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The mind-blowing tech behind microchip manufacturing. Lasers fire 50,000 times per second at tin droplets moving 100 m/s, creating extreme ultraviolet light plasma — and that’s just one step inside ASML’s chip-making machine.

Massimo

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88,112 views • 10 months ago •via X (Twitter)

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This single $300 MILLION machine fires lasers at tin droplets 50,000 times per second creating plasma hotter than the sun and without it your phone and modern AI wouldn’t exist.

This single $300 MILLION machine fires lasers at tin droplets 50,000 times per second creating plasma hotter than the sun and without it your phone and modern AI wouldn’t exist.

Massimo

87,065 views • 6 months ago

Industrial dance, nothing more! 🕺🏻 But it's not just about dancing. These machines are behind one of the biggest chip producers. TRUMPF laser machines play a key role in the manufacturing processes of ASML. These high-precision lasers are used in the production of critical components for ASML’s EUV (Extreme Ultraviolet) lithography systems. The reliability and accuracy of TRUMPF’s laser technology help enable the nanometer-scale precision required in semiconductor fabrication. So actually, these machines might more relevant to AI and robotics industry than ever!

Industrial dance, nothing more! 🕺🏻 But it's not just about dancing. These machines are behind one of the biggest chip producers. TRUMPF laser machines play a key role in the manufacturing processes of ASML. These high-precision lasers are used in the production of critical components for ASML’s EUV (Extreme Ultraviolet) lithography systems. The reliability and accuracy of TRUMPF’s laser technology help enable the nanometer-scale precision required in semiconductor fabrication. So actually, these machines might more relevant to AI and robotics industry than ever!

Lukas Ziegler

176,753 views • 10 months ago

ACCELERATOR-BASED LITHOGRAPHY AND THE INDUCTION STORAGE RING LIGHT SOURCE The future of chipmaking may look less like a factory and more like a power grid. The proposed “Terafab” paradigm reimagines semiconductor manufacturing at utility scale: chip design, wafer fabrication, EUV lithography, memory, advanced packaging, and testing all under one roof, with the ambition of producing more than one terawatt of AI compute capacity per year. Its most radical innovation is treating light as a utility. Today, each EUV scanner relies on its own laser-produced plasma source, firing lasers at molten tin to generate 13.5 nm light. The process is inefficient, intensely hot, debris-heavy, and difficult to scale. Terafab replaces those individual sources with centralized, accelerator-driven free-electron lasers capable of distributing multi-kilowatt EUV light across an entire network of scanners. The advantages could be transformative: • No tin contamination or destructive plasma debris • Higher efficiency through electron-beam energy recovery • Greater photon flux to suppress stochastic defects at sub-3 nm nodes • Redundant accelerators that keep scanners operating during maintenance • Tunable wavelengths, potentially enabling 6.x nm “Beyond EUV” lithography THAT LAST POINT MATTERS ENORMOUSLY Conventional EUV is locked to the atomic emission of tin. Free-electron lasers are not. Their wavelength can be tuned through the energy of the electron beam creating a possible path beyond today’s 13.5 nm limit. If realized, Terafab would represent more than a larger semiconductor plant. It would transform lithography from a collection of isolated tools into shared industrial infrastructure. The next era of chipmaking may not be defined by a better machine. It may be defined by an entirely new architecture for manufacturing intelligence at civilization scale.

ACCELERATOR-BASED LITHOGRAPHY AND THE INDUCTION STORAGE RING LIGHT SOURCE The future of chipmaking may look less like a factory and more like a power grid. The proposed “Terafab” paradigm reimagines semiconductor manufacturing at utility scale: chip design, wafer fabrication, EUV lithography, memory, advanced packaging, and testing all under one roof, with the ambition of producing more than one terawatt of AI compute capacity per year. Its most radical innovation is treating light as a utility. Today, each EUV scanner relies on its own laser-produced plasma source, firing lasers at molten tin to generate 13.5 nm light. The process is inefficient, intensely hot, debris-heavy, and difficult to scale. Terafab replaces those individual sources with centralized, accelerator-driven free-electron lasers capable of distributing multi-kilowatt EUV light across an entire network of scanners. The advantages could be transformative: • No tin contamination or destructive plasma debris • Higher efficiency through electron-beam energy recovery • Greater photon flux to suppress stochastic defects at sub-3 nm nodes • Redundant accelerators that keep scanners operating during maintenance • Tunable wavelengths, potentially enabling 6.x nm “Beyond EUV” lithography THAT LAST POINT MATTERS ENORMOUSLY Conventional EUV is locked to the atomic emission of tin. Free-electron lasers are not. Their wavelength can be tuned through the energy of the electron beam creating a possible path beyond today’s 13.5 nm limit. If realized, Terafab would represent more than a larger semiconductor plant. It would transform lithography from a collection of isolated tools into shared industrial infrastructure. The next era of chipmaking may not be defined by a better machine. It may be defined by an entirely new architecture for manufacturing intelligence at civilization scale.

