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Google laid him off. So he aimed Google own AI at Bitcoin trading and pulled $400,000 profit. It's called TimesFM - a foundation model for time. Think GPT, but instead of the next word, it predicts the next number. He'd spent four years on the team that builds models... like this. Then a spring reorg erased the team and walked him out with a cardboard box. Wallet proof: Here's the part that breaks people: the model was never trained on Bitcoin. Not one candle. Google pretrained TimesFM once on 100 billion time-points - search trends, Wikipedia traffic, demand curves, then froze it. You don't retrain it. You prompt it, the way you prompt GPT. Every five minutes his bot hands it the last few hundred BTC prints. It returns a forecast - not a line, but calibrated quantiles: the 10th through 90th percentile of where the next candle lands. A 30-million-parameter quantile head does nothing but draw that distribution. From it he reads one number: P(close Up). 5min Polymarket contract is that same number, priced by a half-awake crowd: edge = model_p − market_p. Then the filters cut hard. fix_quantile_crossing on, so the distribution stays sane. Skip if the spread's too wide or the edge is under 5¢. Fractional Kelly sizes what's left. Hard daily loss cap, no exceptions. No thesis. No conviction. Just a frozen Google model against a lazy order book, every five minutes, around the clock. His wallet's public, taking the same 5-minute edges right now. Two clicks and every trade it makes lands in yours too:show more

cvxv666

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49,624 次观看 • 1 个月前 •via X (Twitter)

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Jane Street quant just stole and fit a $40-million trading brain inside a box that costs $4,000 and quietly walked out of the building with it. It's the new AMD Ryzen AI Halo. Eleven weeks later, that computer has made him $400,000 - from his bedroom. His wallet: The model doesn't guess where Bitcoin goes. It simulates it. Every five minutes it reads the candle into a pattern net - break of structure, order blocks, the gaps where liquidity hides. Then it hands that read to MiroFish: a simulation that runs the next five minutes forward thousands of times, sorts every path into three outcomes - bear, neutral, breakout and threads the median straight through them. That median is where price actually lands. Not where the crowd hopes. When that fair value drifts 15¢ off the live book, the edge is real. It sizes by Kelly and fires. One button, no nerves. On the firm's cluster, one simulation like that ate serious hardware. The Halo box - 128GB of unified memory, a 200B model held whole, runs thousands a minute, offline, while he sleeps. No cloud. No login the firm can kill. The desk still owns the model. It just can't aim it where he aims it - Polymarket's too small for a fund that size to touch. He took the edge home. Two clicks and you trade right beside it:

Jane Street quant just stole and fit a $40-million trading brain inside a box that costs $4,000 and quietly walked out of the building with it. It's the new AMD Ryzen AI Halo. Eleven weeks later, that computer has made him $400,000 - from his bedroom. His wallet: The model doesn't guess where Bitcoin goes. It simulates it. Every five minutes it reads the candle into a pattern net - break of structure, order blocks, the gaps where liquidity hides. Then it hands that read to MiroFish: a simulation that runs the next five minutes forward thousands of times, sorts every path into three outcomes - bear, neutral, breakout and threads the median straight through them. That median is where price actually lands. Not where the crowd hopes. When that fair value drifts 15¢ off the live book, the edge is real. It sizes by Kelly and fires. One button, no nerves. On the firm's cluster, one simulation like that ate serious hardware. The Halo box - 128GB of unified memory, a 200B model held whole, runs thousands a minute, offline, while he sleeps. No cloud. No login the firm can kill. The desk still owns the model. It just can't aim it where he aims it - Polymarket's too small for a fund that size to touch. He took the edge home. Two clicks and you trade right beside it:

cvxv666

94,183 次观看 • 1 个月前

A 31-year-old blitz chess master who never cracked the top 100 just turned $500 into $153,279 - with Claude and simulation engine. Twenty years reading a board faster than the other guy, for prize pools that barely covered the flights. Then he pointed that speed at Polymarket. His wallet: No tournament ever paid him like a Claude agent running 24/7. Everyone trades Bitcoin like it mean-reverts on the 5-minute. It doesn't. That gap is the whole edge. Here's the stack. Every five minutes a BTC candle closes. A Claude agent reads it into a pattern net - price, momentum, volume in; BOS, order block, FVG out. A neuron fires. It doesn't trade yet. It hands off to MiroFish - a relationship-graph sim that threads a median path through three live clusters: bear, neutral prime, catalyst. That path is where BTC actually lands. Fair value drifts 15¢ off the order book? That's the edge. Size by Kelly. Pull the trigger. Read the candle. Fire the net. Run the graph. No thesis. No conviction. It never stops sharpening, re-running the graph every few seconds, rewriting its own threshold nightly. 92% of Polymarket traders lose. The winners are bots. This one just reads two endpoints and presses one button. 13,290 times. The edge is on-chain. Read it with this bot before it gets crowded:

A 31-year-old blitz chess master who never cracked the top 100 just turned $500 into $153,279 - with Claude and simulation engine. Twenty years reading a board faster than the other guy, for prize pools that barely covered the flights. Then he pointed that speed at Polymarket. His wallet: No tournament ever paid him like a Claude agent running 24/7. Everyone trades Bitcoin like it mean-reverts on the 5-minute. It doesn't. That gap is the whole edge. Here's the stack. Every five minutes a BTC candle closes. A Claude agent reads it into a pattern net - price, momentum, volume in; BOS, order block, FVG out. A neuron fires. It doesn't trade yet. It hands off to MiroFish - a relationship-graph sim that threads a median path through three live clusters: bear, neutral prime, catalyst. That path is where BTC actually lands. Fair value drifts 15¢ off the order book? That's the edge. Size by Kelly. Pull the trigger. Read the candle. Fire the net. Run the graph. No thesis. No conviction. It never stops sharpening, re-running the graph every few seconds, rewriting its own threshold nightly. 92% of Polymarket traders lose. The winners are bots. This one just reads two endpoints and presses one button. 13,290 times. The edge is on-chain. Read it with this bot before it gets crowded:

cvxv666

39,843 次观看 • 2 个月前

A quantum physicist lost his funding last year. He's since pulled $487,000 off Polymarket - with the math his grant was supposed to pay for. He doesn't predict where Bitcoin goes. He reads whether it's pinned in place right now - or free to run. His profile: The lab went dark. He went home with a decade of math and nothing to point it at. Here's the system. He feeds the last few hundred 5-minute BTC prints into TimesFM - Google's foundation model for time series. It hands back a distribution of where price goes next, using only what's known up to this second. No peeking at tomorrow. Then MiroFish turns that into a map. Every recent moment becomes a point on a surface. Red where price is being pulled back to equilibrium - something's holding it. Blue where it's broken free and the path can run. The points cluster: > Red pools into regions that act like a containment field > Blue pools where the structure let go The flip between them isn't gradual. It's a phase transition. That's the whole edge. It answers the only question a 5-minute candle asks: mean reversion, or momentum? He's not guessing which regime is next - he's reading which one is already live. Red, he fades the move. Blue, he rides it. Finding order in noise was his whole career. The market just pays for it directly. The bot's live right now, reading whether this candle is held or running. Two clicks and its trades land in your wallet too:

cvxv666

35,830 次观看 • 1 个月前

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 次观看 • 1 个月前

Japan just changed what an AI model even is. New Sakana Fugu doesn't try to out-think GPT-5, Claude, or Gemini. It conducts all three at once - and beats every one of them. A trader in Tokyo unleashed it on the fastest market alive - 5min Bitcoin binary and turned $6,200 into $304,865. His wallet: The frontier just stopped being which model is smartest. It's who's conducting them - and the market hasn't priced that in yet. Sakana Fugu isn't a bigger model - it's a full multi-agent orchestration system. The coordinator behind it carries about 10,000 parameters, evolved rather than hand-coded, and it runs the most capable models on earth like a single instrument. Pointed at Bitcoin, here's what it does every five minutes. It assembles a team from a pool of frontier models and assigns each one a role: > Thinker - reads the candle, the order book, the news, builds the plan > Worker - turns the plan into one call: up or down, and how much > Verifier - votes ACCEPT or REVISE before a cent moves If the Verifier says REVISE, nothing trades. Fugu reads its own miss, reroutes, even calls itself for a corrective round, and runs it again. No look-ahead, ever - the next candle only appears after it commits. This is what should worry every lab still chasing a bigger model: the edge was never scale. It's orchestration - and Fugu does it better than anything alive. Bookmark this - when the whole timeline is chasing orchestration in six months, you'll already have the breakdown. You're not going to wire up an orchestra of frontier models yourself. Mirror the wallet Fugu runs instead:

Japan just changed what an AI model even is. New Sakana Fugu doesn't try to out-think GPT-5, Claude, or Gemini. It conducts all three at once - and beats every one of them. A trader in Tokyo unleashed it on the fastest market alive - 5min Bitcoin binary and turned $6,200 into $304,865. His wallet: The frontier just stopped being which model is smartest. It's who's conducting them - and the market hasn't priced that in yet. Sakana Fugu isn't a bigger model - it's a full multi-agent orchestration system. The coordinator behind it carries about 10,000 parameters, evolved rather than hand-coded, and it runs the most capable models on earth like a single instrument. Pointed at Bitcoin, here's what it does every five minutes. It assembles a team from a pool of frontier models and assigns each one a role: > Thinker - reads the candle, the order book, the news, builds the plan > Worker - turns the plan into one call: up or down, and how much > Verifier - votes ACCEPT or REVISE before a cent moves If the Verifier says REVISE, nothing trades. Fugu reads its own miss, reroutes, even calls itself for a corrective round, and runs it again. No look-ahead, ever - the next candle only appears after it commits. This is what should worry every lab still chasing a bigger model: the edge was never scale. It's orchestration - and Fugu does it better than anything alive. Bookmark this - when the whole timeline is chasing orchestration in six months, you'll already have the breakdown. You're not going to wire up an orchestra of frontier models yourself. Mirror the wallet Fugu runs instead:

cvxv666

72,821 次观看 • 1 个月前

a Google researcher walked into MIT and made an AI do math correctly by adding seven words to the prompt. the seven words: "you are an MIT mathematician." drop them, model gets it wrong. add them, right. same model. same question. every time. Carter Smith. runs Gemini at Google. 1 hour. free. he then spent the next 50 minutes explaining why. it is the cleanest hour on how LLMs actually work I have seen in two years. you will come back to this. save it now.

a Google researcher walked into MIT and made an AI do math correctly by adding seven words to the prompt. the seven words: "you are an MIT mathematician." drop them, model gets it wrong. add them, right. same model. same question. every time. Carter Smith. runs Gemini at Google. 1 hour. free. he then spent the next 50 minutes explaining why. it is the cleanest hour on how LLMs actually work I have seen in two years. you will come back to this. save it now.

Raytar

20,399 次观看 • 2 个月前

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

A few weeks ago the US government forced Claude Fable 5 offline. It's back. And before they switch it off again, this is your shot to point the most powerful AI on earth at something of your own. Take the 26-year-old in Toronto. His bot went dark the day Fable 5 got pulled. The day it came back, it woke up and started printing again - $568,000 profit and counting. His wallet: Here's the story. When the ban hit, Anthropic had to switch Fable 5 off for everyone. They called it a misunderstanding with the government. Whatever it was, every bot running on that model stopped cold. His was one of them. He didn't rebuild a thing. The loop just sat there, paused, for weeks. Here's what that loop actually does. It's not one prompt to an AI. It's an agentic loop: a team of agents running on repeat. One reads the market, one places the trade, one tries to prove it wrong before a cent moves. Then it goes again, around the clock, keeping what works and dropping what doesn't. Fable 5 is the brain running that team. The exact model the government decided was too powerful to leave on. Point it at Bitcoin's 5-minute markets and it barely misses. Most of its trades close green, and it runs itself. He checks it from his phone. The government could switch off the model. It couldn't switch off the idea. Now the model's back, and so is the money. Save this before next headline. You'll want it the day Fable 5 hits offline again.

cvxv666

24,055 次观看 • 1 个月前

Fired Jane Street quant walked out with ten years of private BTC trading data - and turned it into $1.5M He didn't build a bot. He built a simulator that sees the matrix - it runs every move Bitcoin can make before it makes one. His wallet: Here's what he actually built. A swarm of agents feeds ten years of stolen tick data into MiroFish - a god-tier agentic simulator. It doesn't forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, re-run the sim, and remember every pattern, every reaction, every signal they've ever seen. He doesn't predict the future. The math already knows it. He just reads the numbers and takes the money. I've been copying him for a week and PnL prints like a money printer on steroids. Here's the part the firms don't want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open - barely. Save this and build your own - or skip the homework and copy his with this bot:

Fired Jane Street quant walked out with ten years of private BTC trading data - and turned it into $1.5M He didn't build a bot. He built a simulator that sees the matrix - it runs every move Bitcoin can make before it makes one. His wallet: Here's what he actually built. A swarm of agents feeds ten years of stolen tick data into MiroFish - a god-tier agentic simulator. It doesn't forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, re-run the sim, and remember every pattern, every reaction, every signal they've ever seen. He doesn't predict the future. The math already knows it. He just reads the numbers and takes the money. I've been copying him for a week and PnL prints like a money printer on steroids. Here's the part the firms don't want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open - barely. Save this and build your own - or skip the homework and copy his with this bot:

cvxv666

131,913 次观看 • 1 个月前

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:

Wall Street burns billions trying to predict Bitcoin. A 28-year-old self-taught coder in Warsaw made $377,000 by not even trying. He'd lost money on three trading bots before this one. Each looked perfect on paper, then started losing money the moment he ran it for real market. So he built bot that doesn't trust itself. His wallet: The truth is simple: you can't predict the next five minutes of Bitcoin. It's a coin flip. Anyone selling you a "prediction" is selling you nothing. So he stopped predicting. The bot hunts the moments the crowd is wrong instead. Here's the part that makes the money, and it's the opposite of what everyone builds. Any strategy can be made to look amazing on past data. On a 5-minute chart, most of them are just lucky, not real - and they stop working fast. So the bot treats every strategy it finds as fake until it proves otherwise. Each one has to pass a hard test: > test it on old data → test it on data it's never seen → try to break it on purpose → cut it down to the one thing that matters → run it forward → keep it only if it still works Last round, 10 of its 12 "winning" strategies turned out to be fake. It kept the 2 that actually worked and dropped the rest. And it never stops - building, testing, and dumping strategies around the clock. What worked yesterday can stop working today, so the second one starts losing, it gets cut before it costs you a thing. The result: $433,000 across 2,955 trades All his old bots tried to be right. This one just tries to catch itself being wrong - and that's why it's still alive. Bookmark this article below - it's the breakdown that explains why your last bot died. It's pruning and trading right now. Copy its wallet and skip to the edges that survived:

cvxv666

27,854 次观看 • 1 个月前

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

A PHYSICS PROFESSOR GAVE THE SAME LECTURE 1,742 TIMES TO WARN ABOUT ONE IDEA QUIETLY BREAKING THE HUMAN RACE. IT IS THE WORD EVERY AI LAB THROWS AROUND TODAY AND ALMOST NONE OF THEM ACTUALLY UNDERSTAND IT. 72 minutes from Albert Bartlett, a University of Colorado physicist, on the one piece of math he called humanity's greatest blind spot. -> The idea that lands: a small steady percentage is never small. 7% a year sounds calm, but it doubles in a decade, doubles again in the next, and quietly runs away from you. He shows it with bacteria in a bottle, with money, with whole cities -- the same curve that looks flat for ages and then explodes almost overnight. Everyone calls that curve "Exponential". He proves most people hear it as "Fast" when it actually means something far more violent. Right now every lab and every headline calls AI exponential. This is the talk that shows you what the word truly does -- so you see both the hype and the real danger the way the math demands. You thought it was a boring math word. It was the whole story the entire time. Save this one. You'll never hear "Exponential" the same way ↓

slash1s

99,512 次观看 • 1 个月前

I told Claude to predict the next BTC candle using Kronos. didn't touch anything. +$4,200 overnight. Kronos isn't another LLM wrapper. it's a GPT-style model trained on 12 billion candles from 45 exchanges. Binance, NYSE, NASDAQ, LSE. it reads OHLC the way GPT reads English. > words = candle patterns > sentences = OHLC sequences > the corpus = every trade ever printed > the edge is simple: P(next | history) = softmax(W · h_t) same math GPT uses to predict the next word, except the "word" is the next candle. no twitter sentiment. no blog scraping. no hallucinated confidence. just statistics over 12B real price moves. I plugged Kronos into a 5-min BTC Polymarket bot. input: last 50 candles output: probability next candle closes up trades only when |p_model - p_market| > 0.04 72 hours: $300 → $4,500 ChatGPT looks at a chart and pattern-matches against an article it read. Kronos looks at the chart and remembers 12 billion of them. 87% of Polymarket is exit liquidity. one of these models is actually trading the market. 21.8k stars, free, MIT license. Tsinghua University release.

