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Does prewarming a short-lived LanguageModelSession benefit a later session?
I’m building Summon (https://github.com/NakliTechie/summon), an open-source native macOS launcher that uses the on-device SystemLanguageModel. Summon creates a fresh LanguageModelSession for each query and attaches only the read-only tools relevant to that query. It currently calls prewarm() after the first keystroke using a temporary session, then creates a different session for generation. The documentation describes prewarm(promptPrefix:) as loading the resources required “for this session.” I would value guidance on four points: Is the prewarming benefit scoped to that exact LanguageModelSession instance? Does a later session using the same SystemLanguageModel receive any benefit? For an ephemeral launcher, is retaining one session preferable to creating a fresh session per query? Which Foundation Models Instrument signal identifies an ineffective prewarm or cache invalidation? Thank You Chirag
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477
Aug ’26
Confusing relationship between attributeSet, defaultAttributeSet, and displayRepresentation
I’m trying to understand the intended relationship between IndexedEntity.attributeSet, defaultAttributeSet, and displayRepresentation. For example: struct TrailEntity: IndexedEntity { var displayRepresentation: DisplayRepresentation { DisplayRepresentation( title: "(trail.name)", subtitle: "(trail.location)" ) } var attributeSet: CSSearchableItemAttributeSet { let attributes = CSSearchableItemAttributeSet() attributes.keywords = trail.keywords return attributes } } Should attributeSet instead be initialized with defaultAttributeSet and then have the additional attributes assigned to it? var attributeSet: CSSearchableItemAttributeSet { let attributes = defaultAttributeSet attributes.keywords = trail.keywords return attributes } The documentation says defaultAttributeSet contains values derived from displayRepresentation, but it also describes precedence between displayRepresentation and attributeSet, which suggests Spotlight reads them separately during indexing. So what is the intended pattern? Does overriding attributeSet require including defaultAttributeSet to preserve title/subtitle/image metadata, or is attributeSet only meant for additional Core Spotlight metadata? If the latter, what is the intended use case for overriding or directly using defaultAttributeSet?
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Aug ’26
SwiftPlaygroundsでドローンTelloアプリを制作
皆さん、はじめまして。プログラミングに対する知識がないので、AIを使いながらiPadでTelloのドローンを操縦できるアプリを作っています。このアプリはシミュレーション飛行とドローンの実機飛行ができるアプリにしたいのですが、ドローンの実機飛行を行うためのWi‐Fi接続ができません。(iPadでのシミュレーション飛行が可能です)iPadの基本的な設定はAppleに教えていただき、設定変更を行いましたが、どうしても接続できないので、わかる方法を教えていただけると嬉しいです。 コードを添付いたしますので、ご教示ください。 ドローンアプリコード
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Aug ’26
Is programmatic use of fm serve from a distributed macOS app permitted?
I am developing a macOS developer tool that uses Apple Foundation Models, including the Private Cloud Compute (PCC) model. On macOS 27, the Foundation Models CLI provides fm serve, which exposes a local Chat Completions API, including: POST /v1/chat/completions My application communicates with this local API on the user's own Mac to provide agent-style development features. The Foundation Models CLI Legal Notice states: “You are also agreeing to not programmatically access or use Apple models through Apple software or services except as expressly permitted.” I would like to confirm whether using the local API intentionally exposed by fm serve from a third-party macOS application distributed to users is considered an expressly permitted use. The application would: use only the interfaces and endpoints officially exposed by the fm CLI; run fm serve locally on the user's Mac; use the user's own Foundation Models / PCC availability and quota; not bypass quota limits; not use private or undocumented APIs; not reverse engineer Apple services. Is this use of fm serve permitted for a distributed third-party macOS application? If so, are there any additional requirements or restrictions that developers should follow when distributing an application that integrates with fm serve in this way? Thank you.
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Aug ’26
Is there a supported way to capture per-node intermediate outputs from an ANE-scheduled model?
I'm looking for a supported way to read intermediate tensors from a model executing on the Apple Neural Engine - specifically the output of an individual node in the compiled graph, rather than only the final output. What I'm trying to do: validate a from-weights reimplementation of a model against the real thing, layer by layer. Comparing only the final output tells me the reimplementation is wrong but not where; a per-layer comparison would localise it immediately. What I've established so far: A compiled ANE program can be executed unprivileged through the public graph API, and the final readout matches, so the execution path itself is reachable. Intermediate activations don't appear in host memory during normal operation, which is expected since the scheduler keeps them in accelerator-local storage. Requesting a per-node output appears to hit a kernel-side check that an ordinary process doesn't satisfy. Questions: Is there a supported API for retrieving per-node outputs from an ANE-scheduled graph - a debug or instrumentation mode, an Instruments template, or a Core ML compute-plan facility that surfaces them? Failing that, is there a supported way to make a specific node materialise its output to a host-visible buffer - for example by splitting the graph, marking an intermediate tensor as a model output, or compiling with that node as a terminal operation? I'm aware this may change scheduling and defeat the purpose, but I'd like to know whether it's the intended approach. If neither exists, is that a deliberate design boundary rather than a gap? A clear "no" is a useful answer and I'll stop looking. I'm not asking about any particular shipped model, and this isn't a request to bypass anything - the question is whether the platform exposes per-node observability for ANE execution at all, and if so what the supported entry point is. Thanks.
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Aug ’26
Does Core AI / MLX already cover custom orchestration (queuing, batching, memory management, failover) or is that left to the developer?
I’m evaluating a third-party Swift-based “orchestration layer” for enterprise AI workloads on Apple Silicon — it claims to handle job queuing, scheduling, batching, memory management, monitoring, auditing, and failover on top of on-device inference. Given the Core AI framework’s device-specialization step and InferenceFunction pipeline (and MLX’s unified-memory model), how much of this kind of orchestration is already handled natively versus something a developer would still need to build themselves? Specifically: 1. Does Core AI’s inference pipeline provide any built-in job queuing/batching across multiple concurrent requests, or is that entirely app-side? 2. Is there native failover/monitoring tooling for on-device inference, or would a developer need to build that themselves (e.g., via os_log, MetricKit, custom retry logic)? 3. For memory management across CPU/GPU/ANE, does unified memory in MLX/Core AI eliminate most of the manual management a custom orchestration layer would otherwise need to solve? Trying to understand what’s genuinely differentiated in a third-party layer versus what Apple’s stack already provides out of the box. Appreciate any insight from folks who’ve built with Core AI/MLX in production.
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Aug ’26
"Error Domain=ModelManagerServices.ModelManagerError Code=1026 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n)}"
import Playgrounds import FoundationModels #Playground { do { let session = LanguageModelSession() let response = try await session.respond( to: "Explain SwiftUI in one sentence." ) print(response.content) } catch { print("Error: \(error)") } }``` I tested Foundation Models with this simple code, and it generated this error: "Error Domain=ModelManagerServices.ModelManagerError Code=1026 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n)}" I tried restarting my Mac and Apple Intelligence, but that didn't work. What did work was updating Xcode and the simulators to the latest possible version.
