Clang.jl VS julia

If you are interested in C++ interop you can also have a look at Clang.jl and CxxWrap.jl (the usual Julia package chaos applies, where the package mentioned in old talks and docs that you find on google is superseded by some others...)

I'm really not fond of that agpt landing page. So many red flags; the AI-generated background, mailing letter box with accompanying email-beggar text, the Discord button (!!!) being given as much space as the Github repo click-through... it's a mess. The whole website feels more boilerplate than content. I mean, look at these quotes!

> With the help of the incredible open-source community, we’re making approximately a month’s progress every 48 hours.

> Auto-GPT is pushing for the best, autonomous AI assistant for every device for every person. In the near future, we want you to be able to accomplish more everyday.

> We have come to define ourselves by what we do. If this can be automated, how may we then define ourselves? By what we create!

Every line of copy I read from that site makes me feel like I'm getting dumber instead of learning about their product. If you are building a landing page for a serious software project, you need a more professional approach. You can be playful if you want, but the landing page somehow manages to be less informative than the Github repo in the example you've listed.

Since everyone will ask, here are some software project landing pages that strike me as well-designed:

https://julialang.org/

https://nixos.org/

https://www.scheme.org/

See also https://github.com/JuliaLang/julia/pull/50137 and https://github.com/JuliaLang/julia/pull/50013 that improve the sweeping (the linked PR is only the mark phase)

Julia is a great alternative in terms of raw speed/performance (not a compatible language)

If you are really committed to running on Apple hardware then take a look at Tensorflow for macOS. Another option is the Julia programming language which has very basic Metal support at a CUDA-like level. FluxML would be the ML framework in Julia. I’m not sure either option will be painless or let you do everything you could do with a Nvidia GPU.

Lots of this is in progress and doesn't require a 2.0. Better error messages landed last week, see https://twitter.com/ChrisRackauckas/status/1661014235466563591 which was https://github.com/JuliaLang/julia/pull/49795. That makes them a ton more readable, and there's a few more of these kinds of things in process. The parser change is set to make it into v1.10, https://github.com/JuliaLang/julia/pull/46372. v1.10 is supposed to branch in just a few weeks and will possibly be LTS. With that all in mind, there's a time to take stock of what has been a large amount of pretty huge changes and seeing what's next to help Julia improve static compilation.

Many functions from openlibm has already been transitioned to pure Julia, and openlibm is on the path of being removed from Julia, https://github.com/JuliaLang/julia/pull/42299.

Some things are written in Julia. see this folder for trig functions, log, hyperbolic trig, and cube root.

My favorite change (even though it's not listed in the changelog), is that just-in-time compiled code now has frame pointers[1], making Julia code much more debuggable. Profilers, debuggers, etc. all can now work out of the box.

Extra excited that the project I happen to work (the Parca open source project[2] on influenced this change [3][4]. Shout out to Valentin Churavy for driving this on the Julia front!

[1] https://github.com/JuliaLang/julia/commit/06d4cf072db24ca6df...