I told Claude to predict the next BTC candle using Kronos. didn't touch anything. +$4,200 overnight. Kronos isn't another LLM wrapper. it's a GPT-style model trained on 12 billion candles from 45 exchanges. Binance, NYSE, NASDAQ, LSE. it reads OHLC the way GPT reads English. > words = candle patterns > sentences = OHLC sequences > the corpus = every trade ever printed > the edge is simple: P(next | history) = softmax(W · h_t) same math GPT uses to predict the next word, except the "word" is the next candle. no twitter sentiment. no blog scraping. no hallucinated confidence. just statistics over 12B real price moves. I plugged Kronos into a 5-min BTC Polymarket bot. input: last 50 candles output: probability next candle closes up trades only when |p_model - p_market| > 0.04 72 hours: $300 → $4,500 ChatGPT looks at a chart and pattern-matches against an article it read. Kronos looks at the chart and remembers 12 billion of them. 87% of Polymarket is exit liquidity. one of these models is actually trading the market. 21.8k stars, free, MIT license. Tsinghua University release.

RetroChainer

94,059 次观看 • 3 个月前

$BTC BOTTOM IS WRITTEN IN THE VOLUME You know what the 2018 bottom looked like in real time? Everyone was exhausted Nobody wanted to talk about Bitcoin anymore But then - one single candle printed with volume nobody had seen in months Violent, sudden, unmistakable Price never came back to that level again Then we saw 2022. It was identical Same exhaustion, same silence, then the same candle Smart money absorbed everything retail threw at the market in one session And that was it Now look at where we are Yep, here again But the sudden candle hasn't printed yet When it does, you'll know That's not a price target, not an RSI level, not a CT sentiment shift Just one candle The people who recognize it will be the ones who actually act on it Dont forget: I called the $BTC crash from $126k before it happened - publicly, on this page Every local move since then - mapped out here too The next update is the most important one this cycle - we're approaching the level where the real bottom forms Follow now to not miss the update - turn on notifications to see it first

$BTC BOTTOM IS WRITTEN IN THE VOLUME You know what the 2018 bottom looked like in real time? Everyone was exhausted Nobody wanted to talk about Bitcoin anymore But then - one single candle printed with volume nobody had seen in months Violent, sudden, unmistakable Price never came back to that level again Then we saw 2022. It was identical Same exhaustion, same silence, then the same candle Smart money absorbed everything retail threw at the market in one session And that was it Now look at where we are Yep, here again But the sudden candle hasn't printed yet When it does, you'll know That's not a price target, not an RSI level, not a CT sentiment shift Just one candle The people who recognize it will be the ones who actually act on it Dont forget: I called the $BTC crash from $126k before it happened - publicly, on this page Every local move since then - mapped out here too The next update is the most important one this cycle - we're approaching the level where the real bottom forms Follow now to not miss the update - turn on notifications to see it first

bee🐝

25,764 次观看 • 1 个月前

Persi Diaconis walked into a lecture at the University of Washington, held up a coin, and told a room of physicists that Richard Feynman was fooled by it his entire life. He was right. In 2007, Diaconis proved a coin flip is not 50/50. It lands on the side it started on about 51 percent of the time. The bias comes from the physics of rotation under gravity. Every physicist since Newton had assumed 50/50 without ever testing it. Every trading model built on that assumption is running on the same lie. The lecture was on Feynman's book "The Meaning of it All." Diaconis quoted the most famous line in it: "the first principle is that you must not fool yourself, and you are the easiest person to fool." Then he pointed out that Feynman himself was fooled by every coin he ever flipped. Feynman's own rule would have killed the 50/50 assumption on day one. The market is the same setup at scale. Every model that assumes independent 50/50 outcomes at the base layer is built on a physical impossibility. Order flow, positioning, forced flows, expiries all leave biases larger than 1 percent. Your gut cannot see them. The math already knows they are there. Diaconis's rule: before you trust a random process, check it. Actually check it. Not with a simulation. With a proof or an experiment. The coin is where you start. The chart is where the same rule pays. The only random thing about markets is how thoroughly you refuse to check them.

