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Smarter machines won’t come from piling on compute. They come from smarter system design. Texas Instruments' Mark Ng and Olivier Blanchard 💬 break down why software-defined systems depend on consolidation, not sprawl. Zone architectures replace dozens of legacy edge nodes with a handful of intelligent boards—cutting wiring, weight, and... show more

Six Five Media

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This is what cutting-edge technology and pure ambition look like up close: a 171 ft SpaceX Starship being carefully transported across Starbase, Texas. To move massive rocket components, from boosters to upper-stage sections and even a full Starship after landing SpaceX relies on Self-Propelled Modular Transporters (SPMTs). These heavy-duty, multi-axle machines use dozens of independently controlled wheels and hydraulic leveling systems to distribute weight precisely, making them ideal for transporting enormous, delicate rocket hardware with remarkable finesse.

This is what cutting-edge technology and pure ambition look like up close: a 171 ft SpaceX Starship being carefully transported across Starbase, Texas. To move massive rocket components, from boosters to upper-stage sections and even a full Starship after landing SpaceX relies on Self-Propelled Modular Transporters (SPMTs). These heavy-duty, multi-axle machines use dozens of independently controlled wheels and hydraulic leveling systems to distribute weight precisely, making them ideal for transporting enormous, delicate rocket hardware with remarkable finesse.

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⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

⚡Why Edge Compute Matters for the Future of Agentic AI AI isn’t just about smarter models, it’s about where they run. Centralized clouds are costly, slow, and controlled by a few. That’s not the future. 👉Join the OptimAI Network: With OptimAI Edge Compute and the upcoming Core Node release, every device becomes part of a decentralized supercomputer: + AI models run directly on your devices (phones, desktops, IoT devices) + Data stays local, secure, and real-time + Compute scales with the community, not corporations This is how we power the next generation of Agentic AI - AI systems that don’t just think, but act like you. Lightweight, fast, everywhere, for everyone. Stay tuned. The Edge is coming.

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Robots aren’t just the future — they’re already reshaping our world. At IIT Madras, the new in Robotics, offered under the Department of Engineering Design, is building the brains behind tomorrow’s intelligent machines. From motion planning and control to AI/ML and hands-on labs, this two-year program blends cutting-edge research with real-world industry relevance — powered by a strong robotics faculty team and a legacy of interdisciplinary innovation. Rooted in the success of the Inter-Disciplinary Dual Degree (IDDD) Robotics program, this two-year program brings together expert faculty members and a powerful blend of theory and hands-on learning. From robot design and analysis to motion planning, control systems, and AI/ML, students dive deep into the science and engineering behind next-generation robotic systems. Add immersive lab work and cutting-edge projects, and you have a curriculum that’s as practical as it is pioneering. Manufacturing, healthcare, logistics, and defence — robotics is reshaping them all. This program is about building the minds that will lead to that transformation. If the future runs on robots, it starts here. For more details visit: IIT Madras

Robots aren’t just the future — they’re already reshaping our world. At IIT Madras, the new in Robotics, offered under the Department of Engineering Design, is building the brains behind tomorrow’s intelligent machines. From motion planning and control to AI/ML and hands-on labs, this two-year program blends cutting-edge research with real-world industry relevance — powered by a strong robotics faculty team and a legacy of interdisciplinary innovation. Rooted in the success of the Inter-Disciplinary Dual Degree (IDDD) Robotics program, this two-year program brings together expert faculty members and a powerful blend of theory and hands-on learning. From robot design and analysis to motion planning, control systems, and AI/ML, students dive deep into the science and engineering behind next-generation robotic systems. Add immersive lab work and cutting-edge projects, and you have a curriculum that’s as practical as it is pioneering. Manufacturing, healthcare, logistics, and defence — robotics is reshaping them all. This program is about building the minds that will lead to that transformation. If the future runs on robots, it starts here. For more details visit: IIT Madras

IIT Madras

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After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

