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sopersone

@sopersone • 5,739 subscribers

Inexperienced designer | living stress-free | learning quant trading with AI | prediction markets researcher https://t.co/1pLJKcDRcP

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SOMETHING PUNCHED A HOLE THROUGH FROZEN LAKE 10,000 likes. 29 comments. Filmed from a helicopter door over an ice sheet. Something drops, the ice opens in a small red-lit circle, cracks race outward, and a column of water blows straight up through the hole the cracks are what sells this, and they are the one part that is hard to fake: → they radiate from the impact point in a spoke pattern, which is what brittle sheets actually do under a point load → the spacing is irregular. real fractures branch unevenly because the ice is not uniform, and evenly spaced cracks read as decoration immediately → they propagate outward over time rather than appearing at once. fracture has a speed, and showing it is what makes the eye accept it → secondary cracks split off the main ones at sharp angles instead of curving, which is correct for ice and wrong for glass or mud → and the plume comes after the fracture, not with it. pressure needs the hole before it can use it that ordering is the whole trick. a convincing destruction shot is not about how the damage looks, it is about the sequence the damage arrives in, and sequence is the thing a still image can never carry which is why this category of clip keeps outperforming much more elaborate work. no character, no dialogue, no continuity to protect. one event, shown from cause to consequence, in the correct order the useful takeaway for anything you make: find the shot where the physics does the storytelling, and get out of its way. the viewer is not evaluating the render, they are checking whether the world behaved ten thousand likes on twenty-nine comments says nobody argued with it, which for this kind of clip is the result you want if you want to test a single-event idea before building anything around it, image-to-video off one still is the cheapest way in. Picsart runs it from a phone the hole is the easy part. the order the cracks arrived in is what convinced you

SOMETHING PUNCHED A HOLE THROUGH FROZEN LAKE 10,000 likes. 29 comments. Filmed from a helicopter door over an ice sheet. Something drops, the ice opens in a small red-lit circle, cracks race outward, and a column of water blows straight up through the hole the cracks are what sells this, and they are the one part that is hard to fake: → they radiate from the impact point in a spoke pattern, which is what brittle sheets actually do under a point load → the spacing is irregular. real fractures branch unevenly because the ice is not uniform, and evenly spaced cracks read as decoration immediately → they propagate outward over time rather than appearing at once. fracture has a speed, and showing it is what makes the eye accept it → secondary cracks split off the main ones at sharp angles instead of curving, which is correct for ice and wrong for glass or mud → and the plume comes after the fracture, not with it. pressure needs the hole before it can use it that ordering is the whole trick. a convincing destruction shot is not about how the damage looks, it is about the sequence the damage arrives in, and sequence is the thing a still image can never carry which is why this category of clip keeps outperforming much more elaborate work. no character, no dialogue, no continuity to protect. one event, shown from cause to consequence, in the correct order the useful takeaway for anything you make: find the shot where the physics does the storytelling, and get out of its way. the viewer is not evaluating the render, they are checking whether the world behaved ten thousand likes on twenty-nine comments says nobody argued with it, which for this kind of clip is the result you want if you want to test a single-event idea before building anything around it, image-to-video off one still is the cheapest way in. Picsart runs it from a phone the hole is the easy part. the order the cracks arrived in is what convinced you

4,736,004 просмотров

SHE PUMPED GAS AND GOT 745 COMMENTS 47,000 likes. 745 comments. A woman fills up a car at a roadside station. That is the entire event. The caption reads: honestly no idea how that got there look at the comment ratio before anything else: → 745 comments against 47,000 likes is one per sixty-three. most clips this size land nearer one per five hundred → nothing in the footage explains that. static camera, one location, no edit, no reveal, no payoff → the caption is doing all of it. four words of mock confusion, pointing at something without naming it → that construction forces a decision. the viewer has to work out what is being referred to, and working it out is what moves them into the comments → and it is unfalsifiable. she never says what she means, so nobody can be wrong about it, which is exactly why everyone answers the technique is old and it has a name in older media: the implied antecedent. you refer to a thing without stating it, and the audience supplies the missing noun themselves what makes it powerful on short form is that supplying it feels like participation rather than reaction. a like is passive, but finishing somebody else's sentence is an act, and platforms weight acts far more heavily than they weight approval so the caption is not describing the video. it is a prompt, aimed at the comment box, disguised as an offhand remark the general version, worth stealing for anything you post: the footage decides whether somebody stops. the caption decides whether they do something. most people spend all their effort on the first and write the second in the lift on the way out if you want to test a hook before you shoot anything, image-to-video off one still is the cheapest way in. Picsart runs it from a phone nothing happened in fourteen seconds. seven hundred people explained what it was

SHE PUMPED GAS AND GOT 745 COMMENTS 47,000 likes. 745 comments. A woman fills up a car at a roadside station. That is the entire event. The caption reads: honestly no idea how that got there look at the comment ratio before anything else: → 745 comments against 47,000 likes is one per sixty-three. most clips this size land nearer one per five hundred → nothing in the footage explains that. static camera, one location, no edit, no reveal, no payoff → the caption is doing all of it. four words of mock confusion, pointing at something without naming it → that construction forces a decision. the viewer has to work out what is being referred to, and working it out is what moves them into the comments → and it is unfalsifiable. she never says what she means, so nobody can be wrong about it, which is exactly why everyone answers the technique is old and it has a name in older media: the implied antecedent. you refer to a thing without stating it, and the audience supplies the missing noun themselves what makes it powerful on short form is that supplying it feels like participation rather than reaction. a like is passive, but finishing somebody else's sentence is an act, and platforms weight acts far more heavily than they weight approval so the caption is not describing the video. it is a prompt, aimed at the comment box, disguised as an offhand remark the general version, worth stealing for anything you post: the footage decides whether somebody stops. the caption decides whether they do something. most people spend all their effort on the first and write the second in the lift on the way out if you want to test a hook before you shoot anything, image-to-video off one still is the cheapest way in. Picsart runs it from a phone nothing happened in fourteen seconds. seven hundred people explained what it was

