alt Hacker NewsPart of my job is working on trying to make these models productive for the large corporation I work for. It's a lot of throwing tomatoes at a wall and to a degree I see the issue he is talking about output seemingly having a certain ceiling.
At the same time in no part of his post is any code snippet or anything to latch on to of "the model performed poorly here when it should have done this" - this style of criticism seems to be a pattern of most of these "the LLMs will never work" style posts on blogs and twitter.
They obviously can perform better than autocomplete and in my own day to day development build out huge portions of a codebase that I would have expected a junior or midlevel engineer to perform at.
How are we really supposed to grasp their actual capabilities when no one will actually cite specifically what mistakes they are making.
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This is an excellent point, and as a novice using LLMs for projects I could never previously dream of doing I find myself looking for the same, examples or citations of what exactly agents are writing incorrectly and how would the human do it better. I'm sure they're out there, maybe someone can refer some good content showing such examples.
I have no doubt the top nth percent of coders could write circles around Claude or Codex, but how much worse are they than your average schnook?
This article goes into quite a lot of detailed examples that include code snippets that demonstrate poor architecture: https://blog.k10s.dev/im-going-back-to-writing-code-by-hand/
When people write blog posts about how LLMs failed for some particular task, the responses from boosters invariably fall along the lines of "just use this other model/just tweak your prompt like so/you're just not skilled enough—you can't make fundamental arguments about AI by citing specific examples."
So we can't make arguments by citing specific examples, and also can't make arguments by not citing specific examples. Whelp, I guess that's the ball game.
(yes yes, I'm committing a group attribution error, but still)
> How are we really supposed to grasp their actual capabilities when no one will actually cite specifically what mistakes they are making.
The mistakes they make are pretty subtle. Coding with LLMs can be like that scene in Whiplash – <excellent drumming >, not quite my tempo, <excellent drumming >, downbeat on 18, <excellent drumming>, you’re rushing, <excellent drumming>, dragging, …
Like yeah it produces working code almost always and the code usually does what you asked. And yet it makes you want to throw a chair because it’s not quite right in frustrating ways and it doesn’t even have the taste to know how it’s wrong.