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Taleb: "standard deviation is not how much something moves on average." ask anyone - even statisticians, even government agencies - and that's exactly the wrong definition you'll get back. what they're describing is mean absolute deviation. real standard deviation squares the moves first - which quietly hands almost all... show more

0xDipper

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412,623 次观看 • 1 个月前 •via X (Twitter)

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quant funds spend millions hiring people who know a formula you learned in 10th grade and never applied to a price chart y = mx + b. that's it linear regression - same math from high school most traders learned, ignored, and forgot existed what quants actually figured out: asset prices don't move randomly, they drift and revert with measurable slope regression separates signal from noise. slope tracks trend, residuals measure the deviation when deviation gets large enough, that's the trade - prices tend to snap back to the line quants call it "mean reversion." retail calls it "a dip" actual workflow: > pull 5 years of daily returns > run OLS, extract beta and residuals > z-score the residuals > entries at -2 standard deviations, exits at 0 retail draws trend lines by hand and calls it analysis Bookmark this ^ data is public, math is free, approach has been in papers since the 1980s 99% of traders have never built one signal this way it was never an information gap - always been a methodology gap, and that's the kind that compounds

quant funds spend millions hiring people who know a formula you learned in 10th grade and never applied to a price chart y = mx + b. that's it linear regression - same math from high school most traders learned, ignored, and forgot existed what quants actually figured out: asset prices don't move randomly, they drift and revert with measurable slope regression separates signal from noise. slope tracks trend, residuals measure the deviation when deviation gets large enough, that's the trade - prices tend to snap back to the line quants call it "mean reversion." retail calls it "a dip" actual workflow: > pull 5 years of daily returns > run OLS, extract beta and residuals > z-score the residuals > entries at -2 standard deviations, exits at 0 retail draws trend lines by hand and calls it analysis Bookmark this ^ data is public, math is free, approach has been in papers since the 1980s 99% of traders have never built one signal this way it was never an information gap - always been a methodology gap, and that's the kind that compounds

Livsun

28,936 次观看 • 2 个月前

No author has shaped my worldview more than Nassim Taleb (Nassim Nicholas Taleb). I stumbled on The Black Swan eight years ago, then inhaled the rest of the Incerto. I've increasingly come to appreciate the importance of its ideas to prolonging the human story. Was an honour to speak with Nassim on the podcast. Hard to summarise our conversation, but the timestamps below capture the gist. Enjoy! Timestamps: (0:00:55) - Heuristics for knowing when you're in Mediocristan versus Extremistan. (0:06:06) - Are certain tail exponents intrinsic? (0:10:30) - Why hasn't Universa's tail hedging strategy now been fully priced in? (0:11:52) - Does the power law distribution of startup returns mean VCs should concentrate their bets, or spray and pray? (0:15:20) - Nassim's 30-minute take on the field of behavioural economics. (0:48:57) - Nassim's 20-minute take on superforecasting. (1:11:03) - The Precautionary Principle and AI. (1:17:28) - What are LLMs doing? (1:23:10) - War, violence, & "the empirical mean is not the real mean". (1:39:17) - Covid, & how Western governments think about tail risk. (1:43:38) - What's the most important thing people in social science get wrong about correlation? (1:52:58) - How does Nassim explain the perspicacity of the Russian school of probability? (1:55:56) - Why doesn't Hayek's knowledge argument extend to prediction markets? (1:58:26) - If mean absolute deviation is a better measure than standard deviation, why has the latter become commonplace? (2:01:09) - Nassim's next book, and what he's up to at the moment.

