My IOS app generates pdf files. Every time my users open the generated pdf files, the autofill popup jumps out, but my pdf file is NOT for interacting. I'm here to ask if there's a way to mark my pdf files as "not a form", like in metadata or anywhere else?

Hi everyone, I’m currently learning about ParticleEmitterComponentParticleEmitterComponent and exploring the sample app provided in the Simulating particles in your visionOS app documentation. In the sample app, when I set the EmitterPreset to fireworks from the settings panel on the left side of the window and choose SystemImage, I noticed two issues: The image applied to mainEmitter appears clipped or cropped. The image on spawnedEmitter does not update to the selected SystemImage. What I want to achieve: Apply the same SystemImage to both mainEmittermainEmitter and spawnedEmitterspawnedEmitter so that it displays correctly without clipping. Remove the animation that changes the size of spawnedEmitterspawnedEmitter over time and keep it at a constant size. Could someone explain which properties should be adjusted to achieve this behavior? Any guidance or examples would be greatly appreciated. Thanks in advance!

The code is pretty simple kernel void naive( constant RunParams *param [[ buffer(0) ]], const device float *A [[ buffer(1) ]], // [N, K] device float *output [[ buffer(2) ]], uint2 gid [[ thread_position_in_grid ]]) { uint a_ptr = gid.x * param->K; for (uint i = 0; i < param->K; i++, a_ptr++) { val += A[b_ptr]; } output[ptr] = val; } when uint a_ptr = gid.x * param->K, the code got 150 GFLops when uint a_ptr = gid.y * param->K, the code got 860 GFLops param->K = 256; thread per group: [16, 16] I'd like to understand why the performance is so different, and how can I profile/diagnose this to help with further optimization.

Hello, I’m the developer of the “ StepSquad” app. Our app uses the Game Center achievement feature, but we’ve been encountering a problem: the “Global Players” metric always shows 0%, even though there are friends who have already achieved these achievements. Initially, I thought it might be because the app was newly launched. However, it’s now been over two months since release, and it’s still showing 0%. If anyone has any insight into this issue, please leave a comment.

I am using Unity's GameKit to implement a turnbase game. I want to make a UI in Unity to show all the games I can join. I tried using var matches = await GKTurnBasedMatch.LoadMatches(); to get all the open matches. But it seems that I can only get the matcm related to the current apple account. Can you help me get all the matches? ALSO I used var match = await GKTurnBasedMatchmakerViewController.Request(request); to exit the gamecenter interface and start a game (automatic matching, no one was invited) Another device used var match = await GKTurnBasedMatch.Find(request); to find the game, but it did not find the game, but it start a new game (automatic matching). Can you help me solve these problems?

Hello Everyone I am new here, I am testing game center integration and using a development build of my IOS game. I have set up a couple of achievements in app store connect, but when I trigger them in the game then they do not unlock or show up. Okay so i am signed into the game center with a sandbox account on a test advice. Is there anything else I need to configure, or do achievements usually only work after the game is released?. I will appreciate any guidance… Thanks in Advance!!!

When running on my iPhone SE3 under IOS 18.4.1, achievement banners show as expected. The same code running on my iPad Air2 under IOS 15.8.4, achievement banners do not show, but they are accepted (as shown in the GameCenterViewController). The banners also don't show when running the simulator under iPhone 16 Pro Max under IOS 18.2 or simulator under iPhone SE3 under IOS 18.3. I haven't tried others. [Note that I clear the achievements each run during test so that I can duplicate this]

Hello! I have a question about how thread groups work with tile shading. When running "traditional" compute, I get to choose both thread group size and the grid size. However, when using tile shading kernel I only have dispatchThreadsPerTile method - this controls how many threads will be ran in each tile. So far so good, but what about thread groups? The examples in video "Tile Shading on A11" seem to suggest that there will be only one thread group per tile. In the video, [[thread_index_in_threadgroup]] is called "local_id" and it is used to access the image block. I assume this is the default configuration. So when one does the following: Creates MTLRenderPassDescriptor with tileWidth set to W and tileHeight set to H Fires up the tile shading kernel using dispatchThreadsPerTile with MTLSize size = { W, H, 1 } I understand that the result is 1-to-1 mapping between the tile "pixels" and kernel threads. Now, what I would like to do is to have more than one thread group there. I want this for performance reasons: I have a certain compute kernel which I know executes very well with small thread group size. In fact, { 32, 1, 1 } seems to be the fastest. My understanding is that even if I set tile size to 16x16, and so I am executing 256 threads there, there will only be one SIMD group active in a thread group. Meaning that this SIMD group has to execute 8 times over the tile. Is it possible somehow? Or perhaps the limitations of the API are pointing at the limitations of hardware itself, and if I want to execute with SIMD group sized thread groups I have to use "traditional" compute encoder? Will be grateful for help. Michał

