Building a basic static html landing page is ridiculously easy though. What js is even needed? If it's just an html file and maybe a stylesheet of course it's easy to host. You can apply 20 lines of css and have a decent looking page.

These aren't hard problems.

> These aren't hard problems.

So why do so many LLMs fail at them?

And humans also.

Laziness mostly - no need to think about design, icons and layout (responsiveness and all that stuff).

These are not hard problems obviously, but getting to 80%-90% is faster than doing it by hand and in my cases that was more than enough.

With that being said, AI failed for the rest 10%-20% with various small visual issues.

A big part of my job is building proofs of concept for some technologies and that usually means some webpage to visualize that the underlying tech is working as expected. It’s not hard, doesn’t have to look good at all, and will never be maintained. I throw it away a few weeks later.

It used take me an hr or two to get it all done up properly. Now it’s literal seconds. It’s a handy tool.

> These aren’t hard problems.

Honestly, that’s the best use-case for AI currently. Simple but laborious problems.

Interesting, I'll have to try that. All the "static" page generators I've tried require React....

I like using Vercel v0 for frontend

I’ve gotten 0 production usable python out of any LLM. Small script to do something trivial, sure. Anything I’m going to have to maintain or debug in the future, not even close. I think there is a _lot_ of terrible python code out there training LLMs, so being a more popular language is not helpful. This era is making transparent how low standards really are.

Different experience here. Production code in banking and finance for backend data analysis and reporting. Sure the code isn't perfect, but doesn't need to be. It's saving >50% effort and the analysis results and reporting are of at least as good a standard as human developed alternatives.

> I’ve gotten 0 production usable python out of any LLM

Fascinating, I wonder how you use it because once I decompose code to modules and function signatures, Claude[0] is pretty good at implementing Python functions. I'd say it one-shots 60% of the times, I have to tweak the prompt or adjust the proposed diffs 30%, and the remaining 10% is unusable code that I end up writing by hand. Other things Claude is even better at: writing tests, simple refactors within a module, authoring first-draft docstrings, adding context-appropriate type hints.

0. Local LLMs like Gemma3, Qwen-coder seem to be in the same ballpark in terms of capabilities, it's just that they are much slower on my hardware. Except for the 30b Qwen3 MoE that was released a day ago, that one is freakin' fast.

I agree - you have to treat them like juniors and provide the same context you would someone who is still learning. You can’t assume it’s correct but where it doesn’t matter it is a productivity improvement. The vast majority of the code I write doesn’t even go into production so it’s fantastic for my usage.

What happens to the vast majority of code you write

Try o4-mini-high. It’s getting there.

Maybe with the next got version, gpt-4.003741