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MIT filmed a mathematician proving the theorem behind every equation Maxwell ever wrote. Most engineers use it daily without understanding why any surface gives the same answer - and the ones who can explain it earn $350K at aerospace firms. Stokes' theorem is the reason Maxwell could write four...

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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 Aufrufe • vor 1 Monat

Ten million people have watched an MIT professor teach a course whose first lecture is literally titled "What is a Derivative?" Almost none have written down the two-line answer. He filmed the lecture once in the fall of 2007 and it has been on YouTube ever since. Math tutors charge $200 an hour to teach a diluted version of what he covered in 50 minutes for free. His name is David Jerison. He is a professor of mathematics at MIT and the instructor of 18.01 Single Variable Calculus, one of the most-watched math courses in the history of the internet. The 50-minute clip in this video is Lecture 1, filmed at MIT in the fall of 2007. Jerison is deriving the definition of a derivative from a single tangent line. The whole framework fits on a napkin. A derivative is just how much y changes when x moves a tiny bit. Draw a tangent line to any curve at any point. The slope of that line is the derivative. Memorize one formula, the power rule, and you can differentiate every polynomial on earth in your head. Chain that with a handful of exceptions and you can differentiate almost every function humanity has ever written down. That single set of rules is what every neural network runs on gradient descent, what every rocket landing at SpaceX solves in real time, and what every options desk at Goldman Sachs is running behind every quote you see on the screen. "In mathematics you don't understand things. You just get used to them." That is John von Neumann, the mathematician who helped design the atomic bomb and invent the modern computer. Jerison returns to the same idea in every lecture. Almost no student giving up on calculus has heard von Neumann say it out loud. Every quant fund on Wall Street pays entry-level analysts $250,000 to know the same power rule Jerison derives on the board. Every AI bootcamp charges tens of thousands to teach a diluted version of the same equation on a laptop. The lecture is free on MIT OpenCourseWare. The textbook is under sixty dollars. Almost none of the millions who watched have ever taken the power rule and applied it to their own numbers on their own paper. The math is free. The willingness to actually take one derivative before your next model, trade, or engineering trade-off is the entire edge.
50:48

Ten million people have watched an MIT professor teach a course whose first lecture is literally titled "What is a Derivative?" Almost none have written down the two-line answer. He filmed the lecture once in the fall of 2007 and it has been on YouTube ever since. Math tutors charge $200 an hour to teach a diluted version of what he covered in 50 minutes for free. His name is David Jerison. He is a professor of mathematics at MIT and the instructor of 18.01 Single Variable Calculus, one of the most-watched math courses in the history of the internet. The 50-minute clip in this video is Lecture 1, filmed at MIT in the fall of 2007. Jerison is deriving the definition of a derivative from a single tangent line. The whole framework fits on a napkin. A derivative is just how much y changes when x moves a tiny bit. Draw a tangent line to any curve at any point. The slope of that line is the derivative. Memorize one formula, the power rule, and you can differentiate every polynomial on earth in your head. Chain that with a handful of exceptions and you can differentiate almost every function humanity has ever written down. That single set of rules is what every neural network runs on gradient descent, what every rocket landing at SpaceX solves in real time, and what every options desk at Goldman Sachs is running behind every quote you see on the screen. "In mathematics you don't understand things. You just get used to them." That is John von Neumann, the mathematician who helped design the atomic bomb and invent the modern computer. Jerison returns to the same idea in every lecture. Almost no student giving up on calculus has heard von Neumann say it out loud. Every quant fund on Wall Street pays entry-level analysts $250,000 to know the same power rule Jerison derives on the board. Every AI bootcamp charges tens of thousands to teach a diluted version of the same equation on a laptop. The lecture is free on MIT OpenCourseWare. The textbook is under sixty dollars. Almost none of the millions who watched have ever taken the power rule and applied it to their own numbers on their own paper. The math is free. The willingness to actually take one derivative before your next model, trade, or engineering trade-off is the entire edge.

Lumen

24,218 Aufrufe • vor 4 Tagen

The Fourier transform runs inside every MRI machine, every audio compressor, every signal processor on earth. JPMorgan pays $350K to engineers who can derive it from scratch. There is one professor alive who explains it the way nobody else can. His name is Gilbert Strang. MIT. The most brilliant mathematical mind of his generation. His textbook sits in over 10 million homes. No other person on earth holds this combination of depth and clarity in one head. He opens with one confession. The Fourier transform is unreasonably effective. It solves problems it was never designed for. The goal is not to compute it. The goal is to understand why it works at all. Then the core idea. A periodic signal breaks into pure sine waves. Each with a frequency, amplitude, and phase. The transform finds all three simultaneously. A complicated signal in time becomes a simple picture in frequency. Then 1807. Fourier invented the transform not for sound but for heat. How does warmth spread through a metal rod? In time the equation is a partial differential equation - hard. In frequency space it becomes ordinary - easy. Transform in, solve it, transform back. Then convolution. Convolution in time equals multiplication in frequency. Filtering a signal, removing noise, compressing audio - all multiplication in frequency space. Without the transform: thousands of computations. With it: a few multiplications. Then the delta function. Zero everywhere except one point where it is infinite. Integral equals one. Every mathematician in 1900 said it was not a function. Dirac used it anyway. It took 40 years to justify what engineers had been doing the whole time. Watch the moment Strang shows that the Fourier transform of a delta function is a constant - every frequency in equal measure. The more concentrated in time, the more spread in frequency. This is the uncertainty principle. Not quantum physics. A theorem about any signal at all. A signal processing engineer I know rewatched this before their first project at Apple. Said it was the first time the Fourier transform felt like a change of coordinates rather than a formula to memorize. Free on YouTube, MIT OpenCourseWare. bookmark this and watch later - after this lecture every sound, every image, and every signal will feel like a sum of sine waves waiting to be separated
47:28

