I've been running an experiment.
The Lab is a public project to produce a real, source-backed book on AI engineering by running autonomous research-and-writing loops over a corpus of practitioner talks. The AI Engineer YouTube channel — currently 794 videos and growing — is the primary source brain. Live counts in [STATS.md](https://github.com/isatimur/ai-engineering-book-lab/blob/main/STATS.md).
The deliverable is not just The Manuscript (the chapter drafts you're reading). The deliverable is The Method — a reproducible research-and-writing machine that turns source material into structured notes, theme and concept synthesis, verified Claims with Source Anchors (precise pointers: video id + timestamp), chapter packets, and — eventually — a finished book.
The field evolves too fast, the noise is too high, and the same ideas get repeated in slightly different packaging every week. is partly about writing a book and partly about building a system that helps me stay current, separate signal from noise, and surface the patterns that already have evidence behind them.
The workflow today:
videos → → → chapter drafts → public iterations
And the direction I'm pushing it in, autoresearch-style:
- bounded research passes
- self-improving instructions
- source-fidelity checks
- quality judges for summaries, , and chapters
- coverage, coherence, and drift metrics
The Manuscript is the visible output of a larger experiment: can a book become a public, self-improving research artefact?
This started as "let's make better notes." It's turning into something closer to a public experiment in writing, judging, and continuously improving that produces — with the discipline that no claim ships without a source anchor.
