Hacker Newsnew | past | comments | ask | show | jobs | submitlogin

Do you have a link to some of the code that you have produced using this approach? I am yet to see a public or private repo with non-trivial generated code that is not fundamentally flawed.


This one was a huge success:

https://github.com/micahscopes/radix_immutable

I took an existing MIT licensed prefix tree crate and had Claude+Gemini rewrite it to support immutable quickly comparable views. The execution took about one day's work, following two or three weeks thinking about the problem part time. I scoured the prefix tree libraries available in rust, as well as the various existing immutable collections libraries and found that nothing like this existed. I wanted O(1) comparable views into a prefix tree. This implementation has decently comprehensive tests and benchmarks.

No code for the next two but definitely results...

Tabu search guided graph layout:

https://bsky.app/profile/micahscopes.bsky.social/post/3luh4d...

https://bsky.app/profile/micahscopes.bsky.social/post/3luh4s...

Fast Gaussian blue noise with wgpu:

https://bsky.app/profile/micahscopes.bsky.social/post/3ls3bz...

In both these examples, I leaned on Claude to set up the boilerplate, the GUI, etc, which gave me more mental budget for playing with the challenging aspects of the problem. For example, the tabu graph layout is inspired by several papers, but I was able to iterate really quickly with claude on new ideas from my own creative imagination with the problem. A few of them actually turned out really well.


Not the OP, not my code. But here is Mitchel Hashimoto showing his workflow and code in Zig, created with AI agent assistance: https://youtu.be/XyQ4ZTS5dGw


I think this still is some kind of 'fight' between assisted and more towards 'vibe'. Vibe for me means not reading the generated code, just trying it and the other extreme is writing all without AI. I don't think people here are talking about assisted : they are taking about vibe or almost vibe coding. And its fairly terrible if the llm does not have tons of info. It can loop, hang, remove tons of features, break random things etc all while being cheerful and saying 'this is production code now, ready to deploy'. And people believe it. When you use it to assist, it is great imho.


https://github.com/wglb/gemini-chat Almost entirely generated by gemini based on my english language description. Several rounds with me adding requirements.

(edit)

I asked it to generate a changelog: https://github.com/wglb/gemini-chat/blob/main/CHANGELOG.md


That's disingenuous or naive. Almost nobody decides to expressly highlight the section of code (or whole files generated by ai) they just get on with the job when there's real deadlines and it's not about coding for the sake of the art form...


If the generated implementation is not good, you're trading short-term "getting on with the job" and "real deadlines" for mid-to-long-term slowdown and missed deadlines.

In other words, it matters whether the AI is creating technical debt.


If you're creating technical debt, you're creating technical debt.

That has nothing to do with AI/LLMs.

If you can't understand what the tool spits out either; learn, throw it away, or get it to make something you can understand.


Do you want to clarify your original comment, then? I just read it again, and it really sounds like you're saying that asking to review AI-generated code is "disingenuous or naive".


I am talking about correctness, not style, coding isn't just about being able to show activity (code produced), but rather producing a system that is correctly performing the intended task


Yes, and frankly you should be spending time writing large integration tests correctly not microscopic tests that forgot how tools interact.

It's not about lines of code or quality it's about solving a problem. If the problem creates another problem then it's bad code. If it solves the problem without causing that then great. Move onto the next problem.


Same as pretending that vibe coding isn't producing tons of slop. "Just improve your prompt bro" doesn't work for most real codebases. The recent TEA app leak is a good example of vibe coding gone wrong, I wish I had as much copium as vibe coders to be blind to these things, as most of them clearly are like "it happened to them but surely won't happen to ME."


> The recent TEA app leak is a good example of vibe coding gone wrong

Weren't there 2 or 3 dating apps that were launched before the "vibecoding" craze that went extremely popular and got extremely hacked weeks/months in? I also distinctly remember a social network having firebase global tokens on the clientside, also a few years ago.


So that's an excuse for AI getting it wrong? It should know better if its so much better.


LLMs are not meant to be infallible it's meant to be faster.

Repeat after me, token prediction is not intelligence.


Not an excuse, no. I agree it should be better. And it will get better. Just pointing out that some mistakes were systematically happening before vibecoding became a thing.

We went from "this thing is a stochastic parrot that gives you poems and famous people styled text, but not much else" to "here's a fullstack app, it may have some security issues but otherwise it mainly works" in 2.5 years. People expect perfection, and move the goalposts. Give it a second. Learn what it can do today, adapt, prepare for what it can do tomorrow.


No one is moving the goalposts. There are a ton of people and companies trying to replace large swathes of workers with AI. So it's very reasonable to point out ways in which the AI's output does not measure up to that of those workers.


I thought the idea was that AI would make us collectively better off, not flood the zone with technical debt as if thousands of newly minted CS/bootcamp graduates were unleashed without any supervision.

LLMs are still stochastic parrots, though highly impressive and occasionally useful ones. LLMs are not going to solve problems like "what is the correct security model for this application given this use case".

AI might get there at some point, but it won't be solely based on LLMs.


> "what is the correct security model for this application given this use case".

Frankly I've seen LLMs answer better than people trained in security theatre so be very careful where you draw the line.

If you're trying to say they struggle with what they've not seen before. Yes, provided that what is new isn't within the phase space they've been trained over. Remember there's no photographs of cats riding dinosaurs but SD models can generate them.


Saying that they aren't worse than an incompetent human isn't a ringing endorsement.


I've heard this multiple times (Tea being an example of problems with vibe coding) but my understanding was that the Tea app issues well predated vibe coding.

I have experimented with vibe coding. With Claude Code I could produce a useful and usable small React/TS application, but it was hard to maintain and extend beyond a fairly low level of complexity. I totally agree that vibe coding (at the moment) is producing a lot of slop code, I just don't think Tea is an example of it from what I understand.




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: