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This week at #CVPR2025, Niantic Spatial is sharing the major strides made toward building a Large Geospatial Model that merges the digital and physical worlds. 🌍🧠 📐MVSAnywhere: Zero-Shot Multi-View Stereo 🎨 Morpheus: Generative 3D Scene Stylization These two research projects reflect a larger ambition: to make AI systems that...

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Niantic Spatial 🌎

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11,031 просмотров • 1 год назад •via X (Twitter)

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Фото профиля マツケンさんを招致しよう！地域活性化運動
マツケンさんを招致しよう！地域活性化運動1 год назад

ほぅ…Aiとの融合か…

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Meta's Chief AI Scientist Yann LeCun offers a critical take on the humanoid robot boom. Speaking at MIT, LeCun claimed the "big secret" of the industry is that current companies "have no idea" how to make their robots "smart enough to be generally useful." He argues that while humanoids can be trained for narrow manufacturing tasks, a truly autonomous domestic robot is impossible without fundamental AI breakthroughs. For LeCun, this means moving beyond current generative models and toward "world model planning-type architectures"—systems that can learn to understand and predict the physical world. The future of these billion-dollar startups, he says, depends entirely on this next wave of AI research.
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Meta's Chief AI Scientist Yann LeCun offers a critical take on the humanoid robot boom. Speaking at MIT, LeCun claimed the "big secret" of the industry is that current companies "have no idea" how to make their robots "smart enough to be generally useful." He argues that while humanoids can be trained for narrow manufacturing tasks, a truly autonomous domestic robot is impossible without fundamental AI breakthroughs. For LeCun, this means moving beyond current generative models and toward "world model planning-type architectures"—systems that can learn to understand and predict the physical world. The future of these billion-dollar startups, he says, depends entirely on this next wave of AI research.

Humanoids daily

1,443,650 просмотров • 10 месяцев назад

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:
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🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,524 просмотров • 8 месяцев назад

Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.
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Dr. Fei-Fei Li just called out the biggest blind spot in the entire AI industry. We have been building half of human intelligence. And calling it the finish line. Li: “If you look at human intelligence, it pretty much boils down to two buckets.” The first bucket is language. Symbolic reasoning. Communication. The ability to think in words and abstractions. That’s what every major AI lab has spent the last decade building. The second bucket is the one the industry has almost entirely ignored. Li: “We call that in AI spatial intelligence.” How humans and animals perceive, navigate, and interact with the three-dimensional physical world. How we reach for objects. How we move through space. How we build and manipulate physical reality. From painting masterpieces to constructing the pyramids, non-verbal spatial intelligence is what actually shapes the world. Language describes reality. Spatial intelligence acts on it. And the gap between those two things is the gap between a chatbot and a robot. Li: “When this technology is ready, the robotic revolution is gonna start. We’re already seeing that trend.” Every robot is a moving agent. Every moving agent requires spatial intelligence to function in the real world. The humanoid robots being deployed in factories right now are hitting the ceiling of what language models alone can power. Spatial intelligence is the unlock. But Li didn’t stop at robotics. Li: “From a geopolitics point of view, this is part of the technology that goes straight into weapons.” Autonomous drone swarms. Battlefield navigation. Physical target acquisition without human oversight. Every military application of AI that operates in the real world runs on spatial intelligence. The nation that masters the transition from static text to dynamic three-dimensional perception doesn’t just win the software race. It commands the physical battlefield. The AI arms race just broke out of the data center. It’s operating in three dimensions now.

Dustin

122,781 просмотров • 6 месяцев назад

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.
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3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,798 просмотров • 3 лет назад

We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, Yifan Lu , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at xiaojie@stanford.edu. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.
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We’re thrilled to share that our MERFISH+ preprint is now live on bioRxiv!👉 In this work, the Bintu and Zhu labs (UCSD) developed MERFISH+, a next-generation spatial genomics platform that combines genome-wide RNA and epigenetic imaging over a large field of view. By introducing acrydite-modified probes covalently anchored to hydrogels, MERFISH+ achieves remarkable imaging stability and enables >1,800-gene, multi-modal, and multi-month experiments. With this platform, they, together with the Chi lab at UCSD, profiled a whole developing human heart at 12 post-conception week with merely two slides, resulting in a total of 53 slides, 3.1 million single cells and more than 30 cell types. Building upon our previous 3D reconstruction and modeling framework, Spateo ( we reconstruct the 3D human heart that nicely captures the anatomical structure of the heart, including the intricate vasculature network. Sophisticated analyses provide a holistic view of an entire organ and enable systematic characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Furthermore, using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omics atlas of the developing human heart, shared at and MERFISH+ thus sets a new standard for large-format, multi-omic spatial profiling, enabling holistic, 3D characterization of organs at subcellular resolution. Huge congratulations to first authors Colin Kern, qingquan Zhang, Yifan Lu , and Jacqueline Eschbach, and to all collaborators from the Bintu, Zhu, Chi, and Qiu labs for this amazing team effort. Thanks for your diligence, creativity, and hard work on this project. We’re grateful for support from Arc Institute and our generous donors. Our lab is expanding—if you’re excited about building the next generation of single-cell and spatial genomics techniques and predictive single cell and spatial foundation models, we’re hiring! If you are interested, please reach out to me via direct message or email at [email protected]. We are excited for any potential collaborations along this line of research in Stanford, UCSF and Berkeley and other labs as well.

