Environment MacOC 26 Xcode Version 26.0 beta 7 (17A5305k) simulator: iPhone 16 pro iOS: iOS 26 Problem NLContextualEmbedding.load() fails with the following error In simulator Failed to load embedding from MIL representation: filesystem error: in create_directories: Permission denied ["/var/db/com.apple.naturallanguaged/com.apple.e5rt.e5bundlecache"] filesystem error: in create_directories: Permission denied ["/var/db/com.apple.naturallanguaged/com.apple.e5rt.e5bundlecache"] Failed to load embedding model 'mul_Latn' - '5C45D94E-BAB4-4927-94B6-8B5745C46289' assetRequestFailed(Optional(Error Domain=NLNaturalLanguageErrorDomain Code=7 "Embedding model requires compilation" UserInfo={NSLocalizedDescription=Embedding model requires compilation})) in #Playground I'm new to this embedding model. Not sure if it's caused by my code or environment. Code snippet import Foundation import NaturalLanguage import Playgrounds #Playground { // Prefer initializing by script for broader coverage; returns NLContextualEmbedding? guard let embeddingModel = NLContextualEmbedding(script: .latin) else { print("Failed to create NLContextualEmbedding") return } print(embeddingModel.hasAvailableAssets) do { try embeddingModel.load() print("Model loaded") } catch { print("Failed to load model: \(error)") } }

Lately I am getting this error. GenerativeModelsAvailability.Parameters: Initialized with invalid language code: en-GB. Expected to receive two-letter ISO 639 code. e.g. 'zh' or 'en'. Falling back to: en Does anyone know what this is and how it can be resolved. The error does not crash the app

Trying the Foundation Model framework and when I try to run several sessions in a loop, I'm getting a thrown error that I'm hitting a rate limit. Are these rate limits documented? What's the best practice here? I'm trying to run the models against new content downloaded from a web service where I might get ~200 items in a given download. They're relatively small but there can be that many that want to be processed in a loop.

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?

Greetings! I was trying to get a response from the LanguageModelSession but I just keep getting the following: Error getting response: 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} This occurs both in macOS 15.5 running the new Xcode beta with an iOS 26 simulator, and also on a macOS 26 with Xcode beta. The simulators are both Pro iPhone 16s. I was wondering if anyone had any advice?

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.

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) }

Itself been 4-5 days my Image playground has showing the “Downloading Support for Image Playground “

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).}}}

Hi, unfortunately I am not able to verify this but I remember some time ago I was able to create CoreML models that had one (or more) inputs with an enumerated shape size, and one (or more) inputs with a static shape. This was some months ago. Since then I updated my MacOS to Sequoia 15.5, and when I try to execute MLModels with this setup I get the following error libc++abi: terminating due to uncaught exception of type CoreML::MLNeuralNetworkUtilities::AsymmetricalEnumeratedShapesException: A model doesn't allow input features with enumerated flexibility to have unequal number of enumerated shapes, but input feature global_write_indices has 1 enumerated shapes and input feature input_hidden_states has 3 enumerated shapes. It may make sense (but not really though) to verify that for inputs with a flexible enumerated shape they all have the same number of possible shapes is the same, but this should not impede the possibility of also having static shape inputs with a single shape defined alongside the flexible shape inputs.

We're in the process of migrating our app's custom intents from the older SiriKit Custom Intents framework to App Intents. The migration has been straightforward for our app-specific actions, and we appreciate the improved discoverability and Apple Intelligence integration that App Intents provides. However, we also implement SiriKit domain intents for calling and messaging: INStartCallIntent / INStartCallIntentHandling INSendMessageIntent / INSendMessageIntentHandling These require us to maintain an Intents Extension to handle contact resolution and the actual call/message operations. Our questions: Is there a planned App Intents equivalent for these SiriKit domains (calling, messaging), or is the Intents Extension approach still the recommended path? If we want to support phrases like "Call [contact] on [AppName]" or "Send a message to [contact] on [AppName]" with Apple Intelligence integration, is there any way to achieve this with App Intents today? Are there any WWDC sessions or documentation we may have missed that addresses the migration path for SiriKit domain intents? What we've reviewed: "Migrate custom intents to App Intents" Tech Talk "Bring your app's core features to users with App Intents" (WWDC24) App Intents documentation These resources clearly explain custom intent migration but don't seem to address the system domain intents. Our current understanding: Based on our research, it appears SiriKit domain intents should remain on the older framework, while custom intents should migrate to App Intents. We'd like to confirm this is correct and understand if there's a future direction we should be planning for. Thank you!

I’m seeing consistent failures using SoundAnalysis live classification when my app moves to the background. Setup iOS 17.x AVAudioEngine mic capture SNAudioStreamAnalyzer SNClassifySoundRequest(classifierIdentifier: .version1) UIBackgroundModes = audio AVAudioSession .record / .playAndRecord, active Audio capture + level metering continue working in background (mic indicator stays on) Issue As soon as the app enters background / screen locks: SoundAnalysis starts failing every second with domain:com.apple.SoundAnalysis, code:2(SNErrorCode.operationFailed) Audio capture itself continues normally When the app returns to foreground, classification immediately resumes without restarting the engine/analyzer Question Is live background sound classification with the built-in SoundAnalysis classifier officially unsupported or known to fail in background? If so, is a custom Core ML model the only supported approach for background detection? Or is there a required configuration I’m missing to keep SNClassifySoundRequest(.version1) running in background? Thanks for any clarification.

