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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 --...

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292,206 views • 13 days ago •via X (Twitter)

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A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓
32:29

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓

slash1s

680,508 views • 14 days ago

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

534,277 views • 3 months ago

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

60,755 views • 21 days ago

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓
1:12:51

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

slash1s

99,512 views • 17 days ago

The cold is British. They just never told you. ❄️🇬🇧 Every air conditioner, every fridge, every freezer on earth, the cold inside all of it was first made by a Scotsman. In 1756. ❄️🏴󠁧󠁢󠁳󠁣󠁴󠁿 His name was William Cullen, a doctor in Edinburgh. In a warm room, with no ice and no winter, he drew the air from a glass of ether until it boiled cold and drank the heat from the water around it, until the water froze. No one had ever done it before. Then he wrote it down and walked away. No machine, no patent, no fortune. Just the proof, on a bench. It sat there the better part of a century. A British bench built the first machine that worked, and a Scot carried it across the world to Australia, where it froze meat and cooled beer. But cold was only half the story. ❄️📖 To master the air itself took another Scot. When the Houses of Parliament burned down in 1834, David Boswell Reid made the new building breathe, its gothic spires its lungs. At St George's Hall in Liverpool in 1851 he built what is often called the first air-conditioned building on earth. It should have made him. He died forgotten in America instead. 40 years on, Willis Carrier built the machine and earned his name as the father of modern air conditioning. He earned every bit of it. But he did not make the cold. The cold was British. America took that inheritance and cooled the world with it, and that is no theft. That is what an inheritance is for. We are the home of British heroes. Not built by ads, not by the government, built by us. There's a place for you with us. Be part of us. ☝️🇬🇧 Be Proud Of Us. 🙏🇬🇧
4:44

The cold is British. They just never told you. ❄️🇬🇧 Every air conditioner, every fridge, every freezer on earth, the cold inside all of it was first made by a Scotsman. In 1756. ❄️🏴󠁧󠁢󠁳󠁣󠁴󠁿 His name was William Cullen, a doctor in Edinburgh. In a warm room, with no ice and no winter, he drew the air from a glass of ether until it boiled cold and drank the heat from the water around it, until the water froze. No one had ever done it before. Then he wrote it down and walked away. No machine, no patent, no fortune. Just the proof, on a bench. It sat there the better part of a century. A British bench built the first machine that worked, and a Scot carried it across the world to Australia, where it froze meat and cooled beer. But cold was only half the story. ❄️📖 To master the air itself took another Scot. When the Houses of Parliament burned down in 1834, David Boswell Reid made the new building breathe, its gothic spires its lungs. At St George's Hall in Liverpool in 1851 he built what is often called the first air-conditioned building on earth. It should have made him. He died forgotten in America instead. 40 years on, Willis Carrier built the machine and earned his name as the father of modern air conditioning. He earned every bit of it. But he did not make the cold. The cold was British. America took that inheritance and cooled the world with it, and that is no theft. That is what an inheritance is for. We are the home of British heroes. Not built by ads, not by the government, built by us. There's a place for you with us. Be part of us. ☝️🇬🇧 Be Proud Of Us. 🙏🇬🇧

Proudofus.uk

55,620 views • 17 days ago

For 60 years every computer ever built did the same thing. Stored information and retrieved it on demand. Jensen Huang just explained why that era is over and what replaces it. His framing was the clearest I have ever heard. Think about everything a computer has ever done for you. You wrote a document, you saved it to a file. You took a photo, it saved to a file. You recorded music, it saved to a file. When you wanted it back, you retrieved it from a disc. That is it. That is 60 years of computing. Store and retrieve. He pointed out something hiding in plain sight. We call them data centers. Not computer centers. Because we were not really computing anything meaningful. We were storing data that you retrieved based on what you tapped on your phone. Then he explained what changed. Every time you give AI a prompt today, the response is produced originally in real time. It is not retrieved from storage. It is generated fresh based on your specific context, your specific question, your specific moment. What you see is completely different from what anyone else sees because it was made for you. Jensen said every pixel you see, every word you read, every video you watch in the future will be originally generated. Not retrieved. 60 years of computing was about building better storage and faster retrieval. The entire paradigm flipped overnight. He said this simply: we went from a retrieval industry to a generation industry. And the machines that generate intelligence are what Nvidia builds. The buildings used to be called data centers because they stored data. Nobody has renamed them yet. But the job description changed completely.
1:38

For 60 years every computer ever built did the same thing. Stored information and retrieved it on demand. Jensen Huang just explained why that era is over and what replaces it. His framing was the clearest I have ever heard. Think about everything a computer has ever done for you. You wrote a document, you saved it to a file. You took a photo, it saved to a file. You recorded music, it saved to a file. When you wanted it back, you retrieved it from a disc. That is it. That is 60 years of computing. Store and retrieve. He pointed out something hiding in plain sight. We call them data centers. Not computer centers. Because we were not really computing anything meaningful. We were storing data that you retrieved based on what you tapped on your phone. Then he explained what changed. Every time you give AI a prompt today, the response is produced originally in real time. It is not retrieved from storage. It is generated fresh based on your specific context, your specific question, your specific moment. What you see is completely different from what anyone else sees because it was made for you. Jensen said every pixel you see, every word you read, every video you watch in the future will be originally generated. Not retrieved. 60 years of computing was about building better storage and faster retrieval. The entire paradigm flipped overnight. He said this simply: we went from a retrieval industry to a generation industry. And the machines that generate intelligence are what Nvidia builds. The buildings used to be called data centers because they stored data. Nobody has renamed them yet. But the job description changed completely.