The Fourier transform runs inside every MRI machine, every audio compressor, every signal processor on earth. JPMorgan pays $350K to engineers who can derive it from scratch. There is one professor alive who explains it the way nobody else can. His name is Gilbert Strang. MIT. The most brilliant mathematical mind of his generation. His textbook sits in over 10 million homes. No other person on earth holds this combination of depth and clarity in one head. He opens with one confession. The Fourier transform is unreasonably effective. It solves problems it was never designed for. The goal is not to compute it. The goal is to understand why it works at all. Then the core idea. A periodic signal breaks into pure sine waves. Each with a frequency, amplitude, and phase. The transform finds all three simultaneously. A complicated signal in time becomes a simple picture in frequency. Then 1807. Fourier invented the transform not for sound but for heat. How does warmth spread through a metal rod? In time the equation is a partial differential equation - hard. In frequency space it becomes ordinary - easy. Transform in, solve it, transform back. Then convolution. Convolution in time equals multiplication in frequency. Filtering a signal, removing noise, compressing audio - all multiplication in frequency space. Without the transform: thousands of computations. With it: a few multiplications. Then the delta function. Zero everywhere except one point where it is infinite. Integral equals one. Every mathematician in 1900 said it was not a function. Dirac used it anyway. It took 40 years to justify what engineers had been doing the whole time. Watch the moment Strang shows that the Fourier transform of a delta function is a constant - every frequency in equal measure. The more concentrated in time, the more spread in frequency. This is the uncertainty principle. Not quantum physics. A theorem about any signal at all. A signal processing engineer I know rewatched this before their first project at Apple. Said it was the first time the Fourier transform felt like a change of coordinates rather than a formula to memorize. Free on YouTube, MIT OpenCourseWare. bookmark this and watch later - after this lecture every sound, every image, and every signal will feel like a sum of sine waves waiting to be separated

Zyphor

120,811 Aufrufe • vor 6 Tagen

In Calculus of Variations, we give up the idea of only optimizing values and start optimizing geometries instead. The unknown is not a single x. It is a whole curve/surface y(x). The object you are minimizing is usually not a simple formula that you can guess. It is an integral that judges the whole shape. In our first lecture, we discuss the famous Brachistochrone problem. You choose two points, turn on gravity and ask a question which almost sounds too simple...which track will allow a bead to move from A to B in the minimum time? At your first attempt, your instinct will lead you astray. It is not the straight line. It is not the drop hard then cruise sketch either. The winner turns out to be a cycloid...the curve a point on a rolling circle traces. The track is the moving character in the animation. We begin with a flawed curve, perform gradient descent in curve-space, and observe the geometry change frame by frame as T[y] collapses to the brachistochrone. Please see the comment below for the math breakdown. #CalculusOfVariations #Brachistochrone #Cycloid #Physics #Optimization #Mathematics
0:30

In Calculus of Variations, we give up the idea of only optimizing values and start optimizing geometries instead. The unknown is not a single x. It is a whole curve/surface y(x). The object you are minimizing is usually not a simple formula that you can guess. It is an integral that judges the whole shape. In our first lecture, we discuss the famous Brachistochrone problem. You choose two points, turn on gravity and ask a question which almost sounds too simple...which track will allow a bead to move from A to B in the minimum time? At your first attempt, your instinct will lead you astray. It is not the straight line. It is not the drop hard then cruise sketch either. The winner turns out to be a cycloid...the curve a point on a rolling circle traces. The track is the moving character in the animation. We begin with a flawed curve, perform gradient descent in curve-space, and observe the geometry change frame by frame as T[y] collapses to the brachistochrone. Please see the comment below for the math breakdown. #CalculusOfVariations #Brachistochrone #Cycloid #Physics #Optimization #Mathematics

Mathelirium

23,254 Aufrufe • vor 3 Monaten

Every gambler who ever muttered "I'm due for a win" was one theorem away from understanding why he was about to lose. an Oxford professor teaches that exact theorem for free. Vegas does not mind. the people who need these 50 minutes most are the ones feeding the slot machines instead. his subject is conditional probability. the wheel has no memory. ten reds in a row change nothing. the next spin carries the same edge against you, every single time. that is the whole casino in one idea. and the post above argues the whole market runs on it too. Crawford teaches it to Oxford first-years. no system. no strategy. no hot streak. just the proof that the house edge does not care what already happened. skip to conditional probability. watch him kill "I'm due" in one line. every bet independent, every one bleeding the same tiny percentage, the crowd paying it a million times over. no memory. no streaks. one edge, forever. a trader I know says the day he understood independence was the day he stopped revenge trading. the math has been free the whole time. the house is betting you never learn it.
51:16

Every gambler who ever muttered "I'm due for a win" was one theorem away from understanding why he was about to lose. an Oxford professor teaches that exact theorem for free. Vegas does not mind. the people who need these 50 minutes most are the ones feeding the slot machines instead. his subject is conditional probability. the wheel has no memory. ten reds in a row change nothing. the next spin carries the same edge against you, every single time. that is the whole casino in one idea. and the post above argues the whole market runs on it too. Crawford teaches it to Oxford first-years. no system. no strategy. no hot streak. just the proof that the house edge does not care what already happened. skip to conditional probability. watch him kill "I'm due" in one line. every bet independent, every one bleeding the same tiny percentage, the crowd paying it a million times over. no memory. no streaks. one edge, forever. a trader I know says the day he understood independence was the day he stopped revenge trading. the math has been free the whole time. the house is betting you never learn it.

Rossst.03

10,472 Aufrufe • vor 1 Monat

A Japanese mathematician published a result in 1944 that nobody understood for twenty years. Today it runs inside every options desk on Wall Street. Goldman pays $400K to quants who can derive it from scratch and explain why classical calculus gives the wrong answer without it. His name is Choongbum Lee. MIT, 18.S096, Topics in Mathematics with Applications in Finance. The course that Wall Street watches. This is lecture 17. It derives Ito's Lemma from scratch. He opens with the problem nobody in classical calculus can solve. Then the foundation. Brownian motion is the limit of a random walk taken to infinity. Each trade pushes a price up or down by a tiny amount. A million trades a day. The limit of that process is Brownian motion. Einstein proved this for pollen particles in 1905. The finance world borrowed the math fifty years later. Then three properties that make no sense until you see them derived. Brownian motion crosses zero infinitely often. It never escapes to infinity. And it is nowhere differentiable - with probability one, every path is continuous but has no slope at any point. That last property is why classical calculus breaks completely. Then quadratic variation. For any smooth function, chop an interval into n pieces, square the increments, sum them - the result goes to zero. For Brownian motion it goes to T. The increments are too wild to vanish. That single fact is why Ito's Lemma has a second term that classical calculus does not. Watch the moment he derives it. Taylor expansion applied to a function of Brownian motion. The first term is what you expect. The second term appears precisely because the squared increment does not vanish. Without it, options pricing gives wrong answers. With it, you have Black-Scholes. A quant I know sends this lecture to junior analysts who cannot explain why their pricing model drifts. Says it fixes in ninety minutes what two years of finance courses left open. Free on YouTube, MIT OpenCourseWare, 18.S096. bookmark this and watch later - the math behind every options desk on Wall Street fits on one blackboard, and this is the lecture that shows you why
1:12:31

