Dear Apple Support, I would like to report a long-standing issue affecting Khmer text recognition in Live Text (Vision Framework/OCR). Based on my testing, this issue has persisted for more than two years, from iOS 17 through iOS 27 Beta 3, and is also reproducible on iPadOS and macOS. I would appreciate clarification on the following questions: Is Apple aware of an issue where Live Text (OCR/Text Recognition) incorrectly recognizes Khmer script as Thai script, causing copied text to become Thai characters instead of Khmer? Has this issue been officially logged as a bug within the Vision Framework or Live Text team? Since this behavior has remained reproducible from iOS 17 to iOS 27 Beta 3, why has it not yet been resolved? Is the problem caused by: automatic language detection, the OCR recognition model, the Vision Framework, or another component of Apple's AI pipeline? Does Apple currently have a dedicated OCR and language recognition model for the Khmer script, or is Khmer being inferred through another language model? Is there an estimated timeline for improving Khmer OCR and preventing Khmer text from being misidentified as Thai? Can Apple confirm whether this issue affects all products using Vision Framework, including: Live Text Photos Preview Screenshot OCR APIs provided to third-party developers? How can Apple work with the Khmer technology community to improve OCR accuracy and language support for Khmer? This issue is more than a simple OCR bug. When Khmer text is automatically converted into Thai characters, users lose access to the original text, developers receive incorrect OCR output, and it negatively impacts the digital representation of the Khmer language. For reference, I have documented the issue in detail here: https://app.notion.com/p/Inaccurate-OCR-Language-Inference-Khmer-Script-Misidentified-as-Thai-in-Vision-Framework-2d8a24f4ee6680fcbc49d989f8bb606f I hope Apple can investigate this issue and prioritize improving Khmer language support across Vision Framework and Live Text. Thank you.

We are evaluating various AI frameworks to use within our code, and are hoping to use some of the build-in frameworks in macOS including CoreML and Vision. However, we need to use these frameworks in a background process (system extension) that has no user session attached to it. (To be pedantic, we'll be using an XPC service that is spawned by the system extension, but neither would have an associated user session). Saying the daemon-safe frameworks list has not been updated in a while is an understatement, but it's all we have to go on. CoreGraphics isn't even listed--back then it part of ApplicationServices (I think?) and ApplicationServices is a no go. Vision does use CoreGraphics symbols and data types so I have doubts. We do have a POC that uses both frameworks and they seem to function fine but obviously having something official is better. Any Apple engineers that can comment on this?

Hey everyone, If your iPhone is stuck on "Optimizing Search and Siri" / Indexing at around 85-90% and hard resets or leaving it on wireless charging overnight isn't moving the needle, I found a definitive way to kickstart the daemon and force it to 100%. The Root Cause (Analyzed via macOS Console): By plugging the iPhone into a Mac and checking the Console logs, I noticed that spotlightknowledged and biomed get into an endless loop around Resolved entitled set identifiers to enumerate data resources. The system stalls on verifying developer entitlements and Apple Intelligence security tokens for specific app databases over cellular networks or unstable Wi-Fi. The dasd daemon eventually puts the pipeline into a hidden sleep state to protect the battery, making it look like it's doing nothing. The Solution that forced it to 100%: Use a Wired Connection (Ethernet Adapter): Connect your iPhone directly to your fiber optic router/modem using an RJ-45 Ethernet to USB-C adapter. iOS treats a wired Ethernet connection with the highest background priority. Turn off Cellular/Wi-Fi: Go to Control Center and disable Cellular Data and Wi-Fi entirely to force iOS to route 100% of traffic through the reliable, high-speed wired pipeline. (Verify the "Ethernet" tab appears in Settings). Trigger iCloud Token Refresh: Go to Settings -> [Your Name] -> iCloud -> Saved to iCloud and toggle off/on a major asset like iCloud Drive or Messages. This forces Spotlight to dump the stalled identifiers cache. Force Restart on Cable: Perform a Hard Reset (Vol Up, Vol Down, hold Power) while the Ethernet cable is connected. Let it Cook: Keep Low Power Mode OFF, lock the screen, and leave it alone. How to verify it's definitively done: Keep your Mac Console open and filter for completeness. Gdy to się stanie, zobaczysz ostateczny dziennik świętego Graala: fetchPipelineCompleteness: display=hidden days=3 procent=1.000000 Status the ` spent=1.000000 oznacza solidne 100% uzupełnienia. Zaraz po tym, 「dasd bezpiecznie zabije procesy worker (client process exited /connection invalid), telefon w końcu ostygnie, a żywotność baterii wróci do normy. Zaraz potem wyrzuciłem potoki glp i wszystko jest teraz masłem! Mam nadzieję, że pomoże to każdemu, kto utknął w 88% otchłani!

