[MediaSquirrel]

It sounds like your experience differs from mine. I oversaw teams trying to use CoreML in the 2020 - 2024 era who found it very buggy, as per the screenshots I provided.

More recently, I personally tried to convert Kokoro TTS to run on ANE. After performing surgery on the model to run on ANE using CoreML, I ended up with a recurring Xcode crash and reported the bug to Apple (as reported in the post and copied in part below).

What actually worked for me was using MLX-audio, which has been great as there is a whole enthusiastic developer community around the project, in a way that I haven't seen with CoreML. It also seems to be improving rapidly.

In contrast, I have talked to exactly 1 developer who have ever used CoreML since ChatGPT launched, and all that person did was complain about the experience and explain how it inspired them to abandon on-device AI for the cloud.

___ Crash report:

A Core ML model exported as an `mlprogram` with an LSTM layer consistently causes a hard crash (`EXC_BAD_ACCESS` code=2) inside the BNNS framework when `MLModel.prediction()` is called. The crash occurs on M2 Ultra hardware and appears to be a bug in the underlying BNNS kernel for the LSTM or a related operation, as all input tensors have been validated and match the model's expected shape contract. The crash happens regardless of whether the compute unit is set to CPU-only, GPU, or Neural Engine.

*Steps to Reproduce:* 1. Download the attached Core ML models (`kokoro_duration.mlpackage` and `kokoro_synthesizer_3s.mlpackage`) 2. Create a new macOS App project in Xcode. Add the two `.mlpackage` files to the project's "Copy Bundle Resources" build phase. 3. Replace the contents of `ContentView.swift` with the code from `repro.swift`. 4. Build and run the app on an Apple Silicon Mac (tested on M2 Ultra, macOS 15.6.1). 5. Click the "Run Prediction" button in the app.

*Expected Results:* The `MLModel.prediction()` call should complete successfully, returning an `MLFeatureProvider` containing the output waveform. No crash should occur.

*Actual Results:* The application crashes immediately upon calling `model.prediction(from: inputs, options: options)`. The crash is an `EXC_BAD_ACCESS` (code=2) that occurs deep within the Core ML and BNNS frameworks. The backtrace consistently points to `libBNNS.dylib`, indicating a failure in a low-level BNNS kernel during model execution. The crash log is below.

[llm_nerd]

I can't speak to how CoreML worked for you, or how the sharp edges cut. I triply wouldn't comment on ANE, which is an extremely limited bit of hardware mostly targeted at energy efficient running of small, quantized models with a subset of features. For instance extracting text from images.

CoreML is pervasively used throughout iOS and macOS, and this is more extensive than ever in the 25 versions. Zero percent of the system uses MLX for the runtime. The incredibly weird and nonsensical submissions weird contention that because ANE doesn't work for them, therefore Apple is admitting something is just laughable silliness.

And FWIW, people's impressions of the tech world from their own incredibly small bubble is often deeply misleading. I've read so many developers express with utter conviction that no one uses Oracle, no one uses Salesforce, no one uses Windows, no one uses C++, no one uses...

[MediaSquirrel]

In my conversations with people at Apple, my understanding is that they do not use CoreML. Instead, they have access to lower level libraries that allow more direct programmatic control of the hardware.

CoreML is the crappy middleware they made for 3rd party devs, but never got much love and never took off.

Re: ANE — as stated, ANE is crazy fast when it works. Yes, it’s also more power efficient, but the reason I think it’s actually worth building on is being able to make consumer products where the entire experience depends on speed.

I think you can agree that 5 milliseconds to generate a 512 px resolution image is absolutely insane speed.