After decades of building databases, Databricks VP of Engineering Nikita | Scaling Postgres believes it's time to rethink one of their fundamental assumptions. In this episode of the 𝘐𝘵'𝘴 𝘈𝘣𝘰𝘶𝘵 𝘋𝘢𝘵𝘢 podcast, Nikita discusses why separating compute and storage is the next major architectural shift for operational databases, how storing data on object storage opens it to downstream systems, and why the rise of AI agents is changing how developers build software—and what they need from a database. Watch the full conversation:

Databricks

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Tanay Tandon (Tanay Tandon) is the co-founder and CEO of Athelas/Commure, the AI operating system for healthcare. Commure powers 20M+ appointments annually and processes $25B+ in claims volume, helping clinicians and health systems work faster, smarter, and spend more time on patient care. In this fireside with YC's , Tanay shares how he built one of healthcare's most ambitious companies and why the next decade of medicine will be defined by software. 00:00 – From Rejection Letters to YC Acceptance 02:30 – What Athelas and Commure Actually Do 05:33 – Building a Medical Device at 19 09:00 – Lessons from Clinical Trials and Early Healthcare Startups 16:39 – Pivoting from Hardware to Healthcare Software 21:24 – Fixing Physician Burnout with AI 27:14 – The Rise of Ambient Documentation 32:43 – Stripe for Healthcare: Rebuilding the Financial Layer 36:45 – Competing with Epic and Scaling Nationwide 45:41 – The Next Decade of Medicine

Tanay Tandon (Tanay Tandon) is the co-founder and CEO of Athelas/Commure, the AI operating system for healthcare. Commure powers 20M+ appointments annually and processes $25B+ in claims volume, helping clinicians and health systems work faster, smarter, and spend more time on patient care. In this fireside with YC's , Tanay shares how he built one of healthcare's most ambitious companies and why the next decade of medicine will be defined by software. 00:00 – From Rejection Letters to YC Acceptance 02:30 – What Athelas and Commure Actually Do 05:33 – Building a Medical Device at 19 09:00 – Lessons from Clinical Trials and Early Healthcare Startups 16:39 – Pivoting from Hardware to Healthcare Software 21:24 – Fixing Physician Burnout with AI 27:14 – The Rise of Ambient Documentation 32:43 – Stripe for Healthcare: Rebuilding the Financial Layer 36:45 – Competing with Epic and Scaling Nationwide 45:41 – The Next Decade of Medicine

Y Combinator

64,969 Aufrufe • vor 9 Monaten

Mo Gawdat, ex-CBO Google: For the first time, we may see massive unemployment (30%-50%) not from crisis, but because machines outperform human labor. Capitalism could come to an end in his perspective. That cuts to the core of capitalism: a system built on hiring labor to produce and sell at a profit. If production moves toward near-zero cost, labor arbitrage disappears, and with it, the foundation of pricing and markets. But the real paradox is this: capitalism depends not just on production, but on consumption. AI boosts output while removing workers, undermining the very demand the system needs to survive. As intelligence becomes abundant and production cheap, we’re entering a world where work may no longer define value—and where capitalism itself has to evolve or break.

Mo Gawdat, ex-CBO Google: For the first time, we may see massive unemployment (30%-50%) not from crisis, but because machines outperform human labor. Capitalism could come to an end in his perspective. That cuts to the core of capitalism: a system built on hiring labor to produce and sell at a profit. If production moves toward near-zero cost, labor arbitrage disappears, and with it, the foundation of pricing and markets. But the real paradox is this: capitalism depends not just on production, but on consumption. AI boosts output while removing workers, undermining the very demand the system needs to survive. As intelligence becomes abundant and production cheap, we’re entering a world where work may no longer define value—and where capitalism itself has to evolve or break.

