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Starship’s heat shield solves one of the hardest structural problems in rocketry: ceramic tiles and 300-series stainless steel expand and contract at completely different rates. Three mechanisms make it work: ▷ Precision expansion gaps in a hexagonal tessellation tight enough to block plasma, wide enough to prevent tile crush...

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Lacey

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Starship’s heat shield is one of the most advanced reusable tiles systems flying today Starship uses ~18,000 standardized hexagonal ceramic tiles on its windward side - minimizing unique parts and simplifying mass production The tiles are silica-based ceramics with a high-emissivity black coating, designed to survive extreme re-entry heat while radiating energy away Unlike the Space Shuttle, Starship’s tiles are mechanically pinned, not bonded.... allowing thermal expansion, fast replacement, and far better robustness The entire system is engineered for rapid reuse with minimal refurbishment, a requirement for high-flight-rate, fully reusable spacecraft The rockets that SpaceX is building are fully reusable Starships. When they re-enter Earth's atmosphere, they endure temperatures hotter than molten lava To handle this, the SpaceX team engineered a custom heat shield system SpaceX is building hundreds of Starships in parallel. This flap is getting its tiles installed right now
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Starship’s heat shield is one of the most advanced reusable tiles systems flying today Starship uses ~18,000 standardized hexagonal ceramic tiles on its windward side - minimizing unique parts and simplifying mass production The tiles are silica-based ceramics with a high-emissivity black coating, designed to survive extreme re-entry heat while radiating energy away Unlike the Space Shuttle, Starship’s tiles are mechanically pinned, not bonded.... allowing thermal expansion, fast replacement, and far better robustness The entire system is engineered for rapid reuse with minimal refurbishment, a requirement for high-flight-rate, fully reusable spacecraft The rockets that SpaceX is building are fully reusable Starships. When they re-enter Earth's atmosphere, they endure temperatures hotter than molten lava To handle this, the SpaceX team engineered a custom heat shield system SpaceX is building hundreds of Starships in parallel. This flap is getting its tiles installed right now

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The machines Randall describes operate on a principle that connects directly to his broader research into plasma and toroidal geometry. Microscopic cavitation bubbles are generated and subjected to rapid alternating cycles of vacuum and pressure - produced naturally by the up and down motion of pistons in any conventional engine configuration. The compression phase and vacuum phase act on those bubbles in sequence, and what happens next is the detail Randall finds significant. The cavitation bubbles collapse on their axes and form perfect torus shapes - spontaneously, consistently, and in a way that initiates the same plasma self-organization process he has been tracing across ancient energy systems and sacred geometry traditions. The practical implication is that these toroidal plasma voids can be harvested directly from the machine producing them. Randall points to the vortex tube as a concrete demonstration of the underlying physics - a device that accepts air at room temperature and separates it into two counter-rotating vortices, one inside the other, spinning in opposite directions. The result is a temperature differential of up to several hundred degrees between the hot and cold ends, produced without any additional energy input. Randall’s argument is that this is not an isolated engineering curiosity. It is a visible, reproducible demonstration of the same principles that ancient plasma-based energy systems were built around - and that the machines now being developed around cavitation and toroidal geometry may be the closest modern technology has come to recovering what was lost.
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The machines Randall describes operate on a principle that connects directly to his broader research into plasma and toroidal geometry. Microscopic cavitation bubbles are generated and subjected to rapid alternating cycles of vacuum and pressure - produced naturally by the up and down motion of pistons in any conventional engine configuration. The compression phase and vacuum phase act on those bubbles in sequence, and what happens next is the detail Randall finds significant. The cavitation bubbles collapse on their axes and form perfect torus shapes - spontaneously, consistently, and in a way that initiates the same plasma self-organization process he has been tracing across ancient energy systems and sacred geometry traditions. The practical implication is that these toroidal plasma voids can be harvested directly from the machine producing them. Randall points to the vortex tube as a concrete demonstration of the underlying physics - a device that accepts air at room temperature and separates it into two counter-rotating vortices, one inside the other, spinning in opposite directions. The result is a temperature differential of up to several hundred degrees between the hot and cold ends, produced without any additional energy input. Randall’s argument is that this is not an isolated engineering curiosity. It is a visible, reproducible demonstration of the same principles that ancient plasma-based energy systems were built around - and that the machines now being developed around cavitation and toroidal geometry may be the closest modern technology has come to recovering what was lost.