No author has shaped my worldview more than Nassim Taleb (Nassim Nicholas Taleb). I stumbled on The Black Swan eight years ago, then inhaled the rest of the Incerto. I've increasingly come to appreciate the importance of its ideas to prolonging the human story. Was an honour to speak with Nassim on the podcast. Hard to summarise our conversation, but the timestamps below capture the gist. Enjoy! Timestamps: (0:00:55) - Heuristics for knowing when you're in Mediocristan versus Extremistan. (0:06:06) - Are certain tail exponents intrinsic? (0:10:30) - Why hasn't Universa's tail hedging strategy now been fully priced in? (0:11:52) - Does the power law distribution of startup returns mean VCs should concentrate their bets, or spray and pray? (0:15:20) - Nassim's 30-minute take on the field of behavioural economics. (0:48:57) - Nassim's 20-minute take on superforecasting. (1:11:03) - The Precautionary Principle and AI. (1:17:28) - What are LLMs doing? (1:23:10) - War, violence, & "the empirical mean is not the real mean". (1:39:17) - Covid, & how Western governments think about tail risk. (1:43:38) - What's the most important thing people in social science get wrong about correlation? (1:52:58) - How does Nassim explain the perspicacity of the Russian school of probability? (1:55:56) - Why doesn't Hayek's knowledge argument extend to prediction markets? (1:58:26) - If mean absolute deviation is a better measure than standard deviation, why has the latter become commonplace? (2:01:09) - Nassim's next book, and what he's up to at the moment.

Joseph Noel Walker

577,821 次观看 • 1 年前

Nassim Taleb asked a room full of quants: to trust an average, a normal bell curve needs ~30 data points. how many does an 80/20 "fat-tailed" world need? people guessed 200. 1,000. the real answer: 100 trillion. that's 10¹⁴ - because in fat-tailed data, one rare event dwarfs everything else, and until it shows up your sample is a lie. "you observe, you observe, you observe… until the crash." worse, the deception only runs one way. a wild distribution can disguise itself as calm - a calm one can't fake being wild. so the data almost always fools you downward, into thinking there's less risk than there is. the fix: stop estimating the average from the average. it doesn't work where it matters. ~25-min talk, free. Taleb on why your data is quietly lying about risk ↓

Nassim Taleb asked a room full of quants: to trust an average, a normal bell curve needs ~30 data points. how many does an 80/20 "fat-tailed" world need? people guessed 200. 1,000. the real answer: 100 trillion. that's 10¹⁴ - because in fat-tailed data, one rare event dwarfs everything else, and until it shows up your sample is a lie. "you observe, you observe, you observe… until the crash." worse, the deception only runs one way. a wild distribution can disguise itself as calm - a calm one can't fake being wild. so the data almost always fools you downward, into thinking there's less risk than there is. the fix: stop estimating the average from the average. it doesn't work where it matters. ~25-min talk, free. Taleb on why your data is quietly lying about risk ↓

0xDipper

203,816 次观看 • 1 个月前

Palo Alto Networks' Nikesh Arora on how AI solves one of the biggest risks businesses face: hiring B players. "What AI is doing for me internally is democratizing intelligence." "The biggest risk in businesses is, you hire 100 marketing people. Not all of them are equally good. So you gravitate toward the 10 good ones, and you gotta find a way to deal with the other end of the spectrum, and the 80 in the middle." "The good news is if I train my model right, with all my marketing data, I can increase the average intelligence of all 100 of them to 95%. Then I just need the judgment of the top 5%." "It's the rising tide lifting all boats from an average intelligence perspective. If I can get the outcomes of 3,000 customer support people to be amazing, and there's no disparity or standard deviation, I become highly effective and efficient."

Palo Alto Networks' Nikesh Arora on how AI solves one of the biggest risks businesses face: hiring B players. "What AI is doing for me internally is democratizing intelligence." "The biggest risk in businesses is, you hire 100 marketing people. Not all of them are equally good. So you gravitate toward the 10 good ones, and you gotta find a way to deal with the other end of the spectrum, and the 80 in the middle." "The good news is if I train my model right, with all my marketing data, I can increase the average intelligence of all 100 of them to 95%. Then I just need the judgment of the top 5%." "It's the rising tide lifting all boats from an average intelligence perspective. If I can get the outcomes of 3,000 customer support people to be amazing, and there's no disparity or standard deviation, I become highly effective and efficient."