Chubby♨️

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Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Jensen Huang on "distillation" On his new interview with axios, he was asked this question "Should open source model companies be allowed to distill closed models" "Distillation—learning from AI, learning from other people, and learning from other sources of knowledge, is fundamental to intelligence. We are constantly learning from other people. I am learning from you through the questions you are asking, and you are learning from me. All day long, we are learning from one another. AI also has to learn from something. The original AI models, whether they were open or closed, were trained on previously created knowledge from the internet. Now, AI is generating more content than humans. In a few more years, the internet could be 99% AI-generated content, and that content will have been created by some form of AI. As a result, AI systems will constantly be distilling knowledge and intelligence from other AI systems. The fact that AI can learn is a good thing. We want AI systems to be intelligent because a smarter AI can also be a safer AI." ---- From "Axios" YouTube channel, (full video link in comment)

Rohan Paul

441,746 Aufrufe • vor 24 Tagen

Jensen Huang explains the difference between AI and traditional Software and why there's no bubble. Traditional software was pre-compiled, meaning it was built once, stored, and then executed repeatedly with little computation. It didn’t need constant high-power processing once finished. Users simply ran the completed program as a tool. AI, by contrast, generates its output in real time. It must process context, reason, and produce intelligence at the moment of use, not in advance. This requires ongoing computation for every request. Because of that, AI systems depend on continuous GPU power to “manufacture” responses like a factory producing tokens. So instead of static software tools, AI is an active computational process that needs large-scale, always-on infrastructure to create intelligence dynamically. --- From 'FT Live' YT channel (link in comment)

Jensen Huang explains the difference between AI and traditional Software and why there's no bubble. Traditional software was pre-compiled, meaning it was built once, stored, and then executed repeatedly with little computation. It didn’t need constant high-power processing once finished. Users simply ran the completed program as a tool. AI, by contrast, generates its output in real time. It must process context, reason, and produce intelligence at the moment of use, not in advance. This requires ongoing computation for every request. Because of that, AI systems depend on continuous GPU power to “manufacture” responses like a factory producing tokens. So instead of static software tools, AI is an active computational process that needs large-scale, always-on infrastructure to create intelligence dynamically. --- From 'FT Live' YT channel (link in comment)

Rohan Paul

761,324 Aufrufe • vor 9 Monaten

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.

🚨$OSS is not an AI company. → It is the hardware that lets AI exist where the cloud cannot. Most investors don’t understand $OSS because they think AI = software. $OSS builds the physical “brains” that run AI in extreme environments where cloud computing fails. Jets. Ships. Tanks. Drones. Space. Hospitals. That’s the game. 1) What $OSS actually is $OSS (One Stop Systems) designs rugged high-performance computers and storage systems for AI at the edge. Meaning: They bring data-center-level computing power into harsh environments. Their products include rugged servers, GPU accelerators, storage arrays, and expansion systems used for AI, sensor processing, and autonomous systems. In simple terms: Cloud AI = brain in a safe building. $OSS AI = brain inside machines operating in chaos. 2) Why this is crucial Most AI today runs in data centers. But the future of AI is not in the cloud. It’s on: • autonomous vehicles • military systems • drones • ships • industrial machines • medical devices These systems cannot wait for the cloud. Latency, connectivity, security, and survival demand local AI. $OSS delivers “data-center performance at the edge” across land, sea, and air. Without companies like OSS, autonomous systems simply don’t work. 3) What OSS actually does: Think of OSS as building AI engines that survive reality. 🌊 SEA example: naval surveillance aircraft and ships. $OSS supplies rugged storage and compute systems for U.S. Navy reconnaissance aircraft to collect and process massive sensor data in real time. Translation: Instead of sending raw data back to base, the aircraft analyzes threats instantly onboard. $OSS = the onboard AI brain. 🪖 LAND example: military vehicles and tactical operations. $OSS delivers high-performance servers and FPGA systems for mobile military intelligence platforms used by the U.S. Department of Defense. Translation: Tanks and vehicles detect threats, process sensor data, and make decisions locally. $OSS = the battlefield computer. ✈️ AIR example: airborne AI. $OSS builds GPU-accelerated servers designed for aircraft, described as a “datacenter in the sky.” Translation: Jets and drones run AI models mid-flight. $OSS = flying supercomputers. 🚀 SPACE example: $OSS hardware is designed for extreme environments and autonomous systems across aerospace and defense. Translation: Future satellites, space drones, and autonomous spacecraft need onboard AI. $OSS = the computing core of autonomous space systems. BONUS: CIVILIAN & COMMERCIAL $OSS systems are used in: • autonomous trucking and farming • industrial automation • healthcare imaging • energy and mining • telecom and 5G Example:A medical imaging company uses $OSS hardware to run real-time AI diagnostics in next-gen breast cancer scanners. $OSS = AI where milliseconds matter. 4) Who their customers are (pattern, not names) $OSS sells to: • defense primes • government programs • industrial OEMs • AI infrastructure companies • medical device manufacturers These customers share one trait: They cannot rely on the cloud. That’s why $OSS exists. 5) The mental model that makes $OSS obvious $NVDA = AI chips $PLTR = AI software $OSS = AI hardware in the real world If AI is electricity, $OSS builds the generators that work in storms. Most investors understand AI software. Few understand AI infrastructure at the edge. That gap is the opportunity. 6) The real thesis The world is moving toward: • autonomous warfare • autonomous vehicles • real-time AI systems • distributed intelligence All of that requires rugged edge computing. $OSS is positioned exactly there. Infrastructure. The hardest layer to build. And often the most valuable.