Lacey

12,186 views • 15 days ago

EUV machines are the most complicated tools humans make. Their supply chain has over 10,000 individual suppliers, and any one of them not scaling fast enough can bottleneck the entire AI industry. An EUV tool fires lasers at a tiny tin droplet three times in precise sequence, blasting it hard enough to emit EUV light. That light bounces off 18 multilayer mirrors onto the wafer. Meanwhile, the two platforms inside the machine - one holding the stencil, one holding the chip - are flying back and forth at 9Gs in opposite directions. The successive passes have to land on top of each other to within 3 nanometers. If any part of this is off, yield goes to zero. Take just one component. The mirrors are mostly supplied by Carl Zeiss, who have probably fewer than a thousand people working on them. In turn, Carl Zeiss rely on machines from Switzerland to deposit each of the layers, and use a coating process co-developed with a different German company. None of these companies have woken up. They’re gradually increasing production, but nowhere near the levels necessary for what the labs want by the end of the decade. Dylan Patel predicts production can't scale beyond about 100 EUV machines per year by 2030, no matter how much money gets thrown at the problem. In the medium term this is the key bottleneck on scaling.

EUV machines are the most complicated tools humans make. Their supply chain has over 10,000 individual suppliers, and any one of them not scaling fast enough can bottleneck the entire AI industry. An EUV tool fires lasers at a tiny tin droplet three times in precise sequence, blasting it hard enough to emit EUV light. That light bounces off 18 multilayer mirrors onto the wafer. Meanwhile, the two platforms inside the machine - one holding the stencil, one holding the chip - are flying back and forth at 9Gs in opposite directions. The successive passes have to land on top of each other to within 3 nanometers. If any part of this is off, yield goes to zero. Take just one component. The mirrors are mostly supplied by Carl Zeiss, who have probably fewer than a thousand people working on them. In turn, Carl Zeiss rely on machines from Switzerland to deposit each of the layers, and use a coating process co-developed with a different German company. None of these companies have woken up. They’re gradually increasing production, but nowhere near the levels necessary for what the labs want by the end of the decade. Dylan Patel predicts production can't scale beyond about 100 EUV machines per year by 2030, no matter how much money gets thrown at the problem. In the medium term this is the key bottleneck on scaling.

Dwarkesh Patel

154,271 views • 5 months ago

Former #Patriots WR Josh Gordon says Tom Brady made him and other teammates live with him during the offseason. “He made us live with him in the offseason. Like train right there, his wife making us food and everything like that. It’d be me, [Julian] Edelman, and [Rob Gronkowski]… We would just do the most crazy routine of like standing in one spot and at least 50 times you just catching one specific pass... Alex Guerrero, his coach, is just holding the gun, like a speedometer for baseball, that was just clocking if it stays at 62 miles per hour… Meanwhile your hands are just f***ing taking a beating… That’s about the craziest s**t... He’s obsessive, bro. That’s the craziest s**t. When you see how hard people prepare.” (🎥 N3on on Kick)

Former #Patriots WR Josh Gordon says Tom Brady made him and other teammates live with him during the offseason. “He made us live with him in the offseason. Like train right there, his wife making us food and everything like that. It’d be me, [Julian] Edelman, and [Rob Gronkowski]… We would just do the most crazy routine of like standing in one spot and at least 50 times you just catching one specific pass... Alex Guerrero, his coach, is just holding the gun, like a speedometer for baseball, that was just clocking if it stays at 62 miles per hour… Meanwhile your hands are just f*ing taking a beating… That’s about the craziest st... He’s obsessive, bro. That’s the craziest s**t. When you see how hard people prepare.” (🎥 N3on on Kick)

Carlos A. Lopez

2,887,816 views • 1 month ago

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. 👉

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. 👉

Erika 

162,540 views • 9 months ago

Andrew Huberman explained déjà vu in the most mind-blowing way: Your brain doesn’t replay memories forward… it can fire them backward or all at once. That eerie “I’ve been here before” feeling? Just neurons accidentally playing a sequence that’s close enough to a real memory. Even crazier: During learning (like nailing a kickflip), your brain pauses → then replays the exact same neurons 20–30 times faster in reverse during those 10-second breaks. You’re practicing without moving. Same thing happens in sleep — massive replay, forward and backward. That’s why short, focused pauses + sleep = rocket-fuel learning. 5:53 clip — the brain’s secret replay system exposed 👇 Ever had déjà vu so strong it felt like time travel? Or noticed faster progress when you pause during practice?

Andrew Huberman explained déjà vu in the most mind-blowing way: Your brain doesn’t replay memories forward… it can fire them backward or all at once. That eerie “I’ve been here before” feeling? Just neurons accidentally playing a sequence that’s close enough to a real memory. Even crazier: During learning (like nailing a kickflip), your brain pauses → then replays the exact same neurons 20–30 times faster in reverse during those 10-second breaks. You’re practicing without moving. Same thing happens in sleep — massive replay, forward and backward. That’s why short, focused pauses + sleep = rocket-fuel learning. 5:53 clip — the brain’s secret replay system exposed 👇 Ever had déjà vu so strong it felt like time travel? Or noticed faster progress when you pause during practice?

