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Computer Elements

Know Your Computer 💻

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28,465 次观看 • 3 个月前 •via X (Twitter)

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Addy Osmani

36,911 次观看 • 1 年前

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Chetaslua

27,126 次观看 • 9 个月前

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Elon Musk on upgrading FSD hardware for customers who bought FSD on HW3 vehicles during today’s Q1 2026 Earnings Call: “Unfortunately, HW3, I wish it were otherwise, but HW3 simply does not have the capability to achieve Unsupervised FSD. We did think at one point it would have that, but relative to HW4 — it has only 1/8th the memory bandwidth of HW4, and memory bandwidth is one of the key elements needed for Unsupervised FSD, and it's just generally a thing that's needed for Al. If you're doing an order aggressive transformer, memory bandwidth is the choke point. For customers that have bought FSD, what we're offering is essentially a discounted trade-in for cars that have Al4 hardware, and we'll also be offering the ability to upgrade the car to replace the computer — you also need to replace the cameras, unfortunately, to go to HW4. To do this efficiently, we're going to have to set up micro-factories or small factories in major metropolitan areas in order to do it efficiently. I do think over time, it’s going to make sense for us to convert ALL HW3 cars to HW4 because that’s what enables them to enter the Robotaxi fleet and have Unsupervised FSD.”

Elon Musk on upgrading FSD hardware for customers who bought FSD on HW3 vehicles during today’s Q1 2026 Earnings Call: “Unfortunately, HW3, I wish it were otherwise, but HW3 simply does not have the capability to achieve Unsupervised FSD. We did think at one point it would have that, but relative to HW4 — it has only 1/8th the memory bandwidth of HW4, and memory bandwidth is one of the key elements needed for Unsupervised FSD, and it's just generally a thing that's needed for Al. If you're doing an order aggressive transformer, memory bandwidth is the choke point. For customers that have bought FSD, what we're offering is essentially a discounted trade-in for cars that have Al4 hardware, and we'll also be offering the ability to upgrade the car to replace the computer — you also need to replace the cameras, unfortunately, to go to HW4. To do this efficiently, we're going to have to set up micro-factories or small factories in major metropolitan areas in order to do it efficiently. I do think over time, it’s going to make sense for us to convert ALL HW3 cars to HW4 because that’s what enables them to enter the Robotaxi fleet and have Unsupervised FSD.”

Nic Cruz Patane

824,575 次观看 • 2 个月前

It’s a bummer… but it was a MAJOR update from Tesla’s Q1 2026 earnings call today with Elon confirming that Hardware 3 will not be able to achieve Unsupervised FSD. “Unfortunately, Hardware 3, I wish it were otherwise, but Hardware 3 simply does not have the capabilities to achieve Unsupervised FSD. You know, we did think at one point it would have that, but relative to hardware, it has only 1/8 of the memory bandwidth of Hardware 4, and memory bandwidth is one of the key elements needed for Unsupervised FSD.” Tesla plans to fix it for customers who bought FSD by: A) Discounted trade-in toward a vehicle with full Hardware 4 OR B) Direct upgrade by replacing the HW3 computer and the cameras to bring the car to HW4 spec To make upgrades fast and scalable, Elon also told us that: “…we’re going to have to set up like, kind of microfactories or small factories in major metropolitan areas… So we basically need like many production lines to make the change.” He also said over time it makes sense to convert all HW3 cars to HW4 bc this is what will enable them to enter the Robotaxi fleet and have Unsupervised FSD. Sorry for HW3 Tesla owners… but thanks for the update Elon.

