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a quant who started as a technical trader explained what changed everything for him he stopped trying to predict where price goes he started measuring the probability of each market state and betting only when the math was asymmetric that shift took him from drawing trendlines to writing models...

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112,271 次观看 • 1 个月前 •via X (Twitter)

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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

269,381 次观看 • 1 个月前

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask
1:27

quants don't predict markets. they stopped trying years ago retail is still asking "where is price going?" - quants switched to a different question entirely "given the current state, what's the probability distribution of outcomes" that's not a semantics game. it changes the math entirely stochastic processes model how a system evolves probabilistically - not picking a direction, mapping where each path leads and how likely it is ornstein-uhlenbeck does this for mean reversion: drift rate back to equilibrium, variance around that path same price data everybody has. different equation math is a century old, free in every applied probability textbook Bookmark and learn this before it gone you've been drawing trend lines while they ran probability distributions on same numbers it was never about data it was the question you were taught to ask

Livsun

13,315 次观看 • 1 个月前

Ray Dalio, billionaire founder of Bridgewater ($150B+): "In 1982 I was totally certain the economy would collapse. I bet everything on it, and I was so wrong I nearly went bankrupt. That's when I learned confidence is not skill." the article above is about telling a real edge from luck. dalio built the biggest hedge fund on earth only after learning the hardest way that being certain and being right are not the same thing. he had every reason to feel like a genius. he'd been right before, his conviction was total. then the market wiped him out, and all that confidence turned out to be worth exactly nothing. it didn't care how sure he was. so he made one change that saved his career: he stopped asking "am I right" and started asking "how do I know I'm right." he treated his own certainty as the enemy. that shift is the entire gap between the fool he was in 1982 and the investor he became. he tells the whole story in this free TED talk. here is the trap. a trader on a winning streak feels the exact certainty dalio felt in 1982. it feels like skill. usually it is just the market not having punished you yet. the edge is doubting yourself hardest when you feel surest, stress-testing the idea you love most, and sizing so that being wrong can never end you. the math is free. treating your own confidence as a warning sign is the discipline almost no one has.
16:18

Ray Dalio, billionaire founder of Bridgewater ($150B+): "In 1982 I was totally certain the economy would collapse. I bet everything on it, and I was so wrong I nearly went bankrupt. That's when I learned confidence is not skill." the article above is about telling a real edge from luck. dalio built the biggest hedge fund on earth only after learning the hardest way that being certain and being right are not the same thing. he had every reason to feel like a genius. he'd been right before, his conviction was total. then the market wiped him out, and all that confidence turned out to be worth exactly nothing. it didn't care how sure he was. so he made one change that saved his career: he stopped asking "am I right" and started asking "how do I know I'm right." he treated his own certainty as the enemy. that shift is the entire gap between the fool he was in 1982 and the investor he became. he tells the whole story in this free TED talk. here is the trap. a trader on a winning streak feels the exact certainty dalio felt in 1982. it feels like skill. usually it is just the market not having punished you yet. the edge is doubting yourself hardest when you feel surest, stress-testing the idea you love most, and sizing so that being wrong can never end you. the math is free. treating your own confidence as a warning sign is the discipline almost no one has.

Rossst.03

16,715 次观看 • 24 天前

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

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

slash1s

22,107 次观看 • 1 个月前

100 billion dollars made without predicting a single price direction not a direction call, a macro bet, or a news trade in sight they asked a completely different question than every retail trader alive retail: "will this go up?" quant: "what state is the market in, and where does that state historically lead?" same data. completely different game markets aren't random - they cycle through states: trending, ranging, compressing, reversing each state has a fixed historical probability of transitioning to the next that's the whole edge. not a signal, not an indicator - just conditional probability you don't need to know what fed will do or what earnings will print you need to know which state you're in and what that state leads to 68% of the time framework has been public since 1906 Bookmark it before it gone data is free, implementation is 200 lines of python, paper costs nothing they built 100 billion off math sitting in statistics textbooks your whole life you were staring at candles while they ran probability tables
0:52

100 billion dollars made without predicting a single price direction not a direction call, a macro bet, or a news trade in sight they asked a completely different question than every retail trader alive retail: "will this go up?" quant: "what state is the market in, and where does that state historically lead?" same data. completely different game markets aren't random - they cycle through states: trending, ranging, compressing, reversing each state has a fixed historical probability of transitioning to the next that's the whole edge. not a signal, not an indicator - just conditional probability you don't need to know what fed will do or what earnings will print you need to know which state you're in and what that state leads to 68% of the time framework has been public since 1906 Bookmark it before it gone data is free, implementation is 200 lines of python, paper costs nothing they built 100 billion off math sitting in statistics textbooks your whole life you were staring at candles while they ran probability tables