Camus

311,312 views • 7 months ago

The world runs on a single island (!!) Over 90% of leading-edge chips are manufactured by TSMC in Taiwan — a democratic nation just 100 miles off the coast of China, which the CCP openly claims as its own territory. Given the stakes, it’s astonishing (almost absurd) that the entire global economy rests on a geopolitical fault line this volatile. Once you see what happens inside a TSMC production line, the extreme ultraviolet lithography machines, the atomic-level precision, the thousands of interlocking steps ,you realise TSMC isn’t just hard to replicate. It is, for now, irreplaceable. The U.S. is scrambling to onshore semiconductor manufacturing, while China is aggressively pursuing chip sovereignty and instructing its tech giants to reduce reliance on American hardware. But both sides understand the same reality: if China moved on Taiwan, the balance of global power would shift overnight. This is the wild card at the centre of the AI race — a tiny island everyone depends on, and whose fate may determine the future of global power.

The world runs on a single island (!!) Over 90% of leading-edge chips are manufactured by TSMC in Taiwan — a democratic nation just 100 miles off the coast of China, which the CCP openly claims as its own territory. Given the stakes, it’s astonishing (almost absurd) that the entire global economy rests on a geopolitical fault line this volatile. Once you see what happens inside a TSMC production line, the extreme ultraviolet lithography machines, the atomic-level precision, the thousands of interlocking steps ,you realise TSMC isn’t just hard to replicate. It is, for now, irreplaceable. The U.S. is scrambling to onshore semiconductor manufacturing, while China is aggressively pursuing chip sovereignty and instructing its tech giants to reduce reliance on American hardware. But both sides understand the same reality: if China moved on Taiwan, the balance of global power would shift overnight. This is the wild card at the centre of the AI race — a tiny island everyone depends on, and whose fate may determine the future of global power.

Nina Schick

2,584,252 views • 8 months ago

This is one of the most mind-blowing things you’ll EVER see. (And I swear to G-D if this post only gets a few thousand views on X, then X is simply not fit for purpose for anything. Because this is something everyone should see and be blown away by) You’re watching a laser beam move in real time… at 2 billion frames per second. Light only travels about 15 cm (6 inches) in each frame. That’s why you can actually see the beam crawl across the room, bounce off the mirrors, and come back. Now watch carefully when the camera position changes… The light going away from the camera suddenly looks slow. The light coming toward the camera looks almost instant. It looks like the speed of light is changing. It’s not. You’re seeing the finite speed of light itself. The camera is literally recording the PAST at different moments depending on how far the light has to travel to reach the sensor. This isn’t CGI. This isn’t a simulation. This is a homemade camera filming actual photons. Full credit, and huge respect, to AlphaPhoenix Alpha Phoenix Channel

This is one of the most mind-blowing things you’ll EVER see. (And I swear to G-D if this post only gets a few thousand views on X, then X is simply not fit for purpose for anything. Because this is something everyone should see and be blown away by) You’re watching a laser beam move in real time… at 2 billion frames per second. Light only travels about 15 cm (6 inches) in each frame. That’s why you can actually see the beam crawl across the room, bounce off the mirrors, and come back. Now watch carefully when the camera position changes… The light going away from the camera suddenly looks slow. The light coming toward the camera looks almost instant. It looks like the speed of light is changing. It’s not. You’re seeing the finite speed of light itself. The camera is literally recording the PAST at different moments depending on how far the light has to travel to reach the sensor. This isn’t CGI. This isn’t a simulation. This is a homemade camera filming actual photons. Full credit, and huge respect, to AlphaPhoenix Alpha Phoenix Channel

Mor Edge Insight

228,476 views • 21 days ago

DeepSeek R1 671B is ridiculously good, but running at 158 tokens per second is something out of this world. A model *this good* running *this fast* is 3 years ahead of this time. The secret: DeepSeek in this short video isn't running on GPUs. It's running on custom-made chips! This is running on SambaNova and their SN40L chips, which are designed and optimized for running AI workflows. SambaNova is sponsoring my work. We designed GPUs to run games and later realized we could use them for AI. This is very different from creating a chip specifically for AI from the ground up. Just think about this for a second: One single SNL40 chip can simultaneously hold 100+ models (trillions of parameters) in memory! An agentic workflow that uses multiple models simultaneously won't need to swap models from memory even once! By the way, the Llama family of models *screams* on the SambaNova Cloud. Here are some numbers from my latest tests: • Llama 3.3 70B: 367.32 t/s • Llama 3.2 1B: 2381.82 t/s • Llama 3.2 3B: 1335.71 t/s • Llama 3.1 8B: 929.58 t/s • Llama 3.1 70B: 369.16 t/s • Llama 3.1 405B: 103.37 t/s You can access all of these models for free!

