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The robotics sector has been experiencing explosive growth and while most people are focused on the shiny humanoid robots doing backflips in promotional videos, Auki has been building out the perception layer, or the Real-World Web, that enables robots to do useful things in the real world. In this...

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DePIN Connection

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21,140 просмотров • 4 месяцев назад •via X (Twitter)

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New Episode. Nils Pihl (Nils), CEO of Auki Labs, discuss the "Real World Web" and making physical spaces accessible to robots. We dive into decentralized mapping and Auki’s latest retail deployments. Nils also shares his take on the US-China robotics race, drawing on 15 years of building ventures in the heart of China’s tech ecosystem. 2:05 Nils' journey into the technology 8:36 Six layers of Robotics value chain 11:00 Use case for AUKI's perception stack 17:04 Compute challenge of distributed mapping 20:16 Decentralized solution to privacy 22:36 Programmable robots vs closed approach 25:43 Most valuable layers or robotics ecosystem 28:53 Behind China's rapid humanoid boom 36:06 140 humanoid OEMs, who's gonna win 41:32 US-China geopolitics 44:52 What can the west do to compete with China 46:55 Technology adoption in Asian countries 54:01 Memes
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New Episode. Nils Pihl (Nils), CEO of Auki Labs, discuss the "Real World Web" and making physical spaces accessible to robots. We dive into decentralized mapping and Auki’s latest retail deployments. Nils also shares his take on the US-China robotics race, drawing on 15 years of building ventures in the heart of China’s tech ecosystem. 2:05 Nils' journey into the technology 8:36 Six layers of Robotics value chain 11:00 Use case for AUKI's perception stack 17:04 Compute challenge of distributed mapping 20:16 Decentralized solution to privacy 22:36 Programmable robots vs closed approach 25:43 Most valuable layers or robotics ecosystem 28:53 Behind China's rapid humanoid boom 36:06 140 humanoid OEMs, who's gonna win 41:32 US-China geopolitics 44:52 What can the west do to compete with China 46:55 Technology adoption in Asian countries 54:01 Memes

The Humanoid Hub

39,794 просмотров • 5 месяцев назад

The world today is built for humans, & cities built for cars... The world tomorrow will be built around the evolution of robotics. It was a pleasure hosting this week's Robotics episode of the Super Cycle Pod with Michael Cho - Rbt/Acc (BitRobot Network) & Nils of (Auki) have packed this full of insanely valuable insights: 0:00 - Intro + wen terminator? 5:13 - Robotics vs "GPT moment" 7:08 - Privacy in the world of robotics 11:40 - Web3 robotics + intro to frodobots / bitrobot 14:50 - Impact of $HNT on Michael and Nils 17:06 - Intro to $AUKI 19:26 - Why does dePIN as an investable asset suck? 25:36 - $AUKI community building philosophy 28:30 - Bitrobot mission w/ PhD researchers 31:40 - Bitrobot subnet structure 35:28 - Bitrobot value accrual + Impact 39:40 - $AUKI and proving people wrong 42:20 - Robotics is the biggest industry in the world 50:16 - Dangers of physical AI 55:25 - Human Philosophy in Physical AI 57:05 - How cities will change w/ robots 1:03:20 - Skills of the future 1:06:32 - Responsibility as innovators in robotics Where do you think we are in the robotics cycle? My DMs are open to teams wanting to chat about what they're building.
1:10:20

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0xSammy

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DeFi Dad ⟠ defidad.eth

15,566 просмотров • 5 месяцев назад

SpaceX IPO, Blue Origin rocket go boom, can American manufacturing be saved, is 60 Minutes worth saving and other fascinating things on this week's Core Memory pod. Timestamps 00:00:00 – Intro 00:02:05 – The American Actuator Crisis 00:06:51 – WestMag vs. Atlas Motion Systems 00:14:21 – Uncle Sam Pays Attention 00:16:47 – Chinese Robot Ban 00:21:16 – A Robot in Every Home 00:24:29 – Are You AGI-pilled Yet? 00:27:23 – Shoutout to Micayla Sortland 00:31:01 – Blue Origin’s Explosive Launch 00:42:52 – Low Earth Orbit Drugs 00:49:42 – The Two-Trillion-Dollar Elon Bet 00:57:07 – Founders Fund’s Viral “Mafia” Game Night 01:01:23 – Ashlee Braves His Notifications 01:06:12 – Legacy Media vs. The World
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Core Memory