A Japanese mathematician published a result in 1944 that nobody understood for twenty years. Today it runs inside every options desk on Wall Street. Goldman pays $400K to quants who can derive it from scratch and explain why classical calculus gives the wrong answer without it. His name is Choongbum Lee. MIT, 18.S096, Topics in Mathematics with Applications in Finance. The course that Wall Street watches. This is lecture 17. It derives Ito's Lemma from scratch. He opens with the problem nobody in classical calculus can solve. Then the foundation. Brownian motion is the limit of a random walk taken to infinity. Each trade pushes a price up or down by a tiny amount. A million trades a day. The limit of that process is Brownian motion. Einstein proved this for pollen particles in 1905. The finance world borrowed the math fifty years later. Then three properties that make no sense until you see them derived. Brownian motion crosses zero infinitely often. It never escapes to infinity. And it is nowhere differentiable - with probability one, every path is continuous but has no slope at any point. That last property is why classical calculus breaks completely. Then quadratic variation. For any smooth function, chop an interval into n pieces, square the increments, sum them - the result goes to zero. For Brownian motion it goes to T. The increments are too wild to vanish. That single fact is why Ito's Lemma has a second term that classical calculus does not. Watch the moment he derives it. Taylor expansion applied to a function of Brownian motion. The first term is what you expect. The second term appears precisely because the squared increment does not vanish. Without it, options pricing gives wrong answers. With it, you have Black-Scholes. A quant I know sends this lecture to junior analysts who cannot explain why their pricing model drifts. Says it fixes in ninety minutes what two years of finance courses left open. Free on YouTube, MIT OpenCourseWare, 18.S096. bookmark this and watch later - the math behind every options desk on Wall Street fits on one blackboard, and this is the lecture that shows you why

Lupen

65,255 Aufrufe • vor 5 Tagen

This man teaches at a community college in California. His salary: around $800,000 a year. The engineers who passed calculus because of him: $1,800,000 to start. He has more calculus students than Harvard, MIT, and Stanford combined. This is Professor Leonard's Calculus 2, Lecture 6.2. Free on YouTube. Professor Leonard has taught calculus on YouTube for over a decade. His channel has millions of subscribers across 150 countries. Every major university has students who watch him the night before their exam. Then the concept. An inverse function is a machine that undoes another machine. If a function takes 2 and gives you 8, the inverse takes 8 and gives you back 2. Finding an inverse means switching every x and y in the equation and solving for y again. The graph flips across the line y = x like a mirror. Then the problem. Sometimes it is easy to find the inverse. Sometimes it is impossible to write it explicitly. A function like 3π sin x + sin x cannot be solved for x with algebra. You have to think. What angle makes the whole thing equal to 1? You work backwards through the unit circle until the answer appears. Then the shortcut. If you want the derivative of an inverse at a point, you do not need the inverse itself. You only need the derivative of the original function. The formula: the derivative of the inverse at a point equals 1 divided by the derivative of the original function evaluated at the switched point. The inverse flips the coordinates, so you flip where you plug in. Watch the moment he shows why G prime of 8 equals 1/12 without ever writing the inverse function. Every engineering student memorizes the derivative rules. Professor Leonard's lecture is the one that shows why the inverse derivative formula is just those same rules run backwards. A software engineer at a semiconductor company in Austin said Professor Leonard's channel is the reason she passed Calculus 2 on her second attempt. She graduated, joined the company, and now makes $165,000 a year. Bookmark this and watch later - after this lecture every inverse problem on your exam will feel like a question you already answered.
43:46

This man teaches at a community college in California. His salary: around $800,000 a year. The engineers who passed calculus because of him: $1,800,000 to start. He has more calculus students than Harvard, MIT, and Stanford combined. This is Professor Leonard's Calculus 2, Lecture 6.2. Free on YouTube. Professor Leonard has taught calculus on YouTube for over a decade. His channel has millions of subscribers across 150 countries. Every major university has students who watch him the night before their exam. Then the concept. An inverse function is a machine that undoes another machine. If a function takes 2 and gives you 8, the inverse takes 8 and gives you back 2. Finding an inverse means switching every x and y in the equation and solving for y again. The graph flips across the line y = x like a mirror. Then the problem. Sometimes it is easy to find the inverse. Sometimes it is impossible to write it explicitly. A function like 3π sin x + sin x cannot be solved for x with algebra. You have to think. What angle makes the whole thing equal to 1? You work backwards through the unit circle until the answer appears. Then the shortcut. If you want the derivative of an inverse at a point, you do not need the inverse itself. You only need the derivative of the original function. The formula: the derivative of the inverse at a point equals 1 divided by the derivative of the original function evaluated at the switched point. The inverse flips the coordinates, so you flip where you plug in. Watch the moment he shows why G prime of 8 equals 1/12 without ever writing the inverse function. Every engineering student memorizes the derivative rules. Professor Leonard's lecture is the one that shows why the inverse derivative formula is just those same rules run backwards. A software engineer at a semiconductor company in Austin said Professor Leonard's channel is the reason she passed Calculus 2 on her second attempt. She graduated, joined the company, and now makes $165,000 a year. Bookmark this and watch later - after this lecture every inverse problem on your exam will feel like a question you already answered.

Lupen

650,822 Aufrufe • vor 1 Tag

Cauchy Residue Theorem ✍️ It is given by Augustin-Louis Cauchy, This theorem is a mathematical shortcut that determines the total value of a path around a closed loop based on the "problem points" inside it. Imagine walking in a complete circle. You might expect your net change to be zero. However, if there are specific spots within that circle acting like drains or springs, they add a unique "strength" to your journey. In complex analysis, these spots are called singularities, and their individual strengths are known as residues. Instead of calculating the influence of the function at every step along your path, the theorem lets you identify which singularities are enclosed by your loop, add their residues together, and multiply by a constant factor. This turns a complicated boundary calculation into a simple task of internal accounting, where the behavior of the entire perimeter depends entirely on the special points hidden in its center.
0:28

Cauchy Residue Theorem ✍️ It is given by Augustin-Louis Cauchy, This theorem is a mathematical shortcut that determines the total value of a path around a closed loop based on the "problem points" inside it. Imagine walking in a complete circle. You might expect your net change to be zero. However, if there are specific spots within that circle acting like drains or springs, they add a unique "strength" to your journey. In complex analysis, these spots are called singularities, and their individual strengths are known as residues. Instead of calculating the influence of the function at every step along your path, the theorem lets you identify which singularities are enclosed by your loop, add their residues together, and multiply by a constant factor. This turns a complicated boundary calculation into a simple task of internal accounting, where the behavior of the entire perimeter depends entirely on the special points hidden in its center.

