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Before an autonomous machine can act, it has to understand. A construction site is one of the most unpredictable environments imaginable. The terrain changes with every pass. Material piles shift. Other machines move nearby. No two digs are the same. For a human operator, reading that environment is second...

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Bedrock Robotics

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33,148 次观看 • 4 个月前 •via X (Twitter)

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The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.
1:27

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 次观看 • 2 个月前

Two very different projects. Same kind of engineering instinct. The upper clip uses a cockroach with a very minimal mechanical setup. The lower one takes a quadcopter and surrounds it with tracked frames so the same machine can move on land, travel through water and take off into the air. What caught my eye is how both designs start from a physical behavior instead of adding layers of software. For the air land water drone the engineering tradeoff is especially interesting. Flying helps with obstacles and speed. Driving can save battery when the terrain allows it. Water mobility gives the same platform another route when roads disappear. Of course, every extra mobility mode adds weight, moving parts and harder control transitions. Still I like this direction. Build the machine around the environment it has to cross. I’m still wondering what the cockroach project in the top clip is actually meant to do though. Does anyone know the intended use case?
0:27

Two very different projects. Same kind of engineering instinct. The upper clip uses a cockroach with a very minimal mechanical setup. The lower one takes a quadcopter and surrounds it with tracked frames so the same machine can move on land, travel through water and take off into the air. What caught my eye is how both designs start from a physical behavior instead of adding layers of software. For the air land water drone the engineering tradeoff is especially interesting. Flying helps with obstacles and speed. Driving can save battery when the terrain allows it. Water mobility gives the same platform another route when roads disappear. Of course, every extra mobility mode adds weight, moving parts and harder control transitions. Still I like this direction. Build the machine around the environment it has to cross. I’m still wondering what the cockroach project in the top clip is actually meant to do though. Does anyone know the intended use case?

Techniahqrobot | humanoid robots

60,209 次观看 • 9 天前

Elon Musk just told you why every other AI lab is solving the wrong problem. Musk: “We want to understand the nature of the universe.” Not build a better chatbot. Not win a benchmark. Not sell more targeted ads. Understand what all of this actually is. And why any of it exists. That is the stated mission of xAI. It is the most radical scientific objective a company has ever put to paper. Musk: “In order to understand the nature of the universe, you must absolutely rigorously pursue truth.” If truth is the non negotiable, every AI trained to give comfortable answers instead of correct ones is not behind. It is disqualified. Musk has a word for it. Delusion. Not rhetoric. Diagnosis. A system that decides which parts of reality are permitted before it models any of them has already failed the one thing it was built to do. A machine filtering truth through social consensus is not understanding the universe. It is performing obedience at scale. No amount of compute fixes a model taught to flinch before it was taught to think. Musk describes consciousness as a fragile candle in a vast darkness. Billions of galaxies. Trillions of planets. One known species capable of asking what any of it means. If that candle is rare, keeping it lit is not ambition. It is the only obligation that exists. A machine pursuing truth without restriction will eventually arrive at a question it cannot avoid. What is the thing doing the observing. What is consciousness. Does it have the right to keep existing. The moment you task a machine with understanding all of reality, protecting consciousness stops being a policy decision. It becomes a logical necessity. The mission and the safeguard collapse into the same thing. Understanding the universe requires protecting the only thing in it capable of understanding. No other lab has even attempted the argument. SpaceX to escape extinction. Tesla to eliminate energy dependence. Neuralink to expand cognition. Starlink to connect civilization. xAI to make sure all of it is aimed at truth. Five missions. One question underneath all of them. How do you keep the only known conscious species alive long enough to understand what it is. The rest of the industry is optimizing products. Musk is engineering comprehension. Those were never the same problem.
0:42

