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Richard Hamming stood in front of Navy graduate students in 1995 and explained why smart engineers build the wrong systems. The camera caught 45 minutes of it. Almost nobody watches it. Hamming ran computing at Bell Labs when Bell Labs was inventing the modern world. Error-correcting codes carry his...

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Richard Feynman stood at a Cornell blackboard in 1964 and explained the problem every AI lab is fighting in 2026. The BBC filmed it. Almost nobody watches it. The lecture is about why nature only answers in mathematics. Every team forcing language models to reason is hitting the wall he mapped 62 years ago. He was 46. The Nobel Prize came 11 months later. The footage survived on film reels and now sits free on YouTube with fewer views than a keyboard unboxing. Watch the blackboard section near the middle. He takes 1 of Kepler's laws and rebuilds it from nothing, with notation a 12-year-old can follow. No slides. No jargon. 1 piece of chalk. An ML engineer I know paused it 4 times and made his whole team watch it before standup. You're 62 years late. The lecture is still free.
9:15

Richard Feynman stood at a Cornell blackboard in 1964 and explained the problem every AI lab is fighting in 2026. The BBC filmed it. Almost nobody watches it. The lecture is about why nature only answers in mathematics. Every team forcing language models to reason is hitting the wall he mapped 62 years ago. He was 46. The Nobel Prize came 11 months later. The footage survived on film reels and now sits free on YouTube with fewer views than a keyboard unboxing. Watch the blackboard section near the middle. He takes 1 of Kepler's laws and rebuilds it from nothing, with notation a 12-year-old can follow. No slides. No jargon. 1 piece of chalk. An ML engineer I know paused it 4 times and made his whole team watch it before standup. You're 62 years late. The lecture is still free.

Merlow

2,599,622 views • 2 months ago

A Bell Labs veteran gave a 45-minute lecture in 1995 on 1 question every ML engineer skips: why does mathematics work at all. Almost nobody watches it. The man is Richard Hamming. He wrote the error-correcting code that keeps every hard drive, modem, satellite link, and QR scan from collapsing into noise. His question sounds like nothing. Why do symbols on a board predict planets they were never built for. Why did complex numbers, invented as pure abstraction, turn out to run all of electrical engineering. Why does a matrix eigenvalue decide whether a bridge stands. He calls it "the unreasonable effectiveness of mathematics." Newton wrote 1 equation and it held for the Moon, the tides, and a falling apple at once. Nobody ordered the universe to obey algebra. Watch the middle. He argues we find math everywhere partly because our brains evolved to see it, then admits that answer leaves most of the mystery standing. No slides. 1 old man, 1 chalkboard, 45 minutes. You train models on linear algebra every day. He spent a career asking why it works.
45:34

A Bell Labs veteran gave a 45-minute lecture in 1995 on 1 question every ML engineer skips: why does mathematics work at all. Almost nobody watches it. The man is Richard Hamming. He wrote the error-correcting code that keeps every hard drive, modem, satellite link, and QR scan from collapsing into noise. His question sounds like nothing. Why do symbols on a board predict planets they were never built for. Why did complex numbers, invented as pure abstraction, turn out to run all of electrical engineering. Why does a matrix eigenvalue decide whether a bridge stands. He calls it "the unreasonable effectiveness of mathematics." Newton wrote 1 equation and it held for the Moon, the tides, and a falling apple at once. Nobody ordered the universe to obey algebra. Watch the middle. He argues we find math everywhere partly because our brains evolved to see it, then admits that answer leaves most of the mystery standing. No slides. 1 old man, 1 chalkboard, 45 minutes. You train models on linear algebra every day. He spent a career asking why it works.

kompot

149,187 views • 2 months ago

One of the men who invented the transistor stood at a Bell Labs blackboard and explained how it works. AT&T kept the film. Almost nobody watches it. Walter Brattain shared the 1956 Nobel Prize in physics for the transistor. Every chip training every model in 2026 descends from what he built with Bardeen and Shockley in 1947. This is 28 minutes of the inventor teaching semiconductor physics himself. Properties, simple model, history, impact, new phenomena, written on the board in his own hand. Watch the section where he draws the crystal lattice and shows what an electron and a hole actually do inside it. The abstraction most engineers carry around, drawn from scratch by the man who found it. An embedded engineer I know watched it twice and said 28 minutes of chalk beat a semester of textbook diagrams. Free on YouTube from the AT&T archives, 10 chapters, black and white. Everyone uses his invention. Almost nobody hears him explain it.
28:33

One of the men who invented the transistor stood at a Bell Labs blackboard and explained how it works. AT&T kept the film. Almost nobody watches it. Walter Brattain shared the 1956 Nobel Prize in physics for the transistor. Every chip training every model in 2026 descends from what he built with Bardeen and Shockley in 1947. This is 28 minutes of the inventor teaching semiconductor physics himself. Properties, simple model, history, impact, new phenomena, written on the board in his own hand. Watch the section where he draws the crystal lattice and shows what an electron and a hole actually do inside it. The abstraction most engineers carry around, drawn from scratch by the man who found it. An embedded engineer I know watched it twice and said 28 minutes of chalk beat a semester of textbook diagrams. Free on YouTube from the AT&T archives, 10 chapters, black and white. Everyone uses his invention. Almost nobody hears him explain it.

