Co-founder @ndea. Co-founder @arcprize. Creator of Keras and ARC-AGI. Author of 'Deep Learning with Python'.
United States
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I wrote Deep Learning with Python to be the definitive guide to how deep learning works and how to best make use of it. Tens of thousands of people got their career start via this book. 120,000 copies sold, and downloaded by millions more.
And now it's free to read online: deeplearningwithpython.io/
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François Chollet retweeted
Introducing SKY-001, the first Engineering World Model.
On CAD and BIM tasks, it beats GPT-6 Astra and every frontier model we tested:
-> 51-125x faster
- > 150-210x cheaper
-> Higher IOU than all frontier models
SKY-001 lets you dream a physical object before you build it.
18 months of work. Built by the team out of Maluuba's deep learning lab (acq. by @MSRResearch).
The first ‘AI General Engineer’ will be the backbone for Autonomous Manufacturing.
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When enough people believe something will happen, it doesn't need to happen at all for all of its consequences to be felt
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Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
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Argon has a similar performance profile on ARC-AGI-3 as Astra. Google is back at the frontier.
Gemini 4 Argon from @Google on ARC-AGI (Verified):
- ARC-AGI-3: 97.5%, $4.8K (provider adapter harness), 49.8%, $5.8K (standard harness)
- ARC-AGI-2: 91.3%, $0.32/task
- ARC-AGI-1: 98.5%, $0.16/task
Google DeepMind is the second frontier AI lab to score above 95% on ARC-AGI-3.
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I truly believe conversational AI will drive the biggest educational leap of our generation. 1-on-1 tutoring is the gold standard for education, but it doesn't scale. AI can change that with truly active conversational learning.
Annie and team set out to solve this with @gakkuai, and they've done a great job. Gakku is an AI mentoring app for K-12 students. Rather than doing the work for them, the voice chat coach guides students through 10 min essay writing "missions" that cover brainstorming, drafting, and revising. It actively trains kids to generate their own ideas, structure logical arguments, and articulate themselves clearly.
If you have school-aged kids, I highly recommend checking it out: gakku.ai
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Everyone wants to have solved a hard problem, but almost nobody wants to live inside the grind of actually solving it. The breakthroughs belong to the few who are obsessed with the process, not just the idea of the outcome
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You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomous robotics we have to engineer causality and discrete reasoning back into the loop.
Biwei and the team at Aether AI is doing exactly the kind of foundational, non-consensus work the field actually needs right now.
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting.
Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task.
When we started Aether AI, robotics was the obvious place to begin.
CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room.
Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more.
The technical write-up is linked in the comments.
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String theory is what happens when an intelligent system is starved of sensory input and starts hallucinating. Intelligence needs grounding.
For this reason, intelligence is practically never the bottleneck to solving impactful problems. The bottleneck is iterated contact with reality.
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The ideal outcome for science isn't a couple of labs using proprietary models and millions of dollars to crack trophy problems, it's to have millions of AI-aided human scientists who follow their curiosity and become way more ambitious
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DeepSeek V4.1 Flash from @deepseek_ai on ARC-AGI (Verified):
- ARC-AGI-3: 3.3%, $878 (provider adapter harness), 0.9%, $1.2k (standard harness)
DeepSeek V4.1 Flash is the first model we've evaluated on ARC-AGI-3 to score above 3% for under $1k in total evaluation cost.
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