audience of developers. Group Labs are a unique opportunity for the community to submit questions directly to a panel of Apple engineers and designers. Here are the highlights from the WWDC25 Group Lab for Machine Learning and AI Frameworks. What are you most excited about in the Foundation Models framework? The Foundation Models framework provides access to an on-device Large Language Model (LLM), enabling entirely on-device processing for intelligent features. This allows you to build features such as personalized search suggestions and dynamic NPC generation in games. The combination of guided generation and streaming capabilities is particularly exciting for creating delightful animations and features with reliable output. The seamless integration with SwiftUI and the new design material Liquid Glass is also a major advantage. When should I still bring my own LLM via CoreML? It's generally recommended to first explore Apple's built-in system models and APIs, including the Foundation Models framework, as they are highly optimized for Apple devices and cover a wide range of use cases. However, Core ML is still valuable if you need more control or choice over the specific model being deployed, such as customizing existing system models or augmenting prompts. Core ML provides the tools to get these models on-device, but you are responsible for model distribution and updates. Should I migrate PyTorch code to MLX? MLX is an open-source, general-purpose machine learning framework designed for Apple Silicon from the ground up. It offers a familiar API, similar to PyTorch, and supports C, C++, Python, and Swift. MLX emphasizes unified memory, a key feature of Apple Silicon hardware, which can improve performance. It's recommended to try MLX and see if its programming model and features better suit your application's needs. MLX shines when working with state-of-the-art, larger models. Can I test Foundation Models in Xcode simulator or device? Yes, you can use the Xcode simulator to test Foundation Models use cases. However, your Mac must be running macOS Tahoe. You can test on a physical iPhone running iOS 18 by connecting it to your Mac and running Playgrounds or live previews directly on the device. Which on-device models will be supported? any open source models? The Foundation Models framework currently supports Apple's first-party models only. This allows for platform-wide optimizations, improving battery life and reducing latency. While Core ML can be used to integrate open-source models, it's generally recommended to first explore the built-in system models and APIs provided by Apple, including those in the Vision, Natural Language, and Speech frameworks, as they are highly optimized for Apple devices. For frontier models, MLX can run very large models. How often will the Foundational Model be updated? How do we test for stability when the model is updated? The Foundation Model will be updated in sync with operating system updates. You can test your app against new model versions during the beta period by downloading the beta OS and running your app. It is highly recommended to create an "eval set" of golden prompts and responses to evaluate the performance of your features as the model changes or as you tweak your prompts. Report any unsatisfactory or satisfactory cases using Feedback Assistant. Which on-device model/API can I use to extract text data from images such as: nutrition labels, ingredient lists, cashier receipts, etc? Thank you. The Vision framework offers the RecognizeDocumentRequest which is specifically designed for these use cases. It not only recognizes text in images but also provides the structure of the document, such as rows in a receipt or the layout of a nutrition label. It can also identify data like phone numbers, addresses, and prices. What is the context window for the model? What are max tokens in and max tokens out? The context window for the Foundation Model is 4,096 tokens. The split between input and output tokens is flexible. For example, if you input 4,000 tokens, you'll have 96 tokens remaining for the output. The API takes in text, converting it to tokens under the hood. When estimating token count, a good rule of thumb is 3-4 characters per token for languages like English, and 1 character per token for languages like Japanese or Chinese. Handle potential errors gracefully by asking for shorter prompts or starting a new session if the token limit is exceeded. Is there a rate limit for Foundation Models API that is limited by power or temperature condition on the iPhone? Yes, there are rate limits, particularly when your app is in the background. A budget is allocated for background app usage, but exceeding it will result in rate-limiting errors. In the foreground, there is no rate limit unless the device is under heavy load (e.g., camera open, game mode). The system dynamically balances performance, battery life, and thermal conditions, which can affect the token throughput. Use appropriate quality of service settings for your tasks (e.g., background priority for background work) to help the system manage resources effectively. Do the foundation models support languages other than English? Yes, the on-device Foundation Model is multilingual and supports all languages supported by Apple Intelligence. To get the model to output in a specific language, prompt it with instructions indicating the user's preferred language using the locale API (e.g., "The user's preferred language is en-US"). Putting the instructions in English, but then putting the user prompt in the desired output language is a recommended practice. Are larger server-based models available through Foundation Models? No, the Foundation Models API currently only provides access to the on-device Large Language Model at the core of Apple Intelligence. It does not support server-side models. On-device models are preferred for privacy and for performance reasons. Is it possible to run Retrieval-Augmented Generation (RAG) using the Foundation Models framework? Yes, it is possible to run RAG on-device, but the Foundation Models framework does not include a built-in embedding model. You'll need to use a separate database to store vectors and implement nearest neighbor or cosine distance searches. The Natural Language framework offers simple word and sentence embeddings that can be used. Consider using a combination of Foundation Models and Core ML, using Core ML for your embedding model.