ScieVision

10,947 Aufrufe • vor 6 Monaten

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.
29:44

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 Aufrufe • vor 1 Monat

An MIT professor explained the one idea funds pay $500K for, and he does it in the first hour of freshman calculus. MIT put the whole lecture online. it has been free for twenty years. almost nobody finishes it. his point: calculus is not a hundred formulas to memorize. it is one idea. take two points on a curve, slide them together until the gap vanishes, and the slope you are left with is the derivative. everything else is just consequences. he is David Jerison, and his 18.01 lectures have taught more people calculus than any room on earth. skip to the board, where the power rule, the product rule, the chain rule, the fifty things you were forced to memorize, all fall out of that one limit. no formula sheet. no tricks. one definition, reused. that same slope is velocity. it is marginal cost. it is the gradient that trains a neural network. one operation, hiding everywhere something moves. the math is free. what nobody can sell you is the patience to understand the one limit instead of cramming the fifty formulas that fall out of it. that hour is the foundation, and it is the only part that ever pays.
50:54

An MIT professor explained the one idea funds pay $500K for, and he does it in the first hour of freshman calculus. MIT put the whole lecture online. it has been free for twenty years. almost nobody finishes it. his point: calculus is not a hundred formulas to memorize. it is one idea. take two points on a curve, slide them together until the gap vanishes, and the slope you are left with is the derivative. everything else is just consequences. he is David Jerison, and his 18.01 lectures have taught more people calculus than any room on earth. skip to the board, where the power rule, the product rule, the chain rule, the fifty things you were forced to memorize, all fall out of that one limit. no formula sheet. no tricks. one definition, reused. that same slope is velocity. it is marginal cost. it is the gradient that trains a neural network. one operation, hiding everywhere something moves. the math is free. what nobody can sell you is the patience to understand the one limit instead of cramming the fifty formulas that fall out of it. that hour is the foundation, and it is the only part that ever pays.

Loran

1,075,833 Aufrufe • vor 1 Monat

A woman who spent nine years gluing paper models in a print shop just told a room of physicists their whole field stands on a mistake, one line, no hedging: "Every theory we have takes space and time for granted, like a bagel that forgot it was once a flat sheet of paper." That's Bianca Dittrich. She has a free lecture course that asks one question: what is left of geometry once you quantize it? The answer is: far less than you can picture. Quantum gravity looks like one more field theory. Buried inside is something stranger. Every quantum theory we have puts fields on a fixed stage, flat or curved, and does the physics on top of it. Here the stage itself is the thing being quantized. Your gut reads an atom of spacetime as a tiny grain sitting somewhere. Wrong. It cannot sit anywhere, because it is the somewhere. The move is invisible to human intuition, which is exactly why the people who get anywhere stop asking where the pieces are and start asking what a measurement even means. In 3D the whole thing collapses in a way that should scare you. No matter, no cosmological constant, and gravity is locally flat everywhere. Six degrees of freedom per point, all of them eaten by diffeomorphism symmetry. A field theory that ends up with finitely many real degrees of freedom, sometimes zero. None of it is hidden. Cut a parallelogram out of paper, glue the edges, and you have a torus. Flat everywhere, and yet two numbers survive that no local measurement can see. The lecture is free. Here is the trap: you feel every equation you can solve, every geometry you can draw, as progress. What you cannot feel is the gap you have to cross. Ten to the forty-one, from the Planck scale back up to the world you live in. And crossing it is the only thing that pays. Almost every approach still cannot show it recovers ordinary physics at the far end, and almost everyone quits long before then. The math is free to learn. The nerve to stop trusting your picture of space, that part you still have to bring yourself.
1:02:52

A woman who spent nine years gluing paper models in a print shop just told a room of physicists their whole field stands on a mistake, one line, no hedging: "Every theory we have takes space and time for granted, like a bagel that forgot it was once a flat sheet of paper." That's Bianca Dittrich. She has a free lecture course that asks one question: what is left of geometry once you quantize it? The answer is: far less than you can picture. Quantum gravity looks like one more field theory. Buried inside is something stranger. Every quantum theory we have puts fields on a fixed stage, flat or curved, and does the physics on top of it. Here the stage itself is the thing being quantized. Your gut reads an atom of spacetime as a tiny grain sitting somewhere. Wrong. It cannot sit anywhere, because it is the somewhere. The move is invisible to human intuition, which is exactly why the people who get anywhere stop asking where the pieces are and start asking what a measurement even means. In 3D the whole thing collapses in a way that should scare you. No matter, no cosmological constant, and gravity is locally flat everywhere. Six degrees of freedom per point, all of them eaten by diffeomorphism symmetry. A field theory that ends up with finitely many real degrees of freedom, sometimes zero. None of it is hidden. Cut a parallelogram out of paper, glue the edges, and you have a torus. Flat everywhere, and yet two numbers survive that no local measurement can see. The lecture is free. Here is the trap: you feel every equation you can solve, every geometry you can draw, as progress. What you cannot feel is the gap you have to cross. Ten to the forty-one, from the Planck scale back up to the world you live in. And crossing it is the only thing that pays. Almost every approach still cannot show it recovers ordinary physics at the far end, and almost everyone quits long before then. The math is free to learn. The nerve to stop trusting your picture of space, that part you still have to bring yourself.

Zyron

110,850 Aufrufe • vor 1 Monat

A Stanford neuroscientist has spent thirty years arguing that you have never made a decision. Not one. Every choice you are proud of and every mistake you still think about was the output of biology you did not pick and cannot see. He teaches an entire course on why, and it is free. The man is Robert Sapolsky, who spent decades in Kenya living among wild baboons. He opens with a man who woke up one morning as somebody else. Same brain, same family, same job. One gene had changed. He describes the case and leaves it unresolved, because the point is not the answer. The point is that you already reached for one. Then he goes after the way we reach. His argument is that every explanation you have ever heard for why a person did something is broken in the same way. It is genetic. It is cultural. It is hormones. It is childhood. Each of those is a bucket, and the buckets do not exist anywhere in nature. They exist because universities have separate departments and separate corridors, and the corridors decided what counts as an explanation. Nothing interesting happens inside a bucket. Everything interesting happens where two of them touch, and almost nobody is trained to look there. He asks the room about free will, about God, about evil, about whether biology explains who becomes religious, and whether things that happened before you were born shape your politics thirty years later. Then he refuses to answer any of it, and tells them what they will be able to judge for themselves in ten weeks. The lectures run through aggression, depression, language, schizophrenia, sexuality, and why any of it differs between two people at all. Twenty five hours. One lecture hall. Free.
55:35

