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Jeff Clune
@jeffclune
Co-founder, Recursive. Professor, CS, U. British Columbia. CIFAR AI Chair, Vector Institute. | ML, AI, deep RL, deep learning, AI-Generating Algorithms (AI-GAs)
448 Following    33.5K Followers
thanks for trying Flint! it was cool to see it trending #2# on @huggingface. glad also to hear so many people enjoying it. we're working on improving tool calling, broadening harnesses / inference support, and reducing thinking. anything else you'd like to see?
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✨Thank you to @jeffclune of @ubc for starting off this year's Kempner Seminar Series! We were excited to welcome him to the #KempnerInstitute# earlier this month to discuss his work on open-ended and AI-generating algorithms. 📹ICYMI, watch his talk here:
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Running a team of research agents used to cost API bills or a rack of H100s. Now everyone with a laptop can run an autoresearch swarm!
Amazing and rewarding to see our small-team research effort (led by @ashrewards , @jparkerholder, @_rockt et al.) take on a life of its own as an ever-improving flagship Google product! For me personally, a crazy idea I proposed in my 2019 AI-GA paper (a NN as the entire environment including pixels, sound, etc.) barely worked in Genie1 (2024), worked decently in Genie 2 (8 months later), became incredible in Genie3 (9 months later), and now Genie4 seems ready for Hollywood and VR. What an arc to observe in such a short time. Wild times! Now imagine open-ended learning in Genie. ✨ 🚀
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This is Genie 4, our most realistic and interactive world model yet. 🌐 Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.
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Recursive CEO @RichardSocher explains why open-endedness is crucial for AI breakthroughs: If you only focused on improving fire, you'd never invent the microwave. "If you just go directly after the goal, you’re not thinking enough about clever ideas." "Imagine you started cooking food over fire, and you’re like, let’s make it faster to heat up food. If you just deal with the fire, you would never invent the microwave. The microwave happened because other people were working on radio waves and realized, this chocolate bar in my pocket is melting." "One of the earliest papers is the Darwin Gödel Machine. Four of the five authors are at Recursive. You see the kinds of plots that became popular with Andrej Karpathy’s Auto Research project first in that paper." "An open-ended approach to prompt injection is you ask another LLM to attack the first one, and once it figured out the attack, you inoculate it, and ask it again. You co-evolve an attacker LM with a defender LM. That idea is called rainbow teaming, invented by Tim Rocktäschel and his team." "That’s a better approach long term than just pure RL where you chase after this exact goal." @Recursive_SI
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People say hosting a foreign open-weight model on sovereign compute is safe. This is an example it's not. Sovereign compute controls where a model runs, not what it does.
Scary!
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report:
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I had a deeper look at Recursive's 40B Flint model I ran a few test yesterday but what I hadn't checked was the third-party support for the model. I was using @Prince_Canuma 's mlx-vlm but at the time it wasn't doing justice to the model. I re-ran the tests with mlx-vlm's unmerged PR 2569 (fused metal kernels), the different to stark, up to 30% which is amazing and knowing Prince, we'll see more. As an agentic model this is a beast, up there with the leaders and holding its own. Go team Recursive!
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@jeffclune flint in a real agent loop on day one was a fun one ngl 👀
Recursive Flint 4.7x faster than Nemotron 3.5 🎃 We gave Flint, Ornith and Nemotron the same prompt in Atomic Local AI Agent’s orchestration mode, where Opus plans and the local models get the task done Try Atomic Fusion ->
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I've been a heavy Qwen user. Now 100% on @Recursive_SI 's Flint. I built this side by side test, running both models at 8bit on the same hardware (RTX A6000 48Gb) try it: (This is running locally on two identical cards) BTW 4 bit is even faster.
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This recursive self-improvement development is patently completely insane. Recursive built an LLM completely from scratch (reinventing a lot of stuff), and now it is at the Pareto frontier. Recursive invented the machine that invented the LLM in just a few months! The company was founded just in February this year!!
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Startup Recursive has released Flint, a free, laptop-sized coding model built largely by its own AI research system, as an early test of AI that improves itself.
Incredibly fun journey building the RSI loop(s) that led to a best-in-class local coding LLM. A few simple coding examples Flint could do in < 10 mins. 0 endpoint. 0$. Full CoTs. Stay tuned for more! ps: Fable made this video from the artifacts alone. No editing.
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This = 👀 🏆 👀 🏆 👀 🏆 "Most labs scale by hiring thousands of researchers. We're scaling by building an open-ended AI system that automates this process across ideation, implementation and validation. Every model our system builds will make it better at building the next."
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We’re about to see a breakout in local model capabilities. Flint scores 54.9% on Terminal-Bench 2.1 with ~2B active parameters. That’s ahead of every other small MoE and two of the three dense ~30B models. It only needs about 24GB and runs at 125+ tok/s on a MacBook Pro.
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"I used Flint to build a bunch of programs and found it to be both fast and very capable. In my experience, models of this size can be annoyingly error-prone or slow compared to Claude or Codex, but Flint nailed my coding requests every time." — @willknight, @WIRED
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1/ From OpenAI's Navier–Stokes work to AlphaEvolve's faster algorithms, AI research is getting very real. Now, with Recursive Flint, we can experience that magic locally on our own laptops 💻 The whole research loop runs locally with a selection of tasks! Code + demos 🧵
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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 open-weights pushed on the Hugging Gace hub. Congrats @Recursive_SI !
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I spend increasing amounts of my time all day, every day, using AI. The world of Her is quite close.