Recursive releases Flint, an open-weight, 40B-parameter coding model developed with its Eureka Machine AI system, which automated most of the development

Recursive makes the case that the benefits of using AI to build AI outweigh the risks. — For the past few days I've been testing …

Wired 2026-10-22 Will Knight

Context & Ripple Effects

Flint arrives amid a wider push toward recursive self-improvement, in which AI labs are trying to use models to accelerate the research and engineering behind subsequent models. Anthropic has described Claude as authoring more than 80% of code merged into its codebase, making Recursive’s release a public product-level test of that approach.

The coding-model market was already competing on speed and cost: xAI positioned Grok Code Fast 1 as an economical agentic coding option across major developer tools. Recursive adds open weights and a local-use pitch to that contest, while presenting Eureka Machine’s ideation-to-validation loop as part of the product story.

First-order effects

Developers and organizations gain access to Flint’s weights for coding-agent use, giving them an alternative to closed API-delivered coding models.
Recursive gains a concrete validation vehicle for Eureka Machine: the released model is evidence for its claim that automated research can handle much of a model-development cycle.

Second-order effects

Coding-model vendors face sharper pressure to compete not only on capability, but also on inference speed, cost, local deployment, and whether customers can inspect or adapt model weights.
For teams concerned about API bills or code leaving their environment, an open-weight local model expands the set of deployment choices beyond hosted coding assistants.

Third-order effects

If automated systems repeatedly produce competitive models, AI research teams can shift more engineering effort from executing experiments to setting objectives, evaluating results, and governing the automated loop.
Open-weight releases produced through automated research would widen access to coding-model capabilities while making model access and deployment choices a more consequential governance issue.

The trend: AI labs are moving toward recursive self-improvement, pairing automated research workflows with coding models that compete on accessible, efficient deployment.