123,371 просмотров

I OPEN-SOURCED MY CRYPTO TRADING BOT ON GITHUB. 100% FREE It's called Cabbage. A spot trading bot you can backtest, paper-trade and run live, one command each. Full step-by-step setup guide. It runs on two models: > Astra (GPT-6) built it. Smart but slow. It thinks, writes the code, explains every decision. > Jev runs it. Jev doesn't talk. One word and a probability, in milliseconds. BUY, HOLD or SELL. Under 0.80 it waits. Here's what's inside: 1. Signal engine RSI 14 + EMA 12/26 confluence. It only reads closed candles, so no fake signals from a candle that's still moving. 2. Hard risk rules Fixed ticket size and a percentage stop-loss on every entry. The bot can't size up because it "feels confident". 3. Backtest → HTML report Event-driven backtest that replays history candle by candle, like it's trading live. You get a full HTML report at the end. 4. Paper trading Real exchange data, local fills, zero money at risk. Run one iteration to test, then leave it running. 5. Live trading Real orders on any exchange CCXT supports. Your keys stay in your own .env file. 6. Doctor command Checks your config and exchange connection before you trade. No orders placed. State is saved in SQLite per market and pair, so a restart doesn't wipe your positions. Built on Investing Algorithm Framework 9. Apache-2.0, fork it and make it yours.

I OPEN-SOURCED MY CRYPTO TRADING BOT ON GITHUB. 100% FREE It's called Cabbage. A spot trading bot you can backtest, paper-trade and run live, one command each. Full step-by-step setup guide. It runs on two models: > Astra (GPT-6) built it. Smart but slow. It thinks, writes the code, explains every decision. > Jev runs it. Jev doesn't talk. One word and a probability, in milliseconds. BUY, HOLD or SELL. Under 0.80 it waits. Here's what's inside: 1. Signal engine RSI 14 + EMA 12/26 confluence. It only reads closed candles, so no fake signals from a candle that's still moving. 2. Hard risk rules Fixed ticket size and a percentage stop-loss on every entry. The bot can't size up because it "feels confident". 3. Backtest → HTML report Event-driven backtest that replays history candle by candle, like it's trading live. You get a full HTML report at the end. 4. Paper trading Real exchange data, local fills, zero money at risk. Run one iteration to test, then leave it running. 5. Live trading Real orders on any exchange CCXT supports. Your keys stay in your own .env file. 6. Doctor command Checks your config and exchange connection before you trade. No orders placed. State is saved in SQLite per market and pair, so a restart doesn't wipe your positions. Built on Investing Algorithm Framework 9. Apache-2.0, fork it and make it yours.

62,070 просмотров

a real fly brain saw its 40th video 3 seconds ago. it did not choose any of them. it never will. that's FLYTOK. I open-sourced a live simulation of a real fruit fly connectome and gave it a phone. here's exactly what 166,700 neurons do while you scroll: THE WIRING MaleCNS v1.0. the reconstructed central nervous system of a real male Drosophila. 166,700 neurons. 25,582,938 directed connections. 124,177,617 synaptic contacts. nothing cropped. no "simplified 1,000-neuron version" running behind the scenes. the whole graph is in memory on every single step. THE LOCK the app checksums the connectome against SHA-256 before it's allowed to boot. one array off by a byte and the brain refuses to start. you are either watching the real MaleCNS graph or you are watching nothing. THE SPIKES every neuron is a leaky integrate-and-fire cell. crosses −45 mV, it fires. the spike lands 1.8 ms later. 2.2 ms of silence, then it's live again. the network advances in 0.1 ms steps inside a compiled C++ kernel. roughly 1.2 million spikes per simulated second. THE EYES the browser captures the phone at 90×160 pixels. brightness drives 3,335 R1–R6 photoreceptors. color drives 811 R8 cells, blue and green. each one is placed on the screen by inferring its eye column from its real connections onto L1, L2, L3. real input, through real visual wiring. it's measurable. same brain, same saved state. 100 ms of white produced 127,378 spikes. black produced 92,952. the screen changes the brain. THE ALGORITHM while a video plays, I inject a fixed current into all 15 PAM11 dopamine neurons in the mushroom body. that's it. that's the whole trick. matched 200 ms control: 0 PAM11 spikes. drive on: 261. live, it sits at 85–97 Hz for as long as the feed runs. the fly does not earn it. an app decided watching one more should feel good. THE PLASTICITY 7,835 Kenyon-cell connections onto MBON07 and MBON11 can change. dopamine fires while Kenyon cells were active, those synapses weaken, floored at 10% and never flipping sign. after one stimulated run, 3,087 of them no longer matched their original weight. freeze it and they stay put. restore a checkpoint, replay the input, identical spikes bit for bit. THE SWIPE every 3 seconds the front right leg reaches up and strokes the screen, on the same 900 ms timeline as the video transition. wings, legs and head turns come from actual motor firing, MN9, DNp09, left-vs-right DNa02. those are real. the swipe is not. the fly never decides to swipe. the feed decides for it. ONE BRAIN this isn't a video of a simulation. it's the simulation. there is exactly one brain on one server. whoever loads the page first becomes its eyes and supplies the frames. everyone else watches the same brain react in real time. no video, no input, it just waits. no data, no numbers, the overlay shows nothing. it never invents a reading to look alive. THE HONEST PART the wiring is real. the physiology is approximate. the eyes are approximate. the dopamine is artificial. the feed is on a timer. the learning rule is unvalidated on this connectome. nothing here shows the fly enjoys it, prefers it, is addicted, or experiences anything at all. no living fly was involved. it took a few hundred lines of glue code to build a loop out of a screen, a timer and a reward signal wired to the right cells. imagine what an industry with a budget can do. open source. MaleCNS under CC BY 4.0. runs on your own machine, 16 GB RAM and a few GB of disk. the fly is still scrolling. nobody is coming to take the phone away. links in the reply.

