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Core ML memory usage is dramatically higher with an Xcode 27 build on iOS 27
I’m seeing a major change in reported memory usage (and eventual termination due to memory pressure) when running a Core ML workload built with Xcode 27 on iOS/iPadOS 27. The source code, model files, and MLModelConfiguration are unchanged. Only the Xcode/SDK version used to build the app differs. On the same iPad running iPadOS 27: Xcode 26 build: model loading and prediction complete normally, with a relatively small reported application footprint. Xcode 27 build: the application footprint grows continuously as models are loaded and can exceed 5 GB. The app is eventually terminated unless models are unloaded very aggressively or the increased-memory-limit entitlement is used. I also tested an Xcode 27 build on a device running iOS 26. Its reported peak was only around 300 MB. This suggests the change requires both an Xcode 27-linked binary and the iOS 27 runtime. The workload consists of several compiled Core ML models using .cpuAndNeuralEngine. Loading models sequentially instead of concurrently does not materially change the final footprint. Releasing each MLModel after use does reduce it, so this appears to be model or Neural Engine residency being charged to the application rather than a conventional heap leak. I noticed that the iOS 27 release notes mention Neural Engine memory now being attributed to the application instead of the system. However, I’m unclear about the practical consequences of that change. If the same Neural Engine resources were already physically resident on iOS 26, I would have expected them to contribute to system memory pressure even when they were not attributed directly to the application. Instead, the older configuration runs comfortably, while the Xcode 27/iOS 27 combination approaches or crosses the application’s per-process memory limit. A few additional observations: The problem is more likely to occur after Core ML has already compiled and specialized the models. Cached model loading is much faster and the footprint grows quickly. The first uncached run can survive model preparation because specialization spaces the loads farther apart. Under Instruments, the app often does not terminate, presumably because profiling slows the workload enough to change the peak. os_proc_available_memory() decreases in line with the newly reported footprint. With the increased-memory-limit entitlement, the workload completes, but the reported footprint still reaches several gigabytes. Has anyone else observed a large Core ML memory increase specifically with an Xcode 27 build running on iOS 27? In particular, I’m trying to understand: Is this purely a change in how existing Neural Engine memory is accounted for, or does the new runtime also retain or allocate more memory? Is the new accounting used for the application’s jetsam/per-process memory limit? Is this behavior intentionally gated by the linked SDK version? That would explain why an Xcode 26 build behaves differently on the same iOS 27 device. Should applications now treat the Neural Engine residency of every loaded MLModel as part of their process-memory budget and unload models accordingly? Are there recommended APIs or Core ML loading strategies for controlling this residency? Any confirmation that others are seeing the same Xcode 27/iOS 27 behavior—or clarification of the intended memory-accounting model—would be very helpful.
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A20 pro devices take too long on CoreML model specialization
Hi, I'm a ML developer specializing in local audio related models. I upgraded from a 16 pro max to an 18 pro max. I'm not sure what the reason is, but model specialization (happens only on the first load, cpu and neural engine compute units) is sometimes 2x - 5x longer on the 18 pro max vs the 16 pro max. Both devices are on iOS 27. For example, I have a stem separation model running, and the 18 pro max took 171 seconds (including inference) and the 16 pro max took 37 seconds (including inference). 18 pro max inference is ~55x RTFx and the 16 pro max is ~40 RTFx for this particular model, but I've noticed it as well in TTS models etc. Please look into this, I know the 18 pro max has double the neural engine cores, so I'm not sure if the complexity makes specialization take longer. Subsequent loads are great, same high rtfx for the 18 PM (only 0.5s load), and this is where it beats the 16 PM handily. I attached 2 images showing the 18 PM on a first load (model specialization likely) vs a subsequent run. Please look into this. (Note this is not just my implementation, I tested a range of different models, all with the same outcome).
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Adding MCP and connector support to your own Foundation Models apps
Circling back on the LocalLM Lab arc. With v0.7, we've moved from prompt experimentation into real app development on Apple's Foundation Models local AI. The LocalLM Lab SDK lets you build that same on-device model and MCP client this thread has covered directly into your own app, with real tool and data access (Slack, Todoist, GitHub, Notion, Linear, plus Calendar, Reminders, Contacts and Location). And you can ship your app including through the Mac App Store. This is a big improvement over version 0.6, where the localai-cli toolkit needed LocalLM Lab installed and running. On the other hand, the SDK (LocalLMLabSDKCore) doesn't relay through anything; it links FoundationModels and a real MCP client directly into your own binary and is totally self-contained. The example included in the SDK, Plate Today, has actually been built into a sandboxed test app and verified working, with a signed path to a Mac App Store .pkg (Apple Distribution signing + provisioning profile pipeline). That's "verified signable and sandbox-compatible," to be precise. Entitlements (from personal experience: always a complicated topic): com.apple.security.app-sandbox + com.apple.security.network.client for the app itself, plus the standard personal-information entitlements per connector used (com.apple.security.personal-information.calendars, .addressbook, .location) and matching NS*UsageDescription strings in Info.plist. The one worth flagging specifically: the network entitlement is easy to miss and fails silently rather than throwing. Without it, MCP connections and Weather calls just hang with no error surfaced. OAuth handling requires the app delegate callback (application(_:open:)), not SwiftUI's .onOpenURL. Worth knowing before wiring it up if you're SwiftUI-only. Full entitlements list + SDK guide: https://github.com/ancientcomputing/locallm/blob/main/docs/sdk-guide.md Feature page: thisbrain.ai/locallm/sdk.html I hope the availability of the SDK (free, Apache 2.0 license) will give folks further incentive to explore local AI-enabled applications on the Mac. What else would you want to do that the SDK doesn't currently support? File picker? Calendar/Reminders/Contacts edits & writes?
