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Modern robotic wrist joints often use timing belt differentials to achieve smooth, multi-axis movement within compact spaces. By distributing motion through synchronized belt systems, a single actuator can control multiple rotational outputs with high precision. This design reduces weight, minimizes backlash, and allows for more efficient force transmission compared... show more

Mechanical Knowledge

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117,717 次观看 • 3 个月前 •via X (Twitter)

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NEURALINK: WHERE YOUR THOUGHTS LITERALLY CONTROL THE WORLD AROUND YOU It’s not imagination. It’s movement, precision, and control, all from the mind alone. •⁠ ⁠Neuralink bypasses broken pathways, restoring motion through direct brain signals •⁠ ⁠Thought turns into action in under 100ms: faster than most natural reflexes •⁠ ⁠Robotic limbs respond instantly, moving like seamless extensions of your body •⁠ ⁠Users play games, browse, and type with pure intention, no movement required •⁠ ⁠This is more than tech; it’s the foundation of human-AI symbiosis The mind-machine merge has started, rewriting what it means to be human forever. Source: xAI, Massachusetts Institute of Technology (MIT)

NEURALINK: WHERE YOUR THOUGHTS LITERALLY CONTROL THE WORLD AROUND YOU It’s not imagination. It’s movement, precision, and control, all from the mind alone. •⁠ ⁠Neuralink bypasses broken pathways, restoring motion through direct brain signals •⁠ ⁠Thought turns into action in under 100ms: faster than most natural reflexes •⁠ ⁠Robotic limbs respond instantly, moving like seamless extensions of your body •⁠ ⁠Users play games, browse, and type with pure intention, no movement required •⁠ ⁠This is more than tech; it’s the foundation of human-AI symbiosis The mind-machine merge has started, rewriting what it means to be human forever. Source: xAI, Massachusetts Institute of Technology (MIT)

Mario Nawfal

99,457 次观看 • 6 个月前

🤖 Robotic hands are evolving faster than we realize — and they’re learning to master our world. I find this both fascinating and a little unsettling. At first glance, these robotic hands still look awkward - stiff fingers, clumsy motion. But behind the scenes, every iteration brings: ➡️ Tighter sensors ➡️ Smarter control loops ➡️ Better training on human-designed objects Week by week, they move closer to something astonishing — not imitation, but mastery. They’re learning to grip, twist, and adapt to tools made for our anatomy. And once they reach human-level dexterity, they won’t stop there. They’ll surpass it — steadier than surgeons, faster than assembly lines, tireless and precise. This is more than robotics. It’s the moment machines stop mimicking us… and start outperforming us. How do we redefine “human skill” in a world where robots master it too? #AI #Robotics #Innovation #Technology #Automation #FutureOfWork #Engineering

🤖 Robotic hands are evolving faster than we realize — and they’re learning to master our world. I find this both fascinating and a little unsettling. At first glance, these robotic hands still look awkward - stiff fingers, clumsy motion. But behind the scenes, every iteration brings: ➡️ Tighter sensors ➡️ Smarter control loops ➡️ Better training on human-designed objects Week by week, they move closer to something astonishing — not imitation, but mastery. They’re learning to grip, twist, and adapt to tools made for our anatomy. And once they reach human-level dexterity, they won’t stop there. They’ll surpass it — steadier than surgeons, faster than assembly lines, tireless and precise. This is more than robotics. It’s the moment machines stop mimicking us… and start outperforming us. How do we redefine “human skill” in a world where robots master it too? #AI #Robotics #Innovation #Technology #Automation #FutureOfWork #Engineering