I'm using RealityView in my iOS game mxied with SwiftUI. For the following 2 example usages, the simulator will only render the first RealityView, and the second one is either super laggy or show a black model. Running on the real device is all good, just simualtor has this issue. Have a TabView and each tab has a RealityView. Have a root view and detail view connected via a push navigation, both root and detail have a RealityView. In the Simulator, the second RealityView is going to be very choppy and basically unusable, but on a real iPhone everything looks great. Is this a known simulator issue or I did something bad?

Hey everyone, I am currently developing an app in visionOS and using RealityComposerPro create scenes in put in my app. I have a humanoid model with hair strands, and each strand of hair has an opacity map. However, some reflections are still visible even though the opacity is zero. There are also some weird culling among hair strands (in the left circle) and weird reflections in hair cards (in the right circle). Here's my settings for the materials. Since all the hair strands are interconnected with each other, it is hard to decide the drawing order in Xcode, so I am wondering if there's an easier way to handle transparency objects. Please let me know if you know anything helpful, much appreciated!

I have two devices (iPod, iPhone), each using a different Apple ID. I have an existing game to which I'm adding TBM. When the iPod invites the iPhone, it sends an iMessage invite to the iPhone; when I click on that message, I get "Retrieving", then Game Center in Settings is opened, not my App (same version installed on both devices). I start my App on the iPhone and that match is not shown in the Matchmaker View Controller. When I send an invite from the iPhone to the iPod and I click on the iMessage invite, the app starts but the match isn't listed in the MatchMaker ViewController on the iPod (but is on the iPhone). In addition, when I click on the info circle on the iPhone, it who's the two players and "App Store" under the Game Center name. However, When I do the same on the iPod, it has a "Play your turn" there. Any ideas?

Hello, I am currently working on a research project under ENINCA Consulting, focused on advanced diagnostic tools for pseudorandom number generators (structural metrics, multi-seed stability, cross-architecture reproducibility, and complementary indicators to TestU01). To validate this diagnostic framework, I prototyped a small non-linear 64-bit PRNG (not as a goal in itself, but simply as a vehicle to test the methodology). During these evaluations, I observed something interesting on Apple Silicon (Mac M1): • bit-exact reproducibility between M1 ARM CPU and M1 Metal GPU, • full BigCrush pass on both CPU and Metal backends, • excellent p-values, • stable behaviour across multiple seeds and runs. This was not the intended objective, the goal was mainly to validate the diagnostic concepts, but these results raised some questions about deterministic compute behaviour in Metal. My question: Is there any official guidance on achieving (or expecting) deterministic RNG or compute behaviour across CPU ↔ Metal GPU on Apple Silicon? More specifically: • Are deterministic compute kernels expected or guaranteed on Metal for scientific workloads? • Are there recommended patterns or best practices to ensure reproducibility across GPU generations (M1 → M2 → M3 → M4)? • Are there known Metal features that can introduce non-determinism? I am not sharing the internal recurrence (this work is proprietary), but I can discuss the high-level diagnostic observations if helpful. Thank you for any insight, very interested in how the Metal engineering team views deterministic compute patterns on Apple Silicon. Pascal ENINCA Consulting

How can one match the walls and floor of a given CapturedRoom ? The transform.eulerAngles of a floor z & y are always 0 ! And the polygons seems to have a different orientation than the walls. So how to figure out the rotation and match the one from the walls ?