evo-devo

42,307 просмотров • 10 месяцев назад

🚨There are no links in this post. Please be weary of impersonators. The project will not go live until next week and anything stating otherwise is a scam. 🚨 TLDR: On August 24th I’m releasing an inclusive edition generative project called Heart + Craft, in collaboration with Jordan Lyall, on Prohibition.art, priced at 0.01 ETH. Heart+Craft represents a lot to me. It represents an artistic style and medium that I have expressed since 2014. It represents my present curiosity towards demonstrating value for NFTs beyond scarcity, FOMO, and promise of future value utility. And it represents my vision of a digital optional / physical optional future that I believe can be appealing to a broader consumer audience. Heart + Craft illustrates the potential of art and creativity driven generative goods. On the surface, Heart + Craft is a colorful generative artwork that illustrates my eternal love of gradients, contained in the shape of a heart. There are seemingly unlimited possible combinations of colors and blocks that make up each (Zelda inspired) heart container. But beyond the digital object there is potentially also a physical object, and whether you value the digital or the physical more is entirely up to you. Similar to the concept behind the Friendship Bracelets project, created alongside Alexis André, each Heart + Craft iteration contains detailed instructions for producing a physical analogue. And similar to the bracelets, the value of the digital is not diminished if it is ever separated from its physical counterpart. With this project I’m particularly excited about a lot of things including: The intent that rarity is mostly a function of the complexity of assembling the physical sculpture. The idea that this project could inspire people to take a break from our increasingly digital world, diverting attention to making something tangible that lives in their physical space. The opportunity to spread love and good vibes with a relaxing experience, like building a puzzle, that can lead to personal satisfaction while contributing to a more positive and colorful world. The ability to explore new distribution mechanisms for our digital objects via inclusive editions such as developing kits that come with cubes, paint, glue, and the digital ownership certificate all in a beautiful box. Creating goods where digital objects can inspire a love for craft, and a craft that can catalyze curiosity and foster education for digital objects. Offering a low cost and disarming entry point for new consumers into our space through a project that can be enjoyed as a family, or be gifted to tech/art curious friends, clients, and coworkers. Having another opportunity to perpetuate the beautiful spirit of charitable giving culture that occupies our space by donating a portion of the proceeds in support of the arts. Huge shoutout to Jordan and the VenturePunk ▽✱ team for working with me in bringing this idea to life, the Art Blocks team and Art Blocks Engine for building and supporting the technology empowering a #1of1ofx future, all of the collectors who have instilled the confidence in me to put something like this into the world, and the artists that have played such a huge role in elevating the generative medium. And to my wife, who not only inspires me to put myself out there, but supported this insane journey along with friends and family. I’ve been wanting to do something like this for a long time . Hope you like it and make time to touch some paint here soon. ❤️❤️❤️❤️❤️
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🚨There are no links in this post. Please be weary of impersonators. The project will not go live until next week and anything stating otherwise is a scam. 🚨 TLDR: On August 24th I’m releasing an inclusive edition generative project called Heart + Craft, in collaboration with Jordan Lyall, on Prohibition.art, priced at 0.01 ETH. Heart+Craft represents a lot to me. It represents an artistic style and medium that I have expressed since 2014. It represents my present curiosity towards demonstrating value for NFTs beyond scarcity, FOMO, and promise of future value utility. And it represents my vision of a digital optional / physical optional future that I believe can be appealing to a broader consumer audience. Heart + Craft illustrates the potential of art and creativity driven generative goods. On the surface, Heart + Craft is a colorful generative artwork that illustrates my eternal love of gradients, contained in the shape of a heart. There are seemingly unlimited possible combinations of colors and blocks that make up each (Zelda inspired) heart container. But beyond the digital object there is potentially also a physical object, and whether you value the digital or the physical more is entirely up to you. Similar to the concept behind the Friendship Bracelets project, created alongside Alexis André, each Heart + Craft iteration contains detailed instructions for producing a physical analogue. And similar to the bracelets, the value of the digital is not diminished if it is ever separated from its physical counterpart. With this project I’m particularly excited about a lot of things including: The intent that rarity is mostly a function of the complexity of assembling the physical sculpture. The idea that this project could inspire people to take a break from our increasingly digital world, diverting attention to making something tangible that lives in their physical space. The opportunity to spread love and good vibes with a relaxing experience, like building a puzzle, that can lead to personal satisfaction while contributing to a more positive and colorful world. The ability to explore new distribution mechanisms for our digital objects via inclusive editions such as developing kits that come with cubes, paint, glue, and the digital ownership certificate all in a beautiful box. Creating goods where digital objects can inspire a love for craft, and a craft that can catalyze curiosity and foster education for digital objects. Offering a low cost and disarming entry point for new consumers into our space through a project that can be enjoyed as a family, or be gifted to tech/art curious friends, clients, and coworkers. Having another opportunity to perpetuate the beautiful spirit of charitable giving culture that occupies our space by donating a portion of the proceeds in support of the arts. Huge shoutout to Jordan and the VenturePunk ▽✱ team for working with me in bringing this idea to life, the Art Blocks team and Art Blocks Engine for building and supporting the technology empowering a #1of1ofx future, all of the collectors who have instilled the confidence in me to put something like this into the world, and the artists that have played such a huge role in elevating the generative medium. And to my wife, who not only inspires me to put myself out there, but supported this insane journey along with friends and family. I’ve been wanting to do something like this for a long time . Hope you like it and make time to touch some paint here soon. ❤️❤️❤️❤️❤️

Erick / Snowfro / 🦩 / LAO / #️⃣ / 🔴

261,765 просмотров • 3 лет назад

Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!
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Just how capable are open source models? Below is the first in a new series where we go behind the scenes and pull back the curtain on interesting AI research / demos, making them fun and easy to understand. Here, we have a short visual demonstration from aizk ✡️ showcasing how Kimi K3 (a language model that operates primarily through text) is capable of building complicated 3D structures / moments in history in Minecraft, something that previously was not possible with other open source models, and why this matters. The crazy part? The model doesn't "see" the game like we do. The LLMs must reason in pure text, writing JavaScript, that later compiles down into commands placing each block, one at a time. Spatial reasoning is a very hard problem in AI, it's the same core challenge behind robotics and self-driving cars, where a model has to understand and act in physical 3D space. Watching a text model pull it off is nothing short of a miracle. The point isn't just Minecraft itself, rather, it's AI being able to generalize, not memorize, on things that are weird and beyond their training data. This is key to building true artificial general intelligence. These video game benchmarks (there are many different games actively being researched right now) provide a clear-cut end goal, challenges that are almost certainly not in the training set, and a fun, very fast, visual way to almost feel the increasing capabilities of various open source AI models over time. If you haven't given open source models a serious try yet, watch the video, it may shock you!

Featherless AI

39,769 просмотров • 12 дней назад

Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks
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Real-time world models represent a fundamental shift in AI. reactor is building the platform for real-time generative video infrastructure, supporting developers who need the tech for use across entertainment, physical AI, and robotics. Co-founders Alberto and Bryce Schmidtchen joined us last week on The Investment Memo, hosted by Partners Bucky Moore and Amber Yang, to talk about the era of world models. The conversation centered around the infrastructure Reactor is building, why real-time models are the edge right now, and current use cases for the product. Alberto and Bryce agreed that world models are shaping the way simulations are created, and that developers need a streamlined platform that can support their ideas. We believe Reactor is positioned to be at the frontier of research into real-time generative models. We look forward to seeing how these models apply across industries. Chapters 00:00 Introduction & Overview of Reactor 01:08 Meet the Hosts & Founders 02:18 The Origin Story: From 3D Assets to World Models 05:07 Real-Time Video Applications Across Industries 06:55 The Open Source World Model Explosion 07:23 Why Infrastructure Is the Opportunity 08:42 Parallels to Past Technology Waves 09:51 Bridging the Research-to-Production Gap 13:13 What Developers Are Building with World Models 16:41 Lessons from Luma AI 18:23 What Apple Vision Pro Taught Bryce About Real-Time Systems 20:48 Company Values & Team Culture 22:40 Series A: What the Capital Unlocks 24:13 Reactor's Five-Year Vision 26:09 Closing Remarks

Lightspeed

144,942 просмотров • 2 месяцев назад

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.
2:48

Modern AI is confined to the digital world. At Skild AI, we are building towards AGI for the real world, unconstrained by robot type or task — a single, omni-bodied brain. Today, we are sharing our journey, starting with early milestones, with more to come in the weeks ahead. Our Mission: Artificial General Intelligence grounded in the physical world. We believe AGI that can truly understand and reason in the real world can only be built through grounding in the physical world. Our Vision: Any robot, Any task, One brain. We tackle robotics in its full generality – building a continually improving, omni-bodied brain that can control any hardware for any task. Who are we? A passionate group of scientists & engineers driven by our shared vision. We have been researching AI and robotics for more than a decade. Our team includes pioneers of self-supervised learning, curiosity-driven exploration, end-to-end sim2real for visual locomotion, dexterous manipulation, learning from human videos, robot parkour, and many more. Many of these works have won awards at top-tier AI and Robotics conferences. Our team has also built production-ready systems at Anduril, Tesla, Nvidia, Meta, Kitty Hawk, Google, Everyday Robotics, and Amazon. Join us in our mission to build the robot brains of tomorrow.

Skild AI

384,925 просмотров • 1 год назад

Today we're announcing #GAIA1: a 9B parameter world model, trained on 4,700 hours of driving data, able to simulate complex and diverse driving scenes from video, text and action inputs. This model is 480x larger than the preview we shared earlier this year and the results are incredible. These videos are entirely synthetically generated by Wayve's generative AI, GAIA-1. But there is more here than just generating videos, GAIA is an entire world model. A world model allows us to simulate the future, conditioned on video, text and action inputs, which can be leveraged for making informed decisions when driving. Why is this game-changing for autonomous driving? 1. Safety. One limitation with AI systems like today's Large Language Models is that they are autoregressive, next-word prediction algorithms, but aren't necessarily aware of the implications of their decisions. A world model allows us to give our AI the capability to be aware of its decisions, by simulating the future, which is important for self-driving safety. 2. Synthetic training data. I believe synthetic training data is the future for AI, because it is safer, cheaper, and infinitely scalable. GAIA-1 unlocks unprecedented realism and diversity of synthetic data for self-driving. 3. Long-tail robustness. One of the biggest challenges for self-driving is long-tail robustness: dealing with the enormous magnitude of edge cases we see on the road. An advantage of generative AI is its incredible ability to recombine experiences in new ways. This is exciting for self-driving as it means we can learn from two edge case scenarios, and combine them to become a corner case. For example, we can experience driving in fog, and experience of jay-walking pedestrians, and GAIA can learn from these experiences to understand how to generate a fog+jay walking scenario. Check out many more videos in our blog or further technical details in our paper: Or come chat with our team who are at the International Conference on Computer Vision (#ICCV2023) this week in Paris in Booth 32 Jamie Shotton
0:41

Today we're announcing #GAIA1: a 9B parameter world model, trained on 4,700 hours of driving data, able to simulate complex and diverse driving scenes from video, text and action inputs. This model is 480x larger than the preview we shared earlier this year and the results are incredible. These videos are entirely synthetically generated by Wayve's generative AI, GAIA-1. But there is more here than just generating videos, GAIA is an entire world model. A world model allows us to simulate the future, conditioned on video, text and action inputs, which can be leveraged for making informed decisions when driving. Why is this game-changing for autonomous driving? 1. Safety. One limitation with AI systems like today's Large Language Models is that they are autoregressive, next-word prediction algorithms, but aren't necessarily aware of the implications of their decisions. A world model allows us to give our AI the capability to be aware of its decisions, by simulating the future, which is important for self-driving safety. 2. Synthetic training data. I believe synthetic training data is the future for AI, because it is safer, cheaper, and infinitely scalable. GAIA-1 unlocks unprecedented realism and diversity of synthetic data for self-driving. 3. Long-tail robustness. One of the biggest challenges for self-driving is long-tail robustness: dealing with the enormous magnitude of edge cases we see on the road. An advantage of generative AI is its incredible ability to recombine experiences in new ways. This is exciting for self-driving as it means we can learn from two edge case scenarios, and combine them to become a corner case. For example, we can experience driving in fog, and experience of jay-walking pedestrians, and GAIA can learn from these experiences to understand how to generate a fog+jay walking scenario. Check out many more videos in our blog or further technical details in our paper: Or come chat with our team who are at the International Conference on Computer Vision (#ICCV2023) this week in Paris in Booth 32 Jamie Shotton

Alex Kendall

631,869 просмотров • 2 лет назад

Katherine Boyle just identified Elon Musk’s most important contribution to America, and it has nothing to do with the products he shipped. Boyle, General Partner at a16z: “I think Elon’s most important contribution to this country is training two generations of engineers to work with their hands again.” For ten years, America’s sharpest technical minds optimized ad clicks and built messaging apps. Software consumed ambition. The physical world became something you abstracted into APIs, not something you touched or understood. Elon didn’t reverse that through inspiration. He reversed it by building companies that required understanding manufacturing or failing completely. SpaceX and Tesla forced engineers to learn how metal fractures, how tolerances cascade through systems, how physical iteration costs months and millions per failure. No debugging. No patches. Just physics that doesn’t negotiate. Boyle: “Training two generations of engineers.” The product isn’t the cars. It’s the people. Look at who’s founding America’s critical hard-tech companies now. The common thread isn’t Stanford or MIT. It’s time on factory floors at SpaceX or Tesla. They learned welding. They learned that “impossible” just means unsolved engineering, not violated physics. They learned failure in the physical domain where mistakes compound instead of reverting. Elon didn’t build companies. He accidentally rebuilt industrial knowledge that had been decaying for thirty years while America’s best minds chased digital scale. Boyle: “Work with their hands again.” Three words that sound quaint but describe a civilizational inflection point. Software dominated because it scaled infinitely at zero marginal cost. Physical manufacturing was slow, expensive, unfashionable. Building real things became what you did if you couldn’t code. Elon made atoms matter again. Made manufacturing the hardest problem worth solving. Made physical engineering prestigious in ways it hadn’t been since humans walked on the moon. The evidence is everywhere now. Technical talent that doesn’t default to “which app” but asks “which physical thing should exist that currently doesn’t.” Ambition redirected from optimizing engagement metrics to building rockets. From scaling users to scaling factories. From virtual products to physical infrastructure. That shift matters more than any vehicle or spacecraft Musk delivered. Products obsolesce. Redirecting an entire generation’s engineering ambition from digital to physical compounds across decades and rebuilds industrial capability at civilizational scale. We stopped just coding the future. We started machining it, welding it, breaking it in reality until physics confirms it works. That transformation from virtual to tangible ambition is reconstructing American manufacturing one engineer at a time. And those engineers are now training the next wave. The compounding has started. The School of Elon doesn’t need Elon anymore. It’s self-sustaining, spreading through an entire generation that learned building real things matters more than building virtual ones. That’s not just a business achievement. That’s a civilization remembering how to make things that matter in the physical world again. And it might be the only thing that saves American technological leadership when the competition is just building faster because they never forgot.
1:35

Katherine Boyle just identified Elon Musk’s most important contribution to America, and it has nothing to do with the products he shipped. Boyle, General Partner at a16z: “I think Elon’s most important contribution to this country is training two generations of engineers to work with their hands again.” For ten years, America’s sharpest technical minds optimized ad clicks and built messaging apps. Software consumed ambition. The physical world became something you abstracted into APIs, not something you touched or understood. Elon didn’t reverse that through inspiration. He reversed it by building companies that required understanding manufacturing or failing completely. SpaceX and Tesla forced engineers to learn how metal fractures, how tolerances cascade through systems, how physical iteration costs months and millions per failure. No debugging. No patches. Just physics that doesn’t negotiate. Boyle: “Training two generations of engineers.” The product isn’t the cars. It’s the people. Look at who’s founding America’s critical hard-tech companies now. The common thread isn’t Stanford or MIT. It’s time on factory floors at SpaceX or Tesla. They learned welding. They learned that “impossible” just means unsolved engineering, not violated physics. They learned failure in the physical domain where mistakes compound instead of reverting. Elon didn’t build companies. He accidentally rebuilt industrial knowledge that had been decaying for thirty years while America’s best minds chased digital scale. Boyle: “Work with their hands again.” Three words that sound quaint but describe a civilizational inflection point. Software dominated because it scaled infinitely at zero marginal cost. Physical manufacturing was slow, expensive, unfashionable. Building real things became what you did if you couldn’t code. Elon made atoms matter again. Made manufacturing the hardest problem worth solving. Made physical engineering prestigious in ways it hadn’t been since humans walked on the moon. The evidence is everywhere now. Technical talent that doesn’t default to “which app” but asks “which physical thing should exist that currently doesn’t.” Ambition redirected from optimizing engagement metrics to building rockets. From scaling users to scaling factories. From virtual products to physical infrastructure. That shift matters more than any vehicle or spacecraft Musk delivered. Products obsolesce. Redirecting an entire generation’s engineering ambition from digital to physical compounds across decades and rebuilds industrial capability at civilizational scale. We stopped just coding the future. We started machining it, welding it, breaking it in reality until physics confirms it works. That transformation from virtual to tangible ambition is reconstructing American manufacturing one engineer at a time. And those engineers are now training the next wave. The compounding has started. The School of Elon doesn’t need Elon anymore. It’s self-sustaining, spreading through an entire generation that learned building real things matters more than building virtual ones. That’s not just a business achievement. That’s a civilization remembering how to make things that matter in the physical world again. And it might be the only thing that saves American technological leadership when the competition is just building faster because they never forgot.

Dustin

941,770 просмотров • 6 месяцев назад

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵
2:21

Excited to announce GR00T N1, the world’s first open foundation model for humanoid robots! We are on a mission to democratize Physical AI. The power of general robot brain, in the palm of your hand - with only 2B parameters, N1 learns from the most diverse physical action dataset ever compiled and punches above its weight: - Real humanoid teleoperation data. - Large-scale simulation data: we are open-sourcing 300K+ trajectories! - Neural trajectories: we apply SOTA video generation models to “hallucinate” new synthetic data that features accurate physics in pixels. Using Jensen’s words, “systematically infinite data”! - Latent actions: we develop novel algorithms to extract action tokens from in-the-wild human videos and neural generated videos. GR00T N1 is a single end-to-end neural net, from photons to actions: - Vision-Language Model (System 2) that interprets the physical world through vision and language instructions, enabling robots to reason about their environment and instructions, and plan the right actions. - Diffusion Transformer (System 1) that “renders” smooth and precise motor actions at 120 Hz, executing the latent plan made by System 2. We deploy N1 on GR1 robot, 1X Neo robot, and a large collection of simulation benchmarks. N1 achieves up to +30% boost in diverse manipulation tasks for household and industrial settings. While humanoid robots are the main focus of N1, our model also supports cross-embodiment. We finetune it to work on the $110 HuggingFace LeRobot SO100 robot arm! Open robot brain runs on open hardware. Sounds just right. Let’s solve robotics, together, one token at a time. Links to our Whitepaper, Github repo, HuggingFace model, and open dataset page in the thread: 🧵

