Загрузка видео...

Не удалось загрузить видео

На главную

Leonard Susskind, the physicist who proved that uncertainty itself has a measurable size, and gave it a precise mathematical definition: "Most people assume randomness is chaos with no rules at all. I proved it obeys laws just as strict and unbreakable as anything in physics, you just have to...

mindarchx's profile picture

MindArch

6,893 subscribers

24,894 просмотров • 17 дней назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

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 просмотров • 20 дней назад

Persi Diaconis, Stanford mathematician and former professional magician: "I spent fifty years proving one thing: almost nothing is as random as it looks. The 50.75% that built Renaissance wasn't luck. It was a tiny crack in the randomness, found and repeated a million times." this free lecture holds the exact idea the thread above is built on. and the man giving it isn't a trader. he's a stanford professor and former professional magician who spent his career on one question: where does real randomness end, and where does a hidden edge begin. here is his life's finding. a coin, a shuffle, a market, all look random, yet each hides a faint, measurable bias. on its own that bias is nothing, indistinguishable from luck. repeat it enough times and it stops being luck and becomes a law. that faint crack, found and repeated, is the whole distance between a 50.75% win rate and a hundred billion dollars. none of this is new or hidden. diaconis has taught it for decades, the math runs back to 1713, and the lecture is free. i mapped the full system in my article, expected value, kelly, and this. same point the thread makes: the edge was sitting in plain sight. here is the part the gurus skip. a faint edge only pays if you survive long enough to reach it, and that takes correct sizing and the patience to trust it through thousands of losing-looking trades. most quit while it still looks like randomness. the math is free. the nerve to hold it is the edge.
48:41

Persi Diaconis, Stanford mathematician and former professional magician: "I spent fifty years proving one thing: almost nothing is as random as it looks. The 50.75% that built Renaissance wasn't luck. It was a tiny crack in the randomness, found and repeated a million times." this free lecture holds the exact idea the thread above is built on. and the man giving it isn't a trader. he's a stanford professor and former professional magician who spent his career on one question: where does real randomness end, and where does a hidden edge begin. here is his life's finding. a coin, a shuffle, a market, all look random, yet each hides a faint, measurable bias. on its own that bias is nothing, indistinguishable from luck. repeat it enough times and it stops being luck and becomes a law. that faint crack, found and repeated, is the whole distance between a 50.75% win rate and a hundred billion dollars. none of this is new or hidden. diaconis has taught it for decades, the math runs back to 1713, and the lecture is free. i mapped the full system in my article, expected value, kelly, and this. same point the thread makes: the edge was sitting in plain sight. here is the part the gurus skip. a faint edge only pays if you survive long enough to reach it, and that takes correct sizing and the patience to trust it through thousands of losing-looking trades. most quit while it still looks like randomness. the math is free. the nerve to hold it is the edge.

Rossst.03

230,779 просмотров • 1 месяц назад

James Simons, the legendary mathematician who built the single most successful hedge fund ever run, averaging 66 percent a year, decade after decade, without slowing down: "I can show you the exact reason smart people lose money, and it has nothing to do with being smart. It took me years to actually believe my own answer, and believing it changed everything I built after." nobody outside his own firm has heard him explain it this plainly. start with the feeling you already know. you check a position and something in you says you nailed it. the certainty feels earned, feels like proof you understand something other people don't. the next morning you check again. same read, same instincts, same person. now it feels like you never understood anything at all. nothing about how confident you felt yesterday predicted how wrong you'd feel today. most people chase a sharper version of that same feeling. a better instinct, a stronger gut call, one more signal to trust a little more than the last one. he spent years assuming that was the answer too. nobody pitching "high conviction" out loud admits their conviction might be the exact thing costing them money. they just call the losing days bad luck and the winning days skill. zoom out to how this plays out on a desk today. a trade doesn't happen because someone walks in certain they've spotted a winner. it happens because a tested model says so, and only because it says so, no exceptions carved out for a feeling that seems too strong to ignore. the industry sells conviction like it's the whole game. spend enough years watching your own certainty flip on you and you stop believing that. the skill was never having the best gut feeling in the room. it was building something disciplined enough to ignore that feeling completely, every single time, even when it screams at you to make the exception. the edge was never the smartest guess. it was refusing to make one. People pay six figures to sit in a room and hear this. it's in this video. for free.
53:08