I have been trying to run an open source Windows executable that I would like to help porting to macOS using the Game Porting Toolkit but I stumbled on an issue quite early in the application lifecycle. It looks like the funtion GetThreadDpiHostingBehavior is missing in USER32.dll Has anyone any idea how to solve that? During the startup, it fails with the following error: TiXL crashed. We're really sorry. The last backup was saved Unknown time to... C:\users\crossover\AppData\Roaming\TiXL\Backup Please refer to Help > Using Backups on what to do next. System.EntryPointNotFoundException: Unable to find an entry point named 'GetThreadDpiHostingBehavior' in DLL 'USER32.dll'. at System.Windows.Forms.ScaleHelper.DpiAwarenessScope..ctor(DPI_AWARENESS_CONTEXT context, DPI_HOSTING_BEHAVIOR behavior) at System.Windows.Forms.ScaleHelper.EnterDpiAwarenessScope(DPI_AWARENESS_CONTEXT awareness, DPI_HOSTING_BEHAVIOR dpiHosting) at System.Windows.Forms.NativeWindow.CreateHandle(CreateParams cp) at System.Windows.Forms.Control.CreateHandle() at System.Windows.Forms.Application.ThreadContext.get_MarshallingControl() at System.Windows.Forms.WindowsFormsSynchronizationContext..ctor() at System.Windows.Forms.WindowsFormsSynchronizationContext.InstallIfNeeded() at System.Windows.Forms.Control..ctor(Boolean autoInstallSyncContext) at System.Windows.Forms.ScrollableControl..ctor() at System.Windows.Forms.ContainerControl..ctor() at System.Windows.Forms.Form..ctor() at T3.Editor.SplashScreen.SplashScreen.SplashForm..ctor() at T3.Editor.SplashScreen.SplashScreen.Show(String imagePath) in C:\Users\pixtur\dev\tooll\tixl\Editor\SplashScreen\SplashScreen.cs:line 25 at T3.Editor.Program.Main(String[] args) in C:\Users\pixtur\dev\tooll\tixl\Editor\Program.cs:line 111

After watching WWDC 2025 session "Combine Metal 4 machine learning and graphics", I have decided to give it a shot to integrate the latest MTL4MachineLearningCommandEncoder to my existing render pipeline. After a lot of trial and errors, I managed to set up the pipeline and have the app compiled. However, I am now stuck on creating a MTLLibrary with .mtlpackage. Here is the code I have to create a MTLLibrary according the WWDC session https://developer.apple.com/videos/play/wwdc2025/262/?time=550: let coreMLFilePath = bundle.path(forResource: "my_model", ofType: "mtlpackage")! let coreMLURL = URL(string: coreMLFilePath)! do { metalDevice.makeLibrary(URL: coreMLURL) } catch { print("error: \(error)") } With the above code, I am getting error: Error Domain=MTLLibraryErrorDomain Code=1 "Invalid metal package" UserInfo={NSLocalizedDescription=Invalid metal package} What is the correct way to create a MTLLibrary with .mtlpackage? Do I see this error because the .mtlpackage I am using is incorrect? How should I go with debugging this? I'd really appreciate if I could get some help on this as I have been stuck with it for some time now. Thanks in advance!

