Exactly. Precisely. Definitely.
I see what you describe all the time, because I do review the code the models do produce.
It's not just incredibly verbose: it's constantly missing that there's an obvious, elegant, small, way to solve what was asked and instead it goes ballistic and creates nonsense.
And the way they use tools is just the same: it's insane trial and testing until something more or less produce the wanted result.
I've explained it here already but the craziest I had was, like you, a one line test that was basically the following:
if ( a >= 0xab000000 && a <= 0xabffffff)
But the model decide to go nuts: it noticed a pattern (just like it notices a pattern in your example) and decided to convert the native integers to strings to then do substring matching on the hexadecimal representation of the number.
I.
Shit.
You.
Not.
And all the people here who are saying that "it works" have no idea as to the amount of technical debt they're creating.
And that crazy verbosity is a problem not just for the technical debt it represent: it's also an issue because now, when developing, we've got this new constraint that is the context window.
It's a nice tool but it should be used with caution.
Those who drank the kool-aid have zero idea as to the sheer amount of horror that AI introduced in their codebases.
> And all the people here who are saying that "it works" have no idea as to the amount of technical debt they're creating.
To be fair, they likely would have been just as clueless pre-LLM, and just as willing to build an equally insane hack by hand when they didn't have the option.
Being clueless has in my experience previously been a rate limiter. Without LLMs these people would simply be much less productive than those with a clue, and problems they don't understand would at best compel them to read and learn, and at worst to simply avoid going too far out of their depth. Either way, it would significantly slow them down compared to their more skilled and experienced peers. The resulting rate of output practically limited the burden of reviewing and maintaining it. Then, eventually skill and experience would hopefully just sort of happen to those that work for it, through exercise and exposure to problems and review feedback.
The justification I’ve been getting from others is that future models will resolve all the tech debt, so paying a cost to iterate fast now is worth it.
We may be way past the point.
Do they actually iterate on their code? Or do they claim they are iterating?
Closing a ticket with more code doesn't count as iterating.
lol they're definitely not iterating on code. Instead they iterate on "ideas", with the expectation that code no longer matters because complexity will be self-resolving with agentic loops.
Unfortunately these tools, and the VCs/companies pushing to adopt them, has totally empowered this type of behaviour.