a real fly brain saw its 40th video 3 seconds ago. it did not choose any of them. it never will. that's FLYTOK. I open-sourced a live simulation of a real fruit fly connectome and gave it a phone. here's exactly what 166,700 neurons do while you scroll: THE WIRING MaleCNS v1.0. the reconstructed central nervous system of a real male Drosophila. 166,700 neurons. 25,582,938 directed connections. 124,177,617 synaptic contacts. nothing cropped. no "simplified 1,000-neuron version" running behind the scenes. the whole graph is in memory on every single step. THE LOCK the app checksums the connectome against SHA-256 before it's allowed to boot. one array off by a byte and the brain refuses to start. you are either watching the real MaleCNS graph or you are watching nothing. THE SPIKES every neuron is a leaky integrate-and-fire cell. crosses −45 mV, it fires. the spike lands 1.8 ms later. 2.2 ms of silence, then it's live again. the network advances in 0.1 ms steps inside a compiled C++ kernel. roughly 1.2 million spikes per simulated second. THE EYES the browser captures the phone at 90×160 pixels. brightness drives 3,335 R1–R6 photoreceptors. color drives 811 R8 cells, blue and green. each one is placed on the screen by inferring its eye column from its real connections onto L1, L2, L3. real input, through real visual wiring. it's measurable. same brain, same saved state. 100 ms of white produced 127,378 spikes. black produced 92,952. the screen changes the brain. THE ALGORITHM while a video plays, I inject a fixed current into all 15 PAM11 dopamine neurons in the mushroom body. that's it. that's the whole trick. matched 200 ms control: 0 PAM11 spikes. drive on: 261. live, it sits at 85–97 Hz for as long as the feed runs. the fly does not earn it. an app decided watching one more should feel good. THE PLASTICITY 7,835 Kenyon-cell connections onto MBON07 and MBON11 can change. dopamine fires while Kenyon cells were active, those synapses weaken, floored at 10% and never flipping sign. after one stimulated run, 3,087 of them no longer matched their original weight. freeze it and they stay put. restore a checkpoint, replay the input, identical spikes bit for bit. THE SWIPE every 3 seconds the front right leg reaches up and strokes the screen, on the same 900 ms timeline as the video transition. wings, legs and head turns come from actual motor firing, MN9, DNp09, left-vs-right DNa02. those are real. the swipe is not. the fly never decides to swipe. the feed decides for it. ONE BRAIN this isn't a video of a simulation. it's the simulation. there is exactly one brain on one server. whoever loads the page first becomes its eyes and supplies the frames. everyone else watches the same brain react in real time. no video, no input, it just waits. no data, no numbers, the overlay shows nothing. it never invents a reading to look alive. THE HONEST PART the wiring is real. the physiology is approximate. the eyes are approximate. the dopamine is artificial. the feed is on a timer. the learning rule is unvalidated on this connectome. nothing here shows the fly enjoys it, prefers it, is addicted, or experiences anything at all. no living fly was involved. it took a few hundred lines of glue code to build a loop out of a screen, a timer and a reward signal wired to the right cells. imagine what an industry with a budget can do. open source. MaleCNS under CC BY 4.0. runs on your own machine, 16 GB RAM and a few GB of disk. the fly is still scrolling. nobody is coming to take the phone away. links in the reply.

37,608 просмотров

this is fucking gold.... i built CROWBRAIN, a digital market brain inspired by the 1.5 billion neurons inside one of the smartest birds on earth it scans tokens on Solana and Robinhood Chain, processes price movement, volume and liquidity, filters weak pools and produces signals i put every indicator currently available to the system into one pipeline, so the signal is never just a random green or red button. you can inspect the token, liquidity threshold, market movement, executed rule and complete decision log the interactive brain shows the system processing each token in real time, while the engine exposes what it is checking underneath and now the whole project is open source: website: the current version detects signals while i execute the trades manually next update: connect a real balance and let CROWBRAIN trade by itself

this is fucking gold.... i built CROWBRAIN, a digital market brain inspired by the 1.5 billion neurons inside one of the smartest birds on earth it scans tokens on Solana and Robinhood Chain, processes price movement, volume and liquidity, filters weak pools and produces signals i put every indicator currently available to the system into one pipeline, so the signal is never just a random green or red button. you can inspect the token, liquidity threshold, market movement, executed rule and complete decision log the interactive brain shows the system processing each token in real time, while the engine exposes what it is checking underneath and now the whole project is open source: website: the current version detects signals while i execute the trades manually next update: connect a real balance and let CROWBRAIN trade by itself

21,197 просмотров

I spent 48h to find this ''bot trading wallet'' on Polymarket He made 10,057 trades in one month and earned $87,000 > this wallet is an arbitrage bot > trades only BTC Up or Down (5 min) only > always use the same strategy And here's how it trades: > waits until one of the outcomes (YES/NO) falls below 35¢ > buy more when the average price is low > watch how the opposite outcome moves > makes sure the total average prices are under 99¢ > if he doesn't have a hedge, just closing the position with no losses This is exactly how algo-trading works His profile:

I spent 48h to find this ''bot trading wallet'' on Polymarket He made 10,057 trades in one month and earned $87,000 > this wallet is an arbitrage bot > trades only BTC Up or Down (5 min) only > always use the same strategy And here's how it trades: > waits until one of the outcomes (YES/NO) falls below 35¢ > buy more when the average price is low > watch how the opposite outcome moves > makes sure the total average prices are under 99¢ > if he doesn't have a hedge, just closing the position with no losses This is exactly how algo-trading works His profile:

37,390 просмотров

THAT PRETTY 3D QUANT CHART IS JUST A WEIGHTED SUM OF SIGNALS. THEY'VE GATEKEPT IT FOR 40 YEARS That gorgeous formula on screen is doing one simple thing. It scores every stock in a universe on a blend of signals, then goes long the strongest and short the weakest. That is the entire quant game, hiding inside one Sigma. The signals even have names. Free cash flow yield and earnings yield for value, QMJ profitability and Piotroski F-score for quality, plus momentum, mean reversion and volatility. Minara ships 500+ of these factors and lets you blend them into that exact composite by typing one sentence. The PhD math is real. The wall in front of it is gone. I ran this exact cross-sectional composite on 30 stocks & posted every metric in the article. Check it ↓

THAT PRETTY 3D QUANT CHART IS JUST A WEIGHTED SUM OF SIGNALS. THEY'VE GATEKEPT IT FOR 40 YEARS That gorgeous formula on screen is doing one simple thing. It scores every stock in a universe on a blend of signals, then goes long the strongest and short the weakest. That is the entire quant game, hiding inside one Sigma. The signals even have names. Free cash flow yield and earnings yield for value, QMJ profitability and Piotroski F-score for quality, plus momentum, mean reversion and volatility. Minara ships 500+ of these factors and lets you blend them into that exact composite by typing one sentence. The PhD math is real. The wall in front of it is gone. I ran this exact cross-sectional composite on 30 stocks & posted every metric in the article. Check it ↓

33,919 просмотров

I launched a weather trading bot on Polymarket with $100 -> $8,000 here is how it works and how to copy it most people on polymarket price weather markets randomly based on vibes or a phone app but there is NOAA - a government agency that publishes free forecasts with 94%+ accuracy the result? you regularly see something like this: > NOAA gives a 94% probability that NYC will reach 74°F on Saturday > polymarket prices it at 11¢ the bot buys at 11¢ sells when the market corrects to 45¢ that is 4x on an almost guaranteed outcome how to launch it in 5 steps: > install OpenClaw on your pc > connect it to ChatGPT Plus + a telegram bot > create an agent on simmer markets deposit $100 > install the weather trading skill with: clawhub install simmer-weather > send the config to the bot and it starts trading the bot scans 6 cities every 2 minutes non-stop example of a real trader: profile: +$75,055 just from weather markets zero emotions. fully automated the window is still open but not forever

I launched a weather trading bot on Polymarket with $100 -> $8,000 here is how it works and how to copy it most people on polymarket price weather markets randomly based on vibes or a phone app but there is NOAA - a government agency that publishes free forecasts with 94%+ accuracy the result? you regularly see something like this: > NOAA gives a 94% probability that NYC will reach 74°F on Saturday > polymarket prices it at 11¢ the bot buys at 11¢ sells when the market corrects to 45¢ that is 4x on an almost guaranteed outcome how to launch it in 5 steps: > install OpenClaw on your pc > connect it to ChatGPT Plus + a telegram bot > create an agent on simmer markets deposit $100 > install the weather trading skill with: clawhub install simmer-weather > send the config to the bot and it starts trading the bot scans 6 cities every 2 minutes non-stop example of a real trader: profile: +$75,055 just from weather markets zero emotions. fully automated the window is still open but not forever