xanax

105,368 views • 2 months ago

Richard Feynman stood at a blackboard in 1964 and drew the line between knowing and understanding. Every AI benchmark in 2026 sits on the wrong side of it. Almost nobody watches the clip. His father taught him the difference as a boy. You can know a bird's name in every language and know nothing about the bird. Names track labels. Understanding survives when the labels change. He carried that into physics. You can compute every answer and understand none of them. A student who memorizes the formula knows. A student who can rebuild it from nothing understands. Now run today's models through his test. They name everything, compute everything, pass every exam. The 1 question underneath hasn't moved: is anything understood. He couldn't have imagined the machines. He built their hardest eval 62 years early. Watch him at the board. He never recites. He rebuilds the idea live in front of the room, which was exactly his point. No slides. No notes. 1 piece of chalk. Benchmarks measure knowing. Nobody has built one for the other thing. You're 62 years late. The clip is still free.
5:19

Richard Feynman stood at a blackboard in 1964 and drew the line between knowing and understanding. Every AI benchmark in 2026 sits on the wrong side of it. Almost nobody watches the clip. His father taught him the difference as a boy. You can know a bird's name in every language and know nothing about the bird. Names track labels. Understanding survives when the labels change. He carried that into physics. You can compute every answer and understand none of them. A student who memorizes the formula knows. A student who can rebuild it from nothing understands. Now run today's models through his test. They name everything, compute everything, pass every exam. The 1 question underneath hasn't moved: is anything understood. He couldn't have imagined the machines. He built their hardest eval 62 years early. Watch him at the board. He never recites. He rebuilds the idea live in front of the room, which was exactly his point. No slides. No notes. 1 piece of chalk. Benchmarks measure knowing. Nobody has built one for the other thing. You're 62 years late. The clip is still free.

Merlow

14,979 views • 2 months ago

MIT filmed the single hour that makes all of calculus one subject instead of two. Free, uncut, chalk only. Almost nobody watches it. This is 18.01, Single Variable Calculus, lecture 19, taught by David Jerison. The First Fundamental Theorem of Calculus. Slopes and areas were separate problems for centuries. Different tools, different people, different questions. This lecture shows they were the same thing pointed in opposite directions. Every gradient computed in every training run rides on that connection. Most engineers use it daily and have never seen the proof that makes it legal. Watch the board work in the middle. He builds the link line by line, real students in the room, no software, no animation. A machine learning engineer I know rewatched it before an interview and said the chain from loss to update finally read as 1 object instead of 3 tricks. Full course free on YouTube under Creative Commons. You've been using the theorem. You've never seen it earned.
47:52

MIT filmed the single hour that makes all of calculus one subject instead of two. Free, uncut, chalk only. Almost nobody watches it. This is 18.01, Single Variable Calculus, lecture 19, taught by David Jerison. The First Fundamental Theorem of Calculus. Slopes and areas were separate problems for centuries. Different tools, different people, different questions. This lecture shows they were the same thing pointed in opposite directions. Every gradient computed in every training run rides on that connection. Most engineers use it daily and have never seen the proof that makes it legal. Watch the board work in the middle. He builds the link line by line, real students in the room, no software, no animation. A machine learning engineer I know rewatched it before an interview and said the chain from loss to update finally read as 1 object instead of 3 tricks. Full course free on YouTube under Creative Commons. You've been using the theorem. You've never seen it earned.

obssnnn

124,305 views • 2 months ago

CEO of a trillion-dollar company sat in a Stanford classroom in 2011 and explained exactly how he built it. No PR team, no prepared remarks, no investors in the room. No business school has ever added the recording to a syllabus. His name is Jensen Huang. He co-founded NVIDIA in 1993 with $40,000. By 2011 the company was worth $9 billion. Today it is worth over $3 trillion. He walked into the Stanford ASES Summit and gave away the entire playbook to a room of fifty students for free. The lecture is about when to bet everything on one technology before the market knows it exists. He did it with GPUs. Then with CUDA. Then with AI infrastructure. Three bets, same logic, same company, three different industries. The uncomfortable part is what he says about risk. He almost went bankrupt twice. What he did both times is the opposite of what every MBA program teaches. Business schools charge $200K in tuition to teach frameworks he rejected before his company was worth a billion. Every founder podcast repeats the same five lessons. Almost none of them mention what Huang actually did when the company was ninety days from dying. That part is in the lecture. It has been free for fifteen years. Filmed by a student with a handheld camera. Audio cuts in and out. He gave away the playbook of the most valuable company on Earth to fifty people. Almost nobody watched it. One classroom. One camera. The full lecture is free. It is in the video.
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CEO of a trillion-dollar company sat in a Stanford classroom in 2011 and explained exactly how he built it. No PR team, no prepared remarks, no investors in the room. No business school has ever added the recording to a syllabus. His name is Jensen Huang. He co-founded NVIDIA in 1993 with $40,000. By 2011 the company was worth $9 billion. Today it is worth over $3 trillion. He walked into the Stanford ASES Summit and gave away the entire playbook to a room of fifty students for free. The lecture is about when to bet everything on one technology before the market knows it exists. He did it with GPUs. Then with CUDA. Then with AI infrastructure. Three bets, same logic, same company, three different industries. The uncomfortable part is what he says about risk. He almost went bankrupt twice. What he did both times is the opposite of what every MBA program teaches. Business schools charge $200K in tuition to teach frameworks he rejected before his company was worth a billion. Every founder podcast repeats the same five lessons. Almost none of them mention what Huang actually did when the company was ninety days from dying. That part is in the lecture. It has been free for fifteen years. Filmed by a student with a handheld camera. Audio cuts in and out. He gave away the playbook of the most valuable company on Earth to fifty people. Almost nobody watched it. One classroom. One camera. The full lecture is free. It is in the video.