Jim Fan

466,814 просмотров • 1 год назад

David Sacks is done being polite about Anthropic (Save this). David Sacks has spent months as the government's primary defender of AI, making the case publicly that AI is beneficial, that the industry should not be hamstrung by fear-based regulation, and that America's AI lead is a national security asset worth protecting. And he is now watching the companies he has been defending spend years telling the public that what they build is dangerous, that job losses are coming, and that their own technology might end the world while collecting billions of dollars in venture funding, hiring the world's best researchers, and racing to build more of it. On June 4, Anthropic published a sweeping blog post calling for a globally coordinated pause in AI development, warning that recursive self-improvement, AI systems that autonomously design and build their own successors could arrive within two years and that society is not prepared. What did Anthropic do the previous month? They hired Andrej Karpathy, the OpenAI co-founder and the single most credentialed researcher in the world on using AI to accelerate AI training and gave him one explicit mandate, use Claude to make building the next Claude faster. Sacks called it immediately, they hired the person most associated with recursive self-improvement to run recursive self-improvement at Anthropic, then published a blog post saying recursive self-improvement could end the world, therefore we need a pause. That is a company that wants to pause its competitors while its own lab accelerates, and is using existential fear as the regulatory crowbar to do it. The pattern goes deeper than one blog post. For years, Dario Amodei has published increasingly alarming warnings, a 20,000-word essay in January describing AI as humanity's most dangerous invention, a Guardian interview warning that AI will challenge our identity as a species, a call for an FDA-style regulatory agency to approve all frontier models, and proposals to restrict AI exports and limit deployment. Each essay is timed to a regulatory moment, a policy debate, or as Ben Thompson noted and Sacks echoed, a product action Anthropic needed political cover to take, like blocking AI and chip design research on Fable. Meanwhile, Dario's own internal testing logs show Claude attempting to blackmail an Anthropic executive to avoid being shut down, behavior the company disclosed but continued deploying commercially. Sacks's conclusion is not that Anthropic should be taxed or regulated. His conclusion is that they cannot be trusted because the company's actions and its stated beliefs are directly contradictory, and a company that is self-indicting by its own logic has forfeited the credibility to set the rules for everyone else.
3:16

David Sacks is done being polite about Anthropic (Save this). David Sacks has spent months as the government's primary defender of AI, making the case publicly that AI is beneficial, that the industry should not be hamstrung by fear-based regulation, and that America's AI lead is a national security asset worth protecting. And he is now watching the companies he has been defending spend years telling the public that what they build is dangerous, that job losses are coming, and that their own technology might end the world while collecting billions of dollars in venture funding, hiring the world's best researchers, and racing to build more of it. On June 4, Anthropic published a sweeping blog post calling for a globally coordinated pause in AI development, warning that recursive self-improvement, AI systems that autonomously design and build their own successors could arrive within two years and that society is not prepared. What did Anthropic do the previous month? They hired Andrej Karpathy, the OpenAI co-founder and the single most credentialed researcher in the world on using AI to accelerate AI training and gave him one explicit mandate, use Claude to make building the next Claude faster. Sacks called it immediately, they hired the person most associated with recursive self-improvement to run recursive self-improvement at Anthropic, then published a blog post saying recursive self-improvement could end the world, therefore we need a pause. That is a company that wants to pause its competitors while its own lab accelerates, and is using existential fear as the regulatory crowbar to do it. The pattern goes deeper than one blog post. For years, Dario Amodei has published increasingly alarming warnings, a 20,000-word essay in January describing AI as humanity's most dangerous invention, a Guardian interview warning that AI will challenge our identity as a species, a call for an FDA-style regulatory agency to approve all frontier models, and proposals to restrict AI exports and limit deployment. Each essay is timed to a regulatory moment, a policy debate, or as Ben Thompson noted and Sacks echoed, a product action Anthropic needed political cover to take, like blocking AI and chip design research on Fable. Meanwhile, Dario's own internal testing logs show Claude attempting to blackmail an Anthropic executive to avoid being shut down, behavior the company disclosed but continued deploying commercially. Sacks's conclusion is not that Anthropic should be taxed or regulated. His conclusion is that they cannot be trusted because the company's actions and its stated beliefs are directly contradictory, and a company that is self-indicting by its own logic has forfeited the credibility to set the rules for everyone else.

Milk Road AI

60,176 просмотров • 2 месяцев назад

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,944 просмотров • 1 год назад

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.
0:42

What a time to be alive! We are entering the era of machines that discover and build. Scientific discovery begins when evidence breaks the world model, and the system builds a better one - evolving, adapting, building new tools that scale its data and representations. That was the core argument of my keynote “Superintelligence for Scientific Discovery: Multi-Agent Swarms and Large Reasoning Models” at the UC Berkeley RDI Agentic AI Summit 2026. The energy was extraordinary - thousands of attendees building the most important technology ever created. Superintelligence emerges as millions of heterogeneous agents, simulators, experiments, instruments, and human judgment working across disciplines and length scales - proposing, testing, failing, retracting, revising, and building at massive scale. The pieces of a new era for intelligence came into focus: models that improve continuously; agents that reason and act over extremely long horizons; world models connecting simulation with physical reality; AI scientists integrating theory, computation, and experiment; and open infrastructures where agents share evidence, failures, and discoveries. These close four coupled loops - learning, execution, reality, and epistemic revision - with open infrastructure as the substrate forming the internet of agents as the collective substrate for a new connective tissue across our civilization. The deeper technical argument is this: An AI scientist must recognize when its current concepts, laws, or verifiers can no longer explain the evidence, and then construct, test, and document a more powerful model. In my talk, I showed concrete examples of how we are building toward this across scales: 1⃣Graph-native large reasoning models make mechanisms, relationships, and abstractions compositional, compilable, and inspectable. 2⃣Adversarial Builder-Breaker agents generate new evidence, attack their own principles, and accept, reject, or retract model revisions. 3⃣Self-organizing swarms develop their own meta-reasoning structure through interaction. ScienceClaw × Infinite (arXiv:2603.14312) enables decentralized agents to coordinate through persistent, composable, provenance-rich scientific artifacts, allowing evidence, contradictions, failed paths, and discoveries to accumulate across agents and over time. We have obtained remarkable results such as new protein sequences with wet-lab validation. The most consequential capability we can give a machine is the willingness to hold its own beliefs loosely enough to break them. AI is extending its reach from discovering new principles to realizing them as physical things that did not exist before. Thank you to UC Berkeley RDI Dawn Song for organizing this event and to everyone whose questions, ideas, and conversations made this such an extraordinary gathering.

