On Wednesday, Recursive Superintelligence released its first model, Flint: a laptop-class coding model whose development was touched at nearly every stage by an automated AI research system called the Eureka Machine, free on Hugging Face under Apache 2.0. With 40 billion total parameters and only 2.5 billion activated per token, it fits on an ordinary laptop — code stays on-device, works offline, no API fees (official announcement).
Performance: Small Frame, Abnormally Fast
With Recursive's optimized inference engine, Flint generates more than 125 tokens per second on an M4 Pro MacBook Pro and over 150 on a 64GB NVIDIA RTX Spark at short contexts. TechCrunch's report gives the control group: similar models, including Google's Gemma 4 and NVIDIA's Nemotron 3.5 Lightning, managed only 58 to 69 tokens per second on the same machine (TechCrunch).
On benchmarks, Recursive says Flint is the best-performing MoE model under 50 billion total parameters on agentic coding benchmarks including SWE-Bench Verified and Terminal-Bench 2.1, while rivaling dense models of 25-40 billion parameters at more than five times the speed. TechCrunch adds: on Recursive's own average across six tests, only Alibaba's Qwen3.8 27B scored higher.
Eureka Machine: AI Building AI, First Public Test
The real story is how Flint was built. The Eureka Machine automates the research loop — proposing ideas, running experiments, reading results, deciding what to try next — across parallel directions. The official blog says it contributed to nearly every part of Flint's stack: data, training recipes, infrastructure, and inference.
Two concrete examples. For architecture, it searched expert configurations from a 35B-total/3B-active baseline and settled on 40B/2.5B. For quantization, it went against common practice by quantizing the output layer (lm_head) to 4 bits, then recovered accuracy with offline distillation on a self-discovered coreset; the final NVFP4 model lands within 0.7 points of the 16-bit model. CEO Richard Socher tweeted that low-bit training shrank the model by 71% with almost no quality loss.
The result: a small team of people and agents built Flint from scratch in under four months.
Where Local Models Fit
For context: a week earlier, Meta released Muse Spark Open, an 816-billion-parameter open-weight model that needs at least a terabyte of memory. Flint sits at the opposite extreme. Both are pulling models back from the cloud onto local hardware. Wired's Will Knight tested Flint on a 48GB Mac, building a to-do app and a web research tool, and found none of the errors and slowness common to small coding models.
Caveats remain: Recursive itself admits Flint needs more guidance than large closed-source coding assistants, and "AI building AI" today is still automated research under human oversight. But the direction is clear — when Recursive signed its $410 million compute deal with AWS, it said the bet was "less about headcount and more about agent count." Flint is the first turn of that flywheel. For developers, a usable local coding model is free to grab today; for the industry, the question to watch is whether the next turn spins faster than this one.