Comment by deaddodo

Pretty good at Java, the verbose language, strong type system, and strong static analysis tools that you can run on every edit combine to keep it on the tracks you define

Maybe I should have made it more clear, but it's pretty good if you know how to work with it. The issue is that it's usually faster to just read the documentation and write the code yourself. Depending on what you're working on of course. Like with the yaml, a LLM can write you an ingress config in a second or two from a very short prompt. It can do similar things with Python if you specify exactly how you want something and what dependencies you want.

That's being bad at programming in my opinion. You can mitigate it a lot with how you config you agents. Mine loads our tech stack. The best practices we've decided to use. The fact that I value safety first but am otherwise a fan of the YAGNI philosophy and so on. I spent a little time and build these things into my personal agent on our enterprise AI plan, and I use it a lot. I still have to watch it like a hawk, but I do think it's a great tool.

I guess you could say that your standard LLM will write better Python than I did 10 years ago, but that's not really good enough when you work on systems which can't fail. It's fine on 90% (I made this number up) of software though.

I've had good results with TypeScript. I use a tested project template + .md files as well as ESLint + Stylelint and each project generally turns out pretty clean.

But that was a huge assertion in itself. I’m personally having amazing results with Python in Opus 4.5, so this is very contextual.

One thing copilot seems to be good at for me is python. Other, older languages like VB.NET I found it struggled with.

I did find (weirdly) that it improved when running on WSL rather than windows.

However I did get it to code a script for downloading SharePoint files and even got it to reduce the dependencies down to built-ins which was a massive time saver

It's kinda okay at JS + React + Tailwind. (at least, for reasonably small / not-crazy-complex projects)

Pretty darned good at C++ and typescript too.

Well, OP bar seems super high. Because it isn't entirely perfect in order to allow a non-dev to create apps that doesn't make them "pretty bad" imo.

> So far Opus 4.5 has been the one LLM that keeps mostly coding in a, how to say, predictable way even with a existing code base.

I find this to be true only if you have very explicit rules in CLAUDE.md and even then it still messes up.

I have "you will use the shared code <here>" twice in my CLAUDE.md as it will constantly write duplicate code.

Something that is also annoying is that if it moves some code somewhere with the intent to slightly modify it I've seen it delete the code, then implement from scratch, and then modify it to what it has been specified to do. This completely breaks tests. I then have to say "look at this earlier commit - you've caused a complete regression."

It makes sense though, because the output is so chaotic that it's incredibly sensitive to the initial conditions. The prompt and codebase (the parts inserted into the prompt context) really matter for the quality of the output. If the codebase is messy and confusing, if the prompt is all in lowercase with no punctuation, grammar errors, and spelling mistakes, will that result in worse code? It seems extremely likely to me that the answer is yes. That's just how these things work. If there's bad code already, it biases it to complete more bad code.

Python is very versatile so it's probably a case of the dev not preferring the Python the model produced vs their own. I bet a lot of GenAI created C falls into the same bucket. "..well that's not how i would have done it.."

I use it in a Python/TS codebase (series D B2B SaaS with some AI agent features). It can usually “make it work” in one shot, but the code often requires cleanup.

I start every new feature w/Claude Code in plan mode. I give it the first step, point it to relevant source files, and tell it to generate a plan. I go catch up on my Slack messages.

I check back in and iterate on the plan until I’m happy, then tell it to implement.

I go to a team meeting.

I come back and review all the code. Anything I don’t 100% understand I ask Gemini to explain. I cross-check with primary sources if it’s important.

I tweak the generated code by hand (faster than talking with the agent), then switch back to plan mode and ask for specific tests. I almost always need to clean up the tests for doing way too much manual setup, despite a lot of Claude.md instructions to the contrary.

In the end, I probably get the work done in 30% less wall-clock time of Claude implementing (counting plan time), but I’m also doing other things while the agent crunches. Maybe 50% speed boost in total productivity? I also learn something new on about a third of features, which is way more than I did before.

> why I don't trust or use AI when I code

These are two different concepts. I use AI when coding, but I don't trust it. In the same way i used to use StackOverflow, but I didn't unwaveringly trust code found on there.

I still need to test and make sure the code does the thing I wanted it to do.

I’ve found it to be quite good at Python, JS (Next + Tailwind + TS type of things), and PHP. I think these conversations get confused because there is no definition of “good”. So I’m defining “good” as it can do 50-80% of the work for me, even in a giant code base where call sites are scattered and ever changing. I still have to do some clean up or ask it to do something different, but many times I don’t need to do anything.

As someone else mentions, the best working mode is to think through your problem, write some instructions, and let it do it’s thing while you do other work. Then come back and treat that as a starting point.

I find both chatgpt and Gemini to be very good at writing c++ for Arduino/esp32. Certainly better than me unassisted. Compile errors are very rare, and usually they are just missing declarations. Right now I would say chatgpt is ahead for daily driver use but sometimes Gemini can instantly unlock things that chatgpt is stuck on.

Yeah that list has left me wondering, then what is it good at? HTML, CSS and JavaScript?

True. I do however like the process of working with an AI more in a language like Rust. It's a lot less prone to use ugly hacks to make something that compiles but fail spectacularly at runtime - usually because it can't get the ugly hacks to compile :D

Makes it easier to intercede to steer the AI in the right direction.

How is this an issue specifically with Rust and Haskell? Do you find that LLMs have an easier time proving global correctness with C, Python, or Typescript?

Luckily that's the compiler's job.

Sounds like rubocop though. I used that years ago to update an old legacy ruby codebase. Is that still AI?

Sounds like regular C programming, lol. On a serious note, give Opus 4.5 a try, maybe it would feel better. I’ve experimented with C the other week and it was quite fun. Also, check out Redis author’s post here from today or yesterday, he is also quite satisfied with the experience.