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A GOOGLE ENGINEER STOOD UP AT A C++ CONFERENCE AND TOLD THE ROOM THAT THE DATA STRUCTURE YOU REACH FOR BY REFLEX -- THE LINKED LIST -- IS ALMOST ALWAYS THE WRONG ANSWER. NOT SLOWER BY A LITTLE. SLOWER BY SO MUCH THAT USING IT IS QUIETLY THROWING AWAY...

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slash1s

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55,761 次观看 • 1 个月前 •via X (Twitter)

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THE ENGINE DIRECTOR BEHIND AAA CONSOLE GAMES WALKED ON STAGE AT A C++ CONFERENCE AND TOLD A ROOM OF EXPERTS THAT THE CLEAN, OBJECT ORIENTED CODE THEY ARE PROUD OF IS A LIE AND IT QUIETLY THROWS AWAY 90% OF THE MACHINE THEY PAID FOR 85 minutes from Mike Acton -- engine director at Insomniac Games, where wasting cycles is not an abstract debate, it is the difference between a game that ships and one that dies. -> His first principle is blunt: the only purpose of any program is to transform data. Not to model the world in pretty classes. Move data, fast. 06:33 -- The lie he calls out: "Software is the platform". No. The hardware is the platform. Ignore it and you cannot even reason about the cost of your own code. Most devs never look at what the CPU actually does. They stack abstractions until the machine spends its life waiting on memory it should never have touched. 49:38 -- He prints a value, zips it, and measures the waste directly. A brutal little trick that exposes how much of your data is pure noise. And this is the AI era's blind spot too. Agents generate more abstract code faster, on hardware nobody profiles, while the GPU bill quietly explodes. You thought clean code was the goal. This is the man shipping real engines saying the machine is the goal, and clean code is often how you lose it. Bookmark it & you'll never look at your own abstractions the same ↓
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THE ENGINE DIRECTOR BEHIND AAA CONSOLE GAMES WALKED ON STAGE AT A C++ CONFERENCE AND TOLD A ROOM OF EXPERTS THAT THE CLEAN, OBJECT ORIENTED CODE THEY ARE PROUD OF IS A LIE AND IT QUIETLY THROWS AWAY 90% OF THE MACHINE THEY PAID FOR 85 minutes from Mike Acton -- engine director at Insomniac Games, where wasting cycles is not an abstract debate, it is the difference between a game that ships and one that dies. -> His first principle is blunt: the only purpose of any program is to transform data. Not to model the world in pretty classes. Move data, fast. 06:33 -- The lie he calls out: "Software is the platform". No. The hardware is the platform. Ignore it and you cannot even reason about the cost of your own code. Most devs never look at what the CPU actually does. They stack abstractions until the machine spends its life waiting on memory it should never have touched. 49:38 -- He prints a value, zips it, and measures the waste directly. A brutal little trick that exposes how much of your data is pure noise. And this is the AI era's blind spot too. Agents generate more abstract code faster, on hardware nobody profiles, while the GPU bill quietly explodes. You thought clean code was the goal. This is the man shipping real engines saying the machine is the goal, and clean code is often how you lose it. Bookmark it & you'll never look at your own abstractions the same ↓