Pascal Bornet

97,867 次观看 • 7 个月前

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora Sim: A PyBullet-Powered Environment for Learning Robotic Physical Intelligence We are currently building our Robora simulation environment setup for our sim based learning, leveraging PyBullet, an industry-standard physics engine widely used in AI-driven robotics research and development. The environment is optimized with GPU-accelerated learning algorithms, enabling high-speed imitation learning and reinforcement learning within a safe and controlled virtual setup before shipping out to real world. This simulation platform allows our models to learn, adapt, and generalize across different robot morphologies, terrain types and task objectives - all before deployment to the real world. At it's core, the system combines a VLA-powered high-level planner with low-level motion control algorithms, working cohesively to produce emergent, physically intelligent behaviors. This synergy between simulation, learning, and real-world transfer marks a major step forward in our pursuit of adaptive and intelligent robotic systems. Through advanced domain randomization and synthetic data generation, the Robora Simulation Environment ensures that policies trained in simulation transfer effectively to real-world robots, minimizing the sim-to-real gap. Moreover, users will be able to test and integrate their own hardware kits within selected simulation environments in the Robora Dapp, ensuring seamless compatibility and safer real-world implementation.

Robora

23,489 次观看 • 9 个月前

5-Axis CNC machining of turbocharger impellers sits in the same league as aerospace manufacturing, precision engineering pushed to physical limits. These machines run with positional accuracy in the 1-5 micron range, spindle speeds of 20,000-40,000 RPM, and continuous 5-axis interpolation to carve complex blade geometry without collision, vibration, or thermal drift during cutting of nickel alloys and titanium. A production-grade 5-axis system for impellers typically costs $300,000 to $3 million+ The real constraint is not motion, but stability, suppressing chatter, tool deflection, and microscopic surface defects that would quietly destroy efficiency at 100,000+ RPM operating cycles. Without this class of machining, modern turbocharged engines, high-efficiency industrial compressors, and compact propulsion systems simply would not exist.

5-Axis CNC machining of turbocharger impellers sits in the same league as aerospace manufacturing, precision engineering pushed to physical limits. These machines run with positional accuracy in the 1-5 micron range, spindle speeds of 20,000-40,000 RPM, and continuous 5-axis interpolation to carve complex blade geometry without collision, vibration, or thermal drift during cutting of nickel alloys and titanium. A production-grade 5-axis system for impellers typically costs $300,000 to $3 million+ The real constraint is not motion, but stability, suppressing chatter, tool deflection, and microscopic surface defects that would quietly destroy efficiency at 100,000+ RPM operating cycles. Without this class of machining, modern turbocharged engines, high-efficiency industrial compressors, and compact propulsion systems simply would not exist.

Ammanichanda

48,750 次观看 • 1 个月前

🚨 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.

🚨 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 次观看 • 1 个月前

China now has its own “Bolt” — a robot named after sprint legend Usain Bolt. A Chinese research team has unveiled the world’s first full-size humanoid robot to reach a peak speed of 10 meters per second, setting a new global benchmark for humanoid running. Bolt runs like a body pushed to the limit. Its joints and power systems work in tight coordination, keeping it balanced even at sprint speed. Built to match the build of an adult man—1.75 meters tall and 75 kilograms—it is a life-sized system operating at the edge of physics. Compared with Usain Bolt’s iconic 9.58-second 100-meter world record, which many experts believe may stand for decades, the gap between humans and machines is narrowing fast. Chinese robots are now challenging the ceiling of human performance—much as AlphaGo once challenged Go champion Ke Jie. The breakthrough builds on earlier world-record achievements in high-speed robotic running and marks a giant leap for China in humanoid motion and control. Beyond records, Bolt also carries practical value: robots are leaving the lab and stepping into real-world settings—sports training, emergency response, and demanding industrial tasks where speed, balance and control truly matter.

China now has its own “Bolt” — a robot named after sprint legend Usain Bolt. A Chinese research team has unveiled the world’s first full-size humanoid robot to reach a peak speed of 10 meters per second, setting a new global benchmark for humanoid running. Bolt runs like a body pushed to the limit. Its joints and power systems work in tight coordination, keeping it balanced even at sprint speed. Built to match the build of an adult man—1.75 meters tall and 75 kilograms—it is a life-sized system operating at the edge of physics. Compared with Usain Bolt’s iconic 9.58-second 100-meter world record, which many experts believe may stand for decades, the gap between humans and machines is narrowing fast. Chinese robots are now challenging the ceiling of human performance—much as AlphaGo once challenged Go champion Ke Jie. The breakthrough builds on earlier world-record achievements in high-speed robotic running and marks a giant leap for China in humanoid motion and control. Beyond records, Bolt also carries practical value: robots are leaving the lab and stepping into real-world settings—sports training, emergency response, and demanding industrial tasks where speed, balance and control truly matter.