Persi Diaconis walked into a lecture at the University of Washington, held up a coin, and told a room of physicists that Richard Feynman was fooled by it his entire life. He was right. In 2007, Diaconis proved a coin flip is not 50/50. It lands on the side it started on about 51 percent of the time. The bias comes from the physics of rotation under gravity. Every physicist since Newton had assumed 50/50 without ever testing it. Every trading model built on that assumption is running on the same lie. The lecture was on Feynman's book "The Meaning of it All." Diaconis quoted the most famous line in it: "the first principle is that you must not fool yourself, and you are the easiest person to fool." Then he pointed out that Feynman himself was fooled by every coin he ever flipped. Feynman's own rule would have killed the 50/50 assumption on day one. The market is the same setup at scale. Every model that assumes independent 50/50 outcomes at the base layer is built on a physical impossibility. Order flow, positioning, forced flows, expiries all leave biases larger than 1 percent. Your gut cannot see them. The math already knows they are there. Diaconis's rule: before you trust a random process, check it. Actually check it. Not with a simulation. With a proof or an experiment. The coin is where you start. The chart is where the same rule pays. The only random thing about markets is how thoroughly you refuse to check them.

veles

18,798 次观看 • 19 天前

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

i watched gemma 4 12b build something genuinely impressive today, and then loop itself to death right in front of me. the full run is in the video, sped up but completely uncut, watch it to the end and you will catch the exact moment it stops building and starts looping right in the middle of the work. the task was clean, build a single file gravity simulator, n-body physics, orbits, collisions, running locally on one 3090 through an agent. and for ten minutes it was a joy to watch. it reached for a symplectic integrator on its own, the correct one, the kind that keeps orbits stable instead of spiralling out. real gravity with softening, proper orbital velocities, momentum conserved on collision. the physics was right. the thing actually worked. then on the very last step, writing a few tests to prove its own code, it fell into a loop. not a crash, a loop. it started repeating itself and would not stop. ten more minutes, thirty four thousand tokens into a single answer, the same fragments over and over, until i killed it myself. so it's not that gemma can't code. it did the hard part beautifully. it cannot finish. it cannot hold a long task together without unravelling, and finishing is the entire job in agentic work. here's the part that stings. i run this exact task, same harness, same card, on the chinese open models, qwen especially, and i never see this. they build it, they test it, they stop. every single time. google has the raw capability, you can see it sitting right there in the code, and then the model loops itself to death on a task a 27b from alibaba finishes clean. open weights, apache 2.0, so much to love on paper. i just need it to know when to stop talking.

Sudo su

39,574 次观看 • 1 个月前

A fired Jane Street quant walked out with 10 years of private BTC trading data. Turned it into $1.5M. He did not build a bot. He built a simulator that runs every move Bitcoin can make before it makes one. I found his wallet. Been copying him for a week. PnL prints like clockwork. Here is what he actually built. A swarm of agents feeds 10 years of stolen tick data into MiroFish. A god-tier agentic simulator. It does not forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, rerun the sim, and remember every pattern, every reaction, every signal they have ever seen. He does not predict the future. The math already knows it. He just reads the numbers and takes the money. Here is the part firms do not want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open. Barely. I rebuilt his approach using Claude. One prompt. Fed it the same framework. Let it run. The agent monitors his wallet 24/7. Copies every position in real-time. No delay. No guessing. Just mirror and profit. You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment the word "QUANT" 2. Like and retweet this post 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the copytrading system this week. Start with $200. Scale on evidence.

