Hello,
I am testing the sample project provided here: Bringing advanced speech-to-text capabilities to your app.
On both macOS 26.0 beta and iOS 26.0 beta, the app crashes immediately on launch with a dyld "Symbol not found" error related to FoundationModels.framework.
It feels like this may be related to testing primarily on newer Apple Silicon devices, as I am seeing consistent crashes on an Intel MacBook and on an older iPhone device.
I would appreciate any insight, confirmation, or guidance on whether this is a known limitation or if there is a workaround. Is it planned to be resolved soon?
Environment
macOS:
Device: MacBook Pro (Intel)
Processor: 2 GHz Quad-Core Intel Core i5
Graphics: Intel Iris Plus Graphics 1536 MB
Memory: 16 GB 3733 MHz LPDDR4X
OS: macOS Tahoe Version 26.0 Beta (25A5338b)
iOS:
Device: iPhone 11
Model Number: MHDD3HN/A
OS: iOS 26.0
Xcode:
Version: 26.0 beta 3 (17A5276g)
Crash (macOS)
Abort signal received. Excerpt from crash dump:
dyld`__abort_with_payload:
0x7ff80e3ad4a0 <+0>: movl $0x2000209, %eax
0x7ff80e3ad4a5 <+5>: movq %rcx, %r10
0x7ff80e3ad4a8 <+8>: syscall
-> 0x7ff80e3ad4aa <+10>: jae 0x7ff80e3ad4b4
Console:
dyld[9819]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC
Referenced from: /Users/userx/Library/Developer/Xcode/DerivedData/SwiftTranscriptionSampleApp-*/Build/Products/Debug/SwiftTranscriptionSampleApp.app/Contents/MacOS/SwiftTranscriptionSampleApp.debug.dylib
Expected in: /System/Library/Frameworks/FoundationModels.framework/Versions/A/FoundationModels
Crash (iOS)
Abort signal received. Excerpt from crash dump:
dyld`__abort_with_payload:
0x18f22b4b0 <+0>: mov x16, #0x209
0x18f22b4b4 <+4>: svc #0x80
-> 0x18f22b4b8 <+8>: b.lo 0x18f22b4d8
Console
dyld[2080]: Symbol not found: _$s16FoundationModels20LanguageModelSessionC5model10guardrails5tools12instructionsAcA06SystemcD0C_AC10GuardrailsVSayAA4Tool_pGAA12InstructionsVSgtcfC
Referenced from: /private/var/containers/Bundle/Application/.../SwiftTranscriptionSampleApp.app/SwiftTranscriptionSampleApp.debug.dylib
Expected in: /System/Library/Frameworks/FoundationModels.framework/FoundationModels
Question
Is this crash expected on Intel Macs and older iPhone models with the beta SDKs?
Is there an official statement on whether macOS 26.x releases support Intel, or it exists only until macOS 26.1?
Any suggested workarounds for testing this sample project on current hardware?
Is this a known limitation for the 26.0 beta, and if so, should we expect a fix in 26.0 or only in subsequent releases?
Attaching screenshots for reference.
Thank you in advance.
Explore the power of machine learning and Apple Intelligence within apps. Discuss integrating features, share best practices, and explore the possibilities for your app here.
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In the name of God, please allow initializing GeneratedContent from an array of key-value pairs. It’s literally the same thing KeyValuePairs uses internally, but it would let us initialize structure-like GeneratedContent from dynamic data without resorting to unsafeBitCast hacks.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I'm using Xcode 26 Beta 5 and get errors on any generation I try, however harmless, when wrapped in the #Playground macro.
#Playground {
let session = LanguageModelSession()
let topic = "pandas"
let prompt = "Write a safe and respectful story about (topic)."
let response = try await session.respond(to: prompt)
Not seeing any issues on simulator or device. Anyone else seeing this or have any ideas?
Thanks for any help!
Version 26.0 beta 5 (17A5295f)
macOS 26.0 Beta (25A5316i)
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I am attempting to install Tensorflow on my M1 and I seem to be unable to find the correct matching versions of jax, jaxlib and numpy to make it all work.
I am in Bash, because the default shell gave me issues.
I downgraded to python 3.10, because with 3.13, I could not do anything right.
Current actions:
bash-3.2$ python3.10 -m venv ~/venv-metal
bash-3.2$ python --version
Python 3.10.16
python3.10 -m venv ~/venv-metal
source ~/venv-metal/bin/activate
python -m pip install -U pip
python -m pip install tensorflow-macos
And here, I keep running tnto errors like:
(venv-metal):~$ pip install tensorflow-macos tensorflow-metal
ERROR: Could not find a version that satisfies the requirement tensorflow-macos (from versions: none)
ERROR: No matching distribution found for tensorflow-macos
What is wrong here?
How can I fix that?
It seems like the system wants to use the x86 version of python ... which can't be right.
