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This isn't sci-fi. It's built using Direct Metal Laser Sintering (DMLS), a 3D-printing process that fuses titanium powder layer by layer with high-precision lasers. Each print forms thousands of tiny interlocking links, creating a flexible, chainmail-like sheet that moves almost like real fabric.

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Philosophy Of Physics

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12,854 просмотров • 6 месяцев назад •via X (Twitter)

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🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.
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🚨 CHINESE SCIENTISTS JUST INVENTED 3D PRINTING THAT CREATES OBJECTS IN 0.6 SECONDS USING ONLY LIGHT. Researchers at Tsinghua University have developed a new method called DISH (Digital Incoherent Synthesis of Holographic light fields) that can print complex millimeter-scale objects almost instantly. Instead of slowly building layer by layer, the system fires thousands of precisely patterned light images from multiple angles into a still vat of liquid resin. Where the light overlaps, the resin instantly hardens into a solid 3D object. The entire process takes just 0.6 seconds. Why this matters: • It’s currently the fastest volumetric 3D printing method ever demonstrated • Achieves extremely fine detail features thinner than a human hair • The resin stays completely still, so there’s no vibration or distortion • It can work with watery (low-viscosity) resins, making it suitable for biological applications • The team has already printed complex structures like blood vessel-like tubes and even a tiny bust of a historical figure The deeper implication: Traditional 3D printing has always been limited by speed and the need to move either the print head or the resin. This approach removes both constraints by using light itself as the sculptor. Because it can print directly into still liquid (and potentially onto living tissue), it opens new possibilities in bioprinting, medical devices, and rapid manufacturing. If the technology can be scaled beyond millimeter sizes, it could fundamentally change how we think about making physical objects turning “print” from a slow process into something closer to instantaneous fabrication. We’re moving from “layer by layer” to “all at once.” How do you think instant volumetric 3D printing like this could change medicine, manufacturing, or everyday life if it becomes widely available? Follow for more frontier manufacturing and materials science breakthroughs.

TheNewPhysics

347,458 просмотров • 1 месяц назад

Ok so I've been teasing this Non-Planar print to show off another use case for the E3D #NippleNozzles besides belt printing but now its time to reveal the greater purpose. In collaboration with Michael Wüthrich we developed a technique to illustrate one of practical values of Non-Planar 3D Printing. It may not look like much now but you can clearly see there is much less friction imparted to the steel ball in the front ramp which was 3D Printed using Michaels current NP slicing process as compared to a traditional layer by layer topographical print. What are some other practical uses for Non-Planar? More on this soon!
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Ok so I've been teasing this Non-Planar print to show off another use case for the E3D #NippleNozzles besides belt printing but now its time to reveal the greater purpose. In collaboration with Michael Wüthrich we developed a technique to illustrate one of practical values of Non-Planar 3D Printing. It may not look like much now but you can clearly see there is much less friction imparted to the steel ball in the front ramp which was 3D Printed using Michaels current NP slicing process as compared to a traditional layer by layer topographical print. What are some other practical uses for Non-Planar? More on this soon!

Pooch

26,571 просмотров • 3 лет назад

🚨 OPTIMUS HITS THE FLOOR - AND WE’RE NOT TALKING FACTORY Tesla’s humanoid bot served up a real-time dance drop - and no, it’s not CGI! Optimus is vibing... and evolving like it's got somewhere to be. MOVES THAT MATTER: • No strings, no edits - that’s raw, real-time rhythm • From clunky cosplay to choreographed control • Trained by Tesla’s FSD brain - yeah, that brain • Precision, balance, and flair - built for work, but dancing anyway Forget sci-fi. Optimus is already dancing on your expectations! Source: Tesla Owners Silicon Valley
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🚨 OPTIMUS HITS THE FLOOR - AND WE’RE NOT TALKING FACTORY Tesla’s humanoid bot served up a real-time dance drop - and no, it’s not CGI! Optimus is vibing... and evolving like it's got somewhere to be. MOVES THAT MATTER: • No strings, no edits - that’s raw, real-time rhythm • From clunky cosplay to choreographed control • Trained by Tesla’s FSD brain - yeah, that brain • Precision, balance, and flair - built for work, but dancing anyway Forget sci-fi. Optimus is already dancing on your expectations! Source: Tesla Owners Silicon Valley

