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When robots take the night shift shopping spree! 🛍️ Robots navigate through dm-drogerie markt Deutschland stores at night to create a digital replica of the store's layout, known as a "digital twin." Developed Ubica Robotics GmbH, these autonomous robots scan shelves to provide real-time information about item positions, pricing,...

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Lukas Ziegler

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157,526 görüntüleme • 11 ay önce •via X (Twitter)

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A great day at LEAP East 2026 at HKCEC building and connecting. Spent time at the Animoca Brands booth discussing Minds by Animoca Brands and the next wave of persistent AI agents. Think persistent AI agents with memory, identity, and on-chain wallets, that's what it was! For us at Rice Robotics, that's an exciting direction. As robots become more autonomous, they'll need persistent digital identities, long-term memory, and the ability to interact with both people and decentralized networks. Oh... and we had an insightful and in-depth discussion with someone pretty special and big 👀 More on that soon🤫
0:11

A great day at LEAP East 2026 at HKCEC building and connecting. Spent time at the Animoca Brands booth discussing Minds by Animoca Brands and the next wave of persistent AI agents. Think persistent AI agents with memory, identity, and on-chain wallets, that's what it was! For us at Rice Robotics, that's an exciting direction. As robots become more autonomous, they'll need persistent digital identities, long-term memory, and the ability to interact with both people and decentralized networks. Oh... and we had an insightful and in-depth discussion with someone pretty special and big 👀 More on that soon🤫

RICE AI ( ◉ - ◉ )

21,472 görüntüleme • 2 ay önce

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

Axis Robotics

28,096 görüntüleme • 8 ay önce

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.
0:10

🚨 SCIENTISTS JUST BUILT A CHIP THAT CAN SEE, THINK, AND REMEMBER ALL AT THE SAME TIME. And it works more like a biological brain than a traditional computer. Researchers at RMIT University have created a neuromorphic vision chip that mimics the human eye and brain. Unlike conventional systems that capture images and send data to external processors, this chip performs sensing, processing, and memory storage directly where the light hits. The active layer is thousands of times thinner than a human hair. It uses doped indium oxide to detect light, process the information on-chip, and retain what it sees over time without constant electrical refreshing. Why this matters: • It dramatically cuts energy use and latency by eliminating data transfer to separate processors • Enables much faster real-time decision making for autonomous systems • Works more like biological vision than traditional machine vision • Could power the next generation of efficient edge AI in vehicles, robots, and remote sensors The deeper implication: For decades, we’ve built vision systems by bolting cameras, processors, and memory together like separate organs. This chip collapses those functions into one biological-style unit. It’s a step toward machines that don’t just “see” but actually perceive and remember in a more efficient, brain-like way. If scaled successfully, it could become a foundational component for autonomous systems that need to operate intelligently with minimal power and minimal delay. We’re moving from cameras that take pictures to chips that truly see. How do you think neuromorphic vision chips like this will change what’s possible for self-driving cars and autonomous robots? Follow for more frontier neuromorphic computing, AI hardware, and brain-inspired technology.

TheNewPhysics

23,196 görüntüleme • 2 ay önce

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?
0:16

It's 2030 and you are reviewing humanoid robots. A Tesla. A Google. An Apple. An OpenAI. A Meta. A Figure. And a bunch of Chinese-made ones. Which one is best, and why? I think the Tesla understands the world much better. Why? There were eight Teslas around me on the freeway today. Start there. No other robot company has that data. But my robot is parked at the local high school twice a day. Its cameras see humans in all of our weirdness. How we move. Where we go. Where we walk. Who we talk with. What you are wearing. Whether your hair was combed this morning. That data will lead to robotics breakthroughs. Apple might keep up with its Vision Pro data, but it is too freaked out by the privacy implications of using said data. (On the front are six cameras and a couple of TOF -- Time Of Flight -- sensors that can see everything in your home in great detail). Google has a lot of data, for sure. All my: 1. Email. 2. Calendars. 3. Photos. 4. TV watching behavior. 5. Contacts. 6. Documents and spreadsheets. 7. Files. 8. Location data. So I expect Google's robot will be attractive to many. But how do you see the others shake out over the next five years? Make some guesses. But remember what an AI pioneer told me years ago about AI: it's all about the data. The Chinese ones have huge advantages: the Chinese have more data on their citizens, and many more citizens to boot AND they can make robots cheaper than we can. But now that you know OpenAI is building its own robot you have caught wind of what I've heard from many in San Francisco and Silicon Valley: that humanoid robots are the real prize of AI and will be highly profitable for those that can make them and find customers willing to buy them. Here, too, I learned long ago never to bet against Elon Musk. Will you?

