Eight years of wanting, three months of building with AI

https://lalitm.com/post/building-syntaqlite-ai/

The author shares their experience of building syntaqlite (github) an impressive set of SQLite tools.

Through most of January, I iterated, acting as semi-technical manager and delegating almost all the design and all the implementation to Claude. Functionally, I ended up in a reasonable place: a parser in C extracted from SQLite sources using a bunch of Python scripts, a formatter built on top, support for both the SQLite language and the PerfettoSQL extensions, all exposed in a web playground.

But when I reviewed the codebase in detail in late January, the downside was obvious: the codebase was complete spaghetti. I didn’t understand large parts of the Python source extraction pipeline, functions were scattered in random files without a clear shape, and a few files had grown to several thousand lines. It was extremely fragile; it solved the immediate problem but it was never going to cope with my larger vision, never mind integrating it into the Perfetto tools. The saving grace was that it had proved the approach was viable and generated more than 500 tests, many of which I felt I could reuse.

So they threw away everything they'd gotten so far and started fresh.

For complicated technical projects, you have to keep close control over the output of the LLM or it will churn out a mess.

More importantly, I completely changed my role in the project. I took ownership of all decisions and used it more as “autocomplete on steroids” inside a much tighter process: opinionated design upfront, reviewing every change thoroughly, fixing problems eagerly as I spotted them, and investing in scaffolding (like linting, validation, and non-trivial testing) to check AI output automatically.

Worth reading in full. Excellent technical report on building something complex with LLM tools.

I really admire the author's dedication to their journal, and clear-eyed summary of the process.