Livsun

13,335 次观看 • 1 个月前

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓
0:26

THIS GUY CONNECTED CLAUDE TO TRADINGVIEW AND CUT 200+ HOURS OF MANUAL CHART WORK 40 trades a week used to mean the same loop: open the chart, check RSI, compare EMA, write notes and wait for the next setup now Claude reads TradingView 24/7, tracks volume, indicators and price conditions while he is not even at the desk this is not a $20k trading terminal and not a quant fund setup, just one laptop, one chart and one strategy written in plain English the crazy part is not that Claude predicts the market, it is that a regular trader can remove the boring human loop from every setup ↓

Gipp 🦅

55,610 次观看 • 1 个月前

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart
0:26

a quant at a prop firm showed me a 5x5 grid on a napkin said: > this is our entire edge. we don't predict price. we predict which box the market is in and where that box historically leads i didn't understand it for weeks. then it clicked never looked at a chart the same way since grid is called a Markov Chain transition matrix. the math is from 1906, it's in every probability textbook on earth and hedge funds use it because it asks a completely different question than retail traders ever ask retail: will this go up or down quant: what state is this market in, and where does this state typically go every market lives in one of maybe 5-6 states at any given moment tight range, volatility compression, trending with momentum, post-spike reversal, pre-breakout coil not random labels - clusters you identify from actual data using volatility, volume, and momentum readings stacked together once you have the states, you build the matrix: P(state 2 -> state 4) = 73% P(state 4 -> state 1) = 61% P(state 1 -> state 3) = 68% each cell is a historical probability. now when the market is in state 2, you're not guessing you're betting on 73% historical completion. you size it with Kelly. you take the trade when the math says to, not when it feels right i built this on BTC using 2 years of 4-hour data. identified 5 states one i labeled "volatility compression below 20-day mean for 6+ consecutive candles" transitioned to a directional move above 1.8 ATR in 71% of cases average reward/risk on those trades: 5.4 that's not prediction. that's reading a probability table the market keeps filling in for you every single day the part that should bother you: the data to build this is free. the framework is in any quant textbook python to implement it is maybe 200 lines what Renaissance Technologies has that you don't isn't secret data or proprietary signals it's this framework applied to higher-resolution data with more sophisticated state definitions you're not missing information you're asking the wrong question every single time you open a chart

Livsun

188,258 次观看 • 2 个月前

In 1988, Jim Simons flew to Berkeley to beg a math professor to fix his hedge fund. The professor had never traded a stock. He had spent his career on coding theory and mathematical board games. He agreed to help on the condition he could leave when he wanted. He delivered 55 percent net in his full year running it. Then he handed the whole thing back and went home to teach undergraduates. His name was Elwyn Berlekamp. He is one of two people who ever ran what would become the most profitable trading operation in history. Simons was the other one. MIT math PhD, 1964. Berlekamp wrote foundational papers in coding theory that still run every CD, DVD, satellite link, and QR code on Earth. The Berlekamp-Massey algorithm, published 1968, is why every scratched CD you owned still played through to the end. He also co-wrote "Winning Ways for Your Mathematical Plays" with John Conway and Richard Guy. Four volumes. It became the foundational text of combinatorial game theory. Berlekamp thought about board games the way most mathematicians think about theorems. He proved endgame results in Go that professional masters had assumed were unprovable. His 1994 book "Mathematical Go" reduced the last moves of a Go game to a formula. Top-ranked professionals started studying it. Simons had a problem in the late 1980s. His trading partnership was falling apart. The fund was losing money. He flew west to see the game theorist. Berlekamp bought a controlling stake, cut what was not working, and rebuilt the trading logic from combinatorial game theory principles. The fund returned 55 percent net after fees in his full year running it. In December 1990, Berlekamp sold his stake back to Simons and walked out. He wanted to go back to Berkeley. In interviews he said the same thing many times, in different words: Berkeley was where he belonged. Simons kept building on the system Berlekamp rebuilt. It became the Medallion Fund. Over the next 30 years, Medallion compounded at roughly 66 percent gross per year. It is the most profitable trading strategy in the history of finance. Berlekamp took his cut in 1990 and never went back. He spent the rest of his life at UC Berkeley. He gave a lecture called "Mathematics and Go" that is on YouTube. He died in 2019, aged 78. The paradox is not that Berlekamp made a fortune. It is that he had the door to the biggest fortune in trading history held open for him and walked out. The math was fun. The billions were not.
25:06

