正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Trained a model on 50 digitally sculpted flowers. Wind simulation added, nature meeting computation. The future of AI feels more poetic when it’s built from personal data, not borrowed datasets.

FVCKRENDER

166,858 subscribers

11,381 次观看 • 10 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

What happens when nature becomes a testing ground? In Fish Grid, artist Kevin Abosch explores the intersection of perception, memory and computation. We spotlight works shaping the future of art. Discover more on TAEX.

What happens when nature becomes a testing ground? In Fish Grid, artist Kevin Abosch explores the intersection of perception, memory and computation. We spotlight works shaping the future of art. Discover more on TAEX.

TAEX

3,998,852 次观看 • 4 个月前

AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

AI needs data to grow. But accessing high-quality datasets is becoming harder than ever. · Data collection is increasingly centralized in the hands of a few large players. · Platforms are locking content behind expensive APIs and private data deals. · Specialized datasets (medical, legal, code) can cost 20–40× more than general web data. · And while 94% of companies are exploring Gen-AI, only 12% have reliable data channels. The result? A growing data bottleneck for AI development. The future of AI won’t just depend on better models. It will depend on who can access and activate quality data.

Perceptron Network

75,306 次观看 • 5 个月前

I have a new personal website. I'm not a developer. I built this v2 of my website, all by my OpenClaw agent (on Codex) + built an AI clone of me, with Claude Code. So can designers ship? Yes, its the future.

I have a new personal website. I'm not a developer. I built this v2 of my website, all by my OpenClaw agent (on Codex) + built an AI clone of me, with Claude Code. So can designers ship? Yes, its the future.

Felix Lee

21,923 次观看 • 4 个月前

We’re joining the race. ⚫️ Ever wonder why AI sometimes gives half-baked answers or misses the point? It’s not the model’s fault, it’s running low on good data. Experts say AI is hitting a wall. The internet’s been scraped dry, and without fresh, verified data, progress slows down. That’s where we come in. We’re building the ladder, a network that collects, validates, and shares real, useful datasets. Everything is stored securely in the Vault. We’re not just watching from the sidelines. We’re stepping onto the track, helping AI keep learning with better data from real people.

We’re joining the race. ⚫️ Ever wonder why AI sometimes gives half-baked answers or misses the point? It’s not the model’s fault, it’s running low on good data. Experts say AI is hitting a wall. The internet’s been scraped dry, and without fresh, verified data, progress slows down. That’s where we come in. We’re building the ladder, a network that collects, validates, and shares real, useful datasets. Everything is stored securely in the Vault. We’re not just watching from the sidelines. We’re stepping onto the track, helping AI keep learning with better data from real people.

Perceptron Network

50,958 次观看 • 10 个月前

Imagine this: Data is the lifeblood of AI. No matter how advanced, AI is only as good as the data it learns from. The catch? Most AI runs on outdated, biased, or limited data—like trying to win a marathon in flip-flops. 💡The problem isn’t AI itself—it’s how data is collected, processed & controlled by a few big players. OptimAI Network flips the script! 🔹No centralized silos 🔹No intrusive scraping 🔹A decentralized ecosystem where you power AI’s future Read more:

Imagine this: Data is the lifeblood of AI. No matter how advanced, AI is only as good as the data it learns from. The catch? Most AI runs on outdated, biased, or limited data—like trying to win a marathon in flip-flops. 💡The problem isn’t AI itself—it’s how data is collected, processed & controlled by a few big players. OptimAI Network flips the script! 🔹No centralized silos 🔹No intrusive scraping 🔹A decentralized ecosystem where you power AI’s future Read more:

OptimAI Network

92,298 次观看 • 1 年前

Announcing our commercial partnership with Booster Robotics Booster builds humanoid robot hardware, OS, and developer tools to make humanoid robots more affordable, reliable, and practical. The partnership centers on using simulation to multiply the value of real-robot data—expanding teleoperation demonstrations into scalable training data across diverse tasks and scenes. This joint effort powers sim-real co-training and foundation-model development, accelerating progress from hardware iteration to deployable robot policies. Together, we're building simulation‑powered data infrastructure for Physical AI — making scalable training data accessible to model developers and the broader robotics ecosystem.