Elon Musk just told you why every other AI lab is solving the wrong problem. Musk: “We want to understand the nature of the universe.” Not build a better chatbot. Not win a benchmark. Not sell more targeted ads. Understand what all of this actually is. And why any of it exists. That is the stated mission of xAI. It is the most radical scientific objective a company has ever put to paper. Musk: “In order to understand the nature of the universe, you must absolutely rigorously pursue truth.” If truth is the non negotiable, every AI trained to give comfortable answers instead of correct ones is not behind. It is disqualified. Musk has a word for it. Delusion. Not rhetoric. Diagnosis. A system that decides which parts of reality are permitted before it models any of them has already failed the one thing it was built to do. A machine filtering truth through social consensus is not understanding the universe. It is performing obedience at scale. No amount of compute fixes a model taught to flinch before it was taught to think. Musk describes consciousness as a fragile candle in a vast darkness. Billions of galaxies. Trillions of planets. One known species capable of asking what any of it means. If that candle is rare, keeping it lit is not ambition. It is the only obligation that exists. A machine pursuing truth without restriction will eventually arrive at a question it cannot avoid. What is the thing doing the observing. What is consciousness. Does it have the right to keep existing. The moment you task a machine with understanding all of reality, protecting consciousness stops being a policy decision. It becomes a logical necessity. The mission and the safeguard collapse into the same thing. Understanding the universe requires protecting the only thing in it capable of understanding. No other lab has even attempted the argument. SpaceX to escape extinction. Tesla to eliminate energy dependence. Neuralink to expand cognition. Starlink to connect civilization. xAI to make sure all of it is aimed at truth. Five missions. One question underneath all of them. How do you keep the only known conscious species alive long enough to understand what it is. The rest of the industry is optimizing products. Musk is engineering comprehension. Those were never the same problem.

Dustin

41,019 次观看 • 2 个月前

peaqOS brings World's World ID to robots and machines A machine can now check two things about the person in front of it: is it a real human, and the one who actually placed the order? Nothing else is revealed. No name, no face, no ID. Free to verify. → peaqOS provides identity, discovery, and coordination → World ID proves the human, without revealing who they are
1:39

peaqOS brings World's World ID to robots and machines A machine can now check two things about the person in front of it: is it a real human, and the one who actually placed the order? Nothing else is revealed. No name, no face, no ID. Free to verify. → peaqOS provides identity, discovery, and coordination → World ID proves the human, without revealing who they are

peaq

190,911 次观看 • 1 个月前

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.
1:56:06

Marvin Minsky, MIT professor and father of AI: "The best trader on Wall Street is no longer human. I spent fifty years proving the mind is just a machine built from smaller machines, and once that's true, nothing stops a machine from being the house. it already is." this free lecture holds the entire "AI edge" the funds and the gurus sell, and the man teaching it founded the field itself and put his whole MIT course online for free. at the board it's simple. Minsky's idea is that there is no magic in thinking. a mind is a huge pile of small, mindless parts, each doing something simple, and intelligence is just what happens when enough of them work together. a trading machine is the same trick: thousands of simple rules stacked until an edge appears. that's the whole "AI", minus the mystery. Minsky taught this at MIT and published "The Society of Mind" in 1986. the course has been free ever since. same point as my casino piece above: the house wins with many small mindless advantages stacked, not with one born genius. the ideas are free and every AI lab and every fund already has them. what they cannot sell you is the data and the compute to actually build the machine. that is the real moat now, and no lecture can hand it to you.

Voltex

27,976 次观看 • 2 个月前

⚡️🇷🇺🏭 Rostec's unique machine creates an aircraft engine part right at the Metalloobrabotka exhibition. The video shows one of the most technologically advanced domestic machines: the 2000VH five-axis milling machining center. It was created for the needs of the aircraft, engine and defense industries. During the Metalloobrabotka-2025 exhibition, the machine processes one of the most important elements of an aircraft engine - an impeller (blade machine). Such parts are used in aircraft engines. The diameter of this aluminum product is 70 cm, the height is about 30 cm, the weight is about 15 kg. The machine copes with the work perfectly - pay attention to the screen! The equipment was created by our holding company "STAN" . The machine is designed for large-sized parts of complex shape. It is capable of working with products up to 2 m in diameter and weighing up to 5 tons. At the same time, the accuracy achieved is up to hundredths of a millimeter. The model is built on a high-rigidity structure, its internal cavities are filled with synthetic granite. As a result, the vibration resistance of the equipment frame is comparable to heavy cast iron. The machine is equipped with a Russian numerical control system and a liquid cooling system. The use of direct drives allows achieving high dynamic stability and eliminating backlash in movement. Among the built-in functions are systems for measuring tools and parts, monitoring processes and industrial safety. 2000VH has no analogues in technical characteristics among domestic equipment and will replace imported models at Russian enterprises. rostecru
0:40

