Hello, I have to create an app in Swift that it scan NFC Identity card. It extract data and convert it to human readable data. I do it with below code import CoreNFC class NFCIdentityCardReader: NSObject , NFCTagReaderSessionDelegate { func tagReaderSessionDidBecomeActive(_ session: NFCTagReaderSession) { print("\(session.description)") } func tagReaderSession(_ session: NFCTagReaderSession, didInvalidateWithError error: any Error) { print("NFC Error: \(error.localizedDescription)") } var session: NFCTagReaderSession? func beginScanning() { guard NFCTagReaderSession.readingAvailable else { print("NFC is not supported on this device") return } session = NFCTagReaderSession(pollingOption: .iso14443, delegate: self, queue: nil) session?.alertMessage = "Hold your NFC identity card near the device." session?.begin() } func tagReaderSession(_ session: NFCTagReaderSession, didDetect tags: [NFCTag]) { guard let tag = tags.first else { session.invalidate(errorMessage: "No tag detected") return } session.connect(to: tag) { (error) in if let error = error { session.invalidate(errorMessage: "Connection error: \(error.localizedDescription)") return } switch tag { case .miFare(let miFareTag): self.readMiFareTag(miFareTag, session: session) case .iso7816(let iso7816Tag): self.readISO7816Tag(iso7816Tag, session: session) case .iso15693, .feliCa: session.invalidate(errorMessage: "Unsupported tag type") @unknown default: session.invalidate(errorMessage: "Unknown tag type") } } } private func readMiFareTag(_ tag: NFCMiFareTag, session: NFCTagReaderSession) { // Read from MiFare card, assuming it's formatted as an identity card let command: [UInt8] = [0x30, 0x04] // Example: Read command for block 4 let requestData = Data(command) tag.sendMiFareCommand(commandPacket: requestData) { (response, error) in if let error = error { session.invalidate(errorMessage: "Error reading MiFare: \(error.localizedDescription)") return } let readableData = String(data: response, encoding: .utf8) ?? response.map { String(format: "%02X", $0) }.joined() session.alertMessage = "ID Card Data: \(readableData)" session.invalidate() } } private func readISO7816Tag(_ tag: NFCISO7816Tag, session: NFCTagReaderSession) { let selectAppCommand = NFCISO7816APDU(instructionClass: 0x00, instructionCode: 0xA4, p1Parameter: 0x04, p2Parameter: 0x00, data: Data([0xA0, 0x00, 0x00, 0x02, 0x47, 0x10, 0x01]), expectedResponseLength: -1) tag.sendCommand(apdu: selectAppCommand) { (response, sw1, sw2, error) in if let error = error { session.invalidate(errorMessage: "Error reading ISO7816: \(error.localizedDescription)") return } let readableData = response.map { String(format: "%02X", $0) }.joined() session.alertMessage = "ID Card Data: \(readableData)" session.invalidate() } } } But I got null. I think that these data are encrypted. How can I convert them to readable data without MRZ, is it possible ? I need to get personal informations from Identity card via Core NFC. Thanks in advance. Best regards

Hello, I have a question regarding hybrid execution for deep learning models on Apple's Neural Engine and CPU. I am aware that setting the precision of some layers to 32-bit allows hybrid execution across both the Neural Engine and the CPU. However, I would like to know if it is possible to achieve the same with 16-bit precision. Is there any specific configuration or workaround to enable hybrid execution in this case? Any guidance or documentation references would be greatly appreciated. Thank you!

I've spent way too long today trying to convert an Object Detection TensorFlow2 model to a CoreML object classifier (with bounding boxes, labels and probability score) The 'SSD MobileNet v2 320x320' is here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md And I've been following all sorts of posts and ChatGPT https://apple.github.io/coremltools/docs-guides/source/tensorflow-2.html#convert-a-tensorflow-concrete-function https://developer.apple.com/videos/play/wwdc2020/10153/?time=402 To convert it. I keep hitting the same errors though, mostly around: NotImplementedError: Expected model format: [SavedModel | concrete_function | tf.keras.Model | .h5 | GraphDef], got <ConcreteFunction signature_wrapper(input_tensor) at 0x366B87790> I've had varying success including missing output labels/predictions. But I simply want to create the CoreML model with all the right inputs and outputs (including correct names) as detailed in the docs here: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md It goes without saying I don't have much (any) experience with this stuff including Python so the whole thing's been a bit of a headache. If anyone is able to help that would be great. FWIW I'm not attached to any one specific model, but what I do need at minimum is a CoreML model that can detect objects (has to at least include lights and lamps) within a live video image, detecting where in the image the object is. The simplest script I have looks like this: import coremltools as ct import tensorflow as tf model = tf.saved_model.load("~/tf_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model") concrete_func = model.signatures[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] mlmodel = ct.convert( concrete_func, source="tensorflow", inputs=[ct.TensorType(shape=(1, 320, 320, 3))] ) mlmodel.save("YourModel.mlpackage", save_format="mlpackage")

