I'm sure there are plenty of caveats and breaking points, but if we do adhere to the claim that an LLM coding tool is a nondeterministic sort of compiler then it really does make sense to pick the most performant language available. Obviously there are caveats of libraries and native advantages of various languages. I've been doing stuff in C++ for the past month or so and the only slow down from the language choice is compilation time.

As others have said, the main benefit with Python over Rust is library support especially with ML features. The other gap as I see it with Rust is the lack of native flexible UI support. The nice thing about Rust though is it can serve as a very fast and stable core for an app and offload specifics to TS and Python as their strengths allow, so you get the best of all worlds.

My current goto for desktop apps is Tauri, which give us a rust backend and TS fronted (usually React). Local ML features can be easily loaded as a python sidecar. Production bundling can be a little challenging but it seems to work well so far.

Sidenote: Golang is also an amazing language for LLM use, I generally do most of my "infra" stuff in Golang over Rust, but either work fine most of the time.

Python is the 2nd/3rd best language for almost everything. So I guess it helps.

i would say that object pascal is the clear winner in terms of readability, performance and ease of review bc of the static types.

I had agents code up an app for me in Swift a while back and the entire experience was so much better than your typical Python experience. The agents took full advantage of the compiler and static typing. There were far fewer bugs than expected.

Most of the article makese sense but what is this supposed to mean? "Native Rust binaries are hostile to serverless runtimes" . I don't think that is true.

object pascal is by far superior in combining readability and performance. also the static type system is a huge bonus

If you're using GenAI, you should go through the process of selecting an optimal tech stack for each solution, but also take into consideration that Claude and other services probably the most knowledge of python, javascript, and typescript with go, rust, java, and c# following closely behind. Consider what you're building and what elements of the tech stack is optimal for your problem-space.

I don't know rust at all and I've built three applications using it with Claude because it has speed and correctness built-in.

I use Typescript for 90% of the things I build. For web development I've used a number of tools, but mostly react, nextjs, or raw html/css/js. But if I were building an enterprise application I'd consider my team and whether opinionated (Angular) was optimal over flexible (React).

Each project should consider its own optimal tech stack.

I find if I ask most LLMs to write a self contained script/utility, even in codebases that are 90-100% written in some other language most will default to using python for it, or sometimes bash.

Usually those kinds of utility scripts are one-shotted without any further input from me, and once they're there and doing what I need I usually don't bother converting them to whatever I would have written them in otherwise (bash would be my usual preference for really small scripts, typescript or rust for bigger utilities, I hate writing python but reading it is fine... kind of).

10-100x faster? Maybe for strictly IO bound applications - but if you are building a web app you won't see that performance as network latency dominates.