Access to VisionPro cameras is required for a research project. The project is on mixed reality software development for healthcare applications in dentistry.

Hi all, I’m encountering an issue when trying to run Apple Foundation Models in a blank project targeting iOS 26. Below are the details: Xcode: Latest version with iOS 26 SDK macOS: macOS 26 Tahoe (installed on main disk) Mac: 16” MacBook Pro with M2 Pro chip Apple Intelligence: Available and functional on this machine Problem: I created a new blank iOS project, set the deployment target to iOS 26, and ran the following minimal code using Foundation Models. However, I get no response at all in the output - not even an error. The app runs, but the model does not produce any output. #Playground { let session = LanguageModelSession() let response = try await session.respond(to: "Tell me a story") } Then, I tried to catch an error with this code: #Playground { let session = LanguageModelSession() do { let response = try await session.respond(to: "Tell me a story") print(response) } catch { print("Failed to get response:", error) } print("This line, never gets executed") } And got these results: I’ve done further testing and discovered something important: I tried running the Code Along sample project, and there the #Playground macro worked without issues. The only significant difference I noticed was the Canvas run destination: In my original project, I was using iPhone 16 Pro (iOS 26) as the run target in Canvas. Apple Intelligence was enabled on the simulator, but no response was returned when executing the prompt. In the sample project, the Canvas was running on My Mac. I attempted to match that setup, but at first, my destination was My Mac (Designed for iPad), which still didn’t work. The macro finally executed properly once I switched to My Mac (AppKit). So the question is ... it seems that for now, Foundation Models and the #Playground macro only run correctly when the canvas or destination is set to “My Mac (AppKit)”?

It appears that there is a size limit when training the Tabular Classification model in CreatML. When the training data is small, the training process completes smoothly after a specified period. However, as the data volume increases, the following issues occur: initially, the training process indicates that it is in progress, but after approximately 24 hours, it is automatically terminated after an hour. I am certain that this is not a manual termination by myself or others, but rather an automatic termination by the machine. This issue persists despite numerous attempts, and the only message displayed is “Training Canceled.” I would appreciate it if someone could explain the reason behind this behavior and provide a solution. Thank you for your assistance.

It seems like there was an undocumented change that made Transcript.init(entries: [Transcript.Entry] initializer private, which broke my application, which relies on (manual) reconstruction of Transcript entries. Worked fine on beta 1, on beta 2 there's this error dyld[72381]: Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <44342398-591C-3850-9889-87C9458E1440> /Users/mika/experiments/apple-on-device-ai/fm Expected in: <66A793F6-CB22-3D1D-A560-D1BD5B109B0D> /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels Is this a part of an API transition, if so - Apple, please update your documentation

Hey, Would be great to have an equivalent of toolCallId for both toolCall and toolResult in the transcript. Otherwise, it is hard to connect tool calls with their respective responses, when there were multiple parallel calls to the same tool. Thanks!

I'm seeing this error a lot in my console log of my iPhone 15 Pro (Apple Intelligence enabled): 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: failed at lookup with error 159 - Sandbox restriction." UserInfo={NSDebugDescription=The connection to service named com.apple.modelcatalog.catalog was invalidated: failed at lookup with error 159 - Sandbox restriction.} reached max num connection attempts: 1 Are there entitlements / permissions I need to enable in Xcode that I forgot to do? Code example Here's how I'm initializing the language model session: private func setupLanguageModelSession() { if #available(iOS 26.0, *) { let instructions = """ my instructions """ do { languageModelSession = try LanguageModelSession(instructions: instructions) print("Foundation Models language model session initialized") } catch { print("Error creating language model session: \(error)") languageModelSession = nil } } else { print("Device does not support Foundation Models (requires iOS 26.0+)") languageModelSession = nil } }