A fired Jane Street quant walked out with 10 years of private BTC trading data. Turned it into $1.5M. He did not build a bot. He built a simulator that runs every move Bitcoin can make before it makes one. I found his wallet. Been copying him for a week. PnL prints like clockwork. Here is what he actually built. A swarm of agents feeds 10 years of stolen tick data into MiroFish. A god-tier agentic simulator. It does not forecast the next candle. It spins up a virtual market and plays Bitcoin forward through thousands of scenarios at once. Six agents each validate their own call. A trade only fires when they converge. They collect data 24/7, rerun the sim, and remember every pattern, every reaction, every signal they have ever seen. He does not predict the future. The math already knows it. He just reads the numbers and takes the money. Here is the part firms do not want public: MiroFish just broke algo trading. The desks are quietly building their own simulators right now. The window where one solo wallet can run this is still open. Barely. I rebuilt his approach using Claude. One prompt. Fed it the same framework. Let it run. The agent monitors his wallet 24/7. Copies every position in real-time. No delay. No guessing. Just mirror and profit. You only need Claude + device + 1 hour to deploy. Giving this free for 24 hours. To get it: 1. Comment the word "QUANT" 2. Like and retweet this post 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the copytrading system this week. Start with $200. Scale on evidence.

Himanshu Kumar

63,213 次观看 • 1 个月前

CITADEL QUANTS HIDE THE GAME BEHIND A FORMULA WITH TEN TERMS, PREDICTION MARKETS RUN ON EXACTLY ONE The secret is one line. Edge = P(model) − P(market) That Z-field monster on screen is a stack of simple signals wearing a costume. A prediction market strips all of it down to one number, the crowd's guess at a probability. In plain words. If you can turn fresh news into a probability before the crowd reprices, the gap between your number and their price is the whole edge. That is why the game is not IQ, it is speed to a number. An AI that reads live news does in ninety seconds what the tab takes hours to price in. I broke down the four exact prompts that turn Grok into a probability desk, the sizing rules and the ten minute daily routine in the article. Still bets, not a salary, so size like it & the full playbook is in the article. Read it ↓

CITADEL QUANTS HIDE THE GAME BEHIND A FORMULA WITH TEN TERMS, PREDICTION MARKETS RUN ON EXACTLY ONE The secret is one line. Edge = P(model) − P(market) That Z-field monster on screen is a stack of simple signals wearing a costume. A prediction market strips all of it down to one number, the crowd's guess at a probability. In plain words. If you can turn fresh news into a probability before the crowd reprices, the gap between your number and their price is the whole edge. That is why the game is not IQ, it is speed to a number. An AI that reads live news does in ninety seconds what the tab takes hours to price in. I broke down the four exact prompts that turn Grok into a probability desk, the sizing rules and the ten minute daily routine in the article. Still bets, not a salary, so size like it & the full playbook is in the article. Read it ↓

slash1s

15,977 次观看 • 11 天前

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

Kimi K3 dropped 3 days ago and i already used it to build my own PUBG the model that just took the number one coding spot on earth and it runs on a free tier no team no engine i just fed it free open models and assets that are already public and told it what i wanted it built the entire game itself the shooting the recoil the hit detection all of it came straight out of the model i barely wrote a line what youre watching here is the deathmatch mode and its already live battle royale is next im deep in it and the graphics get reworked right after i know it looks raw right now i wont pretend otherwise but the endgame is a build that runs perfect on phones and the moment its on mobile you drop ads in and it earns on its own the part that still gets me is my whole cost for this was a single subscription

savip

68,860 次观看 • 16 天前

I gave Claude one prompt and it built me a bot that turned $200 into $14,000 on Polymarket No insider info, no lucky guess Just one equation nobody reads - and a bot that runs it 24/7 The price on Polymarket isn't set by people It's set by one formula - LMSR. You trade against pure math, not traders C(q) = b × ln(Σ e^(qi / b)) Someone buys 50 YES shares → price jumps from 50¢ to 62¢ It's softmax — same math GPT uses to pick the next word. Not a coincidence Then I found b — the liquidity parameter b = 50 → one whale swings the market 20% b = 100,000 → barely moves Half my old trades were in thin pools. One order wrecked my fill How much to bet? Kelly criterion f* = (p × b − q) / b 60% edge → Kelly says 20%. Pros use quarter-Kelly → 5% Not exciting. But you won't blow up Last piece. Price impact Contract at 40¢. You buy 1,000 shares - price climbs to 60¢ The edge disappears before you're filled I fed all of this into Claude. One prompt. 40 minutes. The bot checks b, sizes with Kelly, models impact - and executes before I finish my coffee b filters the pool. Kelly sizes the bet. Impact curve tells me where the edge dies I don't predict outcomes. The bot just reads the math