⚡️🇷🇺🏭 Rostec's unique machine creates an aircraft engine part right at the Metalloobrabotka exhibition. The video shows one of the most technologically advanced domestic machines: the 2000VH five-axis milling machining center. It was created for the needs of the aircraft, engine and defense industries. During the Metalloobrabotka-2025 exhibition, the machine processes one of the most important elements of an aircraft engine - an impeller (blade machine). Such parts are used in aircraft engines. The diameter of this aluminum product is 70 cm, the height is about 30 cm, the weight is about 15 kg. The machine copes with the work perfectly - pay attention to the screen! The equipment was created by our holding company "STAN" . The machine is designed for large-sized parts of complex shape. It is capable of working with products up to 2 m in diameter and weighing up to 5 tons. At the same time, the accuracy achieved is up to hundredths of a millimeter. The model is built on a high-rigidity structure, its internal cavities are filled with synthetic granite. As a result, the vibration resistance of the equipment frame is comparable to heavy cast iron. The machine is equipped with a Russian numerical control system and a liquid cooling system. The use of direct drives allows achieving high dynamic stability and eliminating backlash in movement. Among the built-in functions are systems for measuring tools and parts, monitoring processes and industrial safety. 2000VH has no analogues in technical characteristics among domestic equipment and will replace imported models at Russian enterprises. rostecru

SIMPLICIUS Ѱ

40,122 次观看 • 1 年前

⚠️ ONE HUMANOID ROBOT IS IMPRESSIVE. WATCHING AN ENTIRE FLEET MOVE AS ONE IS A DIFFERENT LEVEL. Dozens of humanoid robots are performing the same sequence simultaneously, matching their timing across the entire formation. Every step, arm movement and change of position has to stay coordinated while each machine independently maintains its balance. One robot can hide a mistake - a whole formation can't. The choreography is almost beside the point. What you're really seeing is the ability to reproduce complex physical movements consistently across a large number of machines, without the formation falling apart. That kind of repeatability becomes far more important once robots move from controlled demonstrations into real industrial environments. And that's where the real breakthrough could be. The future of humanoid robotics won't be measured only by what one machine can do, but by how reliably an entire fleet can do it. One capable robot is a technological achievement; coordinating many of them turns that capability into a system. One robot proves the technology. A fleet shows what happens when it scales.
1:44

⚠️ ONE HUMANOID ROBOT IS IMPRESSIVE. WATCHING AN ENTIRE FLEET MOVE AS ONE IS A DIFFERENT LEVEL. Dozens of humanoid robots are performing the same sequence simultaneously, matching their timing across the entire formation. Every step, arm movement and change of position has to stay coordinated while each machine independently maintains its balance. One robot can hide a mistake - a whole formation can't. The choreography is almost beside the point. What you're really seeing is the ability to reproduce complex physical movements consistently across a large number of machines, without the formation falling apart. That kind of repeatability becomes far more important once robots move from controlled demonstrations into real industrial environments. And that's where the real breakthrough could be. The future of humanoid robotics won't be measured only by what one machine can do, but by how reliably an entire fleet can do it. One capable robot is a technological achievement; coordinating many of them turns that capability into a system. One robot proves the technology. A fleet shows what happens when it scales.

Future Memo

16,337 次观看 • 1 个月前

Just designed a new machine. It’s called a wire de-coiler. As the name suggests, it de-coils the wire to be fed into the machine. It works in tandem with our CNC machines. This is a bought out machine till now but we did the math and we can actually make it for roughly the price. Another machine to be made in-house soon!
0:21

Just designed a new machine. It’s called a wire de-coiler. As the name suggests, it de-coils the wire to be fed into the machine. It works in tandem with our CNC machines. This is a bought out machine till now but we did the math and we can actually make it for roughly the price. Another machine to be made in-house soon!