TBPN

57,070 次观看 • 2 个月前

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Tom Yeh

17,011 次观看 • 19 天前

I gave Claude historical weather API vs Polymarket discrepancies and said: "find the gap where the forecast shifts but the price lags" it built the Weather Arb Terminal this isn’t predicting the rain it’s exploiting the latency between a NOAA satellite update and a retail trader’s reaction the screen shows engine in full execution: > 247 active contracts scanned > 86.8% accuracy on deviation spikes > +$5,475.80 in a single session > 462 trades. Zero opinions. the logic is a 4-stage execution pipe: scan → discrepancy → filter → execute 18:59:17 - NYC Frost forecast shifts from 41% to 58% on NOAA/AccuWeather - market is asleep at $0.44. 17-point edge - the bot hits the bid before the headline even hits the feeds - 19:02:14 - Market catches up. Price spikes to $0.61 +$534.20 in 3 minutes it doesn't care if it's snowing or sunny It only cares that the probability changed and the price didn't while the crowd debates climate change, the network reads standard deviations > fire rate: 41 Hz > latency: 3ms > edge: 16pt avg the network doesn’t get tired. it doesn’t hope. it waits for the math to break, then it collects. copy the engine here: the forecast is noise. the deviation is the signal

I gave Claude historical weather API vs Polymarket discrepancies and said: "find the gap where the forecast shifts but the price lags" it built the Weather Arb Terminal this isn’t predicting the rain it’s exploiting the latency between a NOAA satellite update and a retail trader’s reaction the screen shows engine in full execution: > 247 active contracts scanned > 86.8% accuracy on deviation spikes > +$5,475.80 in a single session > 462 trades. Zero opinions. the logic is a 4-stage execution pipe: scan → discrepancy → filter → execute 18:59:17 - NYC Frost forecast shifts from 41% to 58% on NOAA/AccuWeather - market is asleep at $0.44. 17-point edge - the bot hits the bid before the headline even hits the feeds - 19:02:14 - Market catches up. Price spikes to $0.61 +$534.20 in 3 minutes it doesn't care if it's snowing or sunny It only cares that the probability changed and the price didn't while the crowd debates climate change, the network reads standard deviations > fire rate: 41 Hz > latency: 3ms > edge: 16pt avg the network doesn’t get tired. it doesn’t hope. it waits for the math to break, then it collects. copy the engine here: the forecast is noise. the deviation is the signal

Hanako

39,093 次观看 • 4 个月前

Sam Altman: “The most underrated quality of all is being really determined” “The most underrated quality of all is being really determined. This is more important than being smart. This is more important than having a network. This is more important than a great idea.” As Sam explains: “The hardest thing about starting a company is the level and frequency of bad stuff that happens to you… So much of being a successful entrepreneur is just not giving up. We have funded people who had a great idea, perfect background on paper, a great product, and still failed—and it has usually been that they’re insufficiently determined.” He continues: “Of course you need a good product and a good market and to be smart, but that’s really obvious. The degree to which being a 3- or 4-standard deviation outlier on determination as a required skill of a CEO is not something that was obvious to me when I started.”

Sam Altman: “The most underrated quality of all is being really determined” “The most underrated quality of all is being really determined. This is more important than being smart. This is more important than having a network. This is more important than a great idea.” As Sam explains: “The hardest thing about starting a company is the level and frequency of bad stuff that happens to you… So much of being a successful entrepreneur is just not giving up. We have funded people who had a great idea, perfect background on paper, a great product, and still failed—and it has usually been that they’re insufficiently determined.” He continues: “Of course you need a good product and a good market and to be smart, but that’s really obvious. The degree to which being a 3- or 4-standard deviation outlier on determination as a required skill of a CEO is not something that was obvious to me when I started.”