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Researchers at Tokamak Energy have captured for the first time a real-time, high-speed video of plasma behaviour inside their ST40 spherical tokamak, tracking visible green and red light emissions as the fusion process occurs. This visual insight comes via a camera operating at thousands of frames per second, offering unprecedented detail of how the plasma evolves, interacts with the surrounding lithium blanket and outer regions, and ultimately radiates energy. The imaging enables scientists to observe how the ultra-hot core transitions outward into cooler zones, how magnetic confinement shapes the plasma behaviour, and how impurities or outer-region interactions influence the process. By giving a ‘star-in-a-donut’ view of fusion in action, this breakthrough adds a new diagnostic tool to the development of fusion energy, helping engineers refine the magnetic confinement, optimise plasma stability and better understand the heat and light flows at play. It was slowed down by 100x. All this was for 0.3s A tokamak is one of the most advanced devices ever created to achieve controlled nuclear fusion, the same process that powers the Sun. Its goal is simple in principle but incredibly challenging in practice: heat a gas until it becomes plasma, raise that plasma to over 100 million degrees, and confine it long enough for hydrogen nuclei to fuse and release energy. Because no material container can survive such temperatures, a tokamak uses powerful magnetic fields to hold and shape the plasma like an invisible cage. The device has a distinctive doughnut-shaped (toroidal) chamber surrounded by magnetic coils. When the machine is switched on, electric currents and external magnets work together to create helical magnetic fields that trap the plasma and keep it away from the walls. As the plasma spirals around these magnetic lines, it heats up dramatically. Additional heating comes from methods like radio-frequency waves and neutral-beam injection, pushing the plasma toward the extreme temperatures needed for fusion. Inside this tightly controlled environment, hydrogen isotopes such as deuterium and tritium can collide and fuse, releasing fast neutrons and a burst of energy. The goal of tokamak research is to reach a point where the fusion reactions produce more energy than the system consumes, a milestone known as “net energy gain.” Modern machines like ITER, JET, and Tokamak Energy’s ST40 are bringing this vision closer, using advanced diagnostics, superconducting magnets, and increasingly stable plasma control. 👉

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Elon Musk says steel killed carbon fiber on Starship at one-fiftieth the cost. Carbon fiber was supposed to be the future of rocketry. Lighter than aluminum. Stronger than steel. Trusted by every Formula 1 team. SpaceX picked it for Starship. "Particularly if you go for a high-strength specialized carbon fiber that can handle cryogenic oxygen, it's roughly 50 times the cost of steel." Then progress stalled. The autoclaves needed to cure the resin had to outsize every autoclave on Earth, and the team couldn't even produce a clean barrel section without wrinkles. Musk, watching the Mars timeline slip: "At this rate, we're never going to get to Mars. So we've got to think of something else." So he asked the question nobody at SpaceX had asked: "What about steel?" It became known as the cryogenic stainless flip. Musk, who had already shipped Falcon 9 in aluminum-lithium, broke with the textbook. "When you look at the material properties of stainless steel, full-hard, strain hardened stainless steel, at cryogenic temperature the strength to weight is actually similar to carbon fiber." Starship ran on cryogenic methane and oxygen. The airframe lived at temperatures that flipped steel ahead of carbon fiber. "You could smoke a cigar while welding stainless steel." After Musk made the call, steel weighed less than the carbon fiber version. Fifty times cheaper in raw material. Twice the heat tolerance. Half the heat shield mass. Musk, looking back: "In retrospect, we should have started with steel in the beginning. It was dumb not to do steel." What "obvious" material in your work is silently costing you the project? — Elon Musk (.Elon Musk , CEO of Tesla and SpaceX, on Dwarkesh Patel's (.Dwarkesh Patel) podcast
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Elon Musk says steel killed carbon fiber on Starship at one-fiftieth the cost. Carbon fiber was supposed to be the future of rocketry. Lighter than aluminum. Stronger than steel. Trusted by every Formula 1 team. SpaceX picked it for Starship. "Particularly if you go for a high-strength specialized carbon fiber that can handle cryogenic oxygen, it's roughly 50 times the cost of steel." Then progress stalled. The autoclaves needed to cure the resin had to outsize every autoclave on Earth, and the team couldn't even produce a clean barrel section without wrinkles. Musk, watching the Mars timeline slip: "At this rate, we're never going to get to Mars. So we've got to think of something else." So he asked the question nobody at SpaceX had asked: "What about steel?" It became known as the cryogenic stainless flip. Musk, who had already shipped Falcon 9 in aluminum-lithium, broke with the textbook. "When you look at the material properties of stainless steel, full-hard, strain hardened stainless steel, at cryogenic temperature the strength to weight is actually similar to carbon fiber." Starship ran on cryogenic methane and oxygen. The airframe lived at temperatures that flipped steel ahead of carbon fiber. "You could smoke a cigar while welding stainless steel." After Musk made the call, steel weighed less than the carbon fiber version. Fifty times cheaper in raw material. Twice the heat tolerance. Half the heat shield mass. Musk, looking back: "In retrospect, we should have started with steel in the beginning. It was dumb not to do steel." What "obvious" material in your work is silently costing you the project? — Elon Musk (.Elon Musk , CEO of Tesla and SpaceX, on Dwarkesh Patel's (.Dwarkesh Patel) podcast