Hello! I have a swift program that tracks the location of a ball (through the back camera). It seems to be working fine, but the only issue is the run time, particularly my concatenate, normalize, and argmax functions, which are meant to be a 1 to 1 copy of the PyTorch argmax function and the following python lines: imgs = np.concatenate((img, img_prev, img_preprev), axis=2) imgs = imgs.astype(np.float32)/255.0 imgs = np.rollaxis(imgs, 2, 0) inp = np.expand_dims(imgs, axis=0) # used to pass into model However, I need my program to run in real time and in an ideal world, I want it to run way under real time. Below is a run down of the run times that result from my code: Starting model inference Setup took: 0.0 seconds Resize took: 0.03741896152496338 seconds Concatenation took: 0.3359949588775635 seconds Normalization took: 0.9906361103057861 seconds Model prediction took: 0.3425499200820923 seconds Argmax took: 28.17007803916931 seconds Postprocess took: 0.054128050804138184 seconds Model inference took 29.934185028076172 seconds Here are the concatenateBuffers, normalizeBuffers, and argmax functions that I use: func concatenateBuffers(_ buffers: [CVPixelBuffer?]) -> CVPixelBuffer? { guard buffers.count == 3, let first = buffers[0] else { return nil } let width = CVPixelBufferGetWidth(first) let height = CVPixelBufferGetHeight(first) let targetChannels = 9 var concatenated: CVPixelBuffer? let attrs = [kCVPixelBufferCGImageCompatibilityKey: kCFBooleanTrue] as CFDictionary CVPixelBufferCreate(kCFAllocatorDefault, width, height, kCVPixelFormatType_32BGRA, attrs, &concatenated) guard let output = concatenated else { return nil } CVPixelBufferLockBaseAddress(output, []) defer { CVPixelBufferUnlockBaseAddress(output, []) } guard let outputData = CVPixelBufferGetBaseAddress(output) else { return nil } let outputPtr = UnsafeMutablePointer<UInt8>(OpaquePointer(outputData)) // Lock all input buffers at once buffers.forEach { buffer in guard let buffer = buffer else { return } CVPixelBufferLockBaseAddress(buffer, .readOnly) } defer { buffers.forEach { CVPixelBufferUnlockBaseAddress($0!, .readOnly) } } // Process each input buffer for (frameIdx, buffer) in buffers.enumerated() { guard let buffer = buffer, let inputData = CVPixelBufferGetBaseAddress(buffer) else { continue } let inputPtr = UnsafePointer<UInt8>(OpaquePointer(inputData)) let bytesPerRow = CVPixelBufferGetBytesPerRow(buffer) let totalPixels = width * height // Process all pixels in one go for this frame for i in 0..<totalPixels { let y = i / width let x = i % width let inputOffset = y * bytesPerRow + x * 4 let outputOffset = i * targetChannels + frameIdx * 3 // BGR order to match numpy outputPtr[outputOffset] = inputPtr[inputOffset + 2] // B outputPtr[outputOffset + 1] = inputPtr[inputOffset + 1] // G outputPtr[outputOffset + 2] = inputPtr[inputOffset] // R } } return output } func normalizeBuffer(_ buffer: CVPixelBuffer?) -> MLMultiArray? { guard let input = buffer else { return nil } let width = CVPixelBufferGetWidth(input) let height = CVPixelBufferGetHeight(input) let channels = 9 CVPixelBufferLockBaseAddress(input, .readOnly) defer { CVPixelBufferUnlockBaseAddress(input, .readOnly) } guard let inputData = CVPixelBufferGetBaseAddress(input) else { return nil } let shape = [1, NSNumber(value: channels), NSNumber(value: height), NSNumber(value: width)] guard let output = try? MLMultiArray(shape: shape, dataType: .float32) else { return nil } let inputPtr = inputData.assumingMemoryBound(to: UInt8.self) let bytesPerRow = CVPixelBufferGetBytesPerRow(input) let ptr = UnsafeMutablePointer<Float>(OpaquePointer(output.dataPointer)) let totalSize = width * height for c in 0..<channels { for idx in 0..<totalSize { let h = idx / width let w = idx % width let inputIdx = h * bytesPerRow + w * channels + c ptr[c * totalSize + idx] = Float(inputPtr[inputIdx]) / 255.0 } } return output } func argmax(_ array: MLMultiArray) -> MLMultiArray? { let shape = array.shape.map { $0.intValue } guard shape.count == 3, shape[0] == 1, shape[1] == 256, shape[2] == 230400 else { return nil } guard let output = try? MLMultiArray(shape: [1, NSNumber(value: 230400)], dataType: .int32) else { return nil } let ptr = UnsafePointer<Float>(OpaquePointer(array.dataPointer)) let outputPtr = UnsafeMutablePointer<Int32>(OpaquePointer(output.dataPointer)) let channelSize = 230400 for pos in 0..<230400 { var maxValue = -Float.infinity var maxIndex: Int32 = 0 for channel in 0..<256 { let value = ptr[channel * channelSize + pos] if value > maxValue { maxValue = value maxIndex = Int32(channel) } } outputPtr[pos] = maxIndex } return output } Are there any glaring areas of inefficiencies that can be reduced to allow for under real time processing whilst following the same logic as found in the python code exactly? Would using Obj-C speed things up for some reason? Are there any tools I can use so I don't have to write these functions myself? Additionally, in the classes init, function, I tried to check the compute units being used since I feel 0.34 seconds for a singular model prediction is also far too long, but no print statements are showing for some reason: init() { guard let loadedModel = try? BallTrackerModel() else { fatalError("Could not load model") } let config = MLModelConfiguration() config.computeUnits = .all guard let configuredModel = try? BallTrackerModel(configuration: config) else { fatalError("Could not configure model") } self.model = configuredModel print("model loaded with compute units \(config.computeUnits.rawValue)") } Thanks!