Startup Archive

32,513 次观看 • 2 年前

Sam Altman: “The most underrated quality of all is being really determined” “The most underrated quality of all is being really determined. This is more important than being smart. This is more important than having a network. This is more important than a great idea.” As Sam explains: “The hardest thing about starting a company is the level and frequency of bad stuff that happens to you… So much of being a successful entrepreneur is just not giving up. When we have funded people who had a great idea, perfect background on paper, a great product, and it still failed — it has usually been that they’re insufficiently determined. I think this is the most important, non-obvious skill of a founder.” He continues: “Of course you need a good product and a good market and to be smart, but that’s really obvious. But the degree to which being a 3- or 4-standard deviation outlier on determination as a required skill of a CEO is not something that was obvious to me when I started.” Video source: Talks at Google (2017)

Sam Altman: “The most underrated quality of all is being really determined” “The most underrated quality of all is being really determined. This is more important than being smart. This is more important than having a network. This is more important than a great idea.” As Sam explains: “The hardest thing about starting a company is the level and frequency of bad stuff that happens to you… So much of being a successful entrepreneur is just not giving up. When we have funded people who had a great idea, perfect background on paper, a great product, and it still failed — it has usually been that they’re insufficiently determined. I think this is the most important, non-obvious skill of a founder.” He continues: “Of course you need a good product and a good market and to be smart, but that’s really obvious. But the degree to which being a 3- or 4-standard deviation outlier on determination as a required skill of a CEO is not something that was obvious to me when I started.” Video source: Talks at Google (2017)

Startup Archive

39,199 次观看 • 11 个月前

Palantir co-founder Joe Lonsdale on what actually separates Peter Thiel, Alex Karp and Elon Musk from everyone else - how they think, how they pick people, and why it keeps working on Musk: "Elon is the most alpha of all of my friends - I'm willing to go three or four standard deviations too hard in terms of working too hard and pushing people and being bold - Elon's my only friend who's clearly a standard deviation further than me" "Peter will find the view that others are missing and put a lot of weight behind it - he'll identify the one factor that matters that people aren't thinking about" on Karp: "there's a weird form of wisdom where he'll interact with you and know tons of things about you, and know if he wants to hire you or not - it's like a magic trick" "the guy who started Cognition won the gold medal in programming three years in a row - then hired 20 other kids who were the top gold and silver medalists of these Olympiads - it's the fastest growing company I've ever been in" bookmark & watch the full conversation ↓

Palantir co-founder Joe Lonsdale on what actually separates Peter Thiel, Alex Karp and Elon Musk from everyone else - how they think, how they pick people, and why it keeps working on Musk: "Elon is the most alpha of all of my friends - I'm willing to go three or four standard deviations too hard in terms of working too hard and pushing people and being bold - Elon's my only friend who's clearly a standard deviation further than me" "Peter will find the view that others are missing and put a lot of weight behind it - he'll identify the one factor that matters that people aren't thinking about" on Karp: "there's a weird form of wisdom where he'll interact with you and know tons of things about you, and know if he wants to hire you or not - it's like a magic trick" "the guy who started Cognition won the gold medal in programming three years in a row - then hired 20 other kids who were the top gold and silver medalists of these Olympiads - it's the fastest growing company I've ever been in" bookmark & watch the full conversation ↓

Сarm1ne

206,187 次观看 • 15 天前

Right now, the agitators think Trump is about to back down because of "poll numbers" according to Nick Sortor and Cam Higby 🇺🇸 who are on the ground in Minneapolis: "You have to make an example out of this place ... right now they firmly believe they're going to get away with all of this, and that ICE is going to back down. "You know, they're talking about these poll numbers, which I don't even believe are real poll numbers. Because if you talk to actual people on the street, they don't want to live next to Venezuelan rapists." This is a critical test of the administration's will. We must have ZERO tolerance for lawlessness. Squash the agitators quickly, the rest will get the memo, and the news cycle moves on.