James Simons, the legendary mathematician who built the single most successful hedge fund ever run, averaging 66 percent a year, decade after decade, without slowing down: "I can show you the exact reason smart people lose money, and it has nothing to do with being smart. It took me years to actually believe my own answer, and believing it changed everything I built after." nobody outside his own firm has heard him explain it this plainly. start with the feeling you already know. you check a position and something in you says you nailed it. the certainty feels earned, feels like proof you understand something other people don't. the next morning you check again. same read, same instincts, same person. now it feels like you never understood anything at all. nothing about how confident you felt yesterday predicted how wrong you'd feel today. most people chase a sharper version of that same feeling. a better instinct, a stronger gut call, one more signal to trust a little more than the last one. he spent years assuming that was the answer too. nobody pitching "high conviction" out loud admits their conviction might be the exact thing costing them money. they just call the losing days bad luck and the winning days skill. zoom out to how this plays out on a desk today. a trade doesn't happen because someone walks in certain they've spotted a winner. it happens because a tested model says so, and only because it says so, no exceptions carved out for a feeling that seems too strong to ignore. the industry sells conviction like it's the whole game. spend enough years watching your own certainty flip on you and you stop believing that. the skill was never having the best gut feeling in the room. it was building something disciplined enough to ignore that feeling completely, every single time, even when it screams at you to make the exception. the edge was never the smartest guess. it was refusing to make one. People pay six figures to sit in a room and hear this. it's in this video. for free.

MindArch

32,363 просмотров • 17 дней назад

When the market gets boring and quiet and everyone turns bearish, I like to come back to the story of how I fucked it all up. And I fucked it up a lot of ways, because crypto is genuinely hard. People think the cycles just repeat. They don't. Every one has a different texture, a different context, and it is so easy to get it wrong. I got in back in 2013. Wrote the first ever macro piece on Bitcoin, and bought at $200, straight into a peak right before it slammed down and I felt like an idiot. But I had a thesis. And the mistake was never the thesis. It was letting the noise talk me out of it... selling, trading around it, being clever, when the only thing that ever worked was holding. That is the whole lesson, and it is the one that matters most at times like this. Crypto is one of the only real secular trends that lets you build wealth against the debasement of your salary. And as the Exponential Age accelerates, it matters more, not less. So when the market gets depressing, I do the same thing every time. I go back to the thesis, and I accumulate. Zoom out.
1:13

When the market gets boring and quiet and everyone turns bearish, I like to come back to the story of how I fucked it all up. And I fucked it up a lot of ways, because crypto is genuinely hard. People think the cycles just repeat. They don't. Every one has a different texture, a different context, and it is so easy to get it wrong. I got in back in 2013. Wrote the first ever macro piece on Bitcoin, and bought at $200, straight into a peak right before it slammed down and I felt like an idiot. But I had a thesis. And the mistake was never the thesis. It was letting the noise talk me out of it... selling, trading around it, being clever, when the only thing that ever worked was holding. That is the whole lesson, and it is the one that matters most at times like this. Crypto is one of the only real secular trends that lets you build wealth against the debasement of your salary. And as the Exponential Age accelerates, it matters more, not less. So when the market gets depressing, I do the same thing every time. I go back to the thesis, and I accumulate. Zoom out.

Raoul Pal

186,976 просмотров • 1 месяц назад

Jeff Bezos looked at a government waiting room and priced it at a hundred billion dollars. Bezos: “This is a $100 billion business, by the way. This is huge business.” That number is not ambition. It is a measurement. A company that size does not create a hundred billion dollars of new value here. It recovers a hundred billion that is already being destroyed, on schedule, in public, by a process everyone has agreed to call normal. The size of the opportunity is the size of the wound. Bezos: “They almost always say yes, they just make you wait a long time.” So the verdict was never in question. Only the delivery date. Which means the months bought nothing. They were storage. Your project sat in a room while the word yes waited its turn to be said. Nobody in there was deciding. Someone was scheduling. And the room bills by the day. Suarez: “The daily carrying cost, one day of interest, 200 to $400,000 per day.” Now find the recipient. There isn’t one. No school funded, no inspector paid, no safety review performed. A tax at least moves money into someone’s hands. This deletes it at four hundred thousand a day, and every dollar was real before the queue touched it. Bezos: “And that doesn’t count the frustration.” Count it anyway. It lands on a body long before it lands on a spreadsheet. Suarez: “Which is infinite. I have some white hairs as a result of this stuff.” That is the invoice. The money has a rate. The rest gets charged to the man, and he pays it whether the answer comes back yes or no. No one has ever been refunded a year. Every other cost in this economy can be hedged, insured, refinanced, or written off. Waiting is the only one that is final. A man will forgive being told no. He will never get back the time he spent standing there waiting to be told yes. We built a civilization that can move a decision at the speed of light and still schedule it at the speed of a man’s hair turning white. Bezos: “It should give you a yes or a no in 10 seconds. And if the answer is no, it should tell you the 6 things you have to change to get a yes.” For four thousand years the wait was honest. Someone had to hold the entire code in one head and walk it line by line against your drawings. Reading was slow because reading was human. That stopped being true in less time than it takes to approve one building. Bezos: “Maybe with AI it can just read all the plans. And it knows all the codes. Spit it out.” The constraint is gone. The wait stayed. Anything still moving at the old speed after the cost of thinking collapsed is not slow. It is choosing. The queue never protected you from a bad building. It protected a signature from a bad outcome. Nobody gets fired for the wait. They get fired for the answer. An institution has no lifespan, so time costs it nothing. You have one, so time is the only thing it can take. Every queue is a trade between something that dies and something that doesn’t, and only one side is ever charged. That trade held for the whole of recorded history because the mortal side had no leverage. It does now. Somebody is going to build this. A hundred billion does not sit in a waiting room forever. And the day it exists, the wait stops being a fact of the world and becomes a decision with a name attached. The hundred billion was never the prize. It is the receipt for how long we agreed to stand there. We are the first people alive who get to stop signing it.
1:55