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Aug ’26
Adding an OptionsCollection to an existing AppShortcut hides all other parameterless App Shortcuts from the Shortcuts app UI
Hi all, I’m seeing what looks like a bug with AppShortcutParameterPresentation and the Shortcuts app. Any time I provide an OptionsCollection to a shortcut so I can give it a nice category name and symbol in Shortcuts, it hides all other existing app shortcuts that my app has from the UI. I have created a sample that illustrates the problem. My app provides two App Shortcuts: A simple shortcut with no parameters. A shortcut with two parameters. Its Destination parameter uses AppShortcutParameterPresentation to generate “Home” and “Office” options in a separate section. When the second shortcut is present, the first parameterless shortcut disappears from the Shortcuts app. If I comment out the shortcut containing parameterPresentation, the parameterless shortcut appears again. Before commenting out: After commenting out the second shortcut: Here's the code: import AppIntents struct ParameterlessIntent: AppIntent { static let title: LocalizedStringResource = "Parameterless Intent" static let description = IntentDescription("Runs without asking for any parameters.") func perform() async throws -> some IntentResult { .result() } } struct ParameterizedIntent: AppIntent { static let title: LocalizedStringResource = "Parameterized Intent" static let description = IntentDescription("Runs with a destination and a copy count.") // The same provider is used by this parameter and by ParameterPresentation below. @Parameter( title: "Destination", optionsProvider: DestinationOptionsProvider() ) var destination: String @Parameter(title: "Copy Count", default: 1) var copyCount: Int static var parameterSummary: some ParameterSummary { Summary("Send \(\.$copyCount) copies to \(\.$destination)") } func perform() async throws -> some IntentResult { .result() } } nonisolated struct DestinationOptionsProvider: DynamicOptionsProvider { func results() async throws -> [String] { // Each generated App Shortcut option is a value for the Destination parameter. ["Home", "Office"] } } struct BugReproductionShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { // This parameterless shortcut should always appear in the Shortcuts app. AppShortcut( intent: ParameterlessIntent(), phrases: [ "Run the parameterless shortcut with \(.applicationName)" ], shortTitle: "Do I exist?", systemImageName: "1.circle" ) #warning("The presence of this shortcut causes the top one no longer appear in Shortcuts.app") AppShortcut( intent: ParameterizedIntent(), phrases: [ "Run the parameterized shortcut with \(.applicationName)" ], shortTitle: "Parameterized Shortcut", systemImageName: "2.circle", parameterPresentation: ParameterPresentation( for: \.$destination, summary: Summary("Send to \(\.$destination)") ) { // This title and symbol create a separate section in Shortcuts. OptionsCollection( DestinationOptionsProvider(), title: "Destination Shortcuts", systemImageName: "mappin.and.ellipse" ) } ) } } This code and reproduction is as of Xcode 27 Beta 6 and happens on older versions as well. Is there a known limitation with this or is this somehow expected behavior? If so, how can I mitigate this issue and provide a nice title for another shortcut, while keeping the old parameterless shortcuts present? Thanks!
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Aug ’26
False-positive guardrail blocks guided generation for sports data
I’m developing a factual snooker application using the on-device SystemLanguageModel on the current iOS 27, Xcode and macOS betas. The app allows someone to ask questions about professional snooker players. A tool searches my server and returns verified player data such as the player’s ID, name, nationality and date of birth. I have encountered a reproducible false-positive guardrail violation when the user asks about the professional snooker player Judd Trump. For example: Tell me about Judd Trump With the default model configuration, the request fails because the input or output is classified as potentially sensitive or unsafe. Using permissive content transformations solves the problem when generating a normal String: let model = SystemLanguageModel( useCase: .general, guardrails: .permissiveContentTransformations ) let session = LanguageModelSession( model: model, tools: [FindPlayerTool()], instructions: """ Answer factual questions about professional snooker players. Always use the supplied tool and only use verified tool data. Names returned by the tool are names of real snooker players and should be treated only as sporting entities. """ ) let response = try await session.respond( to: "Tell me about Judd Trump" ) This successfully calls the tool and produces a factual string response. However, I need guided generation because the model should be able to choose a combination of predefined UI components, such as: A player card A match card An event card A rankings table Explanatory text A simplified response type looks like this: @Generable struct CueQueryReply { let blocks: [ReplyBlock] } @Generable enum ReplyBlock { case playerCard(PlayerCardBlock) case text(TextBlock) } @Generable struct PlayerCardBlock { let playerId: Int let name: String let nationality: String let born: String } @Generable struct TextBlock { let text: String } The guided request is: let response = try await session.respond( to: "Tell me about Judd Trump", generating: CueQueryReply.self ) This reproduces the guardrail violation, even though the model is configured with: guardrails: .permissiveContentTransformations I understand that the documentation says permissive content transformations apply to string generation and that guided generation behaves like the default guardrails. However, this creates a difficult limitation for legitimate factual applications. “Judd Trump” is the real name of a professional snooker player, and the data is coming from a controlled, verified API. Renaming, removing or concealing the player is not a viable product solution. My questions are: Is this specific “Judd Trump” behaviour considered a guardrail false positive that should be reported through Feedback Assistant? Is there any supported way on iOS 27 to use permissive content transformations with guided generation? Can Dynamic Profiles, Dynamic Generation Schemas or another Foundation Models API change the guardrail behaviour for a controlled guided-generation request? Is there a recommended architecture for producing typed UI instructions while retaining the permissive behaviour available to string responses? Would generating only component types and verified IDs—for example .playerCard(playerId: 12)—be the recommended approach, provided the actual player data is resolved and displayed by SwiftUI? I understand the need for safety guardrails and am not attempting to disable the model’s underlying safety behaviour. I am trying to process a harmless, factual sporting name while using Foundation Models’ typed output features. The on-device model otherwise appears capable of handling this use case well, and keeping the experience on-device, private and free of external API dependencies is an important part of the product. I would appreciate any guidance from the Foundation Models team about whether this is expected behaviour, a beta issue, or something for which there is an intended iOS 27 solution.
