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Competition 2: Mesh Generation 🦾 Until now, our subnet has focused on Gaussian splats. That work led to integrations with Unity, building the world’s largest 3D dataset, and early application deployments with real users and organic traffic growth. 🕹️ The next step is clear. Developers want native mesh generation...

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🚀 Smash The Mesh (STM) is a #GodotEngine add-on that lets you break 3D meshes into multiple pieces and apply physics to each fragment! Now in BETA, with caching for quick loading. Open-source and free to download! Get it here: 🔧 #Godot4 #GameDev
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🚀 Smash The Mesh (STM) is a #GodotEngine add-on that lets you break 3D meshes into multiple pieces and apply physics to each fragment! Now in BETA, with caching for quick loading. Open-source and free to download! Get it here: 🔧 #Godot4 #GameDev

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Just last summer, Netic raised our Seed round. Today, we’re announcing our Series B, led by Founders Fund with Greylock Partners, Hanabi Capital, and Dylan Field. Thank you for tripling down since day zero. We 4x’ed our team with relentless builders from Scale, Databricks, HRT, Meta, MIT, Stanford, and Harvard. But while Silicon Valley accolades are nice, what really matters to us is delivering value for our customers in the real world. In the last year, Netic helped our customers book hundreds of thousands of jobs across services industries in North America, and has become the AI revenue engine for industries that run our country. There are now companies operating entirely AI-first on Netic. Our focus remains unchanged: powering the industries that are the backbone of the American economy. Read more on Bloomberg Bloomberg below—and tune into Bloomberg Technology at 8:40AM PT. If you want to bring AI to the real world, come build with us.

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🚨BREAKING Unitree's IPO application on the Science and Technology Innovation Board has been formally accepted by the Shanghai Stock Exchange, with a planned fundraising of 4.202 billion yuan. Of this, the intelligent robot model R&D project accounts for the majority, amounting to 2 billion yuan. In other words: For Unitree, hardware is no longer the problem,the real bottleneck that urgently needs to be overcome is the development of intelligent robot models. Notably, Unitree reports that it has sold over 5,500 humanoid robots,ranking first globally,making them the growth engine of its robotics business.

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the so101 + leslider frame i've been playing with the past few weeks wasn't just a random rig i built for fun it's what we at LiveKit built to benchmark and debug our infra (s/o livekit-portal), and today we're open-sourcing it so everyone can reproduce and share coolio results at home here is the github: i know this isn’t the first frame designed for evals, but it’s ours, and it’s open, and it’s with a LeSlider! along with this, we’ve released simulations files (URDF, MJCF, and USD), as well as a sample RL env in MJLab for pick-and-place tasks we want people to have fun, being close to the frontier as much as possible, and we want people to know it shouldn’t take thousands of dollars in equipment to reproduce some of the most amazing results the academic world has to offer personally, I’m going to deploy sim2real and real2sim2real pipelines on this soon, for fun, so look out for that.

Binh Pham

16,251 Aufrufe • vor 8 Tagen

World Model is trending— let's revisit our HunyuanWorld journey. We’ve been pioneering open-source 3D world generation in the past two months, and this ride’s only getting started. 🌍 📅 July: HunyuanWorld 1.0 📌 First open-source 3D world model compatible with CG pipelines (Unity/Unreal/Blender) 📌 Hit 2K+ GitHub stars in just two months ⭐—thank you for the love! 📅 August: 1.0-Lite 📌Same top-tier quality, running on consumer GPUs! 📅 September: 1.0-Voyager 📌 Direct 3D output + world memory—taking exploration further! Seamlessly integrated into CG pipelines with layered 3D modeling (assets, terrain, skybox) and fully open-sourced.. we’re fully committed to building open-source spatial intelligence for all! 🚀 💡 Why it matters? ✅ Seamless CG Pipeline Integration: Export generated 3D scenes as standard mesh formats, effortlessly integrating into industry-standard tools like Blender, Unity, and Unreal Engine for direct editing, animation, and physical simulation. ✅ Hierarchical Scene Editing: Deconstruct scenes into semantic layers (sky, background, foreground objects) via instance recognition and layer decomposition, allowing for atomic-level control—independently modify, relocate, or replace objects without rebuilding the entire world. Project page: Github: Amazing creations by Stijn Spanhove camenduru GENEL | AIを用いた動画制作 apolinario 🌐 とりにく Directive Creator 🪥 👇 #AI #3DGeneration #OpenSource #WorldModels #Hunyuan3D #HunyuanWorld
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World Model is trending— let's revisit our HunyuanWorld journey. We’ve been pioneering open-source 3D world generation in the past two months, and this ride’s only getting started. 🌍 📅 July: HunyuanWorld 1.0 📌 First open-source 3D world model compatible with CG pipelines (Unity/Unreal/Blender) 📌 Hit 2K+ GitHub stars in just two months ⭐—thank you for the love! 📅 August: 1.0-Lite 📌Same top-tier quality, running on consumer GPUs! 📅 September: 1.0-Voyager 📌 Direct 3D output + world memory—taking exploration further! Seamlessly integrated into CG pipelines with layered 3D modeling (assets, terrain, skybox) and fully open-sourced.. we’re fully committed to building open-source spatial intelligence for all! 🚀 💡 Why it matters? ✅ Seamless CG Pipeline Integration: Export generated 3D scenes as standard mesh formats, effortlessly integrating into industry-standard tools like Blender, Unity, and Unreal Engine for direct editing, animation, and physical simulation. ✅ Hierarchical Scene Editing: Deconstruct scenes into semantic layers (sky, background, foreground objects) via instance recognition and layer decomposition, allowing for atomic-level control—independently modify, relocate, or replace objects without rebuilding the entire world. Project page: Github: Amazing creations by Stijn Spanhove camenduru GENEL | AIを用いた動画制作 apolinario 🌐 とりにく Directive Creator 🪥 👇 #AI #3DGeneration #OpenSource #WorldModels #Hunyuan3D #HunyuanWorld

