Recursive_SI makes weights for local coding model Flint availableMachine translation

Automated AI research is now producing models people can download and run runs entirely on a local machine under an Apache 2.0 license.

Recursive Flint, a 40B mixture-of-experts coding model, was built with the Eureka Machine, a loop that proposes ideas, implements them, tests them and uses the results to choose what to try next.

Flint writes code at 125+ tokens per second on a MacBook Pro, which works out to about 90 words a second, roughly 20x faster than you can read it.

And it has pushed the Pareto frontier on local coding.

On a 48GB MacBook, local models used to make you pick between smart but slow (Qwen3.8 27B scores highest but runs at about 10 tokens per second) and fast but weak (tiny Qwen3.5 models).

Recursive Flint sits almost as high on quality (about 81% average against about 85%) while running roughly 7x faster across a long 128K-token session, and the shaded area marks the speed and quality combination no model offered before.