slash1s

173,406 次观看 • 1 个月前

IN A 1997 KEYNOTE A DEVELOPER TOLD A ROOM FULL OF PROGRAMMERS THAT THE COMPUTER REVOLUTION HAD NOT ACTUALLY HAPPENED YET. THEN HE PLAYED A CLIP OF WINDOWS, ICONS AND LIVE EDITING RUNNING ON A MACHINE FROM 1973 AND THE ROOM WENT QUIET. 62 minutes from Alan Kay -- the man who invented the word "object-oriented" and helped build the first modern personal computer at Xerox PARC. -> The idea that lands: almost everything you call "computing" is just paper, digitized. Documents, mail, folders. We took the most powerful medium ever made and used it to imitate the office. The real machine -- the one that thinks with you, that you shape live instead of typing at -- was sketched in the 60s and 70s, then quietly abandoned. He calls modern software an Egyptian pyramid: millions of bricks stacked by brute force, no structure underneath. And now AI is quietly hauling us back toward what he wanted -- you describe intent, the machine builds the how. Not a revolution from nowhere. A return to a dream we walked away from. You thought this was the future. This is the talk that shows you it is a detour we have been on for 40 years. Save this. It reframes the whole industry ↓
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IN A 1997 KEYNOTE A DEVELOPER TOLD A ROOM FULL OF PROGRAMMERS THAT THE COMPUTER REVOLUTION HAD NOT ACTUALLY HAPPENED YET. THEN HE PLAYED A CLIP OF WINDOWS, ICONS AND LIVE EDITING RUNNING ON A MACHINE FROM 1973 AND THE ROOM WENT QUIET. 62 minutes from Alan Kay -- the man who invented the word "object-oriented" and helped build the first modern personal computer at Xerox PARC. -> The idea that lands: almost everything you call "computing" is just paper, digitized. Documents, mail, folders. We took the most powerful medium ever made and used it to imitate the office. The real machine -- the one that thinks with you, that you shape live instead of typing at -- was sketched in the 60s and 70s, then quietly abandoned. He calls modern software an Egyptian pyramid: millions of bricks stacked by brute force, no structure underneath. And now AI is quietly hauling us back toward what he wanted -- you describe intent, the machine builds the how. Not a revolution from nowhere. A return to a dream we walked away from. You thought this was the future. This is the talk that shows you it is a detour we have been on for 40 years. Save this. It reframes the whole industry ↓

slash1s

294,466 次观看 • 2 个月前

The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.
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The creator of High Bandwidth Memory (HBM) put a number on the AI build that should stop every infra investor cold. A cluster of a million GPUs runs at roughly 10-20% utilization (Save this). Kim Jung-ho spent thirty years building what feeds the GPU, and his claim is that the GPU is barely working. Here is what is actually happening. Every time a model generates output, the data has to be read out of memory, computed, and written back. The read and the write swallow almost the entire cycle. While that data moves, the GPU does nothing. It sits there, fully powered, fully paid for, waiting. By Kim's estimate the memory is doing only about 30 percent of the work it needs to do. The processor idles the rest. So a million installed GPUs run at 10 to 20 percent. You are not compute constrained. You are memory constrained, and the expensive part is standing around. Adding more GPUs does not fix this. It gives you more processors starving for the same data. Here is the part that decides the next decade. Memory can grow. When a cell cannot shrink any further, you stack it into a high-rise, layer on layer. A GPU cannot be stacked. It runs too hot and needs a cooler bolted to its back, so the one move that rescues memory is closed to the processor. The thing that can keep stacking compounds. The thing that cannot plateaus. The marginal dollar in an AI build now buys more by fixing the memory path than by bolting on another idle GPU. Which is why the companies that control memory bandwidth and supply are not suppliers to the AI trade. They are the AI trade.

Fireside Alpha

38,370 次观看 • 2 个月前

David MacKay, Cambridge professor: "Citadel and Jane Street pay $900K for people who get one idea I taught free at Cambridge: information is measurable, and whoever measures it best wins. the casino knew it first. the machines know it now." this free lecture holds the entire "secret alpha" the funds and the AI gurus sell, and the man teaching it put his whole course and his whole book online for free and never locked a single page. at the board it's simple. information is just how much a signal cuts your uncertainty, and you can measure it in exact bits. the more an edge shrinks what you don't know, the more it is worth, and Shannon proved there is a hard ceiling on how fast you can compound it into money. that is the whole "edge", and the whole "AI magic", minus the marketing. MacKay taught it at Cambridge and gave the book away free in 2003. it has been free ever since. same point as the casino piece above: the house wins with a small, measurable edge, repeated and sized right, not with a secret. the theory is free and every fund and every model already runs on it. what they cannot sell you is the data to feed the machine and the discipline to size the bet. that is the part that actually compounds, and no course can put it in a formula.
1:00:58

David MacKay, Cambridge professor: "Citadel and Jane Street pay $900K for people who get one idea I taught free at Cambridge: information is measurable, and whoever measures it best wins. the casino knew it first. the machines know it now." this free lecture holds the entire "secret alpha" the funds and the AI gurus sell, and the man teaching it put his whole course and his whole book online for free and never locked a single page. at the board it's simple. information is just how much a signal cuts your uncertainty, and you can measure it in exact bits. the more an edge shrinks what you don't know, the more it is worth, and Shannon proved there is a hard ceiling on how fast you can compound it into money. that is the whole "edge", and the whole "AI magic", minus the marketing. MacKay taught it at Cambridge and gave the book away free in 2003. it has been free ever since. same point as the casino piece above: the house wins with a small, measurable edge, repeated and sized right, not with a secret. the theory is free and every fund and every model already runs on it. what they cannot sell you is the data to feed the machine and the discipline to size the bet. that is the part that actually compounds, and no course can put it in a formula.

Rossst.03

24,939 次观看 • 1 个月前

you numbered the steps so it could not get lost it does not get lost that list was written for a model that wandered, and this one plans better than the list does, so every number you added is a place where it is no longer allowed, to be smarter than you state the goal, the constraints, and how you will know it worked keep the numbers only where exactly one sequence is safe, which is deletes, auth and compliance the procedure felt like rigour it is a cage built to the dimensions of a model that is not in the room
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you numbered the steps so it could not get lost it does not get lost that list was written for a model that wandered, and this one plans better than the list does, so every number you added is a place where it is no longer allowed, to be smarter than you state the goal, the constraints, and how you will know it worked keep the numbers only where exactly one sequence is safe, which is deletes, auth and compliance the procedure felt like rigour it is a cage built to the dimensions of a model that is not in the room

Lagosaur

14,857 次观看 • 11 天前

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓
43:30

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 次观看 • 2 个月前

Terence Tao, UCLA professor and the most decorated mathematician alive: "Funds pay $750K to combine weak signals into one real edge. I proved the thing that makes it work and makes it dangerous: in any long enough sequence, hidden structure is unavoidable. it always accumulates. the whole job is telling the real structure from the noise that only looks like it." this free lecture is the most decorated mathematician alive on the exact problem sitting underneath every factor model, and it costs nothing. at the board it's simple. Tao's lifelong theme is the line between structure and randomness. The Erdős discrepancy problem asks a deceptively simple thing: can you write an endless string of plus-ones and minus-ones that stays perfectly balanced forever? Tao proved you cannot. No matter how cleverly you try, imbalance, hidden structure, is forced to accumulate as the sequence grows. There is no such thing as a long stream of pure, structureless noise. That's the whole idea, minus the jargon. Which is exactly why a multi-factor model can work, and exactly why it can kill you. Stack enough weak signals and real structure will appear, because at scale structure is unavoidable. But so will fake structure, patterns that exist only because the data is long enough to force them. Same point as the post above: finding structure is guaranteed. Knowing which structure is an edge is the rare and expensive part. the mathematics is free and public. what nobody can sell you is the judgment to tell the structure the market will pay you for from the structure that exists only because you looked hard enough. That judgment is the alpha, and it takes years to build.
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Terence Tao, UCLA professor and the most decorated mathematician alive: "Funds pay $750K to combine weak signals into one real edge. I proved the thing that makes it work and makes it dangerous: in any long enough sequence, hidden structure is unavoidable. it always accumulates. the whole job is telling the real structure from the noise that only looks like it." this free lecture is the most decorated mathematician alive on the exact problem sitting underneath every factor model, and it costs nothing. at the board it's simple. Tao's lifelong theme is the line between structure and randomness. The Erdős discrepancy problem asks a deceptively simple thing: can you write an endless string of plus-ones and minus-ones that stays perfectly balanced forever? Tao proved you cannot. No matter how cleverly you try, imbalance, hidden structure, is forced to accumulate as the sequence grows. There is no such thing as a long stream of pure, structureless noise. That's the whole idea, minus the jargon. Which is exactly why a multi-factor model can work, and exactly why it can kill you. Stack enough weak signals and real structure will appear, because at scale structure is unavoidable. But so will fake structure, patterns that exist only because the data is long enough to force them. Same point as the post above: finding structure is guaranteed. Knowing which structure is an edge is the rare and expensive part. the mathematics is free and public. what nobody can sell you is the judgment to tell the structure the market will pay you for from the structure that exists only because you looked hard enough. That judgment is the alpha, and it takes years to build.