24,387 просмотров • 1 месяц назад

Robotics is expected to become a multi-trillion dollar sector over the coming years, and we are still in the early stages! However, many of the top companies in the space have yet to go public and are only open to accredited investors. CEO Mauricio Zolliker Mauricio | XMAQUINA created XMAQUINA with the goal of removing the barriers for investment into these companies, opening up access to the normal everyday investor. In this must watch interview, we break down all of the details that you need to know! $DEUS 0:00 Intro 2:38 XMAQUINA Mission 3:14 Accredited Investor Requirements 5:22 Borderless Capital – Investment Waves 8:14 Community Launch – Virtuals 11:10 Messari DePAI Report 13:01 Mauricio’s Background 15:15 Evolution of DePIN 18:16 Avoiding Extraction 19:42 Humanoid Landscape 21:43 XMAQUINA Blog 24:25 DAO Voting Structure 26:27 Treasury Breakdown 30:49 BOT-08 36:05 NEURA Robotics 38:48 Sunday – Memo 40:37 Investment Process 44:19 Physical AI Stack 46:47 Web3 Robotics Projects 51:13 TGE Preparation 53:15 Token Allocation Breakdown 55:43 Treasury mNAV 58:29 DAO Leaderboard 62:00 Closing Thoughts
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DePIN Connection

271,784 просмотров • 6 месяцев назад

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The Humanoid Hub

40,368 просмотров • 10 месяцев назад

1/ New episode – Enabling AI Models to Drive Robots with Michael Cho - Rbt/Acc from BitRobot Network Hosts: Connor Lovely 🥷 EV3 Sal We discuss: - Building the world's largest robotic driving dataset - Working with researchers from Google DeepMind - Launching a memecoin ($SAM) pre-TGE Timestamps: 00:00 - Introduction 01:47 - Intersection of Robotics and AI 02:50 - Real-World Evaluation vs. Digital AI 06:00 - The Importance of Real-World Testing 07:07 - Mapping and Navigation in Robotics 08:12 - Value of Real-World Data for AI 09:38 - Differences in Data Utility 13:07 - Exploring Different Robotic Form Factors 14:04 - Cross-Embodiment in Robotics 16:35 - Introduction to BitRobot 17:30 - Transition from Frotobots to BitRobot 22:05 - Future Plans for BitRobot 24:27 - Meme Coins in Community Building 27:46 - Conclusion Watch on all platforms below ⬇️
29:16

1/ New episode – Enabling AI Models to Drive Robots with Michael Cho - Rbt/Acc from BitRobot Network Hosts: Connor Lovely 🥷 EV3 Sal We discuss: - Building the world's largest robotic driving dataset - Working with researchers from Google DeepMind - Launching a memecoin ($SAM) pre-TGE Timestamps: 00:00 - Introduction 01:47 - Intersection of Robotics and AI 02:50 - Real-World Evaluation vs. Digital AI 06:00 - The Importance of Real-World Testing 07:07 - Mapping and Navigation in Robotics 08:12 - Value of Real-World Data for AI 09:38 - Differences in Data Utility 13:07 - Exploring Different Robotic Form Factors 14:04 - Cross-Embodiment in Robotics 16:35 - Introduction to BitRobot 17:30 - Transition from Frotobots to BitRobot 22:05 - Future Plans for BitRobot 24:27 - Meme Coins in Community Building 27:46 - Conclusion Watch on all platforms below ⬇️

