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Quant from CERN built a model that catches market shocks when everyone panics, it profits the most But when to enter is a separate problem. Math already solved it: R = N/e ≈ 0.37 × N* > skip the first 37% of entry points > then take the first... show more

sopersone

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$Ex-CERN QUANT applied quantum mechanics to the oil market on Polymarket He trades exclusively oil and made $226K in a week His Harmonic Oscillators model sees structure in the chaos: â†|n⟩ = √(n+1)|n+1⟩ -- growth â|n⟩ = √n|n-1⟩ -- decline Eₙ = ℏω(n + ½) -- energy level N̂|n⟩ = n|n⟩ -- particle number Price discretization: n(t) = ⌊(Rₜ - R_min)/ΔR · (N_L - 1)⌋ Rₜ = Σ r̃_τ/σ Market events: âₜ† = 1_{Δnₜ>0} · |Δnₜ| → buy âₜ = 1_{Δnₜ<0} · |Δnₜ| → sell Result: probability density of the next move: P(n,t) = 1/W · Σ δₙ,ₙτ Oil volatility for everyone is risk, for him it's a signal Check his profile:$

Ex-CERN QUANT applied quantum mechanics to the oil market on Polymarket He trades exclusively oil and made $226K in a week His Harmonic Oscillators model sees structure in the chaos: â†|n⟩ = √(n+1)|n+1⟩ -- growth â|n⟩ = √n|n-1⟩ -- decline Eₙ = ℏω(n + ½) -- energy level N̂|n⟩ = n|n⟩ -- particle number Price discretization: n(t) = ⌊(Rₜ - R_min)/ΔR · (N_L - 1)⌋ Rₜ = Σ r̃_τ/σ Market events: âₜ† = 1_{Δnₜ>0} · |Δnₜ| → buy âₜ = 1_{Δnₜ<0} · |Δnₜ| → sell Result: probability density of the next move: P(n,t) = 1/W · Σ δₙ,ₙτ Oil volatility for everyone is risk, for him it's a signal Check his profile:

sopersone

65,045 Aufrufe • vor 3 Monaten

1 week ago 🤔 that first half was one of the worst half’s I’ve seen from a ref this season , but end of the day complaining to the ref won’t change anything when they already calling the match a certain way

1 week ago 🤔 that first half was one of the worst half’s I’ve seen from a ref this season , but end of the day complaining to the ref won’t change anything when they already calling the match a certain way

aa9skillz

64,786 Aufrufe • vor 1 Jahr

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think. The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free. Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. The math is free. The patience to trust it past your own eyes is the edge.

A Stanford mathematician who spent 10 years as a professional magician just described the market in one sentence: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." That's Persi Diaconis. He has a free lecture that asks one question: does anything actually happen at random? The answer is: far less than you think. The market is his first trick in the wild. It looks like pure chance. Buried inside is a faint rig, a 50.75% tilt no eye can see. Your gut reads a losing week as a broken system and a hot streak as skill. Wrong both times. The tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. None of it is hidden. Diaconis has taught it for decades. The probability goes back to 1713. The lecture is free. Here's the trap: you feel every win and every loss, but you cannot feel the average. And the average is the only thing that pays. It takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. The math is free. The patience to trust it past your own eyes is the edge.

Veles

2,181,221 Aufrufe • vor 25 Tagen

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?

A+ NQ setup has exactly three parts. 1. Statistical bias: Probability Map shows 75%+ to one side. Not an opinion. Backtested across 10 years of NQ session data. That number narrows the playing field before price moves an inch. 2. The sweep: Liquidity is resting in the opposite direction. When price grabs it, that is not a fake-out. That is the fuel for the real move. You were expecting it. You were watching for it. 3. The entry model: After the sweep, the structure confirms. iFVG / CISD. A defined ICT concept with clear invalidation. Not a hope, not a gut feel. A model with a precise entry and a reason to be there. Probability tells you where. The sweep tells you when. The entry model tells you how. Remove any one of the three and you do not have an edge. You have a trade. How many of these boxes do you tick before you pull the trigger?