I have a M3 Ultra Mac Studio running RDMA. However, the system is unable to connect all 6 thunderbolt ports when the ethernet cable is also connected. Can anyone help?

Hello Apple Keyboard / Internationalization team, I would like to ask about autocorrection and predictive text support for additional Cyrillic-based languages, especially Kazakh, Kyrgyz, Chuvash, and Ingush. These languages use Cyrillic scripts with their own letters, spelling rules, and word-frequency patterns. When users type in these languages, Russian-based autocorrection or missing language-specific correction can produce incorrect suggestions or replacements. My questions are: Are there plans to expand autocorrection and predictive text support for more Cyrillic-based languages? Is there a recommended way for developers or language communities to provide dictionaries, word-frequency lists, corpora, or other linguistic data to help improve autocorrection? Should this type of request be submitted through Feedback Assistant, Developer Forums, or another Apple channel? I have corpus-based frequency data and language resources for multiple Cyrillic-based languages and would be happy to share them if useful. Thank you. Ali Kuzhuget

I’m trying to group my EntityPropertyQuery selection into sections as well as making it searchable. I know that the EntityStringQuery is used to perform the text search via entities(matching string: String). That works well enough and results in this modal: Though, when I’m using a DynamicOptionsProvider to section my EntityPropertyQuery, it doesn’t allow for searching anymore and simply opens the sectioned list in a menu like so: How can I combine both? I’ve seen it in other apps, but can’t figure out why my code doesn’t allow to section the results and make it searchable? Any ideas? My code (simplified) struct MyIntent: AppIntent { @Parameter(title: "Meter"), optionsProvider: MyOptionsProvider()) var meter: MyIntentEntity? // … struct MyOptionsProvider: DynamicOptionsProvider { func results() async throws -> ItemCollection<MyIntentEntity> { // Get All Data let allData = try IntentsDataHandler.shared.getEntities() // Create Arrays for Sections let fooEntities = allData.filter { $0.type == .foo } let barEntities = allData.filter { $0.type == .bar } return ItemCollection(sections: [ ItemSection("Foo", items: fooEntities), ItemSection("Bar", items: barEntities) ]) } } struct MeterIntentQuery: EntityStringQuery { // entities(for identifiers: [UUID]) and suggestedEntities() functions func entities(matching string: String) async throws -> [MyIntentEntity] { // Fetch All Data let allData = try IntentsDataHandler.shared.getEntities() // Filter Data by String let matchingData = allData.filter { data in return data.title.localizedCaseInsensitiveContains(string)) } return matchingData } }

It has been over 3 years since you chose, to save Apple money, you changes voice to text, and it is worthless. Do you plan on fixing this, or going back to paying for cloud service, instead of being cheap, sacrificing our experience so you can save money your trillion dollar company does not need.

: Hello, I’m seeking clarification on whether Apple provides any framework or API that enables deep integration between Siri and advanced AI assistants (such as ChatGPT), including system-level functions like voice interaction, navigation, cross-platform syncing, and operational access similar to Siri’s own capabilities. If no such option exists today, I would appreciate guidance on the recommended path or approved third-party solutions for building a unified, voice-first experience across Apple’s ecosystem. Thank you for your time and insight.

Sorry I thought it would tell you in the description or in the forum but when I went to try to activate new Siri it says that I have to be on a waitlist and from all that I've checked I'm an Apple developer so I'm wondering if anybody else is waiting or if the system is so busy today. If anybody else is having this issue please let me know.