Sinical

111,374 次观看 • 5 个月前

This isn’t teleportation in the sci-fi sense. It’s something deeper and more fundamental. What’s actually happening is quantum state transfer, where information is reconstructed at another location using entanglement. But here’s the interesting part In my τ-framework, this works because reality isn’t continuous in the way we think. It’s structured through a time-field, where information exists as stable patterns across a temporal lattice. When two systems are entangled, they’re not “connected through space”… they’re sharing the same underlying time-structure. So the information doesn’t travel across distance. It reappears where the time-field allows the same pattern to stabilize. That’s why it looks instant. Not because it breaks physics but because it bypasses distance entirely. If this is correct, then teleportation isn’t movement… it’s reconstruction through temporal symmetry. The real question is: Can we control the time-field enough to scale this beyond quantum systems? Follow me for more answers

This isn’t teleportation in the sci-fi sense. It’s something deeper and more fundamental. What’s actually happening is quantum state transfer, where information is reconstructed at another location using entanglement. But here’s the interesting part In my τ-framework, this works because reality isn’t continuous in the way we think. It’s structured through a time-field, where information exists as stable patterns across a temporal lattice. When two systems are entangled, they’re not “connected through space”… they’re sharing the same underlying time-structure. So the information doesn’t travel across distance. It reappears where the time-field allows the same pattern to stabilize. That’s why it looks instant. Not because it breaks physics but because it bypasses distance entirely. If this is correct, then teleportation isn’t movement… it’s reconstruction through temporal symmetry. The real question is: Can we control the time-field enough to scale this beyond quantum systems? Follow me for more answers

TheNewPhysics

36,141 次观看 • 4 个月前

The difference between SEALSQ silicon-based spin-qubit QPUs and quantum processors built on superconducting circuits or trapped ions comes down to physics, manufacturability, and long-term industrial scalability. SEALSQ’s approach uses electron spins confined in silicon semiconductor structures—essentially quantum dots fabricated with CMOS-compatible processes—where the qubit is the spin state of an electron rather than a macroscopic electrical current or a free ion. This makes spin qubits orders of magnitude smaller, potentially allowing millions of qubits on a single silicon wafer, and critically aligns the technology with existing semiconductor fabs, supply chains, and design tools. In contrast, superconducting qubits rely on exotic materials and microwave resonators that are physically large, wiring-heavy, and difficult to scale beyond a few thousand qubits without massive cryogenic and control overhead. Trapped-ion systems achieve excellent qubit coherence but depend on ultra-high vacuum chambers, precision lasers, and optical alignment, making them closer to scientific instruments than manufacturable chips. Silicon spin qubits also benefit from long intrinsic coherence times (especially in isotopically purified silicon), low power dissipation, and a natural path to tight integration with classical control, cryogenic electronics, and security primitives—an area where SEALSQ’s semiconductor and hardware-security DNA becomes a strategic advantage. The trade-off is that spin qubits are technically harder to control at the single-qubit level and are earlier in large-scale deployment than superconducting systems, but if solved, they offer the most credible route to industrial-scale, cost-effective, secure quantum processors, rather than lab-scale demonstrations.

