Cursor says SQLite rebuild passed all held-out tests, with a 15× cost spread
HamelHusain · x · 2026-07-22
A strong test suite and a clear understanding of how to hold back evals are presented as the key to getting exceptional results from AI.
The quoted Cursor example says a team of agents rebuilt SQLite from its 835-page manual, produced a Rust replica that passed 100% of a held-out test suite, and saw a 15× cost swing depending on the model mix.
The main takeaway is that for agentic coding work, evaluation design matters as much as raw model capability:
- Keep a separate held-out test set
- Use tests to constrain agent output
- Expect large cost differences from model selection
Related event: Cursor's Multi-Agent System Rebuilds SQLite in Rust, Slashing Costs 15x(8 posts)→
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