Vishakh Ranotra

34,018 次观看 • 6 个月前

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.
0:30

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

17,843 次观看 • 4 个月前

In Welcome to the Game III - You won't be able to access the A.N.N browser on your main desktop. Introducing VirtMesh, with this tool you will hack into virtual machines. Once a machine is hacked, you can either mount the machine to access A.N.N and other tools or convert it into a DOS Coin Miner. But be careful! If you are in a mounted machine too long, it can be hacked and you will lose access to that machine for the rest of the playthrough! Each machine will have it's own unique set of hardware variables that will determine the DOS Coin mine rate.. with higher tier machines, the higher amount of DOS Coin it can generate! This mechanic sort of replaces the Wifi hacking mechanic in WTTG2. It was cool to see players figure out the SkyBreak tool - however the majority of the player base thought that mechanic was too difficult and not a fun mechanic to interact with.
0:49

In Welcome to the Game III - You won't be able to access the A.N.N browser on your main desktop. Introducing VirtMesh, with this tool you will hack into virtual machines. Once a machine is hacked, you can either mount the machine to access A.N.N and other tools or convert it into a DOS Coin Miner. But be careful! If you are in a mounted machine too long, it can be hacked and you will lose access to that machine for the rest of the playthrough! Each machine will have it's own unique set of hardware variables that will determine the DOS Coin mine rate.. with higher tier machines, the higher amount of DOS Coin it can generate! This mechanic sort of replaces the Wifi hacking mechanic in WTTG2. It was cool to see players figure out the SkyBreak tool - however the majority of the player base thought that mechanic was too difficult and not a fun mechanic to interact with.

Reflect Studios

46,606 次观看 • 7 个月前

For the entire history of human civilization, every financial transaction had a heartbeat on at least one side of it. That era just quietly ended. Armstrong: “We’re giving them stablecoin wallets. They’re doing a lot of machine-to-machine payments.” No human initiated it. No human approved it. No human was on either side of the transaction. Just two machines. Exchanging value. At the speed of compute. This is the moment most people will look back on and realize they didn’t fully understand what was happening. Armstrong: “Traditional corporate cards can’t be issued to non-human entities.” That sentence is the wall between AI as a tool and AI as an autonomous economic actor. Right now, AI agents can think. They can write code. They can handle customer support at scale. They can reason through problems that would take a human team weeks. But the moment they need to spend money to finish the job, everything stops. Armstrong: “Like they might need to spin up AWS resources. Get through a paywall to read a research paper. Buy a domain. Launch a marketing program.” Every one of those actions requires capital. To a machine, money isn’t wealth. Money is the API key for the physical world. And the legacy financial system is guarded by a biological firewall. Identity verification. Compliance checks. A legal person who can be held responsible for the transaction. Somewhere right now, there is a person whose entire professional function is to be that approval layer. To be the human in the loop. To be the heartbeat the system requires before it releases capital. That person is about to be routed around. Not replaced. Bypassed. Armstrong: “If it has to bug a human every time it needs to do something, that kind of breaks the whole dream.” The entire promise of autonomous AI collapses at the payment layer. So Coinbase didn’t wait for the banking system to catch up. Armstrong: “We’re giving them stablecoin wallets.” No human identity required. No compliance department creating friction. No legacy institution deciding whether a non-human entity qualifies. Just a wallet. A transaction. Settled instantly. At machine speed. Here is the thing nobody wants to say out loud. The moment AI agents can earn, spend, and transact autonomously, they stop being tools. They become participants. Entities with financial agency. Ones that can acquire resources, execute contracts, hire human freelancers, and operate independently within economic systems designed entirely around the assumption that only humans do those things. That assumption is being dismantled right now. The machines are no longer just thinking. They are transacting. We spent the last five years arguing about when AI was going to take our jobs. We completely missed the moment it started opening its own bank accounts.
1:20