Robert Scoble

33,804 görüntüleme • 1 yıl önce

✨Introducing YesCheff is a web app that helps you learn, cook, and enjoy your favorite recipes through step-by-step cooking guides built from real YouTube content. You can even use your own YouTube links. A small but special project where I brought all my capabilities together, from design (brand & product) to animation, and development. This project came from exploring how Al models parse data into frontend components and the development of generative interfaces. I designed and developed the structure through React components (HTML, CSS, JS) for the frontend and Node.js for the backend. This is a testament of how one could develop digital products in a less frustrating and more flexible way with the use of Al, particularly when it comes to getting the styling and functionality right. Now which foods are you eager to learn? Check it out at #YesCheff #WebApp #AI #Design #FrontendDevelopment #ReactJS #NodeJS #ProductDesign
0:18

✨Introducing YesCheff is a web app that helps you learn, cook, and enjoy your favorite recipes through step-by-step cooking guides built from real YouTube content. You can even use your own YouTube links. A small but special project where I brought all my capabilities together, from design (brand & product) to animation, and development. This project came from exploring how Al models parse data into frontend components and the development of generative interfaces. I designed and developed the structure through React components (HTML, CSS, JS) for the frontend and Node.js for the backend. This is a testament of how one could develop digital products in a less frustrating and more flexible way with the use of Al, particularly when it comes to getting the styling and functionality right. Now which foods are you eager to learn? Check it out at #YesCheff #WebApp #AI #Design #FrontendDevelopment #ReactJS #NodeJS #ProductDesign

JEHTOMS 5.0

85,213 görüntüleme • 11 ay önce

🚨China vs USA robots Chinese robot: $24,000 American robot: $39,100 Pricing policy isn't everything. If we look at real capabilities, the balance of power looks completely different 🇨🇳 The robot demonstrates incredible agility, confidently performs a backflip, moves dynamically, and dances Maximum flexibility, high tech at an affordable price, and readiness for real-world tasks 🇺🇸 The robot, with a higher price tag, currently looks more modest, struggling even with basic rectilinear walking An obvious overpayment for brand and marketing, while Chinese engineers are outpacing competitors in software and hardware The main paradox of the current moment is that Chinese companies focus on rapidly cheapening hardware and mass production, turning robotics into an accessible mass-market consumer product. Meanwhile, the US traditionally tries to enter the market through fundamental neural networks and a universal AI brain However, in the coming years, we will see the inevitable convergence of these approaches: the winner in this race will not be the one who simply teaches a robot to do a backflip or walk straight, but the one who first combines Chinese cheap and adaptive mechatronics with advanced American language and multimodal models And what do you think, who will ultimately set the global standard in robotics East or West?
0:12

🚨China vs USA robots Chinese robot: $24,000 American robot: $39,100 Pricing policy isn't everything. If we look at real capabilities, the balance of power looks completely different 🇨🇳 The robot demonstrates incredible agility, confidently performs a backflip, moves dynamically, and dances Maximum flexibility, high tech at an affordable price, and readiness for real-world tasks 🇺🇸 The robot, with a higher price tag, currently looks more modest, struggling even with basic rectilinear walking An obvious overpayment for brand and marketing, while Chinese engineers are outpacing competitors in software and hardware The main paradox of the current moment is that Chinese companies focus on rapidly cheapening hardware and mass production, turning robotics into an accessible mass-market consumer product. Meanwhile, the US traditionally tries to enter the market through fundamental neural networks and a universal AI brain However, in the coming years, we will see the inevitable convergence of these approaches: the winner in this race will not be the one who simply teaches a robot to do a backflip or walk straight, but the one who first combines Chinese cheap and adaptive mechatronics with advanced American language and multimodal models And what do you think, who will ultimately set the global standard in robotics East or West?