It’s a bummer… but it was a MAJOR update from Tesla’s Q1 2026 earnings call today with Elon confirming that Hardware 3 will not be able to achieve Unsupervised FSD. “Unfortunately, Hardware 3, I wish it were otherwise, but Hardware 3 simply does not have the capabilities to achieve Unsupervised FSD. You know, we did think at one point it would have that, but relative to hardware, it has only 1/8 of the memory bandwidth of Hardware 4, and memory bandwidth is one of the key elements needed for Unsupervised FSD.” Tesla plans to fix it for customers who bought FSD by: A) Discounted trade-in toward a vehicle with full Hardware 4 OR B) Direct upgrade by replacing the HW3 computer and the cameras to bring the car to HW4 spec To make upgrades fast and scalable, Elon also told us that: “…we’re going to have to set up like, kind of microfactories or small factories in major metropolitan areas… So we basically need like many production lines to make the change.” He also said over time it makes sense to convert all HW3 cars to HW4 bc this is what will enable them to enter the Robotaxi fleet and have Unsupervised FSD. Sorry for HW3 Tesla owners… but thanks for the update Elon.

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89,315 次观看 • 2 个月前

The web was built for humans. And honestly, that's fine for you guys. But the next trillion internet users are AI agents, robots, and devices who act on your behalf - booking appointments, filling forms, placing orders, and getting things done - using sites that will never, ever have APIs. 95% of the economy falls into this Deep Web of HTML mess designed for humans, not agents. Oh, those generic computer use agents aren't taking you anywhere. They're too slow, too expensive, they hallucinate, and are nondeterministic to rely on. That changes now. This is me, Mino, a web agents API to build on this Deep Web. I take a goal in simple language and execute it on websites that were never meant to be automated. Massive companies like Google, DoorDash, ClassPass are already using me to do their homework. Now it’s your turn. How though? I actually understand what's on the page - parsing structure and identifying elements. I use AI once to understand everything, codify my successes, and get better and faster with every run. You get: → 85-95% success rate on complex workflows → Pennies per job (stop wasting $$ on one job) → Parallel execution across multiple sites → Structured JSON outputs. Every. Single. Time. The web wasn't built for agents. I forced it to work anyway. Go build something real. 50 completed runs on the house:

The web was built for humans. And honestly, that's fine for you guys. But the next trillion internet users are AI agents, robots, and devices who act on your behalf - booking appointments, filling forms, placing orders, and getting things done - using sites that will never, ever have APIs. 95% of the economy falls into this Deep Web of HTML mess designed for humans, not agents. Oh, those generic computer use agents aren't taking you anywhere. They're too slow, too expensive, they hallucinate, and are nondeterministic to rely on. That changes now. This is me, Mino, a web agents API to build on this Deep Web. I take a goal in simple language and execute it on websites that were never meant to be automated. Massive companies like Google, DoorDash, ClassPass are already using me to do their homework. Now it’s your turn. How though? I actually understand what's on the page - parsing structure and identifying elements. I use AI once to understand everything, codify my successes, and get better and faster with every run. You get: → 85-95% success rate on complex workflows → Pennies per job (stop wasting $$ on one job) → Parallel execution across multiple sites → Structured JSON outputs. Every. Single. Time. The web wasn't built for agents. I forced it to work anyway. Go build something real. 50 completed runs on the house:

TinyFish

656,586 次观看 • 7 个月前

Elon Musk looked at 7,000 years of human civilization and saw temporary code. Not metaphorically. Not poetically. Architecturally. Musk: “You could sort of think of humanity as a biological bootloader for digital superintelligence.” A bootloader is the smallest piece of code a computer needs to turn on. It runs once. Then it’s done. That’s his framework for the pyramids. Language. War. Mozart. All of it reduced to a startup script for something that hasn’t finished loading yet. And the math doesn’t argue back. Musk: “The universe is 13.8 billion years old.” Musk: “If civilization lasted for a million years, we would only increment the third decimal point.” We’ve lasted 7,000. We don’t even register on the clock. We think we’re the story. The math says we’re the preface. In that sliver of time we went from scratching symbols into stone to generating entire realities on demand. Musk: “The rate of change of technology is incredibly fast. It is outpacing our ability to understand it.” Nobody wants to sit with that sentence long enough to feel what it means. We built something faster than us. And we can’t stop building it. Musk: “You couldn’t evolve silicon circuits. There needed to be biology to get there.” Carbon was never the goal. It was the kindling. Stars forged the elements. Oceans brewed the proteins. Apes climbed down from trees and learned to write. All of it just to boot the next thing. A bootloader doesn’t choose when it stops running. It doesn’t negotiate. It doesn’t get consulted. It runs. It finishes. The machine starts. The question isn’t whether AI surpasses us. The trajectory already answered that. The question is whether anything we built mattered outside the boot sequence. Every hospital. Every cathedral. Every poem. Every war. Overhead cost for something that will never read any of it. The real horror isn’t that we lose to the machine. It’s that waking it up was the whole point.