Hi, I'm working with a very simple app that tries to read a coordinates card and past the data into diferent fields. The card's layout is COLUMNS from 1-10, ROWs from A-J and a two digit number for each cell. In my app, I have field for each of those cells (A1, A2...). I want that OCR to read that card and paste the info but I just cant. I have two problems. The camera won't close. It remains open until I press the button SAVE (this is not good because a user could take 3, 4, 5... pictures of the same card with, maybe, different results, and then? Which is the good one?). Then, after I press save, I can see the OCR kinda works ( the console prints all the date read) but the info is not pasted at all. Any idea? I know is hard to know what's wrong but I've tried chatgpt and all it does... just doesn't work This is the code from the scanview import SwiftUI import Vision import VisionKit struct ScanCardView: UIViewControllerRepresentable { @Binding var scannedCoordinates: [String: String] var useLettersForColumns: Bool var numberOfColumns: Int var numberOfRows: Int @Environment(.presentationMode) var presentationMode func makeUIViewController(context: Context) -&gt; VNDocumentCameraViewController { let scannerVC = VNDocumentCameraViewController() scannerVC.delegate = context.coordinator return scannerVC } func updateUIViewController(_ uiViewController: VNDocumentCameraViewController, context: Context) {} func makeCoordinator() -&gt; Coordinator { return Coordinator(self) } class Coordinator: NSObject, VNDocumentCameraViewControllerDelegate { let parent: ScanCardView init(_ parent: ScanCardView) { self.parent = parent } func documentCameraViewController(_ controller: VNDocumentCameraViewController, didFinishWith scan: VNDocumentCameraScan) { print("Escaneo completado, procesando imagen...") guard scan.pageCount &gt; 0, let image = scan.imageOfPage(at: 0).cgImage else { print("No se pudo obtener la imagen del escaneo.") controller.dismiss(animated: true, completion: nil) return } recognizeText(from: image) DispatchQueue.main.async { print("Finalizando proceso OCR y cerrando la cámara.") controller.dismiss(animated: true, completion: nil) } } func documentCameraViewControllerDidCancel(_ controller: VNDocumentCameraViewController) { print("Escaneo cancelado por el usuario.") controller.dismiss(animated: true, completion: nil) } func documentCameraViewController(_ controller: VNDocumentCameraViewController, didFailWithError error: Error) { print("Error en el escaneo: \(error.localizedDescription)") controller.dismiss(animated: true, completion: nil) } private func recognizeText(from image: CGImage) { let request = VNRecognizeTextRequest { (request, error) in guard let observations = request.results as? [VNRecognizedTextObservation], error == nil else { print("Error en el reconocimiento de texto: \(String(describing: error?.localizedDescription))") DispatchQueue.main.async { self.parent.presentationMode.wrappedValue.dismiss() } return } let recognizedStrings = observations.compactMap { observation in observation.topCandidates(1).first?.string } print("Texto reconocido: \(recognizedStrings)") let filteredCoordinates = self.filterValidCoordinates(from: recognizedStrings) DispatchQueue.main.async { print("Coordenadas detectadas después de filtrar: \(filteredCoordinates)") self.parent.scannedCoordinates = filteredCoordinates } } request.recognitionLevel = .accurate let handler = VNImageRequestHandler(cgImage: image, options: [:]) DispatchQueue.global(qos: .userInitiated).async { do { try handler.perform([request]) print("OCR completado y datos procesados.") } catch { print("Error al realizar la solicitud de OCR: \(error.localizedDescription)") } } } private func filterValidCoordinates(from strings: [String]) -&gt; [String: String] { var result: [String: String] = [:] print("Texto antes de filtrar: \(strings)") for string in strings { let trimmedString = string.replacingOccurrences(of: " ", with: "") if parent.useLettersForColumns { let pattern = "^[A-J]\\d{1,2}$" // Letras de A-J seguidas de 1 o 2 dígitos if trimmedString.range(of: pattern, options: .regularExpression) != nil { print("Coordenada válida detectada (letras): \(trimmedString)") result[trimmedString] = "Valor" // Asignación de prueba } } else { let pattern = "^[1-9]\\d{0,1}$" // Solo números, de 1 a 99 if trimmedString.range(of: pattern, options: .regularExpression) != nil { print("Coordenada válida detectada (números): \(trimmedString)") result[trimmedString] = "Valor" } } } print("Coordenadas finales después de filtrar: \(result)") return result } } }

In the process of using ARKit's image tracking, we found that different images have significant differences in recognizability. How can we judge the quality of this image in ARKit's image tracking for this situation?