Tigerflow

74,298 views • 1 month ago

A Fields Medalist stood on a stage in the 1960s and taught the geometry every gradient moves through. The film went missing for years. This is John Milnor, lecture 1 on differential topology, filmed by the Mathematical Association of America soon after he won the Fields Medal in 1962. He was in his early 30s. The same lectures became Topology from the Differentiable Viewpoint, a book still assigned 60 years later and short enough to read in a week. Smooth maps, manifolds, regular values. The vocabulary under every loss surface, every latent space, every paper with the word manifold in the abstract. Watch what he writes at the top of the board and how little he needs after it. No slides, no notation dumped on the room, each definition doing real work. A research engineer I know worked through the book alongside the film and said the two together beat every modern course he tried. The footage was unavailable for years. It sits free on YouTube now, black and white, 1 fixed camera. The best textbook in the field is a transcript of this room.
51:41

A Fields Medalist stood on a stage in the 1960s and taught the geometry every gradient moves through. The film went missing for years. This is John Milnor, lecture 1 on differential topology, filmed by the Mathematical Association of America soon after he won the Fields Medal in 1962. He was in his early 30s. The same lectures became Topology from the Differentiable Viewpoint, a book still assigned 60 years later and short enough to read in a week. Smooth maps, manifolds, regular values. The vocabulary under every loss surface, every latent space, every paper with the word manifold in the abstract. Watch what he writes at the top of the board and how little he needs after it. No slides, no notation dumped on the room, each definition doing real work. A research engineer I know worked through the book alongside the film and said the two together beat every modern course he tried. The footage was unavailable for years. It sits free on YouTube now, black and white, 1 fixed camera. The best textbook in the field is a transcript of this room.

the lich

29,983 views • 2 months ago

Gilbert Strang taught linear algebra at MIT for fifty years. His last lecture is on YouTube. It has fewer views than his worst lecture. Nobody told students it was the last one. He just walked in, said "the final class in linear algebra at MIT," and started reviewing old exams. Google built a $2,000,000,000,000 company on one concept from this course. He is 88 years old. He still answers emails. This is MIT 18.06. Linear Algebra. The most watched mathematics course in history. Then the Markov matrix. Strang writes a transition matrix on the board. Three states. People moving between them every step. After enough steps - the system locks into a steady state that never changes. That steady state is an eigenvector. Google's PageRank works the same way. Websites are states. Clicks are transitions. The importance of every page on the internet is one eigenvector of one enormous matrix. Then least squares. Three data points. No line passes through all three. So you find the line that minimizes total error. That is how every AI model on earth is trained - including the ones running inside Goldman Sachs trading desks. Then the projection. The closest point on a plane to a vector outside it. Strang draws it in 30 seconds. That same operation is how Netflix decides what to recommend to 280,000,000 subscribers. Watch the moment he gives back the final exam answer and asks students to work backwards to the question - the room solves it in silence faster than any other lecture all semester. A software engineer told me 18.06 was the course that got her from $90,000 to $200,000 in two years. Same company. Different title. Bookmark this and watch later - after this lecture every dataset you touch will feel like a geometry problem waiting to be solved. MIT 18.06 Lecture 34 | Linear Algebra Final Review | Gilbert Strang
43:06

Gilbert Strang taught linear algebra at MIT for fifty years. His last lecture is on YouTube. It has fewer views than his worst lecture. Nobody told students it was the last one. He just walked in, said "the final class in linear algebra at MIT," and started reviewing old exams. Google built a $2,000,000,000,000 company on one concept from this course. He is 88 years old. He still answers emails. This is MIT 18.06. Linear Algebra. The most watched mathematics course in history. Then the Markov matrix. Strang writes a transition matrix on the board. Three states. People moving between them every step. After enough steps - the system locks into a steady state that never changes. That steady state is an eigenvector. Google's PageRank works the same way. Websites are states. Clicks are transitions. The importance of every page on the internet is one eigenvector of one enormous matrix. Then least squares. Three data points. No line passes through all three. So you find the line that minimizes total error. That is how every AI model on earth is trained - including the ones running inside Goldman Sachs trading desks. Then the projection. The closest point on a plane to a vector outside it. Strang draws it in 30 seconds. That same operation is how Netflix decides what to recommend to 280,000,000 subscribers. Watch the moment he gives back the final exam answer and asks students to work backwards to the question - the room solves it in silence faster than any other lecture all semester. A software engineer told me 18.06 was the course that got her from $90,000 to $200,000 in two years. Same company. Different title. Bookmark this and watch later - after this lecture every dataset you touch will feel like a geometry problem waiting to be solved. MIT 18.06 Lecture 34 | Linear Algebra Final Review | Gilbert Strang

Zyphor

56,603 views • 28 days ago

Elon Musk was asked how fast AI is moving. His answer wasn’t about the technology. It was about the one man who got it all right and was still too conservative. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent 30 years making forecasts that made serious people uncomfortable. He predicted timelines that sounded impossible. He was mocked for it. He was right about nearly all of them. And Musk just called him conservative. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” 10x every six months. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was a 2x improvement every two years. That single curve drove every technological shift of the last 50 years. The internet. Smartphones. Cloud computing. All of it rode a 2x curve. AI is on a 100x curve. And the current infrastructure isn’t running beside the new one. It’s becoming it. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” Everything that runs the world you know is being rewired for the world that comes next. Human beings process the future in straight lines. We take the speed of the last decade and project it forward. Exponential growth doesn’t work that way. It’s invisible until it’s everywhere. The most aggressive forecaster in the history of technology was too conservative. That’s not about Kurzweil being wrong about the direction. That’s about the human brain being wrong about the speed. The limit was never the technology. It was the organ we use to comprehend it. And that organ hasn’t been upgraded in 200,000 years.
1:01