The difference between SEALSQ silicon-based spin-qubit QPUs and quantum processors built on superconducting circuits or trapped ions comes down to physics, manufacturability, and long-term industrial scalability. SEALSQ’s approach uses electron spins confined in silicon semiconductor structures—essentially quantum dots fabricated with CMOS-compatible processes—where the qubit is the spin state of an electron rather than a macroscopic electrical current or a free ion. This makes spin qubits orders of magnitude smaller, potentially allowing millions of qubits on a single silicon wafer, and critically aligns the technology with existing semiconductor fabs, supply chains, and design tools. In contrast, superconducting qubits rely on exotic materials and microwave resonators that are physically large, wiring-heavy, and difficult to scale beyond a few thousand qubits without massive cryogenic and control overhead. Trapped-ion systems achieve excellent qubit coherence but depend on ultra-high vacuum chambers, precision lasers, and optical alignment, making them closer to scientific instruments than manufacturable chips. Silicon spin qubits also benefit from long intrinsic coherence times (especially in isotopically purified silicon), low power dissipation, and a natural path to tight integration with classical control, cryogenic electronics, and security primitives—an area where SEALSQ’s semiconductor and hardware-security DNA becomes a strategic advantage. The trade-off is that spin qubits are technically harder to control at the single-qubit level and are earlier in large-scale deployment than superconducting systems, but if solved, they offer the most credible route to industrial-scale, cost-effective, secure quantum processors, rather than lab-scale demonstrations.

Carlos Creus Moreira

19,616 次观看 • 6 个月前

Cams ARE the original "programmable" automation - these mechanical marvels orchestrate complex sequences without a single line of code. By carefully profiling cam shapes, engineers encode timing, positioning, and sequencing directly into metal. What makes cams brilliant for automation: - Deterministic timing - no software, no edge cases - Self-synchronising - no syncing clocks of different systems - Incredibly durable - cam-driven machines run for decades - Simple maintenance - mechanics can "read" the program by looking at cam profiles The cam followers, springs, and linkages in machines like this execute intricate routines - bending, cutting, feeding, and positioning with clockwork precision. Each bump and valley on those rotating cams tells a different part of the machine exactly when to move. With all the dancing robots, we sometimes forget how high-tech old-school automation is.

Cams ARE the original "programmable" automation - these mechanical marvels orchestrate complex sequences without a single line of code. By carefully profiling cam shapes, engineers encode timing, positioning, and sequencing directly into metal. What makes cams brilliant for automation: - Deterministic timing - no software, no edge cases - Self-synchronising - no syncing clocks of different systems - Incredibly durable - cam-driven machines run for decades - Simple maintenance - mechanics can "read" the program by looking at cam profiles The cam followers, springs, and linkages in machines like this execute intricate routines - bending, cutting, feeding, and positioning with clockwork precision. Each bump and valley on those rotating cams tells a different part of the machine exactly when to move. With all the dancing robots, we sometimes forget how high-tech old-school automation is.

Jack 🤖

40,142 次观看 • 1 年前

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

AI-Powered weed control! 🌱 The LaserWeeder machine from Carbon Robotics has captured the imagination of American farmers. This technology uses AI system to identify weeds in crops and zap them with precision thermal bursts from lasers. Bit of facts about the cool robot: → The machine can remove weeds from over 40 crops and can also be used for thinning crops. → It can operate in virtually all weather conditions, with millimeter accuracy at all times, and can work through the night thanks to its built-in lighting system. → High-resolution cameras and computer machine learning enable it to distinguish weeds from crops in milliseconds. → The LaserWeeder can replace about 70 workers on farms where manual weeding is used, and can weed up to four acres per hour. What other applications can we expect to see in the future in farming applications? Btw. I believe farming robots are A HUGE THING in robotics! 🔥 ~~ ♻ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

54,776 次观看 • 7 个月前

recreated this exact hook with AI in 3 minutes generated first frame with nano banana -> uploaded it along with reference video to Kling motion control, done when the quality is actually there, AI UGC beats real creators every time we built the best system for this right now: - realistic visuals - realistic voice - realistic movement - all optimized for virality and conversions way cheaper than a real creator and built for scale across thousands of posts and multiple accounts this isn't generic AI slop, these are high quality systems built with real effort and ready to scale your business it works across ecom,apps, saas, coaching, DTC... everything yet people are still paying $300-500 per UGC video from creators who ghost halfway through the project

recreated this exact hook with AI in 3 minutes generated first frame with nano banana -> uploaded it along with reference video to Kling motion control, done when the quality is actually there, AI UGC beats real creators every time we built the best system for this right now: - realistic visuals - realistic voice - realistic movement - all optimized for virality and conversions way cheaper than a real creator and built for scale across thousands of posts and multiple accounts this isn't generic AI slop, these are high quality systems built with real effort and ready to scale your business it works across ecom,apps, saas, coaching, DTC... everything yet people are still paying $300-500 per UGC video from creators who ghost halfway through the project