Herman Trading

20,079 Aufrufe • vor 2 Monaten

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j*,j*) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j*,j*) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

THIS WALLET STACKED $230K ON BTC UP/DOWN BETS. THE BLUEPRINT TO AUTOMATE THE SAME EDGE WITH CLAUDE The wallet is $230K all-time, every position a Bitcoin or Ethereum Up or Down market It never guesses direction. It enters only when the math and the market disagree THE STRATEGY: BTC moves are not fully random. When the market enters a committed directional state, continuation is measurable. That is Markov persistence Entry signal: > Δ = p̂ − q ≥ ε Model probability minus market price. Enter only on a 5% gap or more Persistence filter: > p(j,j) ≥ 0.87 Only trade states with 0.87 persistence or higher. Below that, skip. This is what holds the win rate above 65% with zero directional guessing Payout: > r = (1 − q) / q At q = 0.647 that is +54.5% a win. At q = 0.441, +126.7%. Lower entry price, bigger asymmetry Sizing: > f* = p − (1−p)/b Kelly. At p = 0.87, b = 0.647, f* ≈ 0.71. Size to the edge, never to gut HOW TO BUILD IT WITH CLAUDE: What separates this from a static bot: Claude reads its own trade journal every night and rewrites its own thresholds 1. Take an open-source Polymarket bot repo as your base logic. Feed it to Claude and have it migrate to CLOB v2: py_clob_client_v2, Safe wallet support, fee-aware evaluation 2. Hard-code the filters. Enter only when Δ ≥ 0.05 and p(j,j) ≥ 0.87. Apply Kelly on every fill. 3. Run DRY_RUN first. Log every signal, entry price, Markov state, and simulated P/L. No real money until the numbers hold for days 4. The nightly loop. Claude reads the journal, finds which persistence states actually won, adjusts MIN_PROB and MIN_EDGE, ships tomorrow's rules. The agent is sharper after 50 to 100 trades THE SETUP: Claude Opus as the brain. An open-source repo as the starting logic. A Polygon wallet with $50 to $100. Telegram for the morning report Start at $1 to $2 per trade while it learns. Scale only when the dry runs and the live fills line up 17,000 trades compound a thin edge into six figures. The model finds the edge. The nightly loop keeps it sharp Bookmark before you point a bot at your first window

Yarchi

22,966 Aufrufe • vor 2 Monaten

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

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

Rossst.03

269,381 Aufrufe • vor 1 Monat

Barry Silbert backed Bitcoin when it was a cult. Ethereum when it was a scam. Coinbase when it was a joke. He just built an entire company exclusively for $TAO. That is not a trade. That is a pattern. 11.05 million TAO issued. 21 million hard cap. First halving already hit. Next one December 2029. The people who recognised the pattern early never needed to explain their timing later.

Barry Silbert backed Bitcoin when it was a cult. Ethereum when it was a scam. Coinbase when it was a joke. He just built an entire company exclusively for $TAO. That is not a trade. That is a pattern. 11.05 million TAO issued. 21 million hard cap. First halving already hit. Next one December 2029. The people who recognised the pattern early never needed to explain their timing later.

2xnmore

19,228 Aufrufe • vor 1 Monat

A magician spent 10 years rigging card decks. then he became a Stanford professor and proved the market works the same way. he published the math for free. Wall Street still pretends nobody saw it. his name is Persi Diaconis. his quote: "I've spent my life on two tricks. making a rigged deck look random, and making a random one look rigged. the market is the first trick, and almost nobody catches it." the tilt is invisible to human intuition. a 50.75% edge buried inside what looks like pure chance. your gut reads a losing week as a broken system and a hot streak as skill. wrong both times. that's exactly why funds hand the decision to the math. it takes thousands of trades for a 51% edge to separate from luck. almost everyone quits before then. the ones who didn't quit manage the money now. Diaconis has taught this for decades. the probability goes back to 1713. the lecture is free. the patience to trust it past your own eyes is the edge.

A magician spent 10 years rigging card decks. then he became a Stanford professor and proved the market works the same way. he published the math for free. Wall Street still pretends nobody saw it. his name is Persi Diaconis. his quote: "I've spent my life on two tricks. making a rigged deck look random, and making a random one look rigged. the market is the first trick, and almost nobody catches it." the tilt is invisible to human intuition. a 50.75% edge buried inside what looks like pure chance. your gut reads a losing week as a broken system and a hot streak as skill. wrong both times. that's exactly why funds hand the decision to the math. it takes thousands of trades for a 51% edge to separate from luck. almost everyone quits before then. the ones who didn't quit manage the money now. Diaconis has taught this for decades. the probability goes back to 1713. the lecture is free. the patience to trust it past your own eyes is the edge.