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Model Guardrails Too Restrictive?
I'm experimenting with using the Foundation Models framework to do news summarization in an RSS app but I'm finding that a lot of articles are getting kicked back with a vague message about guardrails. This seems really common with political news but we're talking mainstream stuff, i.e. Politico, etc. If the models are this restrictive, this will be tough to use. Is this intended? FB17904424
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Framing an object in real time
Hi, I am currently working on a project which involves giving directions to the user telling them what they should do to frame a given object (also only doing this if sees the object which can be many things, so it needs detection too) so that it can be optimally in view of the camera to be classified. I have figured the classification, but not the real time guidance and detection. I tried training a model that featured all of the different movements ("move up", "move down", etc.). It featured 30 different objects with ten crops per image, and each class featured all 30 images and an even amount per class. It had over 1500 images in total, and I trained using createml and core ml. But, they were very inaccurate in my testing. I also tried using foundation models for this, but it also didn't work. I am quite new to integrating ai/ ml models into a project, and I am unsure of where to go next. If anyone has any suggestions, I would be really appreciative.
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SiriAI Voice my app
Policy or no policy I think its clear if Apple wants to have a premium SiriAI experience its gonna need to allow apps to use the Siri AI Voice engine Developers like me want the privacy/no cloud voice engine service that a SiriAI Voice would provide Seemless conversations with versions of SiriAI Voices let's users know they're still in the SiriAI ecosystem but engaged with a developer app The "hey Siri, ask LinkedIN Hoffman to .." is stunningly haphazard moat gate that Apple can resolve and still keep control with a simple App License so it know every app that uses a SiriAI Voice App engine. I've been waiting over a year for Apple to figure this out. This is an unforced error. This is not a threat its an opportunity for Apple ? Is this a Driver or CoreOS issue? WTF?
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Dutch Siri identifies my App Shortcut but requests Notes or falls back to Home/Intercom — FB25058524
I need a supported way for Dutch Siri to invoke Elegant Cook's built-in grocery actions directly, without requiring users to create personal shortcuts. The installed TestFlight version is 1.5.3 (32), on an iPad Air (5th generation). The owner reports iPadOS 26.6.1; the diagnostic archive identifies build 23G83. Siri is Dutch, with nl_BE runtime assets. Use with Siri is enabled and spoken responses are preferred. The main app uses App Intents and AppShortcutsProvider with three actions: add a grocery, read the list, and check off a grocery. The voice actions use scalar String parameters with requestValueDialog and return ProvidesDialog results. The phrases use the applicationName token and have packaged English and Dutch localizations. We do not register Notes or Home intents. Observed behavior: “Hé Siri, voeg boodschappen toe in Elegant Cook” and “Hé Siri, lees mijn boodschappenlijst in Elegant Cook” ask whether to use Elegant Cook or Notes. After selecting Elegant Cook, adding and reading work by voice. “Hé Siri, vink een boodschap af in Elegant Cook” and the registered app-name-first equivalent “Hé Siri, Elegant Cook boodschappen afvinken” instead produce the instruction to download Apple's Home app to use Intercom. “Hé Siri, open Elegant Cook” opens the installed app correctly. The signed build contains extracted App Intents metadata, compiled Dutch/English nlu.appintents assets and successful phrase-training output. These build checks do not establish successful device routing or App Shortcuts Preview validation. For one captured check-off invocation, local diagnostic analysis identified this sequence: the recognized utterance includes the correct app name; App Shortcuts are enabled; the intended CheckElegantCookGrocery action is a Siri candidate; app disambiguation then fails and explicitly redirects to Home/Intercom before the grocery operation. This trace covers check-off only. It does not prove that the add/read chooser has the same cause, or establish a definitive Apple framework bug. The private bug report is FB25058524. It includes selected evidence and source code. A full sysdiagnose has not been uploaded, and the isolated Swift example is not a compiled or validated reproducing Xcode project. We develop on Windows and use EAS cloud builds, so local Xcode App Shortcuts Preview is unavailable. The code-level support form currently directs us to this forum when we cannot provide a test project demonstrating the issue. Could an App Intents/DTS engineer advise: Which supported registration or metadata correction should we check when Siri identifies an explicitly named App Shortcut but chooses another domain before perform()? What targeted diagnostic evidence can distinguish an app configuration problem from a Siri routing defect without local App Shortcuts Preview? Is there a supported workaround for direct Dutch voice invocation on this OS build, without personal shortcuts or private system settings? We can prepare an isolated native sample project if required, but will clearly distinguish source preparation, cloud compilation and actual device reproduction. We do not want to claim a reproduction that has not been verified.
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iOS 27: concurrent Core ML predictions on the Neural Engine are serialized
Environment iPhone 17 Pro Max, iOS 27.0.1 ([build]) Reference: MacBook Pro (M3 Pro), macOS 26.4.1 ML Program model, MLComputeUnitsCPUAndNeuralEngine. A single prediction takes about 2.8 ms on the ANE. Description On iOS 27, Core ML predictions submitted to the Neural Engine from multiple threads at the same time no longer overlap. The ANE runs them strictly one after another. Adding threads gives no extra throughput, and per-call latency grows linearly with the number of requests in flight. The same code still gets a clear concurrency gain on macOS 26.4.1, and our app's ANE-parallel path on this iPhone became noticeably slower after the iOS 27 update. On iOS 27, latency is about N × single-call time, which means requests are queued and never overlap. Running two different models concurrently on the ANE shows the same behavior. What we tried, with no change in result A separate MLModel instance per thread. The async predictionFromFeatures:options:completionHandler: API. Questions Is this serialization an intended change in iOS 27? Is there a supported way to get concurrent ANE execution back?