MAX

171,662 次观看 • 5 个月前

A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

A New Era with V3🪄 V3's new engine introduces significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.

ALCHEMIST AI 🔮

48,721 次观看 • 1 年前

NEW: After swearing in new recruits at the Los Angeles Military Entrance Processing Station (MEPS), Secretary of War Pete Hegseth departed for Divergent as part of his Arsenal Freedom Tour. Divergent Technologies, Inc. (Divergent) is a Torrance, California-based advanced manufacturing company specializing in defense and aerospace production. Its Divergent Adaptive Production System (DAPS) integrates AI-driven generative design, metal 3D printing, and automated robotic assembly to produce lightweight, high-performance structures rapidly and cost-effectively, reducing weight, part counts, and environmental impact compared to traditional methods. Since pivoting heavily into defense in 2022, Divergent has secured major contracts with prime contractors including General Atomics, Lockheed Martin, Raytheon, and Triumph Group. These cover everything from sustainment parts to full airframe systems and hypersonic components. One thing that makes Divergent unique is they only hire US persons, and they are 100% independent from Chinese supply chains. The company raised $290 million in Series E funding in September 2025, achieving a $2.3 billion valuation to scale production for U.S. military needs. Hegseth’s Pete Hegseth Arsenal Freedom Tour highlights innovative defense manufacturing, AI integration, and technologies to strengthen the U.S. Defense Industrial Base under President Trump's peace-through-strength agenda. Hegseth is traveling with press, myself included, to showcase companies like Divergent that enable faster, more agile production for warfighters. This visit aligns with the Trump administration's push to revitalize American defense manufacturing and rapidly field emerging technologies.

NEW: After swearing in new recruits at the Los Angeles Military Entrance Processing Station (MEPS), Secretary of War Pete Hegseth departed for Divergent as part of his Arsenal Freedom Tour. Divergent Technologies, Inc. (Divergent) is a Torrance, California-based advanced manufacturing company specializing in defense and aerospace production. Its Divergent Adaptive Production System (DAPS) integrates AI-driven generative design, metal 3D printing, and automated robotic assembly to produce lightweight, high-performance structures rapidly and cost-effectively, reducing weight, part counts, and environmental impact compared to traditional methods. Since pivoting heavily into defense in 2022, Divergent has secured major contracts with prime contractors including General Atomics, Lockheed Martin, Raytheon, and Triumph Group. These cover everything from sustainment parts to full airframe systems and hypersonic components. One thing that makes Divergent unique is they only hire US persons, and they are 100% independent from Chinese supply chains. The company raised $290 million in Series E funding in September 2025, achieving a $2.3 billion valuation to scale production for U.S. military needs. Hegseth’s Pete Hegseth Arsenal Freedom Tour highlights innovative defense manufacturing, AI integration, and technologies to strengthen the U.S. Defense Industrial Base under President Trump's peace-through-strength agenda. Hegseth is traveling with press, myself included, to showcase companies like Divergent that enable faster, more agile production for warfighters. This visit aligns with the Trump administration's push to revitalize American defense manufacturing and rapidly field emerging technologies.

