Hello everyone, I hope you’re all doing well. I’m not a developer, but I have an idea for an iOS app that I’d love to get your thoughts on. I wanted to share it here to gather feedback from this knowledgeable community and to learn from your expertise. Idea Overview: Real-Time AI Running Coach for iOS The concept is an iOS application that provides personalized, real-time running coaching by leveraging on-device data sources and Apple’s latest technologies. The app aims to offer an adaptive and motivating running experience while ensuring user privacy through on-device processing. Key Features: • Personalized Coaching: • Utilize real-time biometric data and personal insights to deliver AI-driven coaching tailored to the user’s mental and physical state. • Analyze health metrics, activity data, mood check-ins, and more to provide context-based motivational feedback. • Privacy First: • All data processing occurs on-device using Apple’s frameworks like Core ML, ensuring no personal data leaves the device. • Adaptive Motivation: • Implement Natural Language Processing to analyze user inputs like journal entries or mood check-ins. • Generate personalized coaching cues based on historical performance and mood trends. • Performance Enhancement: • Offer dynamic adjustments to pace, route, and strategy in real time to help improve running performance. • Seamless integration with Apple Watch for real-time data collection and haptic feedback. Technologies and Frameworks Involved: • HealthKit: Access health metrics such as heart rate, distance run, VO₂ max, sleep patterns, etc. • Core ML: On-device machine learning for real-time data analysis without latency. • Natural Language Processing: Analyze personal inputs for better coaching personalization. • Core Motion & Core Location: Track motion data and location services for runs. • AVFoundation & Speech: Provide real-time voice feedback and coaching cues. • SiriKit Integration: Allow users to initiate workouts and receive updates via Siri. Target Audience: • Runners of all levels seeking personalized coaching that adapts to their mental and physical states. • Users who prioritize privacy and want AI-driven insights without their data leaving the device. • Tech-savvy fitness enthusiasts who use iOS devices and Apple wearables. Questions for the Community: 1. Feasibility: Is this idea technically achievable using current iOS frameworks and technologies? 2. Data Access: Are there limitations in accessing and processing the necessary data on-device, especially regarding privacy and permissions? 3. Potential Challenges: What hurdles might developers face in creating such an app, and how could they be addressed? 4. Advice: As someone without a technical background, what steps would you recommend I take to move this idea forward? I truly appreciate any feedback or insights you can provide. I’m excited about the potential of this idea but also aware there may be complexities I’m not considering. Thank you for taking the time to read this! Best regards, Paul

Hi, I'm training a model that should detect a forehand and a backend stroke. The data looks like this: activity,timestamp,Acceleration_X,Acceleration_Y,Acceleration_Z,Rotation_X,Rotation_Y,Rotation_Z forehand,0.0,0.08,-0.08,0.03,0.18,0.26,0.32 I can load it in Create ML but it's showing the acceleration and rotation x,y,z as seperate Doubles and not as one feature. What do I have to change to make this work? Thank you

Hi All, Is it possible to record a video using the Object Capture instead of taking a series of pictures ? Is it possible to get the bounding box coordinates of the object we capture ?

Hello, I’m working on a program that analyzes video files frame by frame to detect human poses in each frame. However, during the process of reading observations from the stream, the analysis frequently stops with the following error: [LOG_ERROR] /Library/Caches/com.apple.xbs/Sources/MediaAnalysis/VideoProcessing/VCPHumanPoseImageRequest.mm[85]: code -18 [LOG_ERROR] /Library/Caches/com.apple.xbs/Sources/MediaAnalysis/VideoProcessing/VCPHumanPoseImageRequest.mm[178]: code -18 The error was caught and printed using a do-catch block, and here is the output: Error Domain=NSOSStatusErrorDomain Code=-18 "Error: failed to processImage" UserInfo={NSLocalizedDescription=Error: failed to processImage} While the do-catch block helps prevent the app from crashing, the frames following the error cannot be analyzed. I’m hoping to understand the cause of this error, or find a way to skip the problematic frames and continue analyzing the subsequent ones. My development environment is Xcode Version 16.0 (16A242d) and iOS 18.0. Thank you for your help. (Attaching my code below.) let videoProcessor = VideoProcessor(videoURL) let bodyPoseRequest = DetectHumanBodyPoseRequest() let asset = AVURLAsset(url: videoURL) let videoTrack = try await asset.loadTracks(withMediaType: .video).first let bodyPoseStream = try await videoProcessor.addRequest(bodyPoseRequest) videoProcessor.startAnalysis() do { for try await observations in bodyPoseStream { guard let observation = observations.first else { continue } if let timeRange = observation.timeRange { /// do something... } } } catch { print("\(error.localizedDescription)") }