DeepSeek R1 671B is ridiculously good, but running at 158 tokens per second is something out of this world. A model this good running this fast is 3 years ahead of this time. The secret: DeepSeek in this short video isn't running on GPUs. It's running on custom-made chips! This is running on SambaNova and their SN40L chips, which are designed and optimized for running AI workflows. SambaNova is sponsoring my work. We designed GPUs to run games and later realized we could use them for AI. This is very different from creating a chip specifically for AI from the ground up. Just think about this for a second: One single SNL40 chip can simultaneously hold 100+ models (trillions of parameters) in memory! An agentic workflow that uses multiple models simultaneously won't need to swap models from memory even once! By the way, the Llama family of models screams on the SambaNova Cloud. Here are some numbers from my latest tests: • Llama 3.3 70B: 367.32 t/s • Llama 3.2 1B: 2381.82 t/s • Llama 3.2 3B: 1335.71 t/s • Llama 3.1 8B: 929.58 t/s • Llama 3.1 70B: 369.16 t/s • Llama 3.1 405B: 103.37 t/s You can access all of these models for free!

Santiago

131,019 views • 1 year ago

The Cybercab is aiming to produce 2 million units per year. Let this sink in. Today, Tesla produces about ~1.7 million vehicles per year total, across its entire lineup. And now Tesla is preparing to outproduce that with one single vehicle, a fully autonomous one. This is Elon and Tesla going ALL-IN on autonomy. Production is scheduled to start April 2026 at Giga Texas, with volume ramping throughout the year. And as of early 2026, Cybercab prototypes are already being tested around the U.S. The Tesla Cybercab is built from the ground up for unsupervised autonomy. There is no steering wheel and no pedals, just cameras, AI, and Tesla’s custom inference computers. No lidar and radar like other companies, just pure vision and software. Elon put it best on the Q3 2024 earnings call: “It’s not just a revolutionary vehicle design, but a revolution in vehicle manufacturing that is also coming with the Cybercab.” That quote matters a lot bc that means the entire way a vehicle is manufactured is changing with the Cybercab. Tesla is designing what Elon calls “the machine that builds the machine.” The Cybercab uses Tesla’s unboxed manufacturing process, where major sections are built in parallel instead of one long assembly line. There are fewer parts, less steps & cost, and faster scale. That’s how you make 2 million Cybercabs per year possible. FYI, this is not going to be easy though. Elon has been brutally honest about production for many years: • “Prototypes are easy, production is hard.” • “The extreme difficulty of scaling production of new technology is poorly understood. It’s 1000% to 10,000% harder than making a few prototypes.” • “For cars, it’s maybe 100 times harder to design the manufacturing system than the car itself.” He reinforced this again in January 2026 when talking about Cybercab and Optimus on 𝕏: “Initial production is always very slow and follows an S-curve. The speed of the production ramp is inversely proportional to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow - but eventually end up being insanely fast.” This is the key thing most people miss about Tesla manufacturing. Early output will be slow by design. Almost everything is new like the vehicle architecture, factory layout, AI hardware, and manufacturing flow. But once it works and clicks, it begins to scale hard. Tesla already proved they can do this. They survived Model 3 production hell. They turned Model Y into the BEST selling car in the world, of any kind. They ramped Cybertruck, which has over 30,000+ unique parts, to meaningful volume. Elon summed it up perfectly in 2024: “Compared to the insane pain of reaching high volume, positive margin production, prototypes are a piece of cake.” That’s why Tesla makes manufacturing look easy bc they already earned the scars from the last vehicle lineups. The Cybercab is aiming to be: 1/ Under $30,000 price 2/ ~$0.20 per mile operating cost 3/ 200+ mile range 4/ Up to 5x utilization vs personal cars 5/ Designed to run nearly nonstop 24/7 This is what you call manufacturing + AI + autonomy converging at scale. The competitors are still showing prototypes and demos, while Tesla is building new production lines, expanding factories, and actually building the product. I remember when Elon told me in the past that one of Tesla’s key advantage long term was going to be manufacturing technology. I get it now.