Trying the Foundation Model framework and when I try to run several sessions in a loop, I'm getting a thrown error that I'm hitting a rate limit.
Are these rate limits documented? What's the best practice here?
I'm trying to run the models against new content downloaded from a web service where I might get ~200 items in a given download. They're relatively small but there can be that many that want to be processed in a loop.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Hi everyone,
I’m an AI engineer working on autonomous AI agents and exploring ways to integrate them into the Apple ecosystem, especially via Siri and Apple Intelligence.
I was impressed by Apple’s integration of ChatGPT and its privacy-first design, but I’m curious to know:
• Are there plans to support third-party LLMs?
• Could Siri or Apple Intelligence call external AI agents or allow extensions to plug in alternative models for reasoning, scheduling, or proactive suggestions?
I’m particularly interested in building event-driven, voice-triggered workflows where Apple Intelligence could act as a front-end for more complex autonomous systems (possibly local or cloud-based).
This kind of extensibility would open up incredible opportunities for personalized, privacy-friendly use cases — while aligning with Apple’s system architecture.
Is anything like this on the roadmap? Or is there a suggested way to prototype such integrations today?
Thanks in advance for any thoughts or pointers!
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
Tags:
SiriKit
Machine Learning
Apple Intelligence
I am writing to inquire about content exclusion capabilities within Apple Intelligence, particularly regarding the use of configuration files such as .aiignore or .aiexclude—similar to what exists in other AI-assisted coding tools. These mechanisms are highly valuable in managing what content AI systems can access, especially in environments that involve sensitive code or proprietary frameworks.
I would appreciate it if anyone could clarify whether Apple Intelligence currently supports any exclusion configuration for AI-assisted features. If so, could you kindly provide documentation or guidance on how developers can implement these controls?
If not, Is there any plan to include such feature in future updates?
I'm interested in using Foundation Models to act as an AI support agent for our extensive in-app documentation. We have many pages of in-app documents, which the user can currently search, but it would be great to use Foundation Models to let the user get answers to arbitrary questions.
Is this possible with the current version of Foundation Models? It seems like the way to add new context to the model is with the instructions parameter on LanguageModelSession. As I understand it, the combined instructions and prompt need to consume less than 4096 tokens.
That definitely wouldn't be enough for the amount of documentation I want the agent to be able to refer to. Is there another way of doing this, maybe as a series of recursive queries? If there is a solution based on multiple queries, should I expect this to be fast enough for interactive use?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I have a Generable type with many elements. I am using a stream() to incrementally process the output (Generable.PartiallyGenerated?) content.
At the end, I want to pass the final version (not partially generated) to another function.
I cannot seem to find a good way to convert from a MyGenerable.PartiallyGenerated to a MyGenerable.
Am I missing some functionality in the APIs?
I have rewatched WWDC22 a few times , but still not getting full understanding how to get .mlmodel model file type from components .
Example with banana ripeness is cool , but what need to be added to actually have output of .mlmodel , is somewhere full sample code for this type of modular project ?
Code is from [https://developer.apple.com/videos/play/wwdc2022/10019)
import CoreImage
import CreateMLComponents
struct ImageRegressor {
static let trainingDataURL = URL(fileURLWithPath: "~/Desktop/bananas")
static let parametersURL = URL(fileURLWithPath: "~/Desktop/parameters")
static func train() async throws -> some Transformer<CIImage, Float> {
let estimator = ImageFeaturePrint()
.appending(LinearRegressor())
// File name example: banana-5.jpg
let data = try AnnotatedFiles(labeledByNamesAt: trainingDataURL, separator: "-", index: 1, type: .image)
.mapFeatures(ImageReader.read)
.mapAnnotations({ Float($0)! })
let (training, validation) = data.randomSplit(by: 0.8)
let transformer = try await estimator.fitted(to: training, validateOn: validation)
try estimator.write(transformer, to: parametersURL)
return transformer
}
}
I have tried to run it in Mac OS command line type app, Swift-UI but most what I had as output was .pkg with
"pipeline.json,
parameters,
optimizer.json,
optimizer"
I am calling into an app extension from a Safari Web Extension (sendNativeMessage, which in turn results in a call to NSExtensionRequestHandling’s beginRequest). My Safari extension aims to make use of the new foundation models for some of the features it provides.
In my testing, I hit the rate limit by sending 4 requests, waiting 30 seconds between each. This makes the FoundationModels framework (which would otherwise serve my use case perfectly well) unusable in this context, because the model is called in response to user input, and this rate of user input is perfectly plausible in a real world scenario.
The error thrown as a result of the rate limit is “Safety guardrail was triggered after consecutive failures during streaming.", but looking at the system logs in Console.app shows the rate limit as the real culprit.
My suggestions:
Please introduce sensible rate limits for app extensions, through an entitlement if need be. If it is rate limited to 1 request per every couple of seconds, that would already fix the issue for me.
