501–510 of 1001 posts
Rust in most cases, especially for back end.
Python when it's low risk (say monitoring dashboard or similar API heavy) or plays to python strengths (e.g. ML/AI - everything ML seems to be python).
Read the first few comments and surprised I didn’t see it, but training data. The voluminous amount of Python in the training data. I could write in brainfuck with ai, but I presume, wouldn’t get the same results than if going with python. My follow up question: with AI now, why care about a lang until you need to?
Surprisingly, LLMs are actually much worse at reasoning in Python than other common programming languages for agentic coding tasks. Data here: https://gertlabs.com/rankings?mode=agentic_coding
GPT 5.5 writes good haskell.
But also, I suspect the article is just wrong. "The hard languages got easy first" isn't true in practice and the impressive examples given are not representative or as magical as the poster makes them out to be.
The takeaway might be right in the end, but the post isn't right in the beginning.
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That's a good idea. Would you rather see Lisp or Scala? Any interest in Prolog? We are trying to be selective to keep the data concentrated, but we will eventually add a couple more, most likely to sample different programming paradigms.
If you are taking request, I was hoping to see clojure on there.
A relative lack of training data might have a bigger effect though.
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That's a good idea. Would you rather see Lisp or Scala? Any interest in Prolog? We are trying to be selective to keep the data concentrated, but we will eventually add a couple more, most likely to sample different programming paradigms.
Just last night I was going down the rabbit hole of "what's the best programming language to use for vibe coding." I came to a short list of: a) Typed Racket b) OCaml c) Julia I would love to see those three added to your benchmarks. And Mistral Medium 3.5 added to the LLM list, please.
How about modern Java? Any experiences?
So I might be biased, but with the correct curation of AGENTS.md files and skills, we're getting extremely good results using Claude Code writing Java.
Another disclaimer: I haven't tried with another language, but we're happy with the results.
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Hah, I was just thinking that Python likely has a vast ocean of training data, but it's likely of lower quality, being much of it is written by beginners and those who aren't primarily programmers.
That was the hardest part of learning PHP, all the code examples online were just awful.
Certain popular PHP codebases appear to use a similar methodology.
One obvious reason is Python's extreme readability, it has often been described as being as close to executable pseudo-code as one can get. If you're using an LLM to write code I think the rules would be 1. Use a language you know really well so you can read it easily, and add to it as needed. 2. Use a language that has a large training set so the LLM can be most efficient. 3. Use a language that is easy to read. If…
1) It's a very consistent language even if you compared to the other popular languages namely Python, Rust, C++ and Go. Try to perform doubly linked list with them and compare them all [1].
2) It's probably the most "Pythonic" among the compiled language according to Walter.
3) It utilizes GC by default, you can also manage your own memory and you can hybrid.
4) It compiled fast and run fast, heck it even has built-in REPL eco-system.
5) Regarding the small training set, with recent self-distillation fine-tuning approach it should be good enough, D (actually D2 version) has been around for more than a decade [2].
[1] Looking for a Simple Doubly Linked List Implementation:
https://forum.dlang.org/thread/osmecwfnpqahoytdqpkr@forum.dl...
[2] Awesome D: