Supabase open-sourced their evals framework last week (supabase/evals, Apache-2.0), and I think it's the most useful thing published about AI coding agent evaluation in months. Not because of which model topped the leaderboard. Because of how they designed the measurement itself. What They're Testing and Why It's Hard Supabase built their evals around a three-axis grid: products (database, auth, storage, edge-functions, realtime, cron, queues, vectors, data-api), topics (RLS, security, migrations, SQL, SDK, observability, self-hosting, declarative-schema), and stages (build, deploy, investigate, resolve). That last axis is where it gets real. "Build" is the easy part. Any capable coding agent can scaffold a schema. "Deploy" and "Investigate" are where agents start to diverge. "Resolve" is where you find out if an agent can fix a broken RLS policy without silently breaking three others it didn't know existed. They're runni...