Proof of Coverage Media 🥷

10,745 просмотров • 1 год назад

Here's my conversation with Marc Raibert, founder and long-time CEO of Boston Dynamics, and head of their new AI Institute, all about the history and future of robotics. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:43 - Early robots 6:47 - Legged robots 25:27 - Boston Dynamics 28:45 - BigDog 36:52 - Hydraulic actuation 38:44 - Natural movement 44:31 - Leg Lab 51:23 - AI Institute 54:41 - Athletic intelligence 1:02:35 - Building a team 1:05:37 - Videos 1:13:25 - Engineering 1:16:53 - Dancing robots 1:21:40 - Hiring 1:25:32 - Optimus robot 1:34:02 - Future of robotics 1:38:56 - Advice for young people
1:43:38

Here's my conversation with Marc Raibert, founder and long-time CEO of Boston Dynamics, and head of their new AI Institute, all about the history and future of robotics. It's here on X in full, and is up on YouTube, Spotify, and everywhere else. Links in comment. Timestamps: 0:00 - Introduction 1:43 - Early robots 6:47 - Legged robots 25:27 - Boston Dynamics 28:45 - BigDog 36:52 - Hydraulic actuation 38:44 - Natural movement 44:31 - Leg Lab 51:23 - AI Institute 54:41 - Athletic intelligence 1:02:35 - Building a team 1:05:37 - Videos 1:13:25 - Engineering 1:16:53 - Dancing robots 1:21:40 - Hiring 1:25:32 - Optimus robot 1:34:02 - Future of robotics 1:38:56 - Advice for young people

Lex Fridman

484,938 просмотров • 2 лет назад

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla
1:06:07

Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

The TWIML AI Podcast

22,533 просмотров • 6 месяцев назад

🔈BidCast - New Interview with Sid Powell 🥞 from Maple on $SYRUP on the back of Cantor x Maple announcement: We discuss: 📌 How Maple pivoted to achieve $2 Bn TVL today 📌 How it plans to achieve $4Bn TVL by year-end 2025 📌 The institutional adoption cycle and pending deals Links 👇 Summary → YouTube → Timestamps 👇 00:00 Opening 00:45 Introduction to Maple Finance and TVL Growth 03:45 Transition to Over-Collateralized Lending 06:46 Loan Economics and Revenue Generation 09:42 Syrup USDC and Syrup USDT 12:42 Client Base and Borrowing Trends 15:47 Market Dynamics and Future Growth Strategies 18:49 Navigating the Credit Cycle in Crypto Lending 21:41 Future Projections and Product Development 28:43 Scaling Bitcoin Staking and Revenue Projections 31:19 Funding Strategies and Revenue Distribution 33:11 Future Growth and Institutional Partnerships 36:17 Navigating Competition and Market Dynamics 40:01 Exploring Bitcoin Treasury Lending Opportunities 41:09 Building a Sustainable Liability Base 46:47 Strategic Focus on Lending and Asset Management
52:28

🔈BidCast - New Interview with Sid Powell 🥞 from Maple on $SYRUP on the back of Cantor x Maple announcement: We discuss: 📌 How Maple pivoted to achieve $2 Bn TVL today 📌 How it plans to achieve $4Bn TVL by year-end 2025 📌 The institutional adoption cycle and pending deals Links 👇 Summary → YouTube → Timestamps 👇 00:00 Opening 00:45 Introduction to Maple Finance and TVL Growth 03:45 Transition to Over-Collateralized Lending 06:46 Loan Economics and Revenue Generation 09:42 Syrup USDC and Syrup USDT 12:42 Client Base and Borrowing Trends 15:47 Market Dynamics and Future Growth Strategies 18:49 Navigating the Credit Cycle in Crypto Lending 21:41 Future Projections and Product Development 28:43 Scaling Bitcoin Staking and Revenue Projections 31:19 Funding Strategies and Revenue Distribution 33:11 Future Growth and Institutional Partnerships 36:17 Navigating Competition and Market Dynamics 40:01 Exploring Bitcoin Treasury Lending Opportunities 41:09 Building a Sustainable Liability Base 46:47 Strategic Focus on Lending and Asset Management