Please document the rate limit.
Please make the thrown error reflect that it is the result of a rate limit and not a generic guardrail violation. IMPORTANT: please indicate in the thrown error when it is safe to try again.
Filed a feedback here: FB18332004
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Attempted to download the Adapter Toolkit linked to from https://developer.apple.com/apple-intelligence/foundation-models-adapter/. Failed on all attempts, with a "403 Forbidden" error. I had accepted the agreement on the first attempt. How would we get access please?
Hello Apple Developer Community,
I'm exploring the integration of Apple Intelligence features into my mobile application and have a couple of questions regarding the current and upcoming API capabilities:
Custom Prompt Support: Is there a way to pass custom prompts to Apple Intelligence to generate specific inferences? For instance, can we provide a unique prompt to the Writing Tools or Image Playground APIs to obtain tailored outputs?
Direct Inference Capabilities: Beyond the predefined functionalities like text rewriting or image generation, does Apple Intelligence offer APIs that allow for more generalized inference tasks based on custom inputs?
I understand that Apple has provided APIs such as Writing Tools, Image Playground, and Genmoji. However, I'm interested in understanding the extent of customization and flexibility these APIs offer, especially concerning custom prompts and generalized inference.
Additionally, are there any plans or timelines for expanding these capabilities, perhaps with the introduction of new SDKs or frameworks that allow deeper integration and customization?
Any insights, documentation links, or experiences shared would be greatly appreciated.
Thank you in advance for your assistance!
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
Lately I am getting this error.
GenerativeModelsAvailability.Parameters: Initialized with invalid language code: en-GB. Expected to receive two-letter ISO 639 code. e.g. 'zh' or 'en'. Falling back to: en
Does anyone know what this is and how it can be resolved. The error does not crash the app
When using Foundation Models, is it possible to ask the model to produce output in a specific language, apart from giving an instruction like "Provide answers in ." ? (I tried that and it kind of worked, but it seems fragile.)
I haven't noticed an API to do so and have a use-case where the output should be in a user-selectable language that is not the current system language.
Hello,
I'm trying to write a model with PyTorch and convert it to CoreML.
I wrote another models and that works succesfully, even the one that gave the problem is, but I can't visualize it with XCode to know where is running.
The error that appear is:
There was a problem decoding this Core ML document
validator error: unable to open file for read
Anyone knows why is this happening?
Thanks a lot,
Álvaro Corrochano
Topic:
Machine Learning & AI
SubTopic:
Core ML
I generate an array of random floats using the code shown below. However, I would like to do this with Double instead of Float. Are there any BNNS random number generators for double values, something like BNNSRandomFillUniformDouble? If not, is there a way I can convert BNNSNDArrayDescriptor from float to double?
import Accelerate
let n = 100_000_000
let result = Array<Float>(unsafeUninitializedCapacity: n) { buffer, initCount in
var descriptor = BNNSNDArrayDescriptor(data: buffer, shape: .vector(n))!
let randomGenerator = BNNSCreateRandomGenerator(BNNSRandomGeneratorMethodAES_CTR, nil)
BNNSRandomFillUniformFloat(randomGenerator, &descriptor, 0, 1)
initCount = n
}
Apple's Image Playground primarily performs image generation on-device, but can use secure Private Cloud Compute for more complex requests that require larger models. Private Cloud Compute (PCC)
For more complex tasks that require greater computational power than the device can provide, Image Playground leverages Apple's Private Cloud Compute. This system extends the privacy and security of the device to the cloud:
Secure Environment: PCC runs on Apple silicon servers and uses a secure enclave to protect data, ensuring requests are processed in a verified, secure environment.
No Data Storage: Data is never stored or made accessible to Apple when using PCC; it is used only to fulfill the specific request.
Independent Verification: Independent experts are able to inspect the code running on these servers to verify Apple's privacy promises.
Hello Apple Team,
Thank you for the recent Group Lab and for your continued work on advancing Xcode and developer tools.
I’d like to submit a feature request:
Are there any plans to introduce support for Agentic AI Mode (MCP protocol) in future versions of iOS or Xcode?
As developer tools evolve toward more intelligent and context-aware environments, the integration of agentic AI capabilities could significantly enhance productivity and unlock new creative workflows.
Looking forward to your consideration, and thank you again for the excellent session.
Best regards
I have an app that stores lots of data that is of interest to the user. Analogies would be the Photos apps or the Health app.
I'm trying to use the Foundation Models framework to allow users to surface information they find interesting using natural language, for example, "Tell me about the widgets from yesterday" or "Tell me about the widgets for the last 3 days". Specifically, I'm trying to get a date range passed down to the Tool so that I can pull the relevant widgets from the database in the call function.
What is the right way to set up the Arguments to get at a date range?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models