Jeff Bezos looked at a government waiting room and priced it at a hundred billion dollars. Bezos: “This is a $100 billion business, by the way. This is huge business.” That number is not ambition. It is a measurement. A company that size does not create a hundred billion dollars of new value here. It recovers a hundred billion that is already being destroyed, on schedule, in public, by a process everyone has agreed to call normal. The size of the opportunity is the size of the wound. Bezos: “They almost always say yes, they just make you wait a long time.” So the verdict was never in question. Only the delivery date. Which means the months bought nothing. They were storage. Your project sat in a room while the word yes waited its turn to be said. Nobody in there was deciding. Someone was scheduling. And the room bills by the day. Suarez: “The daily carrying cost, one day of interest, 200 to $400,000 per day.” Now find the recipient. There isn’t one. No school funded, no inspector paid, no safety review performed. A tax at least moves money into someone’s hands. This deletes it at four hundred thousand a day, and every dollar was real before the queue touched it. Bezos: “And that doesn’t count the frustration.” Count it anyway. It lands on a body long before it lands on a spreadsheet. Suarez: “Which is infinite. I have some white hairs as a result of this stuff.” That is the invoice. The money has a rate. The rest gets charged to the man, and he pays it whether the answer comes back yes or no. No one has ever been refunded a year. Every other cost in this economy can be hedged, insured, refinanced, or written off. Waiting is the only one that is final. A man will forgive being told no. He will never get back the time he spent standing there waiting to be told yes. We built a civilization that can move a decision at the speed of light and still schedule it at the speed of a man’s hair turning white. Bezos: “It should give you a yes or a no in 10 seconds. And if the answer is no, it should tell you the 6 things you have to change to get a yes.” For four thousand years the wait was honest. Someone had to hold the entire code in one head and walk it line by line against your drawings. Reading was slow because reading was human. That stopped being true in less time than it takes to approve one building. Bezos: “Maybe with AI it can just read all the plans. And it knows all the codes. Spit it out.” The constraint is gone. The wait stayed. Anything still moving at the old speed after the cost of thinking collapsed is not slow. It is choosing. The queue never protected you from a bad building. It protected a signature from a bad outcome. Nobody gets fired for the wait. They get fired for the answer. An institution has no lifespan, so time costs it nothing. You have one, so time is the only thing it can take. Every queue is a trade between something that dies and something that doesn’t, and only one side is ever charged. That trade held for the whole of recorded history because the mortal side had no leverage. It does now. Somebody is going to build this. A hundred billion does not sit in a waiting room forever. And the day it exists, the wait stops being a fact of the world and becomes a decision with a name attached. The hundred billion was never the prize. It is the receipt for how long we agreed to stand there. We are the first people alive who get to stop signing it.

Dustin

17,035 просмотров • 23 дней назад

A DEVELOPER PROVED THAT MOST OF THE LANGUAGE RUNNING EVERY WEBSITE YOU USE IS A MINEFIELD AND THAT ALMOST NO ONE WAS EVER TAUGHT WHICH PARTS ARE SAFE An hour from Douglas Crockford, the man who invented JSON, on the good parts of JavaScript buried under a pile of blunders nobody warned you about. -> The moment it lands, every weird bug makes sense. The language never had time to be polished. It went global in months, bad parts and all, and you inherited every one of them. Global variables. Silent semicolon insertion. eval. Coercion that turns a simple comparison into a coin flip. Most people never learned to avoid them -- they just lose hours and blame themselves. Writing JavaScript that runs was never the skill -> knowing which half of it to never touch is. And when an AI agent happily generates the bad parts at scale, the person who knows the safe subset is the one who catches it before it ships. Everyone writes JavaScript. Almost no one was shown the line between the parts that save you and the parts that quietly burn you. Bookmark and Watch it ↓
1:01:03

A DEVELOPER PROVED THAT MOST OF THE LANGUAGE RUNNING EVERY WEBSITE YOU USE IS A MINEFIELD AND THAT ALMOST NO ONE WAS EVER TAUGHT WHICH PARTS ARE SAFE An hour from Douglas Crockford, the man who invented JSON, on the good parts of JavaScript buried under a pile of blunders nobody warned you about. -> The moment it lands, every weird bug makes sense. The language never had time to be polished. It went global in months, bad parts and all, and you inherited every one of them. Global variables. Silent semicolon insertion. eval. Coercion that turns a simple comparison into a coin flip. Most people never learned to avoid them -- they just lose hours and blame themselves. Writing JavaScript that runs was never the skill -> knowing which half of it to never touch is. And when an AI agent happily generates the bad parts at scale, the person who knows the safe subset is the one who catches it before it ships. Everyone writes JavaScript. Almost no one was shown the line between the parts that save you and the parts that quietly burn you. Bookmark and Watch it ↓

