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Robots need strong visuo-motor representations to manipulate objects, but it’s hard to learn these using demo data alone. Our #RSS2024 project vastly improves robotic representations, using human affordances mined from Ego4D! w/ Mohan Kumar Srirama Shikhar Bahl Abhinav Gupta

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Sudeep Dasari

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11,024 просмотров • 2 лет назад •via X (Twitter)

Наука и технологииОбразование#RSS2024
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Фото профиля Sudeep Dasari
Sudeep Dasari2 лет назад

If you’re at @RoboticsSciSys in Delft, drop by our presentation and poster this Wednesday (session 10) at 1:30 PM! Website: Paper:

Фото профиля Yang
Yang1 год назад

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What if robots could learn real-world tasks from your perspective… without ever touching a robot? This is a system that trains robot policies using nothing but human-first, egocentric video data from smart glasses. No robots, no teleop, no sensors, just humans doing real tasks in the real world. Why it matters ✅ Learns robot policies from 20 minutes of human video; zero robot demos ✅ Generalizes to new objects, views, and even robot morphologies ✅ Uses 3D points for interpretable, spatially grounded learning ✅ Deploys directly to real-world robots with strong zero-shot success Thank you, Vincent Liu, for sharing!!! Learn more here: 🔗 Paper: 🌐 Website: 📍 BOOKMARK FOR LATER
0:30

What if robots could learn real-world tasks from your perspective… without ever touching a robot? This is a system that trains robot policies using nothing but human-first, egocentric video data from smart glasses. No robots, no teleop, no sensors, just humans doing real tasks in the real world. Why it matters ✅ Learns robot policies from 20 minutes of human video; zero robot demos ✅ Generalizes to new objects, views, and even robot morphologies ✅ Uses 3D points for interpretable, spatially grounded learning ✅ Deploys directly to real-world robots with strong zero-shot success Thank you, Vincent Liu, for sharing!!! Learn more here: 🔗 Paper: 🌐 Website: 📍 BOOKMARK FOR LATER

Ilir Aliu - eu/acc

10,509 просмотров • 1 год назад

Check out our #PAMI paper with code "Dense Continuous-Time Optical Flow from Event Cameras," where we show how to regress *continuous-time* trajectories of every pixel from event cameras alone or events plus frames! The key idea is to iteratively estimate per-pixel polynomials using a recurrent lookup and update scheme. Paper: Code: DOI: We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. We show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments suggest that our method successfully predicts pixel trajectories in continuous time and is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public. Kudos to Mathias Gehrig Manasi Muglikar
0:23

Check out our #PAMI paper with code "Dense Continuous-Time Optical Flow from Event Cameras," where we show how to regress *continuous-time* trajectories of every pixel from event cameras alone or events plus frames! The key idea is to iteratively estimate per-pixel polynomials using a recurrent lookup and update scheme. Paper: Code: DOI: We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. We show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments suggest that our method successfully predicts pixel trajectories in continuous time and is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public. Kudos to Mathias Gehrig Manasi Muglikar

Davide Scaramuzza

12,637 просмотров • 2 лет назад

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)
1:13

Yann LeCun (Yann LeCun ) beautifully explains how the architecture and principles used to train LLMs can not be extended to teach AI the real-world intelligence. In 1 line: LLMs excel where intelligence equals sequence prediction over symbols. Real-world intelligence requires learned world models, abstraction, causality, and action planning under uncertainty, which current next-token training does not provide. He says current LLMs learn by predicting the next token. That objective works very well when the task itself can be reduced to manipulating discrete symbols and sequences. Math, physics problem solving on paper, and coding fit this pattern because success largely comes from searching and composing the right sequences of symbols, equations, or program tokens. With enough data and scale, these models get very good at that kind of structured sequence prediction. Real-world intelligence is different. The physical world is continuous, noisy, uncertain, and high dimensional. To act in it, a system needs internal models that capture objects, dynamics, causality, constraints from the body, and the outcomes of actions over time. Humans and animals build abstract representations from rich sensory streams, then make predictions in that abstract space, not at the raw pixel level. That is why a child can learn intuitive physics, plan multi-step actions, and adapt quickly in new situations with little data. His claim about saturation follows from this gap. Scaling token prediction keeps improving symbol manipulation tasks like math and code, but it hits limits on embodied reasoning and common sense because text alone does not provide the right learning signals for world models. Predicting the next word cannot efficiently teach contact forces, affordances, occlusion, friction, or how actions change the state of the environment. For that, he argues we need architectures that learn abstractions from sensory data and predict futures in abstract latent spaces, then use those predictions to plan actions toward goals with built-in guardrails. --- From 'Pioneer Works' YT Channel (link in comment)

Rohan Paul

104,460 просмотров • 7 месяцев назад

Welcome to the Lab of the Future! 🧬🤖 Excited to share LUMI-lab, out today in Cell — a self-driving platform that pairs an AI foundation model with a robotic lab to autonomously discover ionizable lipids (LNPs) for mRNA delivery. The core problem: Designing lipid nanoparticles (LNPs) is hard. The chemical space of ionizable lipids is vast, experimental cycles are slow, and — critically — historical LNP datasets are far too small to train a predictive model from scratch. Most AI approaches in this space hit a wall immediately: not enough data to learn from. Our solution: lab-in-the-loop foundation model learning. Instead of training on LNP data alone, LUMI starts as a transformer-based foundation model pretrained across broad chemical space, building rich molecular representations before it ever sees a single LNP experiment. Then it enters a closed loop with a robotic synthesis platform: predict → synthesize → assay → update. Each round of real wet-lab experiments fine-tunes the model, which then proposes smarter candidates for the next round. The lab isn't just validating AI predictions — it's actively teaching the model, continuously. What happened when we let it run: LUMI-lab autonomously synthesized and screened 1,700+ ionizable lipids in human bronchial epithelial cells. The top candidate — LUMI-6 — features a brominated lipid tail, a structural motif that had been largely overlooked in LNP design. LUMI found it without being told where to look. When formulated into LNPs and delivered intratracheally to mice, LUMI-6 achieved 20.3% gene editing efficiency in lung epithelial cells — a compelling result for one of the hardest-to-reach therapeutic targets, directly relevant to diseases like cystic fibrosis and alpha-1 antitrypsin deficiency. Why this matters beyond LNPs: This is a proof of concept for a broader thesis — that foundation model pretraining + active learning + robotic experimentation can overcome the data scarcity bottleneck that plagues AI-driven discovery in biology. You don't need a massive domain-specific dataset to start. You need a model that can generalize, a lab that can generate the right data, and a loop that connects them. Huge congratulations to first authors Yue Xu, Haotian Cui, and Kuan Pang, and to the entire Bowen LI team. Grateful to our collaborators at University Health Network and Leslie Dan Faculty of Pharmacy, and to Princess Margaret Cancer Centre Research Princess Margaret Cancer Centre Research. 📄 Paper:
1:05

