Probably true, but one unique thing about Julia is, that exposes almost all stages of the compilation to the user. From typed IR to native code generation you can customise the compilation in many ways. Together with the power of LISP's metaprogramming features, that's a really fine basis for powerful and performamt DSLs and code transformations.

All those GPU targets are powered by libraries, that are not part of Julia itself (GPUCompiler.jl). The same goes for automatic differentiation. That's remarkable in my opinion.

So you're right, that many programming languages could do it, but it's no wonder, that other languages are lacking in this regard compared to Julia.

Nix devShells works quite well for Python development (don't know about JS) Nixpkgs is also quite up to date. I haven't looked back, since adopting Nix for my dev environments.

I struggled with remote deployment + secret management, too. Like a lot of folks, my nix-config grew over the years as I added secrets management, user management etc ad hoc.

I recently found clan.nix [1] and am quite pleased. It's kind of a framework for writing nixos configurations with a focus on multiple devices. It bundles secrets management and remote deployment into a convenient CLI.

It has the concept of "services", which are e.g. used for user management and VPNs. Services define roles, which can be assigned to machines, e.g. the wireguard service has a controller and a peer role. That feels like the right abstraction and it was very easy to set up a VPN with zerotier like that, something I struggled doing myself in the past.

It's a rather young project, but I converted my nix-config repo after a short evaluation phase to use clan. It's worth taking a look for sure.

[1]: https://clan.lol/

Kartoffel is the standard German word.

Erdäpfel is used in many dialects and has plenty of variants.

Actually the various different words for potatoe and their distribution across Germany, Swiss and Austria is linguistically quite interesting (see this map [1]).

The legend is in German and roughly translates to (from top to bottom):

- Potatoes

- Ground pears

- Earth apples

- Earth pears

- Hearth apples

[1]: http://stepbysteplingue.com/wp-content/uploads/2019/02/karto...

I tried different LLMs with various languages so far: Python, C++, Julia, Elixir and JavaScript.

The SOTA models come do a great job for all of them, but if I had to rank the capabilities for each language it would look like this:

JavaScript, Julia > Elixir > Python > C++

That's just a sample size of one, but I suspect, that for all but the most esoteric programming languages there is more than enough code in the training data.