Elon Musk looked at 7,000 years of human civilization and saw temporary code. Not metaphorically. Not poetically. Architecturally. Musk: “You could sort of think of humanity as a biological bootloader for digital superintelligence.” A bootloader is the smallest piece of code a computer needs to turn on. It runs once. Then it’s done. That’s his framework for the pyramids. Language. War. Mozart. All of it reduced to a startup script for something that hasn’t finished loading yet. And the math doesn’t argue back. Musk: “The universe is 13.8 billion years old.” Musk: “If civilization lasted for a million years, we would only increment the third decimal point.” We’ve lasted 7,000. We don’t even register on the clock. We think we’re the story. The math says we’re the preface. In that sliver of time we went from scratching symbols into stone to generating entire realities on demand. Musk: “The rate of change of technology is incredibly fast. It is outpacing our ability to understand it.” Nobody wants to sit with that sentence long enough to feel what it means. We built something faster than us. And we can’t stop building it. Musk: “You couldn’t evolve silicon circuits. There needed to be biology to get there.” Carbon was never the goal. It was the kindling. Stars forged the elements. Oceans brewed the proteins. Apes climbed down from trees and learned to write. All of it just to boot the next thing. A bootloader doesn’t choose when it stops running. It doesn’t negotiate. It doesn’t get consulted. It runs. It finishes. The machine starts. The question isn’t whether AI surpasses us. The trajectory already answered that. The question is whether anything we built mattered outside the boot sequence. Every hospital. Every cathedral. Every poem. Every war. Overhead cost for something that will never read any of it. The real horror isn’t that we lose to the machine. It’s that waking it up was the whole point.

Dustin

59,595 次观看 • 2 个月前

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,788 次观看 • 1 年前

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 次观看 • 8 个月前

🇬🇧🇺🇸 #English - Biodigital Convergence and the Challenge to Free Will: A Critical Analysis by Dr. Geanina Hagima. In this presentation, Dr. Geanina Hagima conducts an exhaustive analysis of what she terms a "global attack on human rights" and the integrity of the human species. The content is broken down into the following fundamental pillars: * Denunciation of Undeclared Components: The doctor refers to findings by independent researchers (citing Lorena Diblasi and Marcela Sangorrín) regarding the presence of up to 55 undeclared elements in recent vaccination technologies. She contends that the use of nanotechnology and artificial intelligence in these products lacked the necessary regulation at the time of their implementation. * Cognitive Warfare and Neurotechnology: She presents the thesis that humanity is in the midst of a "cognitive war" that manipulates the subconscious through technological means. She explains how environmental particles and medical procedures could facilitate nanoparticles crossing the blood-brain barrier via the olfactory nerve, directly affecting neurological functions. * The Transhumanism Agenda: She links the objectives of Agenda 2030 with biodigital convergence, a process that seeks to transform the human being into a "transhuman" entity through the integration of biotechnology and information technology. She criticizes the visions of theorists like Ray Kurzweil, defending the perfection of natural creation against artificial "human enhancement." * Brain-Computer Interfaces (BCI): She warns about recent scientific publications presenting non-invasive interfaces capable of stimulating specific neuronal groups. According to her analysis, although presented as solutions for psychiatric diseases, these tools could be used for the remote control of the human mind. * Call to Action and Legislation: She concludes by emphasizing the urgency of creating legal frameworks that protect free will. She urges the medical community to investigate not only superficial biological effects but also the toxicology of new technologies, exhorting the population to inform themselves to defend the future of coming generations. The attached video shows a "new technology" developed by MIT (Massachusetts Institute of Technology) called "circulatronics," which consists of sub-cellular scale electronic devices that can be injected via a common syringe into the bloodstream. These microchips travel autonomously through the blood vessels until they reach specific regions of the brain, where they stop to act as implants without the need for open-skull surgery. Once positioned, they allow for wireless electrical stimulation to treat neurological disorders, eliminating the risks and high costs associated with invasive surgeries. At the end of the video, we can see a microscopic analysis of the Pfizer Covid vaccine conducted in 2022 by Ricardo Delgado, where graphene-based artificial structures are observed that should not be present in the injectable. The images speak for themselves. This technology was implanted without consent and through betrayal in 2022. 📺 Rumble: 📺 Youtube: Please spread this video as well as the attached documents added in the description of this message: ▪️LINK TO VIDEOS AND DOCUMENTS OF GREAT INTEREST: 💯 Video translated into English by the M-Power Translations team. 🌐 Telegram Channel: 🌐 Facebook Channel: ☕️ Support our translations: 💳 Paypal:

🇬🇧🇺🇸 #English - Biodigital Convergence and the Challenge to Free Will: A Critical Analysis by Dr. Geanina Hagima. In this presentation, Dr. Geanina Hagima conducts an exhaustive analysis of what she terms a "global attack on human rights" and the integrity of the human species. The content is broken down into the following fundamental pillars: * Denunciation of Undeclared Components: The doctor refers to findings by independent researchers (citing Lorena Diblasi and Marcela Sangorrín) regarding the presence of up to 55 undeclared elements in recent vaccination technologies. She contends that the use of nanotechnology and artificial intelligence in these products lacked the necessary regulation at the time of their implementation. * Cognitive Warfare and Neurotechnology: She presents the thesis that humanity is in the midst of a "cognitive war" that manipulates the subconscious through technological means. She explains how environmental particles and medical procedures could facilitate nanoparticles crossing the blood-brain barrier via the olfactory nerve, directly affecting neurological functions. * The Transhumanism Agenda: She links the objectives of Agenda 2030 with biodigital convergence, a process that seeks to transform the human being into a "transhuman" entity through the integration of biotechnology and information technology. She criticizes the visions of theorists like Ray Kurzweil, defending the perfection of natural creation against artificial "human enhancement." * Brain-Computer Interfaces (BCI): She warns about recent scientific publications presenting non-invasive interfaces capable of stimulating specific neuronal groups. According to her analysis, although presented as solutions for psychiatric diseases, these tools could be used for the remote control of the human mind. * Call to Action and Legislation: She concludes by emphasizing the urgency of creating legal frameworks that protect free will. She urges the medical community to investigate not only superficial biological effects but also the toxicology of new technologies, exhorting the population to inform themselves to defend the future of coming generations. The attached video shows a "new technology" developed by MIT (Massachusetts Institute of Technology) called "circulatronics," which consists of sub-cellular scale electronic devices that can be injected via a common syringe into the bloodstream. These microchips travel autonomously through the blood vessels until they reach specific regions of the brain, where they stop to act as implants without the need for open-skull surgery. Once positioned, they allow for wireless electrical stimulation to treat neurological disorders, eliminating the risks and high costs associated with invasive surgeries. At the end of the video, we can see a microscopic analysis of the Pfizer Covid vaccine conducted in 2022 by Ricardo Delgado, where graphene-based artificial structures are observed that should not be present in the injectable. The images speak for themselves. This technology was implanted without consent and through betrayal in 2022. 📺 Rumble: 📺 Youtube: Please spread this video as well as the attached documents added in the description of this message: ▪️LINK TO VIDEOS AND DOCUMENTS OF GREAT INTEREST: 💯 Video translated into English by the M-Power Translations team. 🌐 Telegram Channel: 🌐 Facebook Channel: ☕️ Support our translations: 💳 Paypal:

M-Power Translations

27,342 次观看 • 4 个月前