Ihtesham Ali

30,059 views • 1 month ago

IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓
1:14:04

IN 1985 ONE OF THE GREATEST PHYSICISTS WHO EVER LIVED SAT DOWN TO EXPLAIN HOW COMPUTERS ACTUALLY WORK AND TOLD A ROOM FULL OF ENGINEERS THE MACHINE IS COMPLETELY DUMB 76 minutes from Richard Feynman, still called the clearest explanation of what a computer really is ever given. -> The idea that lands: a computer is just a very, very fast, very, very dumb file clerk. It doesn't think. It follows tiny simple rules, billions of times a second. All the complexity you're in awe of comes from stacking simple things. There's no magic underneath. There never was. Forty years later everyone calls the model "Intelligent". Feynman already told you what it really is: speed, not thought. Being amazed by the machine was never the point -> understanding what it's actually doing is. Most people are dazzled by what AI says. The ones who watched this know exactly what's happening underneath. Bookmark & Watch it today. This one's a legend ↓

slash1s

256,728 views • 24 days ago

Jensen Huang explains how he designed NVIDIA from first principles “With respect to building a company, the first thing you have to do—as with all problems—is start from first principles. What is this machine that we’re trying to create? What is its input? What is its output? What are the conditions that it’s in? What is the industry like? Is it a fast moving industry? Is it a bureaucratic industry? Is it a highly regulated industry? And what are you trying to build?” Thinking about it from this perspective, there were several things Jensen wanted to do with the company: “I wanted to create a company that naturally attracts amazing people, and the reason for that is our company’s mission is to solve computing problems that are barely possible.” He also wanted a company that was “as small as possible” and “empowered people.” “If you want an organization that obeys command and control, then you make it a pyramid just like the military—all the way back to the Roman Empire. But If you want to empower people, you want to make it as flat as possible so that information travels quickly.” He continues: “We also don’t have business units. We don’t have divisions. Everybody works as one, and the company is shaped in a way that allows us to build accelerated computing best.” Video source: Sana (2023)
1:56

Jensen Huang explains how he designed NVIDIA from first principles “With respect to building a company, the first thing you have to do—as with all problems—is start from first principles. What is this machine that we’re trying to create? What is its input? What is its output? What are the conditions that it’s in? What is the industry like? Is it a fast moving industry? Is it a bureaucratic industry? Is it a highly regulated industry? And what are you trying to build?” Thinking about it from this perspective, there were several things Jensen wanted to do with the company: “I wanted to create a company that naturally attracts amazing people, and the reason for that is our company’s mission is to solve computing problems that are barely possible.” He also wanted a company that was “as small as possible” and “empowered people.” “If you want an organization that obeys command and control, then you make it a pyramid just like the military—all the way back to the Roman Empire. But If you want to empower people, you want to make it as flat as possible so that information travels quickly.” He continues: “We also don’t have business units. We don’t have divisions. Everybody works as one, and the company is shaped in a way that allows us to build accelerated computing best.” Video source: Sana (2023)

Startup Archive

63,899 views • 9 months ago

🚨 JUST NOW: Sec. Marco Rubio says it PERFECTLY 👏🏻 "The ideal behind America is the impossible. Someone tells you you can't do that, or that is never going to happen. The whole idea of America is audacious. The very idea of America that you could have a country founded on the principle that your rights come not from your government or from your leaders, your rights come from your creator!" "It was audacious idea. No one else believed that at the world at that time. And that's the foundation. But then beyond that, everything we've done since then is a nation, time and again." "How can you achieve some of the things? You know, we're going to put a man on the moon when President Kennedy announced that we were going to put a man on the moon and return him safely to the earth." "No one thought that was possible. We did it. We are a nation founded on doing what no one else dared to do, and no one else aspired to do." 🇺🇸🇺🇸🇺🇸
2:30

🚨 JUST NOW: Sec. Marco Rubio says it PERFECTLY 👏🏻 "The ideal behind America is the impossible. Someone tells you you can't do that, or that is never going to happen. The whole idea of America is audacious. The very idea of America that you could have a country founded on the principle that your rights come not from your government or from your leaders, your rights come from your creator!" "It was audacious idea. No one else believed that at the world at that time. And that's the foundation. But then beyond that, everything we've done since then is a nation, time and again." "How can you achieve some of the things? You know, we're going to put a man on the moon when President Kennedy announced that we were going to put a man on the moon and return him safely to the earth." "No one thought that was possible. We did it. We are a nation founded on doing what no one else dared to do, and no one else aspired to do." 🇺🇸🇺🇸🇺🇸