Laura Loomer

109,862 次观看 • 6 个月前

5-axis CNC machining – but different... Most 5-axis machines use serial kinematics: stack a rotary A-axis on top of a rotary B-axis, mount that on linear X/Y/Z stages. Each axis carries the weight of everything after it. Heavy and slow. 🤖 The Sprint Z3 uses parallel kinematics: three linear drives working together to create BOTH the Z-axis motion AND the A/B tilt simultaneously. No stacked rotary joints. No heavy tilting head carrying a motor on top of another motor. The entire barrel-shaped headstock moves as one unit vertically in the column, while the three internal drives handle the spindle positioning and orientation. ⚙️ How it works: Three linear actuators are arranged in a circle, all connected to the spindle platform through pivot arms. - Extend all three equally → spindle moves straight up/down (Z-axis) - Extend them different amounts → spindle tilts (A/B axes) It's using geometry instead of stacked rotary joints to create tilt 🤔 Why this is useful: Way less moving mass than serial kinematics. Less mass = faster accelerations, higher precision, better surface finish. For high-speed machining, dynamics matter more than workspace. Question: Is this a good design or overcomplicated❓

5-axis CNC machining – but different... Most 5-axis machines use serial kinematics: stack a rotary A-axis on top of a rotary B-axis, mount that on linear X/Y/Z stages. Each axis carries the weight of everything after it. Heavy and slow. 🤖 The Sprint Z3 uses parallel kinematics: three linear drives working together to create BOTH the Z-axis motion AND the A/B tilt simultaneously. No stacked rotary joints. No heavy tilting head carrying a motor on top of another motor. The entire barrel-shaped headstock moves as one unit vertically in the column, while the three internal drives handle the spindle positioning and orientation. ⚙️ How it works: Three linear actuators are arranged in a circle, all connected to the spindle platform through pivot arms. - Extend all three equally → spindle moves straight up/down (Z-axis) - Extend them different amounts → spindle tilts (A/B axes) It's using geometry instead of stacked rotary joints to create tilt 🤔 Why this is useful: Way less moving mass than serial kinematics. Less mass = faster accelerations, higher precision, better surface finish. For high-speed machining, dynamics matter more than workspace. Question: Is this a good design or overcomplicated❓

Jack 🤖

118,947 次观看 • 9 个月前

🚨 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.

🚨 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 次观看 • 1 个月前

🚨 THE RACE TO 6G JUST ACCELERATED. Northrop Grumman has developed a W-band GaN chip operating at up to 110 GHz and took it from concept to market-ready hardware in less than six months. The new gallium nitride chip operates in the W-band (75–110 GHz), a frequency range that delivers massive bandwidth, extremely high data rates, and much lower latency than current systems. What makes this impressive is the speed: the chip went from concept to market-ready hardware in less than six months through a U.S. government-backed microelectronics program. That’s unusually fast for advanced defense-grade semiconductors. The chip acts as a high-power signal amplifier that can strengthen wireless links while shrinking the size and power consumption of the hardware. It’s designed for military radar, secure satellite communications, and the coming wave of 6G networks. Why this matters: • W-band offers far more spectrum than current 5G bands, enabling much faster data transmission and higher-resolution sensing • Gallium nitride can handle significantly higher power and frequencies than silicon, making it ideal for these demanding applications • The rapid development cycle shows how public-private collaboration can accelerate critical semiconductor technologies • The same tech that strengthens military radar and satellite links will directly feed into future commercial 6G infrastructure The deeper implication: We’re watching the foundation of next-generation wireless and sensing systems being laid in real time. High-frequency GaN chips like this won’t just improve existing radar and satellite systems they’re likely to become core building blocks for 6G, autonomous systems, and advanced defense platforms. The fact that this moved from lab to market in under six months suggests the pace of high-frequency electronics is accelerating dramatically. The future of wireless isn’t just faster. It’s operating at frequencies most people have never heard of and it’s being built right now. How soon do you think W-band and GaN technology will start appearing in everyday 6G devices? Follow for more frontier semiconductors, defense tech, and next-generation wireless systems.