Rossst.03

213,931 次观看 • 1 个月前

Jensen Huang just made the case that the smartest person in the room is now the most replaceable person on earth. Huang: “The definition of smart is somebody who’s intelligent, solve problems, technical. But I find that that’s a commodity. And we’re about to prove that artificial intelligence is able to handle that part easiest.” The skill you built your entire identity around was the first thing the machine replicated. Not the hardest part. The easiest. Huang: “People who are able to see around corners are truly, truly smart. And their value is incredible. To be able to preempt problems before they show up, just because you feel the vibe.” Huang: “That vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people.” The vibe is not intuition. It is decades of failed bets, human friction, and first-principles thinking compressed into a single read no model can replicate. You cannot prompt your way to it. You earn it by surviving things that had no instructions. Huang: “I think long term the definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, around the corners, the unknowables.” Every institution you ever passed through graded you on the one thing the machine now does for free. The thing they never tested you on is the only thing that still matters. Huang: “And that person might actually score horribly on the SAT.” The SAT did not measure intelligence. It measured obedience to structure. The future does not reward what you can solve inside a framework someone handed you. It rewards what you can see when no framework exists. The machine did not replace human intelligence. It revealed that what we spent a century calling intelligence was never intelligence at all. The people who memorized the answers are about to work for the people who sensed the questions before anyone thought to ask.
2:01

Jensen Huang just made the case that the smartest person in the room is now the most replaceable person on earth. Huang: “The definition of smart is somebody who’s intelligent, solve problems, technical. But I find that that’s a commodity. And we’re about to prove that artificial intelligence is able to handle that part easiest.” The skill you built your entire identity around was the first thing the machine replicated. Not the hardest part. The easiest. Huang: “People who are able to see around corners are truly, truly smart. And their value is incredible. To be able to preempt problems before they show up, just because you feel the vibe.” Huang: “That vibe came from a combination of data, analysis, first principle, life experience, wisdom, sensing other people.” The vibe is not intuition. It is decades of failed bets, human friction, and first-principles thinking compressed into a single read no model can replicate. You cannot prompt your way to it. You earn it by surviving things that had no instructions. Huang: “I think long term the definition of smart is someone who sits at that intersection of being technically astute, but human empathy and having the ability to infer the unspoken, around the corners, the unknowables.” Every institution you ever passed through graded you on the one thing the machine now does for free. The thing they never tested you on is the only thing that still matters. Huang: “And that person might actually score horribly on the SAT.” The SAT did not measure intelligence. It measured obedience to structure. The future does not reward what you can solve inside a framework someone handed you. It rewards what you can see when no framework exists. The machine did not replace human intelligence. It revealed that what we spent a century calling intelligence was never intelligence at all. The people who memorized the answers are about to work for the people who sensed the questions before anyone thought to ask.