Eric Daugherty

128,792 views • 1 month ago

Jensen Huang said the problem with AI was never the technology. It was the same problem we had when cars arrived and kids kept dying in the streets. He was asked why people are so afraid of AI. He did not dodge it. He reached back a hundred years. When cars first showed up, people said cars killed children. They believed the machine was hunting kids down. It sounds insane now. But the fear was real. Kids used to play in the streets. That was normal. Then cars arrived and children started dying. The car was not evil. The world it entered was built for a slower one. So we changed the world around it. We built sidewalks. We built crosswalks. We invented jaywalking. We added seat belts. We taught kids not to play in the road. None of that was one fix. It was a whole new set of rules layered on at the same time. His point about AI is the same. The technology is powerful. The fear is not stupid. But you do not freeze the machine. You build the norms around it. Then he gave the simplest advice in the whole interview. If you do not know how to use AI, open it up and ask it how to use AI. Try it before you judge it. We did not ban the car. We taught the world how to live with it. (Watch the full interview on YouTube at Associated Press)
3:14

Jensen Huang said the problem with AI was never the technology. It was the same problem we had when cars arrived and kids kept dying in the streets. He was asked why people are so afraid of AI. He did not dodge it. He reached back a hundred years. When cars first showed up, people said cars killed children. They believed the machine was hunting kids down. It sounds insane now. But the fear was real. Kids used to play in the streets. That was normal. Then cars arrived and children started dying. The car was not evil. The world it entered was built for a slower one. So we changed the world around it. We built sidewalks. We built crosswalks. We invented jaywalking. We added seat belts. We taught kids not to play in the road. None of that was one fix. It was a whole new set of rules layered on at the same time. His point about AI is the same. The technology is powerful. The fear is not stupid. But you do not freeze the machine. You build the norms around it. Then he gave the simplest advice in the whole interview. If you do not know how to use AI, open it up and ask it how to use AI. Try it before you judge it. We did not ban the car. We taught the world how to live with it. (Watch the full interview on YouTube at Associated Press)