Welcome to the Lab of the Future! 🧬🤖 Excited to share LUMI-lab, out today in Cell — a self-driving platform that pairs an AI foundation model with a robotic lab to autonomously discover ionizable lipids (LNPs) for mRNA delivery. The core problem: Designing lipid nanoparticles (LNPs) is hard. The chemical space of ionizable lipids is vast, experimental cycles are slow, and — critically — historical LNP datasets are far too small to train a predictive model from scratch. Most AI approaches in this space hit a wall immediately: not enough data to learn from. Our solution: lab-in-the-loop foundation model learning. Instead of training on LNP data alone, LUMI starts as a transformer-based foundation model pretrained across broad chemical space, building rich molecular representations before it ever sees a single LNP experiment. Then it enters a closed loop with a robotic synthesis platform: predict → synthesize → assay → update. Each round of real wet-lab experiments fine-tunes the model, which then proposes smarter candidates for the next round. The lab isn't just validating AI predictions — it's actively teaching the model, continuously. What happened when we let it run: LUMI-lab autonomously synthesized and screened 1,700+ ionizable lipids in human bronchial epithelial cells. The top candidate — LUMI-6 — features a brominated lipid tail, a structural motif that had been largely overlooked in LNP design. LUMI found it without being told where to look. When formulated into LNPs and delivered intratracheally to mice, LUMI-6 achieved 20.3% gene editing efficiency in lung epithelial cells — a compelling result for one of the hardest-to-reach therapeutic targets, directly relevant to diseases like cystic fibrosis and alpha-1 antitrypsin deficiency. Why this matters beyond LNPs: This is a proof of concept for a broader thesis — that foundation model pretraining + active learning + robotic experimentation can overcome the data scarcity bottleneck that plagues AI-driven discovery in biology. You don't need a massive domain-specific dataset to start. You need a model that can generalize, a lab that can generate the right data, and a loop that connects them. Huge congratulations to first authors Yue Xu, Haotian Cui, and Kuan Pang, and to the entire Bowen LI team. Grateful to our collaborators at University Health Network and Leslie Dan Faculty of Pharmacy, and to Princess Margaret Cancer Centre Research Princess Margaret Cancer Centre Research. 📄 Paper:

Bo Wang

57,471 просмотров • 5 месяцев назад

We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)
1:24

We are thrilled to share our breakthrough research on "Agile Flight from Pixels without State Estimation," to be presented and live-demonstrated at #RSS2024 next week! You heard well: no state estimation means no explicit visual localization, no SLAM, no VIO, and no IMU! Paper: Video (Narrated): Last year, we demonstrated that #ReinforcementLearning (RL) policies could outperform world-champion drone-racing pilots using the same quadrotor hardware; however, unlike human pilots, these policies continuously estimated an explicit state from known gate positions, the camera feed, and inertial measurements (IMU). In this new work, we tackle the challenge of learning vision-based drone racing using an end-to-end reinforcement learning approach that eliminates the need for IMU data or explicit state estimation. Like professional pilots, we go directly from images to control commands. The training is facilitated by an asymmetric actor-critic with access to privileged information. To overcome the computational complexity during image-based RL training, we use an appropriate sensor representation, which can be efficiently simulated during training without rendering images. We achieve agile flight at speeds up to 40 km/h with accelerations up to 2 g's. Although our demonstration focuses on drone racing, we believe that our method has an impact beyond drone racing and can serve as a foundation for future research into real-world applications in structured environments. Besides the paper presentation, we will also give a live demo next Tuesday and Wednesday between and hrs at TU Delft: Reference: Ismail Geles*, Leonard Bauersfeld*, Angel Romero, Jiaxu Xing, Davide Scaramuzza "Demonstrating Agile Flight from Pixels without State Estimation" Robotics: Science and Systems (RSS), 2024. Kudos to Ismail Geles Leonard Bauersfeld Ángel Romero Jiaxu Xing! University of Zurich UZH Science UZH Space Hub Aerial Core AUTOASSESS European Research Council (ERC)

Davide Scaramuzza

27,917 просмотров • 2 лет назад

A Petri Dish Of HUMAN Brain Cells LEARN TO PLAY THE GAME DOOM! In a groundbreaking fusion of biology and silicon, scientists at Cortical Labs have taught a cluster of lab-grown human neurons to play the iconic video game Doom. Not your typical AI triumph, it’s a petri dish of actual human brain cells, reprogrammed from adult donor skin or blood samples, wired into a $35,000 biological computer called the CL1. Building on their earlier Pong demo, this new feat sees the neurons navigating hellish levels, dodging demons, and even firing shots with surprising efficiency. Programmer Sean Cole pulled it off in just a week using a Python API on GitHub, a stark contrast to the year-plus effort for Pong. Astonishingly, these organic gamers outperform GPT-4 in speed and latency, proving that even a tiny blob of human intelligence can adapt and learn in ways silicon struggles to match. The excitement is palpable: this isn’t just a gimmick; it’s a window into revolutionary medical advancements. Imagine using such bio-computers to model brain diseases, test drugs, or even restore neural functions in patients. With cloud access to CL1 rentals, developers worldwide can experiment, accelerating discoveries that could redefine neuroscience. We’re witnessing the dawn of hybrid intelligence, human biology augmented by tech, evolving beyond our wildest dreams. Yet, amid the thrill, a chill runs down my spine. What are we building here? These neurons aren’t conscious (we hope), but they’re derived from humans and exhibit learning behaviors that echo our own cognition. Echoes of The Matrix or dystopian sci-fi like the “torment nexus” from Doom novels loom large. Could this lead to ethical nightmares—exploiting bio-intelligence for warfare simulations, or worse, creating sentient systems trapped in digital hells? And the philosophical rabbit hole deepens: Is life merely nested Russian dolls (matryoshka, if you prefer) of biological smarts? We, as evolved intelligences, are now crafting our own mini-brains, layering complexity upon complexity. Are we “gods” in the making, or just the next doll in an infinite regress, destined to birth something that surpasses—and perhaps supplants, us? This experiment, detailed in HotHardware’s coverage, pushes boundaries we might not be ready to cross. It’s exhilarating proof of human ingenuity, but let’s proceed with caution lest we summon demons we can’t control and we wind up in the Petri dish?
6:28