The Cybercab is aiming to produce 2 million units per year. Let this sink in. Today, Tesla produces about ~1.7 million vehicles per year total, across its entire lineup. And now Tesla is preparing to outproduce that with one single vehicle, a fully autonomous one. This is Elon and Tesla going ALL-IN on autonomy. Production is scheduled to start April 2026 at Giga Texas, with volume ramping throughout the year. And as of early 2026, Cybercab prototypes are already being tested around the U.S. The Tesla Cybercab is built from the ground up for unsupervised autonomy. There is no steering wheel and no pedals, just cameras, AI, and Tesla’s custom inference computers. No lidar and radar like other companies, just pure vision and software. Elon put it best on the Q3 2024 earnings call: “It’s not just a revolutionary vehicle design, but a revolution in vehicle manufacturing that is also coming with the Cybercab.” That quote matters a lot bc that means the entire way a vehicle is manufactured is changing with the Cybercab. Tesla is designing what Elon calls “the machine that builds the machine.” The Cybercab uses Tesla’s unboxed manufacturing process, where major sections are built in parallel instead of one long assembly line. There are fewer parts, less steps & cost, and faster scale. That’s how you make 2 million Cybercabs per year possible. FYI, this is not going to be easy though. Elon has been brutally honest about production for many years: • “Prototypes are easy, production is hard.” • “The extreme difficulty of scaling production of new technology is poorly understood. It’s 1000% to 10,000% harder than making a few prototypes.” • “For cars, it’s maybe 100 times harder to design the manufacturing system than the car itself.” He reinforced this again in January 2026 when talking about Cybercab and Optimus on 𝕏: “Initial production is always very slow and follows an S-curve. The speed of the production ramp is inversely proportional to how many new parts and steps there are. For Cybercab and Optimus, almost everything is new, so the early production rate will be agonizingly slow - but eventually end up being insanely fast.” This is the key thing most people miss about Tesla manufacturing. Early output will be slow by design. Almost everything is new like the vehicle architecture, factory layout, AI hardware, and manufacturing flow. But once it works and clicks, it begins to scale hard. Tesla already proved they can do this. They survived Model 3 production hell. They turned Model Y into the BEST selling car in the world, of any kind. They ramped Cybertruck, which has over 30,000+ unique parts, to meaningful volume. Elon summed it up perfectly in 2024: “Compared to the insane pain of reaching high volume, positive margin production, prototypes are a piece of cake.” That’s why Tesla makes manufacturing look easy bc they already earned the scars from the last vehicle lineups. The Cybercab is aiming to be: 1/ Under $30,000 price 2/ ~$0.20 per mile operating cost 3/ 200+ mile range 4/ Up to 5x utilization vs personal cars 5/ Designed to run nearly nonstop 24/7 This is what you call manufacturing + AI + autonomy converging at scale. The competitors are still showing prototypes and demos, while Tesla is building new production lines, expanding factories, and actually building the product. I remember when Elon told me in the past that one of Tesla’s key advantage long term was going to be manufacturing technology. I get it now.

Teslaconomics

31,985 views • 7 months ago

$🇮🇱 TECH THURSDAY: GLOBALIZATION IS DYING. ISRAELI TECH COULD BE ONE OF THE BIG WINNERS. (Part of our scheduled content...always pinned) The world is breaking into blocs. Supply chains are collapsing. Wars are redrawing trade routes. Countries are scrambling to bring manufacturing, defense and critical technologies back home. And Israel may be unusually well positioned for what comes next. On the Jerusalem Post’s Defense & Tech podcast, Yoni Heilbon, co-founder and managing partner at IL Ventures, makes a striking argument: Globalization as we knew it is effectively over. What replaces it is a world of technological sovereignty, shortened supply chains and countries increasingly asking one question: What can we no longer afford to depend on someone else for? That is where Israeli tech enters the picture. AI. Robotics. Cybersecurity. Autonomous manufacturing. Supply-chain security. Dual-use technology. Many of these systems were developed under extreme pressure—and some have now been tested in conditions most startups anywhere else in the world could never replicate. But here’s the part that may surprise you: Countries can condemn Israel publicly while quietly continuing to buy Israeli technology behind the scenes. Why? Because when national security, factories, infrastructure and billions of dollars are on the line, politics can suddenly take a back seat to performance. Heilbon believes one of the biggest transformations ahead will be manufacturing itself, as production returns closer to home and automation becomes essential to making that economically possible. And the opportunity for Israeli companies is no longer just America. India. Japan. Europe. Mexico. Brazil. Asia. A fractured world may actually create more demand for the technologies Israel does best. The Startup Nation was built under pressure. Now the rest of the world is entering an age of pressure too. That could change everything. Subscribe to the The Jerusalem Post and its Defense & Tech podcast. Link in comments.$

🇮🇱 TECH THURSDAY: GLOBALIZATION IS DYING. ISRAELI TECH COULD BE ONE OF THE BIG WINNERS. (Part of our scheduled content...always pinned) The world is breaking into blocs. Supply chains are collapsing. Wars are redrawing trade routes. Countries are scrambling to bring manufacturing, defense and critical technologies back home. And Israel may be unusually well positioned for what comes next. On the Jerusalem Post’s Defense & Tech podcast, Yoni Heilbon, co-founder and managing partner at IL Ventures, makes a striking argument: Globalization as we knew it is effectively over. What replaces it is a world of technological sovereignty, shortened supply chains and countries increasingly asking one question: What can we no longer afford to depend on someone else for? That is where Israeli tech enters the picture. AI. Robotics. Cybersecurity. Autonomous manufacturing. Supply-chain security. Dual-use technology. Many of these systems were developed under extreme pressure—and some have now been tested in conditions most startups anywhere else in the world could never replicate. But here’s the part that may surprise you: Countries can condemn Israel publicly while quietly continuing to buy Israeli technology behind the scenes. Why? Because when national security, factories, infrastructure and billions of dollars are on the line, politics can suddenly take a back seat to performance. Heilbon believes one of the biggest transformations ahead will be manufacturing itself, as production returns closer to home and automation becomes essential to making that economically possible. And the opportunity for Israeli companies is no longer just America. India. Japan. Europe. Mexico. Brazil. Asia. A fractured world may actually create more demand for the technologies Israel does best. The Startup Nation was built under pressure. Now the rest of the world is entering an age of pressure too. That could change everything. Subscribe to the The Jerusalem Post and its Defense & Tech podcast. Link in comments.