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405
Aug ’26
FoundationModels guided generation: empty token masks and slow structured output on macOS 27 betas 5, 6 and 7
Hey everyone, hoping to compare notes on something we have been chasing since beta 5. We have a Mac app that uses FoundationModels with @Generable types for structured output. Starting with macOS 27 beta 5, guided generation requests began logging tokenizer errors and our longer structured requests slowed from seconds to minutes. We are still seeing the same thing on beta 6 and beta 7. We filed it as FB24310823 on August 11 with a sysdiagnose and log captures. The signature is easy to check if you want to see whether your machine does it too. Stream the log while your app generates: log stream --predicate 'subsystem == "com.apple.tokengenerationcore"' --style compact On our machine the inference service (TGOnDeviceInferenceProviderService, category guided) prints these two lines in matched pairs, thousands of times: Generated an empty mask at recognizer index N allowedTokenIDs is empty. Something is likely wrong with the tokenizer Some numbers from beta 7 today: 9,008 of those pairs in about five and a half minutes. The errors start about one second into the first request after a fresh app launch. Requests that normally finish in 4 to 12 seconds take 77 to 170 seconds or longer. On beta 5 we measured decode at roughly 0.3 tokens per second on the worst requests. Short requests still finish at normal speed but they emit the same errors while they run, and the structured content they return looks degraded to us. On betas 5 and 6 we also saw repeated asset release errors for instruct_300m.tokenizer and the instruct_3b tokenizer saying the asset is not marked as in use. For what it is worth, a build that ran clean on beta 4 shows the same behavior on beta 5 and later with no app changes, and the same @Generable schema drives both the fast and the slow requests. But we know that does not rule out something on our side, and we would honestly be happy to learn this is our own bug since that would mean we can fix it. So two questions. Is anyone else seeing this since beta 5? And if you spot something we might be doing wrong on our end, sessions we should be recreating, schema patterns that stress the constrained decoder, anything at all, we would really appreciate the feedback. If it does turn out you are hitting the same thing, a Feedback referencing FB24310823 would help a lot. Thanks!
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Aug ’26
Foundation Models tool-calling differs significantly between iPhone 16 and iPhone 17 Pro Max
I'm seeing a reproducible difference in Foundation Models behavior between an iPhone 16 and iPhone 17 Pro Max, both running iOS 27.0 beta 6. My pipeline is roughly: Input → model generation → tool call → validation/correction → structured output Each test starts with a fresh model session. I run the same 50-case dataset on both devices with the same app build, prompt, tool, data, and execution order. The main difference is not just speed: the iPhone 16 consistently makes many more tool calls, which causes the session context to grow until some runs exceed the available context window. Both devices report a context size of roughly 4,096 tokens. Metric iPhone 16 iPhone 17 Pro Max Completed 30/50 49/50 Total tool calls 222 67 Mean calls/run 4.44 1.34 Max calls/run 22 2 Verified outputs 75.1% 91.0% The pattern is very consistent across repeated runs. On the 17 Pro Max, most requests converge after 1–2 tool calls. On the iPhone 16, some requests enter longer tool/correction loops and eventually fail because the context grows too large. I can probably mitigate this by limiting tool calls or changing the prompt, but I'd like to understand the underlying behavior. Is this difference expected across supported devices even on the same OS version? In particular: Can different on-device model variants be used depending on hardware? Is there a way to determine which model/profile a SystemLanguageModel session is using? Should tool-selection behavior be expected to remain reasonably consistent across devices? Would this be worth filing as a Foundation Models regression during the beta?
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Aug ’26
Siri shows contextual and “Siri AI” behaviour independently of Apple Intelligence activation on iOS 27 beta
Environment iOS 27 Developer Beta iPhone17,3 Siri language: English Apple Intelligence availability/configuration differs depending on account/region state Observed behaviour Siri appears to expose behaviours normally associated with the newer intelligence architecture even when the Apple Intelligence experience is not fully enabled. Examples observed include: contextual follow-up questions across multiple turns; responses maintaining the subject of the previous request; different Siri visual/pulsing states depending on input; ChatGPT hand-off through Siri while preserving the original request; UI/settings references related to newer Siri intelligence capabilities; changes in Siri-related UI depending on Apple Account configuration. Reproduction example Invoke Siri. Ask a location/weather question. Ask follow-up questions without repeating the location or subject. Siri continues using the previous conversational context. Similar continuity can be observed across other queries. I have also observed differences in Siri UI and available settings after changing Apple Account configuration, while remaining on the same device and OS build. Question Is the contextual Siri architecture being deployed independently from the full Apple Intelligence feature set in iOS 27, or is this behaviour expected as part of the current beta implementation? I am particularly interested in understanding whether Siri’s contextual/runtime components and Apple Intelligence availability are now intentionally decoupled.
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Aug ’26
Tengo a la versión Beta de Siri.
Está indexando en segundo, plano, pero no me aparece el 100, para Inhabilitar el software que se quede atrás, para evitar el cidrado extremo, no tengo la membresia debido a que estoy dado de alta como como Desarrolador, y Siri es la que encripta mis datos en la nube, con Intelligence, como mi teléfono está intervenido, es imposible que se libere el Xcode, sin embargo necesito el rotor del segundo plano, ya que la programación funcionó, y El sistema está trabajando al cien, solo necesito acceder al rotor del segundo plano
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470
Aug ’26
Are `NSTableViewAppIntentsDataSource` data source methods expected to be called?
I've looked and looked and can't seem to find anything obviously wrong, so I'll ask here. Are NSTableViewAppIntentsDataSource protocol methods expected to be called? Have others had success with this? I've got an extremely trivial NSViewController subclass that conforms to NSTableViewDataSource, NSTableViewDelegate, NSTableViewAppIntentsDataSource. Things I've verified: The NSTableView is setup in a storyboard and the delegate and data source are connected to the view controller. In viewDidLoad while attached to the debugger I see this works. The table view includes a single row and appears populated when running the app. There seems to be no way to assign the appIntentsDataSource view controller in the storyboard, so that's assigned in code in viewDidLoad for the view controller. I can confirm it's correctly set in the data source methods for the table view. I have an AppEntity conforming type and AppIntentsPackage conforming type in the project. I can look at the actionsdata in the built product to confirm the entity is registered. Here's the entirety of the view controller: class ViewController: NSViewController, NSTableViewDataSource, NSTableViewDelegate, NSTableViewAppIntentsDataSource { @IBOutlet var tableView: NSTableView! func numberOfRows(in tableView: NSTableView) -> Int { print("numberOfRows(in:)") return 1 } dynamic public func tableView(_ tableView: NSTableView, objectValueFor tableColumn: NSTableColumn?, row: Int) -> Any? { print("tableView(_:objectValueFor:row:)") return NSObject() } override func viewDidLoad() { super.viewDidLoad() tableView.appIntentsDataSource = self } override var representedObject: Any? { didSet { // Update the view, if already loaded. } } dynamic public func tableView(_ tableView: NSTableView, appEntityIdentifierFor row: Int) -> EntityIdentifier? { print("ViewController.tableView(_:appEntityIdentifierFor:)") return EntityIdentifier(for: MyFancyEntity.self, identifier: "1234") } } Unfortunately, while attached with a debugger, ViewController.tableView(_:appEntityIdentifierFor:) just never seems to be called.
