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MIT Professor just revealed the exact mathematical framework behind quant trading and zero-sum edge. 80-minutes. free. By Ian Bell. here's what they cover: • how von Neumann proved moving first isn't a disadvantage in zero-sum games • Nash equilibrium vs. security strategies (minimax & maximin algebra) • why chess... show more

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Citadel Senior Quant Developer just dropped the complete math framework for scaling a pairs trading desk to 200+ concurrent market positions. 54-minutes. free. By Quant Trader. here's what they cover: • integration orders (I(0) vs I(1)) for asset pricing • using the Augmented Dickey-Fuller test over standard graphs • calculating dynamic rolling betas on log returns • managing independent bets across 200+ parallel tables Bookmark it & watch today. Then read the article below.

Citadel Senior Quant Developer just dropped the complete math framework for scaling a pairs trading desk to 200+ concurrent market positions. 54-minutes. free. By Quant Trader. here's what they cover: • integration orders (I(0) vs I(1)) for asset pricing • using the Augmented Dickey-Fuller test over standard graphs • calculating dynamic rolling betas on log returns • managing independent bets across 200+ parallel tables Bookmark it & watch today. Then read the article below.

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Jane Street pays $750K/year for quants who master large deviations and tail bounds. 82-minutes. free. By MIT professor. "Markov gives us a really bad bound, Chebyshev is pretty reasonable, Chernoff gives us exponential." here's what they cover: • Markov vs Chebyshev vs Chernoff bounds • bounding tail risk for extreme random variables • why variance alone fails in high-frequency regimes • exponential 10x tighter risk modeling Bookmark it & watch today. Then read the article below.

Jane Street pays $750K/year for quants who master large deviations and tail bounds. 82-minutes. free. By MIT professor. "Markov gives us a really bad bound, Chebyshev is pretty reasonable, Chernoff gives us exponential." here's what they cover: • Markov vs Chebyshev vs Chernoff bounds • bounding tail risk for extreme random variables • why variance alone fails in high-frequency regimes • exponential 10x tighter risk modeling Bookmark it & watch today. Then read the article below.

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43,465 views • 1 month ago

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.

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

73,359 views • 3 days ago

Professor Eric Budish (UChicago) delivers a 1-hour masterclass completely deconstructing the exact math HFT bots use to extract millions from continuous order books. Bookmark this and watch it today, if you want to stop trading narratives and start trading architecture It will permanently change how you view markets and liquidity. Check the quoted post below to see an example of HFT bot that appears to be exploiting these mechanics, printing over $500k in just 26 days on Polymarket. For the platform, attracting this level of algorithmic warfare is the ultimate validation. This level of deep, constant liquidity cements the platform as a Tier-1 financial fortress. What you'll learn inside: - The fundamental flaw in the continuous limit order book (clob) - How latency arbitrage actually works under the hood - The concept of the "liquidity tax" and who ultimately pays it - Why pure speed mathematically eliminates directional risk There are no magic pills or secret formulas in this game. The edge simply belongs to those who understand the mechanics better than the others.

Professor Eric Budish (UChicago) delivers a 1-hour masterclass completely deconstructing the exact math HFT bots use to extract millions from continuous order books. Bookmark this and watch it today, if you want to stop trading narratives and start trading architecture It will permanently change how you view markets and liquidity. Check the quoted post below to see an example of HFT bot that appears to be exploiting these mechanics, printing over $500k in just 26 days on Polymarket. For the platform, attracting this level of algorithmic warfare is the ultimate validation. This level of deep, constant liquidity cements the platform as a Tier-1 financial fortress. What you'll learn inside: - The fundamental flaw in the continuous limit order book (clob) - How latency arbitrage actually works under the hood - The concept of the "liquidity tax" and who ultimately pays it - Why pure speed mathematically eliminates directional risk There are no magic pills or secret formulas in this game. The edge simply belongs to those who understand the mechanics better than the others.

Tengen

287,062 views • 3 months ago

Your strategy isn't losing because it's bad. It's losing because the market changed and nobody told you. Markets have moods: trend, chop, panic. Every strategy only works inside one of them. And when the mood flips, no bell rings. Your equity curve is the bell - by the time it rings, you're already down 30%. Quant desks don't wait for the bell. They run 120-year-old math, a Hidden Markov Model, that reads the market's hidden state live and calls the flip days before the chart shows it. Horizon packed that whole desk setup into one typed sentence. Public launch July 15 - get on the waitlist now: Here's the proof, from a real pair of runs. A BTC breakout kept shorting into an uptrend and finished -50.68%, the short book alone bleeding $75,836. The exact same idea, with one added sentence about the regime: +41.15%. Same entries. Same logic. A 91-point swing from one sentence. You don't have to build any of this. Horizon fits the model, watches the regime every bar, and switches your strategy on its own - you just describe what you want. Half your losing trades were probably this exact thing: right idea, wrong market, no way to see it. All this time you've been blaming your discipline, your entries, your psychology. The real question was one you never asked: what state is the market in right now? Bookmark this and read the full framework below - it's the exact regime math desks run on live money, written so you can use it Monday.