In 1988, Jim Simons flew to Berkeley to beg a math professor to fix his hedge fund. The professor had never traded a stock. He had spent his career on coding theory and mathematical board games. He agreed to help on the condition he could leave when he wanted. He delivered 55 percent net in his full year running it. Then he handed the whole thing back and went home to teach undergraduates. His name was Elwyn Berlekamp. He is one of two people who ever ran what would become the most profitable trading operation in history. Simons was the other one. MIT math PhD, 1964. Berlekamp wrote foundational papers in coding theory that still run every CD, DVD, satellite link, and QR code on Earth. The Berlekamp-Massey algorithm, published 1968, is why every scratched CD you owned still played through to the end. He also co-wrote "Winning Ways for Your Mathematical Plays" with John Conway and Richard Guy. Four volumes. It became the foundational text of combinatorial game theory. Berlekamp thought about board games the way most mathematicians think about theorems. He proved endgame results in Go that professional masters had assumed were unprovable. His 1994 book "Mathematical Go" reduced the last moves of a Go game to a formula. Top-ranked professionals started studying it. Simons had a problem in the late 1980s. His trading partnership was falling apart. The fund was losing money. He flew west to see the game theorist. Berlekamp bought a controlling stake, cut what was not working, and rebuilt the trading logic from combinatorial game theory principles. The fund returned 55 percent net after fees in his full year running it. In December 1990, Berlekamp sold his stake back to Simons and walked out. He wanted to go back to Berkeley. In interviews he said the same thing many times, in different words: Berkeley was where he belonged. Simons kept building on the system Berlekamp rebuilt. It became the Medallion Fund. Over the next 30 years, Medallion compounded at roughly 66 percent gross per year. It is the most profitable trading strategy in the history of finance. Berlekamp took his cut in 1990 and never went back. He spent the rest of his life at UC Berkeley. He gave a lecture called "Mathematics and Go" that is on YouTube. He died in 2019, aged 78. The paradox is not that Berlekamp made a fortune. It is that he had the door to the biggest fortune in trading history held open for him and walked out. The math was fun. The billions were not.

veles

68,732 次观看 • 16 天前

a prop trader from chicago made $847k in 180 days just by asking one question every single morning that 99% of traders never ask he didn't build a new model didn't touch machine learning just opened excel and spent 8 minutes on one calculationthe question: what state is the market in right now, and where does it statistically go next most traders ask "will this go up or down". that's 50/50 he started asking "is the market trending, ranging, or reversing" and then looked at the historical probability of each transitionturns out markets don't flip randomly they cycle through states. each state has a fixed probability of shifting to the next onehe built a 5x5 grid on a napkin: trending up -> 68% stays trending, 21% flips to range, 11% reverses ranging -> 54% stays range, 28% breaks up, 18% breaks down trending down -> 61% stays falling, 24% to range, 15% reverses he didn't predict direction he just calculated which state had the highest expected value and sized the position with kelly criterion that's the entire edgethe framework is from 1906 - andrei markov. free in every probability textbook on earthrenaissance technologies has been running this since 1988 37 years of 66% annual returnsdata costs nothing - yahoo finance, federal reserve, any broker implementation is 200 lines of python what separates him from the retail traders losing money isn't intelligence or capital or luck it's that he was willing to think differently about the same data everyone else sees every single day they kept you staring at candles while the people who got it were reading transition matrices bookmark this - you're either asking the wrong question or you're not asking it at all
0:58

a prop trader from chicago made $847k in 180 days just by asking one question every single morning that 99% of traders never ask he didn't build a new model didn't touch machine learning just opened excel and spent 8 minutes on one calculationthe question: what state is the market in right now, and where does it statistically go next most traders ask "will this go up or down". that's 50/50 he started asking "is the market trending, ranging, or reversing" and then looked at the historical probability of each transitionturns out markets don't flip randomly they cycle through states. each state has a fixed probability of shifting to the next onehe built a 5x5 grid on a napkin: trending up -> 68% stays trending, 21% flips to range, 11% reverses ranging -> 54% stays range, 28% breaks up, 18% breaks down trending down -> 61% stays falling, 24% to range, 15% reverses he didn't predict direction he just calculated which state had the highest expected value and sized the position with kelly criterion that's the entire edgethe framework is from 1906 - andrei markov. free in every probability textbook on earthrenaissance technologies has been running this since 1988 37 years of 66% annual returnsdata costs nothing - yahoo finance, federal reserve, any broker implementation is 200 lines of python what separates him from the retail traders losing money isn't intelligence or capital or luck it's that he was willing to think differently about the same data everyone else sees every single day they kept you staring at candles while the people who got it were reading transition matrices bookmark this - you're either asking the wrong question or you're not asking it at all