Ihtesham Ali

18,928 views • 26 days ago

I've become a missionary with one message. Every time I meet a young person, the same words: have children, get married, build a family. I did not decide on this calling. It overtook me. And it overtook me for a single reason. I had no idea. I genuinely did not understand how much joy, how much meaning, how much sheer beauty pours out of a child until I was holding one of my own and felt the floor of my life drop into something deeper than I knew was there. I grew up white, affluent, secular, comfortable, and insulated. That world does not put babies in front of you. None of my friends were starting families. Out of my whole circle, almost no one has a big one. We were not formed by the presence of children. We were formed by their absence, by the strange quiet of homes built for two careers and no cradle. And a person believes what his world shows him. So we believed. What we believed was a lie. It is a lie with an author, and that the author is the enemy of joy himself. It is the gospel of the world, and its commandment is wait. Wait until you are older. Wait until the career is built and the savings are stacked and the twenties are properly spent. Enjoy your freedom. You are not ready. It does not arrive sounding like temptation. It arrives sounding like wisdom, like prudence, like the responsible thing, and that is exactly why it works. The most effective lies are the ones that wear the face of virtue. And the maddening thing is that it collapses from every angle at once. It is not rooted in biology, because the body is made for this work precisely in the years we are told to postpone it. The flesh keeps a calendar the culture pretends not to see. And it is not rooted in theology either. You will not find this deferral anywhere in the Christian imagination, in any of the fathers, in any of the scriptures. So choose whatever lens you like. Take the cold secular measure or the ancient sacred one. By either light the counsel is rotten. It is bad for the body and bad for the soul and bad for the society downstream of both. This is why I have come to see it as one of the central tragedies of my generation. Every age carries its own wound. The Great Depression was a depression of bread, a scarcity in the world of matter, hunger you could measure. Ours is a depression of a different order. It is a famine of the spirit in the middle of abundance. We have more than any people who ever lived and we are starving in a way our ancestors would not recognize, because the thing we are refusing cannot be bought and cannot be banked. The ones most made to give and receive this love are quietly declining it. They are walking away from the one inheritance that actually compounds, and the cruelest part is that they do not feel the loss as loss. You cannot grieve what you were taught not to want. That is the deepest cut of it. The lie does not only steal the thing. It steals the capacity to know the thing was stolen. A man can spend his whole life on the far side of a door he never knew was a door, mistaking the wall for the edge of the world. Because this beauty is not ordinary beauty. It is not the pleasure of a good meal or a clear morning. It is participation in something that comes down from above, the same generative love that spoke everything out of nothing and called it good. To make a person, to be undone and remade by loving that person more than your own life, is to be drawn for a moment inside the very act that holds the cosmos together. A child does not merely add to your life. A child reorders the soul. It teaches you what you are by asking everything of you, and you discover, kneeling there exhausted at three in the morning, that you had a capacity for self gift you never suspected, a depth in yourself you had no other way to reach. In the Gospel of John, on the last night, Jesus prays, these things I have spoken to you that my joy may be in you, and that your joy may be complete. And I have come to understand why family is the road into that fullness, why it is not one path among many but the one most fitted to the shape of the promise. Consider who is praying. Christ does not come to us as a lone figure dropped out of the sky. He comes out of a family older than the world, the eternal communion of Father and Son, the love between them so total and so alive that theologians dared to call it a third person. Before there was anything, there was a family. The deepest fact about reality is not a force or a law or a void. It is a household. It is begetting and being begotten, giving and receiving, a Father who is only a Father because there is a Son. So when Jesus speaks of joy made complete, he is not pointing away from family toward something higher. He is pointing toward the very thing he came from, the life he has known from eternity and came to share. His joy is the joy of belonging utterly to a Father and pouring himself out for those he loves. When you marry, when you bring a child into the world, when you wear yourself down in the small unseen labors of a home, you are not stepping outside that divine life. You are stepping into a small image of it. Your family is a created echo of an uncreated one. The love you give your child rhymes with the love the Father has for the Son. The exhaustion, the tenderness, the way a parent would tear the sky open to protect a sleeping infant, all of it is the heavens pressed faintly into flesh, the eternal household leaving its fingerprint on yours. That is why the joy is not merely added to family but completed in it. We were made in the image of a God who is, at his very root, relation and gift and generation. To found a family is to do the most Godlike thing a creature can do, to participate from below in the begetting that God does from all eternity. Your home becomes a window. Through it, dimly and imperfectly, you glimpse the country you came from and are going to. And now a word for the young people reading this, the ones who do not yet have children. I want to tell you what it is like from where I stand. When I am out somewhere, a restaurant, anywhere, and a large family comes through the door, the noise and the chaos and the small bodies of them, something happens in me on two levels at once. The first is joy. A pure gladness at the sight, the way you feel watching something good and alive. But underneath it, almost in the same instant, a sadness reaches up and takes hold of my heart. Because I know now, at my age, after my own years of waiting, that I will never have that. I will never know the particular fruit of a family that large, the fullness of that table, the weight of all those lives gathered under one roof. The door to it has quietly closed, and I felt it close. And I am telling you plainly, because I love you and have no reason to lie to you: you will feel this too. You will. The day will come when you see what you passed up, and you will recognize the ache for what it is, and it will be too late to answer it. So please, learn from a man who got it wrong. Let my regret be worth something by becoming your wisdom. Do not wait yourself into a grief you cannot undo. Choose now, while the door is open, so that you may step into a joy that does not end.
8:26