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A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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A 40-year-old patent has finally been brought to life. That's the Y-zipper. A 3D-printed three-sided fastener that transitions any object from flexible to rigid and back again. The robotics application is the one that caught my attention. A quadruped robot that adjusts its leg stiffness depending on terrain, switching between rigid and flexible in real time without additional motors or complex mechanical systems. But this goes way beyond robotics. A wrist cast that loosens during the day and stiffens at night. A tent that pops into shape in 90 seconds instead of six minutes. The idea sat in a patent filing for four decades. It took 3D printing to finally make it real. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Taken individually, any single ancient monument can be explained away as a feat of organized labor and brute force. Randall’s argument is that when you take the full picture in totality - the scale, the precision, the sacred geometry, and the global distribution of structures built from stones weighing ten tons or more - a different conclusion forces itself. There is a common system at work. These cultures shared a body of knowledge that we have forgotten, and that knowledge included something that made the manipulation of very large stones considerably easier than simple muscular effort would allow. The question Randall poses is direct - is the sacred geometry embedded in these structures merely an homage to a king's ego, a decorative impulse scaled up in stone? He dismisses that explanation not with contempt but with logic. The consistency of the system across cultures with no documented contact, the precision of the geometry, and the physical improbability of the construction methods conventionally proposed all point in the same direction. What was being built was not a monument to human vanity. It was an application of a knowledge system sophisticated enough to make the seemingly impossible routine - and whatever that system was, its loss is one of the most significant gaps in our understanding of what our predecessors were actually capable of.
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Taken individually, any single ancient monument can be explained away as a feat of organized labor and brute force. Randall’s argument is that when you take the full picture in totality - the scale, the precision, the sacred geometry, and the global distribution of structures built from stones weighing ten tons or more - a different conclusion forces itself. There is a common system at work. These cultures shared a body of knowledge that we have forgotten, and that knowledge included something that made the manipulation of very large stones considerably easier than simple muscular effort would allow. The question Randall poses is direct - is the sacred geometry embedded in these structures merely an homage to a king's ego, a decorative impulse scaled up in stone? He dismisses that explanation not with contempt but with logic. The consistency of the system across cultures with no documented contact, the precision of the geometry, and the physical improbability of the construction methods conventionally proposed all point in the same direction. What was being built was not a monument to human vanity. It was an application of a knowledge system sophisticated enough to make the seemingly impossible routine - and whatever that system was, its loss is one of the most significant gaps in our understanding of what our predecessors were actually capable of.

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Unlike a commercial airplane, the Shuttle did not have its engines on during reentry. Once it left orbit, it became a colossal glider. The pilots had only one chance to land: if they misjudged the aim or the speed, they couldn't "give it gas" and go around for another try. That's why the engineers affectionately called it "the flying brick" (the flying brick). When it reentered the atmosphere, it traveled at about 28,000 km/h (about 25 times the speed of sound). To slow down without disintegrating, it had to hit the air belly-first, using friction as a brake. In the last kilometers before the runway: • It descended at an angle 7 times steeper than that of a commercial airplane. • While a commercial airplane descends gently, the Shuttle came down "in a dive" to maintain enough kinetic energy and avoid stalling. • It touched down at about 350 km/h, a speed so high that it required a rear parachute to stop before the end of the runway. Under that white-and-black "skin" were thousands of ceramic tiles that protected the astronauts from temperatures exceeding 1,500°C. Without them, the aluminum of the structure would have melted like butter in a matter of seconds.
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Unlike a commercial airplane, the Shuttle did not have its engines on during reentry. Once it left orbit, it became a colossal glider. The pilots had only one chance to land: if they misjudged the aim or the speed, they couldn't "give it gas" and go around for another try. That's why the engineers affectionately called it "the flying brick" (the flying brick). When it reentered the atmosphere, it traveled at about 28,000 km/h (about 25 times the speed of sound). To slow down without disintegrating, it had to hit the air belly-first, using friction as a brake. In the last kilometers before the runway: • It descended at an angle 7 times steeper than that of a commercial airplane. • While a commercial airplane descends gently, the Shuttle came down "in a dive" to maintain enough kinetic energy and avoid stalling. • It touched down at about 350 km/h, a speed so high that it required a rear parachute to stop before the end of the runway. Under that white-and-black "skin" were thousands of ceramic tiles that protected the astronauts from temperatures exceeding 1,500°C. Without them, the aluminum of the structure would have melted like butter in a matter of seconds.

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The tighter the fit, the faster the failure. What you’re looking at is a mechanical paradox that took engineers over a century to master. In the world of heavy machinery, "perfectly tight" is a death sentence. Even the most advanced machines on Earth are designed with a deliberate, microscopic gap between every gear tooth. If you make them perfectly flush, they don’t just wear down: they seize, weld, and explode in a matter of seconds. As these gears rotate, the laws of thermodynamics are unforgiving. Friction generates intense heat, causing the metal to expand. Without that sliver of space, the gears have nowhere to grow, turning a million-dollar precision machine into a solid block of scrap in an instant. That tiny gap is where oil breathes and heat escapes. It is the only thing standing between smooth, silent rotation and catastrophic destruction. True precision isn't about zero clearance; it’s about mastering the room for reality.
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The tighter the fit, the faster the failure. What you’re looking at is a mechanical paradox that took engineers over a century to master. In the world of heavy machinery, "perfectly tight" is a death sentence. Even the most advanced machines on Earth are designed with a deliberate, microscopic gap between every gear tooth. If you make them perfectly flush, they don’t just wear down: they seize, weld, and explode in a matter of seconds. As these gears rotate, the laws of thermodynamics are unforgiving. Friction generates intense heat, causing the metal to expand. Without that sliver of space, the gears have nowhere to grow, turning a million-dollar precision machine into a solid block of scrap in an instant. That tiny gap is where oil breathes and heat escapes. It is the only thing standing between smooth, silent rotation and catastrophic destruction. True precision isn't about zero clearance; it’s about mastering the room for reality.