Tigerflow

89,540 Aufrufe • vor 1 Tag

If you’ve lost money in the NGX in the last few weeks, here’s what actually happened and still happening. A lot of people walked into NGX market when it was already trading at a premium. At the top, after the rally had done the heavy lifting. This is the pattern, every single time. Smart money enters early, quietly, before it’s a story. Timelines fill up with green portfolio screenshots. First time investors and the FOMO crowd piles in late, chasing highs. By then, valuations are stretched, P/E’s are ahead of fundamentals, and the cheap entry is long gone. Then market corrects. Early movers take profit. Late entrants panic sells or hold the bag. If that’s you right now, staring at red, you didn’t necessarily pick bad stocks. You picked the wrong entry point. Those are two very different problems. Don’t buy the noise. Buy the value. NGX will give you another entry point but the question is will recognize it when it comes? Listen to Ibukun Awosika, she gave a perfect explanation of NGX market right now. Watch till the end.

If you’ve lost money in the NGX in the last few weeks, here’s what actually happened and still happening. A lot of people walked into NGX market when it was already trading at a premium. At the top, after the rally had done the heavy lifting. This is the pattern, every single time. Smart money enters early, quietly, before it’s a story. Timelines fill up with green portfolio screenshots. First time investors and the FOMO crowd piles in late, chasing highs. By then, valuations are stretched, P/E’s are ahead of fundamentals, and the cheap entry is long gone. Then market corrects. Early movers take profit. Late entrants panic sells or hold the bag. If that’s you right now, staring at red, you didn’t necessarily pick bad stocks. You picked the wrong entry point. Those are two very different problems. Don’t buy the noise. Buy the value. NGX will give you another entry point but the question is will recognize it when it comes? Listen to Ibukun Awosika, she gave a perfect explanation of NGX market right now. Watch till the end.

citykush

38,894 Aufrufe • vor 1 Monat

This 3-hour algorithmic trading video with Python is like getting a mini course from Massachusetts Institute of Technology - for free It reveals more about quant trading and bots than most traders learn in years on the market Bookmark it. Watch it. Then watch it again.

This 3-hour algorithmic trading video with Python is like getting a mini course from Massachusetts Institute of Technology - for free It reveals more about quant trading and bots than most traders learn in years on the market Bookmark it. Watch it. Then watch it again.

sopersone

160,812 Aufrufe • vor 4 Monaten

Persi Diaconis, Stanford mathematician and former professional magician: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." this free lecture asks one question, does anything happen at random, and the answer is: far less than you think. and the man asking isn't a trader. he's a stanford professor and former professional magician who spent his life proving how badly humans read randomness. the market is his first trick in the wild. it looks like pure chance, yet buried inside is a faint rig, a 50.75% tilt no eye can see. your gut reads a losing week as a broken system and a hot streak as skill, and it's wrong both times. the tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. none of it is hidden. diaconis has taught it for decades, the probability goes back to 1713, and the lecture is free. same point the thread makes: the 50.75% is real, but it lives inside noise your gut will always misread. here is the trap. you feel every win and every loss, but you cannot feel the average, and the average is the only thing that pays. it takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. the math is free. the patience to trust it past your own eyes is the edge.

Persi Diaconis, Stanford mathematician and former professional magician: "I've spent my life on two tricks: making a rigged deck look random, and making a random one look rigged. The market is the first trick, and almost nobody catches it." this free lecture asks one question, does anything happen at random, and the answer is: far less than you think. and the man asking isn't a trader. he's a stanford professor and former professional magician who spent his life proving how badly humans read randomness. the market is his first trick in the wild. it looks like pure chance, yet buried inside is a faint rig, a 50.75% tilt no eye can see. your gut reads a losing week as a broken system and a hot streak as skill, and it's wrong both times. the tilt is invisible to human intuition, which is exactly why funds hand the decision to the math. none of it is hidden. diaconis has taught it for decades, the probability goes back to 1713, and the lecture is free. same point the thread makes: the 50.75% is real, but it lives inside noise your gut will always misread. here is the trap. you feel every win and every loss, but you cannot feel the average, and the average is the only thing that pays. it takes thousands of trades for a 51% edge to separate from luck, and almost everyone quits long before then. the math is free. the patience to trust it past your own eyes is the edge.

