> A repeated trend is that Claude Code only gets 70-80% of the way, which is fine and something I wish was emphasized more by people pushing agents.

I have been pretty successful at using llms for code generation.

I have a simple rule that something is either 90%>ai or none at all (exluding inline completions, and very obvious text editing).

The model has an inherent understanding of some problems due to it's training data (e.g. setting up a web server with little to no deps in golang), that it can do with almost 100% certainty, where it's really easy to blaze through in a few minutes, and then I can setup the architecture for some very flat code flows. This can genuinely improve my output by 30%-50%