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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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16,802 görüntüleme • 13 gün önce •via X (Twitter)

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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

64,987 görüntüleme • 2 ay önce

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 görüntüleme • 1 yıl önce

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 görüntüleme • 27 gün önce

Model 1 explained directly by the author, Romeo. Get it from the source, then watch the entry model video for a deeper breakdown. Study 📚

Model 1 explained directly by the author, Romeo. Get it from the source, then watch the entry model video for a deeper breakdown. Study 📚

Brighto

37,851 görüntüleme • 2 ay önce

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

22,871 görüntüleme • 1 ay önce

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

160,348 görüntüleme • 2 ay önce

$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 görüntüleme • 1 ay önce

Most traders know CRT but very few know where to enter, that’s the difference between analysis and precision. In this 12-min lecture, there is an in-depth break down of the Top Entry Models that make CRT deadly accurate: • The TURTLE SOUP MODEL • The MODEL #1 (OB/CSD) • The INVERSION ENTRY (advanced MSS) • The BREAKER ENTRY CRT is not just about spotting the range, it’s about knowing the exact point price is ready to move. This is where most traders get it wrong, they see the setup but enter at the wrong time, after this video you’ll know exactly what to wait for. No more early entries. No more late entries. Just precision. 🎥 12 minutes that can change how you execute forever.

Most traders know CRT but very few know where to enter, that’s the difference between analysis and precision. In this 12-min lecture, there is an in-depth break down of the Top Entry Models that make CRT deadly accurate: • The TURTLE SOUP MODEL • The MODEL #1 (OB/CSD) • The INVERSION ENTRY (advanced MSS) • The BREAKER ENTRY CRT is not just about spotting the range, it’s about knowing the exact point price is ready to move. This is where most traders get it wrong, they see the setup but enter at the wrong time, after this video you’ll know exactly what to wait for. No more early entries. No more late entries. Just precision. 🎥 12 minutes that can change how you execute forever.

R1X TRADES (GER40)

28,674 görüntüleme • 2 ay önce

✨ 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 görüntüleme • 11 ay önce

This is still one of the best videos explaining the differences between Judaism and Christianity when it comes to Jesus. A lot of it is shocking at first, but it is true.

This is still one of the best videos explaining the differences between Judaism and Christianity when it comes to Jesus. A lot of it is shocking at first, but it is true.

Uncommon Sense

230,924 görüntüleme • 1 yıl önce

BREAKING: Trump just bragged that the market is up a lot, and then immediately referred to the same market, when it was 10% higher just weeks ago, as a "sick market." He then pivoted to blame Biden for that supposedly "sick market"—which, again, was 10% better than today. You truly can’t make this up. The contradictions are dizzying.

BREAKING: Trump just bragged that the market is up a lot, and then immediately referred to the same market, when it was 10% higher just weeks ago, as a "sick market." He then pivoted to blame Biden for that supposedly "sick market"—which, again, was 10% better than today. You truly can’t make this up. The contradictions are dizzying.

Brian Krassenstein

611,286 görüntüleme • 1 yıl önce

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

315,019 görüntüleme • 3 ay önce

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

76,215 görüntüleme • 10 gün önce

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 görüntüleme • 2 ay önce

$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 görüntüleme • 2 ay önce

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

507,744 görüntüleme • 2 ay önce

Catching up with China and the US, LG's humanoid robot, KAPEX, was developed jointly with the Korea Institute of Science and Technology (KIST). It is powered by LG's Exaone visual language model, high-end Korean actuators, and a multi-tactile dexterous hand. The humanoid robot is scheduled to be released in November of this year. LG announced its entry into the home humanoid robot market at CES 2025, but first unveiled an AI robot codenamed Q9 as a base model.

Catching up with China and the US, LG's humanoid robot, KAPEX, was developed jointly with the Korea Institute of Science and Technology (KIST). It is powered by LG's Exaone visual language model, high-end Korean actuators, and a multi-tactile dexterous hand. The humanoid robot is scheduled to be released in November of this year. LG announced its entry into the home humanoid robot market at CES 2025, but first unveiled an AI robot codenamed Q9 as a base model.

CyberRobo

25,668 görüntüleme • 8 ay önce

If you're thinking of calling it a night, well, you've got 1:37 here. Video is diabolical. Cigarette didn't stand a chance, but the less said the better here. Enjoy.

If you're thinking of calling it a night, well, you've got 1:37 here. Video is diabolical. Cigarette didn't stand a chance, but the less said the better here. Enjoy.

Smoking Fetish Stuff

69,125 görüntüleme • 2 yıl önce