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Two things LLM coding agents are still bad at

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Re: Two things LLM coding agents are still bad at

#211

Post by majora2007 »

I think LLMs provide value, used it this morning to fix a bug in my PDF Metadata parser without having to get too deep into the PDF spec.

But most of the time, I find that the outputs are nowhere near the effect of just doing it myself. I tried Codex Code the other day to write some unit tests. I had a few setup and wanted to use it (because mocking the data is a pain).

It took about 8 attempts, I had to manually fix code, it couldn't understand that some entities were obsolete (despite being marked and the original service not using them). Overall, was extremely disappointed.

I still don't think LLMs are capable of replacing developers, but they are great at exposing knowledge in fields you might not know and help guide you to a solution, like Stack Overflow used to do (without the snark).

Re: Two things LLM coding agents are still bad at

#212

Post by _1 »

Leynos wrote: post #106
cheema33 wrote: post #71

From the article: > I contest the idea that LLMs are replacing human devs... AI is not able to replace good devs. I am assuming that nobody sane is claiming such a thing today. But, it can probably replace bad and mediocre devs. Even today. In my org we had 3 devs who went through a 6-month code boot camp and got hired a few years ago when it was very difficult to find good devs. They struggled. I would give them eas…

What do you think was the reason that the bootcamp grads struggling to get better at what they do?

My experience with them is that the are taught to cover as much syntax and libraries as possible, without spending time learning how solve problems and develop their own algorithms. They (in general) expect to follow predefined recipes.

Re: Two things LLM coding agents are still bad at

#213

Post by IanCal »

hansmayer wrote: post #201
IanCal wrote: post #189

Earlier quoted context omitted.

> Many of us mastered the hard problem of navigating various abstraction layers of computer over the years, only to be told, we now effing dont know how to write a few sentences in English? Come on. If you're trying to one shot stuff with a few sentences then yes you might be using these things wrong. I've seen people with PhDs fail to use google successfully to find things, were they idiots? If you're using them wro…

Again mate, stop making arrogant assumptions and read some of my previous comments. I and my team are early adopters, since about 2 years. I am even paying for premium-level service. Trust me, it sucks and under-delivers. But good for you and others who claim they are productive with it - I am sure we will see those 10x apps rolling in soon, right? It's only been like 4 years since the revolutionary magic machine was…

I read your comments. Did you read mine? You can pass them into chatgpt or claude or whatever premium services you pay for to summarise them for you if you want.

> Trust me, it sucks

Ok. I'm convinced.

> and under-delivers.

Compared to what promise?

> I am sure we will see those 10x apps rolling in soon, right?

Did I argue that? If you want to look at some massive improvements, I was able to put up UIs to share results & explore them with a client within minutes rather than it taking me a few hours (which from experience it would have done).

> It's only been like 4 years since the revolutionary magic machine was announced.

It's been less than 3 since chatgpt launched, which if you'd been in the AI sphere as long as I had (my god it's 20 years now) absolutely was revolutionary. Over the last 4 years we've seen gpt3 solve a bunch of NLP problems immediately as long as you didn't care about cost to gpt-5-pro with web search and codex/sonnet being able to explore a moderately sized codebase and make real and actual changes (running tests and following up with changes). Given how long I spent stopping a robot hitting the table because it shifted a bit and its background segmentation messed up, or fiddling with classifiers for text, the idea I can get a summary from input without training is already impressive and then to be able to say "make it less wanky" and have it remove the corp speak is a huge shift in the field.

If your measure of success is "the CEOs of the biggest tech orgs say it'll do this soon and I found a problem" then you'll be permanently disappointed. It'd be like me sitting here saying mobile phones are useless because I was told how revolutionary the new chip in an iphone was in a keynote.

Since you don't seem to want to read most of this, most isn't for you. The last bit is, and it's just one question:

Why are you paying for something that solves literally no problems for you?

Re: Two things LLM coding agents are still bad at

#214

Post by troupo »

alzoid wrote: post #179
yodsanklai wrote: post #153

Earlier quoted context omitted.

5 minutes ago, I asked Claude to add some debug statements in my code. It also silently changed a regex in the code. It was easily caught with the diff but can be harder to spot in larger changes.