Mathematica

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Elon Musk says steel killed carbon fiber on Starship at one-fiftieth the cost. Carbon fiber was supposed to be the future of rocketry. Lighter than aluminum. Stronger than steel. Trusted by every Formula 1 team. SpaceX picked it for Starship. "Particularly if you go for a high-strength specialized carbon fiber that can handle cryogenic oxygen, it's roughly 50 times the cost of steel." Then progress stalled. The autoclaves needed to cure the resin had to outsize every autoclave on Earth, and the team couldn't even produce a clean barrel section without wrinkles. Musk, watching the Mars timeline slip: "At this rate, we're never going to get to Mars. So we've got to think of something else." So he asked the question nobody at SpaceX had asked: "What about steel?" It became known as the **cryogenic stainless flip**. Musk, who had already shipped Falcon 9 in aluminum-lithium, broke with the textbook. "When you look at the material properties of stainless steel, full-hard, strain hardened stainless steel, at cryogenic temperature the strength to weight is actually similar to carbon fiber." Starship ran on cryogenic methane and oxygen. The airframe lived at temperatures that flipped steel ahead of carbon fiber. "You could smoke a cigar while welding stainless steel." After Musk made the call, steel weighed less than the carbon fiber version. Fifty times cheaper in raw material. Twice the heat tolerance. Half the heat shield mass. Musk, looking back: "In retrospect, we should have started with steel in the beginning. It was dumb not to do steel." What "obvious" material in your work is silently costing you the project? If you're new here, GeniusThinking is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content. P.S. I've made a free playbook on how to use and create your own mental models. This includes the same thinking strategies Feynman, Munger, and Musk built their careers on. Ttrusted by 5,000+ founders and investors. Grab your copy: — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast
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Elon Musk says steel killed carbon fiber on Starship at one-fiftieth the cost. Carbon fiber was supposed to be the future of rocketry. Lighter than aluminum. Stronger than steel. Trusted by every Formula 1 team. SpaceX picked it for Starship. "Particularly if you go for a high-strength specialized carbon fiber that can handle cryogenic oxygen, it's roughly 50 times the cost of steel." Then progress stalled. The autoclaves needed to cure the resin had to outsize every autoclave on Earth, and the team couldn't even produce a clean barrel section without wrinkles. Musk, watching the Mars timeline slip: "At this rate, we're never going to get to Mars. So we've got to think of something else." So he asked the question nobody at SpaceX had asked: "What about steel?" It became known as the **cryogenic stainless flip**. Musk, who had already shipped Falcon 9 in aluminum-lithium, broke with the textbook. "When you look at the material properties of stainless steel, full-hard, strain hardened stainless steel, at cryogenic temperature the strength to weight is actually similar to carbon fiber." Starship ran on cryogenic methane and oxygen. The airframe lived at temperatures that flipped steel ahead of carbon fiber. "You could smoke a cigar while welding stainless steel." After Musk made the call, steel weighed less than the carbon fiber version. Fifty times cheaper in raw material. Twice the heat tolerance. Half the heat shield mass. Musk, looking back: "In retrospect, we should have started with steel in the beginning. It was dumb not to do steel." What "obvious" material in your work is silently costing you the project? If you're new here, GeniusThinking is a gallery for the greatest minds in economics, psychology, and history. Follow along for more similar content. P.S. I've made a free playbook on how to use and create your own mental models. This includes the same thinking strategies Feynman, Munger, and Musk built their careers on. Ttrusted by 5,000+ founders and investors. Grab your copy: — Elon Musk ( Elon Musk ), CEO of Tesla and SpaceX, on Dwarkesh Patel's ( Dwarkesh Patel ) podcast

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Taming the Edge: How lithium could help us control #fusion plasmas. This video captures the first flashes of lithium being injected into the #plasma of our ST40 tokamak, marking the start of our exploration into its effects. Why lithium? In fusion research, we aim for H-mode, a high-performance state with improved plasma confinement. Future fusion power plants are expected to operate in this mode. But H-mode brings a challenge: ELMs (Edge Localised Modes) are bursts of energy at the plasma edge, similar to mini solar flares. These can reduce plasma temperature and damage the divertor with intense heat and particles. Pioneering work by PPPL and others has shown that lithium can suppress ELMs and increase energy confinement time, leading to higher temperatures. On ST40, we’re currently injecting lithium powder during plasma shots to explore its effects. As part of our upcoming ST40 LEAPS upgrade – in partnership with the U.S. Department of Energy and Department for Energy Security and Net Zero – we’ll go further, coating plasma-facing components with solid lithium using the ‘lithium evaporation’ technique. We’ll be experimentally testing several mechanisms. One key focus is how lithium absorbs hydrogen isotopes and reduces their recycling back into the plasma, lowering the density at the plasma edge, leading to a more stable edge pressure gradient. We’re starting to understand more about lithium’s effect on plasma performance, and early results show lithium isn't getting into the plasma core, which is good news for avoiding diluting the fusion fuel in future plants. The physics is complex, and we’re still learning. But each step brings us closer to fusion energy. By incorporating lithium into ST40, the world’s highest field spherical tokamak, we’re advancing our understanding of this critical enabling technology. #Fusion #FusionEnergy #Innovation #Limitless #EnergyTransition
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Taming the Edge: How lithium could help us control #fusion plasmas. This video captures the first flashes of lithium being injected into the #plasma of our ST40 tokamak, marking the start of our exploration into its effects. Why lithium? In fusion research, we aim for H-mode, a high-performance state with improved plasma confinement. Future fusion power plants are expected to operate in this mode. But H-mode brings a challenge: ELMs (Edge Localised Modes) are bursts of energy at the plasma edge, similar to mini solar flares. These can reduce plasma temperature and damage the divertor with intense heat and particles. Pioneering work by PPPL and others has shown that lithium can suppress ELMs and increase energy confinement time, leading to higher temperatures. On ST40, we’re currently injecting lithium powder during plasma shots to explore its effects. As part of our upcoming ST40 LEAPS upgrade – in partnership with the U.S. Department of Energy and Department for Energy Security and Net Zero – we’ll go further, coating plasma-facing components with solid lithium using the ‘lithium evaporation’ technique. We’ll be experimentally testing several mechanisms. One key focus is how lithium absorbs hydrogen isotopes and reduces their recycling back into the plasma, lowering the density at the plasma edge, leading to a more stable edge pressure gradient. We’re starting to understand more about lithium’s effect on plasma performance, and early results show lithium isn't getting into the plasma core, which is good news for avoiding diluting the fusion fuel in future plants. The physics is complex, and we’re still learning. But each step brings us closer to fusion energy. By incorporating lithium into ST40, the world’s highest field spherical tokamak, we’re advancing our understanding of this critical enabling technology. #Fusion #FusionEnergy #Innovation #Limitless #EnergyTransition