BidClub

19,273 просмотров • 1 год назад

The US vs. China Manufacturing Debate My first debate between Sam D'Amico, founder of Impulse, and Aaron Slodov, founder of Atomic Industries. Timestamps: 0:39 Introductions 11:34 Is it possible to reverse America’s manufacturing decline? 16:37 Where would Sam invest $100M in a factory today? 21:28 How California made it illegal to do chemistry w metals 25:43 Elon on Tesla's Shanghai Gigafactory 32:01 Using the data center boom to pull advanced capabilities onshore 36:51 Building Chinese level capacity in America 38:33 The US Gov hasn't incentivized companies to build base level capacity 42:54 Why space companies have to vertically integrate their supply chains 45:06 The advantage of building a Chinese supply chain 49:10 Why Apple manufactures all their products in China 59:42 Apple exports engineering expertise 1:02:19 Consumer electronics 1:07:55 How the advent of humanoid robots will shift the balance of power 1:12:29 Why Apple spent hundreds of billions building their supply chains in China
1:16:56

The US vs. China Manufacturing Debate My first debate between Sam D'Amico, founder of Impulse, and Aaron Slodov, founder of Atomic Industries. Timestamps: 0:39 Introductions 11:34 Is it possible to reverse America’s manufacturing decline? 16:37 Where would Sam invest $100M in a factory today? 21:28 How California made it illegal to do chemistry w metals 25:43 Elon on Tesla's Shanghai Gigafactory 32:01 Using the data center boom to pull advanced capabilities onshore 36:51 Building Chinese level capacity in America 38:33 The US Gov hasn't incentivized companies to build base level capacity 42:54 Why space companies have to vertically integrate their supply chains 45:06 The advantage of building a Chinese supply chain 49:10 Why Apple manufactures all their products in China 59:42 Apple exports engineering expertise 1:02:19 Consumer electronics 1:07:55 How the advent of humanoid robots will shift the balance of power 1:12:29 Why Apple spent hundreds of billions building their supply chains in China

Ti Morse

227,647 просмотров • 4 месяцев назад

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing
1:15:15

My conversation with Sergey Levine (Sergey Levine). Sergey is the co-founder of Physical Intelligence -- a company building foundation models that can control any robot to do any task in any environment. The company's thesis is that generality is more scalable than specialization, meaning that a model trained across many different robots and tasks will ultimately outperform any system built to do one thing well (eg, just wash dishes). Sergey is a researcher by background, but I think you will appreciate how practical and commercially grounded this conversation is. We discuss: - Why changing a diaper will be the last task a robot masters - The simulation v. real-world data debate - How multimodal LLMs give robots common sense - Moravec's Paradox + Robot Olympics - Why robots can do long-horizon tasks now - A realistic timeline for robots in our homes I should note that I am an investor in Physical Intelligence -- I made the investment because I believe it is one of the most important companies tackling the problem of robotics. Enjoy! Timestamps: 0:00 Intro 2:39 Defining Physical Intelligence 5:19 The Challenge of Building General Models 6:34 The Stakes and Future of General Purpose Robotics 8:15 Pros and Cons of Humanoid Robots 10:12 Historical Milestones in Robotics Research 15:31 Combining Generative AI and Deep RL 21:24 Moravec's Paradox 25:33 Kitchen Robots 29:30 Simulation vs. Real-World Data 30:48 The Robot Olympics 36:31 The Physiological Reality of Embodiment 38:56 Controversies in the Robotics Community 44:18 What Makes a Great Researcher 48:27 How Businesses Should Prepare for Robotics 54:09 Tracking Progress Through Research Papers 57:02 The Next Step: Mid-Level Reasoning 1:02:00 The Kindest Thing