Rossst.03

216,635 Aufrufe • vor 29 Tagen

$A small group of quants wrote one formula, launched it on Polymarket and quietly took $20m in a year Their secret fits in one equation: A = e^(-λ₁T - λ₂y - λ₃R) · (1 + γ₀y · 1_{y>0.7}) · e^(-φ₀R²) · sgn(σₕ - κC)(1-C) These aren't random symbols each factor is a filter: > how stale the position is over time > how overheated the market is by returns > how much volatility justifies the entry > which direction to look at all Only if all four factors align the trade exists No "I feel like it". No "I believe in this asset" Just a number positive or not But there is one tool that changes the game completely Top traders on Polymarket are active simultaneously across multiple high-probability markets every day Each trade brings a small win on its own But combining 5-6 of these trades into one parlay you can increase your return from 80% to 400% ROI instead of five small wins That's exactly what this bot does automatically Try here:$

A small group of quants wrote one formula, launched it on Polymarket and quietly took $20m in a year Their secret fits in one equation: A = e^(-λ₁T - λ₂y - λ₃R) · (1 + γ₀y · 1_{y>0.7}) · e^(-φ₀R²) · sgn(σₕ - κC)(1-C) These aren't random symbols each factor is a filter: > how stale the position is over time > how overheated the market is by returns > how much volatility justifies the entry > which direction to look at all Only if all four factors align the trade exists No "I feel like it". No "I believe in this asset" Just a number positive or not But there is one tool that changes the game completely Top traders on Polymarket are active simultaneously across multiple high-probability markets every day Each trade brings a small win on its own But combining 5-6 of these trades into one parlay you can increase your return from 80% to 400% ROI instead of five small wins That's exactly what this bot does automatically Try here:

sopersone

56,779 Aufrufe • vor 3 Monaten

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

✨ Made a new mini feature on Photo AI: [ Grab from 3d model ] So the problem is we're at that stage in time (typical for AI) where image-to-3d models are not good enough but are fun to play with, but we know they'll be good enough in 1-2 years With [ Make 3d model ] you already can turn any Photo AI pic into a 3d model but it still looks hyper clunky and deformed, but it works! One cool idea I had to make that more useful and made now: Let people make a 3d model then change the view of the it with the 3d viewer, then press [ o ] and it grabs a frame of the 3d That image you can then [ Remix ] (img2img), and it becomes a real photo again and that in turn you can then turn into a video again with [ Make video ] So that essentially gives you a fully freeform camera position control to take photos with One thing I need to fix is the background/skybox, I kinda need to take the original photo and remove the person and just get the background for the 3d model viewer, in this case it should be white, but it's a start!

@levelsio

119,210 Aufrufe • vor 1 Jahr

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dario Amodei just told software engineers exactly how long they have. Six to twelve months. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most powerful AI in history have already stopped writing code. That is not a forecast. That is the current working condition inside the lab closest to the frontier. Amodei: “We might be six to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” The tech industry spent a decade making software engineers its highest-paid, most protected class. That era has a last day now. When a model can execute an entire software build end-to-end, the ability to write syntax stops being a skill. It becomes a credential for a job that no longer exists. Amodei: “And then it’s a question of how fast does that loop close.” That is the sentence everyone skipped. The code was never the hard part. The hard part was everything around it. The model just learned everything around it. Writing the code is already nearly gone. Testing is next. Deployment is next. When all three collapse into a single autonomous execution loop, the machine no longer needs a human in the chain at all. The corporation or sovereign state that closes that loop first does not gain a competitive advantage. It gains a category of speed that biological engineers cannot match, track, or reverse. That is not disruption. That is replacement at a systems level. Amodei is not describing a future disruption. He is describing the current state of his own building. The loop is already closing. The only question is whether you are inside it or outside it when it seals.

