Really great to see a realistic experience sans hype about AI tools and how they can have an impact.

> But when I reviewed the codebase in detail in late January, the downside was obvious: the codebase was complete spaghetti...It was extremely fragile; it solved the immediate problem but it was never going to cope with my larger vision...I decided to throw away everything and start from scratch

This part was interesting to me as it lines up with Fred Brooks "throw one away" philosophy: "In most projects, the first system built is barely usable. Hence plan to throw one away; you will, anyhow."

As indicated by the experience, AI tools provide a much faster way of getting to that initial throw-away version. That's their bread and butter for where they shine.

Expecting AI tools to go directly to production quality is a fool's errand. This is the right way to use AI - get a quick implementation, see how it works and learn from it but then refactor and be opinionated about the design. It's similar to TDD's Red, Green, Refactor: write a failing test, get the test passing ASAP without worrying about code quality, refactor to make the code better and reliable.

In time, after this hype cycle has died down, we'll come to realize that this is the best way to make use of AI tools over the long run.

> When I had energy, I could write precise, well-scoped prompts and be genuinely productive. But when I was tired, my prompts became vague, the output got worse

This part also echoes my experience - when I know well what I want, I'm able to write more specific specifications and guide along the AI output. When I'm not as clear, the output is worse and I need to spend a lot more time figuring it out or re-prompting.