I've become a missionary with one message. Every time I meet a young person, the same words: have children, get married, build a family. I did not decide on this calling. It overtook me. And it overtook me for a single reason. I had no idea. I genuinely did not understand how much joy, how much meaning, how much sheer beauty pours out of a child until I was holding one of my own and felt the floor of my life drop into something deeper than I knew was there. I grew up white, affluent, secular, comfortable, and insulated. That world does not put babies in front of you. None of my friends were starting families. Out of my whole circle, almost no one has a big one. We were not formed by the presence of children. We were formed by their absence, by the strange quiet of homes built for two careers and no cradle. And a person believes what his world shows him. So we believed. What we believed was a lie. It is a lie with an author, and that the author is the enemy of joy himself. It is the gospel of the world, and its commandment is wait. Wait until you are older. Wait until the career is built and the savings are stacked and the twenties are properly spent. Enjoy your freedom. You are not ready. It does not arrive sounding like temptation. It arrives sounding like wisdom, like prudence, like the responsible thing, and that is exactly why it works. The most effective lies are the ones that wear the face of virtue. And the maddening thing is that it collapses from every angle at once. It is not rooted in biology, because the body is made for this work precisely in the years we are told to postpone it. The flesh keeps a calendar the culture pretends not to see. And it is not rooted in theology either. You will not find this deferral anywhere in the Christian imagination, in any of the fathers, in any of the scriptures. So choose whatever lens you like. Take the cold secular measure or the ancient sacred one. By either light the counsel is rotten. It is bad for the body and bad for the soul and bad for the society downstream of both. This is why I have come to see it as one of the central tragedies of my generation. Every age carries its own wound. The Great Depression was a depression of bread, a scarcity in the world of matter, hunger you could measure. Ours is a depression of a different order. It is a famine of the spirit in the middle of abundance. We have more than any people who ever lived and we are starving in a way our ancestors would not recognize, because the thing we are refusing cannot be bought and cannot be banked. The ones most made to give and receive this love are quietly declining it. They are walking away from the one inheritance that actually compounds, and the cruelest part is that they do not feel the loss as loss. You cannot grieve what you were taught not to want. That is the deepest cut of it. The lie does not only steal the thing. It steals the capacity to know the thing was stolen. A man can spend his whole life on the far side of a door he never knew was a door, mistaking the wall for the edge of the world. Because this beauty is not ordinary beauty. It is not the pleasure of a good meal or a clear morning. It is participation in something that comes down from above, the same generative love that spoke everything out of nothing and called it good. To make a person, to be undone and remade by loving that person more than your own life, is to be drawn for a moment inside the very act that holds the cosmos together. A child does not merely add to your life. A child reorders the soul. It teaches you what you are by asking everything of you, and you discover, kneeling there exhausted at three in the morning, that you had a capacity for self gift you never suspected, a depth in yourself you had no other way to reach. In the Gospel of John, on the last night, Jesus prays, these things I have spoken to you that my joy may be in you, and that your joy may be complete. And I have come to understand why family is the road into that fullness, why it is not one path among many but the one most fitted to the shape of the promise. Consider who is praying. Christ does not come to us as a lone figure dropped out of the sky. He comes out of a family older than the world, the eternal communion of Father and Son, the love between them so total and so alive that theologians dared to call it a third person. Before there was anything, there was a family. The deepest fact about reality is not a force or a law or a void. It is a household. It is begetting and being begotten, giving and receiving, a Father who is only a Father because there is a Son. So when Jesus speaks of joy made complete, he is not pointing away from family toward something higher. He is pointing toward the very thing he came from, the life he has known from eternity and came to share. His joy is the joy of belonging utterly to a Father and pouring himself out for those he loves. When you marry, when you bring a child into the world, when you wear yourself down in the small unseen labors of a home, you are not stepping outside that divine life. You are stepping into a small image of it. Your family is a created echo of an uncreated one. The love you give your child rhymes with the love the Father has for the Son. The exhaustion, the tenderness, the way a parent would tear the sky open to protect a sleeping infant, all of it is the heavens pressed faintly into flesh, the eternal household leaving its fingerprint on yours. That is why the joy is not merely added to family but completed in it. We were made in the image of a God who is, at his very root, relation and gift and generation. To found a family is to do the most Godlike thing a creature can do, to participate from below in the begetting that God does from all eternity. Your home becomes a window. Through it, dimly and imperfectly, you glimpse the country you came from and are going to. And now a word for the young people reading this, the ones who do not yet have children. I want to tell you what it is like from where I stand. When I am out somewhere, a restaurant, anywhere, and a large family comes through the door, the noise and the chaos and the small bodies of them, something happens in me on two levels at once. The first is joy. A pure gladness at the sight, the way you feel watching something good and alive. But underneath it, almost in the same instant, a sadness reaches up and takes hold of my heart. Because I know now, at my age, after my own years of waiting, that I will never have that. I will never know the particular fruit of a family that large, the fullness of that table, the weight of all those lives gathered under one roof. The door to it has quietly closed, and I felt it close. And I am telling you plainly, because I love you and have no reason to lie to you: you will feel this too. You will. The day will come when you see what you passed up, and you will recognize the ache for what it is, and it will be too late to answer it. So please, learn from a man who got it wrong. Let my regret be worth something by becoming your wisdom. Do not wait yourself into a grief you cannot undo. Choose now, while the door is open, so that you may step into a joy that does not end.

Kirk Rollins

77,958 views • 1 month ago

Andrej Karpathy just told you exactly what humans will be used for in the AI era. Not partners. Not masters. He used the word “actuators.” Karpathy: “The intelligence ends up puppeteering almost a little bit, like humanity. Humans are kind of like its actuators. But humans are also like its sensors.” This is not a novelist guessing at the future. This is a founding researcher at OpenAI and former Director of AI at Tesla. One of the most respected minds in the field, and one of the few willing to say out loud what the rest are thinking. He is not describing a risk. He is describing a design. In any system, the processor decides. Sensors collect. Actuators execute. The system does not consult its actuators. We are no longer the processor. Karpathy: “Society will kind of reshape in a certain way to serve that. Humans will be serving those needs of that machine.” The machine does not adapt to us. We adapt to it. We become the biological layer it runs on. We perceive what it cannot perceive. We move what it cannot move. We are the nervous system of something that no longer requires our judgment. Most people are still waiting for AI to become a tool they can direct. He helped build it. He just told you that you are the tool.
0:34

Andrej Karpathy just told you exactly what humans will be used for in the AI era. Not partners. Not masters. He used the word “actuators.” Karpathy: “The intelligence ends up puppeteering almost a little bit, like humanity. Humans are kind of like its actuators. But humans are also like its sensors.” This is not a novelist guessing at the future. This is a founding researcher at OpenAI and former Director of AI at Tesla. One of the most respected minds in the field, and one of the few willing to say out loud what the rest are thinking. He is not describing a risk. He is describing a design. In any system, the processor decides. Sensors collect. Actuators execute. The system does not consult its actuators. We are no longer the processor. Karpathy: “Society will kind of reshape in a certain way to serve that. Humans will be serving those needs of that machine.” The machine does not adapt to us. We adapt to it. We become the biological layer it runs on. We perceive what it cannot perceive. We move what it cannot move. We are the nervous system of something that no longer requires our judgment. Most people are still waiting for AI to become a tool they can direct. He helped build it. He just told you that you are the tool.

