Hi,
I have an app that uses Core Data to store user information and display it in various views. I want to know if it's possible to easily integrate this setup with FoundationModels to make it easier for the user to query and manipulate the information, and if so, how would I go about it? Can the model be pointed to the database schema file and the SQLite file sitting in the user's app group container to parse out the information needed? And/or should the NSManagedObjects be made @Generable for better output? Any guidance about this would be useful.
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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Hello. I am willing to hire game developer for cards game called baloot. My question is Can the developer implement an AI when the computer is playing and the computer on the same time the conputer improves his rises level without any interaction?
🌹
Topic:
Machine Learning & AI
SubTopic:
General
Hi Apple team,
When using AppShortcutsProvider, I hit the hard limit:
Each app may have at most 10 App Shortcuts.
This feels limiting for apps that offer multiple workflows and would benefit from deeper Siri integration.
Could this cap be raised — ideally to 30 — to support broader use of AppIntents, enhance Siri automation, and unlock more system-level capabilities?
AppShortcuts are a fantastic tool. Increasing the limit would make them even more powerful.
Thanks!
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
Tags:
Shortcuts
App Intents
Apple Intelligence
I downloaded the new developer beta and then installed xcode. I did the downloads but I couldn't download the Predictive Code Completion Model. When I try to download it I get the error "The operation couldn’t be completed. (ModelCatalog.CatalogErrors.AssetErrors error 1.)". I am using the M3 Pro model.
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
I've downloaded the Xcode-beta and run the sample project "FoundationModelsTripPlanner" but I got this error when trying generate the response.
InferenceError::inferenceFailed::Error Domain=com.apple.UnifiedAssetFramework Code=5000 "There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog" UserInfo={NSLocalizedFailureReason=There are no underlying assets (neither atomic instance nor asset roots) for consistency token for asset set com.apple.modelcatalog}
Device: M1 Pro
Question:
Is it because M1 not supporting this feature?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
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
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
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
I've run into an issue with a small Foundation Models test with Generable. I'm getting a strange error message with this Generable. I was able to get simpler ones to work.
Is this because the Generable is recursive with a property of [HTMLDiv]?
The error message is:
FoundationModels/SchemaAugmentor.swift:209: Fatal error: 'try!' expression unexpectedly raised an error: FoundationModels.GenerationSchema.SchemaError.undefinedReferences(schema: Optional("SafeResponse<HTMLDiv>"), references: ["HTMLDiv"], context: FoundationModels.GenerationSchema.SchemaError.Context(debugDescription: "Undefined types: [HTMLDiv]", underlyingErrors: []))
The code is:
import FoundationModels
import Playgrounds
@Generable
struct HTMLDiv {
@Guide(description: "Optional named ID, useful for nicknames")
var id: String? = nil
@Guide(description: "Optional visible HTML text")
var textContent: String? = nil
@Guide(description: "Any child elements", .count(0...10))
var children: [HTMLDiv] = []
static var sample: HTMLDiv {
HTMLDiv(
id: "profileToolbar",
children: [
HTMLDiv(textContent: "Log in"),
HTMLDiv(textContent: "Sign up"),
]
)
}
}
#Playground {
do {
let session = LanguageModelSession {
"Your job is to generate simple HTML markup"
"Here is an example response to the prompt: 'Make a profile toolbar':"
HTMLDiv.sample
}
let response = try await session.respond(
to: "Make a sign up form",
generating: HTMLDiv.self
)
print(response.content)
} catch {
print(error)
}
}
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
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
}
Hey guys 👋
I’ve been thinking about a feature idea for iOS that could totally change the way we interact with apps like Twitter/X.
Imagine if we could define our own recommendation algorithm, and have an AI on the iPhone that replaces the suggested tweets in the feed with ones that match our personal interests — based on public tweets, and without hacking anything.
Kinda like a personalized "AI skin" over the app that curates content you actually care about. Feels like this would make content way more relevant and less algorithmically manipulative.
Would love to know what you all think — and if Apple could pull this off 🔥
Topic:
Machine Learning & AI
SubTopic:
General
I downloaded Xcode Beta 1 on my mac (did not upgrade the OS). The target OS level of iOS26 and the device simulator for iOS26 is downloaded and selected as the target.
