Recursive, an AI startup founded last year, is starting to live up to its name. The company, which is trying to build AI that can improve itself, released its first model, Flint, on Wednesday. Flint is a free coding model that can run on a laptop and was built largely with the help of AI.
Recursive’s automated research system, which it calls the Eureka Machine, helped with nearly every step of Flint’s development, the company says. That let 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 in turn builds even better AI. It’s also one of the most debated ideas in the industry. Some researchers fear that AI improving itself could slip out of human control. For now, Recursive’s version is more limited: an AI research assistant overseen by people that produced a model small enough for a laptop.
The company emerged from stealth in May with $650 million in funding at a $4.65 billion valuation, from backers including GV, Nvidia, and AMD Ventures. Recursive is led by CEO Richard Socher, Salesforce’s former chief scientist.
Because Flint runs on a user’s own computer rather than a company’s cloud servers, developers can use it offline, keep their code private, and avoid the usage fees that AI cloud services charge based on how much text the model processes. The model is free to download on Hugging Face, the popular site for sharing AI models, under the Apache 2.0 license, an open-source license that 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 settings, known as parameters, at a time. That makes it light enough for an ordinary laptop and far smaller than Meta’s Muse Spark Open, an open model built for Macs that was released last week. Muse Spark Open uses 816 billion parameters and needs at least a terabyte of memory.
Flint is also fast. In Recursive’s tests, it generated about 131 tokens (chunks of text) per second on a MacBook Pro with Apple’s M4 Pro chip — roughly twice the 58 to 69 tokens per second that similar models managed 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 that measures how well AI can fix real-world software bugs, Flint outperformed other models under 50 billion parameters. On Recursive’s own average across six tests, only one model, Alibaba’s Qwen3.8 27B, scored higher than Flint.
The company acknowledged that Flint would need more guidance than the big paid coding assistants, and suggested breaking large tasks into smaller steps. Still, early reviews were 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 that smaller coding models often ran into errors or were slow compared to Claude or Codex, but Flint nailed his coding requests every time.
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 said that Eureka’s contributions include a sparser model shape, low-bit training that shrank Flint by 71% with almost no loss in quality and custom kernels, which are specialized programs that speed up the underlying computations.
Recursive didn’t say how much time the Eureka Machine saved its researchers, but it’s not alone in leaning on AI to do 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.
If Recursive’s bet pays off, each new model should be able to take on longer stretches of work without human help. The company previously told TechCrunch that its $410 million compute deal with Amazon Web Services in June was “less about headcount and more about agent count.”
The longer-term goal, Socher told Wired, 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.
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