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Gen-3 Alpha exhibits several simulation capabilities, including the ability to generate dynamic camera motions, complex fluid motion, and interactions between objects. We expect further simulation capabilities to emerge as we continue to scale our models. To learn more about our long-term research efforts to build General World Models, visit:... show more

Runway

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New Generation Model! 🚨 We're introducing the o3m model to our expanding lineup of generation models. While still in its beta phase, we're actively refining its output to push its capabilities even further. So far, we've observed a significant leap in physics calculations, AI-driven mechanics, and the overall fluidity of app and game interactions. As we continue to tweak and optimize, expect even greater advancements in precision, automation, and creative execution.

New Generation Model! 🚨 We're introducing the o3m model to our expanding lineup of generation models. While still in its beta phase, we're actively refining its output to push its capabilities even further. So far, we've observed a significant leap in physics calculations, AI-driven mechanics, and the overall fluidity of app and game interactions. As we continue to tweak and optimize, expect even greater advancements in precision, automation, and creative execution.

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Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora

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We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

We are entering an extremely exciting era for open-weight models. Kimi K2.6 now feels like a top agentic model. I took it for a spin via Fireworks AI fast inference APIs. Kimi K2.6 has impressive agentic capabilities, design skills, and the ability to synthesize large amounts of information. I built a little Skill that produces survey papers on any AI research topic you want. (see example in the clip) You can use the skill to tell your agent to generate a survey on whatever topic and watch it go to work. The artifact was fully generated by Kimi.ai's Kimi K2.6. It's cheap and fast. Next step for me is to explore ways to continue integrating the capabilities of these models on use cases like automating my LLM knowledge bases and augmenting my agent memory capabilities. Stay tuned for more.

elvis

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We have 6 different models of CNC machines already designed and validated for production but we would need crores to scale up production and offer it to other manufacturers. Hence, we only build machines for our own production facility. Capital is a bottleneck for us. Currently we just have a tiny space within our own factory which is our R&D lab. We are a team of mere three people. Scaling hardware isn’t easy but it’s something that must be done for us to truly be self-reliant. Really appreciate your efforts Sridhar Vembu to build these hardware tools here. We must build more factories and hardware products in the country. Long way to go before I sleep.

We have 6 different models of CNC machines already designed and validated for production but we would need crores to scale up production and offer it to other manufacturers. Hence, we only build machines for our own production facility. Capital is a bottleneck for us. Currently we just have a tiny space within our own factory which is our R&D lab. We are a team of mere three people. Scaling hardware isn’t easy but it’s something that must be done for us to truly be self-reliant. Really appreciate your efforts Sridhar Vembu to build these hardware tools here. We must build more factories and hardware products in the country. Long way to go before I sleep.

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Photorealistic Object Insertion with Diffusion-Guided Inverse Rendering discuss: The correct insertion of virtual objects in images of real-world scenes requires a deep understanding of the scene's lighting, geometry and materials, as well as the image formation process. While recent large-scale diffusion models have shown strong generative and inpainting capabilities, we find that current models do not sufficiently "understand" the scene shown in a single picture to generate consistent lighting effects (shadows, bright reflections, etc.) while preserving the identity and details of the composited object. We propose using a personalized large diffusion model as guidance to a physically based inverse rendering process. Our method recovers scene lighting and tone-mapping parameters, allowing the photorealistic composition of arbitrary virtual objects in single frames or videos of indoor or outdoor scenes. Our physically based pipeline further enables automatic materials and tone-mapping refinement.

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AK

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(1/n) 🚀 With FastVideo, you can now generate a 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licenses

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MrNeRF

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A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