A Petri Dish Of HUMAN Brain Cells LEARN TO PLAY THE GAME DOOM! In a groundbreaking fusion of biology and silicon, scientists at Cortical Labs have taught a cluster of lab-grown human neurons to play the iconic video game Doom. Not your typical AI triumph, it’s a petri dish of actual human brain cells, reprogrammed from adult donor skin or blood samples, wired into a $35,000 biological computer called the CL1. Building on their earlier Pong demo, this new feat sees the neurons navigating hellish levels, dodging demons, and even firing shots with surprising efficiency. Programmer Sean Cole pulled it off in just a week using a Python API on GitHub, a stark contrast to the year-plus effort for Pong. Astonishingly, these organic gamers outperform GPT-4 in speed and latency, proving that even a tiny blob of human intelligence can adapt and learn in ways silicon struggles to match. The excitement is palpable: this isn’t just a gimmick; it’s a window into revolutionary medical advancements. Imagine using such bio-computers to model brain diseases, test drugs, or even restore neural functions in patients. With cloud access to CL1 rentals, developers worldwide can experiment, accelerating discoveries that could redefine neuroscience. We’re witnessing the dawn of hybrid intelligence, human biology augmented by tech, evolving beyond our wildest dreams. Yet, amid the thrill, a chill runs down my spine. What are we building here? These neurons aren’t conscious (we hope), but they’re derived from humans and exhibit learning behaviors that echo our own cognition. Echoes of The Matrix or dystopian sci-fi like the “torment nexus” from Doom novels loom large. Could this lead to ethical nightmares—exploiting bio-intelligence for warfare simulations, or worse, creating sentient systems trapped in digital hells? And the philosophical rabbit hole deepens: Is life merely nested Russian dolls (matryoshka, if you prefer) of biological smarts? We, as evolved intelligences, are now crafting our own mini-brains, layering complexity upon complexity. Are we “gods” in the making, or just the next doll in an infinite regress, destined to birth something that surpasses—and perhaps supplants, us? This experiment, detailed in HotHardware’s coverage, pushes boundaries we might not be ready to cross. It’s exhilarating proof of human ingenuity, but let’s proceed with caution lest we summon demons we can’t control and we wind up in the Petri dish?

Brian Roemmele

657,416 просмотров • 5 месяцев назад

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.
1:06

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

Owen Gregorian

179,005 просмотров • 9 месяцев назад

The interview with Demis Hassabis - the tl;dr (summary) about scaling, AGI and much more: 1. Solving the "Root Node" Problems: DeepMind isn't just building chatbots; they are using AI to solve the hardest scientific problems. After the success of AlphaFold, they are now targeting materials science (room-temperature superconductors, better batteries) and even nuclear fusion to unlock unlimited clean energy. 2. The "Jagged Intelligence" Paradox: Current AI models are in a weird spot—they can win gold medals at the International Math Olympiad but still fail at basic logic puzzles. Hassabis calls this "jagged intelligence." The goal isn't just more data, but fixing these inconsistencies to make models reliable across the board. 3. Scaling is Not Dead (But it’s Changing): Despite rumors of hitting a "data wall," Hassabis says we haven't seen a hard limit yet. However, we are seeing diminishing returns. His bet? Getting to AGI will require 50% scaling and 50% architectural innovation. It’s no longer just about making the models bigger; it’s about making them smarter. 4. The Missing Piece: System 2 Thinking: Today's models are passive—they just spit out an answer. To reach AGI, we need systems that can "think" before they speak. This involves planning, reasoning, and double-checking their own work (similar to human "System 2" thinking) rather than just predicting the next word. 5. Rise of World Models: The next big frontier is "World Models" (like their project Genie). AI needs to understand the physics of the world—gravity, object permanence, and cause-and-effect—not just language. This is crucial for building helpful digital agents and robots that can navigate real-life situations. 6. Is the Universe Computable? On a philosophical level, Hassabis believes that everything in the universe might be computable. His life's work is testing the limits of the "Turing Machine." If we can build an AGI that simulates the human mind perfectly, we might finally understand what (if anything) makes human consciousness unique. 7. Bigger than the Industrial Revolution: We need to prepare for a shift that is 10x faster and bigger than the Industrial Revolution. If AI solves energy (fusion) and labor, we might enter a "post-scarcity" world. Hassabis warns that society, economics, and governments need to adapt quickly to ensure these benefits are shared by everyone, not just a few. And since this is the most important aspect, here is the clip about post labor economy:
1:11

The interview with Demis Hassabis - the tl;dr (summary) about scaling, AGI and much more: 1. Solving the "Root Node" Problems: DeepMind isn't just building chatbots; they are using AI to solve the hardest scientific problems. After the success of AlphaFold, they are now targeting materials science (room-temperature superconductors, better batteries) and even nuclear fusion to unlock unlimited clean energy. 2. The "Jagged Intelligence" Paradox: Current AI models are in a weird spot—they can win gold medals at the International Math Olympiad but still fail at basic logic puzzles. Hassabis calls this "jagged intelligence." The goal isn't just more data, but fixing these inconsistencies to make models reliable across the board. 3. Scaling is Not Dead (But it’s Changing): Despite rumors of hitting a "data wall," Hassabis says we haven't seen a hard limit yet. However, we are seeing diminishing returns. His bet? Getting to AGI will require 50% scaling and 50% architectural innovation. It’s no longer just about making the models bigger; it’s about making them smarter. 4. The Missing Piece: System 2 Thinking: Today's models are passive—they just spit out an answer. To reach AGI, we need systems that can "think" before they speak. This involves planning, reasoning, and double-checking their own work (similar to human "System 2" thinking) rather than just predicting the next word. 5. Rise of World Models: The next big frontier is "World Models" (like their project Genie). AI needs to understand the physics of the world—gravity, object permanence, and cause-and-effect—not just language. This is crucial for building helpful digital agents and robots that can navigate real-life situations. 6. Is the Universe Computable? On a philosophical level, Hassabis believes that everything in the universe might be computable. His life's work is testing the limits of the "Turing Machine." If we can build an AGI that simulates the human mind perfectly, we might finally understand what (if anything) makes human consciousness unique. 7. Bigger than the Industrial Revolution: We need to prepare for a shift that is 10x faster and bigger than the Industrial Revolution. If AI solves energy (fusion) and labor, we might enter a "post-scarcity" world. Hassabis warns that society, economics, and governments need to adapt quickly to ensure these benefits are shared by everyone, not just a few. And since this is the most important aspect, here is the clip about post labor economy:

Chubby♨️

27,473 просмотров • 7 месяцев назад

The tide has turned. In the last week, this is what Sandra Bullock, Reese Witherspoon, Steven Soderbergh, and Christopher Nolan said about AI: "It’s here. We have to observe it. We have to understand it. We have to lean into it. We have to use it in a really constructive and creative way, make it our friend rather than — I mean, we have to be incredibly cautious and aware of it because there are people who will use it for evil and not good. But I do feel that there’s a place for it… it’s here. We have to just be friends in some dark way." - Sandra bullock “The AI revolution has begun, and I need to learn as much as I possibly can about AI and share it with all of you. Also, FYI: the jobs women hold are 3x more likely to be automated by AI, yet women are using AI at a rate 25% lower than men on average. We don’t want to be left behind. So…do you want to learn with me?” - Reese Witherspoon “Five years from now, we all may be going, ‘That was a fun phase.’ We may end up not using it as much as we thought we were going to. There are some people that I have absolute love and respect for that refuse to engage with it. That’s their privilege. But I’m not built that way. You show me a new tool. I want to get my hands on it and see what’s going on.” - Steven Soderbergh “I think any tool, whether AI generated, whether it's computer based, or whatever, it's all another tool for filmmakers to create with. And so as long as we have faith in our in our human beings, creating these tools now, I think the medium of film will continue to develop in exciting ways.” - Christopher Nolan
2:36

The tide has turned. In the last week, this is what Sandra Bullock, Reese Witherspoon, Steven Soderbergh, and Christopher Nolan said about AI: "It’s here. We have to observe it. We have to understand it. We have to lean into it. We have to use it in a really constructive and creative way, make it our friend rather than — I mean, we have to be incredibly cautious and aware of it because there are people who will use it for evil and not good. But I do feel that there’s a place for it… it’s here. We have to just be friends in some dark way." - Sandra bullock “The AI revolution has begun, and I need to learn as much as I possibly can about AI and share it with all of you. Also, FYI: the jobs women hold are 3x more likely to be automated by AI, yet women are using AI at a rate 25% lower than men on average. We don’t want to be left behind. So…do you want to learn with me?” - Reese Witherspoon “Five years from now, we all may be going, ‘That was a fun phase.’ We may end up not using it as much as we thought we were going to. There are some people that I have absolute love and respect for that refuse to engage with it. That’s their privilege. But I’m not built that way. You show me a new tool. I want to get my hands on it and see what’s going on.” - Steven Soderbergh “I think any tool, whether AI generated, whether it's computer based, or whatever, it's all another tool for filmmakers to create with. And so as long as we have faith in our in our human beings, creating these tools now, I think the medium of film will continue to develop in exciting ways.” - Christopher Nolan

Minh Do

23,410 просмотров • 3 месяцев назад

Data has always been the bottleneck for physical AI in self driving and robotics. Tesla is taking two very different approaches for FSD and Optimus. Tesla’s Optimus Training Playbook: 1. Build 30k Optimus Gen 3 robots 2. Operate them in a mock environment where they can perform self-play “Optimus Academy” 3. Train in sim using the real robot data to close sim2real gap. Tesla FSD Training Playbook: 1. Sell millions of cars outfitted with cheap cameras 2. Collect diverse real world driving data (especially intervention and failure recovery data) for free as a byproduct of customers driving the cars. 3. Use driving data to train Autopilot/FSD and deploy policies incrementally as a supervised FSD product 4. Repeat until policy reaches robust unsupervised full self driving for robotaxi launch. The Tesla FSD playbook is a beautiful self-funding, customer subsidized, diverse real world data flywheel. The Optimus playbook is the opposite and shares none of the beautiful attributes of the FSD training flywheel that made FSD successful. The key differences: 1. Instead of having customers pay you for vehicles, Tesla will need to fund 30,000 Optimus robots. Assuming the current landed cost per unit is $100k, that will be $3B to build plus another ~30% per year for maintenance labor and spare parts given it’s still an unhardened pre-production prototype is another $900M per year. For reference, Tesla’s GAAP net income in 2025 was $3.8B. 2. Instead of having customers drive their Teslas on roads all across the world giving Tesla an insanely rich and diverse dataset that Waymo and other AV companies could never collect, the Optimus Academy is doing the equivalent of building a fake town in a parking lot and driving their car in that parking lot. No matter how real you try to make the environments for self-play you can never replicate the diversity, complexity and failure modes of the real world. Data collected in staged environments produces demo-grade policies and will not be rich enough to generalize to the vast diversity of environments, tasks, objects, etc. out of distribution. 3. Instead of having customers collect real world failure recovery data (DAgger style) for free every time FSD disengages, the Optimus Academy will need paid teleoperators or onsite operators to collect the recovery data. Assuming 1 person can manage 2 robots to start that would cost $3.5B in labor per year (30,000 robots, $40/hr fully loaded, 16 hrs/day, 365 days per year, 2:1 robot:operator). Tesla can come up with the money to do this but money doesn’t solve the “mock data” problem. Given the higher degrees of freedom in humanoids vs. cars, training a generalized humanoid will be harder and require more data than a self-driving vehicle. The best way to train your robot is by deploying them in the diverse real world, subsidized by real customer operations. Humanoids face a chicken and egg where it’s very hard to bootstrap your way to a first policy that’s good enough to deploy in real production environments. This is an extremely capital intensive playbook (which doesn’t even include cost of training). Time will tell if it works but a better playbook would be finding a way to copy the FSD playbook.
1:54