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iOS 27: English sentence embedding model unavailable on physical devices
I’m seeing NLEmbedding.sentenceEmbedding(for: .english) return nil on physical devices running iOS 27, both in debug builds under Xcode and in production. The same failure has been observed across thousands of users on iOS 27. One affected device runs iOS 27.0.1 (24A446). Minimal example with diagnostics: import NaturalLanguage let language = NLLanguage.english print("Current revision:", NLEmbedding.currentSentenceEmbeddingRevision(for: language)) print("Supported revisions:", Array(NLEmbedding.supportedSentenceEmbeddingRevisions(for: language))) if let model = NLEmbedding.sentenceEmbedding(for: language) { print("Loaded revision:", model.revision, "dimension:", model.dimension) } else { print("Sentence embedding model unavailable") } On affected devices, the current revision is 1 and supported revisions are [1], but model loading returns nil. The framework logs: Unable to locate Asset for sentence embedding model for local en. The failure occurs during model loading, before any call to vector(for:). This uses Apple’s built-in English sentence model, with no custom embedding file. Is this a known issue on iOS 27? Is there a supported way to request or restore the missing sentence-model asset, or a recommended recovery approach?
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Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0
Application is getting Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0 Crashed: AXSpeech 0 libobjc.A.dylib 0x4820 objc_msgSend + 32 1 libsystem_trace.dylib 0x6c34 _os_log_fmt_flatten_object + 116 2 libsystem_trace.dylib 0x5344 _os_log_impl_flatten_and_send + 1884 3 libsystem_trace.dylib 0x4bd0 _os_log + 152 4 libsystem_trace.dylib 0x9c48 _os_log_error_impl + 24 5 TextToSpeech 0xd0a8c _pcre2_xclass_8 6 TextToSpeech 0x3bc04 TTSSpeechUnitTestingMode 7 TextToSpeech 0x3f128 TTSSpeechUnitTestingMode 8 AXCoreUtilities 0xad38 -[NSArray(AXExtras) ax_flatMappedArrayUsingBlock:] + 204 9 TextToSpeech 0x3eb18 TTSSpeechUnitTestingMode 10 TextToSpeech 0x3c948 TTSSpeechUnitTestingMode 11 TextToSpeech 0x48824 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 12 TextToSpeech 0x49804 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 13 Foundation 0xf6064 __NSThreadPerformPerform + 264 14 CoreFoundation 0x37acc CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION + 28 15 CoreFoundation 0x36d48 __CFRunLoopDoSource0 + 176 16 CoreFoundation 0x354fc __CFRunLoopDoSources0 + 244 17 CoreFoundation 0x34238 __CFRunLoopRun + 828 18 CoreFoundation 0x33e18 CFRunLoopRunSpecific + 608 19 Foundation 0x2d4cc -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 212 20 TextToSpeech 0x24b88 TTSCFAttributedStringCreateStringByBracketingAttributeWithString 21 Foundation 0xb3154 NSThread__start + 732 com.livingMedia.AajTakiPhone_issue_3ceba855a8ad2d1af83655803dc13f70_crash_session_9081fa41ced440ae9a57c22cb432f312_DNE_0_v2_stacktrace.txt 22 libsystem_pthread.dylib 0x24d4 _pthread_start + 136 23 libsystem_pthread.dylib 0x1a10 thread_start + 8
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iOS 27.2 beta: App Shortcut phrase fails, but a named personal shortcut runs the same intent
I'm investigating a Siri invocation failure with an explicitly declared App Shortcut phrase. The same underlying App Intent works when run directly in Shortcuts and when invoked by the name of a saved personal shortcut. Environment: iPhone 17 Pro Max, iOS 27.2 beta (24B5099f), clean restore without a backup. iPhone and Siri languages: Italian. Standard Siri, no Siri AI. Xcode 27.2 (27B5028f), iphoneos 27.2 SDK. Italian App Shortcut phrase and app metadata. PetroCheck 1.3 (637), already open during the failing invocation. The published phrase is “Trova carburanti vicini con PetroCheck”, declared using \(.applicationName). This is a phrase-based App Shortcut; the intent does not adopt an App Schema. What I observe: Saying the App Shortcut phrase produces a generic Siri error: “mi dispiace, ma si è verificato un errore”. Running the action directly in Shortcuts succeeds and records execution in the app's intent journal. Saving a personal shortcut named “Diagnostica Petro” with just that action, then saying “Esegui Diagnostica Petro”, also succeeds and records execution. Returning only a minimal Text snippet from the original intent does not resolve the phrase invocation failure. For two captured failing phrase invocations, the device logs show BackgroundShortcutRunner failing to resolve the workflow reference: -[WFWorkflowDatabaseRunDescriptor(Conversion) workflowReferenceWithDatabase:error:] Couldn't find shortcut with descriptor: <private> reason: unable to resolve workflow reference from descriptor The first of these attempts has no new entry at the recorded start of perform(). The descriptor itself is redacted, so I cannot identify which reference Siri selected. A subsequent attempt also contains these assistantd messages shortly before the descriptor error: Found no AppShortcutTargets! Could not cast to VoiceCommand task to create AppShortcut invocation AppShortcuts enablement result=false I am including these as observations, without interpreting them as proof of a disabled setting. Calling updateAppShortcutParameters() at launch did not resolve the failure. I checked the compiled device bundle's App Intents metadata: the action is discoverable, its fuel parameter is optional, and the declared shortcut points to the correct intent. The Italian training metadata contains the phrase and the application name PetroCheck. This verifies the compiled metadata, not the device's registration database. I then built a separate app, “Prova Petro”, with a different bundle identifier, a fresh phrase, one intent with no parameters, and a dialog-only result. It has no location, networking, snippet or dependencies from the original app. The Siri phrase fails in this app too. I have not yet captured its intent diary or host logs, so I cannot claim that the minimal app fails at exactly the same stage. These are the core declarations from the compiled minimal project; the sample's journal calls are omitted here: import AppIntents struct RoutingProbeIntent: AppIntent { static let title: LocalizedStringResource = "Verifica collegamento Siri" static let supportedModes: IntentModes = [.background] func perform() async throws -> some IntentResult & ProvidesDialog { return .result(dialog: "Il comando Siri ha raggiunto Prova Petro.") } } struct ProbeShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { AppShortcut( intent: RoutingProbeIntent(), phrases: ["Verifica Siri con \(.applicationName)"], shortTitle: "Verifica collegamento Siri", systemImageName: "checkmark.circle" ) } } The minimal app's display name is Prova Petro, and the spoken phrase is “Verifica Siri con Prova Petro”. Its App.init() calls ProbeShortcuts.updateAppShortcutParameters(). The extracted metadata confirms one shortcut, zero parameters, dialog-only output, and the Italian application name and phrase. Has anyone reproduced this difference between an App Shortcut phrase and a named personal shortcut on iOS 27.2 beta, particularly with Italian phrases? Feedback Assistant: FB25077916. The complete minimal Xcode project and scoped diagnostics are attached to the report. Is there anything missing from this setup, or a supported way to diagnose the unresolved shortcut reference? Comparisons with other OS builds, languages or devices would be useful. I have not yet established a regression against a stable OS release. Prova Petro minimal source and Siri routing logs
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JEV
(I don't follow the AI stuff here, so sorry if this is a stupid question. Or the wrong category.) There is a new fangled AI mode called JEV. Can the current Apple Intelligence libraries do something like it, or is this a WWDC27 thing?
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SIRI AI AND APPLE INTELLIGENCE
Since I updated to this os 27 in my iPhone 16 plus the siri ai and Apple Intelligence is got freeze in “ Adding support for Siri is in progress. Siri will be unavailable until the update is complete. “ I updated on 21/09/2026 today date is 30/09/2026 I tried all troubleshooting methods and watch a bunch of YouTube videos, but still stuck in the same position. I contacted Apple support. But , they also do nothing. Please anyone help me to how to get siri ai beta @appleindia @applesupport
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Sep ’26
Create ML Object Tracking training stuck at 97.4% - When pressing 'Resume Training' it PAUSES automatically after 3 seconds
Hi, I’m training an Object Tracking reference object in Create ML using Extended training mode + All Angles for use with visionOS high-frame-rate object tracking. The training ran successfully for approximately 48 hours and reached 97.4%. At that point, Create ML automatically paused. The issue is now completely reproducible: whenever I click Resume, training runs for approximately 3 seconds and then automatically returns to Paused at exactly 97.4%. I captured the system logs while reproducing the issue. MLRecipeExecutionService crashes with: LayerVariable.swift:116: Fatal error: The new value must have the same shape as the current value ([40, 1, 3, 3]), but it has [96, 1, 3, 3]. Immediately afterwards, Create ML reports an interrupted XPC connection to MLRecipeExecutionService. Shortly before the crash, the service also logs: Detector training already finished but its loss file was removed from the cache; reporting loss as 0. and: Error cleaning up detector images: NSCocoaErrorDomain Code=4 NSPOSIXErrorDomain Code=2 "No such file or directory" I would really like to avoid discarding ~48 hours of training if the existing tracker/detector checkpoints can still be recovered. Thanks!
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Exploring Apple Silicon + MLX for a persistent local AI companion architecture
I’m developing an independent project in Scotland called Isla Watson. The architecture is built around a simple principle: the model is replaceable; the identity is not. Long-term memory, persistent internal state and identity are designed to remain outside the foundation model, allowing local models to act as replaceable reasoning and language components without resetting the companion. I’m now exploring whether Apple Silicon and MLX could provide the long-term local compute platform for the system — including specialist Mac nodes for reasoning, memory, speech and perception, with distributed inference when larger models are required. A particular area of interest is whether multiple Macs can be used in two complementary ways: as independent specialist agents during normal operation; and as a distributed MLX inference group when a larger model exceeds the capacity of one machine. The first technical study I’d like to establish is a reproducible 1-node → 2-node baseline, measuring model capacity, unified-memory use, time to first token, generation throughput, power consumption, agent concurrency and distributed scaling efficiency. The wider research goal is to keep persistent identity and state independent from whichever foundation model is currently providing language and reasoning. I’d particularly value guidance from anyone working with MLX distributed inference, Thunderbolt/RDMA multi-Mac setups, or local agent architectures. I’ve also posted an architecture-level overview in the MLX GitHub community and have a one-page public brief available for anyone interested in the wider design. https://github.com/ml-explore/mlx/discussions/4482
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Core ML memory usage is dramatically higher with an Xcode 27 build on iOS 27