Tokamak Energy

66,652 Aufrufe • vor 1 Jahr

A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk. he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth. not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be. fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable. so he stopped trying to do it himself. he handed the control loop to an AI. the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match. this is the exact kind of build people are tearing apart inside Neuro Club. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business. then the post went out. within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive. here is the part that should stop you. he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top. most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it. the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.
1:31

A PhD student built a working nuclear fusion reactor in his garage, let an AI run it, and 400 thousand dollars later he works for Elon Musk. he posted it once. that single post ended with a grant in his account and a job offer from the most powerful man on earth. not a simulation. not a school project. an actual device that fuses atoms, sitting where his car used to be. fusion is the thing governments have been chasing for 70 years with billion dollar labs. the hard part was never the reactor itself. it was the control. the plasma inside has to be held at conditions hotter than the core of the sun, and it shifts and collapses in milliseconds. no human can react fast enough to keep it stable. so he stopped trying to do it himself. he handed the control loop to an AI. the model reads the sensor data hundreds of times a second, predicts how the plasma is about to move, and adjusts the magnetic fields before it ever drifts out of line. it does not wait for the plasma to misbehave. it sees it coming and corrects it before it happens. the same reaction-before-the-event speed no person could ever match. this is the exact kind of build people are tearing apart inside Neuro Club. not to make reactors, but because the workflow is identical for anything hard. let the AI run the loop, predict the problem, fix it before it breaks. same playbook whether it is plasma or a business. then the post went out. within days Elon's fusion team reached out. they did not ask him to interview for an entry role. they handed him a 400 thousand dollar grant and pulled him onto the team building this at scale. one garage build turned a PhD student into an operator for the most ambitious man alive. here is the part that should stop you. he was one guy with a PhD, a garage, and an AI model doing the job that entire teams of physicists used to fail at. the AI was not assisting him. it was the operator. he built the hardware. the machine ran it. and that was enough to get noticed at the very top. most people think AI writes emails and makes pictures. meanwhile someone pointed it at one of the hardest physics problems on earth, held the plasma steady, and got paid by Elon Musk for it. the gap is not between humans and AI anymore. it is between the people who realize what this thing can already do and the people still using it to summarize their inbox.

Born to gamble

347,359 Aufrufe • vor 2 Monaten

Statement by WAYNE OLIVEIRA Voc Tech School Committee Member on the Massachusetts Voc-Tech Admissions Lottery The implementation of a lottery-based admissions system for Massachusetts vocational-technical schools in 2026 has left many students and families deeply disappointed and disillusioned. While the intention behind this shift—to promote equity and expand access—may be well-meaning, the reality is that it has replaced a system based on effort, interest, and demonstrated commitment with one largely dependent on chance and luck. For years, students worked hard to earn their place in these highly sought-after programs by maintaining strong attendance, showing dedication, and actively preparing for a future in the trades. Today, many of those same students find themselves waitlisted or denied admission, not because they lacked motivation or ability, but simply because their name was not drawn. Families have described this transition as a “drastic jump from one extreme to the other,” moving away from merit entirely toward random choice. At the same time, the core issue remains unresolved: there are far more qualified applicants than available seats. Tens of thousands of students apply for just over ten thousand openings each year, meaning that regardless of the admissions method, many will be left out. Instead of addressing this shortage through expansion and investment, the state has chosen to redistribute opportunity through a system that many perceive as arbitrary and discouraging. This approach risks sending a troubling message to students—that hard work, consistency, and passion may not matter when pursuing certain educational paths. It also raises concerns about long-term impacts on student motivation and the development of a skilled workforce. A fair system should strive for both equity and recognition of effort. Massachusetts must re-evaluate this policy and consider solutions that balance opportunity with accountability—while also addressing the fundamental issue of limited access by expanding vocational education capacity. Students deserve more than a lottery. They deserve a system that reflects both fairness and the value of their hard work. If you feel this same way you need to make some noise! Reach out to the State Department of Education and your State Senator and Representative. Tell them this broken system needs to be fixed immediately.
3:59