A Letter to Our Community: The Road Ahead for Robotics To our Community and Partners, As we step into 2026, our mission at Axis is clearer than ever: Constructing the definitive End-to-End Scaling Layer for Robotics. Our goal is to accelerate the transfer of diverse human intelligence into Robotics General Intelligence (RGI). By owning the critical path of intelligence creation, we are turning the physical limitations of robotics into a scalable, software-driven future. Here is our strategic outlook and roadmap for the year ahead. The Core Thesis: Simulation is the Only Way Out The path to RGI is currently blocked by Data Scarcity, Generalization Fragility, and Hardware Fragmentation. At Axis, we believe Simulation is the only way out. Our Simulation Data Platform and Data Augmentation Engine transform raw data into "Synthetic Gold". Backed by academic milestones like Roboverse, Skill Blending, and GraspVLA, we have proven that pure simulation can achieve the generalization required for the real world. We don’t just collect data; we architect it. The Engine: Why Crypto? We believe RGI should come from all, not a few. Crypto is not just a feature; it is the primitive that powers our entire ecosystem flywheel: - Incentive Mechanism: Democratizing contribution and rewarding the trainers and developers. - Assetization: Turning proprietary data and refined models into liquid, ownable assets. - Verifiable Workflow: We are opening the "Black Box" of AI. By bringing total transparency to the Task Generation → Data Collection → Model Training pipeline, we ensure every byte of intelligence is verifiable, traceable, and secure. 2026 Strategic Deliverables This year, we are committed to delivering three foundational pillars: - The World's Largest Training Dataset for Robots: A robot training set—diverse, high-quality interaction data at an unprecedented scale. - A Robotics Foundation Model: A universal robotic brain trained on our pure simulation and synthetic data, capable of robust cross-embodiment transfer and open-world adaptability. - Evolvable Robot Hardware: Robots deployed with Axis models that autonomously evolve through continuous interaction, turning every deployment into a self-improving node within our RGI network. The Ultimate Vision We are building more than models; we are architecting the Distributed Machine Economy. A future where every dataset, model, and robotic embodiment is a verifiable asset in a global, autonomous network. Thank you for building the future of intelligence with us✌️📷

Axis Robotics

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Excited to share a few presentations, demos, and workshop talks from our group and collaborators at #ICRA2026! We will present recent work on real-to-sim-to-real robot policy evaluation, model-based planning with learned dynamics, and multi-modal manipulation. We will also have a joint live demo between SceniX and Analog Devices, Inc. on real-to-sim-to-real cable manipulation at the ICRA exhibition. This is a small teaser of what we have been building, with more to come soon! If you are at ICRA, please stop by the sessions or the demo booth. Happy to chat about robot learning, simulation, world models, and sim-to-real!

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A thousand days of full-scale war is a very difficult path, and we are enduring it because we have friends like Denmark by our side, and leaders like Prime Minister Mette Frederiksen. I am deeply grateful to Mette Frederiksen for her personal leadership, her team, and the Danish people for their support of Ukraine and our people. Today, we have a new support package from Denmark, focused primarily on the long-range capabilities that our country urgently needs. Together, we have created a special Danish model for developing Ukraine’s defense industry, and we will continue to strengthen this cooperation. We are already preparing for further joint efforts at the European institutional level. 🇺🇦🇩🇰

A thousand days of full-scale war is a very difficult path, and we are enduring it because we have friends like Denmark by our side, and leaders like Prime Minister Mette Frederiksen. I am deeply grateful to Mette Frederiksen for her personal leadership, her team, and the Danish people for their support of Ukraine and our people. Today, we have a new support package from Denmark, focused primarily on the long-range capabilities that our country urgently needs. Together, we have created a special Danish model for developing Ukraine’s defense industry, and we will continue to strengthen this cooperation. We are already preparing for further joint efforts at the European institutional level. 🇺🇦🇩🇰

Volodymyr Zelenskyy / Володимир Зеленський

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LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability.

LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability.

AK

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Announcing our $320M Series A at a $2.3B valuation, led by Khosla Ventures, with General Catalyst, Eric Schmidt and Jeff Bezos. General Intuition is the frontier lab for acting in space and time. We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from 17M monthly active users on Medal, and push the frontier of world models to generate infinite training environments.

Announcing our $320M Series A at a $2.3B valuation, led by Khosla Ventures, with General Catalyst, Eric Schmidt and Jeff Bezos. General Intuition is the frontier lab for acting in space and time. We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from 17M monthly active users on Medal, and push the frontier of world models to generate infinite training environments.

General Intuition

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The sixth meeting of the Coalition of the Willing. Together with our partners, we supported the efforts of U.S. President Donald Trump to end the war, stop the killings, and achieve a just and lasting peace. I am grateful to the partners for our shared position: the path to peace cannot be determined without Ukraine, and negotiations can only yield results if they take place under a ceasefire. I am also grateful for the active support of our state as long as Russia has not agreed to a ceasefire. I called on our partners to finance Ukrainian drone production to enhance our country’s defense capabilities and to join the new NATO PURL instrument for the procurement of U.S. weapons. During the meeting, we coordinated the next steps and agreed to continue working in close contact between Ukraine, all of Europe, and the United States. I thank everyone who is working to bring this war to a just end.

