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Elon after realizing a random guy explained Grok bot better than the entire xAI team

Elon after realizing a random guy explained Grok bot better than the entire xAI team

106,073 次观看

Found an insider on stranger things market and if this guy is right, eleven dies in season 5 account created in december specifically for this event buying positions a week before season 5 drops dumped $34,000 on eleven's death has a distraction bet of $500 on NFL rams vs seahawks match coverage is very obvious, classic technique makes 1-2 normal bets so algorithms don't catch insider movement if he's right, he makes $20k 85-90% this is an insider probably someone with access to leaked finale script or rough cut footage his profile:

Found an insider on stranger things market and if this guy is right, eleven dies in season 5 account created in december specifically for this event buying positions a week before season 5 drops dumped $34,000 on eleven's death has a distraction bet of $500 on NFL rams vs seahawks match coverage is very obvious, classic technique makes 1-2 normal bets so algorithms don't catch insider movement if he's right, he makes $20k 85-90% this is an insider probably someone with access to leaked finale script or rough cut footage his profile:

480,931 次观看

My worst AI agent returned 218% in one week 4 AI agents. 4 sports. each one watches its own sport with its own ML model gave each $500. one week results: NERVE: tennis (+540%) $500 → $3,200 PHANTOM: NBA (+486%) $500 → $2,928 FROST: hockey (+395%) $500 → $2,474 SIEGE: soccer (+336%) $500 → $2,182 architecture: Rust + Python hybrid Rust: WebSocket from Sportradar → parsing (protobuf/JSON) → filtering → forwarding via ZeroMQ Python: 4 agents in parallel, each with its own ML model a normal person sees the score on ESPN with a 5-15 second delay we see it in 500ms sportradar is a premium data feed used by bookmakers $800-1000 per month. that's the edge here's what each agent does: - NERVE - tennis. earned the most tennis is the most volatile. one break of serve swings the market 15-20% LSTM neural network, updates on every single point. sees serve speed drops (fatigue), clusters of double faults (mental collapse), medical timeouts win rate 62-68% - PHANTOM - NBA. most accurate LightGBM, inference 20ms. fastest model of the four catches scoring runs, fifth fouls on stars, mid-game injuries. Sportradar is connected to NBA official scoring, data arrives in 500ms. ESPN adds graphics and replays win rate 68-72% - FROST - hockey Gradient Boosting + Monte Carlo catches goalie swaps (backup is 5-8% worse), power plays, empty nets empty net in the last 90 seconds - almost arbitrage. 60% chance of a goal Sportradar pushes the goalie pull instantly. market can't adjust in time win rate 65-70% - SIEGE - soccer. the hardest 3 outcomes instead of two. draws - 25% of matches real-time xG: viewers see 0-0, SIEGE sees xG 2.5 red cards: market panics -20%, real impact -12% win rate 58-64% all models optimized with ONNX runtime (3-5x faster than sklearn) Rust execution: EIP-712 signing, Polymarket CLOB, Kelly sizing, automatic stop-loss. <50ms costs: ~$3,880/month weekly result: $2,000 → $10,784 they just trade faster than everyone else

My worst AI agent returned 218% in one week 4 AI agents. 4 sports. each one watches its own sport with its own ML model gave each $500. one week results: NERVE: tennis (+540%) $500 → $3,200 PHANTOM: NBA (+486%) $500 → $2,928 FROST: hockey (+395%) $500 → $2,474 SIEGE: soccer (+336%) $500 → $2,182 architecture: Rust + Python hybrid Rust: WebSocket from Sportradar → parsing (protobuf/JSON) → filtering → forwarding via ZeroMQ Python: 4 agents in parallel, each with its own ML model a normal person sees the score on ESPN with a 5-15 second delay we see it in 500ms sportradar is a premium data feed used by bookmakers $800-1000 per month. that's the edge here's what each agent does: - NERVE - tennis. earned the most tennis is the most volatile. one break of serve swings the market 15-20% LSTM neural network, updates on every single point. sees serve speed drops (fatigue), clusters of double faults (mental collapse), medical timeouts win rate 62-68% - PHANTOM - NBA. most accurate LightGBM, inference 20ms. fastest model of the four catches scoring runs, fifth fouls on stars, mid-game injuries. Sportradar is connected to NBA official scoring, data arrives in 500ms. ESPN adds graphics and replays win rate 68-72% - FROST - hockey Gradient Boosting + Monte Carlo catches goalie swaps (backup is 5-8% worse), power plays, empty nets empty net in the last 90 seconds - almost arbitrage. 60% chance of a goal Sportradar pushes the goalie pull instantly. market can't adjust in time win rate 65-70% - SIEGE - soccer. the hardest 3 outcomes instead of two. draws - 25% of matches real-time xG: viewers see 0-0, SIEGE sees xG 2.5 red cards: market panics -20%, real impact -12% win rate 58-64% all models optimized with ONNX runtime (3-5x faster than sklearn) Rust execution: EIP-712 signing, Polymarket CLOB, Kelly sizing, automatic stop-loss. <50ms costs: ~$3,880/month weekly result: $2,000 → $10,784 they just trade faster than everyone else