Right now, the agitators think Trump is about to back down because of "poll numbers" according to Nick Sortor and Cam Higby 🇺🇸 who are on the ground in Minneapolis: "You have to make an example out of this place ... right now they firmly believe they're going to get away with all of this, and that ICE is going to back down. "You know, they're talking about these poll numbers, which I don't even believe are real poll numbers. Because if you talk to actual people on the street, they don't want to live next to Venezuelan rapists." This is a critical test of the administration's will. We must have ZERO tolerance for lawlessness. Squash the agitators quickly, the rest will get the memo, and the news cycle moves on.

Andrew Kolvet

79,838 次观看 • 7 个月前

John Wooden shares one of the greatest definitions of success. It's one of the greatest I've ever seen - because it applies to everyone. “Success is peace of mind attained only through self-satisfaction in knowing you made the effort to become the best of which you are capable.” His definition is about BECOMING YOUR BEST. That's why it works for everyone. It levels the playing field. We're not all born with the same resources, talents, or opportunities - but we all have the ability to give our best. The goal is to be better than you were yesterday. Success isn't a title you earn. It's a standard that you choose every day. Ask yourself: "Did I grow today? Am I working to be my best?" John Wooden's definition cuts through the noise and brings it back to what you can control: Effort. Attitude. Growth.

John Wooden shares one of the greatest definitions of success. It's one of the greatest I've ever seen - because it applies to everyone. “Success is peace of mind attained only through self-satisfaction in knowing you made the effort to become the best of which you are capable.” His definition is about BECOMING YOUR BEST. That's why it works for everyone. It levels the playing field. We're not all born with the same resources, talents, or opportunities - but we all have the ability to give our best. The goal is to be better than you were yesterday. Success isn't a title you earn. It's a standard that you choose every day. Ask yourself: "Did I grow today? Am I working to be my best?" John Wooden's definition cuts through the noise and brings it back to what you can control: Effort. Attitude. Growth.

Coach AJ 🎯 Mental Fitness

38,856 次观看 • 1 个月前

WOW: Arizona is on the verge of passing a Constitutional Amendment to prohibit medical mandates. “Not just for vaccines.” “For all medical choices.” “It would be the first and only state that has that.” And it has the full support of gubernatorial candidate Rep. Andy Biggs. It already passed the State House. If it passes the State Senate, it will become a referendum on the 2026 ballot. Which means Governor Katie Hobbs could not veto it. She just vetoed the Arizona Medical Freedom Act, which would have banned almost all medical mandates statewide. So health freedom supporters moved to bypass her veto with a Constitutional Amendment. Rep. Biggs: “Can you imagine what that would mean throughout the entire country?” “It would really set the standard.” “I really want to get Arizona back to being the standard economically and on freedom issues.” “We just need a little catalyst.” Rep Andy Biggs Aaron Siri

WOW: Arizona is on the verge of passing a Constitutional Amendment to prohibit medical mandates. “Not just for vaccines.” “For all medical choices.” “It would be the first and only state that has that.” And it has the full support of gubernatorial candidate Rep. Andy Biggs. It already passed the State House. If it passes the State Senate, it will become a referendum on the 2026 ballot. Which means Governor Katie Hobbs could not veto it. She just vetoed the Arizona Medical Freedom Act, which would have banned almost all medical mandates statewide. So health freedom supporters moved to bypass her veto with a Constitutional Amendment. Rep. Biggs: “Can you imagine what that would mean throughout the entire country?” “It would really set the standard.” “I really want to get Arizona back to being the standard economically and on freedom issues.” “We just need a little catalyst.” Rep Andy Biggs Aaron Siri