I am using Apple’s Vision framework with DetectHorizonRequest to detect the horizon in an image. Here is my code: func processHorizonImage(_ ciImage: CIImage) async { let request = DetectHorizonRequest() do { let result = try await request.perform(on: ciImage) print(result) } catch { print(error) } } After calling the perform method, I am getting result as nil. To ensure the request's correctness, I have verified the following: The input CIImage is valid and contains a visible horizon. No errors are being thrown. The relevant frameworks are properly imported. Given that my image contains a clear horizon, why am I still not getting any results? I would appreciate any help or suggestions to resolve this issue. Thank you for your support! This is the image

In the WWDC24 What’s New In Create ML at 6:03 the presenter introduced TimeSeriesClassifier as a new component of Create ML Components. Where are documentation and code examples for this feature? My app captures accelerometer time series data that I want to classify. Thank you so much!

I'm experiencing issues with the Core ML Async API, as it doesn't seem to be working correctly. It consistently hangs during the "03 performInference, after get smallInput, before prediction" part, as shown in the attached: log1.txt log2.txt Below is my code. Could you please advise on how I should modify it? private func createFrameAsync(for sampleBuffer: CMSampleBuffer ) { guard let pixelBuffer = sampleBuffer.imageBuffer else { return } Task { print("**** createFrameAsync before performInference") do { try await runModelAsync(on: pixelBuffer) } catch { print("Error processing frame: \(error)") } print("**** createFrameAsync after performInference") } } func runModelAsync(on pixelbuffer: CVPixelBuffer) async { print("01 performInference, before resizeFrame") guard let data = metalResizeFrame(sourcePixelFrame: pixelbuffer, targetSize: MTLSize.init(width: InputWidth, height: InputHeight, depth: 1), resizeMode: .scaleToFill) else { os_log("Preprocessing failed", type: .error) return } print("02 performInference, after resizeFrame, before get smallInput") let input = model_smallInput(input: data) print("03 performInference, after get smallInput, before prediction") if let prediction = try? await mlmodel!.model.prediction(from: input) { print("04 performInference, after prediction, before get result") var results: [Float] = [] let output = prediction.featureValue(for: "output")?.multiArrayValue if let bufferPointer = try? UnsafeBufferPointer<Float>(output!) { results = Array(bufferPointer) } print("05 performInference, after get result, before setRenderData") let localResults = results await MainActor.run { ScreenRecorder.shared .setRenderDataNormalized( screenImage: pixelbuffer, depthData: localResults ) } print("06 performInference, after setRenderData") } }

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!