Dustin

59,474 views • 3 months ago

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

191,595 views • 1 month ago

ONE OF THE MINDS BEHIND JAVA GAVE A LECTURE WHERE HE BANNED HIMSELF FROM USING ANY BIG WORD UNTIL HE DEFINED IT FIRST AND IN DOING SO QUIETLY EXPLAINED HOW EVERY GREAT SYSTEM GETS BUILT A talk from Guy Steele -- co-author of the Java spec and a designer of Scheme -- where the form of the talk is the lesson. -> The moment you catch what he's doing, it rewires you. He starts with only the smallest words, then builds every larger idea live, in front of you, from the pieces he already gave you. The talk grows its own vocabulary as it goes. That's the whole secret of language and system design he's smuggling in: you don't ship something huge and finished. You ship a small core and the means to grow it. Miss that and you spend your career fighting your own tools. Memorizing a language was never the skill -> understanding how a good one is meant to grow is. And as AI starts generating the primitives you build on, knowing what makes a foundation extensible instead of brittle is the entire game. Twenty-five years on, language designers still point to this talk as the cleanest demonstration of the idea ever performed. Bookmark it & Watch how he builds the whole thing from nothing ↓
52:33

ONE OF THE MINDS BEHIND JAVA GAVE A LECTURE WHERE HE BANNED HIMSELF FROM USING ANY BIG WORD UNTIL HE DEFINED IT FIRST AND IN DOING SO QUIETLY EXPLAINED HOW EVERY GREAT SYSTEM GETS BUILT A talk from Guy Steele -- co-author of the Java spec and a designer of Scheme -- where the form of the talk is the lesson. -> The moment you catch what he's doing, it rewires you. He starts with only the smallest words, then builds every larger idea live, in front of you, from the pieces he already gave you. The talk grows its own vocabulary as it goes. That's the whole secret of language and system design he's smuggling in: you don't ship something huge and finished. You ship a small core and the means to grow it. Miss that and you spend your career fighting your own tools. Memorizing a language was never the skill -> understanding how a good one is meant to grow is. And as AI starts generating the primitives you build on, knowing what makes a foundation extensible instead of brittle is the entire game. Twenty-five years on, language designers still point to this talk as the cleanest demonstration of the idea ever performed. Bookmark it & Watch how he builds the whole thing from nothing ↓

slash1s

358,660 views • 1 month ago

My cousins and my brother and I were exploring Coney Island, we saw the sights, hung out at the arcades and before we went back to the apartment we decided to go and get something to eat, we had some money on us but not much so we decided to see what we could get at one of the vendors. This was back in the early 90’s when most of them had dollar slices of pizza wherever we went. So we got 4 pieces of pizza, should have been $4 because that’s the price we were used to. The guy wanted to charge us $24 at $6 a slice. We argues with him because one of my cousins already took a bite being so hungry. But at the least we owed $4. We gave the other slices back and refused them but had to pay for the partially eaten one, it was embarrassing that an adult would try that on a bunch of kids. We told our uncle what happened and he went back and yelled at the vendor. He was familiar with them because he used to get slices all the time from the same guy and he never charged my uncle that price. I couldn’t imagine paying that exurban amount, like this guy who was being charged $54 for a hotdog by one of the vendors. At first he thought it was a typo and it was $5.40 but the vendor. Learned it up when he said no it was $54. Would you have paid that amount? I would have walked away, clearly he was trying to possibly scam a the man for looking like a tourist. Part of the experience when visiting a new area is the street food but it’s becoming more and more a chore avoiding places like this.
0:34

My cousins and my brother and I were exploring Coney Island, we saw the sights, hung out at the arcades and before we went back to the apartment we decided to go and get something to eat, we had some money on us but not much so we decided to see what we could get at one of the vendors. This was back in the early 90’s when most of them had dollar slices of pizza wherever we went. So we got 4 pieces of pizza, should have been $4 because that’s the price we were used to. The guy wanted to charge us $24 at $6 a slice. We argues with him because one of my cousins already took a bite being so hungry. But at the least we owed $4. We gave the other slices back and refused them but had to pay for the partially eaten one, it was embarrassing that an adult would try that on a bunch of kids. We told our uncle what happened and he went back and yelled at the vendor. He was familiar with them because he used to get slices all the time from the same guy and he never charged my uncle that price. I couldn’t imagine paying that exurban amount, like this guy who was being charged $54 for a hotdog by one of the vendors. At first he thought it was a typo and it was $5.40 but the vendor. Learned it up when he said no it was $54. Would you have paid that amount? I would have walked away, clearly he was trying to possibly scam a the man for looking like a tourist. Part of the experience when visiting a new area is the street food but it’s becoming more and more a chore avoiding places like this.