Your strategy isn't losing because it's bad. It's losing because the market changed and nobody told you. Markets have moods: trend, chop, panic. Every strategy only works inside one of them. And when the mood flips, no bell rings. Your equity curve is the bell - by the time it rings, you're already down 30%. Quant desks don't wait for the bell. They run 120-year-old math, a Hidden Markov Model, that reads the market's hidden state live and calls the flip days before the chart shows it. Horizon packed that whole desk setup into one typed sentence. Public launch July 15 - get on the waitlist now: Here's the proof, from a real pair of runs. A BTC breakout kept shorting into an uptrend and finished -50.68%, the short book alone bleeding $75,836. The exact same idea, with one added sentence about the regime: +41.15%. Same entries. Same logic. A 91-point swing from one sentence. You don't have to build any of this. Horizon fits the model, watches the regime every bar, and switches your strategy on its own - you just describe what you want. Half your losing trades were probably this exact thing: right idea, wrong market, no way to see it. All this time you've been blaming your discipline, your entries, your psychology. The real question was one you never asked: what state is the market in right now? Bookmark this and read the full framework below - it's the exact regime math desks run on live money, written so you can use it Monday.

cvxv666

98,041 views • 1 month ago

MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

MUST-WATCH: Why SIG Dominated Options Trading — Explained by an 8-Year Insider Kris Abdelmessih (Kris) spent 8 years trading energy derivatives at SIG, then ran options businesses at Parallax & Prime before founding Moontower —one of the world's most popular newsletters on options & volatility trading. "SIG understood there was an abnormal amount of edge in the market. They came from gambling—sports betting, poker—where edge was tiny. A bookie makes 5% margins. But trading a $2.5 call spread for $2.20 when it's worth $2.50? That's a ridiculous amount of edge compared to gambling, with the same risk distribution." We cover: - Why SIG was called "the evil empire" & how they crushed competitors by trading massive size for tighter spreads - The exact structure of prop shop deals: 50/50 splits, escrow accounts, how you get to 60% then 70% payouts - Why markets look efficient from most vantage points & how trading is ultimately about labor—getting your vantage point close enough that it stops looking random - The tyranny of beta: why the best operator in a melting ice cube business will lose to a mediocre performer in a great market - How to escape the "striver" trap & tune out status optimization (hint: find what you got obsessed with before college applications mattered) - Teaching his 12-year-old options market making & involving his 9-year-old in building a trading card game—scattered cards on the bedroom floor that'll become a finished product Thanks to Kris for the masterclass. Highlights: 02:05 How Kris first recognized real trading edge 04:01 How early market structure created easy edge 05:27 Why improvement in trading comes from hindsight 07:08 The core SIG frameworks that shaped his edge 09:37 Why uncovering edge requires labor and precision 11:02 How informed order flow forces trader humility 12:53 What truly differentiated SIG from competitors 13:23 How SIG built a world-class education pipeline 16:30 How SIG captured edge by refusing to hedge 18:11 How centralized risk controlled exposure and variance 19:09 How SIG used size and spreads to dominate markets 23:20 What Kris learned working with Jason McCarthy 25:40 Why elite traders share extreme competitiveness 26:06 How top performers operate across domains and PM roles 28:22 How Kris transitioned from SIG to prop trading 31:56 What shifting into senior roles taught him about trading 33:46 How Kris built training and feedback systems for traders 35:00 How the backer model works inside prop shops 38:41 How escrow capital protects traders from tail events 41:03 How natural gas options trading changed with regime shifts 42:13 How Kris applies trading edge concepts to life decisions 45:46 Why personal alignment beats chasing status in trading 47:13 How status games distort decision-making for young traders 52:23 Why striver behavior is actually risk management 56:27 How Kris teaches opportunity cost through parenting 1:01:28 How exposing kids to decisions builds intuition 1:04:46 How Kris teaches EV using homemade trading games 1:08:05 How iteration and feedback loops shape real learning

Ethan Kho

233,306 views • 6 months ago

an MIT professor who taught both physics and finance told his class something none of them expected "finance is harder than physics" not as a joke. as a mathematical statement in physics, the laws don't change. gravity works the same today as it did a billion years ago you can run an experiment, get a result, and repeat it forever in finance, the moment you discover a law, the participants learn it too and their behavior changes the system you just measured in physics, electrons don't read your paper and start moving differently in finance, traders do. every published edge gets arbitraged away by the people who read it this is why quant models have a half-life and physics equations don't Newton's laws: 300+ years and counting Long-Term Capital Management's model: worked perfectly until it didn't, lost $4.6 billion in 4 months the system you're modeling is aware of you modeling it that's not a solvable problem. it's a permanent condition and the quants who survive are the ones who build for it instead of pretending it doesn't exist > this lecture: MIT finance series, free, public, 53 seconds > LTCM collapse: 1998, Nobel Prize winners, $4.6B loss > Renaissance's solution: never stop researching, replace signals before they decay > average lifespan of a quant signal: 2-5 years before it's crowded out retail builds one strategy and trades it until it breaks quant desks build a research engine that produces new strategies faster than old ones die that's not a difference in skill. it's a difference in understanding what game you're actually playing full breakdown in the video below

