Hi all! Nice to meet you., I am planning to build an iOS application that can: Capture an image using the camera or select one from the gallery. Remove the background and keep only the detected main object. Add a border (outline) around the detected object’s shape. Apply an animation along that border (e.g., moving light or glowing effect). Include a transition animation when removing the background — for example, breaking the background into pieces as it disappears. The app Capword has a similar feature for object isolation, and I’d like to build something like that. Could you please provide any guidance, frameworks, or sample code related to: Object segmentation and background removal in Swift (Vision or Core ML). Applying custom borders and shape animations around detected objects. Recognizing the object name (e.g., “person”, “cat”, “car”) after segmentation. Thank you very much for your support. Best regards, SINN SOKLYHOR

I've built an iOS app with a novel approach to AI safety: a deterministic, pre-inference validation layer called Newton Engine. Instead of relying on the LLM to self-moderate, Newton validates every prompt BEFORE it reaches the model. It uses shape theory and semantic analysis to detect: • Corrosive frames (self-harm language patterns) • Logical contradictions (requests that undermine themselves) • Delegation attempts (asking AI to make human decisions) • Jailbreak patterns (prompt injection, role-play escapes) • Hallucination triggers (requests for fabricated citations) The system achieves a 96% adversarial catch rate across 847 test cases, with zero false positives on benign prompts. Key technical details: • Pure Swift/SwiftUI, no external dependencies • Runs entirely on-device (no server calls for validation) • Deterministic (same input always produces same output) • Auditable (full trace logging for every validation) I'm preparing to submit to the App Store and wanted to ask: Are there specific App Review guidelines I should reference for AI safety claims? Is there interest from Apple in deterministic governance layers for Apple Intelligence integration? Any recommendations for demonstrating safety compliance during review? The app is called Ada, and the engine is open source at: github.com/jaredlewiswechs/ada-newton Happy to share technical documentation or discuss the architecture with anyone interested. See: parcri.net

I’d like to submit a feature request regarding the availability of Foundation Models in MessageFilter extensions. Background MessageFilter extensions play a critical role in protecting users from spam, phishing, and unwanted messages. With the introduction of Foundation Models and Apple Intelligence, Apple has provided powerful on-device natural language understanding capabilities that are highly aligned with the goals of MessageFilter. However, Foundation Models are currently unavailable in MessageFilter extensions. Why Foundation Models Are a Great Fit for MessageFilter Message filtering is fundamentally a natural language classification problem. Foundation Models would significantly improve: Detection of phishing and scam messages Classification of promotional vs transactional content Understanding intent, tone, and semantic context beyond keyword matching Adaptation to evolving scam patterns without server-side processing All of this can be done fully on-device, preserving user privacy and aligning with Apple’s privacy-first design principles. Current Limitations Today, MessageFilter extensions are limited to relatively simple heuristics or lightweight models. This often results in: Higher false positives Lower recall for sophisticated scam messages Increased development complexity to compensate for limited NLP capabilities Request Could Apple consider one of the following: Allowing Foundation Models to be used directly within MessageFilter extensions Providing a constrained or optimized Foundation Model API specifically designed for MessageFilter Enabling a supported mechanism for MessageFilter extensions to delegate inference to the containing app using Foundation Models Even limited access (e.g. short text only, strict execution limits) would be extremely valuable. Closing Foundation Models have the potential to significantly raise the quality and effectiveness of message filtering on Apple platforms while maintaining strong privacy guarantees. Supporting them in MessageFilter extensions would be a major improvement for both developers and users. Thank you for your consideration and for continuing to invest in on-device intelligence.

Hello, I am interested in using jax-metal to train ML models using Apple Silicon. I understand this is experimental. After installing jax-metal according to https://aninterestingwebsite.com/metal/jax/, my python code fails with the following error JaxRuntimeError: UNKNOWN: -:0:0: error: unknown attribute code: 22 -:0:0: note: in bytecode version 6 produced by: StableHLO_v1.12.1 My issue is identical to the one reported here https://github.com/jax-ml/jax/issues/26968#issuecomment-2733120325, and is fixed by pinning to jax-metal 0.1.1., jax 0.5.0 and jaxlib 0.5.0. Thank you!