A Stanford neuroscientist has spent thirty years arguing that you have never made a decision. Not one. Every choice you are proud of and every mistake you still think about was the output of biology you did not pick and cannot see. He teaches an entire course on why, and it is free. The man is Robert Sapolsky, who spent decades in Kenya living among wild baboons. He opens with a man who woke up one morning as somebody else. Same brain, same family, same job. One gene had changed. He describes the case and leaves it unresolved, because the point is not the answer. The point is that you already reached for one. Then he goes after the way we reach. His argument is that every explanation you have ever heard for why a person did something is broken in the same way. It is genetic. It is cultural. It is hormones. It is childhood. Each of those is a bucket, and the buckets do not exist anywhere in nature. They exist because universities have separate departments and separate corridors, and the corridors decided what counts as an explanation. Nothing interesting happens inside a bucket. Everything interesting happens where two of them touch, and almost nobody is trained to look there. He asks the room about free will, about God, about evil, about whether biology explains who becomes religious, and whether things that happened before you were born shape your politics thirty years later. Then he refuses to answer any of it, and tells them what they will be able to judge for themselves in ten weeks. The lectures run through aggression, depression, language, schizophrenia, sexuality, and why any of it differs between two people at all. Twenty five hours. One lecture hall. Free.

Stefan.

71,588 Aufrufe • vor 24 Tagen

An MIT professor solved the equation Wall Street pays $500K for, in a lecture about how to find the top of a hill. MIT put the whole thing online. it has been free for years. almost nobody finishes it. his point: finding the best possible answer to almost anything is one move. write down what you want to grow, take the derivative, and find the exact spot where it stops climbing. that spot is the answer. he is Denis Auroux, and his 18.02 lectures have taught more people calculus than most universities have students. now the equation from the post above, the one that tells you exactly how much to bet. it is not a secret formula. it is this. the growth of your money is a curve. bet too little and you are low on the hill. bet too much and you have gone over the top and started falling. Kelly's number is just the peak. no magic. no fund. one hill, and the single point where it stops going up. a quant I know rewatches this exact lecture before he sizes a single position. you were handed the formula and told to trust it. Auroux shows you where it actually comes from, for free, and it has been sitting there the whole time.
38:17

An MIT professor solved the equation Wall Street pays $500K for, in a lecture about how to find the top of a hill. MIT put the whole thing online. it has been free for years. almost nobody finishes it. his point: finding the best possible answer to almost anything is one move. write down what you want to grow, take the derivative, and find the exact spot where it stops climbing. that spot is the answer. he is Denis Auroux, and his 18.02 lectures have taught more people calculus than most universities have students. now the equation from the post above, the one that tells you exactly how much to bet. it is not a secret formula. it is this. the growth of your money is a curve. bet too little and you are low on the hill. bet too much and you have gone over the top and started falling. Kelly's number is just the peak. no magic. no fund. one hill, and the single point where it stops going up. a quant I know rewatches this exact lecture before he sizes a single position. you were handed the formula and told to trust it. Auroux shows you where it actually comes from, for free, and it has been sitting there the whole time.

Loran

95,357 Aufrufe • vor 1 Monat

The casino is not worried about loaded dice. It is worried about the people who have learned to slide them. Persi Diaconis explains the method plainly. Nevada schools teach players to send the die across the felt so it spins flat and never turns over, keeping one face on top the whole way, and nobody has to be good at it, because being slightly better than chance already beats a house edge measured in fractions of a percent. The counter is sitting in plain sight. That diamond patterned rubber at the back of every craps table exists to force a violent tumble, and once a die hits it, Diaconis says no control of any kind survives. He also takes his own theorem apart with a chocolate bar, arguing a fair five sided die into existence out of a Toblerone and then explaining why the answer quietly depends on whether you catch it, drop it in sand or bounce it on glass. Watch his hands while he describes the sliding throw. For a second the mathematician steps back and the magician answers instead.
8:11

The casino is not worried about loaded dice. It is worried about the people who have learned to slide them. Persi Diaconis explains the method plainly. Nevada schools teach players to send the die across the felt so it spins flat and never turns over, keeping one face on top the whole way, and nobody has to be good at it, because being slightly better than chance already beats a house edge measured in fractions of a percent. The counter is sitting in plain sight. That diamond patterned rubber at the back of every craps table exists to force a violent tumble, and once a die hits it, Diaconis says no control of any kind survives. He also takes his own theorem apart with a chocolate bar, arguing a fair five sided die into existence out of a Toblerone and then explaining why the answer quietly depends on whether you catch it, drop it in sand or bounce it on glass. Watch his hands while he describes the sliding throw. For a second the mathematician steps back and the magician answers instead.

Zyron

1,203,329 Aufrufe • vor 1 Monat

David Jerison, the mathematician who spent decades teaching the exact method for finding the optimal choice when the odds are stacked against you: "I used to think the best decision was whichever option looked strongest at first glance. Then I proved that the real answer almost always hides at a point nobody would guess just by looking, and checking only the obvious choices gets you the worst possible outcome, not the best." this is the exact method quant desks lean on to find the one allocation that survives every constraint thrown at it, and it's been sitting free in a public MIT lecture for almost twenty years. strip away the notation and the mechanism is simple. every optimization problem has a handful of candidate points where the best or worst answer could be hiding, and the obvious middle-of-the-road guess is almost never one of them. check only the points that feel natural, and you don't just miss the best answer. you can land on the exact opposite, the worst possible one, without ever realizing it. nobody presenting a "risk-optimized" portfolio out loud admits how easy it is to stop checking one step too early. zoom out to how this plays out sizing a position or allocating risk under real constraints today. the instinct is to test the option that feels balanced and call it done, when the actual edge is almost always sitting at an extreme nobody thought to check. the industry sells a clean, confident number as proof an allocation is optimal. but that number means nothing until every boundary has been checked, because the same method that finds the best case can just as easily hand you the worst one in disguise. the right answer was never the one that looked most reasonable. it was the one nobody bothered to check.
49:15