I see a linear slow down as more trackers are added in the loop below. According to the WWDC video I was hoping that performAll run all these request in parallel, but apparently not for TrackObjectRequest.... #if DEBUG print("Frame processor: \(requests.count) tracking requests") var observationCount: Int = 0 let trackingStart = Date() #endif for await observation in handler.performAll(requests) { if case .trackObject(let request, let trackedBlock) = observation { #if DEBUG observationCount += 1 #endif guard let trackedBlock = trackedBlock, trackedBlock.confidence <= FrameProcessingThresholds.blockTrackedConfidenceThreshold else { // lost track of the block, remove the tracker blockTrackers.removeValue(forKey: request) continue } trackedBlocks[blockTrackers[request]!] = trackedBlock.boundingBox } } #if DEBUG print("Frame processor: \(observationCount) observations") print("Frame processor: tracker took \(Date().timeIntervalSince(trackingStart)) seconds") #endif

Hi Guys, I want to use the newest Mac Book Pro M6 (Max or Ultra) with at least 256GB RAM for AI development. Will my wish may come true? What do you think? One of Apples most advantage here is unified memory and with the privacy first approach, i want to run local modells and show it to my customer just on the macbook. That has much more magic then first plug the power supply for a sparc, connect a network cable and fiddling around. The perfect match would be a Max Book Pro, M6 Ultra, 512GB. But I guess this is just a dream :-(. Please let me know what you think abou that. Thanks

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)") } }

Product macOS Version macOS 26.4.1 (public release) Hardware Apple M5 Pro, 48 GB unified memory Summary On macOS 26.4.1, the MPS backend consistently reports approximately 40 GiB of “other allocations” on a 48 GB M5 Pro machine, even on a freshly rebooted system with minimal user applications running. This leaves insufficient memory for large GPU tensor operations that previously succeeded on earlier macOS versions. The failure manifests as: RuntimeError: MPS backend out of memory (MPS allocated: 17.60 GiB, other allocations: 40.17 GiB, max allowed: 63.65 GiB). Tried to allocate 7.63 GiB on private pool. The “other allocations: 40.17 GiB” value is consistent across reboots and does not change materially when user applications are quit. This suggests macOS 26.4.1 has increased its baseline GPU/unified memory consumption compared to prior releases in a way that is visible to the MPS allocator. Steps to Reproduce Fresh reboot of M5 Pro, 48 GB, macOS 26.4.1 Launch a PyTorch 2.11.0 application using MPS as the compute device Load a large model into MPS memory (~17 GiB, e.g. a VAE encoder in bfloat16) Attempt to allocate an additional ~7.6 GiB workspace tensor for a matrix multiplication operation (torch.bmm) Result: RuntimeError: MPS backend out of memory, with “other allocations” reported at ~40 GiB despite no large user processes holding GPU memory. Expected: The operation should succeed. 17.60 + 7.63 = 25.23 GiB, which is well within the 48 GiB physical memory of the machine. Additional Observations • vm_stat on a clean boot shows ~24 GB of free system RAM before the PyTorch application launches, consistent with normal OS usage. The 40 GiB figure reported by the MPS allocator as “other allocations” does not correspond to identifiable user processes. • The max allowed: 63.65 GiB ceiling reported by MPS exceeds the physical 48 GiB of the machine, suggesting MPS is using a memory limit calculation that does not account for actual physical constraints on unified memory architectures. • macOS 26.4 introduced a related regression (deterministic RuntimeError: MPSGraph does not support tensor dims larger than INT_MAX) in the same MPS buffer stride arithmetic path. That specific error was resolved in 26.4.1, but the OOM regression described here persists. • This operation succeeded on the same hardware under earlier macOS releases. The increased “other allocations” baseline appears to be specific to macOS 26.x. Impact Machine learning workloads that previously ran successfully on 48 GB Apple Silicon machines are failing on macOS 26.4.1 due to this increased baseline GPU memory consumption. Applications using PyTorch MPS, Core ML, and potentially Metal Performance Shaders directly may be affected. Workaround None identified. Reducing application model size or splitting operations into smaller chunks does not resolve the issue because the constraint is in the “other allocations” baseline, not in the application’s own allocations.

Hello, I spent some time going through the documentation and videos. I did not see how to implement quantized arithmetic for my neural network using BNNSGraph. Could someone please help me.