Mossad Commentary

32,083 views • 10 days ago

America is a failed state, taken over by organized crime. This was not an oversight hearing; it was a mafia lieutenant deliberately creating a scene to distract the cops, while the criminals escape out the back door. All of it was scripted. Count how many times she looks down while she touts the stock market with Epstein’s survivors behind her. She had at least three binders, at least one with opposition research on the Democrats. No information on survivors, or Epstein, or any actual criminals—but lots of ammunition to fire at representatives who were hired by the American people to look over her function. Worse, she had printouts of which unredacted Epstein files they had looked at, giving her advanced notice of what they might ask her—and what they know and don’t know. Surveillance state performance art. One important note, the stock market is one of the only things Trump cares about. That’s why this psychotic rant about it was written down and why she felt compelled to read it. This is a weakness being exploited by many. When it finally crashes, batten down. “The Dow is over $50,000. I don't know why you're laughing. You're a great stock trader as I hear, Raskin. The Dow is over 50,000 right now. The S&P is almost 7,000. And the Nasdaq smashing records. Americans, 401ks and retirement savings are booming. That's what we should be talking about. We should be talking about making Americans safe. We should be talking about what does a Dow have to do with anything? That's what they just asked. Are you kidding? The Dow has shattered 50,000 for the first time. This is crazy. They said it couldn't be done in four years. You have President Trump has done it in one year.”

America is a failed state, taken over by organized crime. This was not an oversight hearing; it was a mafia lieutenant deliberately creating a scene to distract the cops, while the criminals escape out the back door. All of it was scripted. Count how many times she looks down while she touts the stock market with Epstein’s survivors behind her. She had at least three binders, at least one with opposition research on the Democrats. No information on survivors, or Epstein, or any actual criminals—but lots of ammunition to fire at representatives who were hired by the American people to look over her function. Worse, she had printouts of which unredacted Epstein files they had looked at, giving her advanced notice of what they might ask her—and what they know and don’t know. Surveillance state performance art. One important note, the stock market is one of the only things Trump cares about. That’s why this psychotic rant about it was written down and why she felt compelled to read it. This is a weakness being exploited by many. When it finally crashes, batten down. “The Dow is over $50,000. I don't know why you're laughing. You're a great stock trader as I hear, Raskin. The Dow is over 50,000 right now. The S&P is almost 7,000. And the Nasdaq smashing records. Americans, 401ks and retirement savings are booming. That's what we should be talking about. We should be talking about making Americans safe. We should be talking about what does a Dow have to do with anything? That's what they just asked. Are you kidding? The Dow has shattered 50,000 for the first time. This is crazy. They said it couldn't be done in four years. You have President Trump has done it in one year.”

Jim Stewartson, Decelerationist 🇨🇦🇺🇦🇺🇸

112,095 views • 6 months ago

Elon Musk said the most dangerous thing ever spoken on a stage. Not about Mars. Not about rockets. About you. Musk: “The most important mistake I see smart people making is assuming that they’re smart.” He wasn’t talking about IQ. He was talking about what happens when intelligence becomes your identity. Kasparov. Greatest chess mind in human history. Deep Blue crushed him in 1997. Your phone can now crush the world chess champion. Not a close match. Not on his best day. Literally. The world didn’t panic. It adjusted the definition. “Chess isn’t real intelligence.” Nobody flinched. Musk: “We just keep moving the goalposts.” That line should terrify you more than any AI headline ever written. Because he’s not describing technology. He’s describing a pattern that has never once been broken. Machine beats human at chess. “That’s just pattern matching.” Machine beats human at language. “That’s just autocomplete.” Machine beats human at reasoning. “That’s just…” You see where this ends. Every time a machine crosses a line we said only humans could cross, we don’t sit with it. We redraw the line. And call it nuance. That is not intelligence defending itself. That is pride bleeding out slowly enough to mistake for composure. Elon didn’t say machines are smarter to provoke a reaction. He said it because the people who refuse to hear it are the ones it’s coming for. Not from AI. From the version of themselves that would rather redefine reality than face it. That’s the real danger he was pointing at. Not the machine that outperforms you. The story you tell yourself about why it doesn’t count. One day there will be no goalpost left to move. The people who spent years moving them won’t have sharpened a thing. Too busy protecting their ego. The machine didn’t replace you. You were just standing where the goalpost used to be.