Livsun

41,634 次观看 • 1 个月前

a trader in Shanghai has been running 71% annual returns since 2019 without ever touching a Western exchange nobody outside Weibo knows his name he doesn't manage outside capital, never went on a podcast, never posted a P&L screenshot pause at 0:34 - look at the monitor behind him on the right that's not a price chart. that's a 6x6 state transition matrix built from 11 years of Chinese A-share data he found something in 2018 that every quant textbook describes but almost nobody applies CSI 300 price-state transitions are predictable at a level that makes S&P pattern noise look clean by comparison he mapped 6 market states: trending-up, trending-down, range-tight, range-wide, vol-compression, spike-decay then calculated every historical transition probability across 11 years of 30-minute bars: trending-up -> stays trending: 63% vol-compression -> spike-decay: 78% range-tight -> breaks directional: 71% now he's not predicting direction. he's entering when math says 71% historical completion, sizing with Kelly, closing in under 30 minutes 28 min avg hold, worst month -3.1%, best year +94% framework is markov's from 1906. A-share data is free on WIND Terminal implementation: roughly 180 lines of python insight was never about math - it was about where to aim it Chinese A-shares have thinner institutional algo penetration than US equities. patterns don't get arbed out as fast statistical edges persist for months longer than they would on SPY retail in Shanghai trades on gut. US quants are chasing S&P microstructure nobody was running transition matrices on Chinese state sequences at any real scale he aimed a 119-year-old framework at a market nobody was watching and held the edge for 6 years bookmark this before it becomes obvious math is free, data costs nothing what took time was realizing the most exploitable market wasn't the most-watched one they kept you watching SPY candles while the cleanest probability table on earth sat untouched in Shanghai
1:30

a trader in Shanghai has been running 71% annual returns since 2019 without ever touching a Western exchange nobody outside Weibo knows his name he doesn't manage outside capital, never went on a podcast, never posted a P&L screenshot pause at 0:34 - look at the monitor behind him on the right that's not a price chart. that's a 6x6 state transition matrix built from 11 years of Chinese A-share data he found something in 2018 that every quant textbook describes but almost nobody applies CSI 300 price-state transitions are predictable at a level that makes S&P pattern noise look clean by comparison he mapped 6 market states: trending-up, trending-down, range-tight, range-wide, vol-compression, spike-decay then calculated every historical transition probability across 11 years of 30-minute bars: trending-up -> stays trending: 63% vol-compression -> spike-decay: 78% range-tight -> breaks directional: 71% now he's not predicting direction. he's entering when math says 71% historical completion, sizing with Kelly, closing in under 30 minutes 28 min avg hold, worst month -3.1%, best year +94% framework is markov's from 1906. A-share data is free on WIND Terminal implementation: roughly 180 lines of python insight was never about math - it was about where to aim it Chinese A-shares have thinner institutional algo penetration than US equities. patterns don't get arbed out as fast statistical edges persist for months longer than they would on SPY retail in Shanghai trades on gut. US quants are chasing S&P microstructure nobody was running transition matrices on Chinese state sequences at any real scale he aimed a 119-year-old framework at a market nobody was watching and held the edge for 6 years bookmark this before it becomes obvious math is free, data costs nothing what took time was realizing the most exploitable market wasn't the most-watched one they kept you watching SPY candles while the cleanest probability table on earth sat untouched in Shanghai

Livsun

25,048 次观看 • 1 个月前

The man who invented modern fantasy didn't publish his first novel until he was forty-five. By that age, J.R.R. Tolkien had already built a respectable life. He was an Oxford professor, an expert in ancient languages, with a wife and four children and a settled academic career. He was exactly the kind of man who might reasonably have decided that the shape of his life was already fixed. The work he would be remembered for, he had not yet even begun... The story, which Tolkien told himself, is that one summer he was grading examination papers, when he turned a page and found that a student had left it blank. Without quite knowing why, he wrote a single sentence on it: "In a hole in the ground there lived a hobbit." He did not know what a hobbit was. He had spent years inventing languages and mythologies as a private passion, and telling stories to his own children, never imagining any of it would reach the world. But that one line began to grow. It became a story, and then a book, and in 1937, at the age of forty-five, Tolkien published The Hobbit. It was a success, and his publisher asked for a sequel. Tolkien warned them it might take some time. It took 17 years... He wrote it in the margins of a demanding full-time job, revising endlessly, doubting it often. When The Lord of the Rings was finally published, in 1954 and 1955, he was in his early sixties. That book, begun as a middle-aged professor's private side project, went on to sell well over a hundred million copies, to invent modern fantasy as we know it, and to reshape the imagination of the entire world. Tolkien already had a full and respectable life behind him. And still, the thing he is remembered for, the thing that outlived him and reached hundreds of millions of people, was something he began at forty-five, at an age when it would have been the easiest thing in the world to tell himself he had already missed his chance. He didn't. It's never as late as it feels.
3:01