Black Panther Capital

30,138 Aufrufe • vor 6 Monaten

Qasar Younis was COO of Y Combinator when Sam Altman was its president. Peter Ludwig was an early engineer on Android Automotive at Google. Both are Detroit guys with family roots at General Motors. In 2017, they started Applied Intuition to make a billion machines intelligent. Putting intelligence into a machine is one of the hardest problems in technology. A car, a tractor, and a mining vehicle each have different hardware, different sensors, and lives on the line if the software fails. So instead of building one machine, they built the technology that goes into all of them. Cars, trucks, defense vehicles, mining equipment, combines, robots. Like Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence. They are the only company in the world that does this across all of these industries. They have raised about a billion dollars. They have never spent any of it. Qasar believes that when we look back 25 years from now, the most important companies in the world will all be physical AI companies. Last week they launched Dana, an agent platform built to get there faster. Enjoy! Qasar Younis Applied Intuition Timestamps: 0:00 Intro 4:08 Why Physical AI Will Be Enormous 5:54 What Applied Intuition Actually Builds 9:25 Timing the Autonomous Revolution 13:59 From Tools to a Full AI Stack 21:40 Why Chatbots Can’t Build Robots 25:18 The Physical AI Data Flywheel 28:19 The Moat Behind Machine Intelligence 30:13 What’s Slowing Physical AI Down? 32:30 Applied Intuition’s Business Model 36:10 Competing in a Massive Market 39:24 The Hardware Renaissance 41:09 Why They Raised $1 Billion 43:49 A World Filled With Intelligent Machines

Qasar Younis was COO of Y Combinator when Sam Altman was its president. Peter Ludwig was an early engineer on Android Automotive at Google. Both are Detroit guys with family roots at General Motors. In 2017, they started Applied Intuition to make a billion machines intelligent. Putting intelligence into a machine is one of the hardest problems in technology. A car, a tractor, and a mining vehicle each have different hardware, different sensors, and lives on the line if the software fails. So instead of building one machine, they built the technology that goes into all of them. Cars, trucks, defense vehicles, mining equipment, combines, robots. Like Nvidia sells chips into everyone else's machines, Applied Intuition sells intelligence. They are the only company in the world that does this across all of these industries. They have raised about a billion dollars. They have never spent any of it. Qasar believes that when we look back 25 years from now, the most important companies in the world will all be physical AI companies. Last week they launched Dana, an agent platform built to get there faster. Enjoy! Qasar Younis Applied Intuition Timestamps: 0:00 Intro 4:08 Why Physical AI Will Be Enormous 5:54 What Applied Intuition Actually Builds 9:25 Timing the Autonomous Revolution 13:59 From Tools to a Full AI Stack 21:40 Why Chatbots Can’t Build Robots 25:18 The Physical AI Data Flywheel 28:19 The Moat Behind Machine Intelligence 30:13 What’s Slowing Physical AI Down? 32:30 Applied Intuition’s Business Model 36:10 Competing in a Massive Market 39:24 The Hardware Renaissance 41:09 Why They Raised $1 Billion 43:49 A World Filled With Intelligent Machines