Elon Musk said the most dangerous thing ever spoken on a stage. Not about Mars. Not about rockets. About you. Musk: “The most important mistake I see smart people making is assuming that they’re smart.” He wasn’t talking about IQ. He was talking about what happens when intelligence becomes your identity. Kasparov. Greatest chess mind in human history. Deep Blue crushed him in 1997. Your phone can now crush the world chess champion. Not a close match. Not on his best day. Literally. The world didn’t panic. It adjusted the definition. “Chess isn’t real intelligence.” Nobody flinched. Musk: “We just keep moving the goalposts.” That line should terrify you more than any AI headline ever written. Because he’s not describing technology. He’s describing a pattern that has never once been broken. Machine beats human at chess. “That’s just pattern matching.” Machine beats human at language. “That’s just autocomplete.” Machine beats human at reasoning. “That’s just…” You see where this ends. Every time a machine crosses a line we said only humans could cross, we don’t sit with it. We redraw the line. And call it nuance. That is not intelligence defending itself. That is pride bleeding out slowly enough to mistake for composure. Elon didn’t say machines are smarter to provoke a reaction. He said it because the people who refuse to hear it are the ones it’s coming for. Not from AI. From the version of themselves that would rather redefine reality than face it. That’s the real danger he was pointing at. Not the machine that outperforms you. The story you tell yourself about why it doesn’t count. One day there will be no goalpost left to move. The people who spent years moving them won’t have sharpened a thing. Too busy protecting their ego. The machine didn’t replace you. You were just standing where the goalpost used to be.

Dustin

108,099 views • 3 months ago

A Chinese guy spent $20 on an AI video workflow and claims he can replace anyone in any video. He also recorded a full tutorial on creating your own AI avatar. At first, he was just trying to avoid editing the same clip by hand over and over again He spent $20 on the API credits Then he uploaded one normal video and one picture of the character he wanted n8n sent both files to Cloudinary Cloudinary turned them into clean links Wan 2.2 took the video, took the image, and rebuilt the person inside the clip The face changed The body stayed The movement stayed The camera stayed Then he realized this is not a face swap tool anymore It is a machine for making 100 versions of the same video AI is getting scary because now the product is not the video The product is whoever you put inside it

0xTria

28,912 views • 2 months ago

Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind, calls the human brain a "mobile processor" compared to what’s coming. - The human brain is essentially a low-power, 20-watt mobile processor constrained by biology. - While our internal neural signals crawl at just 30 meters per second, AI data travels at the speed of light. - Biological neurons typically fire at rates up to 100–200 Hz (with averages much lower, around 0.1–2 Hz, and some peaking at ~450 Hz), whereas modern silicon chips operate at clock speeds typically up to 6 billion Hz. i.e. Silicon is about 60million times faster - The physical gap is astronomical: AI hardware possesses a 6 to 8 order of magnitude advantage in speed, bandwidth, and scale. - Is human intelligence the universal upper limit? Shane Legg argues "absolutely not." - Just as humans cannot physically outlift a crane or outrun a dragster, our biological cognition cannot compete with industrial-scale compute. - As we master the architecture of intelligence, AI is mathematically destined to far exceed the capabilities of the human mind. --- From 'Google DeepMind and Hannah Fry' YT Channel (link in comment)

Shane Legg, Chief AGI Scientist and co-founder of Google DeepMind, calls the human brain a "mobile processor" compared to what’s coming. - The human brain is essentially a low-power, 20-watt mobile processor constrained by biology. - While our internal neural signals crawl at just 30 meters per second, AI data travels at the speed of light. - Biological neurons typically fire at rates up to 100–200 Hz (with averages much lower, around 0.1–2 Hz, and some peaking at ~450 Hz), whereas modern silicon chips operate at clock speeds typically up to 6 billion Hz. i.e. Silicon is about 60million times faster - The physical gap is astronomical: AI hardware possesses a 6 to 8 order of magnitude advantage in speed, bandwidth, and scale. - Is human intelligence the universal upper limit? Shane Legg argues "absolutely not." - Just as humans cannot physically outlift a crane or outrun a dragster, our biological cognition cannot compete with industrial-scale compute. - As we master the architecture of intelligence, AI is mathematically destined to far exceed the capabilities of the human mind. --- From 'Google DeepMind and Hannah Fry' YT Channel (link in comment)