Dustin

318,457 Aufrufe • vor 4 Monaten

US export-control disable Claude Fable 5 overnight. Not for what it said - for what one trader did with it. He turned an $11,000 wreck into $300,000 profit in five days. Then they pulled Claude Fable 5 from public hands. Too late - his bot's still running. His wallet: Every other Claude model still works. The two best ones don't. Anthropic's calling it a misunderstanding. Maybe. But think about what gets a model killed by export control overnight, no warning. Here's what Fable 5 actually did for him. It doesn't predict price. It computes the whole probability surface of a market - every outcome, weighted, faster than the market can correct itself. It finds the bets the crowd writes off as impossible, runs the real math, and buys them for pennies when the math disagrees. One of those just paid x205. A regular guy, a model this sharp, a market no regulator polices. They closed that door overnight - one night too late. He's holding one of the last builds that ever touched a live market. It's still on Polymarket right now, hunting the same mispriced tails. Fable 5 is locked. The algorithm it wrote is loose. You can't open the model anymore. You can still copy the wallet it built in two clicks:

US export-control disable Claude Fable 5 overnight. Not for what it said - for what one trader did with it. He turned an $11,000 wreck into $300,000 profit in five days. Then they pulled Claude Fable 5 from public hands. Too late - his bot's still running. His wallet: Every other Claude model still works. The two best ones don't. Anthropic's calling it a misunderstanding. Maybe. But think about what gets a model killed by export control overnight, no warning. Here's what Fable 5 actually did for him. It doesn't predict price. It computes the whole probability surface of a market - every outcome, weighted, faster than the market can correct itself. It finds the bets the crowd writes off as impossible, runs the real math, and buys them for pennies when the math disagrees. One of those just paid x205. A regular guy, a model this sharp, a market no regulator polices. They closed that door overnight - one night too late. He's holding one of the last builds that ever touched a live market. It's still on Polymarket right now, hunting the same mispriced tails. Fable 5 is locked. The algorithm it wrote is loose. You can't open the model anymore. You can still copy the wallet it built in two clicks:

cvxv666

77,213 Aufrufe • vor 1 Monat

Dario Amodei, CEO of Anthropic, just shortened your career timeline. His own engineers have stopped writing code. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most advanced AI on Earth are already being replaced by what they built. Not in theory. Not in a forecast. Inside the building. Right now. Amodei: “I think we might be 6 to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” Six to twelve months. Not from automating busywork. From replacing the full scope of what a software engineer does. Architecture. Logic. Debugging. Deployment. The entire chain. Software engineering is not some fading trade. It is the highest-paid, highest-demand, most protected skill the modern economy ever produced. And the man running a frontier lab just gave it a six-month shelf life. If the most technically sophisticated job in the economy falls first, nothing beneath it is safe. That is the inversion no one saw coming. The assumption was always that AI would eat from the bottom. Routine work. Data entry. Simple automation. It started at the top. Engineers first. Then analysts. Then strategists. Then the managers overseeing work that no longer needs them. The displacement doesn’t crawl upward. It cascades downward. Starting with the people closest to the technology itself. Amodei: “If I had to guess, I would guess that this goes faster than people imagine, and that that key element of code, and increasingly research, going faster than we imagine.” Not just code. Research. Hypotheses. Experiments. Interpretation. Discovery itself. If AI closes that loop, it doesn’t just write software. It improves itself. Every iteration compresses the timeline further. Amodei: “It’s very hard for me to see how it could take longer than a few years.” He is not selling optimism. He is setting a ceiling. A few years. Maximum. For AI to absorb the two most important intellectual functions in the economy. The window to position yourself is not a decade. It is already closing.

Dario Amodei, CEO of Anthropic, just shortened your career timeline. His own engineers have stopped writing code. Amodei: “I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code, I edit it, I do the things around it.” The people building the most advanced AI on Earth are already being replaced by what they built. Not in theory. Not in a forecast. Inside the building. Right now. Amodei: “I think we might be 6 to 12 months away from when the model is doing most, maybe all, of what SWEs do end-to-end.” Six to twelve months. Not from automating busywork. From replacing the full scope of what a software engineer does. Architecture. Logic. Debugging. Deployment. The entire chain. Software engineering is not some fading trade. It is the highest-paid, highest-demand, most protected skill the modern economy ever produced. And the man running a frontier lab just gave it a six-month shelf life. If the most technically sophisticated job in the economy falls first, nothing beneath it is safe. That is the inversion no one saw coming. The assumption was always that AI would eat from the bottom. Routine work. Data entry. Simple automation. It started at the top. Engineers first. Then analysts. Then strategists. Then the managers overseeing work that no longer needs them. The displacement doesn’t crawl upward. It cascades downward. Starting with the people closest to the technology itself. Amodei: “If I had to guess, I would guess that this goes faster than people imagine, and that that key element of code, and increasingly research, going faster than we imagine.” Not just code. Research. Hypotheses. Experiments. Interpretation. Discovery itself. If AI closes that loop, it doesn’t just write software. It improves itself. Every iteration compresses the timeline further. Amodei: “It’s very hard for me to see how it could take longer than a few years.” He is not selling optimism. He is setting a ceiling. A few years. Maximum. For AI to absorb the two most important intellectual functions in the economy. The window to position yourself is not a decade. It is already closing.