Hi, I have trained a basic adapter using the adapter training toolkit. I am trying a very basic example of loading it and running inference with it, but am getting the following error: Passing along InferenceError::inferenceFailed::loadFailed::Error Domain=com.apple.TokenGenerationInference.E5Runner Code=0 "Failed to load model: ANE adapted model load failure: createProgramInstanceWithWeights:modelToken:qos:baseModelIdentifier:owningPid:numWeightFiles:error:: Program load new instance failure (0x170006)." UserInfo={NSLocalizedDescription=Failed to load model: ANE adapted model load failure: createProgramInstanceWithWeights:modelToken:qos:baseModelIdentifier:owningPid:numWeightFiles:error:: Program load new instance failure (0x170006).} in response to ExecuteRequest Any ideas / direction? For testing I am including the .fmadapter file inside the app bundle. This is where I load it: @State private var session: LanguageModelSession? // = LanguageModelSession() func loadAdapter() async throws { if let assetURL = Bundle.main.url(forResource: "qasc---afm---4-epochs-adapter", withExtension: "fmadapter") { print("Asset URL: \(assetURL)") let adapter = try SystemLanguageModel.Adapter(fileURL: assetURL) let adaptedModel = SystemLanguageModel(adapter: adapter) session = LanguageModelSession(model: adaptedModel) print("Loaded adapter and updated session") } else { print("Asset not found in the main bundle.") } } This seems to work fine as I get to the log Loaded adapter and updated session. However when the below inference code runs I get the aforementioned error: func sendMessage(_ msg: String) { self.loading = true if let session = session { Task { do { let modelResponse = try await session.respond(to: msg) DispatchQueue.main.async { self.response = modelResponse.content self.loading = false } } catch { print("Error: \(error)") DispatchQueue.main.async { self.loading = false } } } } }

Can't import data in create ML word tagging project training data is 100% correct I guarantee it: I mean look this one has one entry in it. [ { "tokens": [ "a", "august", "gruters" ], "labels": [ "BUILDER", "BUILDER", "BUILDER" ] } ]

For example: I have a list of to-dos, each with a unique id (a GUID). I want to feed them to the LLM model and have the model rewrite the items so they start with an action verb. I'd like to get them back and identify which rewritten item corresponds to which original item. I obviously can't compare the text, as it has changed. I've tried passing the original GUIDs in with each to-do, but the extra GUID characters pollutes the input and confuses the model. I've tried numbering them in order and adding an originalSortOrder field to my generable type, but it doesn't work reliably. Any suggestions? I could do them one at a time, but I also have a use case where I'm asking for them to be organized in sections, and while I've instructed the model not to rename anything, it still happens. It's just all very nondeterministic.

When I try to run visionOS 26 beta 2 on my device the app crashes on Launch: dyld[904]: Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <A71932DD-53EB-39E2-9733-32E9D961D186> /private/var/containers/Bundle/Application/53866099-99B1-4BBD-8C94-CD022646EB5D/VisionPets.app/VisionPets.debug.dylib Expected in: <F68A7984-6B48-3958-A48D-E9F541868C62> /System/Library/Frameworks/FoundationModels.framework/FoundationModels Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <A71932DD-53EB-39E2-9733-32E9D961D186> /private/var/containers/Bundle/Application/53866099-99B1-4BBD-8C94-CD022646EB5D/VisionPets.app/VisionPets.debug.dylib Expected in: <F68A7984-6B48-3958-A48D-E9F541868C62> /System/Library/Frameworks/FoundationModels.framework/FoundationModels dyld config: DYLD_LIBRARY_PATH=/usr/lib/system/introspection DYLD_INSERT_LIBRARIES=/usr/lib/libLogRedirect.dylib:/usr/lib/libBacktraceRecording.dylib:/usr/lib/libMainThreadChecker.dylib:/usr/lib/libViewDebuggerSupport.dylib:/System/Library/PrivateFrameworks/GPUToolsCapture.framework/GPUToolsCapture Symbol not found: _$s16FoundationModels10TranscriptV7entriesACSayAC5EntryOG_tcfC Referenced from: <A71932DD-53EB-39E2-9733-32E9D961D186> /private/var/containers/Bundle/Application/53866099-99B1-4BBD-8C94-CD022646EB5D/VisionPets.app/VisionPets.debug.dylib Expected in: <F68A7984-6B48-3958-A48D-E9F541868C62> /System/Library/Frameworks/FoundationModels.framework/FoundationModels dyld config: DYLD_LIBRARY_PATH=/usr/lib/system/introspection DYLD_INSERT_LIBRARIES=/usr/lib/libLogRedirect.dylib:/usr/lib/libBacktraceRecording.dylib:/usr/lib/libMainThreadChecker.dylib:/usr/lib/libViewDebuggerSupport.dylib:/System/Library/PrivateFrameworks/GPUToolsCapture.framework/GPUToolsCapture Message from debugger: Terminated due to signal 6