Statement by WAYNE OLIVEIRA Voc Tech School Committee Member on the Massachusetts Voc-Tech Admissions Lottery The implementation of a lottery-based admissions system for Massachusetts vocational-technical schools in 2026 has left many students and families deeply disappointed and disillusioned. While the intention behind this shift—to promote equity and expand access—may be well-meaning, the reality is that it has replaced a system based on effort, interest, and demonstrated commitment with one largely dependent on chance and luck. For years, students worked hard to earn their place in these highly sought-after programs by maintaining strong attendance, showing dedication, and actively preparing for a future in the trades. Today, many of those same students find themselves waitlisted or denied admission, not because they lacked motivation or ability, but simply because their name was not drawn. Families have described this transition as a “drastic jump from one extreme to the other,” moving away from merit entirely toward random choice. At the same time, the core issue remains unresolved: there are far more qualified applicants than available seats. Tens of thousands of students apply for just over ten thousand openings each year, meaning that regardless of the admissions method, many will be left out. Instead of addressing this shortage through expansion and investment, the state has chosen to redistribute opportunity through a system that many perceive as arbitrary and discouraging. This approach risks sending a troubling message to students—that hard work, consistency, and passion may not matter when pursuing certain educational paths. It also raises concerns about long-term impacts on student motivation and the development of a skilled workforce. A fair system should strive for both equity and recognition of effort. Massachusetts must re-evaluate this policy and consider solutions that balance opportunity with accountability—while also addressing the fundamental issue of limited access by expanding vocational education capacity. Students deserve more than a lottery. They deserve a system that reflects both fairness and the value of their hard work. If you feel this same way you need to make some noise! Reach out to the State Department of Education and your State Senator and Representative. Tell them this broken system needs to be fixed immediately.

Fall River Reporter

27,042 Aufrufe • vor 4 Monaten

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.
1:05

Experiments in progress. The one on the right has been learning for ~3 hours, the one in the middle for ~1 hour, and the one on the left just started a few minutes ago. The initial motivation for making the physical Atari was just to commit ourselves to a subset of algorithms that can make progress in this setup. This commitment rules out algorithms that require billions of samples to learn (or worse, require multiple environments running in parallel). Atari games are simple enough that we should be able to show learning on them in a short amount of time with no prior knowledge. Since then, I've realized that this setup is also a good way to compare different paradigms in robotics in a principled way. These paradigms are sim2real, learning from tele-operated data, and learning directly on the robots. So far, I have observed that getting sim2real to work reliably is hard. It requires tweaks that don't scale. Policies that can play perfectly in simulation fall apart because of latencies and the messiness of the real world. These aspects could be modeled to improve the simulation, but not without sinking significant human engineering hours. I have higher hopes for learning from tele-operated data, but that requires a human to learn the task first. These experiments are on my to-do list. I have to learn to play some of the games well through the robot. I’m half-decent at playing Pong and Ms Pacman now. Learning directly on robots is looking like the most promising approach. This approach takes away pesky distribution shifts and makes it possible to have algorithms that continually improve with more data and time without any human intervention. It feels great to let experiments run overnight and wake up to find improved policies. With learning on robots, I should, in principle, be able to go on a long vacation and come back to find better policies for complex tasks beyond Atari games. Whether that is possible with current learning algorithms is a different question.

Khurram Javed

52,110 Aufrufe • vor 8 Monaten

AI in CAD just went from “make me a part” to “build me the damn machine.” Using Autodesk Fusion MCP, the agent is working inside a real mechanical assembly with a chassis, shafts, gears, bearings, brackets, repeated hardware, and multiple components that all have to stay aligned with each other. That is a completely different problem from generating one clean 3D object. You can inspect the assembly from different angles, isolate sections, dig into individual joints, and keep working on the same engineering project instead of starting from another prompt-generated blob. The important part is not that AI can create geometry anymore. It is that it can operate inside the structure of a real CAD project where every component depends on what is around it. Once this gets reliable, a lot of mechanical CAD starts looking suspiciously simple: describe the system, inspect what the agent built, then tell it what to change.
0:29

AI in CAD just went from “make me a part” to “build me the damn machine.” Using Autodesk Fusion MCP, the agent is working inside a real mechanical assembly with a chassis, shafts, gears, bearings, brackets, repeated hardware, and multiple components that all have to stay aligned with each other. That is a completely different problem from generating one clean 3D object. You can inspect the assembly from different angles, isolate sections, dig into individual joints, and keep working on the same engineering project instead of starting from another prompt-generated blob. The important part is not that AI can create geometry anymore. It is that it can operate inside the structure of a real CAD project where every component depends on what is around it. Once this gets reliable, a lot of mechanical CAD starts looking suspiciously simple: describe the system, inspect what the agent built, then tell it what to change.