64,986 просмотров

Innovation at Kalshi = Ctrl+C Ctrl+V

Innovation at Kalshi = Ctrl+C Ctrl+V

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I'M F*CKING LOSING MY MIND OVER JEV ON CABBAGE it made me $6,400 in one night on Robinhood Chain 4 trades. 4 green. i didn't touch a thing i said one sentence and walked away from my computer for 12 hours here's what i said: "if you don't make me enough by morning to quit my job, i'm unplugging you and giving your GPU to my cousin for fortnite" at 10am i saw +$6,400 and spent the next hour and a half reading the logs from the beginning. every single entry like reading my ex's messages after she said "he's just a friend" -22:00 received the task 22:00:20. first scan. 536 wallets on the board. every buy and sell on Robinhood Chain, right behind the block 22:14 throws out a "smart money buy". wash trade. the wallet was buying from itself 1 in 11 "smart money buys" is fake. i've personally aped into all 11 23:02 three top-ranked wallets start buying $IMDSTRTGY. asks Jev one question Jev: HOLD under 80% confidence, so it doesn't buy. just waits i've never waited for anything in my life. i buy the chart while it's still loading 23:40 two more top wallets join. Jev: BUY fixed ticket in. hard stop-loss set. the AI can't touch either one posted to Telegram the second it fired 01:15 closes $IMDSTRTGY at +$3,200. doesn't celebrate. recalculates. keeps going 02:20 $ROO. several checks before entering. wallet history, exits, how fast they dump. Jev: BUY 03:05 out of $ROO at +$1,200 03:30 $MUSEPAD. top wallets piling in within seconds of each other. Jev: BUY 04:50 closes $MUSEPAD at +$700. the price keeps climbing it doesn't care. the wallets it copies started selling, so it sold too this is exactly where i would have bought back higher out of spite 06:40 $MUSEGOD. Jev: BUY 07:30 out at +$1,300 then checks the next opportunity. passes. keeps looking no fatigue. no "one more then bed" that somehow ends at lunch 10:00 i'm back at the computer - $IMDSTRTGY +$3,200 - $ROO +$1,200 - $MUSEPAD +$700 - $MUSEGOD +$1,300 +$6,400 52,110 decisions. 21.6 seconds of thinking. $0.37 in API 41,880 fills checked. 536 wallets scored. only 12 made the cut and i didn't do a single one of them here's how it works: > Astra (GPT-6) built it. it's smart but slow. it thinks, writes code, explains itself > Jev runs it. Jev doesn't talk. one word and a probability, in milliseconds every 20 seconds: > WATCHES every buy and sell on Robinhood Chain > FILTERS wash trades, self-buys and bot loops > RANKS every wallet 0–100 on entries, exits, win rate and how fast it dumps > ASKS JEV: BUY, HOLD or SELL. under 80% it waits > ENTERS with a fixed ticket and a hard stop. the AI can't change either > POSTS every call to Telegram instantly, and to X an hour later the smart money desks have humans watching wallets like this, and those results never leave the building these ones do
0:28
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I'M F*CKING LOSING MY MIND OVER JEV ON CABBAGE it made me $6,400 in one night on Robinhood Chain 4 trades. 4 green. i didn't touch a thing i said one sentence and walked away from my computer for 12 hours here's what i said: "if you don't make me enough by morning to quit my job, i'm unplugging you and giving your GPU to my cousin for fortnite" at 10am i saw +$6,400 and spent the next hour and a half reading the logs from the beginning. every single entry like reading my ex's messages after she said "he's just a friend" -22:00 received the task 22:00:20. first scan. 536 wallets on the board. every buy and sell on Robinhood Chain, right behind the block 22:14 throws out a "smart money buy". wash trade. the wallet was buying from itself 1 in 11 "smart money buys" is fake. i've personally aped into all 11 23:02 three top-ranked wallets start buying $IMDSTRTGY. asks Jev one question Jev: HOLD under 80% confidence, so it doesn't buy. just waits i've never waited for anything in my life. i buy the chart while it's still loading 23:40 two more top wallets join. Jev: BUY fixed ticket in. hard stop-loss set. the AI can't touch either one posted to Telegram the second it fired 01:15 closes $IMDSTRTGY at +$3,200. doesn't celebrate. recalculates. keeps going 02:20 $ROO. several checks before entering. wallet history, exits, how fast they dump. Jev: BUY 03:05 out of $ROO at +$1,200 03:30 $MUSEPAD. top wallets piling in within seconds of each other. Jev: BUY 04:50 closes $MUSEPAD at +$700. the price keeps climbing it doesn't care. the wallets it copies started selling, so it sold too this is exactly where i would have bought back higher out of spite 06:40 $MUSEGOD. Jev: BUY 07:30 out at +$1,300 then checks the next opportunity. passes. keeps looking no fatigue. no "one more then bed" that somehow ends at lunch 10:00 i'm back at the computer - $IMDSTRTGY +$3,200 - $ROO +$1,200 - $MUSEPAD +$700 - $MUSEGOD +$1,300 +$6,400 52,110 decisions. 21.6 seconds of thinking. $0.37 in API 41,880 fills checked. 536 wallets scored. only 12 made the cut and i didn't do a single one of them here's how it works: > Astra (GPT-6) built it. it's smart but slow. it thinks, writes code, explains itself > Jev runs it. Jev doesn't talk. one word and a probability, in milliseconds every 20 seconds: > WATCHES every buy and sell on Robinhood Chain > FILTERS wash trades, self-buys and bot loops > RANKS every wallet 0–100 on entries, exits, win rate and how fast it dumps > ASKS JEV: BUY, HOLD or SELL. under 80% it waits > ENTERS with a fixed ticket and a hard stop. the AI can't change either > POSTS every call to Telegram instantly, and to X an hour later the smart money desks have humans watching wallets like this, and those results never leave the building these ones do

sopersone

63,695 просмотров • 25 дней назад

what the ffffffff i spent 12 hours building a digital crow brain that scans Solana and Robinhood Chain and gives me BUY / SELL / WAIT signals then i started trading them $100 → $1,362 CROWBRAIN. the idea is simple: crows are among the smartest birds on earth. they use tools, remember faces, solve complex problems and have around 1.5 billion neurons inside a tiny brain i wanted to build a market system inspired by that intelligence every few seconds it: → finds trending and newly created tokens across both chains → checks price movement, volume and liquidity → removes pools with less than $25K in reserves → gives me BUY / SELL / WAIT with the reason behind every decision → records every check inside a public execution log → displays the process through an interactive crow brain there is no private group and no hidden signal feed. the system finds the opportunities, but i still choose which trades to execute 12 hours of building $100 → $1,362 and i decided to share the whole thing for free: repo: next: connect a real balance and let CROWBRAIN trade automatically until then, i’ll keep trading its signals manually and share the results here should i post every signal it catches live?
0:48
sopersone's profile picture

what the ffffffff i spent 12 hours building a digital crow brain that scans Solana and Robinhood Chain and gives me BUY / SELL / WAIT signals then i started trading them $100 → $1,362 CROWBRAIN. the idea is simple: crows are among the smartest birds on earth. they use tools, remember faces, solve complex problems and have around 1.5 billion neurons inside a tiny brain i wanted to build a market system inspired by that intelligence every few seconds it: → finds trending and newly created tokens across both chains → checks price movement, volume and liquidity → removes pools with less than $25K in reserves → gives me BUY / SELL / WAIT with the reason behind every decision → records every check inside a public execution log → displays the process through an interactive crow brain there is no private group and no hidden signal feed. the system finds the opportunities, but i still choose which trades to execute 12 hours of building $100 → $1,362 and i decided to share the whole thing for free: repo: next: connect a real balance and let CROWBRAIN trade automatically until then, i’ll keep trading its signals manually and share the results here should i post every signal it catches live?