Hello, I am trying to read video frames using AVAssetReaderTrackOutput. Here is the sample code: //prepare assets let asset = AVURLAsset(url: some_url) let assetReader = try AVAssetReader(asset: asset) guard let videoTrack = try await asset.loadTracks(withMediaCharacteristic: .visual).first else { throw SomeErrorCode.error } var readerSettings: [String: Any] = [ kCVPixelBufferIOSurfacePropertiesKey as String: [String: String]() ] //check if HDR video var isHDRDetected: Bool = false let hdrTracks = try await asset.loadTracks(withMediaCharacteristic: .containsHDRVideo) if hdrTracks.count > 0 { readerSettings[AVVideoAllowWideColorKey as String] = true readerSettings[kCVPixelBufferPixelFormatTypeKey as String] = kCVPixelFormatType_420YpCbCr10BiPlanarFullRange isHDRDetected = true } //add output to assetReader let output = AVAssetReaderTrackOutput(track: videoTrack, outputSettings: readerSettings) guard assetReader.canAdd(output) else { throw SomeErrorCode.error } assetReader.add(output) guard assetReader.startReading() else { throw SomeErrorCode.error } //add writer ouput settings let videoOutputSettings: [String: Any] = [ AVVideoCodecKey: AVVideoCodecType.hevc, AVVideoWidthKey: 1920, AVVideoHeightKey: 1080, ] let finalPath = "//some URL oath" let assetWriter = try AVAssetWriter(outputURL: finalPath, fileType: AVFileType.mov) guard assetWriter.canApply(outputSettings: videoOutputSettings, forMediaType: AVMediaType.video) else { throw SomeErrorCode.error } let assetWriterInput = AVAssetWriterInput(mediaType: .video, outputSettings: videoOutputSettings) let sourcePixelAttributes: [String: Any] = [ kCVPixelBufferPixelFormatTypeKey as String: isHDRDetected ? kCVPixelFormatType_420YpCbCr10BiPlanarFullRange : kCVPixelFormatType_32ARGB, kCVPixelBufferWidthKey as String: 1920, kCVPixelBufferHeightKey as String: 1080, ] //create assetAdoptor let assetAdaptor = AVAssetWriterInputTaggedPixelBufferGroupAdaptor( assetWriterInput: assetWriterInput, sourcePixelBufferAttributes: sourcePixelAttributes) guard assetWriter.canAdd(assetWriterInput) else { throw SomeErrorCode.error } assetWriter.add(assetWriterInput) guard assetWriter.startWriting() else { throw SomeErrorCode.error } assetWriter.startSession(atSourceTime: CMTime.zero) //prepare tranfer session var session: VTPixelTransferSession? = nil guard VTPixelTransferSessionCreate(allocator: kCFAllocatorDefault, pixelTransferSessionOut: &session) == noErr, let session else { throw SomeErrorCode.error } guard let pixelBufferPool = assetAdaptor.pixelBufferPool else { throw SomeErrorCode.error } //read through frames while let nextSampleBuffer = output.copyNextSampleBuffer() { autoreleasepool { guard let imageBuffer = CMSampleBufferGetImageBuffer(nextSampleBuffer) else { return } //this part copied from (https://developer.apple.com/videos/play/wwdc2023/10181) at 23:58 timestamp let attachment = [ kCVImageBufferYCbCrMatrixKey: kCVImageBufferYCbCrMatrix_ITU_R_2020, kCVImageBufferColorPrimariesKey: kCVImageBufferColorPrimaries_ITU_R_2020, kCVImageBufferTransferFunctionKey: kCVImageBufferTransferFunction_SMPTE_ST_2084_PQ, ] CVBufferSetAttachments(imageBuffer, attachment as CFDictionary, .shouldPropagate) //now convert to CIImage with HDR data let image = CIImage(cvPixelBuffer: imageBuffer) let cropped = "" //here perform some actions like cropping, flipping, etc. and preserve this changes by converting the extent to CGImage first: //this part copied from (https://developer.apple.com/videos/play/wwdc2023/10181) at 24:30 timestamp guard let cgImage = context.createCGImage( cropped, from: cropped.extent, format: .RGBA16, colorSpace: CGColorSpace(name: CGColorSpace.itur_2100_PQ)!) else { continue } //finally convert it back to CIImage let newScaledImage = CIImage(cgImage: cgImage) //now write it to a new pixelBuffer let pixelBufferAttributes: [String: Any] = [ kCVPixelBufferCGImageCompatibilityKey as String: true, kCVPixelBufferCGBitmapContextCompatibilityKey as String: true, ] var pixelBuffer: CVPixelBuffer? CVPixelBufferCreate( kCFAllocatorDefault, Int(newScaledImage.extent.width), Int(newScaledImage.extent.height), kCVPixelFormatType_420YpCbCr10BiPlanarFullRange, pixelBufferAttributes as CFDictionary, &pixelBuffer) guard let pixelBuffer else { continue } context.render(newScaledImage, to: pixelBuffer) //context is a CIContext reference var pixelTransferBuffer: CVPixelBuffer? CVPixelBufferPoolCreatePixelBuffer(kCFAllocatorDefault, pixelBufferPool, &pixelTransferBuffer) guard let pixelTransferBuffer else { continue } // Transfer the image to the pixel buffer. guard VTPixelTransferSessionTransferImage(session, from: pixelBuffer, to: pixelTransferBuffer) == noErr else { continue } //finally append to taggedBuffer } } assetWriterInput.markAsFinished() await assetWriter.finishWriting() The result video is not in correct color as the original video. It turns out too bright. If I play around with attachment values, it can be either too dim or too bright but not exactly proper as the original video. What am I missing in my setup? I did find that kCVPixelFormatType_4444AYpCbCr16 can produce proper video output but then I can't convert it to CIImage and so I can't do the CIImage operations that I need. Mainly cropping and resizing the CIImage

It's a Broadcast Extension issue: on iOS 26.1 beta the extension never launches—after you tap “Start Broadcast” in the system picker the countdown disappears after 3 s and no broadcast starts, so every live-streaming app（and all other non-system apps that use Broadcast Extension） fails to go live (only the native Photos screen recording still works). Is this a known regression or is a new entitlement required?

I am puzzled by the setAddress(_:attributeStride:index:) of MTL4ArgumentTable. Can anyone please explain what the attributeStride parameter is for? The doc says that it is "The stride between attributes in the buffer." but why? Who uses this for what? On the C++ side in the shaders the stride is determined by the C++ type, as far as I know. What am I missing here? Thanks!