I build agents, RAG systems, and AI infrastructure that run in production. But the more I build with models, the clearer it becomes: the durable edge is understanding how they improve.
So I’m moving from production toward research — papers, experiments, benchmarks, loss curves, and the long work of learning how to ask better questions.
The curriculum I am working through. From engineer with an ML gap to independent research that ships.
read it ↗A field guide. What researchers measure, why they picked it, and how every benchmark eventually saturates. 32 specimens across 8 families.
read it ↗The companion volume. How Veo, Seedance, Kling, Wan, and LTX get graded — arenas, VBench, physics tests, and the hazards of scoring perception.
read it ↗An interactive roadmap from engineer to ML researcher. Progress plotted the only honest way.
github ↗ explore it ↗An agent skill that generates an interactive, self-contained HTML map of any codebase. A dependency graph of its modules, a zoomable lines-of-code treemap, and stack-specific tabs for whatever the repo actually contains (API surface, DB schema, routes, background jobs). One HTML file. No server, no CDNs, works offline. Light + dark themes, colorblind-validated palette.
github ↗Claude Code conventions, hooks, and slash commands for fullstack TypeScript. TanStack Start, tRPC, better-auth, Drizzle.
github ↗
The Cloudflare-native cut. A /setup skill that
bootstraps Workers plus Workers AI in five minutes.