Attempted to download the Adapter Toolkit linked to from https://developer.apple.com/apple-intelligence/foundation-models-adapter/. Failed on all attempts, with a "403 Forbidden" error. I had accepted the agreement on the first attempt. How would we get access please?

When I ran the following code on a physical iPhone device that supports Apple Intelligence, I encountered the following error log. What does this internal error code mean? Image generation failed with NSError in a different domain: Error Domain=ImagePlaygroundInternal.ImageGeneration.GenerationError Code=11 “(null)”, returning a generic error instead let imageCreator = try await ImageCreator() let style = imageCreator.availableStyles.first ?? .animation let stream = imageCreator.images(for: [.text("cat")], style: style, limit: 1) for try await result in stream { // error: ImagePlayground.ImageCreator.Error.creationFailed _ = result.cgImage }

I have an app that streams in data from the Foundation Model and I have a card that shows one of the outputs. I want my card to accept a partially generated model but I keep getting a nonsensical error. The error I get on line 59 is: Cannot convert value of type 'FrostDate.VegetableSuggestion.PartiallyGenerated' (aka 'FrostDate.VegetableSuggestion') to expected argument type 'FrostDate.VegetableSuggestion.PartiallyGenerated' Here is my card with preview: import SwiftUI import FoundationModels struct VegetableSuggestionCard: View { let vegetableSuggestion: VegetableSuggestion.PartiallyGenerated init(vegetableSuggestion: VegetableSuggestion.PartiallyGenerated) { self.vegetableSuggestion = vegetableSuggestion } var body: some View { VStack(alignment: .leading, spacing: 8) { if let name = vegetableSuggestion.vegetableName { Text(name) .font(.headline) .frame(maxWidth: .infinity, alignment: .leading) } if let startIndoors = vegetableSuggestion.startSeedsIndoors { Text("Start indoors: \(startIndoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let startOutdoors = vegetableSuggestion.startSeedsOutdoors { Text("Start outdoors: \(startOutdoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let transplant = vegetableSuggestion.transplantSeedlingsOutdoors { Text("Transplant: \(transplant)") .frame(maxWidth: .infinity, alignment: .leading) } if let tips = vegetableSuggestion.tips { Text("Tips: \(tips)") .foregroundStyle(.secondary) .frame(maxWidth: .infinity, alignment: .leading) } } .padding(16) .frame(maxWidth: .infinity, alignment: .leading) .background( RoundedRectangle(cornerRadius: 16, style: .continuous) .fill(.background) .overlay( RoundedRectangle(cornerRadius: 16, style: .continuous) .strokeBorder(.quaternary, lineWidth: 1) ) .shadow(color: Color.black.opacity(0.05), radius: 6, x: 0, y: 2) ) } } #Preview("Vegetable Suggestion Card") { let sample = VegetableSuggestion.PartiallyGenerated( vegetableName: "Tomato", startSeedsIndoors: "6–8 weeks before last frost", startSeedsOutdoors: "After last frost when soil is warm", transplantSeedlingsOutdoors: "1–2 weeks after last frost", tips: "Harden off seedlings; provide full sun and consistent moisture." ) VegetableSuggestionCard(vegetableSuggestion: sample) .padding() .previewLayout(.sizeThatFits) }

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?