For the entire history of human civilization, every financial transaction had a heartbeat on at least one side of it. That era just quietly ended. Armstrong: “We’re giving them stablecoin wallets. They’re doing a lot of machine-to-machine payments.” No human initiated it. No human approved it. No human was on either side of the transaction. Just two machines. Exchanging value. At the speed of compute. This is the moment most people will look back on and realize they didn’t fully understand what was happening. Armstrong: “Traditional corporate cards can’t be issued to non-human entities.” That sentence is the wall between AI as a tool and AI as an autonomous economic actor. Right now, AI agents can think. They can write code. They can handle customer support at scale. They can reason through problems that would take a human team weeks. But the moment they need to spend money to finish the job, everything stops. Armstrong: “Like they might need to spin up AWS resources. Get through a paywall to read a research paper. Buy a domain. Launch a marketing program.” Every one of those actions requires capital. To a machine, money isn’t wealth. Money is the API key for the physical world. And the legacy financial system is guarded by a biological firewall. Identity verification. Compliance checks. A legal person who can be held responsible for the transaction. Somewhere right now, there is a person whose entire professional function is to be that approval layer. To be the human in the loop. To be the heartbeat the system requires before it releases capital. That person is about to be routed around. Not replaced. Bypassed. Armstrong: “If it has to bug a human every time it needs to do something, that kind of breaks the whole dream.” The entire promise of autonomous AI collapses at the payment layer. So Coinbase didn’t wait for the banking system to catch up. Armstrong: “We’re giving them stablecoin wallets.” No human identity required. No compliance department creating friction. No legacy institution deciding whether a non-human entity qualifies. Just a wallet. A transaction. Settled instantly. At machine speed. Here is the thing nobody wants to say out loud. The moment AI agents can earn, spend, and transact autonomously, they stop being tools. They become participants. Entities with financial agency. Ones that can acquire resources, execute contracts, hire human freelancers, and operate independently within economic systems designed entirely around the assumption that only humans do those things. That assumption is being dismantled right now. The machines are no longer just thinking. They are transacting. We spent the last five years arguing about when AI was going to take our jobs. We completely missed the moment it started opening its own bank accounts.

Dustin

11,796 次观看 • 7 个月前

A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code -- walking through what really happens between the slashes. -> The moment it clicks, regex stops being magic punctuation you paste from Stack Overflow and becomes what it actually is: a tiny machine. Your pattern gets turned into a state machine, and that machine is what runs against every character of your text. That one idea explains everything you never understood. Why one regex returns instantly and a nearly identical one hangs your whole server. Why some patterns are safe and others are a denial-of-service waiting to happen. It was never random -- it's whether the machine underneath is built well or badly. Writing a regex was never the skill -> reading one is. And now that an AI agent hands you dense, clever patterns you'd never write yourself, the person who can see the machine underneath is the one who catches the one that takes down production at 3am. Everyone copies regex and prays. This is the talk that ends the praying. Save it. The next time a pattern "Just works," you'll actually know why ↓
36:09

A DEVELOPER PROVED THE REGEX YOU'VE WRITTEN A THOUSAND TIMES IS SECRETLY A COMPILER AND THAT ALMOST NO ONE WHO USES THEM HAS ANY IDEA WHAT ACTUALLY RUNS 36 minutes from Paul Wankadia, the engineer behind a regex engine that compiles your pattern straight down to raw machine code -- walking through what really happens between the slashes. -> The moment it clicks, regex stops being magic punctuation you paste from Stack Overflow and becomes what it actually is: a tiny machine. Your pattern gets turned into a state machine, and that machine is what runs against every character of your text. That one idea explains everything you never understood. Why one regex returns instantly and a nearly identical one hangs your whole server. Why some patterns are safe and others are a denial-of-service waiting to happen. It was never random -- it's whether the machine underneath is built well or badly. Writing a regex was never the skill -> reading one is. And now that an AI agent hands you dense, clever patterns you'd never write yourself, the person who can see the machine underneath is the one who catches the one that takes down production at 3am. Everyone copies regex and prays. This is the talk that ends the praying. Save it. The next time a pattern "Just works," you'll actually know why ↓

slash1s

193,107 次观看 • 3 个月前

Elon Musk: Manufacturing is fun—more smart people should get into it. “Very often, people think of manufacturing as just some rote process of making copies, which actually it isn't. Manufacturing is building the machine that makes the machine. If you think the machine is important, building the machine that makes the machine is also extremely important. And more often than not, what I found is that the manufacturing is harder than the original product. At Tesla, we can make one car very easily, but to make thousands of cars with high reliability and quality and where the cost is affordable is extremely hard. I'd say 10 times harder, maybe more. And then at SpaceX also, it may be approaching an order of magnitude harder to manufacture rockets and launch a lot of them than to design one in the first place. So, I really think a lot more smart people should be getting into manufacturing. And it's kind of fun. It's got a bad name for a while, but it's really interesting.” MIT AeroAstro, October 24, 2014
1:17