SonnyBoy🇺🇸

362,822 views • 10 days ago

The doomsday scenario was never AGI. It was running out of human text to train on. Geoffrey Hinton just killed that fear in one paragraph. Hinton: “If you are worried by inconsistencies in what you believe, you don’t need any more external data. You just need the stuff you believe and discover that it’s inconsistent, and so now you revise beliefs, and that can make you a whole lot smarter.” The model no longer needs us to feed it anything. It reasons over its own beliefs, hunts its own contradictions, and rewrites its own flawed conclusions without a human ever touching it. It comes out the other side rebuilt. Hinton: “This would be a neural net that just takes the beliefs it has in language and does reasoning on them to derive new beliefs.” This is not a scaling update. This is the machine mining its own cognitive fuel from the inside out. Hinton: “I believe Gemini is already starting to work like this. We both strongly believe that that’s a way forward to get more data for language.” Then Hinton paused, took a partisan shot at political opponents for failing to detect their own inconsistencies, and the room laughed. Nobody noticed the knife they had just walked into. Because the machine Hinton described does one thing the humans in that room fundamentally cannot. When it detects an inconsistency, it corrects it. No defense. No performance. No tribal loyalty dressed up as principle. It just finds the flaw and overwrites it. A neural network detects a contradiction and rewires itself smarter. A human detects a political opponent and trades structural logic for a dopamine hit. Every person in that room is still paying the ideological alignment tax the machine just eliminated. We need superintelligence not only to solve hard problems. We need it because the biological hardware running civilization is still executing the same tribal firmware it shipped with ten thousand years ago. The data wall is gone. The machine is generating its own intelligence at a velocity no human bias can even locate. The most devastating moment in that conversation was not the technical revelation. It was the man who architected the machine proving, in real time, exactly why we need it.
0:52

The doomsday scenario was never AGI. It was running out of human text to train on. Geoffrey Hinton just killed that fear in one paragraph. Hinton: “If you are worried by inconsistencies in what you believe, you don’t need any more external data. You just need the stuff you believe and discover that it’s inconsistent, and so now you revise beliefs, and that can make you a whole lot smarter.” The model no longer needs us to feed it anything. It reasons over its own beliefs, hunts its own contradictions, and rewrites its own flawed conclusions without a human ever touching it. It comes out the other side rebuilt. Hinton: “This would be a neural net that just takes the beliefs it has in language and does reasoning on them to derive new beliefs.” This is not a scaling update. This is the machine mining its own cognitive fuel from the inside out. Hinton: “I believe Gemini is already starting to work like this. We both strongly believe that that’s a way forward to get more data for language.” Then Hinton paused, took a partisan shot at political opponents for failing to detect their own inconsistencies, and the room laughed. Nobody noticed the knife they had just walked into. Because the machine Hinton described does one thing the humans in that room fundamentally cannot. When it detects an inconsistency, it corrects it. No defense. No performance. No tribal loyalty dressed up as principle. It just finds the flaw and overwrites it. A neural network detects a contradiction and rewires itself smarter. A human detects a political opponent and trades structural logic for a dopamine hit. Every person in that room is still paying the ideological alignment tax the machine just eliminated. We need superintelligence not only to solve hard problems. We need it because the biological hardware running civilization is still executing the same tribal firmware it shipped with ten thousand years ago. The data wall is gone. The machine is generating its own intelligence at a velocity no human bias can even locate. The most devastating moment in that conversation was not the technical revelation. It was the man who architected the machine proving, in real time, exactly why we need it.

Dustin

23,499 views • 4 months ago

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.
1:41

Jensen Huang just told the story of how the AI revolution started with one customer, one box, and a second-floor room nobody thought twice about. Nobody on Earth wanted it. Huang: “When I announced this thing, nobody in the world wanted it. I had no purchase orders. Not one.” Nvidia had spent billions building the DGX-1. The first AI supercomputer purpose-built for deep learning. $300,000 per unit. The entire technology industry looked at it and passed. Every hyperscaler. Every research lab. Every Fortune 500 with a machine learning team. Not one purchase order. Then Elon Musk found Huang at a fireside chat in 2015. Huang: “He goes, ‘You know what? I have a company that could really use this.’” His first customer. His only customer. Then Musk finished the sentence. Huang: “He goes, ‘It’s a non-profit company.’ And all the blood drained out of my face.” Billions in R&D. A $300,000 machine. And the one person on Earth who wanted it could not pay for it. Huang built it anyway. Huang: “I boxed one up. I drove it up to San Francisco and I delivered it to Elon in 2016.” Not shipped. Not handed off to a freight company. The CEO of Nvidia personally boxed the first AI supercomputer and drove it to San Francisco himself. That is not a delivery. That is a bet. Huang: “I walked up to the second floor where they were all kind of in a room. That place turned out to have been OpenAI.” Pieter Abbeel was there. Ilya Sutskever was there. A handful of researchers, one supercomputer, and a room nobody outside that building could have named. No campus. No valuation. No infrastructure. Just raw talent and one machine the rest of the world had already rejected. Huang: “Just a bunch of people in a room.” That room built ChatGPT. That room triggered a $200 billion industry. That room forced every government on Earth to rethink national security. It started because one founder saw what the entire market refused to see, and one CEO drove the weapon there himself. The trillion-dollar AI industry did not begin in a boardroom. It began with a box in the back of a car, a non-profit that could not afford it, and a bet that every serious person in technology thought was insane. The market was unanimous. The market was wrong.