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Aug ’26
Supported end-to-end testing route for EU-based developers targeting Siri AI on iOS 27?
Apple's 8 June 2026 announcement states that developers in the EU will not be able to test or use the new Siri AI features in their apps for iOS 27, iPadOS 27 or watchOS 27. I am an EU-based developer building apps for users in multiple markets. App Intents Testing, simulator checks and unit tests can validate parts of an implementation, but they do not appear to replace end-to-end validation of Siri AI behaviour on supported iPhone and iPad hardware. What is Apple's supported route for an EU-based developer to validate the following for users in supported markets? • intent discovery and invocation • parameter resolution and follow-up interaction • error handling and confirmation flows • Siri's presentation and completion of an action • behaviour on supported physical devices Is an official remote-device environment, controlled developer testing mode or another Apple-supported arrangement available or planned? I am not asking for a way to bypass regional restrictions. I am looking for documented, compliant testing guidance for developers serving a global App Store. I have filed Feedback Assistant report FB24276767 about this testing-access issue. Apple source: https://www.apple.com/newsroom/2026/06/due-to-dma-siri-ai-delayed-in-eu-for-ios-27-and-ipados-27/
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Aug ’26
Rate limit from SensitiveContentAnalysisML never lifts when using PCC
I keep running into rate limit issues that never go away while the app is running when trying to analyze images using Private Cloud Compute in iOS 27 Beta 6. After 20 or so images, I get a rate limit error from PCC, but the actual rate limit seems to come from SCML (see relevant log entries below). Once this happens, any attempted PCC requests result in an immediate rate limit error, no matter how long I wait, so long as the app is running. If I kill the app and relaunch, I no longer receive the rate limit error (unless, again I run several images through in succession). So it seems like once this state is triggered, you are stuck in it until you kill and relaunch the app. Has anyone else encountered this or have a workaround? I've filed a feedback already: FB24419603 Passing along Client rate limit exceeded, try again later in response to ExecuteRequest Passing along Client rate limit exceeded, try again later in response to ExecuteRequest systemPromptID failed for task textSafety: Rate limited. Wait a little bit and then try again.::Rate limited. Wait a little bit and then try again.: Client rate limit exceeded, try again later::Client rate limit exceeded, try again later; prompt template also not found: Rate limited. Wait a little bit and then try again.::Rate limited. Wait a little bit and then try again.: Client rate limit exceeded, try again later::Client rate limit exceeded, try again later End sanitizeText with error: Error Domain=com.apple.SensitiveContentAnalysisML Code=15 "SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again." UserInfo={NSLocalizedDescription=SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again., NSUnderlyingError=0x11a632ee0 {Error Domain=SensitiveContentAnalysisML.CombinedTextSanitizerBackend.BackendError Code=1 "SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again." UserInfo={NSUnderlyingError=0x11a5dd380 {Error Domain=com.apple.GenerativeFunctionsFoundation.GenerativeError Code=1010000 "Rate limited. Wait a little bit and then try again."}, NSLocalizedDescription=SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again.}}}
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Aug ’26
Group AppIntents’ Searchable DynamicOptionsProvider in Sections
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 } }
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1.7k
Aug ’26
M3 Neural Engine kernelDMA bandwidth throttled at 1 MiB multiples; splitting restores 3x DRAM bandwidth
A suspected RTL performance erratum in the Apple Neural Engine throttles DRAM weight streaming throughput down to 17–19 GB/s from the nominal 45–60 GB/s, whenever the total weight size is an integer multiple of 1 MiB. This was confirmed on M3 Macbook Air (2024) 24GB. 1 MiB is a common transfer size, and currently affects 7 of ANEMLL’s 15 baseline models. Avoiding the suspected problematic path in the kernel DMA's prefetch ring by splitting 1 MiB transfers into non-multiples of 1 MiB, directly increased Llama 3.2 1B token throughput from 10.0 to 24.3 tokens/s (DRAM usage from 24.7 to 60.0 GB/s), and Qwen3-8B from 1.36 to 2.97 tokens/s (DRAM usage from 22.4 to 48.7 GB/s). Please see writeup here: https://eiln.github.io/posts/ane-dma.html I'm looking to confirm that this behavior is reproducible on your side, and will provide the scripts in my plots.
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519
Aug ’26
Is Small Business Program enrollment (incl. banking/tax info) really required just to evaluate Private Cloud Compute? Extra concerned as a non-US (Japan-based) company
Hi all, We're currently evaluating Private Cloud Compute (PCC) for a technical accuracy assessment. No production release or monetization is planned at this stage — our only goal is to test/evaluate the model. Per the official documentation, PCC access requires: Enrollment in the App Store Small Business Program Fewer than 2 million first-time downloads The Private Cloud Compute entitlement assigned to the account To complete Small Business Program enrollment, our Paid Applications Agreement is currently stuck at "User Information Pending", and we're being asked to submit a bank account and U.S. tax forms (Certificate of Foreign Status of Beneficial Owner / Substitute Form W-8BEN-E) before the agreement can go Active. Honestly, we're having a hard time accepting that submitting banking and revenue-related tax documentation is required when we have no intention of selling a paid app at all. The Small Business Program itself is meant to be a reduced-commission program for developers earning revenue through paid apps/IAP — using it as a gate for free AI model evaluation feels like a mismatch. On top of that, we're a Japan-based company, which raises the bar further. The required tax forms (W-8BEN-E etc.) are aimed at non-US entities and require pulling in our legal/finance teams just to prepare — a fair amount of overhead for what is, on our end, purely a technical evaluation. Before we go through the internal process of preparing this documentation, I wanted to confirm: Is there any path to obtain the PCC entitlement / Small Business Program status for evaluation purposes only, without completing the full Paid Applications Agreement (banking + tax forms)? Is submitting real banking and tax information a hard technical requirement of the Small Business Program itself (i.e., the Paid Apps Agreement cannot go Active without it), or can an account remain PCC-eligible while the agreement is "pending"? Is there an official Apple document (beyond the general Small Business Program / PCC pages) that explicitly confirms banking/tax submission is mandatory before PCC entitlement can be granted? We need something citable for internal approval. For non-US companies (e.g. Japan-based), has anyone gone through this purely for evaluation purposes? Is there any simplified path for foreign entities, or is the full W-8BEN-E process unavoidable? Any pointers to official documentation, or confirmation from anyone who has been through this, would be greatly appreciated — we need a clear, citable answer to justify preparing this documentation internally. Thanks in advance.
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Aug ’26
Does prewarming a short-lived LanguageModelSession benefit a later session?
