[coltonv]

What I'm saying is that the model will get into one of these loops where it needs to be killed, and I'll look at some of the intermediate states and the reasons for failure and they are because it hallucinated things, ran tests, got an error. Does that make sense?

Deleting and re-prompting is fine. I do that too. But even one cycle of that often means the whole prompting exercise takes me longer than if I just wrote the code myself.

[kasey_junk]

I think maybe this is another disconnect. A lot of the advantage I get does not come from the agent doing things faster than me, though for most tasks it certainly can.

A lot of the advantage is that it can make forward progress when I can’t. I can check to see if an agent is stuck, and sometimes reprompt it, in the downtime between meetings or after lunch before I start whatever deep thinking session I need to do. That’s pure time recovered for me. I wouldn’t have finished _any_ work with that time previously.

I don’t need to optimize my time around babysitting the agent. I can do that in the margins. Watching the agents is low context work. That adds the capability to generate working solutions during times that was previously barred from that.

[coltonv]

I've done a few of these types of hands off and go to a meeting style interactions. It has worked a few times, but I tend to just find that they over do it or cause issues. Like you ask them to fix an error and they add a try catch, swallow the error, and call it a day. Or the PR has 1000 line changes when it should have two.

Either way, I'm happy that you are getting so much out of the tools. Perhaps I need to prompt harder, or the codebase I work on has just deviated too much from the stuff the LLMs like and simply isn't a good candidate. Either way, appreciate talking to you!