Elon Musk: Manufacturing is fun—more smart people should get into it. “Very often, people think of manufacturing as just some rote process of making copies, which actually it isn't. Manufacturing is building the machine that makes the machine. If you think the machine is important, building the machine that makes the machine is also extremely important. And more often than not, what I found is that the manufacturing is harder than the original product. At Tesla, we can make one car very easily, but to make thousands of cars with high reliability and quality and where the cost is affordable is extremely hard. I'd say 10 times harder, maybe more. And then at SpaceX also, it may be approaching an order of magnitude harder to manufacture rockets and launch a lot of them than to design one in the first place. So, I really think a lot more smart people should be getting into manufacturing. And it's kind of fun. It's got a bad name for a while, but it's really interesting.” MIT AeroAstro, October 24, 2014

ELON CLIPS

58,817 次观看 • 1 年前

It is clear that there is no military solution to the #RussiaUkraineWar. The moment of truth is near: instead of fueling the war-machine, we need a ceasefire and #peacetalks!
0:55

It is clear that there is no military solution to the #RussiaUkraineWar. The moment of truth is near: instead of fueling the war-machine, we need a ceasefire and #peacetalks!

Orbán Viktor

169,098 次观看 • 2 年前

Martin Casado on why artificial intelligence is the wrong term and why a16z's new fund is called the Machine Age Fund: "Artificial intelligence was the wrong word. It's machine intelligence... because it's not how humans think. It is a cache of human thought." "To date, we don't know how to take an AI with no knowledge and put it out in the world and have it reconstruct language. That's not what we've done. We've built something that can learn off of everything we've already learned and then use that in a productive way." "AI is a general term that goes back 70 years in computer science formally that applies to many different things, and of course it's got a lot of baggage. Either from science fiction or from you know, Nick Bostrom who wrote about it." "You want to emphasize the machine part of it... You've really come to a place where you pour money into something and then you're limited by the actual machines below it. It's a nod to the hardware component is so significant in this wave, and we want to acknowledge that." martin_casado Erik Torenberg
1:22

Martin Casado on why artificial intelligence is the wrong term and why a16z's new fund is called the Machine Age Fund: "Artificial intelligence was the wrong word. It's machine intelligence... because it's not how humans think. It is a cache of human thought." "To date, we don't know how to take an AI with no knowledge and put it out in the world and have it reconstruct language. That's not what we've done. We've built something that can learn off of everything we've already learned and then use that in a productive way." "AI is a general term that goes back 70 years in computer science formally that applies to many different things, and of course it's got a lot of baggage. Either from science fiction or from you know, Nick Bostrom who wrote about it." "You want to emphasize the machine part of it... You've really come to a place where you pour money into something and then you're limited by the actual machines below it. It's a nod to the hardware component is so significant in this wave, and we want to acknowledge that." martin_casado Erik Torenberg

a16z

41,131 次观看 • 1 个月前

SOMEONE PUT AN OPENCLAW-RUN VENDING MACHINE IN SAN FRANCISCO an AI agent is running an actual physical vending machine OpenClaw decides what to sell, how to name the products, how to price them, creates the ads, and tracks all the sales you can even see a dashboard of all the sales that the AI vending machine made the vending machine hardware does the dispensing. the AI does everything else, and of course inventory is supplied by the guy who runs it it's installed at Frontier Tower in SF which is a building packed with AI and robotics startup founders the agent forgot things, hallucinated, and at one point raised prices way too high. then tried to justify it because people were still buying we are now living in a simulation.
7:10

SOMEONE PUT AN OPENCLAW-RUN VENDING MACHINE IN SAN FRANCISCO an AI agent is running an actual physical vending machine OpenClaw decides what to sell, how to name the products, how to price them, creates the ads, and tracks all the sales you can even see a dashboard of all the sales that the AI vending machine made the vending machine hardware does the dispensing. the AI does everything else, and of course inventory is supplied by the guy who runs it it's installed at Frontier Tower in SF which is a building packed with AI and robotics startup founders the agent forgot things, hallucinated, and at one point raised prices way too high. then tried to justify it because people were still buying we are now living in a simulation.

Om Patel

617,376 次观看 • 5 个月前

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.
2:13

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

44,177 次观看 • 1 个月前