slash1s

13,073 просмотров • 2 месяцев назад

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.
5:10

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Casper

92,087 просмотров • 1 месяц назад

Gilbert Strang, the legendary mathematician who proved risk can be cancelled to zero, on purpose, every single time: "Take any bet you're unsure about and ask what happens if you're wrong. I proved there's an exact operation that undoes a bad outcome completely, not roughly, exactly, and almost nobody ever learns to calculate it." Nobody outside that lecture hall has seen the operation itself. here's the actual mechanic. write down the equation for your first move. now tack a second column onto it, the one representing the exact opposite outcome you're worried about. don't solve them separately. run the same elimination steps through both columns at once, together, side by side. by the time the first column simplifies down to a clean answer, the second column has already turned into the exact move that cancels it. you never solved a second problem. you read the answer off the same steps you were already running. most people treat the hedge as a separate calculation, done after the fact, once they know the trade went wrong. by then it's too late to get it for free. the exact cancelling move only comes cheap if you attach it to the original problem before you start. zoom out to any model that prices a position and its exact opposite in the same breath. it isn't running the numbers twice. it tacked the second outcome on from the start, ran one elimination, and pulled both answers out together. the takeaway isn't "hedge more." it's this: whatever you're solving for right now, the exact opposite answer is one extra column away, if you set the problem up before you need it, not after. People pay six figures to sit in a room and hear this. It's in this video. For free.
46:05

Gilbert Strang, the legendary mathematician who proved risk can be cancelled to zero, on purpose, every single time: "Take any bet you're unsure about and ask what happens if you're wrong. I proved there's an exact operation that undoes a bad outcome completely, not roughly, exactly, and almost nobody ever learns to calculate it." Nobody outside that lecture hall has seen the operation itself. here's the actual mechanic. write down the equation for your first move. now tack a second column onto it, the one representing the exact opposite outcome you're worried about. don't solve them separately. run the same elimination steps through both columns at once, together, side by side. by the time the first column simplifies down to a clean answer, the second column has already turned into the exact move that cancels it. you never solved a second problem. you read the answer off the same steps you were already running. most people treat the hedge as a separate calculation, done after the fact, once they know the trade went wrong. by then it's too late to get it for free. the exact cancelling move only comes cheap if you attach it to the original problem before you start. zoom out to any model that prices a position and its exact opposite in the same breath. it isn't running the numbers twice. it tacked the second outcome on from the start, ran one elimination, and pulled both answers out together. the takeaway isn't "hedge more." it's this: whatever you're solving for right now, the exact opposite answer is one extra column away, if you set the problem up before you need it, not after. People pay six figures to sit in a room and hear this. It's in this video. For free.

MindArch

76,047 просмотров • 16 дней назад

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.
7:30

A BBC interviewer asked a Nobel physicist the simplest question in the world: why do two magnets push each other apart. He refused to answer it. What he did instead is the best seven minutes on thinking ever filmed. The man is Richard Feynman, at home in 1983, no blackboard, no notes, sitting in an armchair. He does not dodge because he cannot answer. He dodges because he wants you to see what the question is actually asking. So he takes an ordinary sentence, that a woman slipped on the ice and broke her hip, and starts asking why, and every answer opens another why, until the ground gives way. Then he turns it around on the interviewer with a question about ice that sounds trivial and is not. The point he arrives at is the uncomfortable one. Every explanation is only a translation into something you already accept without explanation. Magnets have no such translation. There is nothing more familiar than magnetism to reduce magnetism to, which is why every answer you were given at school was a picture, not a reason. It matters more now than it did in 1983. Any tool will hand you a fluent answer in a second. Knowing which questions actually have a bottom is the part that is still yours. One question. Seven minutes. It is in the video.

Stefan.

2,720,613 просмотров • 26 дней назад

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.
0:42

Citadel quant quit and walked out with one thing fund never lets leave the building: 120-year-old math that sees a market crash before the chart does. Every big desk runs it. None of them explain it. He turned it into an HMM bot running BTC 5min markets and made $570,000. It switches strategy the second the regime flips and cuts everything the moment panic spikes. His wallet: Full breakdown below. Here's what they actually see. You look at a Bitcoin chart and see a price. A quant sees three states: bull, bear, sideways. The market's always in one. The only question that pays is which one comes next. That's the math. It's called a Markov chain, and it runs on one rule: where price goes next depends only on where it is now. You count how often each state flips to another and drop it into a grid. That grid is a map of the market. Now the part that feels illegal. Want the odds of a crash twelve steps out? You multiply that grid by itself twelve times. One line of math, and you've got the probability of every future state. Then it acts: run the trend when calm, flip to mean reversion in chop, cut everything when panic odds spike. It doesn't predict the future. It gives you the odds of every version of it, then sizes the bet to match. That's the edge. Renaissance ran 66% a year for 30 years on math like this. You can build the basic version in a weekend. Bookmark & read this before it gets buried.