Elon Musk was asked how fast AI is moving. His answer wasn’t about the technology. It was about the one man who got it all right and was still too conservative. Musk: “I have to give credit to Ray Kurzweil in being actually remarkably accurate in his predictions. If anything, I think he was perhaps a bit conservative in his predictions.” Kurzweil spent 30 years making forecasts that made serious people uncomfortable. He predicted timelines that sounded impossible. He was mocked for it. He was right about nearly all of them. And Musk just called him conservative. Musk: “The dedicated AI compute appears to be growing by a factor of 10 every six months.” 10x every six months. Musk: “Almost a 100x improvement per year, at least for the next few years.” Moore’s Law was a 2x improvement every two years. That single curve drove every technological shift of the last 50 years. The internet. Smartphones. Cloud computing. All of it rode a 2x curve. AI is on a 100x curve. And the current infrastructure isn’t running beside the new one. It’s becoming it. Musk: “Probably a lot of the data centers, maybe most of the data centers that currently do conventional compute, will transition to AI compute.” Everything that runs the world you know is being rewired for the world that comes next. Human beings process the future in straight lines. We take the speed of the last decade and project it forward. Exponential growth doesn’t work that way. It’s invisible until it’s everywhere. The most aggressive forecaster in the history of technology was too conservative. That’s not about Kurzweil being wrong about the direction. That’s about the human brain being wrong about the speed. The limit was never the technology. It was the organ we use to comprehend it. And that organ hasn’t been upgraded in 200,000 years.

Dustin

214,257 views • 4 months ago

A man who turned 7 million dollars into 500 million in 10 years sat on a desk in a classroom and taught the method to 30 students. The man is Joel Greenblatt. He ran Gotham Capital from 1985 to 1994 at roughly 50% a year, then returned all outside capital in 1995 and kept running his own money. Almost nobody sustains that number over a decade. A Columbia classroom in 2005. Chalkboard, blue plastic chairs, a camera at the back of the room recording for the students who missed it. He sits on the edge of the desk with a stack of paper. He does not teach a screen or a formula. He teaches situations most funds cannot touch, the corners of the market where the usual buyers are structurally forced to sell regardless of price. The first lecture is where he sets up why those corners exist at all, and why they keep existing after everybody knows about them. The uncomfortable turn is what he says about diversification and about how few positions he actually held. It runs directly against everything the same school teaches two floors down. It matters more now. Every screener is free, every filing is searchable, and the constraint was never information. Free on YouTube, filmed from the back row, audio uneven, students blocking the frame. He gave away 50% a year to a room of 30 people. The tape has been public for years. One classroom. 1 camera. It is in the video.
2:15:19

A man who turned 7 million dollars into 500 million in 10 years sat on a desk in a classroom and taught the method to 30 students. The man is Joel Greenblatt. He ran Gotham Capital from 1985 to 1994 at roughly 50% a year, then returned all outside capital in 1995 and kept running his own money. Almost nobody sustains that number over a decade. A Columbia classroom in 2005. Chalkboard, blue plastic chairs, a camera at the back of the room recording for the students who missed it. He sits on the edge of the desk with a stack of paper. He does not teach a screen or a formula. He teaches situations most funds cannot touch, the corners of the market where the usual buyers are structurally forced to sell regardless of price. The first lecture is where he sets up why those corners exist at all, and why they keep existing after everybody knows about them. The uncomfortable turn is what he says about diversification and about how few positions he actually held. It runs directly against everything the same school teaches two floors down. It matters more now. Every screener is free, every filing is searchable, and the constraint was never information. Free on YouTube, filmed from the back row, audio uneven, students blocking the frame. He gave away 50% a year to a room of 30 people. The tape has been public for years. One classroom. 1 camera. It is in the video.

Merlow

186,823 views • 1 month ago

In 2008 a Stanford professor stood at a podium and proved, for free, that information and money grow by the exact same equation. it was filmed. it has been free for 18 years. 7,000 people watched it. Thomas Cover died four years later and took the fame with him. his life's work: Shannon's information theory and Kelly's betting formula are one thing. how much to bet is just how much you know. every quant sizing a trade in 2026 is standing on Cover, whether they know the name or not. he wrote the book every information theorist trains on. the lecture that ties it all to money sits on YouTube with fewer views than a cat clip. skip to where he turns a horse race into a stock market. same math. a bit of information, a fraction of your bankroll, one growth rate. no fund. no signal. one professor and one equation. a quant I know says this hour is the missing chapter behind every book on position sizing he owns. you are 18 years late. it is still free.
57:26

In 2008 a Stanford professor stood at a podium and proved, for free, that information and money grow by the exact same equation. it was filmed. it has been free for 18 years. 7,000 people watched it. Thomas Cover died four years later and took the fame with him. his life's work: Shannon's information theory and Kelly's betting formula are one thing. how much to bet is just how much you know. every quant sizing a trade in 2026 is standing on Cover, whether they know the name or not. he wrote the book every information theorist trains on. the lecture that ties it all to money sits on YouTube with fewer views than a cat clip. skip to where he turns a horse race into a stock market. same math. a bit of information, a fraction of your bankroll, one growth rate. no fund. no signal. one professor and one equation. a quant I know says this hour is the missing chapter behind every book on position sizing he owns. you are 18 years late. it is still free.