David Jerison, the mathematician who spent decades teaching the exact method for finding the optimal choice when the odds are stacked against you: "I used to think the best decision was whichever option looked strongest at first glance. Then I proved that the real answer almost always hides at a point nobody would guess just by looking, and checking only the obvious choices gets you the worst possible outcome, not the best." this is the exact method quant desks lean on to find the one allocation that survives every constraint thrown at it, and it's been sitting free in a public MIT lecture for almost twenty years. strip away the notation and the mechanism is simple. every optimization problem has a handful of candidate points where the best or worst answer could be hiding, and the obvious middle-of-the-road guess is almost never one of them. check only the points that feel natural, and you don't just miss the best answer. you can land on the exact opposite, the worst possible one, without ever realizing it. nobody presenting a "risk-optimized" portfolio out loud admits how easy it is to stop checking one step too early. zoom out to how this plays out sizing a position or allocating risk under real constraints today. the instinct is to test the option that feels balanced and call it done, when the actual edge is almost always sitting at an extreme nobody thought to check. the industry sells a clean, confident number as proof an allocation is optimal. but that number means nothing until every boundary has been checked, because the same method that finds the best case can just as easily hand you the worst one in disguise. the right answer was never the one that looked most reasonable. it was the one nobody bothered to check.

MindArch

16,232 Aufrufe • vor 26 Tagen

String Theory Lecture 1 A String Does Not Move Like a Point A point particle traces a line through spacetime. A string traces a surface. This is the first geometric shift in String Theory. Particle mechanics asks where one object is at time t, so its history is a curve. String Theory asks where every point of an extended object is at worldsheet time τ, so we need another coordinate telling us where we are along the string. For a point particle x(t) So, for one input of time we get a position in Spacetime. For a string Xᵘ(τ,σ) Here τ plays the role of time on the worldsheet, while σ labels position along the string. Freeze τ and vary σ, and you see the string at one instant. Let τ move, and that curve sweeps out a two-dimensional surface... the worldsheet. The same comparison appears in the action. For a relativistic point particle, the geometric action measures worldline length S = −m ∫ ds If we parameterize the path by t, the action has one integral, one parameter, and one tangent vector dxᵘ/dt For a string, the same idea grows by one dimension. The action measures area, not length. In Nambu-Goto form, S = −T ∫ dτ dσ √[−det hₐᵦ] Here T is the string tension. It plays a role similar to mass, but for an extended object. It weights the area of a surface rather than the length of a line. The particle action has ∫ dt because the history is one-dimensional. The string action has ∫ dτ dσ because the history is two-dimensional. We are no longer summing along a path, we are summing over a surface. The geometry changes for the same reason. For the particle, one derivative is enough dxᵘ/dt For the string, the geometry is built from two derivatives: ∂τXᵘ and ∂σXᵘ The first tells you how the string changes as worldsheet time flows. The second tells you how the embedding changes as you move along the string. Together they define the induced worldsheet metric hₐᵦ = ∂ₐXᵘ ∂ᵦXᵤ In plain terms, hₐᵦ measures tangent lengths and tangent angles on the worldsheet. From it, the area element is dA = dτ dσ √[−det hₐᵦ] This, the Nambu-Goto action is the direct analogue of the point-particle length action. The point particle extremizes length and the string extremizes area. For calculations, people usually switch to the Polyakov action: S = −(T/2) ∫ dτ dσ √[−γ] γᵃᵇ ∂ₐXᵘ ∂ᵦXᵤ This describes the same classical string dynamics, but the algebra is cleaner. After choosing conformal gauge, varying with respect to Xᵘ gives (∂²/∂τ² − ∂²/∂σ²) Xᵘ = 0 This is the first real dynamical payoff... a two-dimensional wave equation on the worldsheet. For a point particle, the equation of motion tells you how one position evolves along one path. For a string, it tells you how an entire curve evolves, with waves traveling along it. The term ∂²Xᵘ/∂τ² measures acceleration in worldsheet time, while ∂²Xᵘ/∂σ² measures curvature along the string. The time evolution is balanced by how the string bends along its own length. This is why strings have oscillation modes. A point particle has one trajectory. A string has many possible vibration patterns, each one a normal mode of the worldsheet wave equation. For a closed string, σ wraps around the loop Xᵘ(τ, σ + 2π) = Xᵘ(τ, σ) For an open string, one standard free-end condition is ∂σXᵘ = 0 at the endpoints. Solving the wave equation gives waves moving in opposite directions along the string Xᵘ(τ,σ) = Fᵘ(τ + σ) + Gᵘ(τ − σ) A function of τ + σ moves one way. A function of τ − σ moves the other. Therefore, a particle has a worldline, its action measures length, and its geometry uses one tangent. The string has a worldsheet, its action measures area, and its geometry uses two tangent directions. #StringTheory #TheoreticalPhysics #MathematicalPhysics #Physics #Spacetime
0:34

String Theory Lecture 1 A String Does Not Move Like a Point A point particle traces a line through spacetime. A string traces a surface. This is the first geometric shift in String Theory. Particle mechanics asks where one object is at time t, so its history is a curve. String Theory asks where every point of an extended object is at worldsheet time τ, so we need another coordinate telling us where we are along the string. For a point particle x(t) So, for one input of time we get a position in Spacetime. For a string Xᵘ(τ,σ) Here τ plays the role of time on the worldsheet, while σ labels position along the string. Freeze τ and vary σ, and you see the string at one instant. Let τ move, and that curve sweeps out a two-dimensional surface... the worldsheet. The same comparison appears in the action. For a relativistic point particle, the geometric action measures worldline length S = −m ∫ ds If we parameterize the path by t, the action has one integral, one parameter, and one tangent vector dxᵘ/dt For a string, the same idea grows by one dimension. The action measures area, not length. In Nambu-Goto form, S = −T ∫ dτ dσ √[−det hₐᵦ] Here T is the string tension. It plays a role similar to mass, but for an extended object. It weights the area of a surface rather than the length of a line. The particle action has ∫ dt because the history is one-dimensional. The string action has ∫ dτ dσ because the history is two-dimensional. We are no longer summing along a path, we are summing over a surface. The geometry changes for the same reason. For the particle, one derivative is enough dxᵘ/dt For the string, the geometry is built from two derivatives: ∂τXᵘ and ∂σXᵘ The first tells you how the string changes as worldsheet time flows. The second tells you how the embedding changes as you move along the string. Together they define the induced worldsheet metric hₐᵦ = ∂ₐXᵘ ∂ᵦXᵤ In plain terms, hₐᵦ measures tangent lengths and tangent angles on the worldsheet. From it, the area element is dA = dτ dσ √[−det hₐᵦ] This, the Nambu-Goto action is the direct analogue of the point-particle length action. The point particle extremizes length and the string extremizes area. For calculations, people usually switch to the Polyakov action: S = −(T/2) ∫ dτ dσ √[−γ] γᵃᵇ ∂ₐXᵘ ∂ᵦXᵤ This describes the same classical string dynamics, but the algebra is cleaner. After choosing conformal gauge, varying with respect to Xᵘ gives (∂²/∂τ² − ∂²/∂σ²) Xᵘ = 0 This is the first real dynamical payoff... a two-dimensional wave equation on the worldsheet. For a point particle, the equation of motion tells you how one position evolves along one path. For a string, it tells you how an entire curve evolves, with waves traveling along it. The term ∂²Xᵘ/∂τ² measures acceleration in worldsheet time, while ∂²Xᵘ/∂σ² measures curvature along the string. The time evolution is balanced by how the string bends along its own length. This is why strings have oscillation modes. A point particle has one trajectory. A string has many possible vibration patterns, each one a normal mode of the worldsheet wave equation. For a closed string, σ wraps around the loop Xᵘ(τ, σ + 2π) = Xᵘ(τ, σ) For an open string, one standard free-end condition is ∂σXᵘ = 0 at the endpoints. Solving the wave equation gives waves moving in opposite directions along the string Xᵘ(τ,σ) = Fᵘ(τ + σ) + Gᵘ(τ − σ) A function of τ + σ moves one way. A function of τ − σ moves the other. Therefore, a particle has a worldline, its action measures length, and its geometry uses one tangent. The string has a worldsheet, its action measures area, and its geometry uses two tangent directions. #StringTheory #TheoreticalPhysics #MathematicalPhysics #Physics #Spacetime