an MIT professor who taught both physics and finance told his class something none of them expected "finance is harder than physics" not as a joke. as a mathematical statement in physics, the laws don't change. gravity works the same today as it did a billion years ago you can run an experiment, get a result, and repeat it forever in finance, the moment you discover a law, the participants learn it too and their behavior changes the system you just measured in physics, electrons don't read your paper and start moving differently in finance, traders do. every published edge gets arbitraged away by the people who read it this is why quant models have a half-life and physics equations don't Newton's laws: 300+ years and counting Long-Term Capital Management's model: worked perfectly until it didn't, lost $4.6 billion in 4 months the system you're modeling is aware of you modeling it that's not a solvable problem. it's a permanent condition and the quants who survive are the ones who build for it instead of pretending it doesn't exist > this lecture: MIT finance series, free, public, 53 seconds > LTCM collapse: 1998, Nobel Prize winners, $4.6B loss > Renaissance's solution: never stop researching, replace signals before they decay > average lifespan of a quant signal: 2-5 years before it's crowded out retail builds one strategy and trades it until it breaks quant desks build a research engine that produces new strategies faster than old ones die that's not a difference in skill. it's a difference in understanding what game you're actually playing full breakdown in the video below

delost

19,002 views • 1 month ago

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Inside the mind of an ex-SIG quant trader who can't turn off the EV brain - even for his kid's school choice Andrew Courtney (Andrew Courtney) ran the International ETFs Trading Desk at Susquehanna International Group for ~15 years before leaving in 2023. He now runs Kalshionomics (Kalshinomics), a prediction markets analytics tool, and writes the Whirligig Bear, one of the sharpest prediction markets Substacks out there. "I think of everything as a bet. I kind of don't understand how you talk to normal people — they do not do that." SIG trains their junior traders with poker, spending 2hrs/day turning over cards after every hand, justifying every decision quantitatively AND qualitatively. 15 years later, Andrew views prediction markets the same way: read who's on the other side, size accordingly, fold when the whale comes back at you 10x. We cover: - Why SIG pays junior traders to play poker for 2hrs/day — & what happens after every single hand - The "one eye on the market, always" attention tax that destroys most people's careers - How to find edge in prediction markets by asking: who am I actually trading against? - Why meme-heavy, overhyped markets (Taylor Swift at the Super Bowl) might be the juiciest trades - The insider trading debate in prediction markets — & why it's "socially corrosive" - Floor trading vs. upstairs quant: why the transition saved his career - 40 connections after ~15 years at one of the world's best firms — the hidden cost of prop trading - Why he doesn't have collision insurance on his car (& the EV math behind it) Thank you so much Andrew Courtney for coming on the pod! Timestamps: 00:00 Intro 05:00 Floor trading vs. electronic trading 06:28 What makes an upstairs trader 10:16 Poker as trader training 13:00 Thinking in bets as a mental framework 15:11 Decision trees in real life 16:40 Where prediction markets actually have edge 19:00 Why the LLM forecasting layer falls short 19:40 Liquidity incentives and trading low-volume markets 22:00 Limiting downside even when the model is wrong 24:32 Executing in illiquid markets 25:44 Fair value vs. directional conviction 27:11 Bayesian updating when liquidity responds 28:40 Fading hype and crowded narratives 31:07 Longshot bias vs. fanbase bias 34:20 How to judge whether you really have edge 36:40 Building analytics tools for prediction markets 38:20 The temporary edge for smart amateurs 40:35 Where prediction markets fit best 41:20 Markets that shouldn’t exist 43:20 Why insider trading corrodes incentives 46:52 Are prediction markets a net good or bad 50:47 Minimizing degeneracy and maximizing signal 53:32 A simple EV mindset anyone can use

Ethan Kho

436,074 views • 5 months ago

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

Ken Griffin, CEO of Citadel, said: "I'm fairly depressed watching AI do a week of PhD work in a few hours" He's worth $51.2 billion and 20% of every stock trade in America goes through his firm. The money isn't what's bothering him. Citadel pays $400,000 to $650,000 a year for that work. New trading AI agent does it now, on a free trial, for people who have never written a line of code. It went live this summer and most of your timeline still hasn't noticed. You type one sentence in plain English. AI agents loop takes it from there: > an AI agent turns your sentence into a real trading strategy > backtests it across 5 years of data in about 12 seconds > scores it 0 to 100 and shows you the exact spot where it leaks > kills the versions that only look good on paper > rebuilds what's left and runs the loop again > stress-tests the survivor on years it has never seen, then puts it live on your exchange in about 90 seconds That list is the entire job description of a junior quant. At a desk, one properly tested idea burns about $87,500 in salary time, and most of what they build is dead by week six. Two clicks and this AI agent is testing your own idea. Costs you nothing and it runs the whole thing by itself: Griffin gets to be depressed about it. He already owns the desk. You just got handed one. Bookmark & read full story of how they got an AI to build trading strategies and kill its own bad ones is in the article below.

cvxv666

222,320 views • 10 days ago

Trading strategy making you money right now is already dying. You just can't feel it yet. Two finance professors tracked 97 published strategies. One year after they went public, the average edge was down 58%. Every edge has a half-life. Nobody hands you the expiration date. So three red weeks hit, and you panic-quit a strategy that was still alive. Or you marry a dead one and bleed for six months defending it. You can't tell the two apart by feel. That's the entire problem. There's now AI tool that draws the line for you. Horizon backtests 5 years in seconds and runs thousands of simulated paths, so before you go live you already know how deep and how long a normal drawdown runs. Drawdown is weather. Decay is information. One you ride out. One you cut. Then it runs the strategy automated -> the exit gets decided by the math you set when you were calm, not the mood you're in at the bottom. The same loop a quant desk pays $25,000 a year for. Yours for $0 while the beta open. Save this -> re-read it the next time you're three red weeks deep and about to fold a strategy that's still alive. Full framework in the article above. Free access while the beta's open:

Trading strategy making you money right now is already dying. You just can't feel it yet. Two finance professors tracked 97 published strategies. One year after they went public, the average edge was down 58%. Every edge has a half-life. Nobody hands you the expiration date. So three red weeks hit, and you panic-quit a strategy that was still alive. Or you marry a dead one and bleed for six months defending it. You can't tell the two apart by feel. That's the entire problem. There's now AI tool that draws the line for you. Horizon backtests 5 years in seconds and runs thousands of simulated paths, so before you go live you already know how deep and how long a normal drawdown runs. Drawdown is weather. Decay is information. One you ride out. One you cut. Then it runs the strategy automated -> the exit gets decided by the math you set when you were calm, not the mood you're in at the bottom. The same loop a quant desk pays $25,000 a year for. Yours for $0 while the beta open. Save this -> re-read it the next time you're three red weeks deep and about to fold a strategy that's still alive. Full framework in the article above. Free access while the beta's open:

cvxv666

71,962 views • 2 months ago

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

MUST-WATCH: Inside high-frequency trading & building the future of markets with Annanay Kapila Annanay Kapila (ak0) — former quant trader at Flow Traders & Tower Research, now founder of QFEX (YC-backed). Building the first 24/7 perpetual futures exchange for equities, commodities & FX — bringing crypto-level UX to traditional assets. "My friends at Cambridge have been making $5M+ a year for the last few years. They don't have to work anymore. But no one retires early. Why? Everyone who gets selected has that desire to win that transcends money, even rationality." We cover: - What it's really like inside elite HFT shops — international math Olympiad winners, professional poker players & zero room for error - Why the smartest traders keep working after making generational wealth (hint: it's pure competitive ego) - The two schools of quant trading: Chicago pit traders vs MIT mathematicians — and which strategies they dominate - How his team navigated the FTX collapse in real-time (including friends who escaped via Dogecoin withdrawals) - Building QFEX : 24/7 perpetual futures on equities with crypto-level UX — "We're building FTX without the fraud" - YC insider stories: meeting Sam Altman & Paul Graham, raising from Paul Graham (Paul Graham) personally, why the $125k for 7% is worth it - Culture secrets from Citadel & Tower: when your dev environment pages you at 2am on Saturday - The one interview hack VCs use that most founders miss (interrupt their CV walkthrough relentlessly) - Why doomerism is dead wrong: "My life would've been worse if I was born 10 years ago. I dread to think how much better it would be if I was born 10 years from now" Huge thanks to ak0 for the transparency. Follow his journey building the future of financial markets. 01:12 Inside high frequency trading culture and competition 03:00 What drives top performers in HFT 03:47 Why elite traders never retire early 04:50 How Tower structures quant trading work 06:14 Chicago versus MIT quant trading styles 10:29 How modern market making actually works 12:25 Why prop firms struggle to scale capital 16:36 Annanay's work at Tower and Flow 19:15 Tower's crypto trading and API edge 19:24 How firms handled the FTX collapse 23:42 Inside FTX culture and HK crypto scene 27:11 What QFEX is building for traders 29:37 QFEX beta launch and early rollout 29:56 Why building an exchange is exciting 32:48 QFEX execution edge and team DNA 36:24 Why quants leave HFT for startups 38:22 QFEX culture versus HFT culture 41:46 How meritocracy works inside QFEX 47:52 How QFEX hires true A players 53:05 Backchanneling and evaluating candidates 53:27 What YC actually teaches founders 55:21 Lessons from meeting Paul Graham and Sam Altman 58:41 Why startups fail and how to pivot 1:00:48 Why young people should stay optimistic 1:11:22 Closing remarks and QFEX vision

Ethan Kho

164,024 views • 6 months ago

A senior quant at an $18.4 billion fund showed his team a study of 50 US stocks and asked: “why are we trading volume when the order book explains price better?” the answer led them into level 3 data during the same 30 seconds that an opening candle printed five values the exchange produced more than 215,000 messages hidden inside them were market makers adding liquidity, canceling bids, and stepping away before a move worth $120,000 appeared on the chart level 3 records every order added, modified, filled, or canceled, along with its price, size, side, and location in the book the team turned that stream into rates of additions, cancellations, and trades per second then they built two signals the continuation model looked for bids entering much faster than asks while the spread stayed tight and activity remained high the reversal model waited until price became stretched then watched bid cancellations jump above the 95th percentile of the previous 60 seconds while new buyers stopped replacing them the candle could still look bullish but the market makers underneath it had already started backing away they do not want thousands of bids filled as price falls and leaves the desk holding millions in unwanted long exposure so they cancel first the visible reversal comes later with $30 million positioned around the open, a 0.4% move equals roughly $120,000 the edge was not predicting every tick it was noticing the exact moment liquidity stopped supporting the price this is the skill firms pay six figures for: turning hundreds of thousands of invisible market events into one signal worth risking capital on I broke down how to build that skill from zero in 16 weeks bookmark this lesson then read the full quant roadmap below ↓