Hi team, I’m exploring the Model Context Protocol (MCP), which is used to connect LLMs/AI agents to external tools in a structured way. It's becoming a common standard for automation and agent workflows. Before I go deeper, I want to confirm: Does Apple currently provide any official MCP server, API surface, or SDK on iOS/macOS? From what I see, only third-party MCP servers exist for iOS simulators/devices, and Apple’s own frameworks (Foundation Models, Apple Intelligence) don’t expose MCP endpoints. Is there any chance Apple might introduce MCP support—or publish recommended patterns for safely integrating MCP inside apps or developer tools? I would like to see if I can share my app's data to the MCP server to enable other third-party apps/services to integrate easily

Hi there, I have a custom keypoint detection model and want to use it via vision's CoremlRequest API. Here's some complication for input and output: For input My model expect 512x512 a image. Which would be resized and padded from a 1920x1080 frame. I use the .scaleToFit option, but can I also specify the color used for padding? For output: My model output a CoreMLFeatureValueObservation, can I have it output in a format vision recognizes? such as joints/keypoints If my model is able to output in a format vision recognizes, would it take care to restoring the coordinates back to the original frame? (undo the padding) If not, how do I restore it from .scaletofit option? Best,

Hello It seems the model Content Tagging doesn't obey when I define the type of tag I wish in the instructions parameters, always the output are the main topics. The unique form to get other type of tags like emotions is using Generable + Guided types. The documentation says it is recommended but not mandatory the use instructions. Maybe I'm setting wrongly the instructions but take a look in the attached snapshot. I copied the definition of tagging emotions from the official documentation. The upper example is employing generable and it works but in the example at the botton I set like instruction the same description of emotion and it doesn't work. I tried with other statements with more or less verbose and never output emotions. Could you provide a state using instruction where it works? Current version of model isn't working with instruction?

I just recently updated to iOS 26 beta (23A5336a) to test an app I am developing I running an MLModel loaded from a .mlmodelc file. On the current iOS version 18.6.2 the model is running as expected with no issues. However on iOS 26 I am now getting error when trying to perform an inference to the model where I pass a camera frame into it. Below is the error I am seeing when I attempt to run an inference. at the bottom it says "Failed with status=0x1d : statusType=0x9: Program Inference error status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model " does this indicate I need to convert my model or something? I don't understand since it runs as normal on iOS 18. Any help getting this to run again would be greatly appreciated. Thank you, processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: Could not process request ret=0x1d lModel=_ANEModel: { modelURL=file:///var/containers/Bundle/Application/04F01BF5-D48B-44EC-A5F6-3C7389CF4856/RizzCanvas.app/faceParsing.mlmodelc/ : sourceURL=(null) : UUID=46228BFC-19B0-45BF-B18D-4A2942EEC144 : key={"isegment":0,"inputs":{"input":{"shape":[512,512,1,3,1]}},"outputs":{"var_633":{"shape":[512,512,1,19,1]},"94_argmax_out_value":{"shape":[512,512,1,1,1]},"argmax_out":{"shape":[512,512,1,1,1]},"var_637":{"shape":[512,512,1,19,1]}}} : identifierSource=1 : cacheURLIdentifier=01EF2D3DDB9BA8FD1FDE18C7CCDABA1D78C6BD02DC421D37D4E4A9D34B9F8181_93D03B87030C23427646D13E326EC55368695C3F61B2D32264CFC33E02FFD9FF : string_id=0x00000000 : program=_ANEProgramForEvaluation: { programHandle=259022032430 : intermediateBufferHandle=13949 : queueDepth=127 } : state=3 : [Espresso::ANERuntimeEngine::__forward_segment 0] evaluate[RealTime]WithModel returned 0; code=8 err=Error Domain=com.apple.appleneuralengine Code=8 "processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error" UserInfo={NSLocalizedDescription=processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error} [Espresso::handle_ex_plan] exception=Espresso exception: "Generic error": ANEF error: /private/var/containers/Bundle/Application/04F01BF5-D48B-44EC-A5F6-3C7389CF4856/RizzCanvas.app/faceParsing.mlmodelc/model.espresso.net, processRequest:model:qos:qIndex:modelStringID:options:returnValue:error:: ANEProgramProcessRequestDirect() Failed with status=0x1d : statusType=0x9: Program Inference error status=-1 Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1). Error Domain=com.apple.Vision Code=3 "The VNCoreMLTransform request failed" UserInfo={NSLocalizedDescription=The VNCoreMLTransform request failed, NSUnderlyingError=0x114d92940 {Error Domain=com.apple.CoreML Code=0 "Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1)." UserInfo={NSLocalizedDescription=Unable to compute the prediction using a neural network model. It can be an invalid input data or broken/unsupported model (error code: -1).}}}

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

Is foundation models matured enough to take input from the Apple Vision framework to generate responses? Something similar to what google's gemini does although in a much smaller scale and for a very specific niche.