Data has always been the bottleneck for physical AI in self driving and robotics. Tesla is taking two very different approaches for FSD and Optimus. Tesla’s Optimus Training Playbook: 1. Build 30k Optimus Gen 3 robots 2. Operate them in a mock environment where they can perform self-play “Optimus Academy” 3. Train in sim using the real robot data to close sim2real gap. Tesla FSD Training Playbook: 1. Sell millions of cars outfitted with cheap cameras 2. Collect diverse real world driving data (especially intervention and failure recovery data) for free as a byproduct of customers driving the cars. 3. Use driving data to train Autopilot/FSD and deploy policies incrementally as a supervised FSD product 4. Repeat until policy reaches robust unsupervised full self driving for robotaxi launch. The Tesla FSD playbook is a beautiful self-funding, customer subsidized, diverse real world data flywheel. The Optimus playbook is the opposite and shares none of the beautiful attributes of the FSD training flywheel that made FSD successful. The key differences: 1. Instead of having customers pay you for vehicles, Tesla will need to fund 30,000 Optimus robots. Assuming the current landed cost per unit is $100k, that will be $3B to build plus another ~30% per year for maintenance labor and spare parts given it’s still an unhardened pre-production prototype is another $900M per year. For reference, Tesla’s GAAP net income in 2025 was $3.8B. 2. Instead of having customers drive their Teslas on roads all across the world giving Tesla an insanely rich and diverse dataset that Waymo and other AV companies could never collect, the Optimus Academy is doing the equivalent of building a fake town in a parking lot and driving their car in that parking lot. No matter how real you try to make the environments for self-play you can never replicate the diversity, complexity and failure modes of the real world. Data collected in staged environments produces demo-grade policies and will not be rich enough to generalize to the vast diversity of environments, tasks, objects, etc. out of distribution. 3. Instead of having customers collect real world failure recovery data (DAgger style) for free every time FSD disengages, the Optimus Academy will need paid teleoperators or onsite operators to collect the recovery data. Assuming 1 person can manage 2 robots to start that would cost $3.5B in labor per year (30,000 robots, $40/hr fully loaded, 16 hrs/day, 365 days per year, 2:1 robot:operator). Tesla can come up with the money to do this but money doesn’t solve the “mock data” problem. Given the higher degrees of freedom in humanoids vs. cars, training a generalized humanoid will be harder and require more data than a self-driving vehicle. The best way to train your robot is by deploying them in the diverse real world, subsidized by real customer operations. Humanoids face a chicken and egg where it’s very hard to bootstrap your way to a first policy that’s good enough to deploy in real production environments. This is an extremely capital intensive playbook (which doesn’t even include cost of training). Time will tell if it works but a better playbook would be finding a way to copy the FSD playbook.

Simon Kalouche

34,671 просмотров • 5 месяцев назад

My crew exposed $100,000,000 in California fraud in one day. But it’s so much darker than stolen taxpayer cash. California is using that money to rig American elections. Here’s How: California leverages their homeless crisis to funnel billions of federal dollars into the state to help homeless Americans. But Whistleblowers tell us +60% of the ‘homeless services’ are actually going to criminal aliens. These illegals live rent-free in long term homeless housing, drive luxury cars and live lavish lifestyles. The shelters guard the criminal aliens from ICE deportations. But why pack your state with homeless illegals? One word: Power. California population loss is set to cost the state major electoral votes in the next election. The only remedy is to pack more bodies into the state ahead of the 2030 census. The census counts persons, not citizens. The federally funded ‘free homeless services’ act as magnets for criminal aliens who fraudulently inflate population numbers. There are 2.5 million illegals in California alone. Finally, California makes it a criminal act to conduct citizenship tests at the shelters, hospitals or the voting booth — and now the fraud is complete. California leaders are trafficking in human misery, intentionally destroying the once great state for power — and using federal tax dollars to do it. This must be stopped. Now. Share this video like wildfire! Americans are getting really sick and tired of watching our hard work and taxes go to pay foreign fraudsters to rig elections for corrupt politicians. If the Trump Administration cut off the funds, the California homeless industrial complex would collapse tomorrow. Time for them to pay.
24:00

My crew exposed $100,000,000 in California fraud in one day. But it’s so much darker than stolen taxpayer cash. California is using that money to rig American elections. Here’s How: California leverages their homeless crisis to funnel billions of federal dollars into the state to help homeless Americans. But Whistleblowers tell us +60% of the ‘homeless services’ are actually going to criminal aliens. These illegals live rent-free in long term homeless housing, drive luxury cars and live lavish lifestyles. The shelters guard the criminal aliens from ICE deportations. But why pack your state with homeless illegals? One word: Power. California population loss is set to cost the state major electoral votes in the next election. The only remedy is to pack more bodies into the state ahead of the 2030 census. The census counts persons, not citizens. The federally funded ‘free homeless services’ act as magnets for criminal aliens who fraudulently inflate population numbers. There are 2.5 million illegals in California alone. Finally, California makes it a criminal act to conduct citizenship tests at the shelters, hospitals or the voting booth — and now the fraud is complete. California leaders are trafficking in human misery, intentionally destroying the once great state for power — and using federal tax dollars to do it. This must be stopped. Now. Share this video like wildfire! Americans are getting really sick and tired of watching our hard work and taxes go to pay foreign fraudsters to rig elections for corrupt politicians. If the Trump Administration cut off the funds, the California homeless industrial complex would collapse tomorrow. Time for them to pay.