I gave Claude one prompt and it built me a bot that turned $200 into $14,000 on Polymarket No insider info, no lucky guess Just one equation nobody reads - and a bot that runs it 24/7 The price on Polymarket isn't set by people It's set by one formula - LMSR. You trade against pure math, not traders C(q) = b × ln(Σ e^(qi / b)) Someone buys 50 YES shares → price jumps from 50¢ to 62¢ It's softmax — same math GPT uses to pick the next word. Not a coincidence Then I found b — the liquidity parameter b = 50 → one whale swings the market 20% b = 100,000 → barely moves Half my old trades were in thin pools. One order wrecked my fill How much to bet? Kelly criterion f* = (p × b − q) / b 60% edge → Kelly says 20%. Pros use quarter-Kelly → 5% Not exciting. But you won't blow up Last piece. Price impact Contract at 40¢. You buy 1,000 shares - price climbs to 60¢ The edge disappears before you're filled I fed all of this into Claude. One prompt. 40 minutes. The bot checks b, sizes with Kelly, models impact - and executes before I finish my coffee b filters the pool. Kelly sizes the bet. Impact curve tells me where the edge dies I don't predict outcomes. The bot just reads the math

rari

52,212 次观看 • 4 个月前

MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang

MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang

MTS

99,784 次观看 • 15 天前

A blackjack dealer in Macau got blacklisted from the VIP rooms last spring for counting cards. By August he couldn't get a floor job at any property in Cotai. So he deposit $500, ran a Hermes trading agent on Polymarket and pulled in $881,319 over the next 14 months. His wallet: The casinos taught him one thing - count, size your bet to your edge, walk away when the edge is gone. So he makes 5 trades a day. Not 50. Not 500. Five. And waits for the rest. Here's the actual stack. Claude Opus 4.7 reads the order book nightly, scores every threshold market by Markov persistence x Kelly edge, and surfaces the 2-3 mispriced ones. Hermes Agent by NousResearch executes. A $10/mo Hetzner VPS runs it 24/7. Telegram pings on every fill. Total cost: $10/month. Setup: 30 minutes. No coding. One trade in April: Will Bitcoin reach $90,000? Market said 1.2¢. He put $3,088 on Yes. It hit. +$123,196. A 3,988% return on a single position. The real edge is the nightly self-learning loop. Every midnight, Opus reads the day's trade journal and rewrites MIN_PROB and MIN_EDGE in the .env file. Last week the threshold was 0.87. This week 0.89. Next week maybe 0.91. His version of the bot has rewritten itself 412 times in 14 months. The Macau syndicate couldn't ban him from Polymarket. The bot doesn't sit at a table. It doesn't show a passport. It just hunts the tails. Save this post - if you want to build something of your own based on Hermes. Or just start copying algorithm that has improved itself 412 times:

A blackjack dealer in Macau got blacklisted from the VIP rooms last spring for counting cards. By August he couldn't get a floor job at any property in Cotai. So he deposit $500, ran a Hermes trading agent on Polymarket and pulled in $881,319 over the next 14 months. His wallet: The casinos taught him one thing - count, size your bet to your edge, walk away when the edge is gone. So he makes 5 trades a day. Not 50. Not 500. Five. And waits for the rest. Here's the actual stack. Claude Opus 4.7 reads the order book nightly, scores every threshold market by Markov persistence x Kelly edge, and surfaces the 2-3 mispriced ones. Hermes Agent by NousResearch executes. A $10/mo Hetzner VPS runs it 24/7. Telegram pings on every fill. Total cost: $10/month. Setup: 30 minutes. No coding. One trade in April: Will Bitcoin reach $90,000? Market said 1.2¢. He put $3,088 on Yes. It hit. +$123,196. A 3,988% return on a single position. The real edge is the nightly self-learning loop. Every midnight, Opus reads the day's trade journal and rewrites MIN_PROB and MIN_EDGE in the .env file. Last week the threshold was 0.87. This week 0.89. Next week maybe 0.91. His version of the bot has rewritten itself 412 times in 14 months. The Macau syndicate couldn't ban him from Polymarket. The bot doesn't sit at a table. It doesn't show a passport. It just hunts the tails. Save this post - if you want to build something of your own based on Hermes. Or just start copying algorithm that has improved itself 412 times:

cvxv666

208,148 次观看 • 2 个月前