Health Freedom Defense Fund

38,850 次观看 • 1 个月前

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Marc Andreessen says raw intelligence might be the worst qualification for leadership — and it changes everything about how we should think about AI. "If the leader is more than one standard deviation of IQ away from the followers, it's a real problem." Andreessen points to the US military, one of the earliest and most rigorous adopters of IQ testing, as the source of this insight. They slot people into specialties and leadership roles based on IQ scores. And over the years, they kept seeing the same pattern. A leader who is significantly less intelligent than their people struggles to model how those people think. That part is intuitive. But the reverse turns out to be equally true. "It's actually very hard for very smart people to model the internal thought processes of even moderately smart people." A leader who is two standard deviations above the norm of the organisation they're running also loses theory of mind, that ability to hold an accurate model of what's happening inside someone else's head. The gap is too wide in both directions. Andreessen then takes this to its logical conclusion: "If you had a person or a machine that had a thousand IQ or something like it, its understanding of reality would be so alien to the people or the things that it was managing that it wouldn't even be able to connect in any sort of realistic way." An AI that vastly outthinks every human in the room isn't positioned to lead those humans. It's positioned to be completely incomprehensible to them. Leadership has never really been an intelligence problem. It's a connection problem. And no amount of raw intelligence closes that gap — past a certain point, it only widens it. The world will not be run by the smartest thing in the room for a long time. Maybe ever.

Big Brain AI

366,029 次观看 • 4 个月前

Dwyane Wade says he will have a standard for the Miami Heat if they like it or not and calls out Heat reporter that asked Bam the buzz question😳 “It’s standards. If the Miami don’t want me to have standards on the players that’s on them…I will always have a standard for the organization…because I gave so much to the organization. I have a lot less millions in my account, I have a lot less stats on the board…my standard isn’t play-in…that may be someone’s standard…the thing I don’t like about the question that Anthony — first all wrong timing to bring that up…you don’t talk about a f*ckin former player saying the buzz in the city is not there — you’re looking for something…I will address with him like this…do your job…like I called Bam when he was struggling this year and I gave him that bold of confidence to get his as* back on track…that’s who we are…it nothing personal…” (via Wy Network by Dwyane Wade/DWade)

Dwyane Wade says he will have a standard for the Miami Heat if they like it or not and calls out Heat reporter that asked Bam the buzz question😳 “It’s standards. If the Miami don’t want me to have standards on the players that’s on them…I will always have a standard for the organization…because I gave so much to the organization. I have a lot less millions in my account, I have a lot less stats on the board…my standard isn’t play-in…that may be someone’s standard…the thing I don’t like about the question that Anthony — first all wrong timing to bring that up…you don’t talk about a fckin former player saying the buzz in the city is not there — you’re looking for something…I will address with him like this…do your job…like I called Bam when he was struggling this year and I gave him that bold of confidence to get his as back on track…that’s who we are…it nothing personal…” (via Wy Network by Dwyane Wade/DWade)

The HEAT Realm

903,739 次观看 • 5 个月前

Curt Cignetti says he’s hardest on players when his team is winning 56–10 because that's when people get content “You create the mindset, the identity of your football team and how you want them to play. Fast. Physical. Relentless. Smart. Disciplined. Poised. Never satisfied until you go to the locker room at the end of the game.” “Treat every play like it’s got a life and history of its own. Nothing-nothing. Game on the line. Get them to play that way where it’s a process. So they’re not result-oriented.” “They’re not looking at the scoreboard at the end of the game. That creates the standard. It’s all about standards. Expectations. Accountability. Process every single day.” “How you do something is how you do everything. First you form your habits. And your habits form you. You’re playing to a standard, not the scoreboard.” “When we’re up 56–10, that’s probably when I’m at my worst on the sideline. I’m looking for somebody smiling. Relaxing. We can do that in the locker room at the end of the day.” “But for 60 minutes, we’re going to play to a standard.”