Business Breakdowns

27,283 Aufrufe • vor 21 Tagen

$Eleven years ago we started Eclipse and bet everything on atoms. Rockets, robots, chips, factories, power systems, defense. The things civilization actually runs on. Today, we're announcing $1.3B in new capital to keep building. The physical world is overdue for transformation. Transportation runs on systems designed decades ago. The energy grid cannot keep up with demand. Healthcare depends on manual procedures that don't scale. Defense development moves at a fraction of the speed threats evolve. These are not software problems. They are full stack engineering and operations problems, and they have been underinvested in for a generation. But there has never been a better moment to solve them. The best engineers are leaving big tech to build in the physical world. AI is compressing timelines from years to quarters. Policy is aligned. And customers are not waiting — the DoD, the hyperscalers, hospitals, and the Fortune 500 are all desperate for technology that makes physical systems smarter, faster, and more resilient. Talent, capital, technology, policy, and demand are all converging at once. This is our moment 🇺🇸 We built Eclipse for this exact moment and in doing so, we launched a movement. Today that movement is 100 companies strong (and growing!). These companies supply each other, share customers, and help solve each other's hardest problems. Propulsion systems powering orbital defense. Modern supply chain infrastructure moving the worlds goods. Autonomous vehicles on three continents. Surgical robots performing procedures that used to require the world's best hands. Cloud hardware powering the AI revolution built on American soil. That's not a fund. That's an economy — an Eclipse Economy. The door is open to rebuild the physical infrastructure of the country. We intend to run through it.$

Eleven years ago we started Eclipse and bet everything on atoms. Rockets, robots, chips, factories, power systems, defense. The things civilization actually runs on. Today, we're announcing $1.3B in new capital to keep building. The physical world is overdue for transformation. Transportation runs on systems designed decades ago. The energy grid cannot keep up with demand. Healthcare depends on manual procedures that don't scale. Defense development moves at a fraction of the speed threats evolve. These are not software problems. They are full stack engineering and operations problems, and they have been underinvested in for a generation. But there has never been a better moment to solve them. The best engineers are leaving big tech to build in the physical world. AI is compressing timelines from years to quarters. Policy is aligned. And customers are not waiting — the DoD, the hyperscalers, hospitals, and the Fortune 500 are all desperate for technology that makes physical systems smarter, faster, and more resilient. Talent, capital, technology, policy, and demand are all converging at once. This is our moment 🇺🇸 We built Eclipse for this exact moment and in doing so, we launched a movement. Today that movement is 100 companies strong (and growing!). These companies supply each other, share customers, and help solve each other's hardest problems. Propulsion systems powering orbital defense. Modern supply chain infrastructure moving the worlds goods. Autonomous vehicles on three continents. Surgical robots performing procedures that used to require the world's best hands. Cloud hardware powering the AI revolution built on American soil. That's not a fund. That's an economy — an Eclipse Economy. The door is open to rebuild the physical infrastructure of the country. We intend to run through it.

Seth Winterroth 🤖

38,820 Aufrufe • vor 4 Monaten

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Touch: A Game-Changer in Dexterity for Robots You can’t be responsive to the world if you can’t feel it. That’s true for people—and it's just as true for robots. We’re zeroed in on a practical, yet innovative, approach to creating general purpose robots designed to perform physical work. And that means hands, powered not just by vision, but by touch. Vision alone falls short. There are many instances where our vision is obstructed while performing work tasks–like grabbing items out of a bin–you lose line of sight. You need tactile intelligence—texture, pressure, contact—with real-time feedback to move faster, smarter, and with confidence. Watch a full explainer video on Sanctuary AI’s latest touch sensors on our blog: #Robotics #AI #HumanoidRobots #Dexterity #TactileSensing #SanctuaryAI