🚨 THE RACE TO 6G JUST ACCELERATED. Northrop Grumman has developed a W-band GaN chip operating at up to 110 GHz and took it from concept to market-ready hardware in less than six months. The new gallium nitride chip operates in the W-band (75–110 GHz), a frequency range that delivers massive bandwidth, extremely high data rates, and much lower latency than current systems. What makes this impressive is the speed: the chip went from concept to market-ready hardware in less than six months through a U.S. government-backed microelectronics program. That’s unusually fast for advanced defense-grade semiconductors. The chip acts as a high-power signal amplifier that can strengthen wireless links while shrinking the size and power consumption of the hardware. It’s designed for military radar, secure satellite communications, and the coming wave of 6G networks. Why this matters: • W-band offers far more spectrum than current 5G bands, enabling much faster data transmission and higher-resolution sensing • Gallium nitride can handle significantly higher power and frequencies than silicon, making it ideal for these demanding applications • The rapid development cycle shows how public-private collaboration can accelerate critical semiconductor technologies • The same tech that strengthens military radar and satellite links will directly feed into future commercial 6G infrastructure The deeper implication: We’re watching the foundation of next-generation wireless and sensing systems being laid in real time. High-frequency GaN chips like this won’t just improve existing radar and satellite systems they’re likely to become core building blocks for 6G, autonomous systems, and advanced defense platforms. The fact that this moved from lab to market in under six months suggests the pace of high-frequency electronics is accelerating dramatically. The future of wireless isn’t just faster. It’s operating at frequencies most people have never heard of and it’s being built right now. How soon do you think W-band and GaN technology will start appearing in everyday 6G devices? Follow for more frontier semiconductors, defense tech, and next-generation wireless systems.

TheNewPhysics

22,647 次观看 • 1 个月前

What is the RAT? The RAT is a small wind turbine stowed within the aircraft fuselage and deployed automatically when certain failure conditions are met. Once extended into the airstream, it uses the forward motion of the aircraft to spin and generate power—mechanical, hydraulic, or electrical. Primary Functions of the RAT on the 787-8 1. Hydraulic Backup Power On deployment, the RAT drives a variable displacement inline hydraulic pump. It pressurizes the center hydraulic system, enabling continued operation of critical flight control surfaces such as the ailerons, elevators, and rudder. This is vital in maintaining aircraft controllability if normal hydraulic sources are lost. 2. Supplementary Electrical Power While the RAT is primarily a hydraulic power source on the 787-8, it can also, in some configurations, drive an emergency generator. This generator provides sufficient AC and DC power to support essential avionics, flight displays, and communications systems. Deployment Scenarios: When Does the RAT Automatically Deploy? The RAT on the Boeing 787-8 deploys automatically—without crew input—under the following emergency conditions: 1. Dual Engine Failure If both engines fail, resulting in the loss of engine-driven electrical and hydraulic generation, the RAT deploys to maintain critical flight control power. 2. Complete Electrical Loss to Flight Instruments If there’s a total loss of electrical power to both the captain’s and first officer’s primary flight instruments, the RAT ensures these systems remain powered. 3. Low Pressure in All Three Hydraulic Systems If all three systems—Left, Center, and Right—lose hydraulic pressure, the RAT provides emergency hydraulic power through the center system. 4. EMP Failure + Engine Loss During Takeoff or Landing If all four Electric Motor Pumps (EMPs) fail and an engine fails during takeoff or landing, the RAT deploys to sustain flight control power during these critical phases. Automatic and Autonomous Operation One of the RAT’s key advantages is its fully autonomous activation. Pilots do not need to manually deploy it; the system is designed to react immediately to predefined failure logic, reducing workload and ensuring flight-critical systems remain powered. In Summary The Ram Air Turbine (RAT) on the Boeing 787-8 is not just a backup—it's a lifesaving last resort. It deploys automatically to supply hydraulic and limited electrical power when all other power sources fail. Designed with layered redundancy in mind, it is one of the unsung heroes of modern aircraft systems, ensuring that even in worst-case scenarios, pilots retain control to guide the aircraft—and its passengers—safely to the ground.