Mario Nawfal

240,266 просмотров • 11 месяцев назад

🚨 AMERICA JUST BUILT THE WORLD’S MOST POWERFUL METAL 3D PRINTER AND IT’S ABOUT TO MASS-PRODUCE ROCKETS AND MISSILES. Divergent Technologies has unveiled the Monolith One, a giant industrial metal printer standing over 8 meters tall and armed with 12 high-powered lasers delivering a combined 24 kilowatts of energy. Unlike typical 3D printers used for prototypes, this machine is built for serious, high-volume production. It can print large, complex aerospace and defense parts in aluminum, titanium, steel, and nickel alloys and it roughly doubles the output of current systems. Why this matters: • Divergent plans to install 64 more of these machines in a massive new 430,000 sq ft factory in Long Beach, California • Once running, the facility aims to produce tens of thousands of munition airframes per year plus hundreds of thousands of critical metal components • It slashes manufacturing time from months down to weeks or even days • The company already supplies major players like Lockheed Martin and RTX The deeper implication: This isn’t just another 3D printer. It represents a shift toward software-defined, on-demand manufacturing at industrial scale for mission-critical hardware. As defense and aerospace demand skyrockets, traditional supply chains are too slow. Systems like Monolith One could become a cornerstone of faster, more resilient domestic production especially for complex structures that are difficult or impossible to make conventionally. We’re watching the industrialization of additive manufacturing in real time. How do you think large-scale 3D printing will change aerospace and defense manufacturing over the next decade? Follow for more frontier manufacturing and defense technology.
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🚨 AMERICA JUST BUILT THE WORLD’S MOST POWERFUL METAL 3D PRINTER AND IT’S ABOUT TO MASS-PRODUCE ROCKETS AND MISSILES. Divergent Technologies has unveiled the Monolith One, a giant industrial metal printer standing over 8 meters tall and armed with 12 high-powered lasers delivering a combined 24 kilowatts of energy. Unlike typical 3D printers used for prototypes, this machine is built for serious, high-volume production. It can print large, complex aerospace and defense parts in aluminum, titanium, steel, and nickel alloys and it roughly doubles the output of current systems. Why this matters: • Divergent plans to install 64 more of these machines in a massive new 430,000 sq ft factory in Long Beach, California • Once running, the facility aims to produce tens of thousands of munition airframes per year plus hundreds of thousands of critical metal components • It slashes manufacturing time from months down to weeks or even days • The company already supplies major players like Lockheed Martin and RTX The deeper implication: This isn’t just another 3D printer. It represents a shift toward software-defined, on-demand manufacturing at industrial scale for mission-critical hardware. As defense and aerospace demand skyrockets, traditional supply chains are too slow. Systems like Monolith One could become a cornerstone of faster, more resilient domestic production especially for complex structures that are difficult or impossible to make conventionally. We’re watching the industrialization of additive manufacturing in real time. How do you think large-scale 3D printing will change aerospace and defense manufacturing over the next decade? Follow for more frontier manufacturing and defense technology.

TheNewPhysics

80,575 просмотров • 1 месяц назад

One of the least understood systems in aerospace manufacturing is the BeAM Magic 800 directed energy deposition platform used for printing turbine nozzles and hot-section components. It uses a laser to melt metal powder or wire feedstock, depositing material layer by layer with positioning accuracy in the 20-50 micron range, building complex nickel superalloy structures that can withstand extreme thermal cycling. A full industrial setup can cost $800,000 to $1.5M+ The materials are not simple metals, but aerospace-grade alloys like Inconel 718 and 625, designed to survive temperatures above 700-900°C while resisting creep and oxidation. But the printed part is never the final part. Critical sealing surfaces, flanges, and precision interfaces are still finished on CNC machines to tolerances under 10 microns, because additive alone cannot guarantee repeatable surface integrity under flight loads. Video :- Fictiv
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One of the least understood systems in aerospace manufacturing is the BeAM Magic 800 directed energy deposition platform used for printing turbine nozzles and hot-section components. It uses a laser to melt metal powder or wire feedstock, depositing material layer by layer with positioning accuracy in the 20-50 micron range, building complex nickel superalloy structures that can withstand extreme thermal cycling. A full industrial setup can cost $800,000 to $1.5M+ The materials are not simple metals, but aerospace-grade alloys like Inconel 718 and 625, designed to survive temperatures above 700-900°C while resisting creep and oxidation. But the printed part is never the final part. Critical sealing surfaces, flanges, and precision interfaces are still finished on CNC machines to tolerances under 10 microns, because additive alone cannot guarantee repeatable surface integrity under flight loads. Video :- Fictiv