Mathelirium

31,560 Aufrufe • vor 4 Monaten

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 Aufrufe • vor 1 Monat

a 25-delta put trades at 22% implied vol. the call at the same distance trades at 15% same index, same expiry, same distance from the money. 7 points apart black-scholes says that gap shouldn't exist the model assumes one volatility number for every strike σ constant across strikes and expiries. one distribution, lognormal returns, flat surface plot the real chain and you don't get a flat line you get a surface. tilted, curved, repricing every second that tilt has a name: skew and it's the closest thing markets have to a live fear gauge the reason it exists is structural, not a pricing error crashes are faster and deeper than rallies. returns have fat left tails so protection below the market costs more than the model says, because the model's normal distribution never priced the tail correctly this wasn't always true before october 1987 the surface was roughly flat. the crash rewrote it permanently one event taught the entire options market that the left tail is real, and the skew has never gone away since the measurement is simple: skew = IV(25-delta put) − IV(25-delta call) on the example above that's 22 − 15 = 7 points when that spread widens, demand for downside protection is rising. someone is paying up to hedge when it flattens, the bid for protection is fading same index level, same price on your chart, two completely different states of institutional fear the chart shows where price is. the surface shows what people are paying to be wrong and the shape carries more than direction steepness tells you how much tail risk is priced term structure tells you whether the fear is about this week or this quarter curvature tells you how much the market disagrees with its own base case retail sees one implied vol number on the option they're about to buy a desk sees a surface and trades the difference between its shape and what that shape usually looks like every input is public. the chain lists IV at every strike and expiry strike on one axis, expiry on another, IV on the third. the surface builds itself black-scholes won a nobel for a formula the market has been visibly disagreeing with since 1987 the disagreement is the signal full breakdown in the article below
0:58

a 25-delta put trades at 22% implied vol. the call at the same distance trades at 15% same index, same expiry, same distance from the money. 7 points apart black-scholes says that gap shouldn't exist the model assumes one volatility number for every strike σ constant across strikes and expiries. one distribution, lognormal returns, flat surface plot the real chain and you don't get a flat line you get a surface. tilted, curved, repricing every second that tilt has a name: skew and it's the closest thing markets have to a live fear gauge the reason it exists is structural, not a pricing error crashes are faster and deeper than rallies. returns have fat left tails so protection below the market costs more than the model says, because the model's normal distribution never priced the tail correctly this wasn't always true before october 1987 the surface was roughly flat. the crash rewrote it permanently one event taught the entire options market that the left tail is real, and the skew has never gone away since the measurement is simple: skew = IV(25-delta put) − IV(25-delta call) on the example above that's 22 − 15 = 7 points when that spread widens, demand for downside protection is rising. someone is paying up to hedge when it flattens, the bid for protection is fading same index level, same price on your chart, two completely different states of institutional fear the chart shows where price is. the surface shows what people are paying to be wrong and the shape carries more than direction steepness tells you how much tail risk is priced term structure tells you whether the fear is about this week or this quarter curvature tells you how much the market disagrees with its own base case retail sees one implied vol number on the option they're about to buy a desk sees a surface and trades the difference between its shape and what that shape usually looks like every input is public. the chain lists IV at every strike and expiry strike on one axis, expiry on another, IV on the third. the surface builds itself black-scholes won a nobel for a formula the market has been visibly disagreeing with since 1987 the disagreement is the signal full breakdown in the article below

delost

89,999 Aufrufe • vor 1 Monat

Millions have watched an MIT professor accidentally destroy the American sports betting industry in a free 12-lecture undergraduate poker course. MIT charges $85,000 a year to sit in that classroom. He posted every lecture on OpenCourseWare for nothing. Almost no one who has ever placed a DraftKings same-game parlay has finished all twelve. His name is Kevin Desmond. He is an MIT alum, a professional poker player, and the instructor of 15.S50 Poker Theory and Analytics, which MIT gave undergraduates college credit for taking during January of 2015. The 43-minute clip in this video is one lecture from that course, filmed at MIT that same month. The chart on the screen behind him looks like a poker graph. It is the exact math that decides whether a Wall Street quant clears $500,000 a year, whether a FanDuel bettor loses their rent money on a Sunday afternoon, and whether a Silicon Valley founder can walk into a term sheet negotiation without being taken apart in the room. Desmond compresses the mathematical foundation of every adversarial decision on earth into five ideas. Ranges. You never know your opponent's exact hand. You know a distribution of hands weighted by probability. Every FanDuel bettor picking a parlay on a hunch is playing without a range. Every retail trader guessing a competitor's next move is guessing blind. Pot odds. The equation that tells you when a call has positive expected value. Every VC term sheet and every insurance premium reduces to it. Every same-game parlay on DraftKings violates it in ways the app is legally allowed to hide from you. Expected value. Sum every outcome weighted by probability. Casinos are built on it. Poker pros live on it. Sports bettors violate it every time they chase a loss hoping for a hot Sunday. Game theory optimal. The Nash equilibrium of poker. The strategy no opponent can exploit no matter how well they read you. Quant funds pay $500,000 bonuses for one senior who can solve for it under pressure. Exploitative play. When to deviate from GTO to punish a specific mistake. What every senior desk on Wall Street does against retail order flow, every trading session, every day. Every quant fund on Wall Street runs a hiring pipeline that starts with this material. Every prop trading desk drills it into juniors before their first live session. The MIT professor who filmed the whole course posted it on OpenCourseWare for the price of an internet connection. "Every time you play a hand differently from the way you would have played it if you could see all your opponent's cards, they gain." That is David Sklansky's Fundamental Theorem of Poker. Desmond opens the course with it. It is also the exact statement of information asymmetry that every trading floor, casino, and DraftKings promo card on earth is built to exploit. The lectures are free on MIT OpenCourseWare. The problem sets are online. Every equation Desmond derives fits on one page. The math is free. The willingness to spend 43 minutes on one lecture before opening a sportsbook app, placing a parlay, or entering a negotiation is a much rarer commodity than the confidence to walk in without it.
43:06