I’m seeing a major change in reported memory usage (and eventual termination due to memory pressure) when running a Core ML workload built with Xcode 27 on iOS/iPadOS 27. The source code, model files, and MLModelConfiguration are unchanged. Only the Xcode/SDK version used to build the app differs. On the same iPad running iPadOS 27: Xcode 26 build: model loading and prediction complete normally, with a relatively small reported application footprint. Xcode 27 build: the application footprint grows continuously as models are loaded and can exceed 5 GB. The app is eventually terminated unless models are unloaded very aggressively or the increased-memory-limit entitlement is used. I also tested an Xcode 27 build on a device running iOS 26. Its reported peak was only around 300 MB. This suggests the change requires both an Xcode 27-linked binary and the iOS 27 runtime. The workload consists of several compiled Core ML models using .cpuAndNeuralEngine. Loading models sequentially instead of concurrently does not materially change the final footprint. Releasing each MLModel after use does reduce it, so this appears to be model or Neural Engine residency being charged to the application rather than a conventional heap leak. I noticed that the iOS 27 release notes mention Neural Engine memory now being attributed to the application instead of the system. However, I’m unclear about the practical consequences of that change. If the same Neural Engine resources were already physically resident on iOS 26, I would have expected them to contribute to system memory pressure even when they were not attributed directly to the application. Instead, the older configuration runs comfortably, while the Xcode 27/iOS 27 combination approaches or crosses the application’s per-process memory limit. A few additional observations: The problem is more likely to occur after Core ML has already compiled and specialized the models. Cached model loading is much faster and the footprint grows quickly. The first uncached run can survive model preparation because specialization spaces the loads farther apart. Under Instruments, the app often does not terminate, presumably because profiling slows the workload enough to change the peak. os_proc_available_memory() decreases in line with the newly reported footprint. With the increased-memory-limit entitlement, the workload completes, but the reported footprint still reaches several gigabytes. Has anyone else observed a large Core ML memory increase specifically with an Xcode 27 build running on iOS 27? In particular, I’m trying to understand: Is this purely a change in how existing Neural Engine memory is accounted for, or does the new runtime also retain or allocate more memory? Is the new accounting used for the application’s jetsam/per-process memory limit? Is this behavior intentionally gated by the linked SDK version? That would explain why an Xcode 26 build behaves differently on the same iOS 27 device. Should applications now treat the Neural Engine residency of every loaded MLModel as part of their process-memory budget and unload models accordingly? Are there recommended APIs or Core ML loading strategies for controlling this residency? Any confirmation that others are seeing the same Xcode 27/iOS 27 behavior—or clarification of the intended memory-accounting model—would be very helpful.
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A20 pro devices take too long on CoreML model specialization
Hi, I'm a ML developer specializing in local audio related models. I upgraded from a 16 pro max to an 18 pro max. I'm not sure what the reason is, but model specialization (happens only on the first load, cpu and neural engine compute units) is sometimes 2x - 5x longer on the 18 pro max vs the 16 pro max. Both devices are on iOS 27. For example, I have a stem separation model running, and the 18 pro max took 171 seconds (including inference) and the 16 pro max took 37 seconds (including inference). 18 pro max inference is ~55x RTFx and the 16 pro max is ~40 RTFx for this particular model, but I've noticed it as well in TTS models etc. Please look into this, I know the 18 pro max has double the neural engine cores, so I'm not sure if the complexity makes specialization take longer. Subsequent loads are great, same high rtfx for the 18 PM (only 0.5s load), and this is where it beats the 16 PM handily. I attached 2 images showing the 18 PM on a first load (model specialization likely) vs a subsequent run. Please look into this. (Note this is not just my implementation, I tested a range of different models, all with the same outcome).
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Adding MCP and connector support to your own Foundation Models apps
Circling back on the LocalLM Lab arc. With v0.7, we've moved from prompt experimentation into real app development on Apple's Foundation Models local AI. The LocalLM Lab SDK lets you build that same on-device model and MCP client this thread has covered directly into your own app, with real tool and data access (Slack, Todoist, GitHub, Notion, Linear, plus Calendar, Reminders, Contacts and Location). And you can ship your app including through the Mac App Store. This is a big improvement over version 0.6, where the localai-cli toolkit needed LocalLM Lab installed and running. On the other hand, the SDK (LocalLMLabSDKCore) doesn't relay through anything; it links FoundationModels and a real MCP client directly into your own binary and is totally self-contained. The example included in the SDK, Plate Today, has actually been built into a sandboxed test app and verified working, with a signed path to a Mac App Store .pkg (Apple Distribution signing + provisioning profile pipeline). That's "verified signable and sandbox-compatible," to be precise. Entitlements (from personal experience: always a complicated topic): com.apple.security.app-sandbox + com.apple.security.network.client for the app itself, plus the standard personal-information entitlements per connector used (com.apple.security.personal-information.calendars, .addressbook, .location) and matching NS*UsageDescription strings in Info.plist. The one worth flagging specifically: the network entitlement is easy to miss and fails silently rather than throwing. Without it, MCP connections and Weather calls just hang with no error surfaced. OAuth handling requires the app delegate callback (application(_:open:)), not SwiftUI's .onOpenURL. Worth knowing before wiring it up if you're SwiftUI-only. Full entitlements list + SDK guide: https://github.com/ancientcomputing/locallm/blob/main/docs/sdk-guide.md Feature page: thisbrain.ai/locallm/sdk.html I hope the availability of the SDK (free, Apache 2.0 license) will give folks further incentive to explore local AI-enabled applications on the Mac. What else would you want to do that the SDK doesn't currently support? File picker? Calendar/Reminders/Contacts edits & writes?
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Model Guardrails Too Restrictive?
