Kind of my fear is that the industry and dev community will ignore new frameworks, languages, architectures etc because the LLM aren't trained on those new things.

For example low level converging to Rust, web frontends to something like React etc.

Agreed. Even if Python or JS was the language I knew well, even if the platform ecosystem is the one I need, I'd _still_ make very sure to use at least strong types (even if not static) for anything an AI co-creates and is maintained longer term.

Rust isn't perfect due to rather long turnaround for compile/test iterations, but a lot of those can be avoided if the type checking is quicker than compilation. Rust is also more verbose than python and other very high level languages, which means your token budget is eaten more quickly as it works on a lower level.

One underrated advantage of using Python or Typescript is that AI agents can inspect the code of installed dependencies.

This means you don't have to muck around with supplying the right documentation for each version of each dependency, or worry about hallucinated interfaces (at least with the latest models).

In the past you'd have to dig through a foreign codebase manually to figure out why a documented interface for a dependency is not working as expected, but frontier models automate that quite well.

This post resonates. I recently built a little web service to scratch an itch I've been having and after discussing the options with Claude we settled on Go, and honestly it's been fantastic. Highly performant, native threading, dead simple to deploy with containers. And I don't even know how to read or write Go.

Honestly use code that you're familiar with. Being able to understand and debug is critical even with AI as it can fall into a weird loop

> Andreas Kling, creator of the Ladybird browser and a career C++ engineer, ported Ladybird’s JavaScript engine from C++ to Rust in two weeks

Discussed here with 698 comments (https://news.ycombinator.com/item?id=47120899)

Really glad to see someone asking this question. After building a fairly significant AI tool using Python tools, I really wish AI/ML tools would all be rewritten to use an actually performant language - say, Rust - without transitive dependency hell on all the package versions.

I know a couple languages fairly well: C, Perl, Python, Bash. I never formally learned Go, but as a test of AI coding, I started some vibe coded projects in Go. It worked very well: the code is minimal, there's few dependencies, and it compiles down to a static app. But most importantly, I can actually read the Go code and understand basically what it's doing. I can also use LLMs to critique the code if I'm uncertain. The big benefit of Go is the simpler language and "batteries included" standard library. This leads to fewer dependencies and less lines of code, which improves overall AI output. In theory, AI should be able to write better code faster in Go than in another language like Rust.

Python does have a much larger ecosystem of course, so with Go you have to develop from scratch what already exists in Python. But for smaller projects, you can also have an AI write a clean-room implementation in Go of some project in Python. So you aren't necessarily locked into one ecosystem anymore.

And in my experience, you don't even need to know the language. I have a co-worker who's basically not a programmer, but got multiple implementations of applications working sooner than our dev teams doing it by hand. You should be a coder so you can architect and orchestrate the coding, but 'language' isn't a barrier anymore.

Actually, I do use compiled languages for this reason. Even Opus 4.7 and GPT-5.5 will leave unassigned variables lying around in Python code of sufficient size. If you've got sufficient testing you'll exercise all paths, and I imagine a good prompt would ensure adding testing with coverage to see that it does happen. However, I do not have (yet) such a system but using Go/Rust helps a lot because the compile phase actually helps detect correctness issues.

My other problem with most of the other ecosystems: ts/npm, python/uv, rust/cargo is that they all have build-time scripts that are controlled by others that execute automatically. This is a real problem because the LLM will just install things and proceed to send your home directory through a juicer. I feel a bit of a paranoiac now doing this, but I have a script that launches a podman container with just the source directory and a binary directory loaded (for caching) which compiles everything.

I know there's some sequence of steps I can take to protect myself, but if the LLM accidentally uses pnpm to run dev build scripts when I had the right config on npm or whatever, I know I'm screwed. So now I do all these shenanigans with Rust (to the extent that I vendor old deps sometimes). So the ideal language to me now is one with very few of these footguns and sandtraps which has a tight iteration loop.

I'm still not sure. Would love thoguhts on this.. but in this new ai world we are in... is it better to go fullstack typescript? or go with proven mature frameworks? .net, ruby, django, etc? Seems TS is moving fast but maybe its time to not reach for the shiny object and stick with proven tech? or in 5 years will we regret it?