[tylerrecall]

Interesting approach. The challenge I keep hitting with AI-generated documentation is that it lacks the persistent context of how the codebase actually evolved - the decisions, the "why we didn't do X" knowledge, the patterns that emerged over time.

I'm working on RecallBricks (memory infrastructure for AI coding tools) and seeing similar problems: AI tools are great at answering questions about code right now, but they don't remember the conversation you had last week about why you chose this architecture over that one.

For documentation specifically, have you thought about combining the AI-generated docs with a memory layer that captures decision history? Like "this API endpoint exists because of issue #247 where users needed X functionality." That context makes docs way more useful than just describing what the code does.

Curious how you're handling the "outdated docs" problem mentioned above - do you have triggers to regenerate when code changes significantly?