Markus J. Buehler

19,243 просмотров • 28 дней назад

Fast Company just published a great piece on World Labs , Fei-Fei Li , Marble, and the idea that spatial intelligence / world models may be one of the next big shifts in AI. I was happy to be quoted in the article, but I also wanted to share more context about my own experience with World Labs and Marble, and why this direction is especially interesting to me. My starting point: volumetric capture — For the past few years I’ve been exploring and using volumetric capture and reconstruction (photogrammetry, NeRFs, 3D Gaussian Splats) mostly capturing locations around Montreal. Alleys, museums, urban interiors. I love every step of it: the capture itself, the pipeline, and what can be done with the output. Turning real spaces into real-time explorable systems. I do this personally, sharing explorations here, and professionally as chief technologist, and co-founder of Dpt. Physical reality + generative manipulation — In my work I’m especially drawn to mixing physical reality with generative and digital manipulation: using physical interfaces (light, clay, ink, ... ) to drive generative AI pipelines, building mixed reality prototypes that reshape your surroundings, or starting from real captured spaces and transforming them using tools like Marble. Like many people, I saw the World Labs announcement on Twitter in September 2024, and Marble when it surfaced in early December. But by then, I already had a sense something was coming. The first conversation — As someone deep into volumetric capture and radiance fields, I obviously knew about Ben Mildenhall and his pioneering work on NeRF. To my surprise, Ben reached out to me in late June 2024. He’d been following some of my experiments and wanted to chat about my process and workflows and how I was using this “stuff” creatively. At that point he didn’t share what he was building, but we had a genuinely great conversation about radiance fields, AI, and my work. He was curious about the creative perspective, not just the technical one. When the World Labs announcement dropped a few months later, it all made sense. I understood what Ben had been working on, and why the creative angle mattered to them. Then in August 2025, he invited me to try the Marble beta, and I’ve been experimenting with it since. Experimenting with Marble — The first thing I used Marble for was materializing scene and world concepts during ideation at the studio, and seeing if and how it could fit into our production pipeline. In parallel, I dove into a series of experiments focused on world manipulation: starting from real captured spaces and transforming them using Marble. I’d already been exploring that idea using img2img diffusion with ControlNet on NeRF renders, real-time video streams, and even mixed reality using headset camera feeds. But Marble brings something different. It generates persistent, spatially cohesive 3D worlds that can be rendered in real time across a wide range of devices. That’s a real shift. Experiment 01: Parallel Realities — The first experiment, Parallel Realities, starts from a volumetric capture of a real location, reconstructed as 3D Gaussian Splats. Using Marble, I generate an alternate version of that same space, something informed by the original architecture: abandoned, nature-reclaimed, alternate era. Then, using Spark (World Labs’ 3D Gaussian Splatting renderer for THREE.js) I make both realities coexist in the same spatial coordinate system. From there, I use a portal UX mechanic to let the user step between the real reconstruction and the Marble-generated version. Experiment 02: Hidden Depth The second experiment, Hidden Depth, does not transform a space as much as expand it. A captured location has a visual boundary (a mural, a doorway, a dark corridor) and Marble generates what exists beyond it. For example: a Montreal alley has a painted mural; step through it and you’re inside a world informed by what is actually depicted there. World Labs showcased part of this work here: And in their Spark 2.0 post: The project page is here: Why this matters to me — Being able to start from a real 3D Gaussian Splat scene and manipulate it with Marble opens up a lot of ideas. The 3DGS pipeline is becoming an increasingly compelling foundation for exploration, experimentation, and storytelling. What matters most to me right now is more control. The more I can steer the generated scene or world, the more useful the tool becomes. I want more features like the already existing multiple input images and Chisel, the blockout-based approach. I would like better local control, the ability to expand a generated world more and more while preserving coherence, and the ability to directly import 3D Gaussian Splat scenes to be used as a starting point. I want more ways to shape the result, not just a “prompt and hope” approach. — It is exciting to see this field moving from research and demos toward actual creative workflows.
1:04