I asked Claude to add a debug endpoint to my hardware device that just gave memory information. It wrote 2600 lines of C that gave information about every single aspect of the system. On the one hand kind of cool. It looked at the MQTT code and the update code, the platform (esp) and generated all kinds of code. It recommended platform settings that could enable more detailed information that checked out when I looke…

> About half of the code generated fake data rather than actually do anything with the system.

All the time

    // fake data. in production this would be real data
    ... proceeds to write sometimes hundreds of lines
    of code to provide fake data

Re: Two things LLM coding agents are still bad at

#215

Post by bunderbunder »

Leynos wrote: post #108
tjansen wrote: post #18

Agreed with the points in that article, but IMHO the no 1 issue is that agents only see a fraction of the code repository. They don't know whether there is a helper function they could use, so they re-implement it. When contributing to UIs, they can't check the whole UI to identify common design patterns, so they re-invent it. The most important task for the human using the agent is to provide the right context. "Loo…

I wonder if a large context model could be employed here via tool call. One of the great things Gemini chat can do is ingest a whole GitHub repo. Perhaps "before implementing a new utility or helper function, ask the not-invented-here tool if it's been done already in the codebase" Of course, now I have to check if someone has done this already.

Large context models don't do a great job of consistently attending to the entire context, so it might not work out as well in practice as continuing to improve the context engineering parts of coding agents would.

I'd bet that most the improvement in Copilot style tools over the past year is coming from rapid progress in context engineering techniques, and the contribution of LLMs is more modest. LLMs' native ability to independently "reason" about a large slushpile of tokens just hasn't improved enough over that same time period to account for how much better the LLM coding tools have become. It's hard to see or confirm that, though, because the only direct comparison you can make is changing your LLM selection in the current version of the tool. Plugging GPT5 into the original version of Copilot from 2021 isn't an experiment most of us are able to try.

Re: Two things LLM coding agents are still bad at

#216

Post by justinhj »

Building an mcp tool that has access to refactoring operations should be straightforward and using it appropriately is well within the capabilities of current models. I wonder if it exists? I don't do a lot of refactoring with llm so haven't really had this pain point.

Re: Two things LLM coding agents are still bad at

#217

Post by causal »

Similar to the copy/paste issue I've noticed LLMs are pretty bad at distilling large documents into smaller documents without leaving out a ton of detail. Like maybe you have a super redundant doc. Give it to an LLM and it won't just deduplicate it, it will water the whole thing down.

Re: Two things LLM coding agents are still bad at

#218

Post by rhetocj23 »

simonw wrote: post #210

I feel like the copy and paste thing is overdue a solution. I find this one particularly frustrating when working directly with ChatGPT and Claude via their chat interfaces. I frequently find myself watching them retype 100+ lines of code that I pasted in just to make a one line change. I expect there are reasons this is difficult, but difficult problems usually end up solved in the end.

Whats the time horizon for said problems to be solved? Because guess what - time is running and people will not continue to aimlessly provide money at this stuff.

Re: Two things LLM coding agents are still bad at

#219

Post by nxpnsv »

d1sxeyes wrote: post #78
nxpnsv wrote: post #46

Codex has got me a few times lately, doing what I asked but certainly not what I intended: - Get rid of these warnings "...": captures and silences warnings instead of fixing them - Update this unit test to relfect the changes "...": changes the code so the outdated test works - The argument passed is now wrong: catches the exception instead of fixing the argument My advice is to prefer small changes and read everyth…

You also have to be a bit careful: “Fix the issues causing these warnings” Retrospectively fixing a test to be passing given the current code is a complex task, instead, you can ask it to write a test that tests the intended behaviour, without needing to infer it. “The argument passed is now wrong” - you’re asking the LLM to infer that there’s a problem somewhere else, and to find and fix it. When you’re asking an LL…

Exactly, I think the takeaway is that being careful when formulating a task is essential with LLMs. They make errors that wouldn’t be expected when asking the same from a person.

Re: Two things LLM coding agents are still bad at

#220

Post by seanw265 »

grey-area wrote: post #59
ivape wrote: post #53

Earlier quoted context omitted.

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?

I suspect you haven't tried a modern mid-to-large-LLM & Agent pair for writing code. They're quite capable, even if not suited for all tasks.
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