Millions have watched an MIT professor accidentally destroy the American sports betting industry in a free 12-lecture undergraduate poker course. MIT charges $85,000 a year to sit in that classroom. He posted every lecture on OpenCourseWare for nothing. Almost no one who has ever placed a DraftKings same-game parlay has finished all twelve. His name is Kevin Desmond. He is an MIT alum, a professional poker player, and the instructor of 15.S50 Poker Theory and Analytics, which MIT gave undergraduates college credit for taking during January of 2015. The 43-minute clip in this video is one lecture from that course, filmed at MIT that same month. The chart on the screen behind him looks like a poker graph. It is the exact math that decides whether a Wall Street quant clears $500,000 a year, whether a FanDuel bettor loses their rent money on a Sunday afternoon, and whether a Silicon Valley founder can walk into a term sheet negotiation without being taken apart in the room. Desmond compresses the mathematical foundation of every adversarial decision on earth into five ideas. Ranges. You never know your opponent's exact hand. You know a distribution of hands weighted by probability. Every FanDuel bettor picking a parlay on a hunch is playing without a range. Every retail trader guessing a competitor's next move is guessing blind. Pot odds. The equation that tells you when a call has positive expected value. Every VC term sheet and every insurance premium reduces to it. Every same-game parlay on DraftKings violates it in ways the app is legally allowed to hide from you. Expected value. Sum every outcome weighted by probability. Casinos are built on it. Poker pros live on it. Sports bettors violate it every time they chase a loss hoping for a hot Sunday. Game theory optimal. The Nash equilibrium of poker. The strategy no opponent can exploit no matter how well they read you. Quant funds pay $500,000 bonuses for one senior who can solve for it under pressure. Exploitative play. When to deviate from GTO to punish a specific mistake. What every senior desk on Wall Street does against retail order flow, every trading session, every day. Every quant fund on Wall Street runs a hiring pipeline that starts with this material. Every prop trading desk drills it into juniors before their first live session. The MIT professor who filmed the whole course posted it on OpenCourseWare for the price of an internet connection. "Every time you play a hand differently from the way you would have played it if you could see all your opponent's cards, they gain." That is David Sklansky's Fundamental Theorem of Poker. Desmond opens the course with it. It is also the exact statement of information asymmetry that every trading floor, casino, and DraftKings promo card on earth is built to exploit. The lectures are free on MIT OpenCourseWare. The problem sets are online. Every equation Desmond derives fits on one page. The math is free. The willingness to spend 43 minutes on one lecture before opening a sportsbook app, placing a parlay, or entering a negotiation is a much rarer commodity than the confidence to walk in without it.

Lumen

57,583 Aufrufe • vor 14 Tagen

A dead MIT professor accidentally destroyed the $20 billion executive coaching industry with one hour of lecture, and ten million people have already watched him do it. He filmed it once in January 2018 and died eighteen months later. Executive coaches charge fifteen thousand dollars a session to teach a third of what he covered in that one hour for free. His name was Patrick Winston. He ran the MIT Artificial Intelligence Laboratory from 1972 to 1997 and wrote the AI textbook every computer science major in the world read for thirty years. Every January for four decades, he gave a lecture called "How to Speak." His entire framework fits on a napkin. Do not read. Be in the image. Keep images simple. Eliminate clutter. Start with an empathetic connection. End with a punch line the audience can repeat over dinner. Never open with a joke. Never end with "thank you." That last rule alone has probably cost the executive coaching industry a hundred million dollars. "Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas. In that order." That is the actual opening line of the lecture. Winston believed it strongly enough to spend fifty years teaching computer scientists how to talk. Founders spend $80,000 on an MBA and then hire a communications coach to teach them the same material Winston filmed once for free. Engineers write brilliant code and lose promotions to teammates who watched this lecture on the train. The lecture is free on MIT OpenCourseWare. The textbook is free on his page. Winston died in 2019. Almost none of the ten million viewers have actually implemented the four rules on the napkin. The napkin is free. The willingness to actually use it in your next meeting is the entire edge.
1:02:04

A dead MIT professor accidentally destroyed the $20 billion executive coaching industry with one hour of lecture, and ten million people have already watched him do it. He filmed it once in January 2018 and died eighteen months later. Executive coaches charge fifteen thousand dollars a session to teach a third of what he covered in that one hour for free. His name was Patrick Winston. He ran the MIT Artificial Intelligence Laboratory from 1972 to 1997 and wrote the AI textbook every computer science major in the world read for thirty years. Every January for four decades, he gave a lecture called "How to Speak." His entire framework fits on a napkin. Do not read. Be in the image. Keep images simple. Eliminate clutter. Start with an empathetic connection. End with a punch line the audience can repeat over dinner. Never open with a joke. Never end with "thank you." That last rule alone has probably cost the executive coaching industry a hundred million dollars. "Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas. In that order." That is the actual opening line of the lecture. Winston believed it strongly enough to spend fifty years teaching computer scientists how to talk. Founders spend $80,000 on an MBA and then hire a communications coach to teach them the same material Winston filmed once for free. Engineers write brilliant code and lose promotions to teammates who watched this lecture on the train. The lecture is free on MIT OpenCourseWare. The textbook is free on his page. Winston died in 2019. Almost none of the ten million viewers have actually implemented the four rules on the napkin. The napkin is free. The willingness to actually use it in your next meeting is the entire edge.

Isa

538,103 Aufrufe • vor 22 Tagen