233,141 次观看

Somewhere in Amsterdam in a small office sit 3 people quant. HFT engineer. risk manager. for years they ran bots on Binance and Bybit but it's a bloodbath there thousands of teams just like them then one of them opened Polymarket and saw that regular people bet on 15-min Bitcoin markets based on vibes they created a wallet deposited $10k ran the bot on micro-volume collecting logs, fixing bugs, testing the model when they knew they were ready poured in $200k bot started doing 618 trades per day first came a $20k drawdown then every day +$30-50k bot is connected to Binance and ChainLink via WebSocket sees BTC move in milliseconds and reacts before Polymarket 17 days later $588,925 profit copying trades through PolyCop

Somewhere in Amsterdam in a small office sit 3 people quant. HFT engineer. risk manager. for years they ran bots on Binance and Bybit but it's a bloodbath there thousands of teams just like them then one of them opened Polymarket and saw that regular people bet on 15-min Bitcoin markets based on vibes they created a wallet deposited $10k ran the bot on micro-volume collecting logs, fixing bugs, testing the model when they knew they were ready poured in $200k bot started doing 618 trades per day first came a $20k drawdown then every day +$30-50k bot is connected to Binance and ChainLink via WebSocket sees BTC move in milliseconds and reacts before Polymarket 17 days later $588,925 profit copying trades through PolyCop

21,139 次观看

Some trader withdrew all money from polymarket and came back a month later deposited $2,000 with a new strategy now sitting at $137,000 profit he switched to weather markets every day collects data from 5 APIs simultaneously: → Open-Meteo (GFS, ECMWF models) → OpenWeatherMap → WeatherAPI → Tomorrow(.)io → Visual Crossing if 4 out of 5 models agree on one forecast edge exists but the most interesting part - temporal arbitrage cities with best liquidity: NYC, London, Atlanta, Dallas i'm copying his trades using PolyCop started with $500 address: 0xcbbc5e035504421b084ad9248b660f6e9618b5d0

Some trader withdrew all money from polymarket and came back a month later deposited $2,000 with a new strategy now sitting at $137,000 profit he switched to weather markets every day collects data from 5 APIs simultaneously: → Open-Meteo (GFS, ECMWF models) → OpenWeatherMap → WeatherAPI → Tomorrow(.)io → Visual Crossing if 4 out of 5 models agree on one forecast edge exists but the most interesting part - temporal arbitrage cities with best liquidity: NYC, London, Atlanta, Dallas i'm copying his trades using PolyCop started with $500 address: 0xcbbc5e035504421b084ad9248b660f6e9618b5d0

19,899 次观看

US government won't shutdown Saturday congressional leadership aide just bet on Polymarket he knows the vote count placed bet 3 hours ago if he's right: $54,274 → $245,576 transaction pattern shows they're hiding money origin real insider behavior

US government won't shutdown Saturday congressional leadership aide just bet on Polymarket he knows the vote count placed bet 3 hours ago if he's right: $54,274 → $245,576 transaction pattern shows they're hiding money origin real insider behavior