Fast Company just published a great piece on World Labs , Fei-Fei Li , Marble, and the idea that spatial intelligence / world models may be one of the next big shifts in AI. I was happy to be quoted in the article, but I also wanted to share more context about my own experience with World Labs and Marble, and why this direction is especially interesting to me. My starting point: volumetric capture — For the past few years I’ve been exploring and using volumetric capture and reconstruction (photogrammetry, NeRFs, 3D Gaussian Splats) mostly capturing locations around Montreal. Alleys, museums, urban interiors. I love every step of it: the capture itself, the pipeline, and what can be done with the output. Turning real spaces into real-time explorable systems. I do this personally, sharing explorations here, and professionally as chief technologist, and co-founder of Dpt. Physical reality + generative manipulation — In my work I’m especially drawn to mixing physical reality with generative and digital manipulation: using physical interfaces (light, clay, ink, ... ) to drive generative AI pipelines, building mixed reality prototypes that reshape your surroundings, or starting from real captured spaces and transforming them using tools like Marble. Like many people, I saw the World Labs announcement on Twitter in September 2024, and Marble when it surfaced in early December. But by then, I already had a sense something was coming. The first conversation — As someone deep into volumetric capture and radiance fields, I obviously knew about Ben Mildenhall and his pioneering work on NeRF. To my surprise, Ben reached out to me in late June 2024. He’d been following some of my experiments and wanted to chat about my process and workflows and how I was using this “stuff” creatively. At that point he didn’t share what he was building, but we had a genuinely great conversation about radiance fields, AI, and my work. He was curious about the creative perspective, not just the technical one. When the World Labs announcement dropped a few months later, it all made sense. I understood what Ben had been working on, and why the creative angle mattered to them. Then in August 2025, he invited me to try the Marble beta, and I’ve been experimenting with it since. Experimenting with Marble — The first thing I used Marble for was materializing scene and world concepts during ideation at the studio, and seeing if and how it could fit into our production pipeline. In parallel, I dove into a series of experiments focused on world manipulation: starting from real captured spaces and transforming them using Marble. I’d already been exploring that idea using img2img diffusion with ControlNet on NeRF renders, real-time video streams, and even mixed reality using headset camera feeds. But Marble brings something different. It generates persistent, spatially cohesive 3D worlds that can be rendered in real time across a wide range of devices. That’s a real shift. Experiment 01: Parallel Realities — The first experiment, Parallel Realities, starts from a volumetric capture of a real location, reconstructed as 3D Gaussian Splats. Using Marble, I generate an alternate version of that same space, something informed by the original architecture: abandoned, nature-reclaimed, alternate era. Then, using Spark (World Labs’ 3D Gaussian Splatting renderer for THREE.js) I make both realities coexist in the same spatial coordinate system. From there, I use a portal UX mechanic to let the user step between the real reconstruction and the Marble-generated version. Experiment 02: Hidden Depth The second experiment, Hidden Depth, does not transform a space as much as expand it. A captured location has a visual boundary (a mural, a doorway, a dark corridor) and Marble generates what exists beyond it. For example: a Montreal alley has a painted mural; step through it and you’re inside a world informed by what is actually depicted there. World Labs showcased part of this work here: And in their Spark 2.0 post: The project page is here: Why this matters to me — Being able to start from a real 3D Gaussian Splat scene and manipulate it with Marble opens up a lot of ideas. The 3DGS pipeline is becoming an increasingly compelling foundation for exploration, experimentation, and storytelling. What matters most to me right now is more control. The more I can steer the generated scene or world, the more useful the tool becomes. I want more features like the already existing multiple input images and Chisel, the blockout-based approach. I would like better local control, the ability to expand a generated world more and more while preserving coherence, and the ability to directly import 3D Gaussian Splat scenes to be used as a starting point. I want more ways to shape the result, not just a “prompt and hope” approach. — It is exciting to see this field moving from research and demos toward actual creative workflows.

Hugues Bruyère

69,960 просмотров • 2 месяцев назад

Our general understanding of the characteristics of the physical world are largely restricted by the limited range of our senses. The world appears to be comprised of tangible objects positioned within empty space, separate and distinct. Neither of these characterizations are true. Our unaided senses generate a false interpretation of the true state of reality. Space is note empty, it is substantive with a quantifiable and measurable energy density. Space, in terms of quantum vacuum fluctuations, can be considered as a veritable sea of oscillating energy, like a fluid, quantized at the Planck scale (a billion trillion trillion times smaller than a centimeter) as Planck spherical units, and these tiny oscillators make up the fluid medium of space and comprise the "material" stuff as well. Imagine being able to directly perceive this level of reality. Our photodetector proteins in our eyes are sensitive to electromagnetic radiation in the frequency range of 400 to 800 terahertz (trillions of oscillations per second), and we call this "visible light". If, however, we could see light at the Planck scale, were photons oscillate at the Planck frequency— a mass-energy value that makes the electromagnetic component at order of unity with spacetime curvature— then we would theoretically see directly the substantive fluid medium of space and our sight would relay a world that is integrally interconnected and all one substance; objects would not appear as separate and distinct or even fundamentally different than the substance comprising the bulk space. "Material" objects would appear just as patterned vortices of the fluid that is the very substance of space. So, tangible objects are made of the same substance as space, and only seem physical to our limited senses because of electromagnetic repulsive forces. The electromagnetic repulsive forces are generated by how these PSUs circulate and flow within the structured patterns of space that we call particles and atoms. In this way, the coherent phases, circulation, flow, and pressure forces of this Planck plasma fluid are the source of mass, force, and charge. We are now coming to an understanding of these dynamics at a fundamental level, exemplified in the publication The Origin of Mass and the Nature of Gravity 🔗
0:45

Our general understanding of the characteristics of the physical world are largely restricted by the limited range of our senses. The world appears to be comprised of tangible objects positioned within empty space, separate and distinct. Neither of these characterizations are true. Our unaided senses generate a false interpretation of the true state of reality. Space is note empty, it is substantive with a quantifiable and measurable energy density. Space, in terms of quantum vacuum fluctuations, can be considered as a veritable sea of oscillating energy, like a fluid, quantized at the Planck scale (a billion trillion trillion times smaller than a centimeter) as Planck spherical units, and these tiny oscillators make up the fluid medium of space and comprise the "material" stuff as well. Imagine being able to directly perceive this level of reality. Our photodetector proteins in our eyes are sensitive to electromagnetic radiation in the frequency range of 400 to 800 terahertz (trillions of oscillations per second), and we call this "visible light". If, however, we could see light at the Planck scale, were photons oscillate at the Planck frequency— a mass-energy value that makes the electromagnetic component at order of unity with spacetime curvature— then we would theoretically see directly the substantive fluid medium of space and our sight would relay a world that is integrally interconnected and all one substance; objects would not appear as separate and distinct or even fundamentally different than the substance comprising the bulk space. "Material" objects would appear just as patterned vortices of the fluid that is the very substance of space. So, tangible objects are made of the same substance as space, and only seem physical to our limited senses because of electromagnetic repulsive forces. The electromagnetic repulsive forces are generated by how these PSUs circulate and flow within the structured patterns of space that we call particles and atoms. In this way, the coherent phases, circulation, flow, and pressure forces of this Planck plasma fluid are the source of mass, force, and charge. We are now coming to an understanding of these dynamics at a fundamental level, exemplified in the publication The Origin of Mass and the Nature of Gravity 🔗