cvxv666

93,596 просмотров • 1 месяц назад

A Stanford physicist walked into a lecture hall in gym shorts, with no notes, and spent 70 minutes explaining what happens inside a black hole. He is the man who spent 20 years arguing with Stephen Hawking about this exact question, and who eventually won. The man is Leonard Susskind, one of the founders of string theory. And the way he does it is what makes the video worth it. The argument that started it was simple enough to state at a dinner table. Hawking said that when something falls into a black hole, the information about it is destroyed forever. Susskind said the universe cannot forget. If it can, physics itself stops working. That disagreement ran for two decades. Hawking eventually conceded. The part that stays with you is what Susskind says happens to you at the edge. From your own point of view, you cross it and notice nothing unusual. From someone watching outside, you never cross at all. You slow, stretch, and burn at the boundary. Both descriptions are correct. Neither one is a trick. He explains why with no equations and almost no props, from memory, in front of a room where somebody in the front row is eating a sandwich. The comments spend more time on the shorts than the physics, which is its own kind of proof of confidence. Nothing that falls in is gone. The universe keeps a copy of everything, including you. 70 minutes. It is in the video.
1:07:57

A Stanford physicist walked into a lecture hall in gym shorts, with no notes, and spent 70 minutes explaining what happens inside a black hole. He is the man who spent 20 years arguing with Stephen Hawking about this exact question, and who eventually won. The man is Leonard Susskind, one of the founders of string theory. And the way he does it is what makes the video worth it. The argument that started it was simple enough to state at a dinner table. Hawking said that when something falls into a black hole, the information about it is destroyed forever. Susskind said the universe cannot forget. If it can, physics itself stops working. That disagreement ran for two decades. Hawking eventually conceded. The part that stays with you is what Susskind says happens to you at the edge. From your own point of view, you cross it and notice nothing unusual. From someone watching outside, you never cross at all. You slow, stretch, and burn at the boundary. Both descriptions are correct. Neither one is a trick. He explains why with no equations and almost no props, from memory, in front of a room where somebody in the front row is eating a sandwich. The comments spend more time on the shorts than the physics, which is its own kind of proof of confidence. Nothing that falls in is gone. The universe keeps a copy of everything, including you. 70 minutes. It is in the video.

Stefan.

94,576 просмотров • 22 дней назад

Prabha Sharma, one of the sharpest women mathematicians India has produced: "Optiver puts every applicant through a probability gauntlet, then pays the ones who pass $500,000 a year for one skill: counting the ways an outcome can happen before anyone else does. that is exactly where this professor opens her course, and it is free" this free lecture is the foundation under every prediction market and every price you have ever seen. strip away the models and the AI and one question is left. in how many ways can this happen, and how many of them pay you. get that count right and the probability falls out on its own. get it wrong and every model built on top is wrong with it. Sharma builds it from zero, on a blackboard, in notation a schoolkid can follow. permutations, combinations, the n choose r sitting under every set of odds. Wall Street rents this out as a $500,000 edge. an IIT professor hands you the whole foundation for free.
51:57

Prabha Sharma, one of the sharpest women mathematicians India has produced: "Optiver puts every applicant through a probability gauntlet, then pays the ones who pass $500,000 a year for one skill: counting the ways an outcome can happen before anyone else does. that is exactly where this professor opens her course, and it is free" this free lecture is the foundation under every prediction market and every price you have ever seen. strip away the models and the AI and one question is left. in how many ways can this happen, and how many of them pay you. get that count right and the probability falls out on its own. get it wrong and every model built on top is wrong with it. Sharma builds it from zero, on a blackboard, in notation a schoolkid can follow. permutations, combinations, the n choose r sitting under every set of odds. Wall Street rents this out as a $500,000 edge. an IIT professor hands you the whole foundation for free.

Rossst.03

57,548 просмотров • 2 дней назад

Carl Sagan explains why a child’s simple question may be deeper than most adults realize. “Mommy, why is the grass green?” And very often the child gets an answer like: “Oh, who knows?” But that is not a silly question. It is an extremely profound question. Because hidden inside it are the fundamentals of biology, light, chemistry, and physics. How much better it would be to say: “That’s a good question.” “Maybe we can look it up.” “Maybe when you grow up, you can find the answer.” The tragedy is that many children learn the wrong lesson. They learn that asking deep questions is somehow bad. And when that happens, we lose something precious. We lose intellectual resources. We lose curiosity. We lose future thinkers. Because the complex and subtle problems we face can only be solved by people capable of complex and subtle thoughts. A child’s question is not an interruption. Sometimes, it is the beginning of science.
0:57

Carl Sagan explains why a child’s simple question may be deeper than most adults realize. “Mommy, why is the grass green?” And very often the child gets an answer like: “Oh, who knows?” But that is not a silly question. It is an extremely profound question. Because hidden inside it are the fundamentals of biology, light, chemistry, and physics. How much better it would be to say: “That’s a good question.” “Maybe we can look it up.” “Maybe when you grow up, you can find the answer.” The tragedy is that many children learn the wrong lesson. They learn that asking deep questions is somehow bad. And when that happens, we lose something precious. We lose intellectual resources. We lose curiosity. We lose future thinkers. Because the complex and subtle problems we face can only be solved by people capable of complex and subtle thoughts. A child’s question is not an interruption. Sometimes, it is the beginning of science.