The man who invented modern fantasy didn't publish his first novel until he was forty-five. By that age, J.R.R. Tolkien had already built a respectable life. He was an Oxford professor, an expert in ancient languages, with a wife and four children and a settled academic career. He was exactly the kind of man who might reasonably have decided that the shape of his life was already fixed. The work he would be remembered for, he had not yet even begun... The story, which Tolkien told himself, is that one summer he was grading examination papers, when he turned a page and found that a student had left it blank. Without quite knowing why, he wrote a single sentence on it: "In a hole in the ground there lived a hobbit." He did not know what a hobbit was. He had spent years inventing languages and mythologies as a private passion, and telling stories to his own children, never imagining any of it would reach the world. But that one line began to grow. It became a story, and then a book, and in 1937, at the age of forty-five, Tolkien published The Hobbit. It was a success, and his publisher asked for a sequel. Tolkien warned them it might take some time. It took 17 years... He wrote it in the margins of a demanding full-time job, revising endlessly, doubting it often. When The Lord of the Rings was finally published, in 1954 and 1955, he was in his early sixties. That book, begun as a middle-aged professor's private side project, went on to sell well over a hundred million copies, to invent modern fantasy as we know it, and to reshape the imagination of the entire world. Tolkien already had a full and respectable life behind him. And still, the thing he is remembered for, the thing that outlived him and reached hundreds of millions of people, was something he began at forty-five, at an age when it would have been the easiest thing in the world to tell himself he had already missed his chance. He didn't. It's never as late as it feels.

James Lucas

93,424 次观看 • 1 个月前

A 26-year-old guy from China runs an agency that gains over 1,000,000 followers a year, and not a single one of his streams is actually live. he started as a real streamer, on camera every night, and made almost nothing. the moment he stopped, the caps stopped. a shop that closes when you sleep isn't a business, it's a shift. so he stopped going live. he built a rig that only looks live: > two vertical 4K monitors looping a clean pre-recorded product demo > phones on tripods filming the screens through anti-moiré glass > TikTok Shop links wired to every "stream" > one operator minding a rack of them instead of a camera the trick was never the tech. it was the reframe: a live-commerce stream never had to be live. it had to sell. a good demo, looped clean, closed the same at 4am as it did at noon. then he ran it for other people. brands handed him their catalog, he spun up always-on storefronts across a dozen accounts, and took a cut of every sale the loops closed. the $100,000 a year didn't come from being on camera. they came the night he realized the camera was the only part that needed a human, so he removed it.
1:45

A 26-year-old guy from China runs an agency that gains over 1,000,000 followers a year, and not a single one of his streams is actually live. he started as a real streamer, on camera every night, and made almost nothing. the moment he stopped, the caps stopped. a shop that closes when you sleep isn't a business, it's a shift. so he stopped going live. he built a rig that only looks live: > two vertical 4K monitors looping a clean pre-recorded product demo > phones on tripods filming the screens through anti-moiré glass > TikTok Shop links wired to every "stream" > one operator minding a rack of them instead of a camera the trick was never the tech. it was the reframe: a live-commerce stream never had to be live. it had to sell. a good demo, looped clean, closed the same at 4am as it did at noon. then he ran it for other people. brands handed him their catalog, he spun up always-on storefronts across a dozen accounts, and took a cut of every sale the loops closed. the $100,000 a year didn't come from being on camera. they came the night he realized the camera was the only part that needed a human, so he removed it.

Nekt0

21,566 次观看 • 1 个月前

Kevin O'Leary reveals the cruelest sentence of his life was the one that saved it. Kevin O'Leary said he has always wanted to be a photographer. He was already working as a cameraman, ready to bet his life on a lens. His stepfather sat him down and said: "I'm sorry, but you're just not good enough. You don't have what it takes to survive against the level of quality that's out there." Kevin calls it the harshest sentence anyone ever delivered to him. He also calls it the most important one. He went back to school. Got a business degree. Started a production company called Special Event Television out of spite — but he didn't put himself behind the camera. He hired a real cameraman. He learned to edit. He sold the company. That was his first deal. The thing he took from that day became his operating system: knowing what you're bad at matters more than knowing what you're good at. Logistics bore him, so he gives up equity to anyone who can do it. He stays in marketing because that's the only seat where he compounds. Then he stopped pretending. The cruelest sentence of his life was the one that saved it. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Kevin O'Leary ( Kevin O'Leary aka Mr. Wonderful ), chairman of O'Leary Ventures and Shark Tank investor, on Graham Stephan's ( Graham Stephan ) and Jack Selby's ( Jack Selby ) Iced Coffee Hour podcast
1:07