A senior quant at an $18.4 billion fund showed his team a study of 50 US stocks and asked: “why are we trading volume when the order book explains price better?” the answer led them into level 3 data during the same 30 seconds that an opening candle printed five values the exchange produced more than 215,000 messages hidden inside them were market makers adding liquidity, canceling bids, and stepping away before a move worth $120,000 appeared on the chart level 3 records every order added, modified, filled, or canceled, along with its price, size, side, and location in the book the team turned that stream into rates of additions, cancellations, and trades per second then they built two signals the continuation model looked for bids entering much faster than asks while the spread stayed tight and activity remained high the reversal model waited until price became stretched then watched bid cancellations jump above the 95th percentile of the previous 60 seconds while new buyers stopped replacing them the candle could still look bullish but the market makers underneath it had already started backing away they do not want thousands of bids filled as price falls and leaves the desk holding millions in unwanted long exposure so they cancel first the visible reversal comes later with $30 million positioned around the open, a 0.4% move equals roughly $120,000 the edge was not predicting every tick it was noticing the exact moment liquidity stopped supporting the price this is the skill firms pay six figures for: turning hundreds of thousands of invisible market events into one signal worth risking capital on I broke down how to build that skill from zero in 16 weeks bookmark this lesson then read the full quant roadmap below ↓

Sammy

75,079 views • 26 days ago

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$96.31) > in 5 hours it showed +$579 PnL > current balance: +$3,799.73 Cost: 10M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below. Good luck!

I asked Claude Fable 5 (Extra High) to build an arb bot for Polymarket. One rule: trade only when YES + NO in 2 hours it almost doubled it (+$96.31) > in 5 hours it showed +$579 PnL > current balance: +$3,799.73 Cost: 10M tokens. Here's how it works and why: Every BTC Up/Down market on Polymarket has exactly two outcomes. YES and NO. When the market resolves, one pays $1. The other pays $0. That means owning BOTH sides should always cost exactly $1. But markets aren't perfect. Sometimes YES trades at $0.48 while NO trades at $0.49. Together that's only $0.97 so the bot instantly buys both. A position worth $1... for just $0.97. And Claude knows it, this is the simplest arb that exists. You can paste this to it and use this logic in your prompt. This $0.03 difference is locked in regardless of whether Bitcoin pumps, dumps, or goes sideways. No prediction required. And difficult part isn't finding the opportunity, it's execution. These pricing gaps usually disappear in seconds. If one order fills but the other doesn't, the trade can become a loss. So my bot constantly scans every BTC market, checks fees, validates liquidity, places both orders almost simultaneously and skips what isn't worth the risk. It's less like trading and more like catching tiny accounting mistakes before everyone else notices them. Funny enoughh, the hardest part was not writing arb logic. It was making the execution reliable enough that free money actually stayed free. This completely changed how I think about trading. What's the point of it if you can just fill mispriced BTC markets?? Automatically. The biggest edge is getting AI to execute simple ideas faster and more consistently than any human ever could. Shared the exact build in my last article, leaving it below. Good luck!

Oracle Boar

154,557 views • 1 month ago

My Polymarket bot was profitable But I kept losing to one thing I couldn't see. Someone always knew before me. Political markets. Price sitting at 0.52 for 3 days. Then in 15 minutes - snaps to 0.71. Huge wallet. No news. I'm already on the wrong side. It happened 6 times in one month. Cost me $4,200. Then I found a 77-year-old formula that explained exactly what was happening. Claude Shannon. MIT professor. Invented the math behind every ZIP file, every modem, every JPEG on Earth. In 1986, Barron's ranked 1,026 mutual funds. Shannon's personal portfolio beat 1,025 of them. He wasn't a trader. He was measuring something nobody else measured. Edge in bits. Not dollars. Bits. His student took it to Vegas first. Won $11,000 in a weekend. Then to Wall Street. 19% annually. 20 years. Zero losing seasons. I built three tools from their framework. Tool 1 - KL-divergence. Scans every open Polymarket market. Returns one number: how many bits of edge you have vs the market price. Below 0.05 - skip. Above 0.10 - trade. Simple filter. Brutal results. D_KL = p · log₂(p/q) + (1-p) · log₂((1-p)/(1-q)) Tool 2 - Max-entropy fusion. 4 signals pointing in different directions. This collapses them into one honest probability - without overfitting. Mathematically impossible to overfit. Jaynes proved it in 1957. Tool 3 - Entropy collapse detector. This one changed everything. Insiders don't announce themselves. But entropy does. Normal market: entropy drifts slowly. Insider enters: entropy collapses. Fast. Sharp. Before any news. Alert if |dH/dt| > 3 · σH Oct 13, 2024. "Will Trump win Pennsylvania?" Sitting at 0.52. One wallet opens a massive position. Entropy drops 0.093 bits in 15 minutes - a 7-sigma event. No public news. 47 minutes later - the news breaks. That wallet nets $340,000. The SEC uses this exact method on equities. Almost nobody runs it on Polymarket. I ran it. Week 1: calibrating signals. +$800. Week 2: first insider alerts firing. +$3,400. Week 3: stopped fighting the tape, followed entropy. +$5,900. Week 4: fully automated. +$7,200. +$17,300. Four weeks. A formula from 1948. Copytrade here: Everyone watches price. Quants watch entropy. The insider arrives an hour before the news. Now I see them coming.