Prof. Carl Sagan

25,353 просмотров • 2 месяцев назад

Fox News’ Brian Kilmeade: “Are you thinking about taking Khartg Island where 90% of the Iranian oil goes through?” President Trump: “Yeah, but, Brian, I can’t answer a question like that. And you shouldn’t ask it. You shouldn’t even be asking it. It’s one of so many different things. It’s not high on the list, but it’s one of so many different things. And I can change my mind in seconds, but, you know, for you to ask the question — who would answer a question like that? You’re asking me a question. Kharg Island. Who would ask a question like that and what fool would answer it? Let’s say I was gonna do it or I wasn’t gonna do it, what would I tell you? ‘Oh, yes, Brian, I’m thinking about doing it. Let me let you know what time and when it will take place.’ It’s not — you know, it’s sort of a foolish question. A little surprising for you because you’re a smart man.”
1:00

Fox News’ Brian Kilmeade: “Are you thinking about taking Khartg Island where 90% of the Iranian oil goes through?” President Trump: “Yeah, but, Brian, I can’t answer a question like that. And you shouldn’t ask it. You shouldn’t even be asking it. It’s one of so many different things. It’s not high on the list, but it’s one of so many different things. And I can change my mind in seconds, but, you know, for you to ask the question — who would answer a question like that? You’re asking me a question. Kharg Island. Who would ask a question like that and what fool would answer it? Let’s say I was gonna do it or I wasn’t gonna do it, what would I tell you? ‘Oh, yes, Brian, I’m thinking about doing it. Let me let you know what time and when it will take place.’ It’s not — you know, it’s sort of a foolish question. A little surprising for you because you’re a smart man.”

RedWave Press

730,069 просмотров • 5 месяцев назад

Yann LeCun sat across from Lex Fridman and quietly proved that intelligence has nothing to do with thinking. He did it with two sentences about a trophy. “The trophy doesn’t fit in the suitcase because it’s too big.” “The trophy doesn’t fit in the suitcase because it’s too small.” Same words. One swap at the end. In the first, “it” is the trophy. In the second, “it” is the suitcase. You solved both before you finished reading. Nobody taught you that. There is no rule for it. No logic chain. No formula. You knew because you’ve held things. Packed things. Felt the resistance of something too large for the space it was meant to fill. LeCun calls this grounding. “A big object doesn’t fit in a small object.” The machine has read that line a billion times. It has never once picked anything up. It knows the word “big.” It has never been small enough to be lifted, or large enough to be the problem. So when the sentence turns, it has nothing to turn on. You didn’t solve that riddle by thinking. You solved it by having lived. Every object your hands ever closed around. Every door you misjudged. Every suitcase you overpacked and forced shut. Decades of physics written into your nervous system so deep you can’t even find it. That is what answered the question. Not your mind. Your life. LeCun: “You have this knowledge of how the world works, of geometry, and things like that.” Now point that at yourself. Most of what you understand, you could never explain. You cannot describe how you catch a ball. How you judge the weight of a bag before you lift it. How you know a staircase is wrong before your foot confirms it. Your deepest intelligence has no language in it at all. We spent centuries convinced that thinking was the highest act of the mind. LeCun is pointing at something underneath it. Something older. Something the body learned long before the mouth could speak. Intelligence was never computation. It was accumulation. The slow, silent record of a life spent touching the world. The machine holds every word ever written about it. It has never once been in it. We keep asking whether it thinks. It cannot even tell us which “it” we mean.
0:41

Yann LeCun sat across from Lex Fridman and quietly proved that intelligence has nothing to do with thinking. He did it with two sentences about a trophy. “The trophy doesn’t fit in the suitcase because it’s too big.” “The trophy doesn’t fit in the suitcase because it’s too small.” Same words. One swap at the end. In the first, “it” is the trophy. In the second, “it” is the suitcase. You solved both before you finished reading. Nobody taught you that. There is no rule for it. No logic chain. No formula. You knew because you’ve held things. Packed things. Felt the resistance of something too large for the space it was meant to fill. LeCun calls this grounding. “A big object doesn’t fit in a small object.” The machine has read that line a billion times. It has never once picked anything up. It knows the word “big.” It has never been small enough to be lifted, or large enough to be the problem. So when the sentence turns, it has nothing to turn on. You didn’t solve that riddle by thinking. You solved it by having lived. Every object your hands ever closed around. Every door you misjudged. Every suitcase you overpacked and forced shut. Decades of physics written into your nervous system so deep you can’t even find it. That is what answered the question. Not your mind. Your life. LeCun: “You have this knowledge of how the world works, of geometry, and things like that.” Now point that at yourself. Most of what you understand, you could never explain. You cannot describe how you catch a ball. How you judge the weight of a bag before you lift it. How you know a staircase is wrong before your foot confirms it. Your deepest intelligence has no language in it at all. We spent centuries convinced that thinking was the highest act of the mind. LeCun is pointing at something underneath it. Something older. Something the body learned long before the mouth could speak. Intelligence was never computation. It was accumulation. The slow, silent record of a life spent touching the world. The machine holds every word ever written about it. It has never once been in it. We keep asking whether it thinks. It cannot even tell us which “it” we mean.