Dustin

123,807 次观看 • 1 个月前

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.
0:56

Elon Musk just put a number on the flaw at the center of Nvidia’s empire. Wall Street has not done the math yet. Nvidia’s Blackwell is the most sought-after silicon on Earth. Every AI lab wants it. Every sovereign nation is bidding for it. Blackwell runs every model, for every company, in every data center on the planet. That universality built the empire. It is also the fracture point. Musk: “We believe the AI5 chip will be about a third of the power of an Nvidia Blackwell for roughly comparable performance. And much less than 10% of the cost.” One-third the power. Comparable performance. Less than ten percent of the cost. Musk: “This is a chip that is very much optimized for the Tesla AI software stack. It’s not meant to be a general purpose chip.” Nvidia builds silicon that serves a million different customers. Every transistor spent on universal compatibility is a transistor not dedicated to one task. Tesla is building silicon for exactly one customer. Itself. When you strip away every function you will never call, you do not get a lesser chip. You get a weapon. Here is what the market refuses to see. Data centers drink unlimited power from the grid. Robots run on batteries. Musk: “In order to have a functional robot, you have to have a great AI chip. And it needs to be an inexpensive chip and it needs to be very power efficient.” You cannot put a Blackwell inside a walking machine. It would drain the battery before it crossed the room. The entire AI revolution lives inside air-conditioned buildings bolted to the electrical grid. Musk is not competing for that market. He is engineering the silicon that survives outside of it. One-third the power is not a spec sheet footnote. It is the physics threshold that severs intelligence from the wall socket. Without that number, every robot on Earth stays tethered. With it, the algorithm walks. Less than ten percent of the cost is not a pricing strategy. It is the line where a machine brain stops being a capital expenditure and becomes a commodity component. When the chip inside a humanoid costs less than the motors in its legs, you do not manufacture hundreds of robots. You manufacture millions. Wall Street is valuing the AI revolution by who dominates the data center. Musk is building the only silicon designed to leave one. Nvidia built the brain of the cloud. Musk is building the brain of the physical world. No one has priced that in yet.

Dustin

160,633 次观看 • 4 个月前

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓
39:31

IN 1999 MIT FILMED A MATH LECTURE THAT QUIETLY BECAME THE FOUNDATION OF EVERY AI MODEL YOU'VE EVER USED AND ALMOST NO ONE WAS TAUGHT TO SEE IT THAT WAY 39 minutes from Gilbert Strang, who taught this at MIT for over 60 years -- the linear algebra course an entire generation of engineers and data scientists grew up on. -> The shift it creates: you stop seeing matrices as boring grids of numbers and start seeing them as the language of space, data, and motion itself. School drilled you to crunch matrices by hand and never told you why. Strang shows you what they actually mean. Every neural net, every embedding, every model you prompt is linear algebra running underneath. The math you skipped is the engine of the thing you use all day. Memorizing the steps was never the skill -> seeing what the numbers do is. This is where it finally clicks. Most people fear linear algebra and move on. The ones who watched this see straight into how AI actually works. Bookmark & Watch it today, this one's a legend ↓

slash1s

433,431 次观看 • 2 个月前

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.
0:31

Larry Ellison just told every software engineer on Earth their job description is dead. Not evolving. Dead. Ellison: “The code that Oracle is writing, Oracle isn’t writing. Our AI models are writing.” This is not a startup demo. This is one of the largest infrastructure monopolies on the planet telling you it already replaced the people who built it. For fifty years, building software meant translating human intent into machine instructions. Line by line. Bug by bug. Sprint by sprint. That entire layer is gone. Ellison: “We don’t write the procedure. We declare our intent.” That sentence just made the entire engineering labor market flinch. The procedure was the job. The procedure was the paycheck. The procedure was what made a developer valuable. And now the machine does it without being asked twice. Ellison: “We just tell the model what we want the program to do, and then the AI comes up with a step-by-step process to actually do it.” You are no longer paid to build. You are paid to think. And most organizations have no idea how to evaluate that. The companies still hiring armies of developers to grind through codebases are paying salaries the machine already made worthless. Not in years. In seconds. When a company worth hundreds of billions hands the keyboard to the machine and tells you the output is better, the debate is not winding down. The debate is over. The enterprise that wins this decade does not write the best code. It removes the human from the process entirely and runs on intent alone. The programmers who survive are the ones who realize the craft is no longer typing. It is architecture. It is judgment. It is knowing what to build and why. Everything else now belongs to the machine. And the machine does not negotiate severance.