Hello, I was successfully able to compile TKDKid1000/TinyLlama-1.1B-Chat-v0.3-CoreML using Core ML, and it's working well. However, I’m now trying to compile the same model using Swift Transformers. With the limited documentation available on the swift-chat and Hugging Face repositories, I’m finding it difficult to understand the correct process for compiling a model via Swift Transformers. I attempted the following approach, but I’m fairly certain it’s not the recommended or correct method. Could someone guide me on the proper way to compile and use models like TinyLlama with Swift Transformers? Any official workflow, example, or best practice would be very helpful. Thanks in advance! This is the approach I have used: import Foundation import CoreML import Tokenizers @main struct HopeApp { static func main() async { print(" Running custom decoder loop...") do { let tokenizer = try await AutoTokenizer.from(pretrained: "PY007/TinyLlama-1.1B-Chat-v0.3") var inputIds = tokenizer("this is the test of the prompt") print("🧠 Prompt token IDs:", inputIds) let model = try float16_model(configuration: .init()) let maxTokens = 30 for _ in 0..<maxTokens { let input = try MLMultiArray(shape: [1, 128], dataType: .int32) let mask = try MLMultiArray(shape: [1, 128], dataType: .int32) for i in 0..<inputIds.count { input[i] = NSNumber(value: inputIds[i]) mask[i] = 1 } for i in inputIds.count..<128 { input[i] = 0 mask[i] = 0 } let output = try model.prediction(input_ids: input, attention_mask: mask) let logits = output.logits // shape: [1, seqLen, vocabSize] let lastIndex = inputIds.count - 1 let lastLogitsStart = lastIndex * 32003 // vocab size = 32003 var nextToken = 0 var maxLogit: Float32 = -Float.greatestFiniteMagnitude for i in 0..<32003 { let logit = logits[lastLogitsStart + i].floatValue if logit > maxLogit { maxLogit = logit nextToken = i } } inputIds.append(nextToken) if nextToken == 32002 { break } let partialText = try await tokenizer.decode(tokens:inputIds) print(partialText) } } catch { print("❌ Error: \(error)") } } }

I've created a "Transfer Learning BERT Embeddings" model with the default "Latin" language family and "Automatic" Language setting. This model performs exceptionally well against the test data set and functions as expected when I preview it in Create ML. However, when I add it to the Xcode project of the application to which I am deploying it, I am getting runtime errors that suggest it can't find the embedding resources: Failed to locate assets for 'mul_Latn' - '5C45D94E-BAB4-4927-94B6-8B5745C46289' embedding model Note, I am adding the model to the app project the same way that I added an earlier "Maximum Entropy" model. That model had no runtime issues. So it seems there is an issue getting hold of the embeddings at runtime. For now, "runtime" means in the Simulator. I intend to deploy my application to iOS devices once GM 26 is released (the app also uses AFM). I'm developing on Tahoe 26 beta, running on iOS 26 beta, using Xcode 26 beta. Is this a known/expected issue? Are the embeddings expected to be a resource in the model? Is there a workaround? I did try opening the model in Xcode and saving it as an mlpackage, then adding that to my app project, but that also didn't resolve the issue.

Hi everyone, I'm experiencing an inconsistent behavior with the Translation framework on iOS 18. The LanguageAvailability.status() API reports language models as .installed, but translation fails with Code 16. Setup: Using translationTask modifier with TranslationSession Batch translation with explicit source/target languages Languages: Portuguese→English, German→English Issue: let status = await LanguageAvailability().status(from: sourceLang, to: targetLang) // Returns: .installed // But translation fails: let responses = try await session.translations(from: requests) // Error: TranslationErrorDomain Code=16 "Offline models not available" Logs: Language model installed: pt -> en Language model installed: de -> en Starting translation: de -> en Error Domain=TranslationErrorDomain Code=16 "Translation failed"NSLocalizedFailureReason=Offline models not available for language pair What I've tried: Re-downloading languages in Settings Using source: nil for auto-detection Fresh TranslationSession.Configuration each time Questions: Is there a way to force model re-validation/re-download programmatically? Should translationTask show download popup when Code 16 occurs? Has anyone found a reliable workaround? I've seen similar reports in threads 791357 and 777113. Any guidance appreciated! Thanks!