Ammanichanda

69,921 просмотров • 28 дней назад

Rhuna has powered more than 165 live events, served over 2M users, and processed $90M+ in volume across its existing infrastructure. At this scale, latency, fragmentation, and settlement uncertainty can have a massive impact on hundreds of thousands of live users. By integrating OmniSet, Rhuna taps into a unified liquidity and settlement layer that can coordinate payments and digital assets across ecosystems. Built on FastSet, OmniSet delivers fast, verifiable settlement that holds up under real-world demand, making it suitable for high-throughput environments like live events. This partnership reflects Pi²’s focus on deploying infrastructure where performance and reliability are essential.
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Rhuna has powered more than 165 live events, served over 2M users, and processed $90M+ in volume across its existing infrastructure. At this scale, latency, fragmentation, and settlement uncertainty can have a massive impact on hundreds of thousands of live users. By integrating OmniSet, Rhuna taps into a unified liquidity and settlement layer that can coordinate payments and digital assets across ecosystems. Built on FastSet, OmniSet delivers fast, verifiable settlement that holds up under real-world demand, making it suitable for high-throughput environments like live events. This partnership reflects Pi²’s focus on deploying infrastructure where performance and reliability are essential.

Pi Squared

11,613 просмотров • 7 месяцев назад

Multi-axis 3D printing with curved layers! 🖨️ Researchers from the The University of Manchester introduced a neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. Traditional 3D printing works like stacking pancakes, flat layers on top of each other. 🥞 This often requires temporary support structures that get thrown away after printing, wastes material, and creates weaker parts. Multi-axis 3D printing can print along curved paths that follow the object's natural shape. This eliminates support structures and makes stronger parts. But figuring out these curved paths is mathematically complex, you need to avoid collisions, respect what the printer can physically do, and optimize for strength. The neural network solves this automatically. It learns to create a "field" around the object, then extracts curved printing paths from this field. Because the entire process is differentiable (translation for non-math specialists, meaning you can optimize it end-to-end), the AI can directly optimize for manufacturing goals like "no support structures needed" and "make it as strong as possible." Here's the project: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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Multi-axis 3D printing with curved layers! 🖨️ Researchers from the The University of Manchester introduced a neural network-based computational pipeline as a representation-agnostic slicer for multi-axis 3D printing. Traditional 3D printing works like stacking pancakes, flat layers on top of each other. 🥞 This often requires temporary support structures that get thrown away after printing, wastes material, and creates weaker parts. Multi-axis 3D printing can print along curved paths that follow the object's natural shape. This eliminates support structures and makes stronger parts. But figuring out these curved paths is mathematically complex, you need to avoid collisions, respect what the printer can physically do, and optimize for strength. The neural network solves this automatically. It learns to create a "field" around the object, then extracts curved printing paths from this field. Because the entire process is differentiable (translation for non-math specialists, meaning you can optimize it end-to-end), the AI can directly optimize for manufacturing goals like "no support structures needed" and "make it as strong as possible." Here's the project: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