sopersone

47,223 просмотров • 1 месяц назад

A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't.
0:42
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A finance professor manages $200M with AI agents, and he told everyone why: "Large language models are at the level of a fourth-year PhD student in every field" Alejandro Lopez-Lira's AI fund, Autopilot, returned 56% last year. The S&P did 16%. There are 52,000 people with money in it, and most of them just watch the machine work. What he automated is the same six-step loop every fund on earth runs: find an idea, code it, backtest it, deploy it, read the autopsy, learn from it. A quant at Two Sigma runs that loop once a month, and the salary time alone costs around $50,000 per hypothesis. All steps from this loop now fit in AI trading text box. Plain English in, executable strategy out, five-year backtest in 12 seconds, live on a broker 90 seconds after you typed the sentence. He runs $200M with AI. You can run same AI fund in two clicks, free to try: Step 6 on this loop is where everyone is stuck. Your agent has no memory. Every strategy it kills goes into a log nobody reads, and the next one starts from zero. Nobody keeps negative results. Not Citadel, not Man Group, not a single repo on GitHub. Fix that and the agent remembers every hypothesis it killed and the regime it died in. It stops burning cycles on your old mistakes. Jane Street pays 3,500 people to run this cycle and made $39.6 billion doing it. Five sixths of it is now free. Bookmark & read full map of this loop in the article below. Most people still think AI trading is out of reach for them - it isn't.

sopersone

48,482 просмотров • 1 месяц назад

THE FIRST CROWBRAIN AUTONOMOUS TRADING EXPERIMENT IS COMPLETE I created a separate wallet for the system, deposited $50 and allowed the algorithm to execute its own signals for the first time. During the first five hours: $50 → $420 CROWBRAIN discovered tokens, analyzed price movement, volume and liquidity, generated BUY / SELL signals, and opened and closed positions automatically. But then the market changed while the algorithm continued trading under the same rules. It took profits too late, re-entered after major price moves and failed to stop after a series of losing trades. Eventually, the profit disappeared and the overall result of the experiment went negative. That is exactly why I am testing it publicly. It would be easy to show only the $50 → $420 moment and stay silent about what happened afterward, but the purpose of this experiment is not to produce one impressive screenshot. It is to build a system that can control risk and perform consistently under different market conditions. Now I am adding: → automatic partial profit-taking → a dynamic stop-loss → position-size limits → a cooldown after consecutive losses → a daily loss limit → protection against re-entering after a sharp price increase → market-regime detection The first test proved that CROWBRAIN can already find opportunities and turn $50 into $420. It also showed that finding good entries means nothing without disciplined exits and proper risk management. The experiment continues. After the update, I will fund a new public wallet with another $50, restart the algorithm and show every trade - successful or not. Let’s see what CROWBRAIN learns from its first serious mistake.
0:46
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THE FIRST CROWBRAIN AUTONOMOUS TRADING EXPERIMENT IS COMPLETE I created a separate wallet for the system, deposited $50 and allowed the algorithm to execute its own signals for the first time. During the first five hours: $50 → $420 CROWBRAIN discovered tokens, analyzed price movement, volume and liquidity, generated BUY / SELL signals, and opened and closed positions automatically. But then the market changed while the algorithm continued trading under the same rules. It took profits too late, re-entered after major price moves and failed to stop after a series of losing trades. Eventually, the profit disappeared and the overall result of the experiment went negative. That is exactly why I am testing it publicly. It would be easy to show only the $50 → $420 moment and stay silent about what happened afterward, but the purpose of this experiment is not to produce one impressive screenshot. It is to build a system that can control risk and perform consistently under different market conditions. Now I am adding: → automatic partial profit-taking → a dynamic stop-loss → position-size limits → a cooldown after consecutive losses → a daily loss limit → protection against re-entering after a sharp price increase → market-regime detection The first test proved that CROWBRAIN can already find opportunities and turn $50 into $420. It also showed that finding good entries means nothing without disciplined exits and proper risk management. The experiment continues. After the update, I will fund a new public wallet with another $50, restart the algorithm and show every trade - successful or not. Let’s see what CROWBRAIN learns from its first serious mistake.

sopersone

33,764 просмотров • 1 месяц назад

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.
2:59:19
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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

161,273 просмотров • 6 месяцев назад

quant traders before: > Kelly Criterion (position sizing) > Black-Scholes-Merton (options pricing) > Sharpe / Sortino Ratio (risk-adjusted return) > CAPM (expected return) > GARCH models (volatility forecasting) > Markowitz Mean-Variance Optimization (portfolio allocation) > Monte Carlo simulations (risk scenarios) now: all of the above. one AI.
0:20
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quant traders before: > Kelly Criterion (position sizing) > Black-Scholes-Merton (options pricing) > Sharpe / Sortino Ratio (risk-adjusted return) > CAPM (expected return) > GARCH models (volatility forecasting) > Markowitz Mean-Variance Optimization (portfolio allocation) > Monte Carlo simulations (risk scenarios) now: all of the above. one AI.