Tencent HY

20,178 Aufrufe • vor 10 Monaten

Native USDC is now live on Aptos! This marks a significant milestone for the Aptos ecosystem, empowering developers and users with access to the world’s largest regulated digital dollar. USDC powers innovative use cases: ✅Build secure apps for peer-to-peer payments, cross-border remittances, RWA settlement, gaming, and more ✅Supercharge DeFi with deep liquidity for digital asset trading and financial services ✅Empower merchants with global, instant, low-cost payment solutions that settle 24/7 Many leading ecosystem apps are expected to support native USDC on Aptos, including: Coinbase 🛡️, Echo Protocol, Petra, Pontem Labs (Liquidswap), Stripe Native USDC is officially issued by Circle and redeemable 1:1 for US dollars. There’s currently a bridged form of USDC in the Aptos ecosystem known as lzUSDC, which is bridged from Ethereum through the AptosBridge built on LayerZero. lzUSDC is not issued by Circle and not redeemable with Circle Mint. Native USDC issued by Circle: Token Name: USDC Token Symbol: USDC Mainnet Address: 0xbae207659db88bea0cbead6da0ed00aac12edcdda169e591cd41c94180b46f3b Testnet Address: 0x69091fbab5f7d635ee7ac5098cf0c1efbe31d68fec0f2cd565e8d168daf52832 Bridged USDC from LayerZero: Token Name: Bridged USDC (LayerZero) Token Symbol: lzUSDC Mainnet Address: 0xf22bede237a07e121b56d91a491eb7bcdfd1f5907926a9e58338f964a01b17fa::asset::USDC Developers can use our step-by-step migration guide for options on migrating bridged USDC to native USDC in their apps: CCTP is coming later this morning: With CCTP launching imminently, leading interoperability providers like Wormhole will enable seamless USDC transfers between Aptos and 9 other blockchains. With the addition of Aptos, USDC is now natively supported on 17 blockchains—with many more expansions planned this year. Start building with USDC on Aptos:
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Native USDC is now live on Aptos! This marks a significant milestone for the Aptos ecosystem, empowering developers and users with access to the world’s largest regulated digital dollar. USDC powers innovative use cases: ✅Build secure apps for peer-to-peer payments, cross-border remittances, RWA settlement, gaming, and more ✅Supercharge DeFi with deep liquidity for digital asset trading and financial services ✅Empower merchants with global, instant, low-cost payment solutions that settle 24/7 Many leading ecosystem apps are expected to support native USDC on Aptos, including: Coinbase 🛡️, Echo Protocol, Petra, Pontem Labs (Liquidswap), Stripe Native USDC is officially issued by Circle and redeemable 1:1 for US dollars. There’s currently a bridged form of USDC in the Aptos ecosystem known as lzUSDC, which is bridged from Ethereum through the AptosBridge built on LayerZero. lzUSDC is not issued by Circle and not redeemable with Circle Mint. Native USDC issued by Circle: Token Name: USDC Token Symbol: USDC Mainnet Address: 0xbae207659db88bea0cbead6da0ed00aac12edcdda169e591cd41c94180b46f3b Testnet Address: 0x69091fbab5f7d635ee7ac5098cf0c1efbe31d68fec0f2cd565e8d168daf52832 Bridged USDC from LayerZero: Token Name: Bridged USDC (LayerZero) Token Symbol: lzUSDC Mainnet Address: 0xf22bede237a07e121b56d91a491eb7bcdfd1f5907926a9e58338f964a01b17fa::asset::USDC Developers can use our step-by-step migration guide for options on migrating bridged USDC to native USDC in their apps: CCTP is coming later this morning: With CCTP launching imminently, leading interoperability providers like Wormhole will enable seamless USDC transfers between Aptos and 9 other blockchains. With the addition of Aptos, USDC is now natively supported on 17 blockchains—with many more expansions planned this year. Start building with USDC on Aptos:

Circle

94,876 Aufrufe • vor 1 Jahr

I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.
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I'm proud to share that Glean has surpassed $300M ARR, just five months after crossing $200M and growing ~3x over the past 15 months. This is an exciting milestone for Glean, and it's a signal about where the enterprise AI market is heading. We’ve long believed the real challenge in enterprise AI is not access to models. It is grounding AI in how a company actually works: its people, knowledge, workflows, permissions, and systems. That’s even clearer now. The companies creating real value with AI are not just adopting better models. They are building systems that understand their business well enough to deliver reliable outcomes at scale. That is the real moat, and it is what we’ve been building at Glean: an unrivaled context layer for enterprise AI. That context has to work across the business, not just inside a single team or use case. We see that in how customers adopt Glean: more than 85% use it across five or more job functions. It also has to meet the security and governance demands of complex enterprises. We see that in who is choosing Glean: our Fortune 500 customer count nearly doubled year over year. And it has to make economic sense as usage grows. In our recent benchmark with Claude Cowork, Glean was preferred roughly 2.5x as often as off-the-shelf MCP tools and used 30% fewer tokens on average. Better context improves both quality and efficiency. I enjoyed talking with CNBC's Deirdre Bosa about this broader shift. In enterprise AI, the winners will not be defined by better models alone. They will be defined by who builds the strongest foundation for enterprise context. Thank you to our customers, partners, and team for helping us build the future of enterprise AI.

Arvind Jain

280,065 Aufrufe • vor 1 Monat

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.
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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

23,489 Aufrufe • vor 9 Monaten