Benny Johnson

2,048,419 просмотров • 6 месяцев назад

Bret Weinstein on the Melania Trump AI teachers: "I get it. And it’s not that it is impossible to imagine robotic teachers doing an excellent job, but it is stunning to watch a sophisticated person fail to recognize what happens when you think that that’s what you’re going to produce, and you set it in motion. Let me point out that Wikipedia has many of the advantages that Melania is describing in this video. It is completely democratizing of knowledge, such that it doesn’t matter where on e arth you are. If you have an internet connection, you’ve got Wikipedia. It’s like an extension of your own mind, and it will make us all brilliant. Now, of course, that didn’t happen, did it? Wikipedia is a hellscape of misinformation, much of it targeted based on a political agenda. We are less certain of what we know, and less capable of reasoning on our own. Now, that doesn’t all come from Wikipedia, but my point is the promise of Wikipedia was not realized. And what we got instead is arguably worse than what we had before it was invented. The same thing is virtually guaranteed here, because you’re talking about not only the capability of educating students using a robot that has vastly more knowledge than a human teacher would, but you’re talking about the irresistible opportunity to capture those minds and steer them in one direction or another, whether that’s political or economic. The idea that these robotic teachers are going to be immune to the kind of flights of fancy that have ruined teaching in the modern era is preposterous. In fact, they will likely be even more easily steered. I would caution everyone to simply realize the distinction between complicated systems and complex systems. AI is a complex system. Human beings are complex systems. And any time you intervene in these systems, thinking you know what’s going to happen, you’re going to be embarrassed by the discovery of the unintended consequences that will come to dominate your project. As much as I like the idea of smarter, wiser, more empathic teachers, and as much as those possibilities do exist in the space of AI, we are still at a very early point in this revolution, and anybody who thinks they can predict it with this kind of precision is actually a hazard."
2:54

Bret Weinstein on the Melania Trump AI teachers: "I get it. And it’s not that it is impossible to imagine robotic teachers doing an excellent job, but it is stunning to watch a sophisticated person fail to recognize what happens when you think that that’s what you’re going to produce, and you set it in motion. Let me point out that Wikipedia has many of the advantages that Melania is describing in this video. It is completely democratizing of knowledge, such that it doesn’t matter where on e arth you are. If you have an internet connection, you’ve got Wikipedia. It’s like an extension of your own mind, and it will make us all brilliant. Now, of course, that didn’t happen, did it? Wikipedia is a hellscape of misinformation, much of it targeted based on a political agenda. We are less certain of what we know, and less capable of reasoning on our own. Now, that doesn’t all come from Wikipedia, but my point is the promise of Wikipedia was not realized. And what we got instead is arguably worse than what we had before it was invented. The same thing is virtually guaranteed here, because you’re talking about not only the capability of educating students using a robot that has vastly more knowledge than a human teacher would, but you’re talking about the irresistible opportunity to capture those minds and steer them in one direction or another, whether that’s political or economic. The idea that these robotic teachers are going to be immune to the kind of flights of fancy that have ruined teaching in the modern era is preposterous. In fact, they will likely be even more easily steered. I would caution everyone to simply realize the distinction between complicated systems and complex systems. AI is a complex system. Human beings are complex systems. And any time you intervene in these systems, thinking you know what’s going to happen, you’re going to be embarrassed by the discovery of the unintended consequences that will come to dominate your project. As much as I like the idea of smarter, wiser, more empathic teachers, and as much as those possibilities do exist in the space of AI, we are still at a very early point in this revolution, and anybody who thinks they can predict it with this kind of precision is actually a hazard."

The DarkHorse Podcast

48,380 просмотров • 4 месяцев назад

China’s construction tech is on another level! If you still picture construction sites with tones of workers swinging hammers, you’re officially stuck in the 90s. In China they’re out here building skyscrapers like it’s a speedrun challenge, with a tech stack that would make Silicon Valley jealous. AI-Powered Smart Sites Drones, IoT sensors, and AI are managing construction sites better than some human managers. Real-time safety alerts, automated progress tracking, and even predictive maintenance are standard tools, not fancy bonuses. Green Building Technologies Solar panels built into facades. Rainwater harvesting embedded into skyscrapers. Buildings that regulate their own temperature to save energy, not future dreams, but today’s standards. Robotics in Construction From brick-laying robots to rebar-tying machines, construction crews in China now have mechanical teammates with no lunch breaks, no grumbling. Welcome to Construction 2.0 (China Edition) Humans supervising, robots building! Gone are the days when a construction site meant a hundred+ guys with shovels. In China’s construction sites today, the machines are the real MVPs, humans mostly sip coffee and supervise. Bricklaying Human hands? Please. Robotic arms now lay bricks faster, straighter, and neater than your uncle’s weekend DIY projects. Some can lay 1,000+ bricks an hour and never ask for overtime. Excavation Remember digging trenches with shovels? That’s adorable. Now AI-guided autonomous excavators carve the earth with GPS-level precision while operators chill in air-conditioned control rooms. Concrete Pouring Giant robotic nozzles squeezing out entire building frames like they’re icing a wedding cake. Layers and layers, no sweat, no mess, no “oops, we need to redo that wall.” Welding You think shaky human hands are still melting steel together? Nah. Robot welders buzz and spark with perfect, tireless accuracy, even in places that are too risky for humans to reach. Inspection Sending humans to climb scaffolding 50 stories up? Very 2002. Now drones buzz around construction sites like paparazzi, scanning every beam, bolt, and brick, spotting defects before a human eye would even blink. Material Transport Why carry things manually when you can have self-driving forklifts and delivery bots moving steel, concrete, and pipes faster than an Amazon Prime truck? Site Monitoring Forget some guy in a neon vest wandering around with a clipboard. Today it’s AI-driven command centers tracking thousands of data points: worker safety, machinery health, material delivery schedules all from one giant screen. Project Planning Old: Paper blueprints and arguing architects. New: AI and BIM (Building Information Modeling) simulate the entire building in virtual reality before the first brick is even printed. This video shows how construction sites in China are now using robots to handle tasks like rebar placement, concrete pouring, and polishing, which has led to a 50% reduction in the workforce at some projects. The robots are proving to be more efficient, with one project's furnace robot achieving a 20% efficiency increase in just 5 days, saving 5% on labor costs. The construction company is developing even more advanced robots for future use as they work to improve smart construction technology for widespread real-time application. At China’s next-gen construction sites, humans don’t sweat, they strategize. Machines are not seen as luxury, they are seen as a necessity. Why does it matter? China is using 5G, AI, IoT, and robotics to build entire smart cities. And when the world comes looking for fast, sustainable, and scalable construction models… guess who’s already handing out the blueprints? Next time you hear: ‘Made in China,’ think bigger, it is: ‘Engineered for the future in China.’
0:36

