Aron Szucs @Skinaron

Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi.ai, Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: platform.kimi.ai 🔗 Tech blog: kimi.ai/blog/kimi-k3-1…

@MatthewBerman @bot Hi Matthew, any chance if still getting this?

Introducing GEN-1.5, a one-shot learner. It can learn new tasks in a few seconds. Show it what to do, and it generalizes. This capability emerged from pretraining on physical data at scale, as a step towards our mission of building general intelligence for the physical world.

Wow! Wow! Did these guys just solve PGI (physical general intelligence) or the physical world equivalent of AGI?! These one-shot physical world learning of the robots looks so incredible! Big congrats to the Generalist team!

Generalist @GeneralistAI

2 months ago

Introducing GEN-1.5, a one-shot learner. It can learn new tasks in a few seconds. Show it what to do, and it generalizes. This capability emerged from pretraining on physical data at scale, as a step towards our mission of building general intelligence for the physical world.

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We’re launching DeepSeek-V4-Pro today! 🚀 🔷 Major Agent upgrades with strong production gains! 🔷 Flexible reasoning effort for V4-Pro & V4-Flash: low for simple tasks, high for daily Agent workflows, max for complex tasks. 🔷 Native OpenAI Responses API support, optimized for Codex with one-click setup. V4 Pro is now available on app/web. Try it via “Expert Mode”. V4 Pro is also available via API. Model names remain unchanged—please refer to the API docs for setup details.

‼️huge ssi news. ilya is about to take his first tentative steps out of the age of research and back into the age of scale. it’s time to smell what ssi is cooking. ssi have built a small reasoning engine that can compete with much larger training runs because his data is better curated to meta learning. but, more importantly. we’re about to step into the era of TTT (test time training. gradient descent happening in real time to solve your problems). so instead of a context window you get actual learning. and because it’s so sample efficient it can be trained on hard to verify tasks that other paradigms can’t touch. everyone else’s weights are frozen, they struggle out of distribution. ssi have created something that has a bundle of knowledge but can truly learn in real time and use that to your advantage. current approaches are trying to hack their way to ‘learn’ with memory tricks, this thing will updates its weights, remember key lessons, and finally feel like a human level reasoner. this is a huge paradigm shift from the king. early results are very impressive. we can stop watching memento on repeat. it’s learning all the way down, the descent is real.

As a professor, I am often asked: Should students use AI coding agents for their research? I have also been trying to understand this question myself, but until now, I had mostly learned about students’ use of AI indirectly, through conversations with them or by seeing the final results. It is rare to have a long, uninterrupted period in which I can closely observe how they actually work, what they delegate to AI, and how their decisions evolve from day to day. The past three weeks at the Telluride Neuromorphic Workshop offered exactly that opportunity. Students propose their own innovative science and engineering projects, form teams, and work toward a demoable final presentation over three weeks. Here are a few things I noticed: 1. Productivity increased dramatically with AI. This was evident across both engineering and scientific projects. The ability to prototype an idea quickly has never felt so powerful or direct until I sat down with the students and observed their coding pipelines. For instance, one integrated-circuit group developed an asynchronous microcontroller with custom instructions, connected it to event-based sensors, deployed it on an FPGA, and produced a runnable demo with C code and a polished, configurable web interface. Completing all of this in three weeks would have been almost unthinkable a few years ago. Contrary to what people sometimes presume, many amazing ideas still emerge from discussion, while the Claude tokens are burning in the background. 2. Students are under peer pressure to use AI to keep up with the rapid pace of development. I noticed that students were more likely to use coding agents when other members of their teams were already using AI to finish their parts. Once some students begin moving much faster, the pressure to produce comparable results becomes very real. 3. The time budget shifts from debugging to planning. I noticed that students spent significantly more time planning their next steps while leaving much of the tedious plumbing work to coding agents. I saw far fewer of the typical frustrations caused by a “stupid bug,” which allowed students to focus more directly on system design and the actual problem they were trying to solve. 4. AI can be more focused on engineering solutions than scientific exploration. Students sometimes found that coding agents concentrated narrowly on solving the problem stated in the prompt, while overlooking instructions to branch out or explore less conventional possibilities. Instead of substantially modifying an established solution or pursuing a risky new direction, coding agents often preferred a safer implementation that was more likely to work. This is useful for engineering, but it can also discourage the kind of uncertain exploration that leads to genuinely novel research. 5. The biggest concern is understanding. Students increasingly delegate entire tasks to AI, which sometimes leads them to present systems or results that they do not fully understand. This is clearly harmful because it removes both the intellectual ownership and the educational value of academic research. A working demo is not enough if the student cannot explain why it works, where it may fail, and which technical decisions shaped the final result. My conclusion is that AI in research is no longer optional. We need to embrace this change, but we must also separate two objectives that are often conflated: education and research project execution. Students benefit most from AI when they already understand the fundamentals, and when they use it to remove repetitive engineering work so that they can focus more deeply on the core scientific questions. Students should still own the questions, the decisions, and the interpretation, even if AI writes much of the plumbing. My lab may soon become more AI-enabled. The challenge will be making sure the agents accelerate the research without becoming the researchers #AI #Research #Engineering #Education

