So yes and no. I often just let it work by itself. Towards the very end when I had more of a deadline I would watch and interrupt it when it was putting implementations in places that broke its architecture.
I think only once did I ever give it an instruction that was related to a handful of lines (There certainly were plenty of opportunities, don't get me wrong).
When troubleshooting occasionally I did read the code. There was an issue with player to player matching where it was just kind of stuck and gave it a simpler solution (conceptually, not actual code) that worked for the design constraints.
I did find myself hinting/telling it to do things like centralize the CSS.
It was a really useful exercise in learning. I'm going to write an article about it. My biggest insight is that "good" architecture for an current generation AI is probably different than for humans because of how attention and context works in the models/tools (at least for the current Claude Code). Essentially "out of sight out of mind" creates a dynamic where decomposing code leads to an increase in entropy when a model is working on it.
I need to experiment with other agentic tools to see how their context handling impacts possible scope of work. I extensively use GitHub Copilot, but I control scope, context, and instructions much tighter there.
I hadn't really used hands off automation much in the past because I didn't think the models were at a level that they could handle a significantly sized unit of work. Now they can with large caveats. There also is a clear upper bound with the Claude Code, but that can probably be significantly improved by better context handling.
I think only once did I ever give it an instruction that was related to a handful of lines (There certainly were plenty of opportunities, don't get me wrong).
When troubleshooting occasionally I did read the code. There was an issue with player to player matching where it was just kind of stuck and gave it a simpler solution (conceptually, not actual code) that worked for the design constraints.
I did find myself hinting/telling it to do things like centralize the CSS.
It was a really useful exercise in learning. I'm going to write an article about it. My biggest insight is that "good" architecture for an current generation AI is probably different than for humans because of how attention and context works in the models/tools (at least for the current Claude Code). Essentially "out of sight out of mind" creates a dynamic where decomposing code leads to an increase in entropy when a model is working on it.
I need to experiment with other agentic tools to see how their context handling impacts possible scope of work. I extensively use GitHub Copilot, but I control scope, context, and instructions much tighter there.
I hadn't really used hands off automation much in the past because I didn't think the models were at a level that they could handle a significantly sized unit of work. Now they can with large caveats. There also is a clear upper bound with the Claude Code, but that can probably be significantly improved by better context handling.