Pretty much as per the title and I suspect I know the answer. Given that Foundation Models run on device, is it possible to use Foundation Models framework inside of a DeviceActivityReport? I've been tinkering with it, and all I get is errors and "Sandbox restrictions". Am I missing something? Seems like a missed trick to utilise on device AI/ML with other frameworks.

Hi everyone, I’m exploring ideas around on-device analysis of user typing behavior on iPhone, and I’d love input from others who’ve worked in this area or thought about similar problems. Conceptually, I’m interested in things like: High-level sentiment or tone inferred from what a user types over time using ML-models Identifying a user’s most important or frequent topics over a recent window (e.g., “last week”) Aggregated insights rather than raw text (privacy-preserving summaries: e.g., your typo-rate by hour to infer highly efficient time slots or "take-a-break" warning typing errors increase) I understand the significant privacy restrictions around keyboard input on iOS, especially for third-party keyboards and system text fields. I’m not trying to bypass those constraints—rather, I’m curious about what’s realistically possible within Apple’s frameworks and policies. (For instance, Grammarly as a correction tool includes some information about tone) Questions I’m thinking through: Are there any recommended approaches for on-device text analysis that don’t rely on capturing raw keystrokes? Has anyone used NLP / Core ML / Natural Language successfully for similar summarization or sentiment tasks, scoped only to user-explicit input? For custom keyboards, what kinds of derived or transient signals (if any) are acceptable to process and summarize locally? Any design patterns that balance usefulness with Apple’s privacy expectations? If you’ve built something adjacent—journaling, writing analytics, well-being apps, etc.—I’d appreciate hearing what worked, what didn’t, and what Apple reviewers were comfortable with. Thanks in advance for any ideas or references 🙏

I have an app that streams in data from the Foundation Model and I have a card that shows one of the outputs. I want my card to accept a partially generated model but I keep getting a nonsensical error. The error I get on line 59 is: Cannot convert value of type 'FrostDate.VegetableSuggestion.PartiallyGenerated' (aka 'FrostDate.VegetableSuggestion') to expected argument type 'FrostDate.VegetableSuggestion.PartiallyGenerated' Here is my card with preview: import SwiftUI import FoundationModels struct VegetableSuggestionCard: View { let vegetableSuggestion: VegetableSuggestion.PartiallyGenerated init(vegetableSuggestion: VegetableSuggestion.PartiallyGenerated) { self.vegetableSuggestion = vegetableSuggestion } var body: some View { VStack(alignment: .leading, spacing: 8) { if let name = vegetableSuggestion.vegetableName { Text(name) .font(.headline) .frame(maxWidth: .infinity, alignment: .leading) } if let startIndoors = vegetableSuggestion.startSeedsIndoors { Text("Start indoors: \(startIndoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let startOutdoors = vegetableSuggestion.startSeedsOutdoors { Text("Start outdoors: \(startOutdoors)") .frame(maxWidth: .infinity, alignment: .leading) } if let transplant = vegetableSuggestion.transplantSeedlingsOutdoors { Text("Transplant: \(transplant)") .frame(maxWidth: .infinity, alignment: .leading) } if let tips = vegetableSuggestion.tips { Text("Tips: \(tips)") .foregroundStyle(.secondary) .frame(maxWidth: .infinity, alignment: .leading) } } .padding(16) .frame(maxWidth: .infinity, alignment: .leading) .background( RoundedRectangle(cornerRadius: 16, style: .continuous) .fill(.background) .overlay( RoundedRectangle(cornerRadius: 16, style: .continuous) .strokeBorder(.quaternary, lineWidth: 1) ) .shadow(color: Color.black.opacity(0.05), radius: 6, x: 0, y: 2) ) } } #Preview("Vegetable Suggestion Card") { let sample = VegetableSuggestion.PartiallyGenerated( vegetableName: "Tomato", startSeedsIndoors: "6–8 weeks before last frost", startSeedsOutdoors: "After last frost when soil is warm", transplantSeedlingsOutdoors: "1–2 weeks after last frost", tips: "Harden off seedlings; provide full sun and consistent moisture." ) VegetableSuggestionCard(vegetableSuggestion: sample) .padding() .previewLayout(.sizeThatFits) }

I'm trying to use Apple's new Visual Intelligence API for recommending content through screenshot image search. The problem I encountered is that the SemanticContentDescriptor labels are either completely empty or super misleading, making it impossible to query for similar content on my app. Even the closest matching example was inaccurate, returning a single label ["cardigan"] for a Supreme T-Shirt. I see other apps using this API like Etsy for example, and I'm wondering if they're using the input pixel buffer to query for similar content rather than using the labels? If anyone has a similar experience or something that wasn't called out in the documentation please lmk! Thanks.