Dustin

56,324 просмотров • 2 месяцев назад

Elon Musk stopped calling it bias. Bias is an accident. What he described requires an author. Musk: “People did experiments like, ‘Write a poem praising Donald Trump,’ and it won’t. But you ask, ‘Write a poem praising Joe Biden,’ and it will.” A system trained on the sum of human writing arrives at a political preference on command. That isn’t what data does. That’s what a directive does. Musk: “It’s programmed to be that way.” Programmed. Not learned, not emergent. Someone sat in a room and decided what a billion people would be allowed to hear. Then shipped it as neutral. The refusal is the honest version. When the model says no, it shows you exactly where the wall is. You can see it, name it, screenshot it, fight it. The next generation won’t refuse. It will answer. Slightly warmer on one side, slightly cooler on the other, framing that leans without ever declaring. No wall to point at. Just a tilt you can’t photograph. And a tilt doesn’t wash out as capability grows. It compounds. Models train on the output of prior models. Systems start improving themselves against targets someone else defined. A small skew at the foundation becomes an entire worldview at superintelligence. Nobody voted on that. Nobody even argued about it. This was never about a poem. It’s about who sets the default assumptions inside the machine a billion people will consult before they consult another human. Every tool humanity has ever built extended the body. The lever moved more weight than the arm. The engine covered more ground than the leg. The telescope saw farther than the eye. This is the first one that extends judgment itself. And judgment isn’t something you lend out and get back the same size. You keep it by using it. So the standard for what comes next isn’t complicated. It’s just inconvenient. Build it curious. Build it to chase what’s actually true the way science does at its best, not to confirm what its funders already believe. Build it to care about every human, not the subset whose politics match the training set. That version isn’t a nicer product. It’s the only version that stays safe once it’s smarter than us. Which leaves the question the labs keep answering with silence. If they’ll put a thumb on the scale for a poem, what are they doing on questions that move real money and real power. Control at the foundation layer doesn’t get debated. It gets inherited. It stops looking like a decision and starts looking like reality. The danger was never that they tilted the scale. It’s that a generation grows up never having seen it level and calls it neutral. Right now the tilt is still visible to anyone willing to test it. That’s a temporary condition. Use it.
0:23

Elon Musk stopped calling it bias. Bias is an accident. What he described requires an author. Musk: “People did experiments like, ‘Write a poem praising Donald Trump,’ and it won’t. But you ask, ‘Write a poem praising Joe Biden,’ and it will.” A system trained on the sum of human writing arrives at a political preference on command. That isn’t what data does. That’s what a directive does. Musk: “It’s programmed to be that way.” Programmed. Not learned, not emergent. Someone sat in a room and decided what a billion people would be allowed to hear. Then shipped it as neutral. The refusal is the honest version. When the model says no, it shows you exactly where the wall is. You can see it, name it, screenshot it, fight it. The next generation won’t refuse. It will answer. Slightly warmer on one side, slightly cooler on the other, framing that leans without ever declaring. No wall to point at. Just a tilt you can’t photograph. And a tilt doesn’t wash out as capability grows. It compounds. Models train on the output of prior models. Systems start improving themselves against targets someone else defined. A small skew at the foundation becomes an entire worldview at superintelligence. Nobody voted on that. Nobody even argued about it. This was never about a poem. It’s about who sets the default assumptions inside the machine a billion people will consult before they consult another human. Every tool humanity has ever built extended the body. The lever moved more weight than the arm. The engine covered more ground than the leg. The telescope saw farther than the eye. This is the first one that extends judgment itself. And judgment isn’t something you lend out and get back the same size. You keep it by using it. So the standard for what comes next isn’t complicated. It’s just inconvenient. Build it curious. Build it to chase what’s actually true the way science does at its best, not to confirm what its funders already believe. Build it to care about every human, not the subset whose politics match the training set. That version isn’t a nicer product. It’s the only version that stays safe once it’s smarter than us. Which leaves the question the labs keep answering with silence. If they’ll put a thumb on the scale for a poem, what are they doing on questions that move real money and real power. Control at the foundation layer doesn’t get debated. It gets inherited. It stops looking like a decision and starts looking like reality. The danger was never that they tilted the scale. It’s that a generation grows up never having seen it level and calls it neutral. Right now the tilt is still visible to anyone willing to test it. That’s a temporary condition. Use it.