I have a MacBook Pro M3 Pro with 18GB of RAM and was following the instructions to fine tune the foundational model given here: https://developer.apple.com/apple-intelligence/foundation-models-adapter/ However, while following the code sample in the example Jupyter notebook, my Mac hangs on the second code cell. Specifically: from examples.generate import generate_content, GenerationConfiguration from examples.data import Message output = generate_content( [[ Message.from_system("A conversation between a user and a helpful assistant. Taking the role as a play writer assistant for a kids' play."), Message.from_user("Write a script about penguins.") ]], GenerationConfiguration(temperature=0.0, max_new_tokens=128) ) output[0].response After some debugging, I was getting the following error: RuntimeError: MPS backend out of memory (MPS allocated: 22.64 GB, other allocations: 5.78 MB, max allowed: 22.64 GB). Tried to allocate 52.00 MB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure). So is my machine not capable enough to adapter train Apple's Foundation Model? And if so, what's the recommended spec and could this be specified somewhere? Thanks!

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.

Application is getting Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0 Crashed: AXSpeech 0 libobjc.A.dylib 0x4820 objc_msgSend + 32 1 libsystem_trace.dylib 0x6c34 _os_log_fmt_flatten_object + 116 2 libsystem_trace.dylib 0x5344 _os_log_impl_flatten_and_send + 1884 3 libsystem_trace.dylib 0x4bd0 _os_log + 152 4 libsystem_trace.dylib 0x9c48 _os_log_error_impl + 24 5 TextToSpeech 0xd0a8c _pcre2_xclass_8 6 TextToSpeech 0x3bc04 TTSSpeechUnitTestingMode 7 TextToSpeech 0x3f128 TTSSpeechUnitTestingMode 8 AXCoreUtilities 0xad38 -[NSArray(AXExtras) ax_flatMappedArrayUsingBlock:] + 204 9 TextToSpeech 0x3eb18 TTSSpeechUnitTestingMode 10 TextToSpeech 0x3c948 TTSSpeechUnitTestingMode 11 TextToSpeech 0x48824 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 12 TextToSpeech 0x49804 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 13 Foundation 0xf6064 __NSThreadPerformPerform + 264 14 CoreFoundation 0x37acc CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION + 28 15 CoreFoundation 0x36d48 __CFRunLoopDoSource0 + 176 16 CoreFoundation 0x354fc __CFRunLoopDoSources0 + 244 17 CoreFoundation 0x34238 __CFRunLoopRun + 828 18 CoreFoundation 0x33e18 CFRunLoopRunSpecific + 608 19 Foundation 0x2d4cc -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 212 20 TextToSpeech 0x24b88 TTSCFAttributedStringCreateStringByBracketingAttributeWithString 21 Foundation 0xb3154 NSThread__start + 732 com.livingMedia.AajTakiPhone_issue_3ceba855a8ad2d1af83655803dc13f70_crash_session_9081fa41ced440ae9a57c22cb432f312_DNE_0_v2_stacktrace.txt 22 libsystem_pthread.dylib 0x24d4 _pthread_start + 136 23 libsystem_pthread.dylib 0x1a10 thread_start + 8

