The only AI benchmark that matters is your own work
Public leaderboards measure something real. It is rarely the thing you are about to use the tool for. A cheaper test: hand it three problems you have already solved.
Every model release arrives with a chart. The numbers are real and the evaluations are honest, and they still tell you very little about whether the thing will help you on Monday morning.
Benchmarks measure average performance on problems chosen because they can be measured. Your work is neither average nor chosen for measurability. That is the whole gap.
A test that costs twenty minutes
Keep three tasks you have already solved. Not toy problems — real ones, out of real work, where you know exactly what good looks like because you produced it yourself.
When something new comes out, give it those three. You are not looking for a score. You are looking for the shape of the failure:
- Does it get the structure right and the details wrong, or the other way round?
- Does it hedge where it should commit?
- Does it invent an API that does not exist, and how confidently?
- When it is wrong, is it wrong in a way you would catch in review?
That last one decides everything. A tool that fails loudly is usable. A tool that fails plausibly costs more than doing the work yourself, because now you are proofreading instead of building.
Why this beats reading reviews
Most write-ups tell you how a tool performed on someone else's problem, in someone else's codebase, under someone else's standard for finished. Any of those three can flip the verdict.
The question is never whether a tool is good. It is whether it is good at the specific thing you were about to do anyway.
Twenty minutes and three saved problems will out-predict every leaderboard you read this year.