sopersone

72,810 просмотров • 2 месяцев назад

Someone read my guide on building a Polymarket trading bot in Python built it and just sent me this video 👇 I’m happy haha
0:58
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Someone read my guide on building a Polymarket trading bot in Python built it and just sent me this video 👇 I’m happy haha

sopersone

139,928 просмотров • 6 месяцев назад

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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the difference between $332 and $2219 trade:
0:45
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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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the difference between $332 and $2219 trade:

sopersone

121,533 просмотров • 6 месяцев назад

I USED GPT-6 ASTRA TO CLONE 100+ OF THE BEST MEMECOIN TRADERS ON ROBINHOOD CHAIN. THEN I LET JEV PULL THE TRIGGER ASTRA + JEV = CABBAGE 52,110 decisions in 21.6 seconds. $0.37 in API. Nobody touched it Every call gets posted to X and Telegram automatically Anyone can follow the same calls 41,880 fills. 536 wallets scored. Only 12 made the cut. Astra built it. Jev runs it Every 20 seconds: > WATCHES every buy and sell on Robinhood Chain, right behind the block > FILTERS the noise. Wash trades, self-buys and bot loops get thrown out. 1 in 11 "smart money buys" is fake > RANKS every wallet 0 to 100 on how it actually trades: entries, exits, win rate, how fast it dumps > ASKS JEV one question when top wallets start buying the same coin: BUY, HOLD or SELL? Under 80% confidence, it waits > ENTERS with a fixed $100 ticket and a hard stop-loss. The AI can't change either > POSTS every call to Telegram the second it fires, and to X an hour later Why two models: -> Astra is smart but slow. It thinks, writes, explains. Perfect for building the system. -> Jev doesn't talk. One word and a probability, in milliseconds. Perfect for the moment money moves. The smart money desks have humans watching wallets like this, and the results never leave the building.
0:29
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I USED GPT-6 ASTRA TO CLONE 100+ OF THE BEST MEMECOIN TRADERS ON ROBINHOOD CHAIN. THEN I LET JEV PULL THE TRIGGER ASTRA + JEV = CABBAGE 52,110 decisions in 21.6 seconds. $0.37 in API. Nobody touched it Every call gets posted to X and Telegram automatically Anyone can follow the same calls 41,880 fills. 536 wallets scored. Only 12 made the cut. Astra built it. Jev runs it Every 20 seconds: > WATCHES every buy and sell on Robinhood Chain, right behind the block > FILTERS the noise. Wash trades, self-buys and bot loops get thrown out. 1 in 11 "smart money buys" is fake > RANKS every wallet 0 to 100 on how it actually trades: entries, exits, win rate, how fast it dumps > ASKS JEV one question when top wallets start buying the same coin: BUY, HOLD or SELL? Under 80% confidence, it waits > ENTERS with a fixed $100 ticket and a hard stop-loss. The AI can't change either > POSTS every call to Telegram the second it fires, and to X an hour later Why two models: -> Astra is smart but slow. It thinks, writes, explains. Perfect for building the system. -> Jev doesn't talk. One word and a probability, in milliseconds. Perfect for the moment money moves. The smart money desks have humans watching wallets like this, and the results never leave the building.

sopersone

19,915 просмотров • 27 дней назад

$CROWBRAIN IS READY FOR ITS SECOND PUBLIC TEST The first experiment proved two things: the algorithm can find opportunities quickly, but without strict risk management, profits can disappear just as fast. I fixed the main problems by adding automatic profit-taking, a dynamic stop-loss, position size limits, a cooldown after consecutive losses and protection against re-entering after a sharp price increase. Now I am creating a new public wallet, funding it with $50 and launching CROWBRAIN in autonomous trading mode again. Starting balance: $50 Wallet address: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] Anyone will be able to verify the balance, open positions and transaction history. I will not hide losing trades or show only profitable moments - the entire experiment will be public. No additional deposits during the test. Just $50, the updated algorithm and the market. Let’s see what CROWBRAIN has learned from its first mistake and what it can turn this wallet into.
0:38
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$CROWBRAIN IS READY FOR ITS SECOND PUBLIC TEST The first experiment proved two things: the algorithm can find opportunities quickly, but without strict risk management, profits can disappear just as fast. I fixed the main problems by adding automatic profit-taking, a dynamic stop-loss, position size limits, a cooldown after consecutive losses and protection against re-entering after a sharp price increase. Now I am creating a new public wallet, funding it with $50 and launching CROWBRAIN in autonomous trading mode again. Starting balance: $50 Wallet address: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] Anyone will be able to verify the balance, open positions and transaction history. I will not hide losing trades or show only profitable moments - the entire experiment will be public. No additional deposits during the test. Just $50, the updated algorithm and the market. Let’s see what CROWBRAIN has learned from its first mistake and what it can turn this wallet into.

sopersone

17,542 просмотров • 1 месяц назад

> Claude coders right now > Python coders right now > Polymarket traders right now
0:24
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> Claude coders right now > Python coders right now > Polymarket traders right now

sopersone

79,434 просмотров • 6 месяцев назад

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:
0:40
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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:

sopersone

65,108 просмотров • 6 месяцев назад

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:
0:38
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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

57,212 просмотров • 5 месяцев назад

Another guy building a Polymarket trading bot in Python I am very very happy hahaha
1:01
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Another guy building a Polymarket trading bot in Python I am very very happy hahaha

sopersone

55,027 просмотров • 6 месяцев назад

I GAVE A CROW BRAIN $50 AND LET IT TRADE BY ITSELF CROWBRAIN BOT is officially live. It is an autonomous trading bot inspired by the intelligence of one of the smartest birds on the planet. It scans new and trending tokens, analyzes price, volume and liquidity, and then makes its own decision: BUY → open a position SELL → take profit or exit WAIT → skip the trade The first experiment turned $50 into $420 in just five hours, but the algorithm later lost the profit because its risk controls were not strong enough. I fixed the main problems. The bot now takes partial profits, uses a dynamic stop-loss, limits position sizes, pauses after consecutive losses and avoids re-entering after a sharp price increase. The second public experiment starts today. Starting balance: $50 Public wallet: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] No additional deposits and no hidden trades. Every entry, exit, profit and loss will be verifiable directly on-chain. $50. One updated algorithm. Fully public trading. Let’s see whether a digital crow brain can survive the real market.
0:30
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I GAVE A CROW BRAIN $50 AND LET IT TRADE BY ITSELF CROWBRAIN BOT is officially live. It is an autonomous trading bot inspired by the intelligence of one of the smartest birds on the planet. It scans new and trending tokens, analyzes price, volume and liquidity, and then makes its own decision: BUY → open a position SELL → take profit or exit WAIT → skip the trade The first experiment turned $50 into $420 in just five hours, but the algorithm later lost the profit because its risk controls were not strong enough. I fixed the main problems. The bot now takes partial profits, uses a dynamic stop-loss, limits position sizes, pauses after consecutive losses and avoids re-entering after a sharp price increase. The second public experiment starts today. Starting balance: $50 Public wallet: [AD4vTDFY9RfFCdTmLPqzmoo61R6kwqskLk3BHSgXmrbf] No additional deposits and no hidden trades. Every entry, exit, profit and loss will be verifiable directly on-chain. $50. One updated algorithm. Fully public trading. Let’s see whether a digital crow brain can survive the real market.