I just recently updated to iOS 26 beta (23A5336a) to test an app I am developing I running an MLModel loaded from a .mlmodelc file. On the current iOS version 18.6.2 the model is running as expected with no issues. However on iOS 26 I am now getting error when trying to perform an inference to the model where I pass a camera frame into it. Below is the error I am seeing when I attempt to run an inference. at the bottom it says "Failed with status=0x1d : statusType=0x9: Program Inference error status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model " does this indicate I need to convert my model or something? I don't understand since it runs as normal on iOS 18. Any help getting this to run again would be greatly appreciated. Thank you, processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: Could not process request ret=0x1d lModel=_ANEModel: { modelURL=file:///var/containers/Bundle/Application/04F01BF5-D48B-44EC-A5F6-3C7389CF4856/RizzCanvas.app/faceParsing.mlmodelc/ : sourceURL=(null) : UUID=46228BFC-19B0-45BF-B18D-4A2942EEC144 : key={"isegment":0,"inputs":{"input":{"shape":[512,512,1,3,1]}},"outputs":{"var_633":{"shape":[512,512,1,19,1]},"94_argmax_out_value":{"shape":[512,512,1,1,1]},"argmax_out":{"shape":[512,512,1,1,1]},"var_637":{"shape":[512,512,1,19,1]}}} : identifierSource=1 : cacheURLIdentifier=01EF2D3DDB9BA8FD1FDE18C7CCDABA1D78C6BD02DC421D37D4E4A9D34B9F8181_93D03B87030C23427646D13E326EC55368695C3F61B2D32264CFC33E02FFD9FF : string_id=0x00000000 : program=_ANEProgramForEvaluation: { programHandle=259022032430 : intermediateBufferHandle=13949 : queueDepth=127 } : state=3 : [Espresso::ANERuntimeEngine::__forward_segment 0] evaluate[RealTime]WithModel returned 0; code=8 err=Error Domain=com.apple.appleneuralengine Code=8 "processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error" UserInfo={NSLocalizedDescription=processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error} [Espresso::handle_ex_plan] exception=Espresso exception: "Generic error": ANEF error: /private/var/containers/Bundle/Application/04F01BF5-D48B-44EC-A5F6-3C7389CF4856/RizzCanvas.app/faceParsing.mlmodelc/model.espresso.net, processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1). Error Domain=com.apple.Vision Code=3 "The VNCoreMLTransform request failed" UserInfo={NSLocalizedDescription=The VNCoreMLTransform request failed, NSUnderlyingError=0x114d92940 {Error Domain=com.apple.CoreML Code=0 "Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1)." UserInfo={NSLocalizedDescription=Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1).}}}

I'd love to add a feature based on FoundationModels to the Mac Catalyst version of my iOS app. Unfortunately I get an error when importing FoundationModels: No such module 'FoundationModels'. Documentation says Mac Catalyst is supported: https://developer.apple.com/documentation/foundationmodels I can create iOS builds using the FoundationModels framework without issues. Hope this will be fixed soon! Config: Xcode 26.0 beta (17A5241e) macOS 26.0 Beta (25A5279m) 15-inch, M4, 2025 MacBook Air

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?

Hi, I have been trying to integrate a CoreML model into Xcode. The model was made using tensorflow layers. I have included both the model info and a link to the app repository. I am mainly just really confused on why its not working. It seems to only be printing the result for case 1 (there are 4 cases labled, case 0, case 1, case 2, and case 3). If someone could help work me through this error that would be great! here is the link to the repository: https://github.com/ShivenKhurana1/Detect-to-Protect-App this file with the model code is called SecondView.swift and here is the model info: Input: conv2d_input-> image (color 224x224) Output: Identity -> MultiArray (Float32 1x4)

Almost everywhere else you see Apple Intelligence, you get to select whether it's on device, private cloud compute, or ChatGPT. Is there a way to do that via code in the Foundation Model? I searched through the docs and couldn't find anything, but maybe I missed it.