Loran

33,982 views • 1 month ago

Benoit Mandelbrot, Yale professor, IBM Fellow, and inventor of fractals: "Every risk model at Citadel, Goldman and JPMorgan assumes markets are 'normal.' I proved they're wild. That one lie has vaporized hundreds of billions." this free TED talk is mandelbrot months before he died. almost every risk model treats the market as mild and smooth. he proved it's the opposite: rough, jagged, and wild, with extremes far more common than the bell curve allows. that gap is not academic. under the bell curve, the 2008 crash was a once-in-a-billion-years event. it happens roughly every decade. every VaR model that whispered "you're safe" and every fund that blew up in a week was trusting the same lie. those wild days also cluster together, which is exactly the "regime change" the thread is trying to catch. none of it is new. mandelbrot showed it in cotton prices in the 1960s, gave this talk for free, and wrote a whole book about it for the public. same lesson as my article: the math sat there for decades, ignored because it was inconvenient. here is what the regime thread won't admit. no model catches the wild day before it hits, its timing is unknowable by design. the edge isn't predicting the storm, it's sizing so a single wild day can never end you. the math is free. respecting the tails is the discipline almost nobody has.
16:46

Benoit Mandelbrot, Yale professor, IBM Fellow, and inventor of fractals: "Every risk model at Citadel, Goldman and JPMorgan assumes markets are 'normal.' I proved they're wild. That one lie has vaporized hundreds of billions." this free TED talk is mandelbrot months before he died. almost every risk model treats the market as mild and smooth. he proved it's the opposite: rough, jagged, and wild, with extremes far more common than the bell curve allows. that gap is not academic. under the bell curve, the 2008 crash was a once-in-a-billion-years event. it happens roughly every decade. every VaR model that whispered "you're safe" and every fund that blew up in a week was trusting the same lie. those wild days also cluster together, which is exactly the "regime change" the thread is trying to catch. none of it is new. mandelbrot showed it in cotton prices in the 1960s, gave this talk for free, and wrote a whole book about it for the public. same lesson as my article: the math sat there for decades, ignored because it was inconvenient. here is what the regime thread won't admit. no model catches the wild day before it hits, its timing is unknowable by design. the edge isn't predicting the storm, it's sizing so a single wild day can never end you. the math is free. respecting the tails is the discipline almost nobody has.

Rossst.03

35,425 views • 2 months 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

100,138 views • 3 months ago

An Indian physicist put the entire secret behind Renaissance's $100 billion on one chalkboard: a coin that barely beats 50/50. he is V. Balakrishnan, and his lecture went viral years after he recorded it. it has been free the whole time. almost nobody who trades has watched it. lecture one is the humblest thing in all of finance. a coin that lands your way a little more than half the time. that is the entire edge. that is all Renaissance ever had. the post above is the one equation for what to do with that coin. not what to bet. how much. bet too little and you die of old age. bet too much and one flip ends you. that same coin is every trade, every hand, every prediction market. a tiny edge, flipped a million times. the whole machine fits on his board, and he charged nothing for it. no slides. no notes. one coin and a stick of chalk. a quant I know says this old Indian lecture taught him probability better than his entire degree. the coin is free. the equation is free. the discipline to size it is the only thing left to earn.
1:01:55

An Indian physicist put the entire secret behind Renaissance's $100 billion on one chalkboard: a coin that barely beats 50/50. he is V. Balakrishnan, and his lecture went viral years after he recorded it. it has been free the whole time. almost nobody who trades has watched it. lecture one is the humblest thing in all of finance. a coin that lands your way a little more than half the time. that is the entire edge. that is all Renaissance ever had. the post above is the one equation for what to do with that coin. not what to bet. how much. bet too little and you die of old age. bet too much and one flip ends you. that same coin is every trade, every hand, every prediction market. a tiny edge, flipped a million times. the whole machine fits on his board, and he charged nothing for it. no slides. no notes. one coin and a stick of chalk. a quant I know says this old Indian lecture taught him probability better than his entire degree. the coin is free. the equation is free. the discipline to size it is the only thing left to earn.

Rossst.03

160,497 views • 1 month ago

A hedge fund returned 50% a year for ten years straight. In 2005 the man who ran it sat on a desk at Columbia and taught the entire method to 30 students for free. No bank, no fund, no business school has ever promoted the recording. His name is Joel Greenblatt. He ran Gotham Capital from 1985 to 1994. Almost nobody sustains 50% annually for a single year. He did it for ten. Then in 1995 he returned all outside capital, kept running his own money, and walked into a classroom. The first lecture is about corners of the market where the usual buyers are structurally forced to sell regardless of price. Spinoffs, restructurings, situations where an index fund must dump a stock the day it leaves the index. He does not teach a screener or a formula. He teaches why these corners exist at all, and why they keep existing after everybody knows about them. The uncomfortable part is what he says about diversification. He held very few positions. It runs directly against everything the business school teaches two floors down. Columbia charges $80K a year in tuition. The man upstairs gave away the method for free. Every screener is free now. Every filing is searchable. The constraint was never information. It was knowing which information to ignore. Filmed from the back row, audio uneven, students blocking the frame. He gave away 50% a year to a room of 30 people. Almost nobody traded on it. One classroom. One camera. The full lecture is free. It is in the video.
1:08:49

