> Half an hour and fifty dollars later, I realized I had spent fifty dollars on this, and that this was not sustainable because, if anything, the code was getting more and more buggy the more Claude fixed it. Off topic, this has been my experience with AI so often that it prevents me from exploring AI uses more. I liked Cursor’s “auto” plan but that now seems gone. I’d happily switch to a provider that offers a simil…
It is possible to radically increase your chances of success. You have to speak the LLM’s language, just like you write Java or Rust. But it doesn’t come with a language spec, so you get to figure it out by trial and error. And a model change means revisiting what works. Lots of tips on how to do this out there but one thing I do is have it try, throw away everything it it did, and try again with a completely restate…
This is the thing that worries me about AI/LLMs and how people "profess they're actually really useful when you use them right": the cliff to figuring out if they're useful is vertical.
"You’ve still come out ahead even if you take over fully."
I just finished a weeklong saga of redoing a bunch of Claude's work because instead of figuring out how to properly codegen some files it just manually updated them and ignored a bunch of failing tests.
With another human I can ask, "Hey, wtf were you thinking when you did [x]?" and peer into their mind-state. With Claude, it's time to stir the linear algebra again. How can I tell when I'm near a local or global maxima when all the prevailing advice is "I dunno man, just `git reset --hard origin/master` and start again but like, with different words I guess."
We have studies that show people feel like they're more productive using AI while they're actually getting less done [1], and "throw away everything it did and try again" based on :sparkle: vibes :sparkle: is the state of the art on how to "actually" use this stuff, I just feel more and more skeptical.
[1]: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...