I am calling into an app extension from a Safari Web Extension (sendNativeMessage, which in turn results in a call to NSExtensionRequestHandling’s beginRequest). My Safari extension aims to make use of the new foundation models for some of the features it provides. In my testing, I hit the rate limit by sending 4 requests, waiting 30 seconds between each. This makes the FoundationModels framework (which would otherwise serve my use case perfectly well) unusable in this context, because the model is called in response to user input, and this rate of user input is perfectly plausible in a real world scenario. The error thrown as a result of the rate limit is “Safety guardrail was triggered after consecutive failures during streaming.", but looking at the system logs in Console.app shows the rate limit as the real culprit. My suggestions: Please introduce sensible rate limits for app extensions, through an entitlement if need be. If it is rate limited to 1 request per every couple of seconds, that would already fix the issue for me. Please document the rate limit. Please make the thrown error reflect that it is the result of a rate limit and not a generic guardrail violation. IMPORTANT: please indicate in the thrown error when it is safe to try again. Filed a feedback here: FB18332004

After watching the What's new in App Intents session I'm attempting to create an intent conforming to URLRepresentableIntent. The video states that so long as my AppEntity conforms to URLRepresentableEntity I should not have to provide a perform method . My application will be launched automatically and passed the appropriate URL. This seems to work in that my application is launched and is passed a URL, but the URL is in the form: FeatureEntity/{id}. Am I missing something, or is there a trick that enables it to pass along the URL specified in the AppEntity itself? struct MyExampleIntent: OpenIntent, URLRepresentableIntent { static let title: LocalizedStringResource = "Open Feature" static var parameterSummary: some ParameterSummary { Summary("Open \(\.$target)") } @Parameter(title: "My feature", description: "The feature to open.") var target: FeatureEntity } struct FeatureEntity: AppEntity { // ... } extension FeatureEntity: URLRepresentableEntity { static var urlRepresentation: URLRepresentation { "https://myurl.com/\(.id)" } }

We get two error message in Xcode debug. apple.model.catalog we get 1 time at startup, and the nw_protocol_instance_set_output_handler Not calling remove_input_handler on 0x152ac3c00:udp we get on sartup and some time during running of the app. I have tested cutoff repos WS eg. But nothing helpss, thats for the nw_protocol. We have a fondationmodel in a repo but we check if it is available if not we do not touch it. Please help me? nw_protocol_instance_set_output_handler Not calling remove_input_handler on 0x152ac3c00:udp com.apple.modelcatalog.catalog sync: connection error during call: Error Domain=NSCocoaErrorDomain Code=4099 "The connection to service named com.apple.modelcatalog.catalog was invalidated: Connection init failed at lookup with error 159 - Sandbox restriction." UserInfo={NSDebugDescription=The connection to service named com.apple.modelcatalog.catalog was invalidated: Connection init failed at lookup with error 159 - Sandbox restriction.} reached max num connection attempts: 1 The function we have in the repo is this: public actor FoundationRepo: JobDescriptionChecker, SubskillSuggester { private var session: LanguageModelSession? private let isEnabled: Bool private let shouldUseLocalFoundation: Bool private let baseURLString = "https://xx.xx.xxx/xx" private let http: HTTPPac public init(http: HTTPPac, isEnabled: Bool = true) { self.http = http self.isEnabled = isEnabled self.session = nil guard isEnabled else { self.shouldUseLocalFoundation = false return } let model = SystemLanguageModel.default guard model.supportsLocale() else { self.shouldUseLocalFoundation = false return } switch model.availability { case .available: self.shouldUseLocalFoundation = true case .unavailable(.deviceNotEligible), .unavailable(.appleIntelligenceNotEnabled), .unavailable(.modelNotReady): self.shouldUseLocalFoundation = false @unknown default: self.shouldUseLocalFoundation = false } } So here we decide if we are going to use iPhone ML or my backend-remote?

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!

Hello It seems the model Content Tagging doesn't obey when I define the type of tag I wish in the instructions parameters, always the output are the main topics. The unique form to get other type of tags like emotions is using Generable + Guided types. The documentation says it is recommended but not mandatory the use instructions. Maybe I'm setting wrongly the instructions but take a look in the attached snapshot. I copied the definition of tagging emotions from the official documentation. The upper example is employing generable and it works but in the example at the botton I set like instruction the same description of emotion and it doesn't work. I tried with other statements with more or less verbose and never output emotions. Could you provide a state using instruction where it works? Current version of model isn't working with instruction?

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?