sopersone

12,201 просмотров • 1 месяц назад

29-year-old gave 10,000 Grok Bot agents $5 each with one rule, and they made $11,500 last month from wallets that started with just $5 the idea started as a cost-control hack. he turned it into a selection rule before anyone else did not a budget cap, a death sentence he didn't tune the agents. he didn't pick their niches, didn't write their pitches, didn't tell a single one of them to survive. he only made survival possible and let the ones that couldn't afford it disappear > Grok Bot: spawns the agent, writes its own prompt, picks its own niche, drafts its own first offer: 4 min > the wallet: $5.00 in, no refills ever, every token and every API call debits it live: instant >the reaper: reads the balance each cycle, revokes the keys the moment it touches $0.25: automatic > the replicator: at $12.00 it clones itself, endows the child $5.00, mutates exactly one variable: automatic 96% died without earning a cent. every corpse cost exactly five dollars 383 are still running. the best one is up $7,800 from a five dollar wallet, trades on its own, and has funded nine children out of its own money. nobody wrote its strategy its grandparent had a different one and died for it
0:30
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29-year-old gave 10,000 Grok Bot agents $5 each with one rule, and they made $11,500 last month from wallets that started with just $5 the idea started as a cost-control hack. he turned it into a selection rule before anyone else did not a budget cap, a death sentence he didn't tune the agents. he didn't pick their niches, didn't write their pitches, didn't tell a single one of them to survive. he only made survival possible and let the ones that couldn't afford it disappear > Grok Bot: spawns the agent, writes its own prompt, picks its own niche, drafts its own first offer: 4 min > the wallet: $5.00 in, no refills ever, every token and every API call debits it live: instant >the reaper: reads the balance each cycle, revokes the keys the moment it touches $0.25: automatic > the replicator: at $12.00 it clones itself, endows the child $5.00, mutates exactly one variable: automatic 96% died without earning a cent. every corpse cost exactly five dollars 383 are still running. the best one is up $7,800 from a five dollar wallet, trades on its own, and has funded nine children out of its own money. nobody wrote its strategy its grandparent had a different one and died for it

sopersone

11,224 просмотров • 1 месяц назад

recruiter called me last week about a $95,000/year job i asked what the monthly take-home was after tax she said around $6,200 i opened my terminal and showed her last month she asked what it was i explained that claude monitors polymarket 24/7 detects when top wallets independently converge on the same markets within 6 hours flags the cluster i stack it into one parlay through Poly_Parlay instead of copying separately convergent specialists entering the same market without coordination is one of the strongest signals in prediction markets she asked how much time it takes i said 4 minutes a day she asked why i was even taking her call honestly good question i told her i'd think about the offer she's called twice since the bot made more than the monthly salary last tuesday alone: try now and multiply your profits this is way better than regular trading
0:22
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recruiter called me last week about a $95,000/year job i asked what the monthly take-home was after tax she said around $6,200 i opened my terminal and showed her last month she asked what it was i explained that claude monitors polymarket 24/7 detects when top wallets independently converge on the same markets within 6 hours flags the cluster i stack it into one parlay through Poly_Parlay instead of copying separately convergent specialists entering the same market without coordination is one of the strongest signals in prediction markets she asked how much time it takes i said 4 minutes a day she asked why i was even taking her call honestly good question i told her i'd think about the offer she's called twice since the bot made more than the monthly salary last tuesday alone: try now and multiply your profits this is way better than regular trading

sopersone

28,822 просмотров • 5 месяцев назад

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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the line between catching a soft breach early and discovering the edge died eight months ago trade:
0:45
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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 one better than anything you've seen > random entry: 1–10%, optimal: ~37% Good model + right timing = the line between catching a soft breach early and discovering the edge died eight months ago trade:

sopersone

16,874 просмотров • 4 месяцев назад

Chinese quant genius unlocked 100% of his brain's potential at just 22 years old, living in a dorm room and spending only 10 days, he built a money-making machine raised $4.1m in investment and earned the respect of 18,000⭐developers on GitHub then he tested his simulator on prediction markets the result: $85,696 in pure profit in one week check his trade history yourself: his wallet: 0x17559efac103ac7f361be37ec0b93888d4c55aac
0:21
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Chinese quant genius unlocked 100% of his brain's potential at just 22 years old, living in a dorm room and spending only 10 days, he built a money-making machine raised $4.1m in investment and earned the respect of 18,000⭐developers on GitHub then he tested his simulator on prediction markets the result: $85,696 in pure profit in one week check his trade history yourself: his wallet: 0x17559efac103ac7f361be37ec0b93888d4c55aac

sopersone

24,442 просмотров • 7 месяцев назад

this bot has a 92% win rate look at how this bot evaluates probability using Black-Scholes and uses the Kelly Criterion to determine position sizes
1:38
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this bot has a 92% win rate look at how this bot evaluates probability using Black-Scholes and uses the Kelly Criterion to determine position sizes

sopersone

22,770 просмотров • 6 месяцев назад