Two things LLM coding agents are still bad at

I think the issue with them making assumptions and failing to properly diagnose issues comes more from fine-tuning than any particular limitation in LLMs themselves. When fine tuned on a set of problem->solution data it kind of carries the assumption that the problem contains enough data for the solution.

What is really needed is a tree of problems which appear identical at first glance, but the issue and the solution is something that is one of many possibilities which can only be revealed by finding what information is lacking, acquiring that information, testing the hypothesis then, if the hypothesis is shown to be correct, then finally implementing the solution.

That's a much more difficult training set to construct.

The editing issue, I feel needs something more radical. Instead of the current methods of text manipulation, I think there is scope to have a kind of output position encoding for a model to emit data in a non-sequential order. Again this presents another training data problem, there are limited natural sources to work from showing programming in the order a programmer types it. On the other hand I think it should be possible to do synthetic training examples by translating existing model outputs that emit patches, search/replaces, regex mods etc. and translate those to a format that directly encodes the final position of the desired text.

At some stage I'd like to see if it's possible to construct the models current idea of what the code is purely by scanning a list of cached head_embeddings of any tokens that turned into code. I feel like there should be enough information given the order of emission and the embeddings themselves to reconstruct a piecemeal generated program.

A friendly reminder that "refactor" means "make and commit a tiny change in less than a few minutes" (see links below). The OP and many comments here use "refactor" when they actually mean "rewrite".

I hear from my clients (but have not verified myself!) that LLMs perform much better with a series of tiny, atomic changes like Replace Magic Literal, Pull Up Field, and Combine Functions Into Transform.

[1] https://martinfowler.com/books/refactoring.html [2] https://martinfowler.com/bliki/OpportunisticRefactoring.html [3] https://refactoring.com/catalog/

The URLs being wrong in that specific case is one where they were using the "wrong tool". I can name you at least a dozen other cases from own experience, where too, they appear to be the wrong tool, for example for working with Terraform or for not exposing secrets by hardcoding them in the frontend. Et cetera. Many other people will have contributed thousands if not more similar but different cases. So what good are these tools then for really? Are we all really that stupid? 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. I'd be happy to use them in whatever specific domain they supposedly excel at. But no-one seems to be able to identify one for sure. The problem is, the folks pushing or better said, shoving down these bullshit generators down our throats are trying to sell us the promise of an "everything oracle". What did old man Altman tell us about ChatGPT 5? PhD level tool for code generation or some similar nonsense? But it turns out it only gets one metric right each time - generating a lot of text. So, essentially, great for bullshit jobs (i count some of the IT jobs as such too), but not much more.

This isn't ai maximalist though, it's explicitly pointing out something that regularly does not work!

> Sure, but conceivably, you could also be informed of this second hand, through any publication about LLMs, so it is very odd to say "you don't use them enough" rather than "you're ignorant" or "you're uninformed".

But this is to someone who is actively using them, and the suggestion of "if you were using them more actively you'd know this, this is a very common issue" is not at all weird. There are other ways they could have known this, but they didn't.

"You haven't got the experience yet" is a much milder way of saying someone doesn't know how to use a tool properly than "you're ignorant".

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 looked at the docs. I ran it and it worked. On the other hand, most of the code was just duplicated over and over again ex: 3 different endpoints that gave overlapping information. About half of the code generated fake data rather than actually do anything with the system.

I rolled back and re-prompted and got something that looked good and worked. The LLMs are magic when they work well but they can throw a wrench into your system that will cost you more if you don't catch it.

I also just had a 'senior' developer tell me that a feature in one of our platforms was deprecated. This was after I saw their code which did some wonky hacky like stuff to achieve something simple. I checked the docs and said feature (URL Rewriting) was obviously not deprecated. When I asked how they knew it was deprecated they said Chat GPT told them. So now they are fixing the fix chat gpt provided.