In Claude Code, it always shows the diff between current and proposed changes and I have to explicitly allow it to actually modify the code. Doesn’t that “fix” the copy-&-paste issue?
Two things LLM coding agents are still bad at
51–60 of 382 posts
Re: Two things LLM coding agents are still bad at
#52I see a pattern in these discussions all the time: some people say how very, very good LLMs are, and others say how LLMs fail miserably; almost always the first group presents examples of simple CRUD apps, frontend "represent data using some JS-framework" kind of tasks, while the second group presents examples of non-trivial refactoring, stuff like parsers (in this thread), algorithms that can't be found in leetcode,…
> almost always the first group presents examples of simple CRUD apps
How about a full programming language written by cc "in a loop" in ~3 months? With a compiler and stuff?
It might be a meme project, but it's still impressive as hell we're here.
I learned about this from a yt content creator that took that repo, asked cc to "make it so that variables can be emojis", and cc did that 5$ later. Pretty cool.
Re: Two things LLM coding agents are still bad at
#53Recently, I asked Codex CLI to refactor some HTML files. It didn't literally copy and pasted snippets here and there as I would have done myself, it rewrote them from memory, removing comments in the process. There was a section with 40 successive links with complex URLs. A few days later, just before deployment to production, I wanted to double check all 40 links. First one worked. Second one worked. Third one worke…
You’re just not using LLMs enough. You can never trust the LLM to generate a url, and this was known over two years ago. It takes one token hallucination to fuck up a url.
It’s very good at a fuzzy great answer, not a precise one. You have to really use this thing all the time and pick up on stuff like that.
Re: Two things LLM coding agents are still bad at
#54Earlier quoted context omitted.
But, just like lots of people expect/want self-driving to outperform humans even on edge cases in order to trust them, they also want "AI" to outperform humans in order to trust it. So: "humans are bad at this too" doesn't have much weight (for people with that mindset). It makes sense to me, at least.
If we had a knife that most of the time cuts a slice of bread like the bottom p50 of humans cutting a slice of bread with their hands, we wouldn't call the knife useful. Ok, this example is probably too extreme, replace the knife with an industrial machine that cut bread vs a human with a knife. Nobody would buy that machine either if it worked like that.
Agreed in a general sense, but there's a bit more nuance.
If a knife slices bread like a normal human at p50, it's not a very good knife.
If a knife slices bread like a professional chef at p50, it's probably a very decent knife.
I don't know if LLMs are better at asking questions than a p50 developer. In my original comment I wanted to raise the question of whether the fact that LLMs are not good at asking questions makes them still worse than human devs.
The first LLM critique in the original article is that they can't copy and paste. I can't argue with that. My 12 year old copies-and-pastes better than top coding agents.
The second critique says they can't ask questions. Since many developers also are not good at this, how does the current state of the art LLM compare to a p50 developer in this regard?
Re: Two things LLM coding agents are still bad at
#55Recently, I asked Codex CLI to refactor some HTML files. It didn't literally copy and pasted snippets here and there as I would have done myself, it rewrote them from memory, removing comments in the process. There was a section with 40 successive links with complex URLs. A few days later, just before deployment to production, I wanted to double check all 40 links. First one worked. Second one worked. Third one worke…
You’re just not using LLMs enough. You can never trust the LLM to generate a url, and this was known over two years ago. It takes one token hallucination to fuck up a url. It’s very good at a fuzzy great answer, not a precise one. You have to really use this thing all the time and pick up on stuff like that.
I would generalise it to you can’t trust LLMs to generate any kind of unique identifier. Sooner or later it will hallucinate a fake one.
Re: Two things LLM coding agents are still bad at
#56@kixpanganiban Do you think it will work if for refactoring tasks, we take aways OpenAI's `apply_patch` tool and just provide `cut` and `paste` for the first few steps?
I can run this experiment using ToolKami[0] framework if there is enough interest or if someone can give some insights.
Re: Two things LLM coding agents are still bad at
#57LLMs are great at asking questions if you ask them to ask questions. Try it: "before writing the code, ask me about anything that is nuclear or ambiguous about the task".
Re: Two things LLM coding agents are still bad at
#58Earlier quoted context omitted.
I've found codex to be better here than Claude. It has stopped many times and said hey you might be wrong. Of course this changes with a larger context. Claude is just chirping away "You're absolutely right" and making me to turn on caps lock when I talk to it and it's not even noon yet.
i find the chirpy affirmative tone of claude to be rage inducing
This. The biggest reason I went with OpenAI this month...
Re: Two things LLM coding agents are still bad at
#59Recently, I asked Codex CLI to refactor some HTML files. It didn't literally copy and pasted snippets here and there as I would have done myself, it rewrote them from memory, removing comments in the process. There was a section with 40 successive links with complex URLs. A few days later, just before deployment to production, I wanted to double check all 40 links. First one worked. Second one worked. Third one worke…
You’re just not using LLMs enough. You can never trust the LLM to generate a url, and this was known over two years ago. It takes one token hallucination to fuck up a url. It’s very good at a fuzzy great answer, not a precise one. You have to really use this thing all the time and pick up on stuff like that.
Or just not bother. It sounds pretty useless if it flunks on basic tasks like this.
Perhaps you’ve been sold a lie?
Re: Two things LLM coding agents are still bad at
#60I'd argue LLM coding agents are still bad at many more things. But to comment on the two problems raised in the post:
> LLMs don’t copy-paste (or cut and paste) code.
The article is confusing the architectural layers of AI coding agents. It's easy to add "cut/copy/paste" tools to the AI system if that shows improvement. This has nothing to do with LLM, it's in the layer on top.
> Good human developers always pause to ask before making big changes or when they’re unsure [LLMs] keep trying to make it work until they hit a wall -- and then they just keep banging their head against it.
Agreed - LLMs don't know how to back track. The recent (past year) improvements in thinking/reasoning do improve in this regard (it's the whole "but wait..." RL training that exploded with OpenAI o1/o3 and DeepSeek R1, now done by everyone), but clearly there's still work to do.