The sixth meeting of the Coalition of the Willing. Together with our partners, we supported the efforts of U.S. President Donald Trump to end the war, stop the killings, and achieve a just and lasting peace. I am grateful to the partners for our shared position: the path to peace cannot be determined without Ukraine, and negotiations can only yield results if they take place under a ceasefire. I am also grateful for the active support of our state as long as Russia has not agreed to a ceasefire. I called on our partners to finance Ukrainian drone production to enhance our country’s defense capabilities and to join the new NATO PURL instrument for the procurement of U.S. weapons. During the meeting, we coordinated the next steps and agreed to continue working in close contact between Ukraine, all of Europe, and the United States. I thank everyone who is working to bring this war to a just end.

Volodymyr Zelenskyy / Володимир Зеленський

180,451 просмотров • 1 год назад

With 3 more sales today, we've now hit the milestone and officially burned 20.2% of the entire supply of NODESTR burned in 6.5 months. That comes out to an average of more than 0.1% of supply burned EVERY DAY since we began. Even the most well known flywheel DAT on ETH only burned 6.3% before death spiraling, but we structured our project for long term success, and we remain committed to our long term goals. Some of our initial project design choices weren't popular at the time, but our ironclad foundation is being proven as correct as more time goes on. Our project has always been a long term asymmetric bet on the providence of the most rare digital assets on the original chain where NFT's were originally born. We don't know how long the journey will take, but we will continue to plow forward relentlessly.

With 3 more sales today, we've now hit the milestone and officially burned 20.2% of the entire supply of NODESTR burned in 6.5 months. That comes out to an average of more than 0.1% of supply burned EVERY DAY since we began. Even the most well known flywheel DAT on ETH only burned 6.3% before death spiraling, but we structured our project for long term success, and we remain committed to our long term goals. Some of our initial project design choices weren't popular at the time, but our ironclad foundation is being proven as correct as more time goes on. Our project has always been a long term asymmetric bet on the providence of the most rare digital assets on the original chain where NFT's were originally born. We don't know how long the journey will take, but we will continue to plow forward relentlessly.

NodeStrategy

10,526 просмотров • 4 месяцев назад

NeuralAI Full Rebranding Begins Now! The new face of NeuralAI awakens: our updated logotype and branding symbolizes adaptability, growth, and transformation—core to our mission of transforming virtual worlds into dynamic, intelligent ecosystems. Our updated brand profile reflects who we are and where we're headed: to build living, AI-driven experiences at large. As we now initiate a full rebranding process, we urge you to be vigilant and bear in mind that this change is bound to take a little while, but is already in full motion. We are super excited to show you the new brand for NeuralAI that's being implemented from this moment and beyond. We hope you are going to love it as much as we do 🤝 Let's build the future together 🥇

NeuralAI

27,726 просмотров • 1 год назад

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

🚀Thrilled to share what we’ve been building at TRI over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!

Zubair Irshad

20,377 просмотров • 1 год назад

SMRT x NVIDIA🤝 We are excited to announce we have been enrolled into the NVIDIA Developer program! Included in this prestigious program is access to advanced tools and SDKs, training resources, early access programs and unlimited use of NVIDIA On-Demand Services. Additionally, the SmartMoney team will be attending the NVIDIA GTC AI Conference, networking with the top brains in AI development who will be in attendance, including Jensen Huang CEO of NVIDIA and Brad Lightcap COO of OpenAI . As a nascent AI Blockchain project, this partnership will be crucial as we scale and build out our platform and services, given NVIDIA's wealth of resources in the AI industry. Make sure to follow our twitter for more updates as we continue to partner with and learn from the strongest projects and firms in our field.

SMRT x NVIDIA🤝 We are excited to announce we have been enrolled into the NVIDIA Developer program! Included in this prestigious program is access to advanced tools and SDKs, training resources, early access programs and unlimited use of NVIDIA On-Demand Services. Additionally, the SmartMoney team will be attending the NVIDIA GTC AI Conference, networking with the top brains in AI development who will be in attendance, including Jensen Huang CEO of NVIDIA and Brad Lightcap COO of OpenAI . As a nascent AI Blockchain project, this partnership will be crucial as we scale and build out our platform and services, given NVIDIA's wealth of resources in the AI industry. Make sure to follow our twitter for more updates as we continue to partner with and learn from the strongest projects and firms in our field.

SmartMoney

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