I'm experimenting with using the Foundation Models framework to do news summarization in an RSS app but I'm finding that a lot of articles are getting kicked back with a vague message about guardrails. This seems really common with political news but we're talking mainstream stuff, i.e. Politico, etc. If the models are this restrictive, this will be tough to use. Is this intended? FB17904424
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Framing an object in real time
Hi, I am currently working on a project which involves giving directions to the user telling them what they should do to frame a given object (also only doing this if sees the object which can be many things, so it needs detection too) so that it can be optimally in view of the camera to be classified. I have figured the classification, but not the real time guidance and detection. I tried training a model that featured all of the different movements ("move up", "move down", etc.). It featured 30 different objects with ten crops per image, and each class featured all 30 images and an even amount per class. It had over 1500 images in total, and I trained using createml and core ml. But, they were very inaccurate in my testing. I also tried using foundation models for this, but it also didn't work. I am quite new to integrating ai/ ml models into a project, and I am unsure of where to go next. If anyone has any suggestions, I would be really appreciative.
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398
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2w
SiriAI Voice my app
Policy or no policy I think its clear if Apple wants to have a premium SiriAI experience its gonna need to allow apps to use the Siri AI Voice engine Developers like me want the privacy/no cloud voice engine service that a SiriAI Voice would provide Seemless conversations with versions of SiriAI Voices let's users know they're still in the SiriAI ecosystem but engaged with a developer app The "hey Siri, ask LinkedIN Hoffman to .." is stunningly haphazard moat gate that Apple can resolve and still keep control with a simple App License so it know every app that uses a SiriAI Voice App engine. I've been waiting over a year for Apple to figure this out. This is an unforced error. This is not a threat its an opportunity for Apple ? Is this a Driver or CoreOS issue? WTF?
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Dutch Siri identifies my App Shortcut but requests Notes or falls back to Home/Intercom — FB25058524
I need a supported way for Dutch Siri to invoke Elegant Cook's built-in grocery actions directly, without requiring users to create personal shortcuts. The installed TestFlight version is 1.5.3 (32), on an iPad Air (5th generation). The owner reports iPadOS 26.6.1; the diagnostic archive identifies build 23G83. Siri is Dutch, with nl_BE runtime assets. Use with Siri is enabled and spoken responses are preferred. The main app uses App Intents and AppShortcutsProvider with three actions: add a grocery, read the list, and check off a grocery. The voice actions use scalar String parameters with requestValueDialog and return ProvidesDialog results. The phrases use the applicationName token and have packaged English and Dutch localizations. We do not register Notes or Home intents. Observed behavior: “Hé Siri, voeg boodschappen toe in Elegant Cook” and “Hé Siri, lees mijn boodschappenlijst in Elegant Cook” ask whether to use Elegant Cook or Notes. After selecting Elegant Cook, adding and reading work by voice. “Hé Siri, vink een boodschap af in Elegant Cook” and the registered app-name-first equivalent “Hé Siri, Elegant Cook boodschappen afvinken” instead produce the instruction to download Apple's Home app to use Intercom. “Hé Siri, open Elegant Cook” opens the installed app correctly. The signed build contains extracted App Intents metadata, compiled Dutch/English nlu.appintents assets and successful phrase-training output. These build checks do not establish successful device routing or App Shortcuts Preview validation. For one captured check-off invocation, local diagnostic analysis identified this sequence: the recognized utterance includes the correct app name; App Shortcuts are enabled; the intended CheckElegantCookGrocery action is a Siri candidate; app disambiguation then fails and explicitly redirects to Home/Intercom before the grocery operation. This trace covers check-off only. It does not prove that the add/read chooser has the same cause, or establish a definitive Apple framework bug. The private bug report is FB25058524. It includes selected evidence and source code. A full sysdiagnose has not been uploaded, and the isolated Swift example is not a compiled or validated reproducing Xcode project. We develop on Windows and use EAS cloud builds, so local Xcode App Shortcuts Preview is unavailable. The code-level support form currently directs us to this forum when we cannot provide a test project demonstrating the issue. Could an App Intents/DTS engineer advise: Which supported registration or metadata correction should we check when Siri identifies an explicitly named App Shortcut but chooses another domain before perform()? What targeted diagnostic evidence can distinguish an app configuration problem from a Siri routing defect without local App Shortcuts Preview? Is there a supported workaround for direct Dutch voice invocation on this OS build, without personal shortcuts or private system settings? We can prepare an isolated native sample project if required, but will clearly distinguish source preparation, cloud compilation and actual device reproduction. We do not want to claim a reproduction that has not been verified.
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3w
iOS 27: concurrent Core ML predictions on the Neural Engine are serialized
Environment iPhone 17 Pro Max, iOS 27.0.1 ([build]) Reference: MacBook Pro (M3 Pro), macOS 26.4.1 ML Program model, MLComputeUnitsCPUAndNeuralEngine. A single prediction takes about 2.8 ms on the ANE. Description On iOS 27, Core ML predictions submitted to the Neural Engine from multiple threads at the same time no longer overlap. The ANE runs them strictly one after another. Adding threads gives no extra throughput, and per-call latency grows linearly with the number of requests in flight. The same code still gets a clear concurrency gain on macOS 26.4.1, and our app's ANE-parallel path on this iPhone became noticeably slower after the iOS 27 update. On iOS 27, latency is about N × single-call time, which means requests are queued and never overlap. Running two different models concurrently on the ANE shows the same behavior. What we tried, with no change in result A separate MLModel instance per thread. The async predictionFromFeatures:options:completionHandler: API. Questions Is this serialization an intended change in iOS 27? Is there a supported way to get concurrent ANE execution back?