I’m building Summon (https://github.com/NakliTechie/summon), an open-source native macOS launcher that uses the on-device SystemLanguageModel. Summon creates a fresh LanguageModelSession for each query and attaches only the read-only tools relevant to that query. It currently calls prewarm() after the first keystroke using a temporary session, then creates a different session for generation. The documentation describes prewarm(promptPrefix:) as loading the resources required “for this session.” I would value guidance on four points: Is the prewarming benefit scoped to that exact LanguageModelSession instance? Does a later session using the same SystemLanguageModel receive any benefit? For an ephemeral launcher, is retaining one session preferable to creating a fresh session per query? Which Foundation Models Instrument signal identifies an ineffective prewarm or cache invalidation? Thank You Chirag
Replies
1
Boosts
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477
Activity
Aug ’26
Confusing relationship between attributeSet, defaultAttributeSet, and displayRepresentation
I’m trying to understand the intended relationship between IndexedEntity.attributeSet, defaultAttributeSet, and displayRepresentation. For example: struct TrailEntity: IndexedEntity { var displayRepresentation: DisplayRepresentation { DisplayRepresentation( title: "(trail.name)", subtitle: "(trail.location)" ) } var attributeSet: CSSearchableItemAttributeSet { let attributes = CSSearchableItemAttributeSet() attributes.keywords = trail.keywords return attributes } } Should attributeSet instead be initialized with defaultAttributeSet and then have the additional attributes assigned to it? var attributeSet: CSSearchableItemAttributeSet { let attributes = defaultAttributeSet attributes.keywords = trail.keywords return attributes } The documentation says defaultAttributeSet contains values derived from displayRepresentation, but it also describes precedence between displayRepresentation and attributeSet, which suggests Spotlight reads them separately during indexing. So what is the intended pattern? Does overriding attributeSet require including defaultAttributeSet to preserve title/subtitle/image metadata, or is attributeSet only meant for additional Core Spotlight metadata? If the latter, what is the intended use case for overriding or directly using defaultAttributeSet?
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381
Activity
Aug ’26
SwiftPlaygroundsでドローンTelloアプリを制作
皆さん、はじめまして。プログラミングに対する知識がないので、AIを使いながらiPadでTelloのドローンを操縦できるアプリを作っています。このアプリはシミュレーション飛行とドローンの実機飛行ができるアプリにしたいのですが、ドローンの実機飛行を行うためのWi‐Fi接続ができません。(iPadでのシミュレーション飛行が可能です)iPadの基本的な設定はAppleに教えていただき、設定変更を行いましたが、どうしても接続できないので、わかる方法を教えていただけると嬉しいです。 コードを添付いたしますので、ご教示ください。 ドローンアプリコード
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744
Activity
Aug ’26
Is programmatic use of fm serve from a distributed macOS app permitted?
I am developing a macOS developer tool that uses Apple Foundation Models, including the Private Cloud Compute (PCC) model. On macOS 27, the Foundation Models CLI provides fm serve, which exposes a local Chat Completions API, including: POST /v1/chat/completions My application communicates with this local API on the user's own Mac to provide agent-style development features. The Foundation Models CLI Legal Notice states: “You are also agreeing to not programmatically access or use Apple models through Apple software or services except as expressly permitted.” I would like to confirm whether using the local API intentionally exposed by fm serve from a third-party macOS application distributed to users is considered an expressly permitted use. The application would: use only the interfaces and endpoints officially exposed by the fm CLI; run fm serve locally on the user's Mac; use the user's own Foundation Models / PCC availability and quota; not bypass quota limits; not use private or undocumented APIs; not reverse engineer Apple services. Is this use of fm serve permitted for a distributed third-party macOS application? If so, are there any additional requirements or restrictions that developers should follow when distributing an application that integrates with fm serve in this way? Thank you.
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709
Activity
Aug ’26
Is there a supported way to capture per-node intermediate outputs from an ANE-scheduled model?
I'm looking for a supported way to read intermediate tensors from a model executing on the Apple Neural Engine - specifically the output of an individual node in the compiled graph, rather than only the final output. What I'm trying to do: validate a from-weights reimplementation of a model against the real thing, layer by layer. Comparing only the final output tells me the reimplementation is wrong but not where; a per-layer comparison would localise it immediately. What I've established so far: A compiled ANE program can be executed unprivileged through the public graph API, and the final readout matches, so the execution path itself is reachable. Intermediate activations don't appear in host memory during normal operation, which is expected since the scheduler keeps them in accelerator-local storage. Requesting a per-node output appears to hit a kernel-side check that an ordinary process doesn't satisfy. Questions: Is there a supported API for retrieving per-node outputs from an ANE-scheduled graph - a debug or instrumentation mode, an Instruments template, or a Core ML compute-plan facility that surfaces them? Failing that, is there a supported way to make a specific node materialise its output to a host-visible buffer - for example by splitting the graph, marking an intermediate tensor as a model output, or compiling with that node as a terminal operation? I'm aware this may change scheduling and defeat the purpose, but I'd like to know whether it's the intended approach. If neither exists, is that a deliberate design boundary rather than a gap? A clear "no" is a useful answer and I'll stop looking. I'm not asking about any particular shipped model, and this isn't a request to bypass anything - the question is whether the platform exposes per-node observability for ANE execution at all, and if so what the supported entry point is. Thanks.
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503
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Aug ’26
Does Core AI / MLX already cover custom orchestration (queuing, batching, memory management, failover) or is that left to the developer?
I’m evaluating a third-party Swift-based “orchestration layer” for enterprise AI workloads on Apple Silicon — it claims to handle job queuing, scheduling, batching, memory management, monitoring, auditing, and failover on top of on-device inference. Given the Core AI framework’s device-specialization step and InferenceFunction pipeline (and MLX’s unified-memory model), how much of this kind of orchestration is already handled natively versus something a developer would still need to build themselves? Specifically: 1. Does Core AI’s inference pipeline provide any built-in job queuing/batching across multiple concurrent requests, or is that entirely app-side? 2. Is there native failover/monitoring tooling for on-device inference, or would a developer need to build that themselves (e.g., via os_log, MetricKit, custom retry logic)? 3. For memory management across CPU/GPU/ANE, does unified memory in MLX/Core AI eliminate most of the manual management a custom orchestration layer would otherwise need to solve? Trying to understand what’s genuinely differentiated in a third-party layer versus what Apple’s stack already provides out of the box. Appreciate any insight from folks who’ve built with Core AI/MLX in production.
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409
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Aug ’26
"Error Domain=ModelManagerServices.ModelManagerError Code=1026 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n)}"
import Playgrounds import FoundationModels #Playground { do { let session = LanguageModelSession() let response = try await session.respond( to: "Explain SwiftUI in one sentence." ) print(response.content) } catch { print("Error: \(error)") } }``` I tested Foundation Models with this simple code, and it generated this error: "Error Domain=ModelManagerServices.ModelManagerError Code=1026 \"(null)\" UserInfo={NSMultipleUnderlyingErrorsKey=(\n)}" I tried restarting my Mac and Apple Intelligence, but that didn't work. What did work was updating Xcode and the simulators to the latest possible version.