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.

This is my code: witch SystemLanguageModel.default.availability { case .available: ContentView() .popover(isPresented: $showSettings) { SettingsView().presentationCompactAdaptation(.popover) } case .unavailable(.modelNotReady): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("Please come back later.")) case .unavailable(.appleIntelligenceNotEnabled): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("Please turn on Apple Intelligence.")) case .unavailable(.deviceNotEligible): ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark", description: Text("This device is not eligible for Apple Intelligence.")) case .unavailable: ContentUnavailableView("Apple Intelligence is unavailable", systemImage: "apple.intelligence.badge.xmark") } When I switch off Apple Intelligence, I expected "Please turn on Apple Intelligence.", but instead I get "Please come back later." This seems to be wrong error?

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'm experimenting with downloading an audio file of spoken content, using the Speech framework to transcribe it, then using FoundationModels to clean up the formatting to add paragraph breaks and such. I have this code to do that cleanup: private func cleanupText(_ text: String) async throws -> String? { print("Cleaning up text of length \(text.count)...") let session = LanguageModelSession(instructions: "The content you read is a transcription of a speech. Separate it into paragraphs by adding newlines. Do not modify the content - only add newlines.") let response = try await session.respond(to: .init(text), generating: String.self) return response.content } The content length is about 29,000 characters. And I get this error: InferenceError::inferenceFailed::Failed to run inference: Context length of 4096 was exceeded during singleExtend.. Is 4096 a reference to a max input length? Or is this a bug? This is running on an M1 iPad Air, with iPadOS 26 Seed 1.

I am using a contact tool to help get contact from my address book. but the model ins't invoking my tool call method. Even tried with a simple tool the outcome is the same my simple tool is not being invoked.

