[dingnuts]

> you lose nothing but your own time when things don't go well and you have to reset

Crucially, you lose money with a lot of these models when they output the wrong thing, because you pay by token whether the tokens coming out are what you want or not.

It's a bit like a slot machine. You write your prompt, insert some money, and pull the lever. Sometimes it saves you a lot of time! Sometimes, not so much. Sometimes it gets 80% of the way and you think oh, let me just put in another coin and tweak my prompt and pull the lever, this time it will get me 100%

Listening to people justify pulling the lever over and over again is a little bit like listening to an addict excusing their behavior.

I realize there are flat rate plans like Kagi offers, but the API offerings and IDE integrations all feature the slot machine and sunk cost effects that I describe.

[or_am_i]

Yeah, I guess I'm silently assuming the developer's time is more valuable than the API costs (which is true in the majority of use cases in US+EU, unless using parallel/multi-shot strategies or hyper-expensive frontier models).

I agree, it can feel a lot like a slot machine at times, and it's a failure mode somewhat unique to developing with LLM, where it doesn't just fail outright or tell you "I don't know how to do that", but instead you find yourself in the end of a sometimes hours long spiral of trying just-one-more-prompt.

It's important to experience this mode of failure and learn to notice the "spiral" early and adjust the approach. Sometimes it's enough to switch to a different model, often an explicit planning step helps. But more likely than not, a "spiral" means approaching the frontier of LLM possibility. In my experience, certain types of changes are really hard for current gen LLM to pull off, like large scale refactorings changing the project architecture, or implementing genuinely novel algorithmic ideas, so we still need a human touch for these (yay?)