Rina

14,154 Aufrufe • vor 8 Tagen

Alexandr Wang offers a contrarian take on management: the more brilliant your people, the less you should tell them what to do. Most leadership instinct says that as you hire better people and take on bigger problems, you direct more closely. Wang's approach to running MSL goes the other way. "In general, in terms of my management philosophy for MSL, [it] is not to boss people around," he says. Alexandr Wang points to a line from Steve Jobs that flips the usual logic: "Most companies hire people and tell them what to do, but we hire people for them to tell us what to do." That inversion sits at the foundation of everything. For Wang, the entire bet behind MSL and TBD rests on it: "That is like pretty core to the entire thesis of TBD and MSL and how we've built it… we're going to hire brilliant researchers and create the best environment for them to do the work of their careers and the work of their lives." So instead of positioning himself as the person with the answers, he positions himself as the person who removes everything standing between great researchers and great work: "Long story short, I'm not trying to boss anyone around. Actually, I'm trying to create the best environment for researchers to do incredible work."
0:46

Alexandr Wang offers a contrarian take on management: the more brilliant your people, the less you should tell them what to do. Most leadership instinct says that as you hire better people and take on bigger problems, you direct more closely. Wang's approach to running MSL goes the other way. "In general, in terms of my management philosophy for MSL, [it] is not to boss people around," he says. Alexandr Wang points to a line from Steve Jobs that flips the usual logic: "Most companies hire people and tell them what to do, but we hire people for them to tell us what to do." That inversion sits at the foundation of everything. For Wang, the entire bet behind MSL and TBD rests on it: "That is like pretty core to the entire thesis of TBD and MSL and how we've built it… we're going to hire brilliant researchers and create the best environment for them to do the work of their careers and the work of their lives." So instead of positioning himself as the person with the answers, he positions himself as the person who removes everything standing between great researchers and great work: "Long story short, I'm not trying to boss anyone around. Actually, I'm trying to create the best environment for researchers to do incredible work."

Big Brain Business

12,986 Aufrufe • vor 2 Monaten

Plasma is better in colour! Watch one of our latest #plasma pulses in our ST40 tokamak, filmed using our new high-speed colour camera at an incredible 16,000 frames per second. Each pulse lasts around a fifth of a second. What you’re seeing is mostly visible light from the plasma’s edge, glowing pink. The core is simply too hot to emit visible light. In this footage, lithium is dropped into the plasma. As it interacts, it glows red when excited, then turns green as it becomes ionised, losing an electron. From there, it traces the magnetic field lines, revealing the plasma’s path around the tokamak. Lithium is the focus of our $52 million ST40 upgrade programme, in partnership with U.S. Department of Energy and Department for Energy Security and Net Zero. This builds on pioneering work at PPPL and others that has shown that lithium can significantly improve plasma performance. This video comes from ongoing research into X-point radiator (XPR) regimes, a promising operating mode for future #fusion power plants that aims to cool the plasma before it reaches plasma-facing components (PFCs), helping to reduce wear without compromising performance. Fusion research just got a lot more colourful! 👇 #Fusion #FusionEnergy #EnergyTransition #Innovation
0:33

Plasma is better in colour! Watch one of our latest #plasma pulses in our ST40 tokamak, filmed using our new high-speed colour camera at an incredible 16,000 frames per second. Each pulse lasts around a fifth of a second. What you’re seeing is mostly visible light from the plasma’s edge, glowing pink. The core is simply too hot to emit visible light. In this footage, lithium is dropped into the plasma. As it interacts, it glows red when excited, then turns green as it becomes ionised, losing an electron. From there, it traces the magnetic field lines, revealing the plasma’s path around the tokamak. Lithium is the focus of our $52 million ST40 upgrade programme, in partnership with U.S. Department of Energy and Department for Energy Security and Net Zero. This builds on pioneering work at PPPL and others that has shown that lithium can significantly improve plasma performance. This video comes from ongoing research into X-point radiator (XPR) regimes, a promising operating mode for future #fusion power plants that aims to cool the plasma before it reaches plasma-facing components (PFCs), helping to reduce wear without compromising performance. Fusion research just got a lot more colourful! 👇 #Fusion #FusionEnergy #EnergyTransition #Innovation

Tokamak Energy

2,085,566 Aufrufe • vor 10 Monaten

To build a terawatt-scale orbital AI data center network, SpaceX must deliver 10 million tons of infrastructure to orbit every year. Starship V4 @ 200 tons payload = 50,000 launches per year. → ~135 Starships per day. One every ~10 minutes. 24/7. Sounds impossible… until you understand what SpaceX is actually solving. Elon is right. True rapid reusability isn’t just hard it’s two civilization-level engineering problems that must be cracked at the same time. SpaceX isn’t building another rocket. They’re building the first orbital airliner. Engines: Raptor Designed from day one for liquid methane + full-flow staged combustion. - Burns clean (no coking) - Turbines run cooler - Raptor 3 strips shields & welds flanges shut Goal: 1,000 flights with minimal maintenance. Heat Shield: The unsolvable problem… until now Thousands of hexagonal silica tiles mechanically pinned to stainless steel. Tiles and hull expand independently. No glue. No months-long refurbishment like the Shuttle. On top of these sit thousands of smaller issues that only real flights reveal. The method: Fly. Break. Learn. Iterate. Fast. Solve this stack → marginal cost drops to mostly propellant. That unlocks 200-ton payloads every ~10 minutes… enough for orbital AI data centers at terawatt scale, lunar bases, and a city on Mars. This is why Starship is the hardest machine humans have ever attempted. Source: Elon Musk SpaceX
1:08