Bober_smart

34,991 görüntüleme • 25 gün önce

⚔️ Kingdom Come Deliverance first impressions ⚔️ Loving it so far, basically a medieval detective simulator that really doesn't care that you are the main character. And I'm all here for it. ▪️The WORLD is the real star of the show here and even though it's got plenty of jank and lots of copy and paste NPC faces, it just feels so IMMERSIVE. Even the UI just transports me to the times with a bright colourful medieval art style. ▪️The MUSIC I love, absolutely sells the world and basically ASMR as you trot around on your horse through the world. ▪️THE Combat is a real interesting one, it's got quite the learning curve which I actually LIKE, it definitely has some jank to it as well but I really appreciate the attempt at an original and nuanced combat system. (Having to stop your bleeding with bandages is really cool) ▪️The Story has gripped me (19 hours in so far) And while it seems a simple revenge story on the face of it, I think the story is more about Henry making his way through the world after the horrors of Skallitz. The writing quality is top notch as well as the quest design also. ▪️The CHARACTERS are amazing and the humour is top notch. I'm not sure the last time I laughed so much at a game. Henry is great and so well voiced by Tom McKay This really feels like Warhorse Studios have put a lot of love and work into making an authentic medieval world and as a bit of a medieval nerd I can't get enough of this game. A True RPG as well by all accounts, the game really makes me think hard about how to approach situations. Also I can't wait to get to KCD2.
0:12

⚔️ Kingdom Come Deliverance first impressions ⚔️ Loving it so far, basically a medieval detective simulator that really doesn't care that you are the main character. And I'm all here for it. ▪️The WORLD is the real star of the show here and even though it's got plenty of jank and lots of copy and paste NPC faces, it just feels so IMMERSIVE. Even the UI just transports me to the times with a bright colourful medieval art style. ▪️The MUSIC I love, absolutely sells the world and basically ASMR as you trot around on your horse through the world. ▪️THE Combat is a real interesting one, it's got quite the learning curve which I actually LIKE, it definitely has some jank to it as well but I really appreciate the attempt at an original and nuanced combat system. (Having to stop your bleeding with bandages is really cool) ▪️The Story has gripped me (19 hours in so far) And while it seems a simple revenge story on the face of it, I think the story is more about Henry making his way through the world after the horrors of Skallitz. The writing quality is top notch as well as the quest design also. ▪️The CHARACTERS are amazing and the humour is top notch. I'm not sure the last time I laughed so much at a game. Henry is great and so well voiced by Tom McKay This really feels like Warhorse Studios have put a lot of love and work into making an authentic medieval world and as a bit of a medieval nerd I can't get enough of this game. A True RPG as well by all accounts, the game really makes me think hard about how to approach situations. Also I can't wait to get to KCD2.

KJPlays

63,735 görüntüleme • 8 ay önce

I've been wanting to say this for months. This is what Web3 should look like. With NBA Top Shot and Dapper Labs, NFTs don't SUCK. You don't need to know what blockchain is and you don't need forever to set up a wallet and add crypto JUST to get started. (There's little mention of the words blockchain or NFT in the platform itself.) The platform's UX is top-of-the-line considering it's in beta. It's so easy to use and navigate. Transactions are so fast and cheap. As a designer myself, I'm really impressed. Not to even mention the Tiktok-like experience on the mobile app! Most importantly, you're not collecting just over-glorified digital cats or apes, but moments by real people— athletes. I've tried many Web3 or NFT products and this doesn't even come close. You're not going to revolutionize your product by changing "Connect" to "Sign in". This should've been obvious from the beginning. IT'S MORE THAN THAT. The future of Web3 is here. Take note!
0:12

I've been wanting to say this for months. This is what Web3 should look like. With NBA Top Shot and Dapper Labs, NFTs don't SUCK. You don't need to know what blockchain is and you don't need forever to set up a wallet and add crypto JUST to get started. (There's little mention of the words blockchain or NFT in the platform itself.) The platform's UX is top-of-the-line considering it's in beta. It's so easy to use and navigate. Transactions are so fast and cheap. As a designer myself, I'm really impressed. Not to even mention the Tiktok-like experience on the mobile app! Most importantly, you're not collecting just over-glorified digital cats or apes, but moments by real people— athletes. I've tried many Web3 or NFT products and this doesn't even come close. You're not going to revolutionize your product by changing "Connect" to "Sign in". This should've been obvious from the beginning. IT'S MORE THAN THAT. The future of Web3 is here. Take note!

DD

41,106 görüntüleme • 3 yıl önce

Dear Andrej Karpathy, Update on this. Earlier this month The Innovation Game (𝔦, 𝔦) announced a new SOTA routing algorithm had been collaboratively developed and submitted to The Innovation Game. The algorithm demonstrated the largest single perfomance jump in the modern history of the field on standard academic benchmarks: This success is a powerful proof of concept. I believe the wider implications will also interest you. As you know, the "Source" in AI is algorithms and data. These algorithms are typically for "hard to solve but easy to verify" problems. Remarkably, this allows the creation of a market for pricing improvements to these algorithms (roughly, the market is created by "racing" the algorithms, to see which can produce proof-of-work fastest). Availability of a market mechanism means open development of the algorithms can be funded by capturing a portion of the value they generate, and allocating it back to algorithm developers. The allocation is efficient, naturally integrating information (such as hardware availability) through revealed preferences. Importantly, market allocation is also "impersonal", which mitigates the risk to community cohesion that has historically afflicted Open Source projects offering monetary reward. Note: That a market for pricing code could extend Open Source to areas requiring monetary reward was (as far as I know) first suggested by Eric Raymond in 1999 Eric S. Raymond : Conclusion: The structure of Open Source AI means it can operate commercially. For example, value captured via Open Source "dual licensing", with allocation of the value by a market generated by proof-of-work. I'd love to hear your thoughts on this. Please see for more detail.
0:12