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iOS 27: English sentence embedding model unavailable on physical devices
I’m seeing NLEmbedding.sentenceEmbedding(for: .english) return nil on physical devices running iOS 27, both in debug builds under Xcode and in production. The same failure has been observed across thousands of users on iOS 27. One affected device runs iOS 27.0.1 (24A446). Minimal example with diagnostics: import NaturalLanguage let language = NLLanguage.english print("Current revision:", NLEmbedding.currentSentenceEmbeddingRevision(for: language)) print("Supported revisions:", Array(NLEmbedding.supportedSentenceEmbeddingRevisions(for: language))) if let model = NLEmbedding.sentenceEmbedding(for: language) { print("Loaded revision:", model.revision, "dimension:", model.dimension) } else { print("Sentence embedding model unavailable") } On affected devices, the current revision is 1 and supported revisions are [1], but model loading returns nil. The framework logs: Unable to locate Asset for sentence embedding model for local en. The failure occurs during model loading, before any call to vector(for:). This uses Apple’s built-in English sentence model, with no custom embedding file. Is this a known issue on iOS 27? Is there a supported way to request or restore the missing sentence-model asset, or a recommended recovery approach?
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Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0
Application is getting Crashed: AXSpeech EXC_BAD_ACCESS KERN_INVALID_ADDRESS 0x000056f023efbeb0 Crashed: AXSpeech 0 libobjc.A.dylib 0x4820 objc_msgSend + 32 1 libsystem_trace.dylib 0x6c34 _os_log_fmt_flatten_object + 116 2 libsystem_trace.dylib 0x5344 _os_log_impl_flatten_and_send + 1884 3 libsystem_trace.dylib 0x4bd0 _os_log + 152 4 libsystem_trace.dylib 0x9c48 _os_log_error_impl + 24 5 TextToSpeech 0xd0a8c _pcre2_xclass_8 6 TextToSpeech 0x3bc04 TTSSpeechUnitTestingMode 7 TextToSpeech 0x3f128 TTSSpeechUnitTestingMode 8 AXCoreUtilities 0xad38 -[NSArray(AXExtras) ax_flatMappedArrayUsingBlock:] + 204 9 TextToSpeech 0x3eb18 TTSSpeechUnitTestingMode 10 TextToSpeech 0x3c948 TTSSpeechUnitTestingMode 11 TextToSpeech 0x48824 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 12 TextToSpeech 0x49804 AXAVSpeechSynthesisVoiceFromTTSSpeechVoice 13 Foundation 0xf6064 __NSThreadPerformPerform + 264 14 CoreFoundation 0x37acc CFRUNLOOP_IS_CALLING_OUT_TO_A_SOURCE0_PERFORM_FUNCTION + 28 15 CoreFoundation 0x36d48 __CFRunLoopDoSource0 + 176 16 CoreFoundation 0x354fc __CFRunLoopDoSources0 + 244 17 CoreFoundation 0x34238 __CFRunLoopRun + 828 18 CoreFoundation 0x33e18 CFRunLoopRunSpecific + 608 19 Foundation 0x2d4cc -[NSRunLoop(NSRunLoop) runMode:beforeDate:] + 212 20 TextToSpeech 0x24b88 TTSCFAttributedStringCreateStringByBracketingAttributeWithString 21 Foundation 0xb3154 NSThread__start + 732 com.livingMedia.AajTakiPhone_issue_3ceba855a8ad2d1af83655803dc13f70_crash_session_9081fa41ced440ae9a57c22cb432f312_DNE_0_v2_stacktrace.txt 22 libsystem_pthread.dylib 0x24d4 _pthread_start + 136 23 libsystem_pthread.dylib 0x1a10 thread_start + 8
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3w
iOS 27.2 beta: App Shortcut phrase fails, but a named personal shortcut runs the same intent
I'm investigating a Siri invocation failure with an explicitly declared App Shortcut phrase. The same underlying App Intent works when run directly in Shortcuts and when invoked by the name of a saved personal shortcut. Environment: iPhone 17 Pro Max, iOS 27.2 beta (24B5099f), clean restore without a backup. iPhone and Siri languages: Italian. Standard Siri, no Siri AI. Xcode 27.2 (27B5028f), iphoneos 27.2 SDK. Italian App Shortcut phrase and app metadata. PetroCheck 1.3 (637), already open during the failing invocation. The published phrase is “Trova carburanti vicini con PetroCheck”, declared using \(.applicationName). This is a phrase-based App Shortcut; the intent does not adopt an App Schema. What I observe: Saying the App Shortcut phrase produces a generic Siri error: “mi dispiace, ma si è verificato un errore”. Running the action directly in Shortcuts succeeds and records execution in the app's intent journal. Saving a personal shortcut named “Diagnostica Petro” with just that action, then saying “Esegui Diagnostica Petro”, also succeeds and records execution. Returning only a minimal Text snippet from the original intent does not resolve the phrase invocation failure. For two captured failing phrase invocations, the device logs show BackgroundShortcutRunner failing to resolve the workflow reference: -[WFWorkflowDatabaseRunDescriptor(Conversion) workflowReferenceWithDatabase:error:] Couldn't find shortcut with descriptor: <private> reason: unable to resolve workflow reference from descriptor The first of these attempts has no new entry at the recorded start of perform(). The descriptor itself is redacted, so I cannot identify which reference Siri selected. A subsequent attempt also contains these assistantd messages shortly before the descriptor error: Found no AppShortcutTargets! Could not cast to VoiceCommand task to create AppShortcut invocation AppShortcuts enablement result=false I am including these as observations, without interpreting them as proof of a disabled setting. Calling updateAppShortcutParameters() at launch did not resolve the failure. I checked the compiled device bundle's App Intents metadata: the action is discoverable, its fuel parameter is optional, and the declared shortcut points to the correct intent. The Italian training metadata contains the phrase and the application name PetroCheck. This verifies the compiled metadata, not the device's registration database. I then built a separate app, “Prova Petro”, with a different bundle identifier, a fresh phrase, one intent with no parameters, and a dialog-only result. It has no location, networking, snippet or dependencies from the original app. The Siri phrase fails in this app too. I have not yet captured its intent diary or host logs, so I cannot claim that