A hedge fund returned 50% a year for ten years straight. In 2005 the man who ran it sat on a desk at Columbia and taught the entire method to 30 students for free. No bank, no fund, no business school has ever promoted the recording. His name is Joel Greenblatt. He ran Gotham Capital from 1985 to 1994. Almost nobody sustains 50% annually for a single year. He did it for ten. Then in 1995 he returned all outside capital, kept running his own money, and walked into a classroom. The first lecture is about corners of the market where the usual buyers are structurally forced to sell regardless of price. Spinoffs, restructurings, situations where an index fund must dump a stock the day it leaves the index. He does not teach a screener or a formula. He teaches why these corners exist at all, and why they keep existing after everybody knows about them. The uncomfortable part is what he says about diversification. He held very few positions. It runs directly against everything the business school teaches two floors down. Columbia charges $80K a year in tuition. The man upstairs gave away the method for free. Every screener is free now. Every filing is searchable. The constraint was never information. It was knowing which information to ignore. Filmed from the back row, audio uneven, students blocking the frame. He gave away 50% a year to a room of 30 people. Almost nobody traded on it. One classroom. One camera. The full lecture is free. It is in the video.

Tigerflow

1,328,623 views • 1 month ago

Jeff Bezos said the AI companies everyone is watching are the smallest part of the story. Bezos: “Today we talk about AI-first companies like OpenAI and Anthropic and Mistral and so on and so on and so on. There are so many startup companies that are kind of AI companies of various kinds, and that’s normal for this phase.” He called it a phase, and phases end. There was such a thing as an internet company for about ten years, and then every company had a website and the word stopped meaning anything. Bezos: “But that is not the biggest impact that AI is gonna have.” The people selling a new technology are always the loudest part of it. The change lands on everyone using it. Bezos: “The biggest impact that AI is gonna have is it is going to affect every company in the world.” Every company. That includes the ones with no engineers, no research budget, and no interest in any of this. Bezos: “It is gonna make their quality go up- And their productivity go up.” Those two numbers decide whether a business survives a decade. He is describing both of them moving everywhere at once, whether or not anyone asked for it. Bezos: “Uh, it- it’s I mean, by every company, I literally mean every company. Every manufacturing company, every hotel, every, you know, consumer products company, et cetera, et cetera, et cetera.” He reached for the word literally because the claim sounds like exaggeration, and he needed it heard as a measurement. Electricity worked the same way. The companies selling generators had a good run, and the bigger fortunes were made by the factories that used them. Factories electrified in the 1890s and productivity barely moved for thirty years. They had swapped the steam engine for one big electric motor and kept the same building, the same belts, the same layout. The gains came when someone put a small motor on every machine and rebuilt the floor around the work instead of around the power source. Same technology, four decades apart. The difference was willingness to change the shape of the company. That gap is opening again. Access to these models is close to universal. Almost nobody has redesigned the actual work around them. The tools that changed the most lives were the ones that stopped being remarkable. Everyone ends up with the same models. The gap comes from who was willing to rebuild around them.
0:44

Jeff Bezos said the AI companies everyone is watching are the smallest part of the story. Bezos: “Today we talk about AI-first companies like OpenAI and Anthropic and Mistral and so on and so on and so on. There are so many startup companies that are kind of AI companies of various kinds, and that’s normal for this phase.” He called it a phase, and phases end. There was such a thing as an internet company for about ten years, and then every company had a website and the word stopped meaning anything. Bezos: “But that is not the biggest impact that AI is gonna have.” The people selling a new technology are always the loudest part of it. The change lands on everyone using it. Bezos: “The biggest impact that AI is gonna have is it is going to affect every company in the world.” Every company. That includes the ones with no engineers, no research budget, and no interest in any of this. Bezos: “It is gonna make their quality go up- And their productivity go up.” Those two numbers decide whether a business survives a decade. He is describing both of them moving everywhere at once, whether or not anyone asked for it. Bezos: “Uh, it- it’s I mean, by every company, I literally mean every company. Every manufacturing company, every hotel, every, you know, consumer products company, et cetera, et cetera, et cetera.” He reached for the word literally because the claim sounds like exaggeration, and he needed it heard as a measurement. Electricity worked the same way. The companies selling generators had a good run, and the bigger fortunes were made by the factories that used them. Factories electrified in the 1890s and productivity barely moved for thirty years. They had swapped the steam engine for one big electric motor and kept the same building, the same belts, the same layout. The gains came when someone put a small motor on every machine and rebuilt the floor around the work instead of around the power source. Same technology, four decades apart. The difference was willingness to change the shape of the company. That gap is opening again. Access to these models is close to universal. Almost nobody has redesigned the actual work around them. The tools that changed the most lives were the ones that stopped being remarkable. Everyone ends up with the same models. The gap comes from who was willing to rebuild around them.