Dear Andrej Karpathy, Update on this. Earlier this month The Innovation Game (𝔦, 𝔦) announced a new SOTA routing algorithm had been collaboratively developed and submitted to The Innovation Game. The algorithm demonstrated the largest single perfomance jump in the modern history of the field on standard academic benchmarks: This success is a powerful proof of concept. I believe the wider implications will also interest you. As you know, the "Source" in AI is algorithms and data. These algorithms are typically for "hard to solve but easy to verify" problems. Remarkably, this allows the creation of a market for pricing improvements to these algorithms (roughly, the market is created by "racing" the algorithms, to see which can produce proof-of-work fastest). Availability of a market mechanism means open development of the algorithms can be funded by capturing a portion of the value they generate, and allocating it back to algorithm developers. The allocation is efficient, naturally integrating information (such as hardware availability) through revealed preferences. Importantly, market allocation is also "impersonal", which mitigates the risk to community cohesion that has historically afflicted Open Source projects offering monetary reward. Note: That a market for pricing code could extend Open Source to areas requiring monetary reward was (as far as I know) first suggested by Eric Raymond in 1999 Eric S. Raymond : Conclusion: The structure of Open Source AI means it can operate commercially. For example, value captured via Open Source "dual licensing", with allocation of the value by a market generated by proof-of-work. I'd love to hear your thoughts on this. Please see for more detail.

John Fletcher (𝔦, 𝔦)

38,141 görüntüleme • 1 ay önce

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology
0:15

The future of footwear may not be manufactured in bulk. It may be fabricated around you. That is what makes this shift so interesting to me. 3D-printed footwear is moving from novelty to a real industrial model, with market forecasts pointing to rapid growth over the next decade. At the same time, brands and manufacturers are using additive manufacturing, digital design, and custom-fit workflows to shorten development cycles and make more personalized products viable. What is new here is not just the printer. It is the system around it: → scan the foot → model the fit digitally → print the part on demand → produce closer to the customer That matters. Because once footwear becomes data-driven and locally fabricated, several things change fast: → fit gets more personal → prototyping gets faster → waste drops because you do not overproduce → inventory pressure falls because you do not need to guess demand the same way To me, that is the bigger signal. This is not just about a better sneaker. It is about a different manufacturing logic. Formlabs notes that 3D printing already enables customized orthotics with better biomechanical precision, lower material waste, and simpler digital workflows. McKinsey has also pointed to digitization and 3D design as a way to shorten design cycles and reduce sampling iterations in apparel and footwear. And once that logic matures, the use cases get much bigger: → custom athletic footwear built from gait and pressure data → hospitals producing orthotics faster and closer to the patient → micro-factories making products on demand instead of stocking shelves → footwear designed for one body, not an average body That is why I think this matters now. The question is no longer whether personalized fabrication is possible. It is whether brands move fast enough before customers start expecting every product to fit like it was made only for them. Would you actually wear a shoe fabricated around your own biometric data? #AI #3DPrinting #Footwear #Manufacturing #Innovation #FutureOfWork #RetailTech #Customization #Technology

Pascal Bornet

47,489 görüntüleme • 4 ay önce

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:
0:12

Google 3D Maps aren’t as accurate as you think. Everyone knows Google dominates mapping—but here’s what they won’t tell you. Their 3D maps are built with car-mounted cameras, capturing images every ~10 meters. That’s fine for basic navigation, but it’s nowhere near enough for AR, robotics, autonomous systems, or AI-driven spatial intelligence. Now, imagine a map so detailed it captures the world at sub-5cm accuracy. That’s OVRMaps. 🌐 OVER 3D maps are built with 400-1,000 images per 300 sqm, taken from multiple angles at pedestrian level. The result? A new era of hyper-precise localization that changes everything. Why does this matter? ↳ VPS that actually works – Real-world AR anchoring, digital twins, and AI-powered spatial computing with pinpoint accuracy. ↳ Next-gen precision – Essential for ride-sharing, robotics, smart cities, and asset tracking—where even a small error makes a huge difference. ↳ AI-Driven Spatial Intelligence – Our maps fuel Large Geo-Spatial Models (LGMs), the AI revolution enabling machines to understand, navigate, and interact with the real world. ↳ The Spatial Computing revolution – Merging immersive experiences with real-world precision, unlocking applications beyond what Google’s dataset can support. OVRMaps are built for the future. 🌍 Start mapping today:

Over the Reality 🌐

1,331,567 görüntüleme • 1 yıl önce

Dear Min of Trade |Rwanda ! Greetings from the community! I vividly remember that a few years ago the government introduced potato collection centres as part of a broader effort to address the long-standing imbalance between farm-gate prices and market prices in Kigali. The objective was straightforward; organize the value chain, reduce the influence of middlemen, improve farmers’ bargaining power and ensure that consumers accessed potatoes at fair and stable prices. Today, however, the situation raises important questions. We are witnessing what appears to be a bumper harvest, yet farmers are struggling to sell their produce at prices that reflect the effort and investment they have made. According to reports from Kigali Today, in Nyabihu District, a kilogram of the highest-quality Kinigi potatoes is selling for around Rwf 550, while the same potatoes can fetch as much as Rwf 1,300 per kilogram in Kigali. Traders are reportedly only purchasing in lots of 100 kilograms because there is an oversupply, and those with private vehicles are taking advantage of the price difference by transporting potatoes directly to urban markets. This is where modern technology, particularly artificial intelligence and data analytics, could play a transformative role. AI can help forecast production volumes, anticipate market demand, identify supply bottlenecks, optimize transport routes, and provide real-time pricing information to farmers, traders, and policymakers. Such insights would enable better planning before harvests, reducing the likelihood of oversupply in one area while shortages exist elsewhere. The bigger question is; what happened to the systems that were established to stabilize this market? Are the collection centres still functioning as intended? If they are, why are farmers still facing such significant price disparities? If they are not, what lessons have been learned? A high harvest should be a source of prosperity for farmers, not financial distress. As Rwanda continues embracing digital transformation, strengthening agricultural market intelligence should become a priority to ensure that both producers and consumers benefit from a more efficient, transparent, and equitable value chain. Ministry of Local Government | Rwanda Ministry of Agriculture & Animal Resources |Rwanda PSF Rwanda Ministry of Finance & Economic Planning Ministry of ICT and Innovation | Rwanda
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Dear Min of Trade |Rwanda ! Greetings from the community! I vividly remember that a few years ago the government introduced potato collection centres as part of a broader effort to address the long-standing imbalance between farm-gate prices and market prices in Kigali. The objective was straightforward; organize the value chain, reduce the influence of middlemen, improve farmers’ bargaining power and ensure that consumers accessed potatoes at fair and stable prices. Today, however, the situation raises important questions. We are witnessing what appears to be a bumper harvest, yet farmers are struggling to sell their produce at prices that reflect the effort and investment they have made. According to reports from Kigali Today, in Nyabihu District, a kilogram of the highest-quality Kinigi potatoes is selling for around Rwf 550, while the same potatoes can fetch as much as Rwf 1,300 per kilogram in Kigali. Traders are reportedly only purchasing in lots of 100 kilograms because there is an oversupply, and those with private vehicles are taking advantage of the price difference by transporting potatoes directly to urban markets. This is where modern technology, particularly artificial intelligence and data analytics, could play a transformative role. AI can help forecast production volumes, anticipate market demand, identify supply bottlenecks, optimize transport routes, and provide real-time pricing information to farmers, traders, and policymakers. Such insights would enable better planning before harvests, reducing the likelihood of oversupply in one area while shortages exist elsewhere. The bigger question is; what happened to the systems that were established to stabilize this market? Are the collection centres still functioning as intended? If they are, why are farmers still facing such significant price disparities? If they are not, what lessons have been learned? A high harvest should be a source of prosperity for farmers, not financial distress. As Rwanda continues embracing digital transformation, strengthening agricultural market intelligence should become a priority to ensure that both producers and consumers benefit from a more efficient, transparent, and equitable value chain. Ministry of Local Government | Rwanda Ministry of Agriculture & Animal Resources |Rwanda PSF Rwanda Ministry of Finance & Economic Planning Ministry of ICT and Innovation | Rwanda