Sanctuary AI

53,269 Aufrufe • vor 1 Jahr

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

Robots can already fold laundry, make espresso, clean kitchens, and assemble things. The harder problem is getting them to do those tasks reliably, for long periods of time, without a human babysitting them. At Startup School 2026, Physical Intelligence cofounder Chelsea Finn explains what it takes to build general-purpose robots that work in the real world. She shares how reinforcement learning pushed robot throughput up 2x, how their systems can run autonomously for hours, and why she believes robotics is entering its GPT era: moving from specialized models toward general-purpose systems that can work across tasks, robots, and environments. 00:00 — The State of Physical Intelligence 01:23 — What It Takes to Make Robots Useful 05:11 — The Reliability Problem 07:43 — Reinforcement Learning for Robotics 09:35 — Learning From Failures 12:43 — Training Robots to Improve Themselves 14:21 — Can a Robot Work for 13 Hours Straight? 17:36 — Why Robots Need Memory 21:22 — Building a General-Purpose Robot 25:02 — From Fine-Tuning to Out-of-the-Box Models 27:35 — Training on All the Data 30:20 — One Model That Beats the Specialists 31:21 — Compositional Generalization 37:49 — The GPT Era of Robotics 39:49 — Q&A

Y Combinator

94,148 Aufrufe • vor 5 Tagen

The NHS’s Federated Data Platform (FDP) - which is powered by Palantir - can feel complicated. But if you want a simple explanation of how it is transforming the lives of clinicians and patients, I’d urge you to watch this short video from NHS England. NHS trusts across England use the FDP to pull together data from different IT systems. This helps to improve patient care, while cutting the administrative work for NHS staff. Each year, more than two million people in England navigate the difficult journey of cancer diagnosis, testing and treatment. That’s why we’ve introduced the Cancer 360 tool, built on the #FDP, to speed up this process. It cuts down paper work, tracks progress, prevents delays and helps to create personalised treatment plans - so nothing important gets missed. Without this system, #NHS staff are stuck pulling together information from multiple systems, spreadsheets and emails - taking up valuable time that could be spent on patient care. We are incredibly proud of the difference this software is making to the NHS and are incredibly excited about what’s coming next. $PLTR #PLTR

The NHS’s Federated Data Platform (FDP) - which is powered by Palantir - can feel complicated. But if you want a simple explanation of how it is transforming the lives of clinicians and patients, I’d urge you to watch this short video from NHS England. NHS trusts across England use the FDP to pull together data from different IT systems. This helps to improve patient care, while cutting the administrative work for NHS staff. Each year, more than two million people in England navigate the difficult journey of cancer diagnosis, testing and treatment. That’s why we’ve introduced the Cancer 360 tool, built on the #FDP, to speed up this process. It cuts down paper work, tracks progress, prevents delays and helps to create personalised treatment plans - so nothing important gets missed. Without this system, #NHS staff are stuck pulling together information from multiple systems, spreadsheets and emails - taking up valuable time that could be spent on patient care. We are incredibly proud of the difference this software is making to the NHS and are incredibly excited about what’s coming next. $PLTR #PLTR

Louis Mosley

52,452 Aufrufe • vor 1 Jahr

Simulation-first approach to robotic production lines! 👾 At Hannover Messe, Amazon Web Services (AWS) and SoftServe are demonstrating a fully autonomous, AI-powered manufacturing workflow. Design a product using generative AI, then watch it move through the entire process: engraving, inspection, and delivery, handled by a mix of robots working together powered by physical AI on AWS. The interesting part is how the system is built. Getting robots into production is usually where things fall apart. SoftServe takes a simulation-first approach using AWS. They build and test the entire system virtually: robot behavior, production flow, before deploying anything physically. This enables faster iteration, safe edge case testing, and significantly reduced sim-to-real gap. Their demo includes AMRs handling transport, cobots doing precise manipulation, AI-driven quality inspection, and humanoid interaction at the end. All connected through a unified, software-defined layer powered by AWS. It's about building a system where robots can adapt, coordinate, and scale without rebuilding everything from scratch every time. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Simulation-first approach to robotic production lines! 👾 At Hannover Messe, Amazon Web Services (AWS) and SoftServe are demonstrating a fully autonomous, AI-powered manufacturing workflow. Design a product using generative AI, then watch it move through the entire process: engraving, inspection, and delivery, handled by a mix of robots working together powered by physical AI on AWS. The interesting part is how the system is built. Getting robots into production is usually where things fall apart. SoftServe takes a simulation-first approach using AWS. They build and test the entire system virtually: robot behavior, production flow, before deploying anything physically. This enables faster iteration, safe edge case testing, and significantly reduced sim-to-real gap. Their demo includes AMRs handling transport, cobots doing precise manipulation, AI-driven quality inspection, and humanoid interaction at the end. All connected through a unified, software-defined layer powered by AWS. It's about building a system where robots can adapt, coordinate, and scale without rebuilding everything from scratch every time. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