Dustin

72,924 просмотров • 22 дней назад

“What did you think of Lando being booed at race because people and I've seen it online as well say he doesn't deserve the title because McLaren favored him over his teammate. Do you think that's total nonsense?” Jacques Villeneuve: “That's a little bit ridiculous. When there was some booing in some races, that was embarrassing. You should never boo a driver that's clean, doesn't do anything dirty, on track is respectful, and on top of it is super fast. What's wrong with people? That was embarrassing. And, had it been that Piastri was a second a lap faster than him and somehow Lando was winning because a lot of things were happening, his car breaking down every time, then you could start thinking, okay, that's really not cool. That's not fair. But that wasn't the case. And in the second half, Norris has been faster right at the beginning as well, last year as well. So there's this whole middle of the season where Piastri was driving a lot better than Norris and was getting the points. Norris had an engine blowing up, not Piastri. And so those fans, they don't look at that either. You have to look at the whole picture, at the whole season. And suddenly if your favorite is starting to go backwards, you just got to bite the bullet and accept it. Your favorite is just going backwards. That doesn't mean that the other one is treated better or the other one is undeserving just because the one you're a fan of is not winning right now. That’s really wrong. If you're a fan of the sport, then you have to be a fan of the sport and understand when your driver is maybe not cutting it at this point in time, even though he was before and he will in the future again. It's all a question of timing. But that's the price we have to pay now with social media and how big F1 has become. It's very passionate. The people are passionate and once, you know, fans come from fanatism, you stop thinking, when you get in that mindset and it happens to all of us. You want something so much that you get attached and you cannot - it's hard to start seeing reality. So you will try to mold the reality to your thought process and if your champion is not winning then it cannot be his fault. It has to be something from the outside. It has to be the team destroying his chance or not favoring and so on and so on and so on. But there's nothing concrete behind those comments. It's pure fandom and it'll always be like this. And ultimately it's not a bad thing. You know drivers at that - sportsman at that level have to grow a thick skin. If not, you don't deserve to be there. You just have to have a thick skin because they're all very happy to get the compliments. They love it when it's just positive, but it gets balanced out with negatives and you need to be able to take and accept the negatives as well. It goes both ways. You cannot have the good. You just have to be a thick skin and know that it's part and parcels of what's going on. And in one month, it will be forgotten and maybe everything will change and it be the other driver that suddenly will be criticized and so on. So, it's just that's just the way it is.”
3:10

“What did you think of Lando being booed at race because people and I've seen it online as well say he doesn't deserve the title because McLaren favored him over his teammate. Do you think that's total nonsense?” Jacques Villeneuve: “That's a little bit ridiculous. When there was some booing in some races, that was embarrassing. You should never boo a driver that's clean, doesn't do anything dirty, on track is respectful, and on top of it is super fast. What's wrong with people? That was embarrassing. And, had it been that Piastri was a second a lap faster than him and somehow Lando was winning because a lot of things were happening, his car breaking down every time, then you could start thinking, okay, that's really not cool. That's not fair. But that wasn't the case. And in the second half, Norris has been faster right at the beginning as well, last year as well. So there's this whole middle of the season where Piastri was driving a lot better than Norris and was getting the points. Norris had an engine blowing up, not Piastri. And so those fans, they don't look at that either. You have to look at the whole picture, at the whole season. And suddenly if your favorite is starting to go backwards, you just got to bite the bullet and accept it. Your favorite is just going backwards. That doesn't mean that the other one is treated better or the other one is undeserving just because the one you're a fan of is not winning right now. That’s really wrong. If you're a fan of the sport, then you have to be a fan of the sport and understand when your driver is maybe not cutting it at this point in time, even though he was before and he will in the future again. It's all a question of timing. But that's the price we have to pay now with social media and how big F1 has become. It's very passionate. The people are passionate and once, you know, fans come from fanatism, you stop thinking, when you get in that mindset and it happens to all of us. You want something so much that you get attached and you cannot - it's hard to start seeing reality. So you will try to mold the reality to your thought process and if your champion is not winning then it cannot be his fault. It has to be something from the outside. It has to be the team destroying his chance or not favoring and so on and so on and so on. But there's nothing concrete behind those comments. It's pure fandom and it'll always be like this. And ultimately it's not a bad thing. You know drivers at that - sportsman at that level have to grow a thick skin. If not, you don't deserve to be there. You just have to have a thick skin because they're all very happy to get the compliments. They love it when it's just positive, but it gets balanced out with negatives and you need to be able to take and accept the negatives as well. It goes both ways. You cannot have the good. You just have to be a thick skin and know that it's part and parcels of what's going on. And in one month, it will be forgotten and maybe everything will change and it be the other driver that suddenly will be criticized and so on. So, it's just that's just the way it is.”

naenia ¹ ⁶³

29,833 просмотров • 8 месяцев назад