Dustin

536,121 次观看 • 5 个月前

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast
1:29

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

AI Evolution

15,565 次观看 • 3 个月前

Most C Programmers don't have a clue as to how the CPU works. Here is a lecture from my course on the C Language that introduces how to visualise the CPU as a C (or general software) Programmer.... More Details are at: Being able to model the behaviour of CPU provides a solid base to reason about your code. The summary is - - The CPU is a state machine which maintains it's states in registers. - It has busses to memory for requesting data/instruction read/writes. - Interrupts to attend to events. I discuss this and more in my course on the C Language.
11:41

Most C Programmers don't have a clue as to how the CPU works. Here is a lecture from my course on the C Language that introduces how to visualise the CPU as a C (or general software) Programmer.... More Details are at: Being able to model the behaviour of CPU provides a solid base to reason about your code. The summary is - - The CPU is a state machine which maintains it's states in registers. - It has busses to memory for requesting data/instruction read/writes. - Interrupts to attend to events. I discuss this and more in my course on the C Language.

Piyush Itankar

28,019 次观看 • 1 年前

IN 1982 GRACE HOPPER WALKED INTO THE NSA AND EXPLAINED THE NEXT 40 YEARS OF COMPUTING 48 minutes from the woman who invented the compiler -- the first person who made a machine understand human words. -> The idea that lands: stop bending humans to the machine. Bend the machine to humans. She built the first compiler when the experts swore computers could only crunch numbers. She wanted you to write in plain words and let the machine handle the translation. Forty years later you barely write code at all -- you tell the model what you want in plain English and it writes it for you. The compiler was step one. The LLM is the same idea, finished. Memorizing syntax was never the skill -- saying clearly what you actually want is. This is where that idea was born. Most people think talking to your computer in plain English started in 2023. The ones who watch this know a Navy admiral called it in 1982. Bookmark & Watch it today ↓
47:50

IN 1982 GRACE HOPPER WALKED INTO THE NSA AND EXPLAINED THE NEXT 40 YEARS OF COMPUTING 48 minutes from the woman who invented the compiler -- the first person who made a machine understand human words. -> The idea that lands: stop bending humans to the machine. Bend the machine to humans. She built the first compiler when the experts swore computers could only crunch numbers. She wanted you to write in plain words and let the machine handle the translation. Forty years later you barely write code at all -- you tell the model what you want in plain English and it writes it for you. The compiler was step one. The LLM is the same idea, finished. Memorizing syntax was never the skill -- saying clearly what you actually want is. This is where that idea was born. Most people think talking to your computer in plain English started in 2023. The ones who watch this know a Navy admiral called it in 1982. Bookmark & Watch it today ↓

slash1s

61,013 次观看 • 2 个月前

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below
2:00

the CEO of NVIDIA just said the PC you use today will be gone, replaced by an AI supercomputer that lives in your house Jensen Huang stood on stage holding the machine and said it becomes less like a computer and more like R2-D2, a thing in your home that just does work for you all day he put it bluntly, this is as big as the day the phone became the smartphone and the wild part is the hardware to do it already exists and ships this year here is what he actually unveiled: > a desktop that runs a one trillion parameter AI model locally, 768GB of memory, sitting by your desk > agents that run 24/7 with no meter, no cloud bill, no rental, doing work while you sleep > NVIDIA and Microsoft rebuilding the PC from the ground up for the first time in 40 years > a full lineup, from a $249 chip to enterprise monsters, and almost nobody knows which one they actually need the hype is going to push everyone toward the biggest most expensive machine i wrote the honest breakdown, every NVIDIA AI box, what each really does, the real math, and which one is actually yours the full guide is in the article below