the minimal app fails at exactly the same stage. These are the core declarations from the compiled minimal project; the sample's journal calls are omitted here: import AppIntents struct RoutingProbeIntent: AppIntent { static let title: LocalizedStringResource = "Verifica collegamento Siri" static let supportedModes: IntentModes = [.background] func perform() async throws -> some IntentResult & ProvidesDialog { return .result(dialog: "Il comando Siri ha raggiunto Prova Petro.") } } struct ProbeShortcuts: AppShortcutsProvider { static var appShortcuts: [AppShortcut] { AppShortcut( intent: RoutingProbeIntent(), phrases: ["Verifica Siri con \(.applicationName)"], shortTitle: "Verifica collegamento Siri", systemImageName: "checkmark.circle" ) } } The minimal app's display name is Prova Petro, and the spoken phrase is “Verifica Siri con Prova Petro”. Its App.init() calls ProbeShortcuts.updateAppShortcutParameters(). The extracted metadata confirms one shortcut, zero parameters, dialog-only output, and the Italian application name and phrase. Has anyone reproduced this difference between an App Shortcut phrase and a named personal shortcut on iOS 27.2 beta, particularly with Italian phrases? Feedback Assistant: FB25077916. The complete minimal Xcode project and scoped diagnostics are attached to the report. Is there anything missing from this setup, or a supported way to diagnose the unresolved shortcut reference? Comparisons with other OS builds, languages or devices would be useful. I have not yet established a regression against a stable OS release. Prova Petro minimal source and Siri routing logs
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3w
JEV
(I don't follow the AI stuff here, so sorry if this is a stupid question. Or the wrong category.) There is a new fangled AI mode called JEV. Can the current Apple Intelligence libraries do something like it, or is this a WWDC27 thing?
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SIRI AI AND APPLE INTELLIGENCE
Since I updated to this os 27 in my iPhone 16 plus the siri ai and Apple Intelligence is got freeze in “ Adding support for Siri is in progress. Siri will be unavailable until the update is complete. “ I updated on 21/09/2026 today date is 30/09/2026 I tried all troubleshooting methods and watch a bunch of YouTube videos, but still stuck in the same position. I contacted Apple support. But , they also do nothing. Please anyone help me to how to get siri ai beta @appleindia @applesupport
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Sep ’26
Create ML Object Tracking training stuck at 97.4% - When pressing 'Resume Training' it PAUSES automatically after 3 seconds
Hi, I’m training an Object Tracking reference object in Create ML using Extended training mode + All Angles for use with visionOS high-frame-rate object tracking. The training ran successfully for approximately 48 hours and reached 97.4%. At that point, Create ML automatically paused. The issue is now completely reproducible: whenever I click Resume, training runs for approximately 3 seconds and then automatically returns to Paused at exactly 97.4%. I captured the system logs while reproducing the issue. MLRecipeExecutionService crashes with: LayerVariable.swift:116: Fatal error: The new value must have the same shape as the current value ([40, 1, 3, 3]), but it has [96, 1, 3, 3]. Immediately afterwards, Create ML reports an interrupted XPC connection to MLRecipeExecutionService. Shortly before the crash, the service also logs: Detector training already finished but its loss file was removed from the cache; reporting loss as 0. and: Error cleaning up detector images: NSCocoaErrorDomain Code=4 NSPOSIXErrorDomain Code=2 "No such file or directory" I would really like to avoid discarding ~48 hours of training if the existing tracker/detector checkpoints can still be recovered. Thanks!
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361
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Sep ’26
Exploring Apple Silicon + MLX for a persistent local AI companion architecture
I’m developing an independent project in Scotland called Isla Watson. The architecture is built around a simple principle: the model is replaceable; the identity is not. Long-term memory, persistent internal state and identity are designed to remain outside the foundation model, allowing local models to act as replaceable reasoning and language components without resetting the companion. I’m now exploring whether Apple Silicon and MLX could provide the long-term local compute platform for the system — including specialist Mac nodes for reasoning, memory, speech and perception, with distributed inference when larger models are required. A particular area of interest is whether multiple Macs can be used in two complementary ways: as independent specialist agents during normal operation; and as a distributed MLX inference group when a larger model exceeds the capacity of one machine. The first technical study I’d like to establish is a reproducible 1-node → 2-node baseline, measuring model capacity, unified-memory use, time to first token, generation throughput, power consumption, agent concurrency and distributed scaling efficiency. The wider research goal is to keep persistent identity and state independent from whichever foundation model is currently providing language and reasoning. I’d particularly value guidance from anyone working with MLX distributed inference, Thunderbolt/RDMA multi-Mac setups, or local agent architectures. I’ve also posted an architecture-level overview in the MLX GitHub community and have a one-page public brief available for anyone interested in the wider design. https://github.com/ml-explore/mlx/discussions/4482
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Sep ’26
New Siri AI Stuck
I’ve tried every online recommendation but nothing works, someone helps me out , my WiFi is pretty good, 100GB storage space and I have left the phone plugged in for 5 consecutive days
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Sep ’26