Patrick OShaughnessy

133,833 просмотров • 3 месяцев назад

INTERVIEW: “ZODL is to zcash:native what Coinbase was to bitcoin:native .” Josh Swihart 🛡 joins Bankless to unpack ZEC's awakening and why Zcash 🛡️ may be entering its most important chapter yet. We cover: - Zodl (fka Zashi)'s plan for Zcash - the rise of shielded ZEC - privacy in the AI era - why Bitcoiners are paying attention - making private money too big to kill --- TIMESTAMPS 0:00 Intro 4:17 Privacy in the AI Era 6:19 Crypto’s Missing Privacy Layer 9:49 The Political Fight for Privacy 14:42 Can Crypto Retain Privacy? 16:47 From Zashi to ZODL 19:48 The Zcash Reawakening 21:41 Balaji, Funding, and the Mission 24:49 Product Market Fit for Shielded ZEC 27:51 ZODL's Role in the Zcash Stack 29:36 The Zcash Roadmap 32:56 What Is ZEC and is it Undervalued? 36:56 The Shielded Pool Explained 45:12 The $25M ZODL Raise 47:16 Compliance and Shielded ZEC 51:25 Institutions vs Cypherpunk Values 53:58 Personal Privacy in an AI World
56:55

INTERVIEW: “ZODL is to zcash:native what Coinbase was to bitcoin:native .” Josh Swihart 🛡 joins Bankless to unpack ZEC's awakening and why Zcash 🛡️ may be entering its most important chapter yet. We cover: - Zodl (fka Zashi)'s plan for Zcash - the rise of shielded ZEC - privacy in the AI era - why Bitcoiners are paying attention - making private money too big to kill --- TIMESTAMPS 0:00 Intro 4:17 Privacy in the AI Era 6:19 Crypto’s Missing Privacy Layer 9:49 The Political Fight for Privacy 14:42 Can Crypto Retain Privacy? 16:47 From Zashi to ZODL 19:48 The Zcash Reawakening 21:41 Balaji, Funding, and the Mission 24:49 Product Market Fit for Shielded ZEC 27:51 ZODL's Role in the Zcash Stack 29:36 The Zcash Roadmap 32:56 What Is ZEC and is it Undervalued? 36:56 The Shielded Pool Explained 45:12 The $25M ZODL Raise 47:16 Compliance and Shielded ZEC 51:25 Institutions vs Cypherpunk Values 53:58 Personal Privacy in an AI World

Bankless

119,130 просмотров • 1 месяц назад

My first interview with Cameron Schiller (Cameron Schiller) co-founder & CEO of Rangeview 0:27 History of casting 2:01 Finding the American Dream in China 3:26 Never listen to McKinsey 7:26 Building America’s first Cyber Foundry 8:25 Manufacturing things in China as a kid 11:57 Made in China Initiative 14:28 Growing up doing robotics 20:53 Building factories 27:47 Manufacturing vs assembly 32:56 Charging bird scooters to fund his first business 39:55 Manufacturing is not fintech 47:18 SpaceX had to build their own foundry 50:55 Robotics competitions in China 53:13 New facility 59:30 Engineering emotions & beautiful spaces 1:02:25 Understanding a society through their cars 1:06:13 Why the GTI is an amazing car 1:09:27 Dropping out of college
1:12:12

My first interview with Cameron Schiller (Cameron Schiller) co-founder & CEO of Rangeview 0:27 History of casting 2:01 Finding the American Dream in China 3:26 Never listen to McKinsey 7:26 Building America’s first Cyber Foundry 8:25 Manufacturing things in China as a kid 11:57 Made in China Initiative 14:28 Growing up doing robotics 20:53 Building factories 27:47 Manufacturing vs assembly 32:56 Charging bird scooters to fund his first business 39:55 Manufacturing is not fintech 47:18 SpaceX had to build their own foundry 50:55 Robotics competitions in China 53:13 New facility 59:30 Engineering emotions & beautiful spaces 1:02:25 Understanding a society through their cars 1:06:13 Why the GTI is an amazing car 1:09:27 Dropping out of college