Curt Cignetti says he’s hardest on players when his team is winning 56–10 because that's when people get content “You create the mindset, the identity of your football team and how you want them to play. Fast. Physical. Relentless. Smart. Disciplined. Poised. Never satisfied until you go to the locker room at the end of the game.” “Treat every play like it’s got a life and history of its own. Nothing-nothing. Game on the line. Get them to play that way where it’s a process. So they’re not result-oriented.” “They’re not looking at the scoreboard at the end of the game. That creates the standard. It’s all about standards. Expectations. Accountability. Process every single day.” “How you do something is how you do everything. First you form your habits. And your habits form you. You’re playing to a standard, not the scoreboard.” “When we’re up 56–10, that’s probably when I’m at my worst on the sideline. I’m looking for somebody smiling. Relaxing. We can do that in the locker room at the end of the day.” “But for 60 minutes, we’re going to play to a standard.”

Josh Chambers

41,284 次观看 • 8 天前

Nassim Taleb ran a simple test: take two completely random, unrelated columns of numbers - noise, nothing else - and measure their correlation. with a small sample, you'll routinely see correlations of 0.4, 0.6, even higher. from pure randomness. now the trap. give a researcher many variables and few data points - say 38 variables, 18 points each, which he says is far more common than you'd think - and let them hunt for the highest correlation. they'll always find a "strong" one. it means nothing. it's just the max of a pile of noise. "when you hear the word correlation, be suspicious." by his estimate, about half of published research is nonsense for exactly this reason. ~14-min talk, free. Taleb on the number that fools almost everyone ↓

Nassim Taleb ran a simple test: take two completely random, unrelated columns of numbers - noise, nothing else - and measure their correlation. with a small sample, you'll routinely see correlations of 0.4, 0.6, even higher. from pure randomness. now the trap. give a researcher many variables and few data points - say 38 variables, 18 points each, which he says is far more common than you'd think - and let them hunt for the highest correlation. they'll always find a "strong" one. it means nothing. it's just the max of a pile of noise. "when you hear the word correlation, be suspicious." by his estimate, about half of published research is nonsense for exactly this reason. ~14-min talk, free. Taleb on the number that fools almost everyone ↓

0xDipper

174,392 次观看 • 1 个月前

Elon Musk explains why government efficiency matters: “I mean, North Korea, they're starving. South Korea, it's like amazing. And so in North Korea, you've got 100% government. In South Korea, you've got probably, I don't know, 40% government. It's not zero. And yet you've got a standard of living that is probably 10 times higher in South Korea. At least. And then East and West Germany, in West Germany, you had, just thinking in terms of cars, I mean, you had BMW, Porsche, Audi, Mercedes. And East Germany, which is a random line on a map, the only car you could get was a Trabant, which is basically a lawnmower with a shell on it. And it was extremely unsafe. There was a 20-year wait. So you put your kid on the list as soon as they're conceived. Even then, only, I think, a quarter of people maybe got this lousy car. So that's just an interesting example of basically the same people, different operating system.”

Elon Musk explains why government efficiency matters: “I mean, North Korea, they're starving. South Korea, it's like amazing. And so in North Korea, you've got 100% government. In South Korea, you've got probably, I don't know, 40% government. It's not zero. And yet you've got a standard of living that is probably 10 times higher in South Korea. At least. And then East and West Germany, in West Germany, you had, just thinking in terms of cars, I mean, you had BMW, Porsche, Audi, Mercedes. And East Germany, which is a random line on a map, the only car you could get was a Trabant, which is basically a lawnmower with a shell on it. And it was extremely unsafe. There was a 20-year wait. So you put your kid on the list as soon as they're conceived. Even then, only, I think, a quarter of people maybe got this lousy car. So that's just an interesting example of basically the same people, different operating system.”