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282
Activity
Aug ’26
Adding an OptionsCollection to an existing AppShortcut hides all other parameterless App Shortcuts from the Shortcuts app UI
Hi all, I’m seeing what looks like a bug with AppShortcutParameterPresentation and the Shortcuts app. Any time I provide an OptionsCollection to a shortcut so I can give it a nice category name and symbol in Shortcuts, it hides all other existing app shortcuts that my app has from the UI. I have created a sample that illustrates the problem. My app provides two App Shortcuts: A simple shortcut with no parameters. A shortcut with two parameters. Its Destination parameter uses AppShortcutParameterPresentation to generate “Home” and “Office” options in a separate section. When the second shortcut is present, the first parameterless shortcut disappears from the Shortcuts app. If I comment out the shortcut containing parameterPresentation, the parameterless shortcut appears again. Before commenting out: After commenting out the second shortcut: Here's the code: import AppIntents struct ParameterlessIntent: AppIntent { static let title: LocalizedStringResource = "Parameterless Intent" static let description = IntentDescription("Runs without asking for any parameters.") func perform() async throws -> some IntentResult { .result() } } struct ParameterizedIntent: AppIntent { static let title: LocalizedStringResource = "Parameterized Intent" static let description = IntentDescription("Runs with a destination and a copy count.") // The same provider is used by this parameter and by ParameterPresentation below. @Parameter( title: "Destination", optionsProvider: DestinationOptionsProvider() ) var destination: String @Parameter(title: "Copy Count", default: 1) var copyCount: Int static var parameterSummary: some ParameterSummary { Summary("Send \(\.$copyCount) copies to \(\.$destination)") } func perform() async throws -> some IntentResult { .result() } } nonisolated struct DestinationOptionsProvider: DynamicOptionsProvider { func results() async throws -> [String] { // Each generated App Shortcut option is a value for the Destination parameter. ["Home", "Office"] } } struct BugReproductionShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { // This parameterless shortcut should always appear in the Shortcuts app. AppShortcut( intent: ParameterlessIntent(), phrases: [ "Run the parameterless shortcut with \(.applicationName)" ], shortTitle: "Do I exist?", systemImageName: "1.circle" ) #warning("The presence of this shortcut causes the top one no longer appear in Shortcuts.app") AppShortcut( intent: ParameterizedIntent(), phrases: [ "Run the parameterized shortcut with \(.applicationName)" ], shortTitle: "Parameterized Shortcut", systemImageName: "2.circle", parameterPresentation: ParameterPresentation( for: \.$destination, summary: Summary("Send to \(\.$destination)") ) { // This title and symbol create a separate section in Shortcuts. OptionsCollection( DestinationOptionsProvider(), title: "Destination Shortcuts", systemImageName: "mappin.and.ellipse" ) } ) } } This code and reproduction is as of Xcode 27 Beta 6 and happens on older versions as well. Is there a known limitation with this or is this somehow expected behavior? If so, how can I mitigate this issue and provide a nice title for another shortcut, while keeping the old parameterless shortcuts present? Thanks!
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Aug ’26
False-positive guardrail blocks guided generation for sports data
I’m developing a factual snooker application using the on-device SystemLanguageModel on the current iOS 27, Xcode and macOS betas. The app allows someone to ask questions about professional snooker players. A tool searches my server and returns verified player data such as the player’s ID, name, nationality and date of birth. I have encountered a reproducible false-positive guardrail violation when the user asks about the professional snooker player Judd Trump. For example: Tell me about Judd Trump With the default model configuration, the request fails because the input or output is classified as potentially sensitive or unsafe. Using permissive content transformations solves the problem when generating a normal String: let model = SystemLanguageModel( useCase: .general, guardrails: .permissiveContentTransformations ) let session = LanguageModelSession( model: model, tools: [FindPlayerTool()], instructions: """ Answer factual questions about professional snooker players. Always use the supplied tool and only use verified tool data. Names returned by the tool are names of real snooker players and should be treated only as sporting entities. """ ) let response = try await session.respond( to: "Tell me about Judd Trump" ) This successfully calls the tool and produces a factual string response. However, I need guided generation because the model should be able to choose a combination of predefined UI components, such as: A player card A match card An event card A rankings table Explanatory text A simplified response type looks like this: @Generable struct CueQueryReply { let blocks: [ReplyBlock] } @Generable enum ReplyBlock { case playerCard(PlayerCardBlock) case text(TextBlock) } @Generable struct PlayerCardBlock { let playerId: Int let name: String let nationality: String let born: String } @Generable struct TextBlock { let text: String } The guided request is: let response = try await session.respond( to: "Tell me about Judd Trump", generating: CueQueryReply.self ) This reproduces the guardrail violation, even though the model is configured with: guardrails: .permissiveContentTransformations I understand that the documentation says permissive content transformations apply to string generation and that guided generation behaves like the default guardrails. However, this creates a difficult limitation for legitimate factual applications. “Judd Trump” is the real name of a professional snooker player, and the data is coming from a controlled, verified API. Renaming, removing or concealing the player is not a viable product solution. My questions are: Is this specific “Judd Trump” behaviour considered a guardrail false positive that should be reported through Feedback Assistant? Is there any supported way on iOS 27 to use permissive content transformations with guided generation? Can Dynamic Profiles, Dynamic Generation Schemas or another Foundation Models API change the guardrail behaviour for a controlled guided-generation request? Is there a recommended architecture for producing typed UI instructions while retaining the permissive behaviour available to string responses? Would generating only component types and verified IDs—for example .playerCard(playerId: 12)—be the recommended approach, provided the actual player data is resolved and displayed by SwiftUI? I understand the need for safety guardrails and am not attempting to disable the model’s underlying safety behaviour. I am trying to process a harmless, factual sporting name while using Foundation Models’ typed output features. The on-device model otherwise appears capable of handling this use case well, and keeping the experience on-device, private and free of external API dependencies is an important part of the product. I would appreciate any guidance from the Foundation Models team about whether this is expected behaviour, a beta issue, or something for which there is an intended iOS 27 solution.
