Recursive releases the open-weight Flint local coding model
Recursive released Flint, an Apache 2.0-licensed mixture-of-experts model with 40 billion total parameters and 2.5 billion active parameters per token, optimized for coding agents on local hardware. Recursive says its Eureka Machine automated most of the development pipeline, while reported benchmark and speed figures are based on the company’s own tests.
Flint provides a concrete release built around AI-assisted model research, although the reports do not establish that it achieves full recursive self-improvement.
For the past few days I’ve been testing an impressive open-weight AI coding model called Flint. It suggests that fully recursive self-improvement, or RSI—an exciting and controversial approach that uses AI to build AI—is getting closer.
Flint is the first offering from Recursive, a company that aims to use RSI to advance both AI itself and other areas of science using AI. “Nearly every aspect of the entire model pipeline was actually done and improved upon by AI,” Recursive CEO and cofounder Richard Socher told me.
Socher says his company used its recursive AI system, dubbed the Eureka Machine, to automate most elements of the model’s development. This enabled a company with a handful of researchers to build a world-class model in just a few months, he says.
Socher told me that the Eureka Machine devised a number of innovations, including a new way of training the base model, new model architectures, and code optimized for specific silicon chips. Recursive shared benchmark scores that suggest Flint outperforms other models of similar size and architecture on coding tasks.