John Doe

194,375 次观看 • 3 个月前

EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.
0:34

EVERYONE'S BUYING THE M4 MAC MINI AS A CHEAP DESKTOP. HERE'S WHY IT ENDS UP UNUSED. the pitch is irresistible: a tiny mac that runs cool, quiet, and cheap. then the receipt shows up. the mac mini ships with no screen, no keyboard, no mouse. add a monitor, a decent keyboard and mouse, and more storage, and the "$799 mac" is past $1,000 fast. and macos isn't windows. if your world is gaming, specific windows apps, or the setup you already know, the mini fights you the whole way. that's how so many end up back on a pc, the mini in a drawer. so what is the mac mini actually great at? not being your main desktop. it's the box you never look at. headless in a corner, it's a near-silent always-on server: run local ai models on it, host your automations, keep your files, and reach it from the laptop you already use. it earns its keep by disappearing. the honest turn: the mac mini isn't a cheap desktop. it's a cheap server pretending to be one. buy it for the job it's quietly brilliant at, not the one the ads imply. want a main computer? price the whole setup first. want a silent little box that runs ai and never bothers you? this is the one. no monitor in the box, no keyboard, no "just $599". save this before you buy the sticker price instead of the real one.

RetroChainer

12,520 次观看 • 1 个月前

You already know the story of the bullet holes on the planes. You have heard it a dozen times. And you are still making the exact mistake it warns about, probably today. The man behind it was Abraham Wald, 1943. You know how it goes. Bombers came home covered in bullet holes, wings and fuselage riddled, engines almost clean. The officers said armor the holes. Wald said armor everywhere else. Because the data was only the planes that came back. The ones hit in the engines never returned to be counted. The clean spots on the survivors were the deadly spots on the dead. That part you remember. Here is the part you skip. You repeat this story, then you open a list of the best investments of the last ten years, and every name on it is a plane that made it home. The winners survived. The ones that blew up are not on the list. They never come back to be counted. You know the bias by name and you buy the survivors anyway. Every track record you are shown was selected for coming home. The failures are silent, and the silence is not empty. It is the whole answer. Your eyes read the bullet holes as danger. The real danger is always the plane that is not on the chart. Wald saw it with pen and paper because he counted what was missing instead of what was in front of him. That is the entire skill. Not knowing the story. Doing what it tells you, which almost nobody does. The idea is free. It has been famous for 80 years. Most people still armor the bullet holes with their own money.
6:09

You already know the story of the bullet holes on the planes. You have heard it a dozen times. And you are still making the exact mistake it warns about, probably today. The man behind it was Abraham Wald, 1943. You know how it goes. Bombers came home covered in bullet holes, wings and fuselage riddled, engines almost clean. The officers said armor the holes. Wald said armor everywhere else. Because the data was only the planes that came back. The ones hit in the engines never returned to be counted. The clean spots on the survivors were the deadly spots on the dead. That part you remember. Here is the part you skip. You repeat this story, then you open a list of the best investments of the last ten years, and every name on it is a plane that made it home. The winners survived. The ones that blew up are not on the list. They never come back to be counted. You know the bias by name and you buy the survivors anyway. Every track record you are shown was selected for coming home. The failures are silent, and the silence is not empty. It is the whole answer. Your eyes read the bullet holes as danger. The real danger is always the plane that is not on the chart. Wald saw it with pen and paper because he counted what was missing instead of what was in front of him. That is the entire skill. Not knowing the story. Doing what it tells you, which almost nobody does. The idea is free. It has been famous for 80 years. Most people still armor the bullet holes with their own money.

Zyron

71,727 次观看 • 1 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 • 2 个月前