I get more use out of them every single day and certainly with every model release (mostly for generating absolutely not trivial code) and it's not subtle.
I’m tired of this argument. I’ll even grant you: both sides of it.
It seems as though we prepared our selves to respond to llms in this manner with people memeing, or simply recognizing, that there was a “way” to ask questions to get better results early on when ranked search broadened the appeal of search engines.
The reality is that both you and the op are talking about the opinion of the thing, but leaving out the thing itself.
You could say “git gud”, but what if you showed op what “gud” output to you was, and they recognized it as the same sort of output that they were saying was repetitive?
It’s ambiguity based on opinion.
I fear so many are taking part each other.
Perhaps linking to example prompts and outputs that can be directly discussed is the only way to give specificity to the ambiguous language.
a) the code is bad b) the problem is beneath what they consider non-trivial
The way that OP structured the response, I frankly got a similar impression (although the follow up feels much different). I just don't see the point in engaging in that here, but I take your criticism: Why engage at all. I should probably not, then.
But I am not saying LLMs are impotent - the other week Claude happily churned me ~3500 lines of C code that allowed to implement a prototype capture facility for network packets with flexible filters and saving the contents into pcapng files. I had to fix a couple of bugs that it made, but overall it was certainly at least 5x-10x productivity improvement compared to me typing these lines of code by hand. I don’t dispute that it’s a pretty useful tool in coding, or as a thinking assistant (see the last paragraph of my comment).
What I challenged is the submissive self deprecating adoration across the entire spectrum.
Anyway: I found the writers perspective on this whole subject to be interesting, and agree on the merits — I definitely think they are correct on their analysis and outlook, and here the two of us apparently disagree – but I don't share their concluding feelings.
But I can see, how they got there.
They can't do anything elaborate or interesting for me beyond literal tiny pet project proof of concepts. They could potentially help me uncover a bug, explain some code, or implement a small feature.
As soon as the complexity of the feature goes up either in its side-effects, dependencies, or the customization of the details of the feature, they are quite unhelpful. I doubt even one senior engineer at a large company is using LLMs for major feature updates in codebases that have a lot of moving parts and significant complexity and many LOC.