It uses Go, which is more verbose than Python would be, so he takes 300 lines to do it. Also, his edit_file tool could be a lot simpler (I just make my minimal agent "edit" files by overwriting the entire existing file).
I keep meaning to write a similar blog post with Python, as I think it makes it even clearer how simple the stripped-down essence of a coding agent can be. There is magic, but it all lives in the LLM, not the agent software.
I dictate rambling, disorganized, convoluted thoughts about a new feature into a text file.
I tell Claude Code or Gemini CLI to read my slop, read the codebase, and write a real functional design doc in Markdown, with a section on open issues and design decisions.
I'll take a quick look at its approach and edit the doc to tweak its approach and answer a few open questions, then I'll tell it to answer the remaining open questions itself and update the doc.
When that's about 90% good, I'll tell the local agent to write a technical design doc to think through data flow, logic, API endpoints and params and test cases.
I'll have it iterate on that a couple more rounds, then tell it to decompose that work into a phased dev plan where each phase is about a week of work, and each task in the phase would be a few hours of work, with phases and tasks sequenced to be testable on their own in frequent small commits.
Then I have the local agent read all of that again, the codebase, the functional design, the technical design, and the entire dev plan so it can build the first phase while keeping future phases in mind.
It's cool because the agent isn't only a good coder, it's also a decent designer and planner too. It can read and write Markdown docs just as well as code and it makes surprisingly good choices on its own.
And I have complete control to alter its direction at any point. When it methodically works through a series of small tasks it's less likely to go off the rails at all, and if it does it's easy to restore to the last commit and run it again.