China’s construction tech is on another level! If you still picture construction sites with tones of workers swinging hammers, you’re officially stuck in the 90s. In China they’re out here building skyscrapers like it’s a speedrun challenge, with a tech stack that would make Silicon Valley jealous. AI-Powered Smart Sites Drones, IoT sensors, and AI are managing construction sites better than some human managers. Real-time safety alerts, automated progress tracking, and even predictive maintenance are standard tools, not fancy bonuses. Green Building Technologies Solar panels built into facades. Rainwater harvesting embedded into skyscrapers. Buildings that regulate their own temperature to save energy, not future dreams, but today’s standards. Robotics in Construction From brick-laying robots to rebar-tying machines, construction crews in China now have mechanical teammates with no lunch breaks, no grumbling. Welcome to Construction 2.0 (China Edition) Humans supervising, robots building! Gone are the days when a construction site meant a hundred+ guys with shovels. In China’s construction sites today, the machines are the real MVPs, humans mostly sip coffee and supervise. Bricklaying Human hands? Please. Robotic arms now lay bricks faster, straighter, and neater than your uncle’s weekend DIY projects. Some can lay 1,000+ bricks an hour and never ask for overtime. Excavation Remember digging trenches with shovels? That’s adorable. Now AI-guided autonomous excavators carve the earth with GPS-level precision while operators chill in air-conditioned control rooms. Concrete Pouring Giant robotic nozzles squeezing out entire building frames like they’re icing a wedding cake. Layers and layers, no sweat, no mess, no “oops, we need to redo that wall.” Welding You think shaky human hands are still melting steel together? Nah. Robot welders buzz and spark with perfect, tireless accuracy, even in places that are too risky for humans to reach. Inspection Sending humans to climb scaffolding 50 stories up? Very 2002. Now drones buzz around construction sites like paparazzi, scanning every beam, bolt, and brick, spotting defects before a human eye would even blink. Material Transport Why carry things manually when you can have self-driving forklifts and delivery bots moving steel, concrete, and pipes faster than an Amazon Prime truck? Site Monitoring Forget some guy in a neon vest wandering around with a clipboard. Today it’s AI-driven command centers tracking thousands of data points: worker safety, machinery health, material delivery schedules all from one giant screen. Project Planning Old: Paper blueprints and arguing architects. New: AI and BIM (Building Information Modeling) simulate the entire building in virtual reality before the first brick is even printed. This video shows how construction sites in China are now using robots to handle tasks like rebar placement, concrete pouring, and polishing, which has led to a 50% reduction in the workforce at some projects. The robots are proving to be more efficient, with one project's furnace robot achieving a 20% efficiency increase in just 5 days, saving 5% on labor costs. The construction company is developing even more advanced robots for future use as they work to improve smart construction technology for widespread real-time application. At China’s next-gen construction sites, humans don’t sweat, they strategize. Machines are not seen as luxury, they are seen as a necessity. Why does it matter? China is using 5G, AI, IoT, and robotics to build entire smart cities. And when the world comes looking for fast, sustainable, and scalable construction models… guess who’s already handing out the blueprints? Next time you hear: ‘Made in China,’ think bigger, it is: ‘Engineered for the future in China.’

Evrim Kanbur

71,826 просмотров • 1 год назад

Sharing a super simple, user-owned memory module we've been playing around: nanomem The basic idea is to treat memory as a pure intelligence problem: ingestion, structuring, and (selective) retrieval are all just LLM calls & agent loops on a on-device markdown file tree. Each file lists a set of facts w/ metadata (timestamp, confidence, source, etc.); no embeddings/RAG/training of any kind. For example: - `nanomem add ` starts an agent loop to walk the tree, read relevant files, and edit. - `nanomem retrieve ` walks the tree and returns a single summary string (possibly assembled from many subtrees) related to the query. What’s nice about this approach is that the memory system is, by construction: 1. partitionable (human/agents can easily separate `hobbies/snowboard.md` from `tax/residency.md` for data minimization + relevance) 2. portable and user-owned (it’s just text files) 3. interpretable (you know exactly what’s written and you can manually edit) 4. forward-compatible (future models can read memory files just the same, and memory quality/speed improves as models get better) 5. modularized (you can optimize ingestion/retrieval/compaction prompts separately) Privacy & utility. I'm most excited about the ability to partition + selectively disclose memory at inference-time. Selective disclosure helps with both privacy (principle of least privilege & “need-to-know”) and utility (as too much context for a query can harm answer quality). Composability. An inference-time memory module means: (1) you can run such a module with confidential inference (LLMs on TEEs) for provable privacy, and (2) you can selectively disclose context over unlinkable inference of remote models (demo below). We built nanomem as part of the Open Anonymity project ( but it’s meant to be a standalone module for humans and agents (e.g., you can write a SKILL for using the CLI tool). Still polishing the rough edges! - GitHub (MIT): - Blog: - Beta implementation in chat client soon: Work done with amazing project co-leads Amelia Kuang Coco Xu Erik Chi !!
0:29

Sharing a super simple, user-owned memory module we've been playing around: nanomem The basic idea is to treat memory as a pure intelligence problem: ingestion, structuring, and (selective) retrieval are all just LLM calls & agent loops on a on-device markdown file tree. Each file lists a set of facts w/ metadata (timestamp, confidence, source, etc.); no embeddings/RAG/training of any kind. For example: - `nanomem add ` starts an agent loop to walk the tree, read relevant files, and edit. - `nanomem retrieve ` walks the tree and returns a single summary string (possibly assembled from many subtrees) related to the query. What’s nice about this approach is that the memory system is, by construction: 1. partitionable (human/agents can easily separate `hobbies/snowboard.md` from `tax/residency.md` for data minimization + relevance) 2. portable and user-owned (it’s just text files) 3. interpretable (you know exactly what’s written and you can manually edit) 4. forward-compatible (future models can read memory files just the same, and memory quality/speed improves as models get better) 5. modularized (you can optimize ingestion/retrieval/compaction prompts separately) Privacy & utility. I'm most excited about the ability to partition + selectively disclose memory at inference-time. Selective disclosure helps with both privacy (principle of least privilege & “need-to-know”) and utility (as too much context for a query can harm answer quality). Composability. An inference-time memory module means: (1) you can run such a module with confidential inference (LLMs on TEEs) for provable privacy, and (2) you can selectively disclose context over unlinkable inference of remote models (demo below). We built nanomem as part of the Open Anonymity project ( but it’s meant to be a standalone module for humans and agents (e.g., you can write a SKILL for using the CLI tool). Still polishing the rough edges! - GitHub (MIT): - Blog: - Beta implementation in chat client soon: Work done with amazing project co-leads Amelia Kuang Coco Xu Erik Chi !!