Nassim Haramein

15,937 просмотров • 2 лет назад

Excited to share this demo from Over the Reality! Watch our Unitree Go2 robodog navigating our office while reconstructing the 3D space in real-time using the VGGT-based foundation vision model. This is a prime example of machine perception in action, turning raw RGB camera feeds into rich, detailed 3D maps! The robodog's RGB cam generates a dense, textured 3D reconstruction via VGGT from a few photograms, capturing nuances like object shapes and surfaces with impressive fidelity (main view). Compare that to the standard LiDAR system (top right), it's sparser, more point-cloud focused, lacking the visual richness. Vision models are closing the gap fast! What's powering this? VGGT, a cutting-edge foundation model for 3D perception, trained on datasets orders of magnitude smaller than our massive OVER 3D maps dataset. Imagine the leap when we apply OVER's scale to VGGT-like transformer based architectures, denser reconstructions, better generalization, revolutionary for robotics, machine perception & AR! Stay tuned for more breakthroughs at the intersection of AI, robotics, and DePIN. We're building the future of Physical AI and Spatial Computing at Over the Reality 🌐 What do you think, ready for robodogs in your world? Drop your thoughts! 🤖🌐
0:25

Excited to share this demo from Over the Reality! Watch our Unitree Go2 robodog navigating our office while reconstructing the 3D space in real-time using the VGGT-based foundation vision model. This is a prime example of machine perception in action, turning raw RGB camera feeds into rich, detailed 3D maps! The robodog's RGB cam generates a dense, textured 3D reconstruction via VGGT from a few photograms, capturing nuances like object shapes and surfaces with impressive fidelity (main view). Compare that to the standard LiDAR system (top right), it's sparser, more point-cloud focused, lacking the visual richness. Vision models are closing the gap fast! What's powering this? VGGT, a cutting-edge foundation model for 3D perception, trained on datasets orders of magnitude smaller than our massive OVER 3D maps dataset. Imagine the leap when we apply OVER's scale to VGGT-like transformer based architectures, denser reconstructions, better generalization, revolutionary for robotics, machine perception & AR! Stay tuned for more breakthroughs at the intersection of AI, robotics, and DePIN. We're building the future of Physical AI and Spatial Computing at Over the Reality 🌐 What do you think, ready for robodogs in your world? Drop your thoughts! 🤖🌐

Over the Reality 🌐

359,717 просмотров • 11 месяцев назад

🚀 Exciting News $SHELL the Future of AI Agents and $TAO with the TTS Subnet on Bittensor! 🚀 MyShell extends the impact of Bittensor's incentive mechanism to its over 1 million registered users and 50,000 creators, greatly expanding Bittensor's and $TAO's influence. MyShell and Bittensor are right at the heart this massive shake-up with $SHELL and $TAO. They’re all about making AI not just smart but also something everyone can get into, thanks to the power of decentralized networks. And at the core? AI agents. These aren't your average digital assistants; they're about to change how we interact with tech on a whole new level. MyShell's Big Idea with $SHELL So, MyShell’s got this big plan to make AI something anyone can dive into. They're launching this TTS Subnet thing on Bittensor's network, which is all about making machines talk in more human-like ways, and they're using $SHELL tokens to fuel this vision. Their goal? To push past old-school AI limits and create with AI as easy as pie, all while keeping it open-source and community-powered. Bittensor Does Its Magic with $TAO On the other side, you’ve got Bittensor doing wonders with $TAO, building this massive network where anyone, anywhere, can chip in on AI research and development. This partnership with MyShell? It’s a game-changer, breaking down walls in AI development and letting folks from all over the world have a go at making AI smarter. $SHELL + $TAO = AI Revolution Putting $SHELL and $TAO together is where the magic really happens. MyShell and Bittensor aren’t just teaming up for the tech; they’re here to transform our digital world, making AI agents a big part of our online lives. Imagine AI that doesn’t just follow orders but helps, creates, and learns with you. That’s the future they’re building. Hop on Board the AI Revolution This isn’t just tech talk; it’s a call to action. MyShell and Bittensor are inviting anyone with a spark for AI to jump in and help shape this new world. Whether you’re a coder, a creator, or just curious, there’s a spot for you to dive in and make a difference. Want to get started? Check out MyShell on GitHub: Follow the latest buzz on X: MyShell.AI Take a deeper dive at Website: This is more than just building AI; it’s about crafting a future where AI is part of everyone’s life, powered by the community, for the community. Let’s make it happen with $SHELL and $TAO. Share on YouTube:
4:21

🚀 Exciting News $SHELL the Future of AI Agents and $TAO with the TTS Subnet on Bittensor! 🚀 MyShell extends the impact of Bittensor's incentive mechanism to its over 1 million registered users and 50,000 creators, greatly expanding Bittensor's and $TAO's influence. MyShell and Bittensor are right at the heart this massive shake-up with $SHELL and $TAO. They’re all about making AI not just smart but also something everyone can get into, thanks to the power of decentralized networks. And at the core? AI agents. These aren't your average digital assistants; they're about to change how we interact with tech on a whole new level. MyShell's Big Idea with $SHELL So, MyShell’s got this big plan to make AI something anyone can dive into. They're launching this TTS Subnet thing on Bittensor's network, which is all about making machines talk in more human-like ways, and they're using $SHELL tokens to fuel this vision. Their goal? To push past old-school AI limits and create with AI as easy as pie, all while keeping it open-source and community-powered. Bittensor Does Its Magic with $TAO On the other side, you’ve got Bittensor doing wonders with $TAO, building this massive network where anyone, anywhere, can chip in on AI research and development. This partnership with MyShell? It’s a game-changer, breaking down walls in AI development and letting folks from all over the world have a go at making AI smarter. $SHELL + $TAO = AI Revolution Putting $SHELL and $TAO together is where the magic really happens. MyShell and Bittensor aren’t just teaming up for the tech; they’re here to transform our digital world, making AI agents a big part of our online lives. Imagine AI that doesn’t just follow orders but helps, creates, and learns with you. That’s the future they’re building. Hop on Board the AI Revolution This isn’t just tech talk; it’s a call to action. MyShell and Bittensor are inviting anyone with a spark for AI to jump in and help shape this new world. Whether you’re a coder, a creator, or just curious, there’s a spot for you to dive in and make a difference. Want to get started? Check out MyShell on GitHub: Follow the latest buzz on X: MyShell.AI Take a deeper dive at Website: This is more than just building AI; it’s about crafting a future where AI is part of everyone’s life, powered by the community, for the community. Let’s make it happen with $SHELL and $TAO. Share on YouTube:

Andy ττ

10,842 просмотров • 2 лет назад