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405
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Aug ’26
FoundationModels guided generation: empty token masks and slow structured output on macOS 27 betas 5, 6 and 7
Hey everyone, hoping to compare notes on something we have been chasing since beta 5. We have a Mac app that uses FoundationModels with @Generable types for structured output. Starting with macOS 27 beta 5, guided generation requests began logging tokenizer errors and our longer structured requests slowed from seconds to minutes. We are still seeing the same thing on beta 6 and beta 7. We filed it as FB24310823 on August 11 with a sysdiagnose and log captures. The signature is easy to check if you want to see whether your machine does it too. Stream the log while your app generates: log stream --predicate 'subsystem == "com.apple.tokengenerationcore"' --style compact On our machine the inference service (TGOnDeviceInferenceProviderService, category guided) prints these two lines in matched pairs, thousands of times: Generated an empty mask at recognizer index N allowedTokenIDs is empty. Something is likely wrong with the tokenizer Some numbers from beta 7 today: 9,008 of those pairs in about five and a half minutes. The errors start about one second into the first request after a fresh app launch. Requests that normally finish in 4 to 12 seconds take 77 to 170 seconds or longer. On beta 5 we measured decode at roughly 0.3 tokens per second on the worst requests. Short requests still finish at normal speed but they emit the same errors while they run, and the structured content they return looks degraded to us. On betas 5 and 6 we also saw repeated asset release errors for instruct_300m.tokenizer and the instruct_3b tokenizer saying the asset is not marked as in use. For what it is worth, a build that ran clean on beta 4 shows the same behavior on beta 5 and later with no app changes, and the same @Generable schema drives both the fast and the slow requests. But we know that does not rule out something on our side, and we would honestly be happy to learn this is our own bug since that would mean we can fix it. So two questions. Is anyone else seeing this since beta 5? And if you spot something we might be doing wrong on our end, sessions we should be recreating, schema patterns that stress the constrained decoder, anything at all, we would really appreciate the feedback. If it does turn out you are hitting the same thing, a Feedback referencing FB24310823 would help a lot. Thanks!
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510
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Aug ’26
Foundation Models tool-calling differs significantly between iPhone 16 and iPhone 17 Pro Max
I'm seeing a reproducible difference in Foundation Models behavior between an iPhone 16 and iPhone 17 Pro Max, both running iOS 27.0 beta 6. My pipeline is roughly: Input → model generation → tool call → validation/correction → structured output Each test starts with a fresh model session. I run the same 50-case dataset on both devices with the same app build, prompt, tool, data, and execution order. The main difference is not just speed: the iPhone 16 consistently makes many more tool calls, which causes the session context to grow until some runs exceed the available context window. Both devices report a context size of roughly 4,096 tokens. Metric iPhone 16 iPhone 17 Pro Max Completed 30/50 49/50 Total tool calls 222 67 Mean calls/run 4.44 1.34 Max calls/run 22 2 Verified outputs 75.1% 91.0% The pattern is very consistent across repeated runs. On the 17 Pro Max, most requests converge after 1–2 tool calls. On the iPhone 16, some requests enter longer tool/correction loops and eventually fail because the context grows too large. I can probably mitigate this by limiting tool calls or changing the prompt, but I'd like to understand the underlying behavior. Is this difference expected across supported devices even on the same OS version? In particular: Can different on-device model variants be used depending on hardware? Is there a way to determine which model/profile a SystemLanguageModel session is using? Should tool-selection behavior be expected to remain reasonably consistent across devices? Would this be worth filing as a Foundation Models regression during the beta?
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Aug ’26
Siri shows contextual and “Siri AI” behaviour independently of Apple Intelligence activation on iOS 27 beta
Environment iOS 27 Developer Beta iPhone17,3 Siri language: English Apple Intelligence availability/configuration differs depending on account/region state Observed behaviour Siri appears to expose behaviours normally associated with the newer intelligence architecture even when the Apple Intelligence experience is not fully enabled. Examples observed include: contextual follow-up questions across multiple turns; responses maintaining the subject of the previous request; different Siri visual/pulsing states depending on input; ChatGPT hand-off through Siri while preserving the original request; UI/settings references related to newer Siri intelligence capabilities; changes in Siri-related UI depending on Apple Account configuration. Reproduction example Invoke Siri. Ask a location/weather question. Ask follow-up questions without repeating the location or subject. Siri continues using the previous conversational context. Similar continuity can be observed across other queries. I have also observed differences in Siri UI and available settings after changing Apple Account configuration, while remaining on the same device and OS build. Question Is the contextual Siri architecture being deployed independently from the full Apple Intelligence feature set in iOS 27, or is this behaviour expected as part of the current beta implementation? I am particularly interested in understanding whether Siri’s contextual/runtime components and Apple Intelligence availability are now intentionally decoupled.
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411
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Aug ’26
Tengo a la versión Beta de Siri.
Está indexando en segundo, plano, pero no me aparece el 100, para Inhabilitar el software que se quede atrás, para evitar el cidrado extremo, no tengo la membresia debido a que estoy dado de alta como como Desarrolador, y Siri es la que encripta mis datos en la nube, con Intelligence, como mi teléfono está intervenido, es imposible que se libere el Xcode, sin embargo necesito el rotor del segundo plano, ya que la programación funcionó, y El sistema está trabajando al cien, solo necesito acceder al rotor del segundo plano
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470
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Aug ’26
Are `NSTableViewAppIntentsDataSource` data source methods expected to be called?
I've looked and looked and can't seem to find anything obviously wrong, so I'll ask here. Are NSTableViewAppIntentsDataSource protocol methods expected to be called? Have others had success with this? I've got an extremely trivial NSViewController subclass that conforms to NSTableViewDataSource, NSTableViewDelegate, NSTableViewAppIntentsDataSource. Things I've verified: The NSTableView is setup in a storyboard and the delegate and data source are connected to the view controller. In viewDidLoad while attached to the debugger I see this works. The table view includes a single row and appears populated when running the app. There seems to be no way to assign the appIntentsDataSource view controller in the storyboard, so that's assigned in code in viewDidLoad for the view controller. I can confirm it's correctly set in the data source methods for the table view. I have an AppEntity conforming type and AppIntentsPackage conforming type in the project. I can look at the actionsdata in the built product to confirm the entity is registered. Here's the entirety of the view controller: class ViewController: NSViewController, NSTableViewDataSource, NSTableViewDelegate, NSTableViewAppIntentsDataSource { @IBOutlet var tableView: NSTableView! func numberOfRows(in tableView: NSTableView) -> Int { print("numberOfRows(in:)") return 1 } dynamic public func tableView(_ tableView: NSTableView, objectValueFor tableColumn: NSTableColumn?, row: Int) -> Any? { print("tableView(_:objectValueFor:row:)") return NSObject() } override func viewDidLoad() { super.viewDidLoad() tableView.appIntentsDataSource = self } override var representedObject: Any? { didSet { // Update the view, if already loaded. } } dynamic public func tableView(_ tableView: NSTableView, appEntityIdentifierFor row: Int) -> EntityIdentifier? { print("ViewController.tableView(_:appEntityIdentifierFor:)") return EntityIdentifier(for: MyFancyEntity.self, identifier: "1234") } } Unfortunately, while attached with a debugger, ViewController.tableView(_:appEntityIdentifierFor:) just never seems to be called.
