Believe it or not I agree.

Let's repeat this process for 100 coding examples and see how many it can complete "hands-off" especially where (a) it isn't a case of here is a spec and I need you to implement it and (b) it isn't for a a use for which there is already publicly available code.

Otherwise your claim of "this seems true, right now!" is baseless.

I tried to have an LLM fully write a Python library for me. I wrote an extensive, detailed requirements doc, and it looked close enough that I accepted the code. But as I read through it more closely, I realized it was duplicating code in confusing ways, and overall it took longer than just getting the bones down myself, first.

Some coding agents are now more actively indexing code, I think that should help with this problem

[1] https://jdsemrau.substack.com/p/memory-and-context

its a very niche app, and havent used it as much since buying it, but there's that https://repoprompt.com/

Today I spent easily half an hour trying to make it solve a layout issue it itself introduced when porting a component.

It was a complex port it executed perfectly. And then it completely failed to even create a simple wrapper that fixed a flexbox issue.

BTW. Claude (Code and Cursor) is over-indexed on "let's randomly add and remove h-full/overflow-auto and pretend it works ad infinitum"

i have cursor through work but i am tempted to shell out $100 because of this hype.

is it better than using claude models in cursor?

> This is the single most impressive code-gen project I’ve seen so far. I did not think this was possible yet.

To get that sort of acclaim, a human had to build an embedded programming language from scratch to get to that point. And even with all that effort, the agent itself took $631 and 119 hours to complete the task. I actually don't think this is a knock on the idea at all, this is the direction I think most engineers should be thinking about.

That agent-built HTTP/2 server they're referencing is apparently the only example of this sort of output they've seen to date. But if you're active in this particular space, especially on the open source side of the fence, this kind of work is everywhere. But since they don't manifest themselves as super generic tooling that you can apply to broad task domains as a turnkey solution, they don't get much attention.

I've continually held the line that if any given LLM agent platform works well for your use case and you haven't built said agent platform yourself, the underlying problem likely isn't that hard or complex. For the hard problems, you gotta do some first-principles engineering to make these tools work for you.

Writing hint: Your last paragraph stands well on its own. Especially if this is, in fact, your actual experience.

Nothing in that paragraph requires the negativity or inaccuracies of the preceding two paragraphs.

There should be a name for the human tendency (we have all done/do it) to weigh down good points with unnecessary and often inaccurate contrast/competition.