Kevin O'Leary reveals the cruelest sentence of his life was the one that saved it. Kevin O'Leary said he has always wanted to be a photographer. He was already working as a cameraman, ready to bet his life on a lens. His stepfather sat him down and said: "I'm sorry, but you're just not good enough. You don't have what it takes to survive against the level of quality that's out there." Kevin calls it the harshest sentence anyone ever delivered to him. He also calls it the most important one. He went back to school. Got a business degree. Started a production company called Special Event Television out of spite — but he didn't put himself behind the camera. He hired a real cameraman. He learned to edit. He sold the company. That was his first deal. The thing he took from that day became his operating system: knowing what you're bad at matters more than knowing what you're good at. Logistics bore him, so he gives up equity to anyone who can do it. He stays in marketing because that's the only seat where he compounds. Then he stopped pretending. The cruelest sentence of his life was the one that saved it. P.S. Pull the thread on any story like this and you'll find the hidden incentive at the other end. As Munger said: "Show me the incentive and I'll show you the outcome." So I wrote a short book on how to spot them and design your own. Comment "INCENTIVES" and I'll send you the details. If you're new here, follow GeniusThinking for content on the greatest minds in economics, psychology, and history. — Kevin O'Leary ( Kevin O'Leary aka Mr. Wonderful ), chairman of O'Leary Ventures and Shark Tank investor, on Graham Stephan's ( Graham Stephan ) and Jack Selby's ( Jack Selby ) Iced Coffee Hour podcast

GeniusThinking

25,562 次观看 • 2 个月前

How Jeffrey Epstein rose from maths nerd, to a financial fixer for elites, to the boss of blackmail: ‘If you look at Epstein’s operation, what it looks like to me is that it’s a criminal operation on a number of different levels. Epstein is an interesting case because where does he come from? Well, he doesn’t come from a wealthy family. He doesn’t come from an influential family. He was a Long Island kid who was good at mathematics…he was a kind of smart, nerdy kid who made friends by doing their math homework. You’re not popular because you’re a jock. You’re not popular necessarily because you’re that good-looking or you dance well. You’re popular because you can do other people’s math homework and get them to pass. His whole career is ingratiating himself to wealthy, powerful people. What did he do as a financial advisor? Look at it. Just stand back and look at what he did. And what he did was that he helped them dodge taxes. He also helped them hide money. He could help people discover money that had been hidden abroad. He could help them hide money. If you do one, you can do the other. And that’s how he moved as a kind of fixer and arranger into the realm of the rich and powerful. And in that process, either he or other people who were working with him found out that these people can be compromised in a number of ways. And so then you start installing cameras in your residences, in the bedrooms and the bathrooms. There’s only one reason you do that. It’s fairly simple. The only reason why you collect all of this video information on your rich and powerful friends is to potentially use it as leverage against them. You don’t have to actually use it. You simply have to make them aware that you have it and could use it.’ -Prof. Richard Spence on the latest episode of Going Underground FULL INTERVIEW:
3:19

How Jeffrey Epstein rose from maths nerd, to a financial fixer for elites, to the boss of blackmail: ‘If you look at Epstein’s operation, what it looks like to me is that it’s a criminal operation on a number of different levels. Epstein is an interesting case because where does he come from? Well, he doesn’t come from a wealthy family. He doesn’t come from an influential family. He was a Long Island kid who was good at mathematics…he was a kind of smart, nerdy kid who made friends by doing their math homework. You’re not popular because you’re a jock. You’re not popular necessarily because you’re that good-looking or you dance well. You’re popular because you can do other people’s math homework and get them to pass. His whole career is ingratiating himself to wealthy, powerful people. What did he do as a financial advisor? Look at it. Just stand back and look at what he did. And what he did was that he helped them dodge taxes. He also helped them hide money. He could help people discover money that had been hidden abroad. He could help them hide money. If you do one, you can do the other. And that’s how he moved as a kind of fixer and arranger into the realm of the rich and powerful. And in that process, either he or other people who were working with him found out that these people can be compromised in a number of ways. And so then you start installing cameras in your residences, in the bedrooms and the bathrooms. There’s only one reason you do that. It’s fairly simple. The only reason why you collect all of this video information on your rich and powerful friends is to potentially use it as leverage against them. You don’t have to actually use it. You simply have to make them aware that you have it and could use it.’ -Prof. Richard Spence on the latest episode of Going Underground FULL INTERVIEW:

Going Underground

68,766 次观看 • 7 个月前

"[Lewis Hamilton] was really down on himself. And no one likes to see that because at the end of the day, whether you're a fan or not, Lewis Hamilton is the greatest racing driver we've ever seen." "Statistically, he is the greatest. I think he's the greatest even without the stats." "He wears his heart on his sleeve and should be applauded for that because it does a lot of good when he wears his heart on his sleeve. He did apologise to Rachel Brookes for being so down in the dumps off camera. He said, 'I'm really sorry about that. I'm just so down.' And that just shows the extent that he really was down. He's not putting this on for the cameras, trust me." "And once again, it's right after the race. He's not had time for a debrief. He's not had time to have a chat with his race engineer. So he probably didn't know the extent of whatever damage the car had at the time he spoke to Rachel. And he probably didn't realise that it was the car that was hindering him more so than his driving in that one. And look, when you're in a Ferrari and you're overtaken by Nico Hülkenberg, albeit in a Ferrari powered Sauber, you're going to question, 'is it me really? Am I doing something wrong here?' Because it just didn't go for him really from the outset." [📹 The Fast And The Curious]
1:11

"[Lewis Hamilton] was really down on himself. And no one likes to see that because at the end of the day, whether you're a fan or not, Lewis Hamilton is the greatest racing driver we've ever seen." "Statistically, he is the greatest. I think he's the greatest even without the stats." "He wears his heart on his sleeve and should be applauded for that because it does a lot of good when he wears his heart on his sleeve. He did apologise to Rachel Brookes for being so down in the dumps off camera. He said, 'I'm really sorry about that. I'm just so down.' And that just shows the extent that he really was down. He's not putting this on for the cameras, trust me." "And once again, it's right after the race. He's not had time for a debrief. He's not had time to have a chat with his race engineer. So he probably didn't know the extent of whatever damage the car had at the time he spoke to Rachel. And he probably didn't realise that it was the car that was hindering him more so than his driving in that one. And look, when you're in a Ferrari and you're overtaken by Nico Hülkenberg, albeit in a Ferrari powered Sauber, you're going to question, 'is it me really? Am I doing something wrong here?' Because it just didn't go for him really from the outset." [📹 The Fast And The Curious]

sim 🇧🇷🇲🇽🇸🇳🇫🇷🇭🇹

97,656 次观看 • 1 年前

Tupac was a performer through and through. At his core, he was an actor, a poet, and a thinker—not a gangster. Sure, he rapped about the streets and the struggles that came with them, but that was more about telling the stories of the people around him than living that life himself. He had the smarts and the charisma to pull people in, whether it was on stage, on screen, or in his music. He trained at the Baltimore School for the Arts, where he honed his craft, showing he was born to entertain. The "gangster" image? That was more about the industry and the media pushing a narrative than who Tupac really was at heart. Tupac was an artist first, using his voice and talent to speak for the people, share their pain, and make you feel something. He wasn’t about the gangster life—he was about the art and the message.
2:18

Tupac was a performer through and through. At his core, he was an actor, a poet, and a thinker—not a gangster. Sure, he rapped about the streets and the struggles that came with them, but that was more about telling the stories of the people around him than living that life himself. He had the smarts and the charisma to pull people in, whether it was on stage, on screen, or in his music. He trained at the Baltimore School for the Arts, where he honed his craft, showing he was born to entertain. The "gangster" image? That was more about the industry and the media pushing a narrative than who Tupac really was at heart. Tupac was an artist first, using his voice and talent to speak for the people, share their pain, and make you feel something. He wasn’t about the gangster life—he was about the art and the message.

Gerry

33,998 次观看 • 1 年前

Jonni Skinner is a heroic young man Jonni S🦎. If you are willing to understand what Gender Affirming "Care" actually entails from what it looks like in a gender clinic here in Michigan, please listen to his experience. He was naturally effeminate and gay as a boy where he was therefore not accepted by many around him. He sought answers for the traumas he was living through. At 13 years old, he was placed on a medicalized path at the University of Michigan to attempt to change him into a girl. The doctor contended that he was not gay, he was just a girl. He was diagnosed with "tall stature" because the doctor told him if he was too tall as a girl, he would not be able to find a mate. They therefore deliberately stunted his growth as if being tall was a disease. He was 5'7 at the time. Nine years later, he is 5'8. He was diagnosed with an endocrine disorder related puberty that he did not have to be placed on puberty blockers and cross sex hormones. The doctor used fraudulent coding more than 100 times in his medical records. He was prescribed "affirming gear" where they sent him to a sex shop across the state in Wyandotte. His doctors fearmongered him and his mother saying the phrase "would you like to have a dead son, or a living daughter". They threatened to take him from his mother if she refused their malicious pharmaceutical path. His doctor ignored the ailments related to the powerful drugs he was placed on and dismissed the sexual disfunction they were causing him by saying he could seek other means of "pleasure". The doctors ultimately ghosted him in 2021 by legitimately hanging up on him and never speaking with him again. His unique ailments related to this "care" have him being turned away from doctors today. He deserves justice and actual care. We hope to aid him in that endeavor.
52:17

Jonni Skinner is a heroic young man Jonni S🦎. If you are willing to understand what Gender Affirming "Care" actually entails from what it looks like in a gender clinic here in Michigan, please listen to his experience. He was naturally effeminate and gay as a boy where he was therefore not accepted by many around him. He sought answers for the traumas he was living through. At 13 years old, he was placed on a medicalized path at the University of Michigan to attempt to change him into a girl. The doctor contended that he was not gay, he was just a girl. He was diagnosed with "tall stature" because the doctor told him if he was too tall as a girl, he would not be able to find a mate. They therefore deliberately stunted his growth as if being tall was a disease. He was 5'7 at the time. Nine years later, he is 5'8. He was diagnosed with an endocrine disorder related puberty that he did not have to be placed on puberty blockers and cross sex hormones. The doctor used fraudulent coding more than 100 times in his medical records. He was prescribed "affirming gear" where they sent him to a sex shop across the state in Wyandotte. His doctors fearmongered him and his mother saying the phrase "would you like to have a dead son, or a living daughter". They threatened to take him from his mother if she refused their malicious pharmaceutical path. His doctor ignored the ailments related to the powerful drugs he was placed on and dismissed the sexual disfunction they were causing him by saying he could seek other means of "pleasure". The doctors ultimately ghosted him in 2021 by legitimately hanging up on him and never speaking with him again. His unique ailments related to this "care" have him being turned away from doctors today. He deserves justice and actual care. We hope to aid him in that endeavor.

Brad Paquette

110,000 次观看 • 10 个月前

This woman rented a horse drawn carriage for a wedding procession, everything was going according to plan he showed up on time at the proper location everything was perfect in his eyes however the lady who rented the buggy took exception to the way the horse handler/groom was dressed and came completely unglued and started cursing at the buggy driver and threw a drink on him, when he returned serve and started moving away with the buggy the whole crowd started to converge on him ready to pounce forcing him to go to the whip on the horses to make his getaway. Why is it everywhere you look People ! think this is acceptable behavior ?
0:42

This woman rented a horse drawn carriage for a wedding procession, everything was going according to plan he showed up on time at the proper location everything was perfect in his eyes however the lady who rented the buggy took exception to the way the horse handler/groom was dressed and came completely unglued and started cursing at the buggy driver and threw a drink on him, when he returned serve and started moving away with the buggy the whole crowd started to converge on him ready to pounce forcing him to go to the whip on the horses to make his getaway. Why is it everywhere you look People ! think this is acceptable behavior ?

ℳ𝒜𝒢𝒜_𝒳_𝒯𝒾𝓂𝑒𝓈 𝒟𝒶𝒾𝓁𝓎 𝒩𝑒𝓌𝓈🇺🇸

1,057,920 次观看 • 5 个月前

ABC News photojournalist Chris Plater, a witness to the scene after two National Guardsmen were shot in D.C.: "One person said something like, you know, man, the guy went up there and started shooting at the National Guard. I didn't get to talk to him, but I didn't get to, you know, know who he was. But it seemed like he suggested that somebody went up and started shooting at the National Guard." "One National Guard was lying on his side. They were rendering aid to him. He wasn't moving, but he was on his side. And then I looked at the the other National Guard member that was down, and I saw compressions going on. I, you know, it was -- it was a lot of people around there. So I don't think it was the person that had done the shooting. And all I could tell you is that I identified two National Guard members that were being worked on. Eventually, the one National Guard that was on the side, he got to move a little bit while he was on his side. And they were still, you know, kind of, you know, working with him. But the other national Guard member, I just saw compression going on with him in the chest area, and that's that's all I can tell you."
1:25

ABC News photojournalist Chris Plater, a witness to the scene after two National Guardsmen were shot in D.C.: "One person said something like, you know, man, the guy went up there and started shooting at the National Guard. I didn't get to talk to him, but I didn't get to, you know, know who he was. But it seemed like he suggested that somebody went up and started shooting at the National Guard." "One National Guard was lying on his side. They were rendering aid to him. He wasn't moving, but he was on his side. And then I looked at the the other National Guard member that was down, and I saw compressions going on. I, you know, it was -- it was a lot of people around there. So I don't think it was the person that had done the shooting. And all I could tell you is that I identified two National Guard members that were being worked on. Eventually, the one National Guard that was on the side, he got to move a little bit while he was on his side. And they were still, you know, kind of, you know, working with him. But the other national Guard member, I just saw compression going on with him in the chest area, and that's that's all I can tell you."

Curtis Houck

697,899 次观看 • 8 个月前