17,709 Aufrufe • vor 3 Monaten

What is your BLOOD TYPE ? Why does New Age say that blood group O is that of the old souls and the young souls would be A, B and AB! ! Once again, the New Age is lying to us and reversing reality ! Once again, a PSYOP is trying to control us, we the starseeds and consciousness awakeners ! The O’s are not older souls than the A’s or AB’s or B’s !! The O’s did not come first… because they are psychically more evolved or because they have been on Earth longer they would be more evolved… NO !! no… What they want to HIDE from us with this childish logic of “you came first so you’re more evolved,” what they want to hide from us is : that in reality the groups A, B and AB come from OTHERS GALAXIES. Those who come from the farthest away are the ABs. The A’s, B’s and AB’s develop a lot of allergies… symbolically expressed in the physical body. They do not like the matrix systems like that of money (wheat), of punishments (almond), imprisonment (earth), the mental egregoric connections (pollen)…

What is your BLOOD TYPE ? Why does New Age say that blood group O is that of the old souls and the young souls would be A, B and AB! ! Once again, the New Age is lying to us and reversing reality ! Once again, a PSYOP is trying to control us, we the starseeds and consciousness awakeners ! The O’s are not older souls than the A’s or AB’s or B’s !! The O’s did not come first… because they are psychically more evolved or because they have been on Earth longer they would be more evolved… NO !! no… What they want to HIDE from us with this childish logic of “you came first so you’re more evolved,” what they want to hide from us is : that in reality the groups A, B and AB come from OTHERS GALAXIES. Those who come from the farthest away are the ABs. The A’s, B’s and AB’s develop a lot of allergies… symbolically expressed in the physical body. They do not like the matrix systems like that of money (wheat), of punishments (almond), imprisonment (earth), the mental egregoric connections (pollen)…

AuroraArabella

31,031 Aufrufe • vor 3 Monaten

Over the years, I’ve learned something important about how people actually learn. It doesn’t happen when someone talks at you. It happens when you’re close enough to think together. When you can pause, question, challenge, and build on an idea in real time. Learning lives in presence. And Jupiter understands that. Some of the most important lessons I’ve ever learned did not come from slides or stages. They came from late conversations. From shared confusion. From moments where someone reframed a problem and everything clicked. That is how systems thinking is born. That is how communities form. Most events miss this. They optimize for optics, not understanding. They focus on speakers instead of signals. But progress does not come from rows of chairs. It comes from curiosity, and shared work. That is why CatLumpurr 2026 feels different. Jupiter chooses intention built for how people actually learn, build, and align. The difference becomes clear when you look closer 🧵⤵️

Over the years, I’ve learned something important about how people actually learn. It doesn’t happen when someone talks at you. It happens when you’re close enough to think together. When you can pause, question, challenge, and build on an idea in real time. Learning lives in presence. And Jupiter understands that. Some of the most important lessons I’ve ever learned did not come from slides or stages. They came from late conversations. From shared confusion. From moments where someone reframed a problem and everything clicked. That is how systems thinking is born. That is how communities form. Most events miss this. They optimize for optics, not understanding. They focus on speakers instead of signals. But progress does not come from rows of chairs. It comes from curiosity, and shared work. That is why CatLumpurr 2026 feels different. Jupiter chooses intention built for how people actually learn, build, and align. The difference becomes clear when you look closer 🧵⤵️

Yeesha🧡

17,595 Aufrufe • vor 7 Monaten