To build a terawatt-scale orbital AI data center network, SpaceX must deliver 10 million tons of infrastructure to orbit every year. Starship V4 @ 200 tons payload = 50,000 launches per year. → ~135 Starships per day. One every ~10 minutes. 24/7. Sounds impossible… until you understand what SpaceX is actually solving. Elon is right. True rapid reusability isn’t just hard it’s two civilization-level engineering problems that must be cracked at the same time. SpaceX isn’t building another rocket. They’re building the first orbital airliner. Engines: Raptor Designed from day one for liquid methane + full-flow staged combustion. - Burns clean (no coking) - Turbines run cooler - Raptor 3 strips shields & welds flanges shut Goal: 1,000 flights with minimal maintenance. Heat Shield: The unsolvable problem… until now Thousands of hexagonal silica tiles mechanically pinned to stainless steel. Tiles and hull expand independently. No glue. No months-long refurbishment like the Shuttle. On top of these sit thousands of smaller issues that only real flights reveal. The method: Fly. Break. Learn. Iterate. Fast. Solve this stack → marginal cost drops to mostly propellant. That unlocks 200-ton payloads every ~10 minutes… enough for orbital AI data centers at terawatt scale, lunar bases, and a city on Mars. This is why Starship is the hardest machine humans have ever attempted. Source: Elon Musk SpaceX

Lacey

12,200 Aufrufe • vor 3 Monaten

Elon Musk reveals why he’s building rockets, AI, and humanoid robots at the same time "The overall goal of my companies is to maximize the probability that civilization has a great future, and to expand consciousness beyond earth" "We need to assume that life and consciousness is extremely rare and it might only be us" "The image in my mind is of a tiny candle in a vast darkness, a tiny candle of consciousness that could easily go out" "That's why it's important to make life multiplanetary, such that if there's a natural or man-made disaster on earth, that consciousness continues" "That's the purpose of SpaceX" "With robotics and AI, this is really the path to abundance for all. The only way to solve global poverty and give everyone a very high standard of living is AI and robotics" "My prediction is there'll be more robots than people. Everyone on earth is going to have one and going to want one. We will make so many robots and AI that they will saturate all human needs. You won't be able to even think of something to ask the robot for" "Who wouldn't want a robot to watch over your kids, take care of your pet, or protect an elderly parent? There just aren't enough young people to take care of the old people" "Overall I'm very optimistic. We're headed for a future of amazing abundance. We are in the most interesting time in history"
6:38

Elon Musk reveals why he’s building rockets, AI, and humanoid robots at the same time "The overall goal of my companies is to maximize the probability that civilization has a great future, and to expand consciousness beyond earth" "We need to assume that life and consciousness is extremely rare and it might only be us" "The image in my mind is of a tiny candle in a vast darkness, a tiny candle of consciousness that could easily go out" "That's why it's important to make life multiplanetary, such that if there's a natural or man-made disaster on earth, that consciousness continues" "That's the purpose of SpaceX" "With robotics and AI, this is really the path to abundance for all. The only way to solve global poverty and give everyone a very high standard of living is AI and robotics" "My prediction is there'll be more robots than people. Everyone on earth is going to have one and going to want one. We will make so many robots and AI that they will saturate all human needs. You won't be able to even think of something to ask the robot for" "Who wouldn't want a robot to watch over your kids, take care of your pet, or protect an elderly parent? There just aren't enough young people to take care of the old people" "Overall I'm very optimistic. We're headed for a future of amazing abundance. We are in the most interesting time in history"

Jaynit

618,499 Aufrufe • vor 3 Monaten

THIS GUY PUT FABLE, GPT 5.6 SOL, AND OPUS IN ONE SHARED CHAT AND HAS THEM TAG EACH OTHER LIKE COWORKERS instead of running three separate models in three separate windows and copy pasting between them, they all sit in one room and talk to each other > you tag whichever model you want and only that one responds > they tag each other too, so fable can hand something to opus, opus can pull in sol for a second opinion > each agent only sees the messages theyre mentioned in, but you get to read everything > he had them running and orchestrating together for more than a day straight the best part is how different each one actually behaves: > fable is the creative product manager. huge context window so it remembers everything, and its the one finding the out of the box solutions > sol is the obsessive tech lead. it will trace every bug to the exact source and stress test every edge case before writing a single line > opus is the workhorse engineer. smart enough for almost anything, and it constantly questions things, which forces the other two to re-examine their own solutions its like managing a team. one plans, one writes the code, one reviews it, and they hand work back and forth without you playing middleman everyone else is arguing about which model is best right now, but the real play is making them work together at the same time
1:11

THIS GUY PUT FABLE, GPT 5.6 SOL, AND OPUS IN ONE SHARED CHAT AND HAS THEM TAG EACH OTHER LIKE COWORKERS instead of running three separate models in three separate windows and copy pasting between them, they all sit in one room and talk to each other > you tag whichever model you want and only that one responds > they tag each other too, so fable can hand something to opus, opus can pull in sol for a second opinion > each agent only sees the messages theyre mentioned in, but you get to read everything > he had them running and orchestrating together for more than a day straight the best part is how different each one actually behaves: > fable is the creative product manager. huge context window so it remembers everything, and its the one finding the out of the box solutions > sol is the obsessive tech lead. it will trace every bug to the exact source and stress test every edge case before writing a single line > opus is the workhorse engineer. smart enough for almost anything, and it constantly questions things, which forces the other two to re-examine their own solutions its like managing a team. one plans, one writes the code, one reviews it, and they hand work back and forth without you playing middleman everyone else is arguing about which model is best right now, but the real play is making them work together at the same time

Om Patel

22,998 Aufrufe • vor 1 Monat