alt Hacker NewsOne thing I learned about AI is that, e.g., if you're making websites you're much better off going for php/ruby/elixir, even if you don't like the languages much.
Pipelines and deploys gets much easier and faster than the very common TypeScript monorepo, and so does communication between server and client.
And I say that as a TypeScript and Effect aficionado who has no particular love for neither php or ruby, but they are extremely solid choices to move fast, well, and get excellent performance and tooling out of the box.
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PHP has had a lot of conventions so the training data is all over the place. Ruby is a nightmare in that regard. Can't speak to Elixir but I'm surprised to hear someone say LLMs producing good code in either language.
On the other hand, Go code from 2012 and Go code from 2026 looks virtually the same. Conventions are respected, go fmt is the one single formatter, "use the stdlib" is a popular mantra and the code is readable by design.
If I were to codegen a project I wouldn't use anything but Go at this point.
> Pipelines and deploys gets much easier and faster than the very common TypeScript monorepo
Can you elaborate more on the slowness you've seen with deploying TypeScript codebases? My experience is that it's improved significantly over the past decade or so. tsc is still kinda slow for large projects, but the rewrite to Go should improve things significantly. Pre-install/post-install scripts can also be slow, but I've always disabled those and haven't yet run into issues. The JS-written bundlers can also be slow, but I use the non-JS ones (esbuild, parcel, bun, etc.) and they're fast enough for me to not care. The main build bottleneck I can think of for a modern TypeScript project would be build plugins, since those are still written in JS/TS.
The willingness to reshape society for the benefit of AI rather than shaping AI around humanity paints a very dire picture of the coming decade.
Great point! I'd also add that Django's another solid choice for boring tech that LLM agents will know very well and will very likely continue to do so. A fair bit of SWE Bench and other Python benchmarks are Django related tests, which the LLM vendors care very deeply about keeping their scores up. Also, Django's docs are excellent, so strategically pointing an LLM to them in a prompt can often produce great results.