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Aug ’26
Supported end-to-end testing route for EU-based developers targeting Siri AI on iOS 27?
Apple's 8 June 2026 announcement states that developers in the EU will not be able to test or use the new Siri AI features in their apps for iOS 27, iPadOS 27 or watchOS 27. I am an EU-based developer building apps for users in multiple markets. App Intents Testing, simulator checks and unit tests can validate parts of an implementation, but they do not appear to replace end-to-end validation of Siri AI behaviour on supported iPhone and iPad hardware. What is Apple's supported route for an EU-based developer to validate the following for users in supported markets? • intent discovery and invocation • parameter resolution and follow-up interaction • error handling and confirmation flows • Siri's presentation and completion of an action • behaviour on supported physical devices Is an official remote-device environment, controlled developer testing mode or another Apple-supported arrangement available or planned? I am not asking for a way to bypass regional restrictions. I am looking for documented, compliant testing guidance for developers serving a global App Store. I have filed Feedback Assistant report FB24276767 about this testing-access issue. Apple source: https://www.apple.com/newsroom/2026/06/due-to-dma-siri-ai-delayed-in-eu-for-ios-27-and-ipados-27/
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Aug ’26
Rate limit from SensitiveContentAnalysisML never lifts when using PCC
I keep running into rate limit issues that never go away while the app is running when trying to analyze images using Private Cloud Compute in iOS 27 Beta 6. After 20 or so images, I get a rate limit error from PCC, but the actual rate limit seems to come from SCML (see relevant log entries below). Once this happens, any attempted PCC requests result in an immediate rate limit error, no matter how long I wait, so long as the app is running. If I kill the app and relaunch, I no longer receive the rate limit error (unless, again I run several images through in succession). So it seems like once this state is triggered, you are stuck in it until you kill and relaunch the app. Has anyone else encountered this or have a workaround? I've filed a feedback already: FB24419603 Passing along Client rate limit exceeded, try again later in response to ExecuteRequest Passing along Client rate limit exceeded, try again later in response to ExecuteRequest systemPromptID failed for task textSafety: Rate limited. Wait a little bit and then try again.::Rate limited. Wait a little bit and then try again.: Client rate limit exceeded, try again later::Client rate limit exceeded, try again later; prompt template also not found: Rate limited. Wait a little bit and then try again.::Rate limited. Wait a little bit and then try again.: Client rate limit exceeded, try again later::Client rate limit exceeded, try again later End sanitizeText with error: Error Domain=com.apple.SensitiveContentAnalysisML Code=15 "SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again." UserInfo={NSLocalizedDescription=SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again., NSUnderlyingError=0x11a632ee0 {Error Domain=SensitiveContentAnalysisML.CombinedTextSanitizerBackend.BackendError Code=1 "SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again." UserInfo={NSUnderlyingError=0x11a5dd380 {Error Domain=com.apple.GenerativeFunctionsFoundation.GenerativeError Code=1010000 "Rate limited. Wait a little bit and then try again."}, NSLocalizedDescription=SCML.CombinedTextSanitizerBackend.BackendError("SafetyGuardrailTextSanitizerBackend"): Rate limited. Wait a little bit and then try again.}}}
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247
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Aug ’26
Group AppIntents’ Searchable DynamicOptionsProvider in Sections
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 } }
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Activity
Aug ’26
M3 Neural Engine kernelDMA bandwidth throttled at 1 MiB multiples; splitting restores 3x DRAM bandwidth
A suspected RTL performance erratum in the Apple Neural Engine throttles DRAM weight streaming throughput down to 17–19 GB/s from the nominal 45–60 GB/s, whenever the total weight size is an integer multiple of 1 MiB. This was confirmed on M3 Macbook Air (2024) 24GB. 1 MiB is a common transfer size, and currently affects 7 of ANEMLL’s 15 baseline models. Avoiding the suspected problematic path in the kernel DMA's prefetch ring by splitting 1 MiB transfers into non-multiples of 1 MiB, directly increased Llama 3.2 1B token throughput from 10.0 to 24.3 tokens/s (DRAM usage from 24.7 to 60.0 GB/s), and Qwen3-8B from 1.36 to 2.97 tokens/s (DRAM usage from 22.4 to 48.7 GB/s). Please see writeup here: https://eiln.github.io/posts/ane-dma.html I'm looking to confirm that this behavior is reproducible on your side, and will provide the scripts in my plots.
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519
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Aug ’26
Is Small Business Program enrollment (incl. banking/tax info) really required just to evaluate Private Cloud Compute? Extra concerned as a non-US (Japan-based) company
Hi all, We're currently evaluating Private Cloud Compute (PCC) for a technical accuracy assessment. No production release or monetization is planned at this stage — our only goal is to test/evaluate the model. Per the official documentation, PCC access requires: Enrollment in the App Store Small Business Program Fewer than 2 million first-time downloads The Private Cloud Compute entitlement assigned to the account To complete Small Business Program enrollment, our Paid Applications Agreement is currently stuck at "User Information Pending", and we're being asked to submit a bank account and U.S. tax forms (Certificate of Foreign Status of Beneficial Owner / Substitute Form W-8BEN-E) before the agreement can go Active. Honestly, we're having a hard time accepting that submitting banking and revenue-related tax documentation is required when we have no intention of selling a paid app at all. The Small Business Program itself is meant to be a reduced-commission program for developers earning revenue through paid apps/IAP — using it as a gate for free AI model evaluation feels like a mismatch. On top of that, we're a Japan-based company, which raises the bar further. The required tax forms (W-8BEN-E etc.) are aimed at non-US entities and require pulling in our legal/finance teams just to prepare — a fair amount of overhead for what is, on our end, purely a technical evaluation. Before we go through the internal process of preparing this documentation, I wanted to confirm: Is there any path to obtain the PCC entitlement / Small Business Program status for evaluation purposes only, without completing the full Paid Applications Agreement (banking + tax forms)? Is submitting real banking and tax information a hard technical requirement of the Small Business Program itself (i.e., the Paid Apps Agreement cannot go Active without it), or can an account remain PCC-eligible while the agreement is "pending"? Is there an official Apple document (beyond the general Small Business Program / PCC pages) that explicitly confirms banking/tax submission is mandatory before PCC entitlement can be granted? We need something citable for internal approval. For non-US companies (e.g. Japan-based), has anyone gone through this purely for evaluation purposes? Is there any simplified path for foreign entities, or is the full W-8BEN-E process unavoidable? Any pointers to official documentation, or confirmation from anyone who has been through this, would be greatly appreciated — we need a clear, citable answer to justify preparing this documentation internally. Thanks in advance.
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391
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Aug ’26