metal-cpp VS cortex.cpp

My understanding is the proliferation of “XYZ-cpp” AI frameworks is due to the c++ support in Apple’s gpu library ‘Metal’, and the popularity of apple silicon for inference (and there are a few technical reasons for this): https://developer.apple.com/metal/cpp/

This is basically also what the "official" C++ API for Metal does (https://developer.apple.com/metal/cpp/), it's an automatically generated bindings wrapper which calls into ObjC runtime functions.

I also dabbled a bit with this idea by parsing clang AST-dumps of macOS system headers:

https://github.com/floooh/objc-ast-experiments

Unfortunately this is very brittle, and also broke on ARM CPUs, I guess the shim code needs some ABI adjustments (famously, objc_msgSend has multiple "ABI shapes": https://www.mikeash.com/pyblog/objc_msgsends-new-prototype.h...).

There's official C++-interface: https://developer.apple.com/metal/cpp/

It doesn't matter if the project driven by Microsoft or not, the cat (of automatically generated language bindings) is out of the bag. E.g. Zig is using the same approach without being an official MS project: https://github.com/marlersoft/zigwin32, and Apple has an automatically generated C++ API for Metal (https://developer.apple.com/metal/cpp/).

In the future, the question won't be "what language do I need to learn to code on this platform", but instead "are there language bindings for my favourite language".

this? https://developer.apple.com/metal/cpp/

Doesn't it just use objc/runtime.h and if anything is missing you can just add your custom api calls?

Metal has C++ bindings (which cover a full app lifecycle so you don’t have to touch Objective-C/Swift at all) but they’re based on the Objective-C memory model. There are some helper structs mimicking shared pointers, but you’ll still need to understand the basics of how an autorelease pool is used to avoid memory leaks and/or bad access crashes.

On https://developer.apple.com/metal/cpp/ check Foundation folder and all those nice Object::sendMessage().

There is https://cortex.so/ that I’m looking forward too.

This is the one you are looking for. https://github.com/janhq/cortex

Jan incorporates a lightweight, built-in inference server called Nitro. Nitro supports both llama.cpp and NVIDIA's TensorRT-LLM engines. This means many open LLMs in the GGUF format are supported. Jan's Model Hub is designed for easy installation of pre-configured models but it also allows you to install virtually any model from Hugging Face or even your own.

I'd like to see a comparison to nitro https://github.com/janhq/nitro which has been fantastic for running a local LLM.