57,048 просмотров • 4 месяцев назад

🚨 SCIENTISTS JUST INVENTED A WAY TO PRINT CIRCUIT BOARDS WITH LIQUID METAL AND IT LOOKS LIKE SOMETHING OUT OF TERMINATOR. A startup called Itera has developed a system that can create working PCB prototypes in minutes instead of weeks. You upload your design, and electric fields force a liquid metal alloy into the exact shape of the traces on a glass substrate. The board is then tested and ready almost instantly. Why this matters: • Traditional PCB prototyping can take days or even weeks this could reduce it to minutes • It uses liquid metal instead of etched copper, making it potentially much faster and more flexible for rapid iteration • Backed by $12 million in funding, the company is already focusing on single-layer boards with surface-mount components • The process looks genuinely futuristic glowing rivers of metal flowing into place on command The deeper implication is enormous: We may be watching the beginning of a completely new era of hardware development. Instead of waiting days for a prototype, engineers and makers could design, test, and iterate multiple versions in a single afternoon. This could dramatically speed up innovation in electronics, robotics, AI hardware, and even consumer devices. What happens when making a new circuit board becomes as fast and easy as printing a document? Follow for more frontier physics and future technology.
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🚨 SCIENTISTS JUST INVENTED A WAY TO PRINT CIRCUIT BOARDS WITH LIQUID METAL AND IT LOOKS LIKE SOMETHING OUT OF TERMINATOR. A startup called Itera has developed a system that can create working PCB prototypes in minutes instead of weeks. You upload your design, and electric fields force a liquid metal alloy into the exact shape of the traces on a glass substrate. The board is then tested and ready almost instantly. Why this matters: • Traditional PCB prototyping can take days or even weeks this could reduce it to minutes • It uses liquid metal instead of etched copper, making it potentially much faster and more flexible for rapid iteration • Backed by $12 million in funding, the company is already focusing on single-layer boards with surface-mount components • The process looks genuinely futuristic glowing rivers of metal flowing into place on command The deeper implication is enormous: We may be watching the beginning of a completely new era of hardware development. Instead of waiting days for a prototype, engineers and makers could design, test, and iterate multiple versions in a single afternoon. This could dramatically speed up innovation in electronics, robotics, AI hardware, and even consumer devices. What happens when making a new circuit board becomes as fast and easy as printing a document? Follow for more frontier physics and future technology.

TheNewPhysics

19,878 просмотров • 1 месяц назад

This is the kind of project that quietly changes an industry. Instead of using traditional slicers, this creator built an app that converts hand-drawn curves directly into printable G-code for spiral vase mode prints. App → G-code → Print. - No complicated workflow. - No heavy setup. - Just turning ideas into physical objects almost instantly. The interesting part isn’t just the print quality. It’s how tools are becoming so simple that more people can create complex things without needing advanced technical knowledge. Would tools like this make 3D printing finally mainstream? 🎥 Media: ( Instagram ) ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.
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This is the kind of project that quietly changes an industry. Instead of using traditional slicers, this creator built an app that converts hand-drawn curves directly into printable G-code for spiral vase mode prints. App → G-code → Print. - No complicated workflow. - No heavy setup. - Just turning ideas into physical objects almost instantly. The interesting part isn’t just the print quality. It’s how tools are becoming so simple that more people can create complex things without needing advanced technical knowledge. Would tools like this make 3D printing finally mainstream? 🎥 Media: ( Instagram ) ⚠️ This content is shared for informational purposes only. CTO Robotics Media is a media platform and does not own or develop the technology shown. Credit belongs to the original creators.

CTO ROBOTICS Media

132,290 просмотров • 2 месяцев назад

A metal origami! 🪭 This method is called Hyperbolic Metal Forming and is hypnotizing to watch. Instead of shaping metal with slow mechanical force, HMF uses controlled shockwaves to form complex geometries at extreme speed, often without the need for heavy dies or post-processing. The result is stronger, lighter parts with shapes that are almost impossible using traditional stamping. That’s why you see it popping up in aerospace, automotive structures, and defense components. Think of it like metal origami, but driven by high-energy pulses instead of presses. A small reminder that some things in manufacturing come from physics, not just automation. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
0:18

A metal origami! 🪭 This method is called Hyperbolic Metal Forming and is hypnotizing to watch. Instead of shaping metal with slow mechanical force, HMF uses controlled shockwaves to form complex geometries at extreme speed, often without the need for heavy dies or post-processing. The result is stronger, lighter parts with shapes that are almost impossible using traditional stamping. That’s why you see it popping up in aerospace, automotive structures, and defense components. Think of it like metal origami, but driven by high-energy pulses instead of presses. A small reminder that some things in manufacturing come from physics, not just automation. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