Rohan Paul

236,133 views • 7 months ago

🚨 Chinese developers just dropped a 4-minute video and GitHub Repo that’s exploding the internet. They opened a terminal… and in just 4 minutes built a team of 7 AI agents that completely replace an entire customer support department. Cost? > Only $50 a month. > One agent classifies tickets. > One reads the knowledge base. > One handles billing. > One watches the customer’s tone. > One decides when to escalate. > One writes the reports. And one agent runs all the other six. The results? Brutal. 1,200 tickets a day per agent. More than a full call center of 8 human operators. Companies used to spend $25,000–$40,000 per month on salaries. Now they pay just $50 for the API. The video hit 4 million views in 72 hours. Every CEO in China was forwarding it. Every support team in America is panicking. While the West is still debating “Will AI replace jobs?”… China just published the actual manual on how to fire an entire department. But wait it gets way crazier. Pause the video at 0:39. Ignore the guy in the blue shirt. Look at the second monitor on the right. That’s not a support dashboard. That’s a live trading wallet. > Username: gabagool22 > $868,862 profit > 28,620 predictions > All on Bitcoin > All 15-minute windows Every single one green. Profile: Copying his trades: GitHub: The comment section turned into a detective agency. People slowed the video to 0.5x, screenshot every frame, and stitched the full wallet together from just 6 seconds of background footage. Entry prices: 2 to 10 cents. Payouts: thousands of dollars. Not one red trade in 28,620 bets. Biggest single win? $4,696 in one 15-minute window. Here’s the insane part: The 7 AI agents save a company $40K a month. The wallet on their second monitor makes that money in one good week. They were teaching the world how to replace humans with cheap AI. Meanwhile, their own setup behind the camera was already replacing traders. They deleted the wallet zoom in the next upload. Too late. Someone screen-recorded it anyway. The tutorial got 4 million views. The secret wallet clip got another 800K. Now 727K people are staring at that wallet. The 7 agents are still answering tickets somewhere for $50 a month. But the wallet behind them? It doesn’t need 7 agents. It only needs one. And that one was already running while they were filming. Mind officially blown Screenshot it. Send it to a friend. And ask yourself: what’s running on your second monitor?

AdiiX

23,279 views • 3 months ago

The speed of light is too slow. That is the buried physics problem inside Elon Musk's plan for 1 million AI satellites, and his solution is a constellation that refuses to think together. The world pictures the same machine, a million Starlink satellites wired into one giant brain in the sky. Physics vetoes it on contact. Inside a single ground rack, AI chips exchange 260 terabytes a second across 2 meters of copper and synchronize in millionths of a second. Between satellites hundreds of kilometers apart, light itself needs a full millisecond to cross the gap, a thousand times too slow. The fastest thing that exists cannot keep up with the conversation inside one server rack. A computer cannot be stretched across the sky. Think about it. Maybe Elon will agree. So SpaceX collapsed the computer into the satellite. The spacecraft is the rack. Each AI1 carries a complete data center rack's worth of compute, 120 kilowatts on average, on wings spanning 70 meters, wider than a Boeing 747 jet. The spec Elon posted today pushes further, a full NVIDIA Rubin rack in every Starlink satellite, 72 GPUs, 250 kilowatts peak. Nothing important ever leaves the machine. The weights, the memory, the model itself, all stay home. The laser links between Starlink satellites carry questions and finished answers, never the thinking in between. For 20 odd years computing moved in one direction, toward the cloud, thousands of machines pretending to be one. Orbit just reversed it. A million computers pretending to be nothing but themselves. The moat is not beating the speed of light. If you think about it it is all about building the machine so the most valuable data never enters the race. Two prototypes fly in early 2027. The rocket that has to carry them flies tonight. The piece works out whether the sky becomes one mind or a million. My money is on Elon!

The speed of light is too slow. That is the buried physics problem inside Elon Musk's plan for 1 million AI satellites, and his solution is a constellation that refuses to think together. The world pictures the same machine, a million Starlink satellites wired into one giant brain in the sky. Physics vetoes it on contact. Inside a single ground rack, AI chips exchange 260 terabytes a second across 2 meters of copper and synchronize in millionths of a second. Between satellites hundreds of kilometers apart, light itself needs a full millisecond to cross the gap, a thousand times too slow. The fastest thing that exists cannot keep up with the conversation inside one server rack. A computer cannot be stretched across the sky. Think about it. Maybe Elon will agree. So SpaceX collapsed the computer into the satellite. The spacecraft is the rack. Each AI1 carries a complete data center rack's worth of compute, 120 kilowatts on average, on wings spanning 70 meters, wider than a Boeing 747 jet. The spec Elon posted today pushes further, a full NVIDIA Rubin rack in every Starlink satellite, 72 GPUs, 250 kilowatts peak. Nothing important ever leaves the machine. The weights, the memory, the model itself, all stay home. The laser links between Starlink satellites carry questions and finished answers, never the thinking in between. For 20 odd years computing moved in one direction, toward the cloud, thousands of machines pretending to be one. Orbit just reversed it. A million computers pretending to be nothing but themselves. The moat is not beating the speed of light. If you think about it it is all about building the machine so the most valuable data never enters the race. Two prototypes fly in early 2027. The rocket that has to carry them flies tonight. The piece works out whether the sky becomes one mind or a million. My money is on Elon!

Shanaka Anslem Perera ⚡

56,952 views • 1 month ago