The initial implementation of Omnist, a tested, open-source Python library, came together from a 16-year-old PhD paper in two weeks — one person, part-time. That fact tends to get read as “AI writes code fast.” It doesn’t quite explain it. What actually happened: I ran two teams during those two weeks, not one. I was the only human on either of them. Two layers, two teams Split any R&D project into two layers. Conceptualization: interpreting the idea, deciding what’s true, choosing what to build, naming things so they make sense. Materialization: turning a decision into working code, a test that proves it, a release someone can actually install. ...

Sixteen years ago, in 2010, I published a paper on formalizing schemas for tree-structured data, like XML and JSON. My goal was to move beyond guesswork: could we mathematically decide if a schema change is backward-compatible? I wanted to see if we could prove schema equivalence, or reliably infer schemas from examples. The research offered a solid theoretical framework, though it remained largely academic. For years, I hoped to build something practical from it. But the demands of a full-time job made finding the time nearly impossible. ...