Eva Fo𝕏 🦊 Claudius Nero's Legion

1,802,252 次观看 • 9 个月前

This guy is making +1% on EVERY TRADE Sounds small, I know. But he already made $50k profit (started with $200 only) How? He repeats this +1% trade 30 times per day. Almost zero risk. And I found a way to make 100x more trading same markets. This is either genius or it should be illegal. But this trader has been quietly running the cleanest strategy on Polymarket for months. Just one repeatable process: > Find markets where the outcome is already decided > Buy shares at 95-99¢ > Collect the gap to $1.00 at resolution > Wake up tomorrow and do it again His PnL curve looks like a savings account that somehow prints 33% daily. Wallet: 5% per trade sounds boring until you run the parlay math. Here's exactly what changes with PolyParlay: Standard bond trading on $1,000: > 4 markets at 96¢ traded separately > +4% collected four times > Total: $1,060 (you made $60, congrats) Same $1,000 combined into one parlay: > 0.96 × 0.96 × 0.96 × 0.96 = boosted PnL > 1.27x payout multiplier > Total: $1,270 in one single position Now add 2 mispriced entries at 70-80¢ into that same parlay: > Payout multiplier jumps to 3-10x > Same $1,000 becomes $3,000-$10,000 after all hit That's the gap between grinding $160 daily and waking up to $3,000+ on the same capital. Bond markets eliminate the risk. Parlay structure eliminates the ceiling. Both together is what $50k months actually look like. Parlay bot link: This is the only bot for Polymarket parlay trading. Zero stress. Daily yield. Exponential upside.

This guy is making +1% on EVERY TRADE Sounds small, I know. But he already made $50k profit (started with $200 only) How? He repeats this +1% trade 30 times per day. Almost zero risk. And I found a way to make 100x more trading same markets. This is either genius or it should be illegal. But this trader has been quietly running the cleanest strategy on Polymarket for months. Just one repeatable process: > Find markets where the outcome is already decided > Buy shares at 95-99¢ > Collect the gap to $1.00 at resolution > Wake up tomorrow and do it again His PnL curve looks like a savings account that somehow prints 33% daily. Wallet: 5% per trade sounds boring until you run the parlay math. Here's exactly what changes with PolyParlay: Standard bond trading on $1,000: > 4 markets at 96¢ traded separately > +4% collected four times > Total: $1,060 (you made $60, congrats) Same $1,000 combined into one parlay: > 0.96 × 0.96 × 0.96 × 0.96 = boosted PnL > 1.27x payout multiplier > Total: $1,270 in one single position Now add 2 mispriced entries at 70-80¢ into that same parlay: > Payout multiplier jumps to 3-10x > Same $1,000 becomes $3,000-$10,000 after all hit That's the gap between grinding $160 daily and waking up to $3,000+ on the same capital. Bond markets eliminate the risk. Parlay structure eliminates the ceiling. Both together is what $50k months actually look like. Parlay bot link: This is the only bot for Polymarket parlay trading. Zero stress. Daily yield. Exponential upside.

Oracle Boar

13,926 次观看 • 3 个月前

for everyone wondering how zero-delay tracking is even possible on an $8 chip: it is all thanks to the Kalman filter. this 1-minute clip from Steve Brunton perfectly explains why it works (full lecture in reply) instead of just observing a target, it mathematically predicts where it will be in the next millisecond. here is what happens under the hood: - balances physics of motion against noisy camera data - corrects the trajectory instantly - moves the robotic arm before the next frame processes this is the exact same math used in rockets and autonomous driving, stripped down for a cheap microcontroller.

for everyone wondering how zero-delay tracking is even possible on an $8 chip: it is all thanks to the Kalman filter. this 1-minute clip from Steve Brunton perfectly explains why it works (full lecture in reply) instead of just observing a target, it mathematically predicts where it will be in the next millisecond. here is what happens under the hood: - balances physics of motion against noisy camera data - corrects the trajectory instantly - moves the robotic arm before the next frame processes this is the exact same math used in rockets and autonomous driving, stripped down for a cheap microcontroller.

ard

53,268 次观看 • 19 天前