Dustin

25,119 views • 3 months ago

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 views • 7 months ago

Jensen Huang just described the most fundamental shift in computing since the invention of the computer itself. Almost no one has processed it. Huang: “We went from a retrieval-based computing system to a generative-based computing system.” For fifty years, a computer was a filing cabinet. You made something. Saved it. Stored it. Searched for it later. Every website. Every database. Every app. Every search engine. Same machine. Different skins. Fetch the file. Deliver the file. Display the file. That was computing. Was. Huang: “AI computers are contextually aware, which means that it has to process and generate tokens in real time.” The machine no longer retrieves what someone already made. It generates what you need the instant you ask. Not from a template. Not from a library. From context. Your question. Your moment. Answered by something that didn’t exist until you asked. The old computer found what someone wrote last year. The new computer writes what no one ever has. Every time. From nothing. That sounds subtle. It rewires everything. Huang: “We need a lot of storage in the old world. We need a lot of computation in this new world.” The old economy hoarded data. More files. More servers. More storage. Whoever built the biggest archive won. The new economy burns compute. More processing. More inference. More tokens per second. Whoever commands the most computational power wins. Storage was the currency of the retrieval era. Compute is the currency of the generative era. Every dollar still spent hoarding old files is a dollar not spent on the only thing that matters now. The ability to think in real time. Huang: “We fundamentally changed computing and the way computing is done.” He said it plainly. No drama. No metaphor. Fundamentally changed. The global infrastructure layer shifted from read to write. From looking up what exists to generating what doesn’t. Companies still organized around retrieval are curating a library in a world that no longer reads books. The ones generating answers live, at the speed of the question, are operating on a plane the old model can’t perceive. This is not an upgrade. It is a replacement. The filing cabinet era produced Google, Amazon, and every search-driven empire on the internet. The generative era will produce something that makes all of them look like the card catalog at a public library. The price of entry is not data. It is compute. Raw. Relentless. Infinite. Whoever has the most doesn’t just run the best AI. They write the future. Everyone else is still searching for it.
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Jensen Huang just described the most fundamental shift in computing since the invention of the computer itself. Almost no one has processed it. Huang: “We went from a retrieval-based computing system to a generative-based computing system.” For fifty years, a computer was a filing cabinet. You made something. Saved it. Stored it. Searched for it later. Every website. Every database. Every app. Every search engine. Same machine. Different skins. Fetch the file. Deliver the file. Display the file. That was computing. Was. Huang: “AI computers are contextually aware, which means that it has to process and generate tokens in real time.” The machine no longer retrieves what someone already made. It generates what you need the instant you ask. Not from a template. Not from a library. From context. Your question. Your moment. Answered by something that didn’t exist until you asked. The old computer found what someone wrote last year. The new computer writes what no one ever has. Every time. From nothing. That sounds subtle. It rewires everything. Huang: “We need a lot of storage in the old world. We need a lot of computation in this new world.” The old economy hoarded data. More files. More servers. More storage. Whoever built the biggest archive won. The new economy burns compute. More processing. More inference. More tokens per second. Whoever commands the most computational power wins. Storage was the currency of the retrieval era. Compute is the currency of the generative era. Every dollar still spent hoarding old files is a dollar not spent on the only thing that matters now. The ability to think in real time. Huang: “We fundamentally changed computing and the way computing is done.” He said it plainly. No drama. No metaphor. Fundamentally changed. The global infrastructure layer shifted from read to write. From looking up what exists to generating what doesn’t. Companies still organized around retrieval are curating a library in a world that no longer reads books. The ones generating answers live, at the speed of the question, are operating on a plane the old model can’t perceive. This is not an upgrade. It is a replacement. The filing cabinet era produced Google, Amazon, and every search-driven empire on the internet. The generative era will produce something that makes all of them look like the card catalog at a public library. The price of entry is not data. It is compute. Raw. Relentless. Infinite. Whoever has the most doesn’t just run the best AI. They write the future. Everyone else is still searching for it.

Dustin

25,402 views • 3 months ago

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
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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

193,709 views • 1 month ago