Announcing our commercial partnership with Booster Robotics Booster builds humanoid robot hardware, OS, and developer tools to make humanoid robots more affordable, reliable, and practical. The partnership centers on using simulation to multiply the value of real-robot data—expanding teleoperation demonstrations into scalable training data across diverse tasks and scenes. This joint effort powers sim-real co-training and foundation-model development, accelerating progress from hardware iteration to deployable robot policies. Together, we're building simulation‑powered data infrastructure for Physical AI — making scalable training data accessible to model developers and the broader robotics ecosystem.

Axis Robotics

68,804 次观看 • 1 个月前

Pi Ventures Joins Hack VC To Back AI Robotics Startup On Base Axis Robotics (Axis Robotics) has raised a $12 million seed round led by Hack VC, with participation from Pi Network (Pi Network) Ventures, Nomad Capital, 10K Ventures, and other angel investors. The company is building a data engine for Physical AI that combines simulation, real world data capture, and human feedback to generate scalable robotics datasets. Axis said the funding will accelerate development of its global human in the loop data engine. Base (Base APAC) congratulated the team, calling Axis one of the leading scalable Physical AI and robotics platforms building on the network.

Pi Ventures Joins Hack VC To Back AI Robotics Startup On Base Axis Robotics (Axis Robotics) has raised a $12 million seed round led by Hack VC, with participation from Pi Network (Pi Network) Ventures, Nomad Capital, 10K Ventures, and other angel investors. The company is building a data engine for Physical AI that combines simulation, real world data capture, and human feedback to generate scalable robotics datasets. Axis said the funding will accelerate development of its global human in the loop data engine. Base (Base APAC) congratulated the team, calling Axis one of the leading scalable Physical AI and robotics platforms building on the network.

BSCN

51,711 次观看 • 1 个月前

Bengaluru first, politics later! The Tunnel Road is not my personal project - it’s a public initiative for Bengaluru’s future. We had a meeting with Union Minister Shri Nitin Gadkari who advised us and supported on the Tunnel Project as a solution to our city’s traffic woes. State BJP leaders, who are on a warpath against the project, should first listen to their own Union Minister. When tunnel roads are built by the Central government across India, it’s called development - but when we plan one for Bengaluru, it becomes controversial. Bengaluru deserves constructive solutions and progress - not politics. BJP Karnataka R. Ashoka

Bengaluru first, politics later! The Tunnel Road is not my personal project - it’s a public initiative for Bengaluru’s future. We had a meeting with Union Minister Shri Nitin Gadkari who advised us and supported on the Tunnel Project as a solution to our city’s traffic woes. State BJP leaders, who are on a warpath against the project, should first listen to their own Union Minister. When tunnel roads are built by the Central government across India, it’s called development - but when we plan one for Bengaluru, it becomes controversial. Bengaluru deserves constructive solutions and progress - not politics. BJP Karnataka R. Ashoka

DK Shivakumar

111,931 次观看 • 10 个月前

Grok 4.7 is just around the corner. And it’s bringing a much bigger brain. The new model is coming with 2.1 trillion parameters, up 40% from Grok 4.6. It’s also trained on SpaceX engineering data and built to power persistent Grok Bot teams that keep working in the cloud after you log off. Bigger model. Better engineering context. Agents that don’t clock out. Grok 4.7 is coming in hot. Grok SpaceXAI SpaceX / Writer: Annette, Grok Imagine Designer: Janné

Grok 4.7 is just around the corner. And it’s bringing a much bigger brain. The new model is coming with 2.1 trillion parameters, up 40% from Grok 4.6. It’s also trained on SpaceX engineering data and built to power persistent Grok Bot teams that keep working in the cloud after you log off. Bigger model. Better engineering context. Agents that don’t clock out. Grok 4.7 is coming in hot. Grok SpaceXAI SpaceX / Writer: Annette, Grok Imagine Designer: Janné

Mario Nawfal

81,033 次观看 • 2 天前

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

🚀 Introducing EgoExo Forge - built on top of Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tuned

Pablo Vela

35,626 次观看 • 1 年前

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.