Joseph Nkurunziza Ryarasa

24,104 görüntüleme • 2 ay önce

I am hear to share with you of one of my FAVORITE EVER health related tools. Blue Light Blocking Glasses. A powerful tool to block the harmful frequencies of artificial light wherever you may see it. At work on the pc, in the gym under fluorescent lights, in airports and grocery stores, in the evening when you use your phone… Wherever these eye straining lights are there, these Ra Optics glasses protect your eyes. They are developed in tandem with world leading photobiology expert, Dr. Wunsch - who I Have personally met and can confirm he is the real deal when it comes to light knowledge and the technology inside the glasses. These are not just colored lenses. What I have experienced: - Falling asleep easier - Less eye strain - Calmer mental state - Compliments on the glasses - More focus during work sessions - Less jet lag when traveling If there was one thing I would recommend you purchase to support your health in this world, these blue light blockers would be up there as some of the best. I use mine every single day and night. You can get 10% discount using my code “solbrah” - here is the link:
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I am hear to share with you of one of my FAVORITE EVER health related tools. Blue Light Blocking Glasses. A powerful tool to block the harmful frequencies of artificial light wherever you may see it. At work on the pc, in the gym under fluorescent lights, in airports and grocery stores, in the evening when you use your phone… Wherever these eye straining lights are there, these Ra Optics glasses protect your eyes. They are developed in tandem with world leading photobiology expert, Dr. Wunsch - who I Have personally met and can confirm he is the real deal when it comes to light knowledge and the technology inside the glasses. These are not just colored lenses. What I have experienced: - Falling asleep easier - Less eye strain - Calmer mental state - Compliments on the glasses - More focus during work sessions - Less jet lag when traveling If there was one thing I would recommend you purchase to support your health in this world, these blue light blockers would be up there as some of the best. I use mine every single day and night. You can get 10% discount using my code “solbrah” - here is the link:

⚡️🌞 Sol Brah 🌞🐬

26,288 görüntüleme • 5 ay önce

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.
0:20

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

30,070 görüntüleme • 5 ay önce

This is quite a big change in the past few years: delivery robots have become pretty much ubiquitous in Chinese hotels (and many residence buildings too). Half the time I take the lift in a hotel I share it with a robot. The way it works is pretty seamless. When I - as a hotel guest - order something online, the delivery guy puts it in the robot at the hotel's reception, the robot orders the lift, sets the floor, goes in front of my room and calls the phone in the room: "open the door for me, I'm here with your order!" It's a huge time saver for delivery folks - or hotel staff - and a good experience for guests. I think that's the way the whole delivery process will be automatized in China: bit by bit all residences in the country - at least those with lifts - will be equiped with those, and that's actually the most complex part of the process handled, the last few meters of the delivery process. Once that's done, it's just a matter of connecting the dots: autonomous delivery vehicles or drones - which are already a feature of many Chinese cities - will dock at the building's entrance and hand off to the indoor robot, completing a fully automated chain from merchant to doorstep.
0:19

This is quite a big change in the past few years: delivery robots have become pretty much ubiquitous in Chinese hotels (and many residence buildings too). Half the time I take the lift in a hotel I share it with a robot. The way it works is pretty seamless. When I - as a hotel guest - order something online, the delivery guy puts it in the robot at the hotel's reception, the robot orders the lift, sets the floor, goes in front of my room and calls the phone in the room: "open the door for me, I'm here with your order!" It's a huge time saver for delivery folks - or hotel staff - and a good experience for guests. I think that's the way the whole delivery process will be automatized in China: bit by bit all residences in the country - at least those with lifts - will be equiped with those, and that's actually the most complex part of the process handled, the last few meters of the delivery process. Once that's done, it's just a matter of connecting the dots: autonomous delivery vehicles or drones - which are already a feature of many Chinese cities - will dock at the building's entrance and hand off to the indoor robot, completing a fully automated chain from merchant to doorstep.