Ti Morse

48,354 просмотров • 11 месяцев назад

Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.
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Godfather of AI Yann LeCun reveals the humanoid robot industry's secret: the companies building the robots have no idea how to make them smart enough to be useful. "There is a lot of expectations that robotics is going to be a revolution, essentially, in a big market, eventually." "But the secret of the industry is that none of the companies at the moment that are building robots, like humanoid robots, none of those companies has any idea how to make those robots smart enough to be useful." "There's a big push for AI to make progress towards... understanding the physical world. That's what is prompting this investment." "There's nothing wrong with investing in LLMs. LLMs are useful. They're just addressing a completely different set of applications, really." LeCun is not describing someone else's problem. He has spent 15 years on world models, raised more than $1 billion for AMI Labs to build that missing intelligence layer, and in the same interview he calls 2026 the year of the world model and puts consumer robots 3 to 5 years out. Every humanoid robot demo you have seen is a body waiting for a brain. The winner in robotics will not be the company that shipped the body; it will be the one that ships the intelligence. - Yann LeCun (Yann LeCun), Turing Award winner and founder of AMI Labs, on Bloomberg Television.

Karl Mehta

41,759 просмотров • 1 день назад

Crypto Is About To Go Parabolic - Here’s Why With avery.apt 🇺🇸, Aptos Timestamps: 0:00 Intro 2:30 Crypto’s Time to Shine 5:15 Aptos Growth in 2024 8:00 Blockchain Scaling for Adoption 10:45 Real-World Blockchain Use Cases 13:30 Stablecoins and Global Payments 16:15 Future of Decentralized Trading 19:00 Why Aptos is Different 21:45 AI and Blockchain Merge 24:30 Government and Crypto Innovation 27:15 The Next Big Crypto Wave 30:00 How Institutions Are Adopting 32:45 Trading and Market Evolution 35:30 The Future of Web3 38:15 RWA and Tokenized Assets 41:00 Private vs Public Blockchains 43:45 AI-Powered Trading and Agents 46:30 AI in Legacy Finance 49:15 Crypto’s Biggest Adoption Hurdles 52:00 The Role of Aptos Token 54:45 What’s Coming in 2025 57:30 Final Thoughts
54:06

Crypto Is About To Go Parabolic - Here’s Why With avery.apt 🇺🇸, Aptos Timestamps: 0:00 Intro 2:30 Crypto’s Time to Shine 5:15 Aptos Growth in 2024 8:00 Blockchain Scaling for Adoption 10:45 Real-World Blockchain Use Cases 13:30 Stablecoins and Global Payments 16:15 Future of Decentralized Trading 19:00 Why Aptos is Different 21:45 AI and Blockchain Merge 24:30 Government and Crypto Innovation 27:15 The Next Big Crypto Wave 30:00 How Institutions Are Adopting 32:45 Trading and Market Evolution 35:30 The Future of Web3 38:15 RWA and Tokenized Assets 41:00 Private vs Public Blockchains 43:45 AI-Powered Trading and Agents 46:30 AI in Legacy Finance 49:15 Crypto’s Biggest Adoption Hurdles 52:00 The Role of Aptos Token 54:45 What’s Coming in 2025 57:30 Final Thoughts

The Wolf Of All Streets

90,974 просмотров • 1 год назад

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses
1:14:20

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - Brex: The intelligent finance platform: - Granola: The app that might actually make you love meetings: Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Mario Gabriele 🦊