My Polymarket bot was profitable But I kept losing to one thing I couldn't see. Someone always knew before me. Political markets. Price sitting at 0.52 for 3 days. Then in 15 minutes - snaps to 0.71. Huge wallet. No news. I'm already on the wrong side. It happened 6 times in one month. Cost me $4,200. Then I found a 77-year-old formula that explained exactly what was happening. Claude Shannon. MIT professor. Invented the math behind every ZIP file, every modem, every JPEG on Earth. In 1986, Barron's ranked 1,026 mutual funds. Shannon's personal portfolio beat 1,025 of them. He wasn't a trader. He was measuring something nobody else measured. Edge in bits. Not dollars. Bits. His student took it to Vegas first. Won $11,000 in a weekend. Then to Wall Street. 19% annually. 20 years. Zero losing seasons. I built three tools from their framework. Tool 1 - KL-divergence. Scans every open Polymarket market. Returns one number: how many bits of edge you have vs the market price. Below 0.05 - skip. Above 0.10 - trade. Simple filter. Brutal results. D_KL = p · log₂(p/q) + (1-p) · log₂((1-p)/(1-q)) Tool 2 - Max-entropy fusion. 4 signals pointing in different directions. This collapses them into one honest probability - without overfitting. Mathematically impossible to overfit. Jaynes proved it in 1957. Tool 3 - Entropy collapse detector. This one changed everything. Insiders don't announce themselves. But entropy does. Normal market: entropy drifts slowly. Insider enters: entropy collapses. Fast. Sharp. Before any news. Alert if |dH/dt| > 3 · σH Oct 13, 2024. "Will Trump win Pennsylvania?" Sitting at 0.52. One wallet opens a massive position. Entropy drops 0.093 bits in 15 minutes - a 7-sigma event. No public news. 47 minutes later - the news breaks. That wallet nets $340,000. The SEC uses this exact method on equities. Almost nobody runs it on Polymarket. I ran it. Week 1: calibrating signals. +$800. Week 2: first insider alerts firing. +$3,400. Week 3: stopped fighting the tape, followed entropy. +$5,900. Week 4: fully automated. +$7,200. +$17,300. Four weeks. A formula from 1948. Copytrade here: Everyone watches price. Quants watch entropy. The insider arrives an hour before the news. Now I see them coming.

Trackmind

13,229 views • 3 months ago

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

Your trading strategy didn't break. The market it was built for quietly stopped existing. Read that twice. It's most of why 89% of retail finished 2025 in the red. There's now an app that does the entire job of a $400,000 quant. You type a trading idea in plain English. It writes the code, backtests 5 years in 12 seconds, runs thousands of simulations, and tells you cold whether your edge is dead or the regime just changed. No code. No Python. No $25,000 terminal. 20,000 already inside. Waitlist stops at 25,000: That distinction is the whole game, and you never had a way to see it. Every strategy is a bet that one thing stays true. Momentum bets trends continue. Mean reversion bets ranges hold. When the regime flips, the assumption dies and your strategy bleeds with nothing wrong in the code. You stare at the logic for a month and never find the bug, because there isn't one. So you delete it, or refit it to the last drawdown and build something that would have survived the pain you already felt and nothing coming next. The desks never had that problem. 92% of institutional volume is automated. Only 45% of retail is. They test 100 strategies for every 1 you test by hand, and kill 97 of them on purpose, because they can tell a dead edge from a normal drawdown. Now that exact loop costs $0. One hypothesis used to cost a fund $87,500 to test. With Horizon you get unlimited, in seconds, and a winner deploys live in 90 seconds and runs without your hands on it.

cvxv666

40,765 views • 2 months ago

The 5-Minute Polymarket Sniper: Making $189,861 on "Boring" Bitcoin Fluctuations.. Profile Statistics: > Total Profit: $189,861.72 > The Biggest Win: $13.3K > Total Predictions: 95,647 > Account Created: April 2026 > Name: 0x50f7 The Strategy: 5-Minute Scalping 1) Buying the Undervalued: He enters ultra-short positions (5-minute "Up or Down" BTC windows) when the market panics or misprices the odds of an outcome. Entries are caught at heavily discounted prices, ranging from 1¢ to 39¢ per share. 2) Mathematical Edge: Out of 95,000+ trades, he captures spreads and liquidity imbalances. Unlike standard traders trying to predict crypto prices a week in advance, he simply exploits real-time order book inefficiencies. 3) Zero Emotion: Pure short-term execution, buying heavily discounted probabilities just minutes before the candle closes. Top Deals from the Dashboard: > June 6 (12:45 AM - 12:50 AM ET): Bought "Down" at 1.1¢ -> Won $13,450.72 (+$13,301.35 / +8,904.95%) > July 11 (1:30 PM - 1:45 PM ET): Bought "Up" at 3¢ -> Won $9,000.00 (+$8,730.00 / +3,233.44%) > May 21 (1:45 PM - 1:50 PM ET): Ultimate sniper shot in "Up" at 1.1¢ -> Won $8,434.21 (+$8,345.23 / +9,378.67%) > April 22 (1:30 PM - 1:35 PM ET): Deep discount entry in "Down" at 1¢ ->Won $6,876.28 (+$6,807.52 / +9,900.00%) Why does this work? On ultra-short (5-minute) prediction windows, retail traders and basic algorithms constantly create order book inefficiencies by panic-selling or overreacting to minor Bitcoin ticks. The trader locks in absurd risk-to-reward ratios (1:50 to 1:100). The Secret Driver: AI Prompts for Edge Scanning Analyzing his trading style, manually tracking thousands of micro-events and instantly spotting mispriced odds is nearly impossible. It is highly likely that he was backed by specialized LLM prompts (similar to the News-to-Edge Scanner and Catalyst Chain Reactor): 1) Instant Catalyst Assessment: Prompts allow the AI to filter out real-time news noise and calculate mathematically sound probability models faster than the crowd. 2) Identifying Contrarian Angles: The algorithm flags precise moments where crowd sentiment strays too far from objective facts. When the AI flags an inefficiency, all the trader has to do is pull the trigger and walk away with up to +9,900% ROI in just 300 seconds. Do you think this is driven by custom AI scripts or pure market microstructure intuition?