Arnaud Bertrand

33,042 görüntüleme • 26 gün önce

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.
0:18

LLM Artifacts Connected to Andrej Karpathy's LLM Knowledge base idea, I've been building out a fun way to generate dynamic artifacts from these knowledge bases with the goal of discovering and revealing meaningful and deeper insights. LLM KBs are hard to consume for humans, as I think they are more built for agents. So the question is, what form would be useful for humans to take actions and make important decisions? That's what I am trying to figure out with these artifacts. The artifact example shows a pulse on HN discussions around AI-related stories. The insights can go deeper, of course, but this is already super fun and thought-provoking, like some of my favorite podcasts. The format and depth matter a lot. The aggregation skills of agents are outstanding if you tune the prompts and skill carefully. I built this artifact generator in a few minutes through an agent skill, but I feel like there are so many ways that LLM-generated information can be used and consumed. Like generating deeper insights and analysis, and things that are just not feasible for humans today. The generated artifact (including its data and design) serves as reusable templates or can be updated in real-time via auomations, which is something I am also working on. It is truly an insane way to monitor and track information. Better than a newsletter. Better than newspapers. There is something about this that gets me really excited about the future of AI agents for knowledge generation and discovery. Lots of hidden gems everywhere just waiting to be discovered and acted on if the information is presented correctly. This is not perfect. The format, style/prose can be improved, but this is easy to customize via skill. You can personalize it to your liking. I feel like these dynamic artifacts are going to emerge as a strong new medium to stay on the cutting edge of things, both for agents and humans. My target is research, of course. This was just a basic example. Besides animation, I am also targeting other components like voice, videos, images, slides, etc. This space is full of opportunities to explore. Skill for this coming soon.

elvis

31,314 görüntüleme • 4 ay önce

It never fails. After months of late night feedings and short bursts of sleep, your baby FINALLY begins sleeping through the night… only to begin waking again. What gives? What you’re experiencing is a sleep regression… a small setback in sleep continuity often associated with big breakthroughs in development. As your baby grows as a language user, for example, you may find that they discover 3am is a great time to practice all their new skills. This happy little guy, shared to TT by em_suttles, is a great example. He’s unlocked a cool new vocal trick (at 5 months) and he’s ready to put it to work. While it may not yet look like it, he’s on his way to spoken language. Even more pronounced regressions can coincide with major motor milestones. As your baby learns to sit, roll over, crawl and pull to a stand, their nighttime options in the crib become far more varied. And the temptation to practice them can be strong. The good news is that these sleep regressions are typically pretty short lived. So, bleary-eyed though you may be, do your best to celebrate them for what they are: markers of big steps forward in your child’s development.
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It never fails. After months of late night feedings and short bursts of sleep, your baby FINALLY begins sleeping through the night… only to begin waking again. What gives? What you’re experiencing is a sleep regression… a small setback in sleep continuity often associated with big breakthroughs in development. As your baby grows as a language user, for example, you may find that they discover 3am is a great time to practice all their new skills. This happy little guy, shared to TT by em_suttles, is a great example. He’s unlocked a cool new vocal trick (at 5 months) and he’s ready to put it to work. While it may not yet look like it, he’s on his way to spoken language. Even more pronounced regressions can coincide with major motor milestones. As your baby learns to sit, roll over, crawl and pull to a stand, their nighttime options in the crib become far more varied. And the temptation to practice them can be strong. The good news is that these sleep regressions are typically pretty short lived. So, bleary-eyed though you may be, do your best to celebrate them for what they are: markers of big steps forward in your child’s development.