Hey Devs, I'm trying to create my own Real Time Text detection like this Apple project. https://developer.apple.com/documentation/vision/extracting-phone-numbers-from-text-in-images I want to use the new iOS18 RecognizeTextRequest instead of the old VNRecognizeTextRequest in my SwiftUI project. This is my delegate code with the camera setup. I removed region of interest for debugging but I'm trying to scan English words in books. The idea is to get one word in the ROI in the future. But I can't even get proper words so testing without ROI incase my math is wrong. @Observable class CameraManager: NSObject, AVCapturePhotoCaptureDelegate ... override init() { super.init() setUpVisionRequest() } private func setUpVisionRequest() { textRequest = RecognizeTextRequest(.revision3) } ... func setup() -> Bool { captureSession.beginConfiguration() guard let captureDevice = AVCaptureDevice.default( .builtInWideAngleCamera, for: .video, position: .back) else { return false } self.captureDevice = captureDevice guard let deviceInput = try? AVCaptureDeviceInput(device: captureDevice) else { return false } /// Check whether the session can add input. guard captureSession.canAddInput(deviceInput) else { print("Unable to add device input to the capture session.") return false } /// Add the input and output to session captureSession.addInput(deviceInput) /// Configure the video data output videoDataOutput.setSampleBufferDelegate( self, queue: videoDataOutputQueue) if captureSession.canAddOutput(videoDataOutput) { captureSession.addOutput(videoDataOutput) videoDataOutput.connection(with: .video)? .preferredVideoStabilizationMode = .off } else { return false } // Set zoom and autofocus to help focus on very small text do { try captureDevice.lockForConfiguration() captureDevice.videoZoomFactor = 2 captureDevice.autoFocusRangeRestriction = .near captureDevice.unlockForConfiguration() } catch { print("Could not set zoom level due to error: \(error)") return false } captureSession.commitConfiguration() // potential issue with background vs dispatchqueue ?? Task(priority: .background) { captureSession.startRunning() } return true } } // Issue here ??? extension CameraManager: AVCaptureVideoDataOutputSampleBufferDelegate { func captureOutput( _ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection ) { guard let pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return } Task { textRequest.recognitionLevel = .fast textRequest.recognitionLanguages = [Locale.Language(identifier: "en-US")] do { let observations = try await textRequest.perform(on: pixelBuffer) for observation in observations { let recognizedText = observation.topCandidates(1).first print("recognized text \(recognizedText)") } } catch { print("Recognition error: \(error.localizedDescription)") } } } } The results I get look like this ( full page of English from a any book) recognized text Optional(RecognizedText(string: e bnUI W4, confidence: 0.5)) recognized text Optional(RecognizedText(string: ?'U, confidence: 0.3)) recognized text Optional(RecognizedText(string: traQt4, confidence: 0.3)) recognized text Optional(RecognizedText(string: li, confidence: 0.3)) recognized text Optional(RecognizedText(string: 15,1,#, confidence: 0.3)) recognized text Optional(RecognizedText(string: jllÈ, confidence: 0.3)) recognized text Optional(RecognizedText(string: vtrll, confidence: 0.3)) recognized text Optional(RecognizedText(string: 5,1,: 11, confidence: 0.5)) recognized text Optional(RecognizedText(string: 1141, confidence: 0.3)) recognized text Optional(RecognizedText(string: jllll ljiiilij41, confidence: 0.3)) recognized text Optional(RecognizedText(string: 2f4, confidence: 0.3)) recognized text Optional(RecognizedText(string: ktril, confidence: 0.3)) recognized text Optional(RecognizedText(string: ¥LLI, confidence: 0.3)) recognized text Optional(RecognizedText(string: 11[Itl,, confidence: 0.3)) recognized text Optional(RecognizedText(string: 'rtlÈ131, confidence: 0.3)) Even with ROI set to a specific rectangle Normalized to Vision, I get the same results with single characters returning gibberish. Any help would be amazing thank you. Am I using the buffer right ? Am I using the new perform(on: CVPixelBuffer) right ? Maybe I didn't set up my camera properly? I can provide code

I have a mac (M4, MacBook Pro) running Tahoe 26.0 beta. I am running Xcode beta. I can run code that uses the LLM in a #Preview { }. But when I try to run the same code in the simulator, I get the 'device not ready' error and I see the following in the Settings app. Is there anything I can do to get the simulator to past this point and allowing me to test on it with Apple's LLM?

I've been following along with "App Shortcuts" development but cannot get Siri to run my Intent. The intent on its own works in Shortcuts, along with a couple others that aren't in the AppShortcutsProvder structure. I keep getting the following two errors, but cannot figure out why this is occurring with documentation or other forum posts. No ConnectionContext found for 12909953344 Attempted to fetch App Shortcuts, but couldn't find the AppShortcutsProvider. Here are the relevant snippets of code - (1) The AppIntent definition struct SetBrightnessIntent: AppIntent { static var title = LocalizedStringResource("Set Brightness") static var description = IntentDescription("Set Glass Display Brightness") @Parameter(title: "Level") var level: Int? static var parameterSummary: some ParameterSummary { Summary("Set Brightness to \(\.$level)%") } func perform() async throws -> some IntentResult { guard let level = level else { throw $level.needsValueError("Please provide a brightness value") } if level > 100 || level <= 0 { throw $level.needsValueError("Brightness must be between 1 and 100") } // do stuff with level return .result() } } (2) The AppShortcutsProvider (defined in my iOS app target, there are no other targets) struct MyAppShortcuts: AppShortcutsProvider { static var shortcutTileColor: ShortcutTileColor = .grayBlue @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: SetBrightnessIntent(), phrases: [ "set \(.applicationName) brightness to \(\.$level)", "set \(.applicationName) brightness to \(\.$level) percent" ], shortTitle: LocalizedStringResource("Set Glass Brightness"), systemImageName: "sun.max" ) } } Does anything here look wrong? Is there some magical key that I need to specify in Info.plist to get Siri to recognize the AppShortcutsProvider? On Xcode 16.2 and iOS 18.2 (non-beta).

基于iPhone 14 Max相机，实现模型识别，并在识别对象周围画一个矩形框。宽度和高度使用激光雷达计算，并在实时更新的图像上以厘米为单位显示。 swift code