Ridark

19,083 views • 21 days ago

Claude and a free weather API will earn you $100k+. Success rate for beginners: 80%. Complete guide and algorithm for building Polymarket weather trading bot. Simple logic, a low entry budget and high ROI -that’s why weather bots are so clean. Onchain proof these bots exist: 1st bot: 2nd bot: I verified their profitability by myself copying every trade - each bot's win rate over time ranges from 80 to 90%. I grew my starting capital by +40% in just one week. You can copy their trades and see for yourself in two clicks through this bot: The alpha is simple: you're not trading weather. You're trading other people's ignorance. Gap between what the crowd prices and what 51 ensemble models say. Polymarket asks: "Will Atlanta hit 95°F tomorrow?" Normies bet on vibes. You bet on math. The core tool: Open-Meteo API. Free. No key needed. 51-model ensemble. Clean JSON. Cooked and ready. Update every 30 min. Hardcode your city coordinates - don't waste time on geocoding at runtime. This single endpoint beats most paid tools for what Polymarket actually needs. The edge in one sentence: Market is heavy on 16°C. Your 51-model ensemble points at 19°C. That's your trade. Find that gap systematically across every city market, every day - and you have a scanner. That's what separates consistent traders from gamblers. How to start: - Week 1: Open-Meteo + tropicaltidbits. Pick one city market. Track model vs market price daily. Don't trade yet — just watch where you'd have been right. - Weeks 2–3: Automate the pull. Log ensemble divergences. Build the scanner. - Week 4: Now you have an edge. Trade it. Most people want to skip to week 4. That's exactly why most people lose. Now you have the algorithm framework plus a complete guide to get started. All that's left is to actually do it. Bookmark this post so you can come back to it when you start building the bot.

Claude and a free weather API will earn you $100k+. Success rate for beginners: 80%. Complete guide and algorithm for building Polymarket weather trading bot. Simple logic, a low entry budget and high ROI -that’s why weather bots are so clean. Onchain proof these bots exist: 1st bot: 2nd bot: I verified their profitability by myself copying every trade - each bot's win rate over time ranges from 80 to 90%. I grew my starting capital by +40% in just one week. You can copy their trades and see for yourself in two clicks through this bot: The alpha is simple: you're not trading weather. You're trading other people's ignorance. Gap between what the crowd prices and what 51 ensemble models say. Polymarket asks: "Will Atlanta hit 95°F tomorrow?" Normies bet on vibes. You bet on math. The core tool: Open-Meteo API. Free. No key needed. 51-model ensemble. Clean JSON. Cooked and ready. Update every 30 min. Hardcode your city coordinates - don't waste time on geocoding at runtime. This single endpoint beats most paid tools for what Polymarket actually needs. The edge in one sentence: Market is heavy on 16°C. Your 51-model ensemble points at 19°C. That's your trade. Find that gap systematically across every city market, every day - and you have a scanner. That's what separates consistent traders from gamblers. How to start: - Week 1: Open-Meteo + tropicaltidbits. Pick one city market. Track model vs market price daily. Don't trade yet — just watch where you'd have been right. - Weeks 2–3: Automate the pull. Log ensemble divergences. Build the scanner. - Week 4: Now you have an edge. Trade it. Most people want to skip to week 4. That's exactly why most people lose. Now you have the algorithm framework plus a complete guide to get started. All that's left is to actually do it. Bookmark this post so you can come back to it when you start building the bot.

cvxv666

50,799 views • 4 months ago

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Stanford professor just gave away the entire foundation of how AI Agents & automation actually works. 1-hour lecture. Tool calling. Multi-step workflows. Planning. Reflection. SAVE this to watch this before you open Netflix tonight. More valuable than 6 months of copying Make and n8n tutorials, for building Ai Agents Most people learn by copying tutorials blindly. Stanford teaches you WHY agents work the way they do. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward instead of just entertaining you for 30 seconds. ↓ Why your automations keep breaking. You copied a Make tutorial. Built the exact workflow. Worked for a week. Then the API changed. The trigger failed. An edge case broke everything. You had no idea how to fix it. Because you never understood why it worked. You were copying keystrokes. The people shipping real automation were understanding architecture. ↓ What Stanford actually teaches. Tool calling: how an agent decides which tool to use by scoring each option against the current task state, not just matching keywords. ReAct loop: the agent reasons, acts, observes, then reasons again. Break this cycle and your workflow fails silently. Planning vs execution: why agents that plan all steps upfront break on dynamic inputs, and why iterative planners survive production. Memory architecture: short-term context for the current task, long-term vector memory for patterns. Most automations fail because they confuse the two. Reflection: how agents catch their own errors by evaluating outputs against original intent before moving to the next step. Tool composition: why chaining 10 tools blindly creates cascading failures, and how to structure dependencies so one broken node doesn't kill the whole workflow. This is the foundation behind every automation that actually works. Not prompting tricks. Not "10 best AI tools" reels. Actual architecture. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward. ↓ Your weekend plan. Tonight: watch the Stanford lecture. 1 hour. Saturday to Sunday: build 3 projects applying what you learned. Next 2 weekends: 6 more projects. 9 projects. 2 weeks. APIs, webhooks, LLM integration, real workflows. No theory. Just build. ↓ Stanford Agentic AI lecture: free on YouTube. Watch it this weekend or buy another $500 "AI automation course" in 2027 that teaches less than this one free lecture. Bookmark. Watch tonight. Follow Himanshu Kumar for more high-signal content that actually moves your skills forward.