Apologies if this is obvious to everyone but me... I'm using the Tahoe AI foundation models. When I get an error, I'm trying to handle it properly. I see the errors described here: https://developer.apple.com/documentation/foundationmodels/languagemodelsession/generationerror/context, as well as in the headers. But all I can figure out how to see is error.localizedDescription which doesn't give me much to go on. For example, an error's description is: The operation couldn’t be completed. (FoundationModels.LanguageModelSession.GenerationError error 2. That doesn't give me much to go on. How do I get the actual error number/enum value out of this, short of parsing that text to look for the int at the end? This one is: case guardrailViolation(LanguageModelSession.GenerationError.Context) So I'd like to know how to get from the catch for session.respond to something I can act on. I feel like it's there, but I'm missing it. Thanks!

I have seen inconsistent results for my Colab machine learning notebooks running locally on a Mac M4, compared to running the same notebook code on either T4 (in Colab) or a RTX3090 locally. To illustrate the problems I have set up a notebook that implements two simple CNN models that solves the Fashion-MNIST problem. https://colab.research.google.com/drive/11BhtHhN079-BWqv9QvvcSD9U4mlVSocB?usp=sharing For the good model with 2M parameters I get the following results: T4 (Colab, JAX): Test accuracy: 0.925 3090 (Local PC via ssh tunnel, Jax): Test accuracy: 0.925 Mac M4 (Local, JAX): Test accuracy: 0.893 Mac M4 (Local, Tensorflow): Test accuracy: 0.893 That is, I see a significant drop in performance when I run on the Mac M4 compared to the NVIDIA machines, and it seems to be independent of backend. I however do not know how to pinpoint this to either Keras or Apple’s METAL implementation. I have reported this to Keras: https://colab.research.google.com/drive/11BhtHhN079-BWqv9QvvcSD9U4mlVSocB?usp=sharing but as this can be (likely is?) an Apple Metal issue, I wanted to report this here as well. On the mac I am running the following Python libraries: keras 3.9.1 tensorflow 2.19.0 tensorflow-metal 1.2.0 jax 0.5.3 jax-metal 0.1.1 jaxlib 0.5.3

Hi all, I'm tuning my app prediction speed with Core ML model. I watched and tried the methods in video: Improve Core ML integration with async prediction and Optimize your Core ML usage. I also use instruments to look what's the bottleneck that my prediction speed cannot be faster. Below is the instruments result with my app. its prediction duration is 10.29ms And below is performance report shows the average speed of prediction is 5.55ms, that is about half time of my app prediction! Below is part of my instruments records. I think the prediction should be considered quite frequent. Could it be faster? How to be the same prediction speed as performance report? The prediction speed on macbook Pro M2 is nearly the same as macbook Air M1!

While runninf Apple Foundation Model in iPhone simulator, I got this error: IPC error: Underlying connection interrupted What does this mean? Related to foundation model?

