Recursive's Flint: A $4.65 Billion Startup's Laptop Coding Model Built by AI

Recursive, a startup valued at $4.65 billion, released its first AI model on Wednesday: Flint, a free coding model that runs on an ordinary laptop and was built largely by AI itself. The company's automated research system, the Eureka Machine, helped with nearly every step of Flint's development, letting a small team build the model from scratch in under four months.

Flint is an early public test of recursive self-improvement — the idea that AI can build better AI, which then builds even better AI. It's also one of the most debated concepts in the industry. Some researchers fear AI improving itself could slip out of human control. Recursive's current version is more limited: an AI research assistant, overseen by people, that produced a model small enough for a laptop.

Who is behind Recursive?

The company emerged from stealth in May with $650 million in funding at a $4.65 billion valuation, backed by GV, Nvidia, and AMD Ventures. CEO Richard Socher is Salesforce's former chief scientist. In June, Recursive signed a $410 million compute deal with Amazon Web Services, telling TechCrunch the arrangement was "less about headcount and more about agent count."

What makes Flint different?

Because Flint runs on a user's own computer rather than cloud servers, developers can work offline, keep their code private, and avoid usage fees that cloud AI services charge based on how much text the model processes. The model is free to download on Hugging Face under the Apache 2.0 license, which allows free use and modification.

Flint is a mixture-of-experts model, meaning it uses only a small slice of itself for any given task: just 2.5 billion of its 40 billion parameters at a time. That keeps it light enough for a laptop and far smaller than Meta's Muse Spark Open, an open model built for Macs released last week, which uses 816 billion parameters and needs at least a terabyte of memory.

Speed is a selling point. In Recursive's tests, Flint generated about 131 tokens per second on a MacBook Pro with Apple's M4 Pro chip — roughly twice the 58 to 69 tokens per second managed by similar models on the same machine, including Google's Gemma 4 and Nvidia's Nemotron 3.5 Lightning. On Nvidia's RTX Spark, Flint topped 150 tokens per second.

On SWE-Bench Verified, a benchmark measuring how well AI fixes real-world software bugs, Flint outperformed other models under 50 billion parameters. On Recursive's own average across six tests, only one model scored higher: Alibaba's Qwen3.8 27B.

How much did AI actually build?

The Eureka Machine comes up with ideas, runs experiments, and uses the results to decide what to try next. According to the company's launch video, it contributed to nearly every part of building Flint — from pre-training to post-training and inference. Recursive credits Eureka with three specific contributions:

• a sparser model shape;
• low-bit training that shrank Flint by 71% with almost no loss in quality;
• custom kernels, specialized programs that speed up the underlying computations.

Recursive didn't say how much time the Eureka Machine saved its researchers. It's not alone in leaning on AI for research: OpenAI said last month that by mid-August, its AI agents were logging 3.1 workdays for every one worked by a human researcher.

What do early reviews say?

The company acknowledged that Flint needs more guidance than the big paid coding assistants and suggested breaking large tasks into smaller steps. Early reviews were still positive. Wired's Will Knight used Flint on a Mac with 48GB of memory to build several small programs, including a to-do list app and a tool that scours the web for research. He found smaller coding models often ran into errors or lagged behind Claude or Codex, but Flint nailed his coding requests every time.

What comes next?

If Recursive's bet pays off, each new model should handle longer stretches of work without human help. Socher told Wired the long-term goal is for the Eureka Machine to achieve full recursive self-improvement, with humans overseeing the process and able to stop it if something goes wrong. Recursive says each new model should make the Eureka Machine a better researcher, helping it build the next one faster.