I’ve been thinking about how I use AI tools recently and I feel like I’m on the other side of a bell curve. I mostly use sonnet/local llms these days for building, binned off opus except for tracking down gnarly bugs, because honestly with a bit of baby sitting you can end up with better results. Sure, you can set the more advanced models off on long running tasks, go to sleep, do whatever and after a few hours, burn through millions of tokens (and loads of cash) and you’ll end up with something that works and is along the lines of what you’ve asked for, but it feels as though the tendancy is to then just accept it for what it is and think it’s the best that it could have been, when in reality it’s just a local maxima, and you could have found a better one with a bit more thought and guidance. Sitting with a ‘dumber’ model which forces you to engange with it more makes me feel as though I always get better results in the long run, for cheaper, and often quicker as you’re forced to think a bit more about what you’re trying to achieve, and have more checkpoints to make adjustments along the way even sometimes rethink what you’re building entirely. Unless the problem you’re trying to solve is extremely well defined, it just feels like the latest claude and gpt models are overkill and a complete waste of money, maybe they’re useful if you’re lazy and want to push slop out with as little effort as possible. Just my personal observation. submitted by /u/Dredgefort
Originally posted by u/Dredgefort on r/ArtificialInteligence