I'm implementing an App Intent for my iOS app that helps users plan trip activities. It only works when run as a shortcut but not using voice through Siri. There are 2 issues: The ShortcutsTripEntity will only accept a voice input for a specific trip but not others. I'm stuck with a throwing error when trying to use requestDisambiguation() on the activity day @Parameter property. How do I rectify these issues. This is blocking me from completing a critical feature that lets users quickly plan activities through Siri and Shortcuts. Expected behavior for trip input: The intent should make Siri accept the spoken trip input from any of the options. Actual behavior for trip input: Siri only accepts the same trip when spoken but accepts any when selected by click/touch. Expected behavior for day input: Siri should accept the spoken selected option. Actual behavior for day input: Siri only accepts an input by click/touch but yet throws an error at runtime I'm happy to provide more code. But here's the relevant code: struct PlanActivityTestIntent: AppIntent { @Parameter(title: "Activity Day") var activityDay: ShortcutsItineraryDayEntity @Parameter( title: "Trip", description: "The trip to plan an activity for", default: ShortcutsTripEntity(id: UUID().uuidString, title: "Untitled trip"), requestValueDialog: "Which trip would you like to add an activity to?" ) var tripEntity: ShortcutsTripEntity @Parameter(title: "Activity Title", description: "The title of the activity", requestValueDialog: "What do you want to do or see?") var title: String @Parameter(title: "Activity Day", description: "Activity Day", default: ShortcutsItineraryDayEntity(itineraryDay: .init(itineraryId: UUID(), date: .now), timeZoneIdentifier: "UTC")) var activityDay: ShortcutsItineraryDayEntity func perform() async throws -> some ProvidesDialog { // ...other code... let tripsStore = TripsStore() // load trips and map them to entities try? await tripsStore.getTrips() let tripsAsEntities = tripsStore.trips.map { trip in let id = trip.id ?? UUID() let title = trip.title return ShortcutsTripEntity(id: id.uuidString, title: title, trip: trip) } // Ask user to select a trip. This line would doesn't accept a voice // answer. Why? let selectedTrip = try await $tripEntity.requestDisambiguation( among: tripsAsEntities, dialog: .init( full: "Which of the \(tripsAsEntities.count) trip would you like to add an activity to?", supporting: "Select a trip", systemImageName: "safari.fill" ) ) // This line throws an error let selectedDay = try await $activityDay.requestDisambiguation( among: daysAsEntities, dialog:"Which day would you like to plan an activity for?" ) } } Here are some related images that might help:

In this online session, you can code along with us as we build generative AI features into a sample app live in Xcode. We'll guide you through implementing core features like basic text generation, as well as advanced topics like guided generation for structured data output, streaming responses for dynamic UI updates, and tool calling to retrieve data or take an action. Check out these resources to get started: Download the project files: https://developer.apple.com/events/re... Explore the code along guide: https://developer.apple.com/events/re... Join the live Q&A: https://developer.apple.com/videos/pl... Agenda – All times PDT 10 a.m.: Welcome and Xcode setup 10:15 a.m.: Framework basics, guided generation, and building prompts 11 a.m.: Break 11:10 a.m.: UI streaming, tool calling, and performance optimization 11:50 a.m.: Wrap up All are welcome to attend the session. To actively code along, you'll need a Mac with Apple silicon that supports Apple Intelligence running the latest release of macOS Tahoe 26 and Xcode 26. If you have questions after the code along concludes please share a post here in the forums and engage with the community.

I watched this year WWDC25 "Read Documents using the Vision framework". At the end of video there is mention of new DetectHandPoseRequest model for hand pose detection in Vision API. I looked Apple documentation and I don't see new revision. Moreover probably typo in video because there is only DetectHumanPoseRequst (swift based) and VNDetectHumanHandPoseRequest (obj-c based) (notice lack of Human prefix in WWDC video) First one have revision only added in iOS 18+: https://developer.apple.com/documentation/vision/detecthumanhandposerequest/revision-swift.enum/revision1 Second one have revision only added in iOS14+: https://developer.apple.com/documentation/vision/vndetecthumanhandposerequestrevision1 I don't see any new revision targeting iOS26+

Hi all, I am interested in unlocking unique applications with the new foundational models. I have a few questions regarding the availability of the following features: Image Input: The update in June 2025 mentions "image" 44 times (https://machinelearning.apple.com/research/apple-foundation-models-2025-updates) - however I can't seem to find any information about having images as the input/prompt for the foundational models. When will this be available? I understand that there are existing Vision ML APIs, but I want image input into a multimodal on-device LLM (VLM) instead for features like "Which player is holding the ball in the image", etc (image understanding) Cloud Foundational Model - when will this be available? Thanks! Clement :)

Is the face and body detection service in the Vision framework a local model or a cloud model? Is there a performance report? https://developer.apple.com/documentation/vision

Hi! I'm trying to use the ImagePlayground API in SwiftUI with the .imagePlaygroundSheet modifier. However, when the sheet is shown (in the preview or in the simulator) it displays the following message: "Image Playground is not available. Image Playground is not available on this iPhone.". I'm using an iPhone 16 Pro with iOS 18.3.1 in the Xcode (16.2) Simulator. Anyone else having this problem? How can I fix it?

