Standard AI benchmarks are like the SATs: they ask whether AI can replace you on generic tests, and at this point every frontier model scores near the top—which tells you almost nothing about which one is actually good at *your* job. What you want instead is a reference check that asks whether a model can be put to work for you and help you do your job better. Coding took off with AI first because code has verifiable rewards, while knowledge work depends on human taste. A personal benchmark makes your taste testable by turning your real work and corrections into repeatable test cases and simple yes/no checks based on your judgment. Today in Every Checks, we do this for our own editorial work—like testing models on a published article opening with yes/no checks for whether the headline makes the strongest possible claim and the opening uses concrete wording, or turning our editor-in-chief Kate’s copy edits in Google Docs into checks (**KateBench**). Next, whenever you correct an AI in your workflow—“that’s not quite right, do this instead”—one click will save that correction as a case and a check automatically. Because no model company can credibly tell you when a competitor’s model is better, your personal benchmark compares models across every provider against your standards, shows you which one works best or most cheaply for your specific tasks, helps you switch when a better model comes along, and gives your skills (**KatePass**) a clear target to improve against so you can share them with your team (**EveryAgent**).