I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model. Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intelligence Index at 59. It also leads across key cybersecurity evaluations scoring 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on CWE-bench for patching. We’re seeing significant improvements in long-horizon tasks even compared to Argon, and we’ve strengthened frontier safeguards. As we did with Argon, we’re going to roll it out gradually + responsibly. Some trusted cyber defenders will get access starting this week. We'll be expanding it to more partners through our Fairwind Program, ahead of wider availability soon, so hang tight!

Le Chonk cooked on visual grounding. Pinpoints exactly where things are in an image. 42% on Dense 200, ahead of even frontier closed models..

R.I.P.

Speeding up Gemma! Gemini 4 Argon enabled a 2.85x speedup on an A100 via vLLM, jumping from ~37,900 to 108,000+ tokens/sec on gemma-4-E2B-it. ✅ 100% token parity ✅ Zero quality drop ✅ Done in days, not months See the exact changes across 8 PRs: goo.gle/4zNtANl

Our latest open-source research has a pretty interesting name, inspired by the story of the Tower of Babel. What interests us about that story is how language barriers can get in the way of people working toward the same goal And when it comes to agents, there are a few more places for that understanding to break, like tool arguments, database entries, and search queries One example from BabelArena (yes that's the name, surprise) stuck with me: Qwen-3.8-Max completed the same shopping task in English and Telugu, getting the same four products. But it took 25 tool calls in English and 201 in Telugu, with repeated empty searches against the localized catalog. Even though both runs passed, we’d still want to see that difference before shipping. Sorry for the long story, hope you guys are still here. What I want to share is that we’ve open-sourced BabelArena to study these workflows across 23 languages. Code, datasets, and paper in the comment

If you were thinking concentrating AI behind closed doors is safe, it is time to update your priors!

Today, we hold Japan Day @cityoflondon to celebrate the strong ties between 🇯🇵🇬🇧financial sectors. The @citylordmayor and I published a special joint article for this occassion. Looking forward to further strengthening🇯🇵🇬🇧partnership! asia.nikkei.com/opinion/from…

まずはシミュレータ上で歩行確認！✌️ #超エレキ万博2026

If you don't want to wait to experience the magic of HF open-source robots, you should get a Reachy mini, ships much faster than microduck and as much fun! store.pollen-robotics.com/

Seeing some bad takes on British Government AI funding: some notes. 1. The Government has announced a tranche of funding for a number of different AI-related organisations and initiatives. It comes to roughly £600m (excluding private sector input.) This has led to a series of partisan bad takes by people who don’t really follow this conversation. That is annoying because only I can have partisan bad takes. 2. Two common takes: “this is nothing, AI labs spend hundreds of millions every day/month etc! We can’t compete! Common British L” Or “Government is terrible at picking winners and investing, should be left to the private sector.” These takes reflect usually partisan pre-existing notions, neither actually understands what this money does or doesn’t do, and neither actually examines what the money is going towards either. Apples to oranges type stuff. 3. Let’s break down two of the more significant sets. The first is more funding for AISI (+£120m), and the second is for a new AI Biosecurity Lab (£100m, + around £200m from frontier companies). 4. AISI is the world’s best resourced institute doing this work and leading in its class. Don’t take my word for it - feel free to read MI5 Chief’s recent speech, or any number of expert statements from frontier companies. IT IS NOT a frontier AI company competitor. IT IS NOT designed to produce cutting edge LLMs or agents or consumer applications. It is, effectively, a research organisation staffed by clever people whose job is to analyse what frontier AI companies are working on and test their stuff. So if you’re measuring this announcement of investment and saying “it’s nothing compared to what AI firms spend”, you’re comparing totally different things. Instead, look how much spending Britain’s AISI gets relative to its international counterparts on getting access and insight into this frontier. 5. The UK’s AI Biosecurity Lab (AIBL) is even newer than AISI, and a world first. IT IS NOT a competitor to private sector biolabs. IT IS NOT an example of Government intervening and ‘picking a winner.’ Its job - modelled after AISI - is to “develop technical capabilities that help the UK better understand, prevent, detect and respond to biological threats.” It will do this by attracting best in class talent + working with frontier companies + working with the UK’s wider biosecurity landscape + other agencies. 6. Both of these organisations represent continuity in British Government thinking: ‘AI brings with it a whole bunch of positive things, but also risks. We should create organisations that attract top talent to help us analyse and understand this technology.’ You see this in MI5’s recently launched AI Threat Assessment Centre (AITAC) and the AI Economics Institute (AIEI) launched earlier this year. You can absolutely think this is the right or wrong approach btw but that’s the broad strategy. 7. So the comparisons to frontier companies’ spending is just misplaced. You’re working yourself into a tizzy over a misunderstanding of why the state is allocating resources. You shouldn’t compare these to frontier companies, but instead to other AISIs or other foreign govt AI investments I guess. Finally, if I asked many of those commenting what they would do differently, I would guess they wouldn’t really know.

We’re excited to announce an additional £120m for Sovereign AI to back the future of AI in Britain. An extra £60m to invest in the UK’s most promising AI companies, expanding into Physical and Industrial AI. And £60m more for R&D procurement, backing innovation in areas such as AI security and AI for healthcare. We want to help founders start and scale in Britain, and turn innovation into impact.

Today we're setting out a British approach to AI: backing innovation and growth, managing risks responsibly, and making sure the benefits of AI-enabled growth are felt right across the country.

Huge. British Govt announces: • £165m: National AI Biosecurity Initiative: Includes £100m for a new AI Biosecurity Lab, using AI to detect emerging biological threats. • £120m: AI Security Institute (AISI): Additional funding to strengthen AI risk research and evaluation. • £70m: National Exposure and Real-Time Vulnerability (NERV) Centre: Identifying vulnerabilities in critical infrastructure. • £60m: National Cyber Shield: Strengthening Britain’s cyber defences. • £60m: UK Sovereign AI: Expanding investment into physical and industrial AI. • £60m: R&D Procurement Scheme: Supporting companies developing AI security and NHS innovations. • £30m: Mayoral AI Partnerships: Helping local businesses adopt AI, create jobs and improve public services. • £23m : AI Economics Institute: Research into AI’s effects on productivity, employment and living standards. • £18m: UK Advanced Chip Design Academy: Developing domestic semiconductor design expertise and reducing overseas dependencies.

Get in loser, we are going to evaporate the oceans.

士郎正宗によるサイバーパンクの金字塔 「攻殻機動隊」 新作アニメーション、制作始動。 アニメーション制作：サイエンスSARU #攻殻機動隊 #theghostintheshell

Heads up regarding @Recursive_SI's flint, it seems to be suffering from launch bugs in llama.cpp at present. I'd say give it a week before testing.

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 scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.

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chills #hearheartart

… i drive … #heartheartart