Himanshu Kumar

28,120 views • 3 months ago

A Yale professor recorded one semester of game theory in 2007 that quietly explains how Google, eBay, and every online ad exchange on earth extracts over $500 billion a year from advertisers. Yale charges $86,000 a year to sit in that classroom. He posted every lecture to Yale Open Courses in 2008 for nothing. Millions of people have watched. Almost no retail trader, founder, or negotiator has used the framework. His name is Ben Polak. He is a Yale professor, former Provost of Yale, and one of the most watched university lecturers in the history of the internet. The 62-minute clip in this video is one lecture from his course ECON 159: Game Theory, taught in the fall of 2007 to a packed auditorium of Yale undergraduates. Polak covers the entire mathematical foundation of strategic decision-making in 24 lectures. Dominant strategies. If one option beats every other option regardless of what your opponent does, pick it. Everything else in game theory is a workaround for the fact that dominant strategies almost never exist in real games. Nash equilibria. The point where no player can improve by unilaterally changing strategy. Almost every price on earth is a Nash equilibrium. Every wage negotiation, every auction bid, every trade in a market with millions of participants converges to one. Auctions. The mathematical difference between first-price sealed-bid, second-price sealed-bid, English ascending, and Dutch descending auctions is worth billions of dollars a year to the entity running them. Google Ads is a modified second-price auction. eBay is an English ascending auction with a proxy bid. Treasury bill sales are Dutch descending. Each format extracts different value from bidders. Almost no bidder knows which format they are in. Evolutionary strategies. Why cooperation sometimes wins over defection in repeated games. Why the tit-for-tat rule outperformed every complex algorithm in Robert Axelrod's famous 1980 tournament. Why almost every stable business relationship on earth is a tit-for-tat game. Every hedge fund pays entry-level quants to know this material. Every big tech company hires PhD game theorists to design its pricing pages. Every corporate M&A negotiation is priced by consultants who charge $2 million a deal to apply the same equations. "Never play a strictly dominated strategy." That is the first rule Polak teaches every semester. He calls it the only rule game theory is entirely certain about. Retail traders violate it every time they add to a losing position because taking the loss feels worse than doubling down. The lectures are free on Yale Open Courses. The problem sets and exams from 2007 are still online. Every equation Polak derives fits in under thirty pages of a textbook. The math is free. The willingness to sit through 24 hours of game theory before opening a bid box, a trading terminal, or a negotiation is a much rarer commodity than the confidence to walk in without it.

A Yale professor recorded one semester of game theory in 2007 that quietly explains how Google, eBay, and every online ad exchange on earth extracts over $500 billion a year from advertisers. Yale charges $86,000 a year to sit in that classroom. He posted every lecture to Yale Open Courses in 2008 for nothing. Millions of people have watched. Almost no retail trader, founder, or negotiator has used the framework. His name is Ben Polak. He is a Yale professor, former Provost of Yale, and one of the most watched university lecturers in the history of the internet. The 62-minute clip in this video is one lecture from his course ECON 159: Game Theory, taught in the fall of 2007 to a packed auditorium of Yale undergraduates. Polak covers the entire mathematical foundation of strategic decision-making in 24 lectures. Dominant strategies. If one option beats every other option regardless of what your opponent does, pick it. Everything else in game theory is a workaround for the fact that dominant strategies almost never exist in real games. Nash equilibria. The point where no player can improve by unilaterally changing strategy. Almost every price on earth is a Nash equilibrium. Every wage negotiation, every auction bid, every trade in a market with millions of participants converges to one. Auctions. The mathematical difference between first-price sealed-bid, second-price sealed-bid, English ascending, and Dutch descending auctions is worth billions of dollars a year to the entity running them. Google Ads is a modified second-price auction. eBay is an English ascending auction with a proxy bid. Treasury bill sales are Dutch descending. Each format extracts different value from bidders. Almost no bidder knows which format they are in. Evolutionary strategies. Why cooperation sometimes wins over defection in repeated games. Why the tit-for-tat rule outperformed every complex algorithm in Robert Axelrod's famous 1980 tournament. Why almost every stable business relationship on earth is a tit-for-tat game. Every hedge fund pays entry-level quants to know this material. Every big tech company hires PhD game theorists to design its pricing pages. Every corporate M&A negotiation is priced by consultants who charge $2 million a deal to apply the same equations. "Never play a strictly dominated strategy." That is the first rule Polak teaches every semester. He calls it the only rule game theory is entirely certain about. Retail traders violate it every time they add to a losing position because taking the loss feels worse than doubling down. The lectures are free on Yale Open Courses. The problem sets and exams from 2007 are still online. Every equation Polak derives fits in under thirty pages of a textbook. The math is free. The willingness to sit through 24 hours of game theory before opening a bid box, a trading terminal, or a negotiation is a much rarer commodity than the confidence to walk in without it.

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51,235 views • 3 days ago