When I try to use VNRecognizeTextRequest in a simple program on apple silicon .accurate works, but when I add the same code to a helper process in a larger project, .accurate doesn’t return any results while only .fast works. This happens on apple silicon machines but not older intel ones. When I call VNRecognizeTextRequest I see the error [Espresso::handle_ex_plan] exception= in the logs along with (TextRecognition) Error loading network 0, -1. And when I catch the exception in lldb and print it I see Null bundleID. In the code, [[NSBundle mainBundle] returns null even though plutil -p on the helper process binary shows an embedded plist, as well as on the process that spawns the helper.

I am a professional user of the 2019 Mac Pro (7,1) with dual AMD Radeon Pro W6900X MPX modules (32GB VRAM each). This hardware is designed for high-performance compute, but it is currently crippled for modern local LLM/AI workloads under Linux due to Apple's EFI/PCIe routing restrictions. Core Issue: rocminfo reports "No HIP GPUs available" when attempting to use ROCm/amdgpu on Linux Apple's custom EFI firmware blocks full initialization of professional GPU compute assets The dual W6900X GPUs have 64GB combined VRAM and high-bandwidth Infinity Fabric Link, but cannot be fully utilized for local AI inference/training My Specific Request: Apple should provide an official, one-click deployable application that enables full utilization of dual W6900X GPUs for local large language model (LLM) inference and training under Linux. This application must: Fully initialize both W6900X GPUs via HIP/ROCm, establishing valid compute contexts Bypass artificial EFI/PCIe routing restrictions that block access to professional GPU resources Provide a stable, user-friendly one-click deployment experience (similar to NVIDIA's AI Enterprise or AMD's ROCm Hub) Why This Matters: The 2019 Mac Pro is Apple's flagship professional workstation, marketed for compute-intensive workloads. Its high-cost W6900X GPUs should not be locked down for modern AI/LLM use cases. An official one-click deployment solution would demonstrate Apple's commitment to professional AI and unlock significant value for professional users. I look forward to Apple's response and a clear roadmap for enabling this critical capability. #MacPro #Linux #ROCm #LocalLLM #W6900X #CoreML

Hi, I think many of us would love to be able to use our GPUs for Jax on the new Apple Silicon devices, but currently, the Jax-metal plugin is, for all effects and purposes, broken. Is it still under active development? Is there a planned release for a new version? thanks!

I want to introduce how is usefully AI

In Oct25, using mlx_lm.lora I created an adapter and a fused model uploaded to Huggingface. I was able to incorporate this model into my SwiftUI app using the mlx package. MLX-libraries 2.25.8. My base LLM was mlx-community/Mistral-7B-Instruct-v0.3-4bit. Looking at LLMModelFactory.swift the current version 2.29.1 the only changes are the addition of a few models. The earlier model was called: pharmpk/pk-mistral-7b-v0.3-4bit The new model is called: pharmpk/pk-mistral-2026-03-29 The base model (mlx-community/Mistral-7B-Instruct-v0.3-4bit.) must still be available. Could the error 'unsupportedTokenizer' be related to changes in the mlx package? I noticed mention of splitting the package into two parts but don't see anything at github. Feeling rather lost. Does anone have any thoguths and/or suggestions. Thanks, David

I have a series of shortcuts that I’ve written that use the “Use Model” action to do various things. For example, I have a shortcut “Clipboard Markdown to Notes” that takes the content of the clipboard, creates a new note in Notes, converts the markdown content to rich text, adds it to the note etc. One key step is to analyze the markdown content with “Use Model” and generate a short descriptive title for the note. I use the on-device model for this, but sometimes the content and prompt exceed the context window size and the action fails with an error message to that effect. In that case, I’d like to either repeat the action using the Cloud model, or, if the error was a refusal, to prompt the user to enter a title to use. I‘ve tried using an IF based on whether the response had any text in it, but that didn’t work. No matter what I’ve tried, I can’t seem to find a way to catch the error from Use Model, determine what the error was, and take appropriate action. Is there a way to do this? (And by the way, a huge ”thank you” to whoever had the idea of making AppIntents visible in Shortcuts and adding the Use Model action — has made a huge difference already, and it lets us see what Siri will be able to use as well.)