Ken Liu

73,900 просмотров • 3 месяцев назад

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the form’s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKit’s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasn’t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️
1:00

Hell froze over: announcing FormKit for React. Secretly framework-agnostic since inception, today we’re open sourcing the most popular Vue form library…for React. Why is this a big deal? 1. Forms are still hard. We (the creators of FormKit) thought form libraries were no longer necessary, given the trajectory of coding agents. It turns out we were wrong, and we learned this the hard way. Need repeating conditional fields nested 3 layers deep inside a dynamic component, with accessibility, validation, internationalization, and backend error placement? Turns out coding agents aren’t great at that. It’s table stakes for FormKit. 2. Single component. This matters more than you would think, but FormKit doesn’t ship lots of different components each with its own props. Instead, it has a single one: and unified props. This was done to provide a better DX to human engineers. It makes it easy to spot when a given component was part of the form’s data structure vs a presentational component. It turns out this matters even more to coding agents than humans. No matter where your coding agent is, whenever it sees “FormKit” it immediately knows “oh, that’s part of the form’s data”. 3. No plumbing. FormKit doesn’t require any manual data collection, event listening, or state tracking. It does all this for you on a heavily tested, framework agnostic, self-assembling graph. The only code your agent needs to write is declarative templates and submission handlers that respond to the state. 4. Dense colocation. FormKit’s syntax happens to be ideal for coding agents; nearly everything you need to know about a given input is *on* the input: Colocation dramatically improves the efficacy of coding agents. 5. DOM. FormKit, unlike most form frameworks in React, renders the actual DOM. This also increases colocation and best practices, meaning your coding agent is far more likely to produce consistent and high-quality output that looks and acts the way its supposed to. 6. Schema. FormKit’s own inputs are not written using Vue or React — instead, FormKit has its own render schema — think of it like an AST for the DOM — and you can modify it on the fly. It’s not very human-friendly to write, but it turns out most models are already pretty well trained on FormKit’s schema. Want your inputs to look a bit different on one form than another? No problem, your coding agent can easily make those changes *without* modifying the JSX structure at all. Oh, and any inputs you create for Vue work with React and vice versa. 7. Plugins. FormKit leans into the unstructured tree graph hard. The graph doesn’t just collect data, it also passes down configuration and plugins. Want one form to work a bit differently than another one? No problem — just add a plugin to the top of that form or group and its children will all receive that feature. You can even mass assign props and configuration this way. Of course, FormKit has been solving these exact issues for a long time, but it wasn’t until we started using it on our own projects with coding agents that we realized what a huge advantage it is. With so many people using coding agents with React, it made sense to unveil FormKit for what it has always been — a completely framework-agnostic form framework that happens to unlock your coding agents. ➡️

Justin Schroeder

11,549 просмотров • 4 месяцев назад

We need builders in the identity space and we need to work together! Billions co-founder and CEO Evin McMullen evin on: Worldcoin and Billions Network I think of projects like Worldcoin as colleagues. I've actually had the privilege to speak with them and meet with them. I even went into their offices to see how the Orb is made and then have given a ton of feedback on papers and documentation before it goes out. Because none of us in the identity space are really competing with each other. Right now we are all competing with obscurity. It is essential that we as builders and as developers in the identity space work together, that we provide rigorous, honest respectful feedback to one another so that we can all improve because we all are working in the same service industry in service of human beings. I think of Worldcoin like a proprietary camera company: They make a special kind of camera that takes a particular kind of picture and produces a specific type of data. But unlike their approach, with Billions Network we have a very flexible approach to identity. Which means that you don't have to start with a special camera to get on board and to start using Billions Network. We've actually integrated with 100+ different government systems. We've integrated with many different providers of trust and data. So our flexible framework allows for there to be these many different sources and proven authorities of trust, as opposed to just ONE. See our identity framework in action in the tomorrow’s builders webinar: 🔑 "Login with Billions" Live demo: 🤝 Human-verified DID login ✅ Privacy-first vs Google/Apple login ✅ No passwords, no central ID ✅ No personal data (PII) stored on your backend Register now: Please select the session that works best for you if you can’t make it tomorrow. Video clip from The Upwind Podcast episode 14 🙏
1:23

We need builders in the identity space and we need to work together! Billions co-founder and CEO Evin McMullen evin on: Worldcoin and Billions Network I think of projects like Worldcoin as colleagues. I've actually had the privilege to speak with them and meet with them. I even went into their offices to see how the Orb is made and then have given a ton of feedback on papers and documentation before it goes out. Because none of us in the identity space are really competing with each other. Right now we are all competing with obscurity. It is essential that we as builders and as developers in the identity space work together, that we provide rigorous, honest respectful feedback to one another so that we can all improve because we all are working in the same service industry in service of human beings. I think of Worldcoin like a proprietary camera company: They make a special kind of camera that takes a particular kind of picture and produces a specific type of data. But unlike their approach, with Billions Network we have a very flexible approach to identity. Which means that you don't have to start with a special camera to get on board and to start using Billions Network. We've actually integrated with 100+ different government systems. We've integrated with many different providers of trust and data. So our flexible framework allows for there to be these many different sources and proven authorities of trust, as opposed to just ONE. See our identity framework in action in the tomorrow’s builders webinar: 🔑 "Login with Billions" Live demo: 🤝 Human-verified DID login ✅ Privacy-first vs Google/Apple login ✅ No passwords, no central ID ✅ No personal data (PII) stored on your backend Register now: Please select the session that works best for you if you can’t make it tomorrow. Video clip from The Upwind Podcast episode 14 🙏

Billions

14,831 просмотров • 5 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.
3:08

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

122,680 просмотров • 5 месяцев назад