Somehow I'm not able to decrypt our ml models on my machine. It does not matter: If I clean the build / delete the build folder If it's a local build or a build downloaded from our build server I log in as a different user I reboot my system (15.4.1 (24E263) I use a different network Re-generate the encryption keys. I'm the only one in my team confronted with this issue. Using the encrypted models works fine for everyone else. As soon as our application tries to load the bundled ml model the following error is logged and returned: Could not create persistent key blob for CD49E04F-1A42-4FBE-BFC1-2576B89EC233 : error=Error Domain=com.apple.CoreML Code=9 "Failed to generate key request for CD49E04F-1A42-4FBE-BFC1-2576B89EC233 with error: -42908" Error code 9 points to a decryption issue, but offers no useful pointers and suggests that some sort of network request needs to be made in order to decrypt our models. /*! Core ML throws/returns this error when the framework encounters an error in the model decryption subsystem. The typical cause for this error is in the key server configuration and the client application cannot do much about it. For example, a model loading method will throw/return the error when it uses incorrect model decryption key. */ MLModelErrorModelDecryption API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) = 9, I could not find a reference to error '-42908' anywhere. ChatGPT just lied to me, as usual... How do can I resolve this or diagnose this further? 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?

The documentation for the Create ML tool ("Building an object detector data source") mentions that there are options for using normalized values instead of pixels and also different anchor point origins ("MLBoundingBoxCoordinatesOrigin") instead of always using "center". However, the JSON format for these does not appear in any examples. Does anyone know the format for these options?

I am writing an app that parses text and conducts some actions. I don't want to give too much away ;) However, I am having a huge problem with token sizes. LanguageModelSession will of course give me the on device model 4096 available, but when you go over 4096, my code doesn't seem to be falling back to PCC, or even the system configured ChatGPT. Can anyone assist me with this? For some reason, after reading the docs, it's very unclear how this transition between the three takes place.

Hello everybody! I’m encountering an unexpected guardrailViolation error when using Foundation Models on macOS Beta 3 (Tahoe) with an Apple M2 Pro chip. This issue didn’t occur on Beta 1 or Beta 2 using the same codebase. Reproduction Context I’m developing an app that leverages Foundation Models for structured generation, paired with a local database tool. After upgrading to macOS Beta 3, I started receiving this error consistently, despite no changes in the generation logic. To isolate the issue, I opened the official WWDC sample project from the Adding intelligent app features with generative models and the same guardrailViolation error appeared without any modifications. Simplified Working Example I attempted to narrow down the issue by starting with a minimal prompt structure. This basic case works fine: import Foundation import Playgrounds import FoundationModels @Generable struct GeneableLandmark { @Guide(description: "Name of the landmark to visit") var name: String } final class LandmarkSuggestionGenerator { var landmarkSuggestion: GeneableLandmark.PartiallyGenerated? private var session: LanguageModelSession init(){ self.session = LanguageModelSession( instructions: Instructions { """ generate a list of landmarks to visit """ } ) } func createLandmarkSuggestion(location: String) async throws { let stream = session.streamResponse( generating: GeneableLandmark.self, options: GenerationOptions(sampling: .greedy), includeSchemaInPrompt: false ) { """ Generate a list of landmarks to viist in \(location) """ } for try await partialResponse in stream { landmarkSuggestion = partialResponse } } } #Playground { let generator = LandmarkSuggestionGenerator() Task { do { try await generator.createLandmarkSuggestion(location: "New york") if let suggestion = generator.landmarkSuggestion { print("Suggested landmark: \(suggestion)") } else { print("No suggestion generated.") } } catch { print("Error generating landmark suggestion: \(error)") } } } But as soon as I use the Sample ItineraryPlanner: #Playground { // Example landmark for demonstration let exampleLandmark = Landmark( id: 1, name: "San Francisco", continent: "North America", description: "A vibrant city by the bay known for the Golden Gate Bridge.", shortDescription: "Iconic Californian city.", latitude: 37.7749, longitude: -122.4194, span: 0.2, placeID: nil ) let planner = ItineraryPlanner(landmark: exampleLandmark) Task { do { try await planner.suggestItinerary(dayCount: 3) if let itinerary = planner.itinerary { print("Suggested itinerary: \(itinerary)") } else { print("No itinerary generated.") } } catch { print("Error generating itinerary: \(error)") } } } The error pops up: Multiline Error generating itinerary: guardrailViolation(FoundationModels.LanguageModelSession. >GenerationError.Context(debug Description: "May contain sensitive or unsafe content", >underlyingErrors: [FoundationModels. LanguageModelSession. Gene >rationError.guardrailViolation(FoundationMo dels. >LanguageModelSession.GenerationError.C ontext (debugDescription: >"May contain unsafe content", underlyingErrors: []))])) Based on my tests: The error may not be tied to structure complexity (since more nested structures work) The issue may stem from the tools or prompt content used inside the ItineraryPlanner The guardrail sensitivity may have increased or changed in Beta 3, affecting models that worked in earlier betas Thank you in advance for your help. Let me know if more details or reproducible code samples are needed - I’m happy to provide them. Best, Sasha Morozov

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)

When I use the FoundationModel framework to generate long text, it will always hit an error. "Passing along Client rate limit exceeded, try again later in response to ExecuteRequest" And stop generating. eg. for the prompt "Write a long story", it will almost certainly hit that error after 17 seconds of generation. do{ let session = LanguageModelSession() let prompt: String = "Write a long story" let response = try await session.respond(to: prompt) }catch{} If possible, I want to know how to prevent that error or at least how to handle it.

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