When I try a simple Playground in Xcode ( #Playground ) I get a session error.
#Playground {
let avail = SystemLanguageModel.default.availability
if avail != .available {
print("SystemLanguageModel not available")
return
}
let session = LanguageModelSession()
do {
let response = try await session.respond(to: "Create a recipe for apple pie")
} catch {
print(error)
}
}
The error I get is:
Asset com.apple.gm.safety_deny_input.foundation_models.framework.api not found in Model Catalog
Is there a way to test drive the FoundationModel code without upgrading to macos26?
How reliable is the Models, to use as a comparison, such as a cholesterol test, to inform, for example, whether it is worth it to go see a doctor?
I would like to use Tool to attach the simple blood test data to the session and with this the Model can analyse and made a simple suggestion if is necessary to see a doctor etc.. ?
ps.: Local model
iOS26 is supported by a wider range of devices than are able to run AI, e.g iPhone 12 runs iOS26, but does not support AI.
How do we determine in code if AI is supported on a device ?
How do we determine what features use AI under the hood ?
Thanks,
Steve.
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
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
Hello,
We find that models sometimes load very fast (<< 1 second) and sometimes encounter very long load times (>> 120 seconds). During such slow load times, the model is being compiled.
We would greatly appreciate the ability to check cache validity via CoreML and determine that we are about to encounter long load times so that we can mitigate and provide a good user experience.
A secondary issue: sometimes the cache is corrupted (typically .mpsgraphpackage yielding Metal cold asserts). This yields load failures and OS errors that persist between launches, and we have to manually nuke the cache (~/Library/..../my-app/...) for the CoreML assets. A CoreML API for clearing caches and hardening from asserts across the load paths would be appreciated
Topic:
Machine Learning & AI
SubTopic:
Core ML
Hi, I'm looking for the best way to use MLX models, particularly those I've fine-tuned, within a React Native application on iOS devices. Is there a recommended integration path or specific API for bridging MLX's capabilities to React Native for deployment on iPhones and iPads?
Posting a follow up question after the WWDC 2025 Machine Learning AI & Frameworks Group Lab on June 12.
In regards to the on-device API of any of the AI frameworks (foundation model, vision framework, ect.), is there a response condition or path where the API outsources it's input to ChatGPT if the user has allowed this like Siri does?
Ignore this if it's a no: is this handled behind the scenes or by the developer?
Topic:
Machine Learning & AI
SubTopic:
Apple Intelligence
Tags:
Machine Learning
VisionKit
Apple Intelligence
Is it possible to train an Adaptor for the Foundation Models to produce Generable output? If so what would the response part of the training data need to look like? Presumably, under the hood, the model is outputting JSON (or some other similar structure) that can be decoded to a Generable type. Would the response part of the training data for an Adaptor need to be in that structured format?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
I am experimenting with Foundation Models in my time tracking app to analyze users tracked events, but I am finding that the model struggles with even basic computation of time. Specifically converting from seconds to hours and minutes.
To give just one example, when I prompt:
"Convert 3672 seconds to hours, minutes, and seconds. Don't include the calculations in the resulting output"
I get this:
"3672 seconds is equal to 1 hour, 0 minutes, and 36 seconds".
Which is clearly wrong - it should be 1 hour, 1 minute, and 12 seconds. Another issue that I saw a lot is that seconds were considered to be minutes, or that the hours were just completely off.
What can I do to make the support for math better? Or is that just something that the model is not meant to be used for?
Topic:
Machine Learning & AI
SubTopic:
Foundation Models
Is anything configurable for LanguageModelSession.Guardrails besides the default? I'm prototyping a camping app, and it's constantly slamming into guardrail errors when I use the new foundation model interface. Any subjects relating to fishing, survival, etc. won't generate.
For example the prompt "How can I kill deer ticks using a clothing treatment?" returns a generation error.
The results that I get are great when it works, but so far the local model sessions are extremely unreliable.
Topic:
Machine Learning & AI
SubTopic:
Foundation Models