27,871 просмотров • 4 месяцев назад

2026 is the year of Agents OpenClaw, Claude Code, Codex and Cursor... In this video, I broke it all down with Micky. Consider this a 2026, year in review for AI agents. 00:00 Intro 01:35 Q1 Belonged to Anthropic 03:02 Opus 4.5 Inflection 08:42 Codex vs Claude UX War 12:53 Cursor was first? 14:27 SpaceX Acquisition Deal with Cursor 21:45 OpenClaw and the Agentic Personal Computer 28:57 Reactive vs Proactive Work 30:30 Keep Agents Narrow 33:12 Memory Layer Matters 36:23 Computer Use will get FAST 39:28 Who Wins the Super App? (Codex so far) 41:06 The Problem With Google... 45:17 Prompting and Skills creation 51:30 Codex Skill Example 56:56 Integrations Beat Prompts 59:39 All the Muxes (CMUX, TMUX) 01:02:04 Bold Predictions 01:06:38 Agent Commerce
1:23:01

2026 is the year of Agents OpenClaw, Claude Code, Codex and Cursor... In this video, I broke it all down with Micky. Consider this a 2026, year in review for AI agents. 00:00 Intro 01:35 Q1 Belonged to Anthropic 03:02 Opus 4.5 Inflection 08:42 Codex vs Claude UX War 12:53 Cursor was first? 14:27 SpaceX Acquisition Deal with Cursor 21:45 OpenClaw and the Agentic Personal Computer 28:57 Reactive vs Proactive Work 30:30 Keep Agents Narrow 33:12 Memory Layer Matters 36:23 Computer Use will get FAST 39:28 Who Wins the Super App? (Codex so far) 41:06 The Problem With Google... 45:17 Prompting and Skills creation 51:30 Codex Skill Example 56:56 Integrations Beat Prompts 59:39 All the Muxes (CMUX, TMUX) 01:02:04 Bold Predictions 01:06:38 Agent Commerce

Riley Brown

20,112 просмотров • 2 месяцев назад

Why Galaxy Might Be the Most Undervalued Crypto x AI Stock w/ Duncan What do Meta, Amazon, Google, and Microsoft …have in common? They’re spending $330B on AI infra this year. And one crypto company is building the AI infrastructure to power them. Tune in to know more ⏱ TIME POINTS ⏱ 00:00 – Intro 00:58 – Where Are We in the Bull Market? 03:11 – Is the 4-Year Cycle Dead? 06:25 – How Public Markets Are Changing Crypto 12:13 – Figure Markets 12:48 – KGeN 13:18 – Why Duncan Loves $GLXY 16:10 – The AI Data Center Opportunity 27:23 – Risks of Building at AI Scale 33:33 – What’s Galaxy’s Endgame? 36:05 – AI & Robots: What’s Coming and When 43:46 – How to Invest in Humanoid Robots 47:50 – What Could Slow Down Robot Adoption? 50:18 – Where Duncan Finds Alpha 53:29 – Final Thoughts
55:26

Why Galaxy Might Be the Most Undervalued Crypto x AI Stock w/ Duncan What do Meta, Amazon, Google, and Microsoft …have in common? They’re spending $330B on AI infra this year. And one crypto company is building the AI infrastructure to power them. Tune in to know more ⏱ TIME POINTS ⏱ 00:00 – Intro 00:58 – Where Are We in the Bull Market? 03:11 – Is the 4-Year Cycle Dead? 06:25 – How Public Markets Are Changing Crypto 12:13 – Figure Markets 12:48 – KGeN 13:18 – Why Duncan Loves $GLXY 16:10 – The AI Data Center Opportunity 27:23 – Risks of Building at AI Scale 33:33 – What’s Galaxy’s Endgame? 36:05 – AI & Robots: What’s Coming and When 43:46 – How to Invest in Humanoid Robots 47:50 – What Could Slow Down Robot Adoption? 50:18 – Where Duncan Finds Alpha 53:29 – Final Thoughts

Milk Road

59,357 просмотров • 10 месяцев назад

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏
18:51

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏

Andreas Klinger 🦾

14,699 просмотров • 4 месяцев назад