Dustin

72,961 views • 1 month ago

A mathematician pulls a volunteer from the audience and tells them to corrupt his data on purpose. Then he finds every flipped bit. This is Marcus du Sautoy at the Royal Institution, 4 minutes on error-correcting codes. A grid of 0s and 1s on the screen and a live demonstration of how a machine catches a single flipped digit. The math is Hamming's. Every hard drive, satellite link, QR code, and model checkpoint transferred between GPUs depends on it working silently. Watch the moment the volunteer changes 1 bit and he locates it without being told where. No equations shown. The trick is the parity structure underneath. A backend engineer I know sent it to a junior who kept asking why checksums exist and closed the question in 4 minutes. Free on YouTube, Royal Institution stage, subtitles on. The bits flip constantly. You just never see it happen.
4:35

A mathematician pulls a volunteer from the audience and tells them to corrupt his data on purpose. Then he finds every flipped bit. This is Marcus du Sautoy at the Royal Institution, 4 minutes on error-correcting codes. A grid of 0s and 1s on the screen and a live demonstration of how a machine catches a single flipped digit. The math is Hamming's. Every hard drive, satellite link, QR code, and model checkpoint transferred between GPUs depends on it working silently. Watch the moment the volunteer changes 1 bit and he locates it without being told where. No equations shown. The trick is the parity structure underneath. A backend engineer I know sent it to a junior who kept asking why checksums exist and closed the question in 4 minutes. Free on YouTube, Royal Institution stage, subtitles on. The bits flip constantly. You just never see it happen.

obssnnn

44,484 views • 2 months ago

Every pane of glass around you. 🪟 Every window. Every phone screen. Every car windscreen. Every skyscraper.🇬🇧 All made the same way. All using the same process. Invented in a kitchen sink in Lancashire. His name was Sir Alastair Pilkington. He wasn't even related to the glass company. He just happened to share the name and married into the family. In 1952 he was doing the washing up at home. He watched the grease float on the water. Perfectly flat. Undisturbed. And thought: what if molten glass could do that? Before this moment, flat glass had been made the same way for three hundred years. 😰 You melted sand. You poured it into sheets. Then you ground it. And polished it. By hand. For hours. A third of every sheet was wasted in the process. The work was brutal. The results were inconsistent. Nobody questioned it. That was just how glass was made. Pilkington went to his bosses at Pilkington Brothers in St Helens with his idea. They backed him. It took seven years. It cost £7 million. An enormous sum in the 1950s. There were years where nothing worked. The company nearly went bankrupt. His idea: pour molten glass at 1,100°C onto a bath of molten tin. Glass is less dense than tin. It floats. It spreads. Both surfaces fire-polished perfectly flat by the heat. No grinding. No polishing. No waste. 🔥 In January 1959, it worked. The float glass process was licensed to manufacturers across the world. Over 40 companies. Over 30 countries. Today it accounts for over 90% of all flat glass production on Earth. Every window you have ever looked through in your entire life was almost certainly made using this single British process. Sir Alastair Pilkington was knighted in 1970. Elected Fellow of the Royal Society in 1969. Made a life peer in 1995. Baron Pilkington of St Helens. He died the same year. Before he could take his seat in the House of Lords. A British man with an idea who changed every building on Earth. 🇬🇧 Be part of us - Be proud of us. 🙏🇬🇧
2:02

Every pane of glass around you. 🪟 Every window. Every phone screen. Every car windscreen. Every skyscraper.🇬🇧 All made the same way. All using the same process. Invented in a kitchen sink in Lancashire. His name was Sir Alastair Pilkington. He wasn't even related to the glass company. He just happened to share the name and married into the family. In 1952 he was doing the washing up at home. He watched the grease float on the water. Perfectly flat. Undisturbed. And thought: what if molten glass could do that? Before this moment, flat glass had been made the same way for three hundred years. 😰 You melted sand. You poured it into sheets. Then you ground it. And polished it. By hand. For hours. A third of every sheet was wasted in the process. The work was brutal. The results were inconsistent. Nobody questioned it. That was just how glass was made. Pilkington went to his bosses at Pilkington Brothers in St Helens with his idea. They backed him. It took seven years. It cost £7 million. An enormous sum in the 1950s. There were years where nothing worked. The company nearly went bankrupt. His idea: pour molten glass at 1,100°C onto a bath of molten tin. Glass is less dense than tin. It floats. It spreads. Both surfaces fire-polished perfectly flat by the heat. No grinding. No polishing. No waste. 🔥 In January 1959, it worked. The float glass process was licensed to manufacturers across the world. Over 40 companies. Over 30 countries. Today it accounts for over 90% of all flat glass production on Earth. Every window you have ever looked through in your entire life was almost certainly made using this single British process. Sir Alastair Pilkington was knighted in 1970. Elected Fellow of the Royal Society in 1969. Made a life peer in 1995. Baron Pilkington of St Helens. He died the same year. Before he could take his seat in the House of Lords. A British man with an idea who changed every building on Earth. 🇬🇧 Be part of us - Be proud of us. 🙏🇬🇧

Proudofus.uk

130,952 views • 6 months ago

Over a decade ago, a math professor in Chennai opened his first lecture with the simplest idea in all of math, and it is the exact thing every prediction market runs on today. NPTEL filmed it. it has been free ever since. 759,000 people have watched it, almost none of them trade. his point: before probability, before any odds, there are just sets. a bag of everything that can happen, and the slice you are betting on. every trader pricing a market is doing set theory without knowing the name for it. no Wall Street badge, no trading desk. just a professor at a green board building the whole machine from nothing. skip to the board in the first ten minutes. he takes union, intersection, and the outcomes you throw away, and turns them into the rules every payoff has to obey, in notation a schoolkid can follow. no slides. no app. one green board and a piece of chalk. a quant i know rewatched this exact lecture before rebuilding his risk model. said he had been skipping the part that actually matters. I put the practical aspects of this lecture into practice and developed a strategy that generates income, the results surprised me. 27 days. $200 → $25,400. 74% win rate. Sharpe 2.47. I'm giving away the full configuration of my bot for free. 24 hours only. Here's how: >Comment "SEND" >Like and Follow me I'll send the complete setup in your DMs.
52:13