Dustin

16,132 Aufrufe • vor 3 Monaten

$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's *240. The law of large numbers turns noise into an exponent. Kelly criterion f* ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.

$1,331,821 IN 30 DAYS. 3 BOTS. 48,061 TRADES. ONE FORMULA. They don't predict price. They measure what state the market is in right now. Markov chains, transition matrix, each cell - the probability of transitioning from state A to state B. The matrix diagonal - the probability that the market stays where it is. Entry only when the diagonal is above 0.87. Two conditions: the gap between model and market is greater than 5%, and the state is stable. Both must be true. One function, runs every minute. Bot 1 - Bonereaper. BTC and ETH, hourly windows, entry at 83-97¢. The market agrees with the direction but underestimates the confidence. 4-19% on every resolution. Low variance. Bot 2 - 0xe1D6. Dual mode, directional scalps at 64-83¢ deliver 20-54% per trade. In parallel, locks at 99.5-99.8¢. Best trade: entry at 64.7¢, return 54.6%. Bot 3 - 0xB27BC. Five assets: BTC, ETH, SOL, BNB, XRP. Five-minute windows. One trade every 1.7 minutes. Variance 55% lower at the same expected return. The real edge - 3:00 AM. People are asleep. The market posts lazy, stale prices. The gap between model and market is maximal when no one is watching. 0.034% per trade sounds like nothing. Over 16,000 trades that's 240. The law of large numbers turns noise into an exponent. Kelly criterion f ≈ 0.71 - aggressive enough to grow, conservative enough not to go to zero. As long as people misprice short windows - the edge exists. You don't need to predict. You need to measure. The market rewards those who understand probability. The rest just provide liquidity.

zostaff

61,629 Aufrufe • vor 3 Monaten

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.

Karpathy told Dwarkesh that a 1 billion parameter model, trained on clean data, could hit the intelligence of today's 1.8 trillion parameter frontier. That is a 1,800x compression claim. The math behind it is more defensible than it sounds. When researchers at frontier labs look at random samples from their training corpus, they see stock ticker symbols, broken HTML, forum spam, autogenerated gibberish. Not Wikipedia. Not the Wall Street Journal. The actual pretraining dataset is mostly noise, and the model is burning parameters to vaguely remember all of it. One estimate pegs Llama 3's information compression at 0.07 bits per token. Well-structured English carries around 1.5 bits per token of real information. The trillion-parameter model is holding a roughly 5% resolution image of the internet it trained on. So when a lab ships a 1.8 trillion parameter model, the overwhelming majority of those weights are handling rough memorization. They are compression overhead for a noisy training set, taking up capacity that could be doing reasoning instead. Karpathy's proposal is to separate the two. Build a cognitive core: a small model that contains only the algorithms for reasoning and problem-solving, stripped of encyclopedic memorization. Pair it with external memory the model queries when it needs a fact. A 1 billion parameter reasoner plus retrieval beats a 1.8 trillion parameter model trying to do both. The data already supports this direction. GPT-4o runs at roughly 200 billion parameters and outperforms the original 1.8 trillion GPT-4. Inference costs for GPT-3.5 level performance fell 280x between 2022 and 2024, driven almost entirely by smaller, cleaner, better-architected models. The trend line is pointing where Karpathy says it should. The real implication for anyone tracking the AI trade: data quality is the actual constraint. The companies winning the next phase will be the ones who figured out what to train on, and what to throw away.

Aakash Gupta

508,149 Aufrufe • vor 3 Monaten

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

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

Casper

91,587 Aufrufe • vor 16 Tagen