Chinese quant built a simulation of how SPX price reacts to any global event. He’s already made over $100k - with full blockchain proof. He knows exactly where price will go. More than 40 years of SPX trading history have been loaded into MiroFish simulator (18k stars on GitHub) AI analyzed every single moment in that trading history. Now this guy has a fully functional SPX price prediction system. His wallet: Dozens of successful SPX price-prediction trades and hundreds of tests across other stock markets. Here’s exactly what you need to replicate his stack: - market data APIs (SPX price, use Alpha Vantage or Quandl) - data pipeline (use Python) - feature engineering (for output signals like RSI, MACD) - seed dataset for MiroFish (convert data into structured context) - multi-agent simulation (macro strategist, earnings analyst, sentiment analyst agents etc.) - probability forecast (run different scenarios) - trading / decision Model (SPX futures ES, SPY ETF) Save this pipeline if you want to run a similar simulation on your own data. You can feed the whole thing to your Claude and build your first (even small) simulation model together.

cvxv666

2,427,583 次观看 • 5 个月前

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

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

Axis Robotics

28,096 次观看 • 8 个月前

🔐 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗪𝗮𝘃𝗲 𝗼𝗳 𝗔𝗜 We're proud to partner with Stempoint to secure the future of decentralized AI compute. StemPoint is building one of the most advanced distributed AI infrastructures on the planet, sharded GPU compute, open model libraries, and secure data flows across a global mesh. Now with Naoris Protocol’s Sub-Zero Layer and dPoSec consensus, every node, model, and data packet gains: • Post-quantum cryptographic protection • Real-time validation of compute and algorithms • A decentralized trust layer for sovereign AI 🚫 No more trusting black boxes. ✅ Verifiable AI infrastructure at scale.

🔐 𝗣𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗪𝗮𝘃𝗲 𝗼𝗳 𝗔𝗜 We're proud to partner with Stempoint to secure the future of decentralized AI compute. StemPoint is building one of the most advanced distributed AI infrastructures on the planet, sharded GPU compute, open model libraries, and secure data flows across a global mesh. Now with Naoris Protocol’s Sub-Zero Layer and dPoSec consensus, every node, model, and data packet gains: • Post-quantum cryptographic protection • Real-time validation of compute and algorithms • A decentralized trust layer for sovereign AI 🚫 No more trusting black boxes. ✅ Verifiable AI infrastructure at scale.

NaoX Protocol

23,336 次观看 • 1 年前

There are some small differences in UI between Tesla FSD V14 Lite and HW4 V14. • Starting Self-Driving requires brake to confirm. It’s on by default. • There is no FSD intervention survey, just a voice message. • No Self-Driving stats (hope that can be added in the future). • Arrival options do not appear when approaching the destination. • It does not say “Driving to Whole Foods” when FSD is activated (I hope that’s added in the future as well). Keep in mind, this is a legacy Model Y with an Intel chip. All in all, V14 delivers on the main features from HW4 FSD. It parks in your driveway, in parking lots, at Superchargers, etc. It’s been really incredible. A night-and-day difference compared to V12.

There are some small differences in UI between Tesla FSD V14 Lite and HW4 V14. • Starting Self-Driving requires brake to confirm. It’s on by default. • There is no FSD intervention survey, just a voice message. • No Self-Driving stats (hope that can be added in the future). • Arrival options do not appear when approaching the destination. • It does not say “Driving to Whole Foods” when FSD is activated (I hope that’s added in the future as well). Keep in mind, this is a legacy Model Y with an Intel chip. All in all, V14 delivers on the main features from HW4 FSD. It parks in your driveway, in parking lots, at Superchargers, etc. It’s been really incredible. A night-and-day difference compared to V12.