567,473 просмотров • 8 месяцев назад

🚨 Quantum computers don’t run on electricity. They run on light. And that’s been the problem. Each quantum system needs very specific laser colors to control atoms and qubits. Until now… those lasers were: huge expensive stuck in labs But scientists just changed that. They built a chip that can generate ANY wavelength of light inside a tiny circuit. ~10,000 photonic circuits full spectrum control on a single chip This is massive. Because it means: Quantum computers could go from room-sized labs… to portable systems. So the real question is If we can control reality at the quantum level with light… what happens when that control fits in your hand? Follow me the future won’t be powered by electrons.
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🚨 Quantum computers don’t run on electricity. They run on light. And that’s been the problem. Each quantum system needs very specific laser colors to control atoms and qubits. Until now… those lasers were: huge expensive stuck in labs But scientists just changed that. They built a chip that can generate ANY wavelength of light inside a tiny circuit. ~10,000 photonic circuits full spectrum control on a single chip This is massive. Because it means: Quantum computers could go from room-sized labs… to portable systems. So the real question is If we can control reality at the quantum level with light… what happens when that control fits in your hand? Follow me the future won’t be powered by electrons.

TheNewPhysics

14,321 просмотров • 3 месяцев назад

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️
0:12

Check out this Stereo4D paper from Google DeepMind. It's a pretty clever approach to a persistent problem in computer vision -- getting good training data for how things move in 3D. The key insight is using VR180 videos -- those stereo fisheye videos we launched back in 2017 for YouTubeVR. It was always clear that structured stereo datasets would be valuable for computer vision -- and we launched some powerful VR tools with it back in 2017 (link below). But what's the game changer now in 2024 is the scale -- they're providing 110K high quality clips :-) That's the kind of massive, real-world AI dataset that was just a dream back then! They're using it to train this model called DynaDUSt3R that can predict both 3D structure and motion from video frames. Which means it tracks how objects move between frames while simultaneously reconstructing their 3D shape. And given we're dealing with real stereoscopic content, results are notably better than synthetic data, giving you a faithful rendition of the real-world with a diverse set of subject matter. It's one of those through lines when tackling a timeless mission like mapping the world or spatial computing -- VR content created for immersion becoming the foundation for teaching machines to understand how the world moves. Sometimes innovation chains together in unexpected ways! Links to projects below⛓️

Bilawal Sidhu

67,919 просмотров • 1 год назад

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.
0:16

Jeff Bezos just told you exactly how to price AI. Nobody listened. Bezos: “AI is real and it is going to change every industry. In fact it’s a very unusual technology in that regard in that it’s a horizontal enabling layer.” Horizontal enabling layer. Three words that reprice the entire technology sector. The iPhone was a vertical. One product. One new market. Electricity was a horizontal. One substrate that rewired every market on Earth. Wall Street is pricing AI like it is the next iPhone. Bezos is telling you it is the next electrical grid. Right now, thousands of companies are trying to sell AI as a product. A feature. A tool. A subscription tier. Every single one of them will be priced to zero. You do not sell a horizontal layer. You do not compete with it. You build on top of it or you disappear beneath it. For a century, entire industries survived on one thing. Complexity. The friction of navigating law, medicine, logistics, finance. That was the moat. If you could not memorize the maze, you could not compete. A horizontal layer does not navigate the maze. It dissolves the walls. Electricity did not compete with the candle industry. It erased the need for one. The most dangerous part of a horizontal shift is how quiet it is. It moves underneath the economy. The surface looks normal. Revenue still holds. Every day you operate on the old substrate, you accumulate a debt you cannot see and cannot repay. The internet repriced distribution. AI is repricing cognition itself. When intelligence becomes a utility that runs through the walls of every company on Earth, the premium on human expertise does not erode. It evaporates. This is not a disruption. Disruptions replace products. This replaces the ground you are standing on.