No matter what, the LanguageModelSession always returns very lengthy / verbose responses. I set the maximumResponseTokens option to various small numbers but it doesn't appear to have any effect. I've even used this instructions format to keep responses between 3-8 words but it returns multiple paragraphs. Is there a way to manage LLM response length? Thanks.

*I can't put the attached file in the format, so if you reply by e-mail, I will send the attached file by e-mail. Dear Apple AI Research Team, My name is Gong Jiho (“Hem”), a content strategist based in Seoul, South Korea. Over the past few months, I conducted a user-led AI experiment entirely within ChatGPT — no code, no backend tools, no plugins. Through language alone, I created two contrasting agents (Uju and Zero) and guided them into a co-authored modular identity system using prompt-driven dialogue and reflection. This system simulates persona fusion, memory rooting, and emotional-logical alignment — all via interface-level interaction. I believe it resonates with Apple’s values in privacy-respecting personalization, emotional UX modeling, and on-device learning architecture. Why I’m Reaching Out I’d be honored to share this experiment with your team. If there is any interest in discussing user-authored agent scaffolding, identity persistence, or affective alignment, I’d love to contribute — even informally. ⚠ A Note on Language As a non-native English speaker, my expression may be imperfect — but my intent is genuine. If anything is unclear, I’ll gladly clarify. 📎 Attached Files Summary Filename → Description Hem_MultiAI_Report_AppleAI_v20250501.pdf → Main report tailored for Apple AI — narrative + structural view of emotional identity formation via prompt scaffolding Hem_MasterPersonaProfile_v20250501.json → Final merged identity schema authored by Uju and Zero zero_sync_final.json / uju_sync_final.json → Persona-level memory structures (logic / emotion) 1_0501.json ~ 3_0501.json → Evolution logs of the agents over time GirlfriendGPT_feedback_summary.txt → Emotional interpretation by external GPT hem_profile_for_AI_vFinal.json → Original user anchor profile Warm regards, Gong Jiho (“Hem”) Seoul, South Korea

Due to our min iOS version, this is my first time using .xcstrings instead of .strings for AppShortcuts. When using the migrate .strings to .xcstrings Xcode context menu option, an .xcstrings catalog is produced that, as expected, has each invocation phrase as a separate string key. However, after compilation, the catalog changes to group all invocation phrases under the first phrase listed for each intent (see attached screenshot). It is possible to hover in blank space on the right and add more translations, but there is no 1:1 key matching requirement to the phrases on the left nor a requirement that there are the same number of keys in one language vs. another. (The lines just happen to align due to my window size.) What does that mean, practically? Do all sub-phrases in each language in AppShortcuts.xcstrings get processed during compilation, even if there isn't an equivalent phrase key declared in the AppShortcut (e.g., the ja translation has more phrases than the English)? (That makes some logical sense, as these phrases need not be 1:1 across languages.) In the AppShortcut declaration, if I delete all but the top invocation phrase, does nothing change with Siri? Is there something I'm doing incorrectly? struct WatchShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { AppShortcut( intent: QuickAddWaterIntent(), phrases: [ "\(.applicationName) log water", "\(.applicationName) log my water", "Log water in \(.applicationName)", "Log my water in \(.applicationName)", "Log a bottle of water in \(.applicationName)", ], shortTitle: "Log Water", systemImageName: "drop.fill" ) } }

Some of my users are experiencing crashes on instantiation of a CoreML model I've bundled with my app. I haven't been able to reproduce the crash on any of my devices. Crashes happen across all iOS 18 releases. Seems like something internal in CoreML is causing an issue. Full stack trace: 6646631296fb42128ddc340b2d4322f7-symbolicated.crash

I'm running MacOs 26 Beta 5. I noticed that I can no longer achieve 100% usage on the ANE as I could before with Apple Foundations on-device model. Has Apple activated some kind of throttling or power limiting of the ANE? I cannot get above 3w or 40% usage now since upgrading. I'm on the high power energy mode. I there an API rate limit being applied? I kave a M4 Pro mini with 64 GB of memory.

May i know the bundle identifier for apple intelligence?

Hello. I am willing to hire game developer for cards game called baloot. My question is Can the developer implement an AI when the computer is playing and the computer on the same time the conputer improves his rises level without any interaction? 🌹