Over a decade ago, a math professor in Chennai opened his first lecture with the simplest idea in all of math, and it is the exact thing every prediction market runs on today. NPTEL filmed it. it has been free ever since. 759,000 people have watched it, almost none of them trade. his point: before probability, before any odds, there are just sets. a bag of everything that can happen, and the slice you are betting on. every trader pricing a market is doing set theory without knowing the name for it. no Wall Street badge, no trading desk. just a professor at a green board building the whole machine from nothing. skip to the board in the first ten minutes. he takes union, intersection, and the outcomes you throw away, and turns them into the rules every payoff has to obey, in notation a schoolkid can follow. no slides. no app. one green board and a piece of chalk. a quant i know rewatched this exact lecture before rebuilding his risk model. said he had been skipping the part that actually matters. I put the practical aspects of this lecture into practice and developed a strategy that generates income, the results surprised me. 27 days. $200 → $25,400. 74% win rate. Sharpe 2.47. I'm giving away the full configuration of my bot for free. 24 hours only. Here's how: >Comment "SEND" >Like and Follow me I'll send the complete setup in your DMs.

Voltex

28,331 views • 1 month ago

In 1950 a scientist at Bell Labs admitted, out loud, that he was envious of the geniuses in the offices around him. He was sharing an attic with Claude Shannon. So instead of resenting them, he spent the rest of his career studying exactly what separated the great ones from everyone else, and near the end of his life he laid the whole answer out in a single talk. His name was Richard Hamming. He was honest about where he started. At Los Alamos, he called himself a janitor of science, one of the people who kept things running while the important decisions happened without him. He was, in his own words, plain envious. And rather than swallow it, he turned it into a question. What actually is the difference between the first-class people and the rest of us? His first answer is uncomfortable, because it removes every excuse. It is not luck, though luck plays a part. It is not raw IQ, since plenty of the greats did not test especially well, and Einstein spent seven years in a patent office before anyone noticed him. What it comes down to, again and again, is that the able people simply work on important problems, and they work on them relentlessly, while everyone else stays busy with whatever happens to be in front of them. Then he tells the story that stays with you. For years he ate lunch with the physicists, the Nobel winners, and learned how they thought. One day he turned to a chemist at the table and asked, if what you are working on is not important, and not likely to lead to anything important, why are you working on it? The man was offended. But months later he stopped Hamming in the hallway and admitted the question had gotten under his skin. That chemist went on to run his department and join the National Academy. Of everyone else at that table, Hamming says, he never heard of a single one again. He noticed the same pattern in something as small as an office door. The people who worked with their doors closed got more done in the moment, but ten years later they no longer knew what was worth doing. The ones who left their doors open stayed connected to what actually mattered, even at the cost of constant interruption. And underneath all of it was a habit most people never build. He set aside Friday afternoons, every week, for nothing but great thoughts. No task, no email, just the question of what the important problems in his field really were, and whether he was working on any of them. He was not claiming to be the smartest man in the building. He knew he was not. That was the entire point. He watched the smartest men closely, took what he could use, and made himself into someone who did work that lasted. As he put it near the end, no one ever told him these things. He had to find them alone. You have no such excuse.
44:02

In 1950 a scientist at Bell Labs admitted, out loud, that he was envious of the geniuses in the offices around him. He was sharing an attic with Claude Shannon. So instead of resenting them, he spent the rest of his career studying exactly what separated the great ones from everyone else, and near the end of his life he laid the whole answer out in a single talk. His name was Richard Hamming. He was honest about where he started. At Los Alamos, he called himself a janitor of science, one of the people who kept things running while the important decisions happened without him. He was, in his own words, plain envious. And rather than swallow it, he turned it into a question. What actually is the difference between the first-class people and the rest of us? His first answer is uncomfortable, because it removes every excuse. It is not luck, though luck plays a part. It is not raw IQ, since plenty of the greats did not test especially well, and Einstein spent seven years in a patent office before anyone noticed him. What it comes down to, again and again, is that the able people simply work on important problems, and they work on them relentlessly, while everyone else stays busy with whatever happens to be in front of them. Then he tells the story that stays with you. For years he ate lunch with the physicists, the Nobel winners, and learned how they thought. One day he turned to a chemist at the table and asked, if what you are working on is not important, and not likely to lead to anything important, why are you working on it? The man was offended. But months later he stopped Hamming in the hallway and admitted the question had gotten under his skin. That chemist went on to run his department and join the National Academy. Of everyone else at that table, Hamming says, he never heard of a single one again. He noticed the same pattern in something as small as an office door. The people who worked with their doors closed got more done in the moment, but ten years later they no longer knew what was worth doing. The ones who left their doors open stayed connected to what actually mattered, even at the cost of constant interruption. And underneath all of it was a habit most people never build. He set aside Friday afternoons, every week, for nothing but great thoughts. No task, no email, just the question of what the important problems in his field really were, and whether he was working on any of them. He was not claiming to be the smartest man in the building. He knew he was not. That was the entire point. He watched the smartest men closely, took what he could use, and made himself into someone who did work that lasted. As he put it near the end, no one ever told him these things. He had to find them alone. You have no such excuse.

Zyron

15,886 views • 2 months ago