Dustin

540,708 просмотров • 3 месяцев назад

🚨 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.
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🚨 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 просмотров • 1 месяц назад

Grok Imagine Cinematic Prompt A cinematic, wide-angle shot of a massive, heavily armored mecha sniper emerging from a thick layer of ice and snow in a desolate arctic tundra. The mecha is pristine white and industrial, featuring a colossal railgun-style sniper rifle with glowing blue energy components. It shifts into a stable firing stance, shedding chunks of ice as it moves. The Shot Sequence: The Activation: Close-up on the barrel as blue electrical arcs and a swirling energy vortex charge up at the muzzle. A high-tech digital HUD overlay flickers across the screen with targeting data. The Firing: The railgun fires a powerful kinetic blast, creating a massive sonic boom and shockwave that clears the snow. The Impact: The camera follows a high-speed projectile or a jet-propelled craft streaking across the frozen plains at ground level, leaving a dual trail of displaced snow behind it, culminating in a massive explosion against a distant ice wall. Environment & Atmosphere: Setting: A vast, wind-swept arctic landscape with towering icebergs and a heavy, overcast grey sky. Lighting: Cold, diffused natural light with high-contrast glowing blue accents from the mecha’s sensors and weaponry. VFX: Realistic particle effects for falling snow, shattering ice, and swirling mist. Heavy motion blur on the high-speed chase sequence. Style: * Ultra-realistic 3D animation, 8k resolution, cinematic sci-fi aesthetic, reminiscent of Armored Core or Metal Gear Solid. Quick Tips for AI Generation: Keywords: "Mecha," "Railgun," "Arctic Tundra," "Energy Vortex," and "Cinematic HUD" are your best friends here. Lighting: Specifying "Cold lighting" or "Overcast" helps maintain that desolate, frosty mood.
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Grok Imagine Cinematic Prompt A cinematic, wide-angle shot of a massive, heavily armored mecha sniper emerging from a thick layer of ice and snow in a desolate arctic tundra. The mecha is pristine white and industrial, featuring a colossal railgun-style sniper rifle with glowing blue energy components. It shifts into a stable firing stance, shedding chunks of ice as it moves. The Shot Sequence: The Activation: Close-up on the barrel as blue electrical arcs and a swirling energy vortex charge up at the muzzle. A high-tech digital HUD overlay flickers across the screen with targeting data. The Firing: The railgun fires a powerful kinetic blast, creating a massive sonic boom and shockwave that clears the snow. The Impact: The camera follows a high-speed projectile or a jet-propelled craft streaking across the frozen plains at ground level, leaving a dual trail of displaced snow behind it, culminating in a massive explosion against a distant ice wall. Environment & Atmosphere: Setting: A vast, wind-swept arctic landscape with towering icebergs and a heavy, overcast grey sky. Lighting: Cold, diffused natural light with high-contrast glowing blue accents from the mecha’s sensors and weaponry. VFX: Realistic particle effects for falling snow, shattering ice, and swirling mist. Heavy motion blur on the high-speed chase sequence. Style: * Ultra-realistic 3D animation, 8k resolution, cinematic sci-fi aesthetic, reminiscent of Armored Core or Metal Gear Solid. Quick Tips for AI Generation: Keywords: "Mecha," "Railgun," "Arctic Tundra," "Energy Vortex," and "Cinematic HUD" are your best friends here. Lighting: Specifying "Cold lighting" or "Overcast" helps maintain that desolate, frosty mood.

