@omarsar0: Recommended reading. (bookmark it) Pay attention to the prices and what a combination of models can unlock for you. You…
Summary
A thread discussing Cursor's experiment where a team of AI agents rebuilt SQLite from its manual in Rust, achieving 100% test pass rate with significant cost variation depending on model mix. Takeaways include using frontier models for decomposition and cheaper workers for implementation.
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Cached at: 07/21/26, 02:45 PM
Recommended reading.
(bookmark it)
Pay attention to the prices and what a combination of models can unlock for you.
You don’t need the absolute best model for everything.
I am not that impressed with the Rust SQLite replica. A totally new and improved SQLite (or something more creative and new) could be a more interesting test.
Takeaways:
- Use a frontier model for decomposition, architecture, key design choices, and trade-offs.
- Have cheaper, faster workers execute well-defined, narrow implementation tasks.
- Do not have planners implement or workers make broad design decisions.
Use a swarm or harness with a recursive task tree: planners break the original spec into subtrees, delegate them, and workers operate at the leaves. Cursor argues this helps mostly because each agent has a constrained context, rather than simply because many agents run in parallel. Pay extra attention to biases and failures that might emerge from poor/inefficient agent coordination.
Cursor (@cursor_ai): We had a team of agents rebuild SQLite from its 835-page manual.
It created a replica in Rust which passed 100% of a held-out test suite.
Interestingly, cost varied 15x depending on which model mix we used.
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