Nic Cruz Patane

57,204 次观看 • 2 个月前

A week ago, we launched the OptimAI Core Node. Today, the numbers speak for themselves; but the story behind them speaks even louder. More than 3,000 active Core Nodes now power the OptimAI Network: + 43,831 CPU cores + 75,289 GB RAM + Over 1,000 GPUs spanning NVIDIA, AMD, Intel, Apple, and more Together, they form a distributed AI supercomputer - built not by a corporation, but by the community. This is how the future of Agentic AI begins: AI that learns from live data, reasons across the open web, and evolves through human collaboration. We’re not just running nodes. We’re building the backbone of decentralized intelligence. 👉Join the movement:

A week ago, we launched the OptimAI Core Node. Today, the numbers speak for themselves; but the story behind them speaks even louder. More than 3,000 active Core Nodes now power the OptimAI Network: + 43,831 CPU cores + 75,289 GB RAM + Over 1,000 GPUs spanning NVIDIA, AMD, Intel, Apple, and more Together, they form a distributed AI supercomputer - built not by a corporation, but by the community. This is how the future of Agentic AI begins: AI that learns from live data, reasons across the open web, and evolves through human collaboration. We’re not just running nodes. We’re building the backbone of decentralized intelligence. 👉Join the movement:

OptimAI Network

20,649 次观看 • 9 个月前

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Something big is happening in robotics - and it’s hiding in plain sight. This post is not about dancing robots but in the data that powers them. Open robotics datasets have exploded this year, turning the field into a more scalable and collaborative ecosystem. In just two years, Hugging Face datasets grew from 11k to over 600k - and robotics is by far the fastest-growing segment. We went from 1k robotics datasets in 2024 to 27k in 2025! For comparison, text generation, the second-largest category, has only around 5k datasets in 2025. That gap is massive. Open datasets are important because robotics lives and dies by real-world robot data - video, actions, sensors, failures. By making this data easy to upload, reuse, and benchmark, researchers, startups, and large players are now releasing real-robot datasets that would have stayed locked inside labs just a few years ago. Major contributors include NVIDIA, LeRobot initiative, and a rapidly growing maker community. This surge is also enabled by cheaper video storage, better tooling, and an open-source AI culture now spilling into the physical world. And it really matters: open robotics data dramatically lowers entry barriers, accelerates learning-by-doing, and speeds up progress toward generalist and humanoid robots. Robotics won’t scale through hardware alone - but to a large extent through shared data. Viz below from AI World - link to the story and more viz/filters in comment.

Pierre-Alexandre Balland

186,094 次观看 • 8 个月前

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

Stefan 3D AI

36,034 次观看 • 1 个月前

Nice sunny day in Bavaria. Another day when all these wind turbines stand perfectly still， as expensive land decorations without generating a single watt of electricity. I saw more than 50 wind turbines today while driving and hiking through the landscape. Not a single one of them was turning. I used to support the so called "renewables". But having eyes to observe and a brain to think make me doubt the renewable maximumist narrative. Southern Germany is simply not suitable to build more wind turbines， because it is just not windy enough. No amount of government mandate or subsidies can change that. Green's 100% renewable fantasy is an expensive war against nature.

Nice sunny day in Bavaria. Another day when all these wind turbines stand perfectly still， as expensive land decorations without generating a single watt of electricity. I saw more than 50 wind turbines today while driving and hiking through the landscape. Not a single one of them was turning. I used to support the so called "renewables". But having eyes to observe and a brain to think make me doubt the renewable maximumist narrative. Southern Germany is simply not suitable to build more wind turbines， because it is just not windy enough. No amount of government mandate or subsidies can change that. Green's 100% renewable fantasy is an expensive war against nature.

Dr. Dale Wen

54,131 次观看 • 4 个月前

Introducing Poetic: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.

Introducing Poetic: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.

Markie Wagner

1,376,271 次观看 • 2 个月前