What is the RAT? The RAT is a small wind turbine stowed within the aircraft fuselage and deployed automatically when certain failure conditions are met. Once extended into the airstream, it uses the forward motion of the aircraft to spin and generate power—mechanical, hydraulic, or electrical. Primary Functions of the RAT on the 787-8 1. Hydraulic Backup Power On deployment, the RAT drives a variable displacement inline hydraulic pump. It pressurizes the center hydraulic system, enabling continued operation of critical flight control surfaces such as the ailerons, elevators, and rudder. This is vital in maintaining aircraft controllability if normal hydraulic sources are lost. 2. Supplementary Electrical Power While the RAT is primarily a hydraulic power source on the 787-8, it can also, in some configurations, drive an emergency generator. This generator provides sufficient AC and DC power to support essential avionics, flight displays, and communications systems. Deployment Scenarios: When Does the RAT Automatically Deploy? The RAT on the Boeing 787-8 deploys automatically—without crew input—under the following emergency conditions: 1. Dual Engine Failure If both engines fail, resulting in the loss of engine-driven electrical and hydraulic generation, the RAT deploys to maintain critical flight control power. 2. Complete Electrical Loss to Flight Instruments If there’s a total loss of electrical power to both the captain’s and first officer’s primary flight instruments, the RAT ensures these systems remain powered. 3. Low Pressure in All Three Hydraulic Systems If all three systems—Left, Center, and Right—lose hydraulic pressure, the RAT provides emergency hydraulic power through the center system. 4. EMP Failure + Engine Loss During Takeoff or Landing If all four Electric Motor Pumps (EMPs) fail and an engine fails during takeoff or landing, the RAT deploys to sustain flight control power during these critical phases. Automatic and Autonomous Operation One of the RAT’s key advantages is its fully autonomous activation. Pilots do not need to manually deploy it; the system is designed to react immediately to predefined failure logic, reducing workload and ensuring flight-critical systems remain powered. In Summary The Ram Air Turbine (RAT) on the Boeing 787-8 is not just a backup—it's a lifesaving last resort. It deploys automatically to supply hydraulic and limited electrical power when all other power sources fail. Designed with layered redundancy in mind, it is one of the unsung heroes of modern aircraft systems, ensuring that even in worst-case scenarios, pilots retain control to guide the aircraft—and its passengers—safely to the ground.

Turbine Traveller

293,307 次观看 • 1 年前

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

🦿Xpeng showed a humanoid robot called IRON whose movement looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.

Rohan Paul

3,802,402 次观看 • 8 个月前

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out yes! 🥳 tl;dr: Faster, better RL than PPO in continuous control 💪 The answer lies in using more information from the simulation. We are juicing the simulation on GPU as it is, why not use it for gradients as well? This has been a driving question in a series of our works. We first studied this problem in ICLR 2022 paper on Short Horizon Actor Critic Naive gradient based methods are stuck in local minima and have exploding/vanishing gradients. SHAC solved this problem truncated rollouts and model based value estimation, where the model is Differentiable Sim. This boosted sample efficiency and wall-clock time immensely especially in high dimensional systems such as humanoids Yet, given enough compute PPO often caught up. Our follow up paper on on Adaptive Horizon Actor Critic at ICML 2024 discovers the cause and provides a fix. However, we find that even when given ground-truth dynamics, not all gradients are useful due to sample error. 1st-Order Model-Based Reinforcement Learning methods employing differentiable simulation provide gradients with reduced variance but are susceptible to bias in scenarios involving stiff dynamics, such as physical contact. We find that back-propagating through contact and long trajectories drastically reduces gradient accuracy. Using this insight, we propose AHAC to dynamically adapt its roll-out horizon to avoid differentiating through stiff contact. AHAC is a first-order model-based RL algorithm that learns high-dimensional tasks in minutes (wall clock) and outperforms PPO by 40%, even in the limit of data provided to PPO. This work is led by Ignat Georgiev alongside Krishnan Srinivasan, Jie Xu, Eric Heiden and ample assistance from warp team at NVIDIA Robotics (Miles Macklin)

Animesh Garg

52,300 次观看 • 2 年前