Hi, I'm working on training a createML object detector model; I've run into an issue that has me stumped - when I reach somewhere between 100,000 and 150,000 iterations my model will stop training and error out. More Details: CreateML gives me the error prompt that says it is unable to train the model please delete the model source and start from the beginning or duplicate the model and start from the beginning (slightly paraphrased) I see the following error in the createML console (my user name and UUIDs have been redacted) Unable to load model from file:///Users/<my user name>/Library/Caches/com.apple.dt.createml/projects/<UUID HERE>/sessions/checkpoint.sessions/<UUID Here>//training-000132500.checkpoint: Cannot open file:///Users/<my user name>/Library/Caches/com.apple.dt.createml/projects/<UUID Here>/sessions/checkpoint.sessions/<uuid here> //training-000132500.checkpoint/dir_archive.ini for read. Cannot open /Users/<my username>/Library/Caches/com.apple.dt.createml/projects/<UUID>/sessions/checkpoint.sessions/<UUID>//training-000132500.checkpoint/dir_archive.ini for reading I've gone into my Caches in my Library directory and I see each piece of the file path in finder UNTIL the //training-00132500 piece of the path, so I can at least confirm that createML appears to be unable to create or open the file it needs for this training session. Technology Used: Xcode 16 Apple M1 Pro MacOS 14.6.1 (23G93) I've also verified that Xcode and terminal have full disk permissions in my system preferences - I didn't see an option to add CreateML to this list. I've also ensured that my createML project and its data sources are not in iCloud and are indeed local on my desktop. Lastly, I made more space on my machine, so I should have a little over 1 TB of space. Has anybody experienced this before? Any advice? I am majorly blocked on this issue, so I hope somebody else can help shed some light on this issue! Thanks!

With apple intelligence enabled I am seeing a 10-15 sec delay in push notification. This is actually causing issue with my alarm system, door bells and 2FA requests. I'm using iPhone 16 Pro. First installed 18.1 Dev beta 5 and AI and noticed this. When beta 6 was released it continued. I put in FB15389900.

Hi Ty for playing

Can vector embeddings be used in a SwiftData Model? If yes, are there resources available to learn more about it? or at least a guided step on how to make it work?

WWDC 2024 mentioned that the OCR feature from the Vision framework has support for "Korean, Swedish, and Chinese", but the Swedish support does not seem to be available... Running either print(try? VNRecognizeTextRequest().supportedRecognitionLanguages()) or var ocrRequest = RecognizeTextRequest(.revision3) print(ocrRequest.supportedRecognitionLanguages) did not print out Swedish as one of the supported languages, but Korean and Chinese are. Tested on early versions of iOS 18 developer beta, and the latest version of iOS 18.1 (22B5054e).

why it’s taking too long to download appl intelligence on iPhone 15 pro max . how long it will take to download. I use 5G now it counting 4 weeks still downloading.

Every time I click join the waitlist, there's is no reaction. My phone just redirects me back to this page

I was working on my project and when I tried to train a model the kernel crashed, so I restarted the kernel and tried the same and still I got the same crashing issue. Then I read one of the thread having the same issue where the apple support was saying to install tensorflow-macos and tensorflow-metal and read the guide from this site: https://developer.apple.com/metal/tensorflow-plugin/ and I did so, I tried every single thing and when I tried the test code provided in the site, I got the same error, here's the code and the output. Code: import tensorflow as tf cifar = tf.keras.datasets.cifar100 (x_train, y_train), (x_test, y_test) = cifar.load_data() model = tf.keras.applications.ResNet50( include_top=True, weights=None, input_shape=(32, 32, 3), classes=100,) loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False) model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"]) model.fit(x_train, y_train, epochs=5, batch_size=64) and here's the output: Epoch 1/5 The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details. And here's the half of log file as it was not fully coming: metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M1 2024-10-06 23:30:49.894405: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 8.00 GB 2024-10-06 23:30:49.894420: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 2.67 GB 2024-10-06 23:30:49.894444: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support. 2024-10-06 23:30:49.894460: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: ) 2024-10-06 23:30:56.701461: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled. [libprotobuf FATAL google/protobuf/message_lite.cc:353] CHECK failed: target + size == res: libc++abi: terminating due to uncaught exception of type google::protobuf::FatalException: CHECK failed: target + size == res: Please respond to this post as soon as possible as I am working on my project now and getting this error again n again. Device: Apple MacBook Air M1.

Why does Apple sell Iphones 16 with AI that won’t work in europe? You do not sell a car without a engine. Was it not better to wait to bring it to europe?

I don’t see the Apple Intelligence tap, even with iOS 18.1, language and region United States, Sri in English US… what can I do?

