For me, it has gone through stages.

Initially I was astounded by the results.

Then I wrote a large feature (ad pacing) on a site using LLMs. I learned the LLMs did not really understand what they were doing. The algorithm (PID controller) itself was properly implemented (as there is plenty of data to train on), but it was trying to optimize the wrong thing. There were other similar findings where LLM was doing very stupid mistakes. So I went through a disillusionment stage and kind of gave up for a while.

Since then, I have learned how to use Claude Code effectively. I have used it mostly on existing Django code bases. I think everybody has a slightly different take on how it works well. Probably the most reasonable advice is to just keep going and try different kind of things. Existing code bases seem easier, as well as working on a spec beforehand, requiring tests etc. basic SWE principles.

> I have learned how to use Claude Code effectively

This is step 3 of “draw the rest of the owl” :-)

> the most reasonable advice is to just keep going and try different kind of things.

This is where I’ve been at for a while now. Every couple of months I try again with latest models and latest techniques I hear people talking about but there’s very little concrete info there that works for me.

Then I wonder if it’s just my spend? I don’t mind spending $30/month to experiment but I’m not going to drop $300/month unless I can see evidence that it’ll be worth it, which I haven’t really seen, but maybe there’s a dependency and you don’t get the result without increased spend?

Some posts I’ve seen claim spending of $1,500/month, which would be worth it if it could increase productivity enough, but there’s very few specifics on workflows and results.