Thoughts Creator

23,678 просмотров • 3 месяцев назад

🚨 GRAPHENE MAY BE ONE OF THE MOST IMPORTANT MATERIALS EVER DISCOVERED. It’s nearly transparent. Almost weightless. Thinner than paper. Yet stronger than steel. Graphene is made from a single layer of carbon atoms arranged in a perfect hexagonal lattice a structure so precise that electrons can move through it almost like light itself. Why this matters: This material could eventually transform: • batteries • quantum computing • flexible electronics • spacecraft materials • neural interfaces • ultra-fast processors • next-generation energy systems Some scientists believe graphene may help unlock an entirely new era of technology where materials are engineered atom-by-atom for specific realities. The strange part? It’s just carbon. The same element found in pencil lead. Sometimes the biggest revolutions begin with structures hiding in plain sight. Follow for more future physics and technology breakthroughs.
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🚨 GRAPHENE MAY BE ONE OF THE MOST IMPORTANT MATERIALS EVER DISCOVERED. It’s nearly transparent. Almost weightless. Thinner than paper. Yet stronger than steel. Graphene is made from a single layer of carbon atoms arranged in a perfect hexagonal lattice a structure so precise that electrons can move through it almost like light itself. Why this matters: This material could eventually transform: • batteries • quantum computing • flexible electronics • spacecraft materials • neural interfaces • ultra-fast processors • next-generation energy systems Some scientists believe graphene may help unlock an entirely new era of technology where materials are engineered atom-by-atom for specific realities. The strange part? It’s just carbon. The same element found in pencil lead. Sometimes the biggest revolutions begin with structures hiding in plain sight. Follow for more future physics and technology breakthroughs.

TheNewPhysics

23,737 просмотров • 2 месяцев назад

Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.
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Two weeks ago I fixed one of my teeth with algorithms I wrote a couple of years ago! I got hooked by 3D scanning when I started to work for a software shop in Zurich that was programming 3D computational geometry algorithms for denture scanning to produce crowns (and more). Back then, a typical reconstruction pipeline was like: scan the patient’s teeth using an intraoral scanner, reconstruct the surface mesh, design the restoration digitally, and finally mill the crown out of ceramic. We were working mostly with point clouds and meshes, but it wasn’t just math, it was craftsmanship translated into a digital process. Every micron mattered. You could literally see how a good algorithm meant a better fit in someone’s mouth. Gaussian Splatting isn’t about surface reconstruction, it’s about appearance reconstruction. It doesn’t care about explicit topology, it captures how light interacts with the scene. In a sense, it’s the opposite philosophy of the dental world: instead of modeling what the object is, it models how the object looks. 3D Gaussian Splatting enables applications like training self driving cars, teaching robots to understand their environment, creating virtual worlds, or monitoring real sites. It represents scenes as millions of small Gaussians rendered in real time without the need for meshes or textures. Coming from a world where precision geometry was everything, this shift felt natural. It’s still about reconstruction, but with a different goal: not manufacturing a perfect object, but reproducing how the world actually looks. Two weeks ago I got my first dental crown, made with the same software, reconstruction algorithms, and Swiss precision I once helped develop. I haven’t worked there in two years, but sitting in that chair and seeing the process from the other side was a proud moment. It reminded me why I love this field.

MrNeRF

290,140 просмотров • 8 месяцев назад

Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.
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Most recent diffusion language model research (that I’ve seen) seems to be using masking as the noising process. It looks like, however, most closed-source models (Google Gemini Diffusion and possibly Inception Labs’ Mercury) use a different noising process, where instead of masking tokens, they replace them with different tokens (either with a random token or a semantically similar token). I wondered how they were getting such high throughput with the latter noising process, since I believed that optimizing inference with KVCache approximation would be more difficult (for various reasons). I visualized this noising process with tiny-diffusion and compared it to normal unmasking, and was very surprised to see how fast the generation “settles” into a reasonable output, and then only slightly refines afterwards, requiring much fewer steps in total. Unmasking (where tokens are never remasked, the typical implementation) is inherently limited in generation speed by the fact that an increase in tokens decoded per step leads to more errors due to the mismatch between individual and marginal token probability distributions we sample from. The token replacement noising process seems to have a much different set of characteristics. Because we sample each token per step, every token makes “progress” towards the final output each iteration (in addition to *potentially* giving other tokens more information in future steps). Generally, masking has outperformed other noising processes, which is probably why most research focused on it (using smaller models). But the paper referred to in the retweet shows that random replacement as a noising process may scale better as model size increases. Big labs might have noticed these results much earlier (due to having drastically more training resources and being able to test larger models), which may explain the discrepancy in the choice of noising process. I’m gonna test this with larger models, since tiny-diffusion only has 10M parameters.

nathan (in sf)

40,440 просмотров • 6 месяцев назад