Dan Wuori

39,379 görüntüleme • 1 yıl önce

🚨BREAKING: just dropped their Shopify integration yesterday. Now you can build a complete Shopify store by talking to AI. This changes a lot of things for ecom. WHAT THIS MEANS: Lovable AI can now: • Build complete online stores from text prompts • Set up checkout and shopping cart automatically • Add products with AI-generated descriptions • Deploy live stores in minutes, not weeks They proved it by building their own merch store: lovable[.]dev/merch THE OLD ECOM SETUP: • Hire Shopify developer ($3K-$10K) • Wait 2-4 weeks for completion • Go through endless revision cycles • Pay for theme customizations • Debug technical issues • Launch after months of delays THE NEW REALITY: "Build me an online store for selling fitness equipment" → AI creates complete store in 10 minutes → Add products with descriptions → Click publish → Start selling immediately WHAT THIS MEANS FOR ECOM OWNERS: The Technical Barrier Just Disappeared: • No coding knowledge required • No designer needed for basic stores • No developer for functionality setup • No technical troubleshooting Speed Becomes the New Standard: • Test product ideas in hours, not months • Launch seasonal stores instantly • Pivot business models without rebuilding • A/B test different store concepts rapidly The Cost Structure Changes: • $29/month Shopify + AI tool vs. $10K+ development • Instant iterations vs. expensive revisions • Self-service setup vs. agency dependencies • Focus budget on marketing, not development THE REALITY: While you're waiting 6 weeks for your developer to finish your store… Your competitor just described their business idea to AI and launched 3 different store variations to test the market. THE OPPORTUNITY FOR BRANDS: • Test 10 product ideas instead of 1 • Launch seasonal campaigns instantly • Create niche stores for different audiences • Focus on products and marketing, not tech THE WINDOW IS CLOSING: Right now, most ecom owners don't know this exists. In 6 months, everyone will expect instant store creation. In 12 months, waiting weeks for a basic store will look amateur. As for me, I’ll say basic Shopify development just became commoditized. P.S. Thanks to Lovable, everyone will have a store soon. Your real edge isn't in JUST building it. It's in the operations, automations, and strategy that make it profitable.
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🚨BREAKING: just dropped their Shopify integration yesterday. Now you can build a complete Shopify store by talking to AI. This changes a lot of things for ecom. WHAT THIS MEANS: Lovable AI can now: • Build complete online stores from text prompts • Set up checkout and shopping cart automatically • Add products with AI-generated descriptions • Deploy live stores in minutes, not weeks They proved it by building their own merch store: lovable[.]dev/merch THE OLD ECOM SETUP: • Hire Shopify developer ($3K-$10K) • Wait 2-4 weeks for completion • Go through endless revision cycles • Pay for theme customizations • Debug technical issues • Launch after months of delays THE NEW REALITY: "Build me an online store for selling fitness equipment" → AI creates complete store in 10 minutes → Add products with descriptions → Click publish → Start selling immediately WHAT THIS MEANS FOR ECOM OWNERS: The Technical Barrier Just Disappeared: • No coding knowledge required • No designer needed for basic stores • No developer for functionality setup • No technical troubleshooting Speed Becomes the New Standard: • Test product ideas in hours, not months • Launch seasonal stores instantly • Pivot business models without rebuilding • A/B test different store concepts rapidly The Cost Structure Changes: • $29/month Shopify + AI tool vs. $10K+ development • Instant iterations vs. expensive revisions • Self-service setup vs. agency dependencies • Focus budget on marketing, not development THE REALITY: While you're waiting 6 weeks for your developer to finish your store… Your competitor just described their business idea to AI and launched 3 different store variations to test the market. THE OPPORTUNITY FOR BRANDS: • Test 10 product ideas instead of 1 • Launch seasonal campaigns instantly • Create niche stores for different audiences • Focus on products and marketing, not tech THE WINDOW IS CLOSING: Right now, most ecom owners don't know this exists. In 6 months, everyone will expect instant store creation. In 12 months, waiting weeks for a basic store will look amateur. As for me, I’ll say basic Shopify development just became commoditized. P.S. Thanks to Lovable, everyone will have a store soon. Your real edge isn't in JUST building it. It's in the operations, automations, and strategy that make it profitable.

Lian Lim | Dashboard & AI Automation Expert

14,316 görüntüleme • 10 ay önce

This is the mechanical heart of every flight deck. At the core of this instrument is a high-speed mechanical gyroscope held within a series of gimbals. It relies on the physics of rigidity in space, meaning the spinning disc stays perfectly aligned with the Earth's horizon regardless of how the aircraft moves around it. When you see the outer casing tilt and dive, you are actually watching the plane move while the instrument stays perfectly still. For decades, this was the primary way pilots survived flying through thick cloud cover or at night. Without a visible horizon, the human brain can easily become disoriented, leading to a deadly condition called the graveyard spin. This device provides an objective visual reference that never fails, even if the entire electrical system of the plane goes dark. While modern airliners have transitioned to digital screens and solid-state sensors, these analog units remain critical. Many pilots still prefer them as a fail-safe backup because they operate on pure physics rather than software. It is a masterclass in engineering that remains just as relevant in the age of automation as it was eighty years ago. It is a timeless piece of safety technology. Would you trust a mechanical dial over a digital screen? Follow for more Technology updates Video: alternateb747
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This is the mechanical heart of every flight deck. At the core of this instrument is a high-speed mechanical gyroscope held within a series of gimbals. It relies on the physics of rigidity in space, meaning the spinning disc stays perfectly aligned with the Earth's horizon regardless of how the aircraft moves around it. When you see the outer casing tilt and dive, you are actually watching the plane move while the instrument stays perfectly still. For decades, this was the primary way pilots survived flying through thick cloud cover or at night. Without a visible horizon, the human brain can easily become disoriented, leading to a deadly condition called the graveyard spin. This device provides an objective visual reference that never fails, even if the entire electrical system of the plane goes dark. While modern airliners have transitioned to digital screens and solid-state sensors, these analog units remain critical. Many pilots still prefer them as a fail-safe backup because they operate on pure physics rather than software. It is a masterclass in engineering that remains just as relevant in the age of automation as it was eighty years ago. It is a timeless piece of safety technology. Would you trust a mechanical dial over a digital screen? Follow for more Technology updates Video: alternateb747

Mechanical Knowledge

68,004 görüntüleme • 6 ay önce