Discussion

@richardsocher Richard Socher on x
Introducing Flint, @Recursive_SI 's first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from…
@mortezabihzadeh Morteza on x
When I vibe code, two things are always in the back of my mind: the API bill & where my code is going A strong coding model running on my own laptop solves both Thats why Flint from @Recursive_SI caught my attention, this one is worth watching closely [image] [embedded post]
@richardsocher Richard Socher on x
Flint is also a real-world test of the Eureka Machine, our system for automated AI research. It runs the full research loop: ideation, implementation, and validation. Then it uses the results to decide what to try next. We used it in nearly every part of how Flint was built. This…
@recursive_si @recursive_si on x
Recursive Flint is here: our first model, built with the Eureka Machine. Fast, local coding. Open weights available on Hugging Face.
@josh_tobin_ Josh Tobin on x
3/ You'll want agents that can take on enormous amounts of work and keep going for hours or days. You'll also want something fast beside you while you're making things. Try an idea, see what happens, change your mind, try again.
@celineodier @celineodier on x
ok so AI is now helping design the next AI. and the result runs on a macbook 💀 Flint from @recursive_si: 40B, open weights, built in under 4 months with their own automated research system [embedded post]
@miaai_lab Mia on x
Flint - 40B params total, 2.5B active It's @Recursive_SI's first model. Claims to run at 150 tok/s on a DGX/RTX Spark. Looks interesting enough to test! Could it beat the legendary Qwen3.6-35B? [embedded post]
@ivanfioravanti Ivan Fioravanti on x
My dream one day is a DGX Station! 🤩 Image taken from the blog post - Bringing the Spark to Europe: Local Intelligence Powers Next Wave of Agents: https://blogs.nvidia.com/...: [image]
@josh_tobin_ Josh Tobin on x
1/ Introducing Flint. It's a pretty good model for agentic coding locally. We @Recursive_SI think a lot of everyday coding will move to models running on your laptop or a server down the hall. Here's why we built it.
@thom_wolf Thomas Wolf on x
My bar for AGI is still an AI winning a Nobel. But an AI researcher looking at the lm_head (that *everyone* tells you to keep in high precision) deciding to quantize it to 4 bits AND being actually right (0.7pt from bf16) is definitely impressive. Even better when the result are …
@diptish09 Dipti Sharma on x
A coding model that's fast, private, open-weight, and works offline? That's a compelling combination. Flint delivers 125+ tokens/sec on an M4 Pro MacBook Pro, with options to run it through tools like Ollama, LM Studio, and llama.cpp. Big win for developers who want capable AI wi…
@_rockt Tim Rocktäschel on x
We are releasing our first model, Flint, a best-in-class local OSS coding model that runs at 150+ tokens/s on an @nvidia DGX Spark and 125+ tokens/s on a MacBook Pro M4—developed from scratch in less than four months with help from @Recursive_SI's autoresearch system.
@tydsh Yuandong Tian on x
Today, we release our first model, Flint-40B-A2B, a fast local coding model built …
@josh_tobin_ Josh Tobin on x
7/ My bet is that we'll use both: powerful agents in the cloud, and increasingly capable models right beside us. Flint is our first step toward that local future. Try it on something you're building: https://docs.recursive.com/
@lmstudio @lmstudio on x
We're excited to bring you Flint by @Recursive_SI in LM Studio on day 0! • 40B MoE, 2.5B active • 262K token context window • Apache 2.0 • Trained for local agentic coding model Requires about 24GB to run locally on your machine, performs impressively on benchmarks.
@richardsocher Richard Socher on x
Flint is the best-performing model in its class (MoE models under 50B) on agentic coding benchmarks like SWE-bench Pro v2, Terminal-Bench 2.1, and SWE-bench Verified. It also rivals similarly sized dense models that are much slower to run.
@datachaz Charly Wargnier on x
i don't think people realize how big this is... A US lab just released a 40B open-weight coding model leading its class on three major coding benchmarks. 125+ tok/s on a MacBook. 150+ on RTX Spark. no per-token fees. no usage limits. your laptop is now the endpoint. [image] [embe…
@shiri_shh Shirish on x
sir... they just dropped a coding model that runs LOCALLY on MacBook Pro at 125 tokens/sec [embedded post]
@documentingagi @documentingagi on x
Recursive SI launches Flint, its first model: a fast local coding model built …
@josh_tobin_ Josh Tobin on x
4/ My ideal version of that everyday experience is fast, customizable, private, and free. Flint is a step toward that: open weights, running on your hardware, without API fees. Over 125 tokens/sec on an M4 Pro MacBook Pro at short contexts.
@dreamtgn @dreamtgn on x
Flint by Recursive is an interesting step for local AI • A 40B coding model built to run on local hardware …
@0xenesonchain @0xenesonchain on x
One thing that's always bothered me with AI coding tools is having to choose between model capability, speed, cost and privacy. …
@codemarch @codemarch on x
I've been looking forward to the day when capable coding models can run locally without sacrificing so much performance. …
@zephyr_z9 @zephyr_z9 on x
Very cool Matching the small Qwen with their first model release [image] [embedded post]
@pidotdev @pidotdev on x
@Recursive_SI shipped its first model, Flint! Exciting to see they did extensive RL training on the Pi harness, so it works well out of the box. 40B MoE (2.5B active) for fast local coding. Apache 2.0. Available now for free in Pi via Radius. [embedded post]
Cong Lu Cong Lu on linkedin
We just released Flint: a blazing-fast 40B model shaped by autoresearch. — Built from scratch in under four months, with our Eureka Machine and an incredible team. …
@timkellogg.me Mr. Tim on bluesky
Flint: a brand new, from scratch, small LLM that's a heck of a lot faster than Qwen3.8-27B and basically as good — recursive.com/updates/intr... [image]
r/LocalLLM r on reddit
a new model? Flint 40b
@josh_tobin_ Josh Tobin on x
5/ We built Flint from scratch in under four months with a small team and our automated research system. It leads MoE models under 50B total parameters on agentic coding benchmarks. The system helped develop everything from training recipes to inference kernels.
@ollama @ollama on x
Meet Flint from @Recursive_SI! A ~40B parameter MoE open model for fast, accurate agentic coding use case made to run on-device. Flint can generate >125 tps on a M4 Pro Macbook Pro and >150 tps on a 64GB NVIDIA RTX Spark on short contexts. Try it on Ollama today: ollama run flint…
@eliebakouch Elie on x
SLIDING WINDOW AGAIN and huge congrats, this is a very cool release :) [image] [embedded post]
r/LocalLLaMA r on reddit
Recursive Flint - the “Qwen3.8 35B” we've all been waiting for?
@josh_tobin_ Josh Tobin on x
2/ At OpenAI, when we were building the first version of Codex, there was a debate: focus on the local, interactive experience, or build for long-running agents in the cloud? I always thought we'd need both.
@svpino @svpino on x
Here is an open-weight coding model you can run locally on your computer. …
@openrouter @openrouter on x
Flint from @Recursive_SI is now on OpenRouter! Recursive's first model: an open-weight 40B MoE …

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