Here's a bug I kept hitting. I'd ask an AI assistant to write some code against a library — Prisma, the Vercel AI SDK, Zod — and the code would look completely right. Clean, idiomatic, exactly the shape I expected. Then I'd run it, and it wouldn't compile.
The reason was always the same: the library had shipped a new major version, and the model was writing the previous major's API from memory. parameters instead of inputSchema. required_error instead of error. A new PrismaClient({ datasources }) call that no longer exists. Small things — but enough to break the build on the first try.
Models are frozen at their training cutoff. Libraries are not. So there's a gap between "the API the model reaches for" and "the API you actually have installed" — and that gap is widest right after a library ships a breaking change.
I wanted to know: how big is that gap, exactly — and can I measure it objectively? So I built SDKProof.
The method: let the compiler be the judge