Introducing HRM-Text. An ultra-lean 1B-parameter reasoning language model designed to deliver strong general performance with a fraction of the data, compute, and infrastructure. Trained on just 40B structured tokens, HRM-Text achieves competitive performance while using ~1/1000 of the training data of comparable models. The kicker? The full model trains in roughly one day on a $1,000 budget. This opens the door to a new generation of AI that is powerful, accessible, and radically easier to adapt. Theories and research concepts once deemed too expensive to test are officially back in the game. Sapient Intelligence invites you to help us shape a new paradigm for general intelligence.

I’ve always believed the No.1 application of AI should be to improve human health. That work started with AlphaFold, and now at @IsomorphicLabs with the mission to reimagine drug discovery and one day solve all disease! We are turbocharging that goal with $2.1B in new funding.

People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the same way. We share our approach, early results, and a quick look at our model in action. thinkingmachines.ai/blog/interacti…

The most comprehensive RL overview I've ever seen. Kevin Murphy from Google DeepMind, who has over 128k citations, wrote this. What makes this different from other RL resources: → It bridges classical RL with the modern LLM era: There's an entire chapter dedicated to "LLMs and RL" covering: - RLHF, RLAIF, and reward modeling - PPO, GRPO, DPO, RLOO, REINFORCE++ - Training reasoning models - Multi-turn RL for agents - Test-time compute scaling → The fundamentals are crystal clear Every major algorithm, like value-based methods, policy gradients, and actor-critic are explained with mathematical rigor. → Model-based RL and world models get proper coverage Covers Dreamer, MuZero, MCTS, and beyond, which is exactly where the field is heading. → Multi-agent RL section Game theory, Nash equilibrium, and MARL for LLM agents. I have shared the arXiv paper in the replies!

Former BYD and Huawei engineers made a device that turns any bike electric, reaching 32 km/h ⚡

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI

Anthropic just took a big swipe at OpenAI's decision to put ads in ChatGPT. Anthropic is airing ads mocking ChatGPT ads during the Super Bowl, and they're hilarious 😅 Anthropic is also committing to no ads in Claude theverge.com/ai-artificial-…

Open Source Robotics A curated collection of high-quality open source robotics projects, tools, and software to propel the robotics community forward. robotics.growbotics.ai —- Weekly robotics and AI insights. Subscribe free: scalingdeep.tech

AI agent for browser automation github.com/normal-computi…

We just upgraded XLeRobot 🚀 Built by the MakerMods team @IsaacSin12, @ThomasSchicksal and @QILIU9203. • Easier to build • Improved chassis • Reduced 3D-print time and material • Designed in collaboration with the original author @VectorWang2 Fully open-source Full build guide GitHub release coming soon

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Leading AI scientists from China and the West including Turing award winners Bengio, Yao and Hinton call for global emergency preparedness measures to avert catastrophic risks from AI at the latest International Dialogue on AI Safety.