Recently, I'm trying to deploy some third-party LLM to Apple devices. The methodoloy is similar to https://github.com/Anemll/Anemll. The biggest issue I'm having now is the runtime memory usage. When there are multiple functions in a model (mlpackage or mlmodelc), the runtime memory usage for weights is somehow duplicated when I load all of them. Here's the detail: I created my multifunction mlpackage following https://apple.github.io/coremltools/docs-guides/source/multifunction-models.html I loaded each of the functions using the generated swift class: let config = MLModelConfiguration() config.computeUnits = MLComputeUnits.cpuAndNeuralEngine config.functionName = "infer_512"; let ffn1_infer_512 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_1024"; let ffn1_infer_1024 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) config.functionName = "infer_2048"; let ffn1_infer_2048 = try! mimo_FFN_PF_lut4_chunk_01of02(configuration: config) I observed that RAM usage increases linearly as I load each of the functions. Using instruments, I see that there are multiple HWX files generated and loaded, each of which contains all the weight data. My understanding of what's happening here: The CoreML framework did some MIL->MIL preprocessing before further compilation, which includes separating CPU workload from ANE workload. The ANE part of each function is moved into a separate MIL file then compile separately into a HWX file each. The problem is that the weight data of these HWX files are duplicated. Since that the weight data of LLMs is huge, it will cause out-of-memory issue on mobile devices. The improvement I'm hoping from Apple: I hope we can try to merge the processed MIL files back into one before calling ANECCompile(), so that the weights can be merged. I don't have control over that in user space and I'm not sure if that is feasible. So I'm asking for help here. Thanks.

Hello, We find that models sometimes load very fast (<< 1 second) and sometimes encounter very long load times (>> 120 seconds). During such slow load times, the model is being compiled. We would greatly appreciate the ability to check cache validity via CoreML and determine that we are about to encounter long load times so that we can mitigate and provide a good user experience. A secondary issue: sometimes the cache is corrupted (typically .mpsgraphpackage yielding Metal cold asserts). This yields load failures and OS errors that persist between launches, and we have to manually nuke the cache (~/Library/..../my-app/...) for the CoreML assets. A CoreML API for clearing caches and hardening from asserts across the load paths would be appreciated

Hello, My goal is to enable users to perform a freeform search request for any product I sell using a spoken phrase, for example, "Hey Siri, search GAMING CONSOLES on MyCatalogApp". The result would launch MyCatalogApp and navigate to a search results page displaying gaming consoles. I have defined a SearchIntent (using the .system.search schema) and a Shortcut to accomplish this. However, Siri doesn't seem to be able to correctly parse the spoken phrase, extract the search string, and provide it as the critiria term within SearchIntent. What am I doing wrong? Here is the SearchIntent. Note the print() statement outputs the search string--which in the scenario above would be "GAMING CONSOLES"--but it doesn't work. import AppIntents @available(iOS 17.2, *) @AppIntent(schema: .system.search) struct SearchIntent: ShowInAppSearchResultsIntent { static var searchScopes: [StringSearchScope] = [.general] @Parameter(title: "Criteria") var criteria: StringSearchCriteria static var title: LocalizedStringResource = "Search with MyCatalogApp" @MainActor func perform() async throws -> some IntentResult { let searchString = criteria.term print("**** Search String: \(searchString) ****") // tmp debugging try await MyCatalogSearchHelper.search(for: searchString) // fetch results via my async fetch API return .result() } } Here's the Shortcuts definition: import AppIntents @available(iOS 17.2, *) struct Shortcuts: AppShortcutsProvider { @AppShortcutsBuilder static var appShortcuts: [AppShortcut] { AppShortcut( intent: SearchIntent(), phrases: ["Search for \(\.$criteria) on \(.applicationName)."], shortTitle: "Search", systemImageName: "magnifyingglass" ) } } Thanks for any help!