Hi everyone, I’m currently in the process of converting and optimizing the Stable Diffusion XL model for iOS 18. I followed the steps from the WWDC 2024 session on model optimization, specifically the one titled "Bring your machine learning and AI models to Apple Silicon." I utilized the Stable Diffusion XL model and the tools available in the ml-stable-diffusion GitHub repository and ran the following script to convert the model into an .mlpackage: python3 -m python_coreml_stable_diffusion.torch2coreml \ --convert-unet \ --convert-vae-decoder \ --convert-text-encoder \ --xl-version \ --model-version stabilityai/stable-diffusion-xl-base-1.0 \ --bundle-resources-for-swift-cli \ --refiner-version stabilityai/stable-diffusion-xl-refiner-1.0 \ --attention-implementation SPLIT_EINSUM \ -o ../PotraitModel/ \ --custom-vae-version madebyollin/sdxl-vae-fp16-fix \ --latent-h 128 \ --latent-w 96 \ --chunk-unet The model conversion worked without any issues. However, when I proceeded to optimize the model in a Jupyter notebook, following the same process shown in the WWDC session, I encountered an error during the post-training quantization step. Here’s the code I used for that: op_config = cto_coreml.0pPalettizerConfig( nbits=4, mode="kmeans", granularity="per_grouped_channel", group_size=16, ) config = cto_coreml.OptimizationConfig(op_config) compressed_model = cto_coreml.palettize_weights(mlmodel, config) Unfortunately, I received the following error: AssertionError: The IOS16 only supports per-tensor LUT, but got more than one lut on 0th axis. LUT shape: (80, 1, 1, 1, 16, 1) It appears that the minimum deployment target of the MLModel is set to iOS 16, which might be causing compatibility issues. How can I update the minimum deployment target to iOS 18? If anyone has encountered this issue or knows a workaround, I would greatly appreciate your guidance! Thanks in advance for any help!

Xcode Version 16.0 (16A242d) iOS18 - Swift There seems to be a behavior change on iOS18 when using AppShortcuts and AppIntents to pass string parameters. After Siri prompts for a string property requestValueDialog, if the user makes a statement the string is passed. If the user's statement is a question, however, the string is not sent to the AppIntent and instead Siri attempts to answer that question. Example Code: struct MyAppNameShortcuts: AppShortcutsProvider { @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: AskQuestionIntent(), phrases: [ "Ask \(.applicationName) a question", ] ) } } struct AskQuestionIntent: AppIntent { static var title: LocalizedStringResource = .init(stringLiteral: "Ask a question") static var openAppWhenRun: Bool = false static var parameterSummary: some ParameterSummary { Summary("Search for \(\.$query)") } @Dependency private var apiClient: MockApiClient @Parameter(title: "Query", requestValueDialog: .init(stringLiteral: "What would you like to ask?")) var query: String // perform is not called if user asks a question such as "What color is the moon?" in response to requestValueDialog // iOS 17, the same string is passed though @MainActor func perform() async throws -> some IntentResult & ProvidesDialog & ShowsSnippetView { print("Query is: \(query)") let queryResult = try await apiClient.askQuery(queryString: query) let dialog = IntentDialog( full: .init(stringLiteral: queryResult.answer), supporting: .init(stringLiteral: "The answer to \(queryResult.question) is...") ) let view = SiriAnswerView(queryResult: queryResult) return .result(dialog: dialog, view: view) } } Given the above mock code: iOS17: Hey Siri Ask (AppName) a question Siri responds "What would you like to ask?" Say "What color is the moon?" String of "What color is the moon?" is passed to the AppIntent iOS18: Hey Siri Ask (AppName) a question Siri responds "What would you like to ask?" Say "What color is the moon?" Siri answers the question "What color is the moon?" Follow above steps again and instead reply "Moon" "Moon" is passed to AppIntent Basically any interrogative string parameters seem to be intercepted and sent to Siri proper rather than the provided AppIntent in iOS 18