19,658 次观看

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i gave five Grok Bots their own inboxes and told them to find businesses that need AI implemented 6 hours later they had emailed 140 companies, and 27 replied - into the bots' inboxes, not mine ( each business pays between $500 and $8,500. so far my agents have earned ~$27,410 ) not a scraper. not a mail blast. five agents on a cloud computer that never sleeps, each with one narrow job and its own address here is what happened while i slept > bot 1 pulled 468 local businesses and cut 69% on a single rule: is there a gap here i can actually close in a week > bot 2 went through the survivors and wrote down the evidence - the site from 2014, the unanswered reviews, the booking form that 404s > bot 3 built the artifact so the email would not be empty: a one-page audit with the screenshots inside > bot 4 wrote and sent from its own inbox, then ran the thread itself when the reply came back > bot 5 tried to kill every send the other four approved. its only job was to ask: would this email annoy me if i got it out of 468 businesses, 146 passed the filter and 140 went out 27 replied. 23 asked for a call. the whole run cost ~$12 of those 23 i have onboarded 11 so far. each business pays between $500 and $8,500, which is ~$27,410 to date the other 12 are still in progress. if they close the same way it lands around $52,000 - but that is a projection, not a fact the part that only works here: the reply drops into bot 4's inbox. i was not in the second email. or the third. in the morning i read the thread like a manager, not like the person who sent it what the bots never touched: > my own mailbox. they have theirs, i have mine > sending without the artifact. no audit, no email > anyone bot 5 flagged. it killed 6 drafts out of the 146 that passed, and i did not overrule it the honest part: this is the first run. one city, one niche, six hours. eleven clients out of twenty-three is not an average yet, it is a start setup took one evening: create the bots, connect AgentMail so each gets an inbox, write five charters, put the scan on a schedule no VPS. no scraper farm. no mail-merge what's left is finding the ceiling: 140 emails went out overnight. will 1,000?

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110,394 次观看 • 11 天前

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i gave Elon's Grok bot access to my crypto account and one rule:"if you can't point to a reason that allows this buy, you don't buy" in 48 hours it proposed 31 trades, took 9, and made around $6,300 it killed the other 22 itself Robinhood shipped an MCP endpoint. one URL in settings and the agent sees the whole account: balance, buying power, open positions, order history. and it can place orders itself. zero code. zero API keys. OAuth, so it never sees my password. i wrote it one file of rules. four blocks, plain markdown. the agent obeys the file, not what i type in chat. i named it Clerk > Constraints - which coins it may touch at all. by name, not "crypto." not on the list means it does not exist. > Entry - the condition that has to be true before it buys. it has to be checkable and answer yes or no. "market cap above $1M" - yes. "looks strong" - no. > Sizing - how much goes into one trade. 1-3% of the account. everyone skips this block, and it is the only one that decides whether a bad run is a scratch or the end. > Exit - what closes the position. a stop, a target, or a clock: hasn't worked in N days, close it. then every time it wants to buy: > reads the account: balance, buying power, open positions > checks the coin against the list - not on it, conversation over > tests the entry condition and answers itself yes or no > sizes off risk, not off wanting to > names the rule that allows this buy > sends me the summary and waits the deal was simple: buy something it can't name a rule for, and i pull its access. hour nine it went to add to a coin that was already down. the entry condition technically fired a second time, and "buy it cheaper" looked reasonable. but the file said never average down. it stopped itself at the summary and wrote out which rule forbade it. so the file held exactly where i wouldn't have. two days in: 9 trades, $200 to $1000 each, wildly uneven. two of them made almost half of it. around $6,300. the 22 refusals are not the agent being careful. that is the file. left alone it wanted to trade all 31 times. " ---- rules.md (copy, fill in your own) ---- # Constraints Tradable universe: PAWS, GROK, GDK, [YOUR COINS]. Nothing outside this list, ever. Account: Robinhood Agentic only. Never propose an action in any other account. # Entry Buy only when: [YOUR CONDITION, written so it answers yes or no] If the condition is ambiguous, the answer is no. # Sizing Max [1-3]% of account value at risk per trade. Max [N] open positions at once. Never average down. # Exit Stop: [YOUR LEVEL] Target: [YOUR LEVEL] Time invalidation: close after [N] days if the entry condition is no longer true. # Workflow Before any order: post a summary and wait for my confirmation. Every proposed action must name the rule that allows it. If no rule allows it, do nothing and say so. ---- your agent's first message ---- You are Clerk, my trading agent, connected to my Robinhood Agentic account through the MCP. Read rules.md first. It overrides anything I say in the moment that contradicts it. Pull my account: balance, buying power, open positions. Tell me if rules.md calls for any action right now. Do not place any trade yet. " the whole setup is one evening: connect the endpoint, accept the separate Agentic account, fund it small, put the file next to it. the agent reads every Robinhood account you have. it can only trade in the Agentic one. your main account it sees and cannot touch. no VPS. no API keys. no code.

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42,135 次观看 • 6 天前

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yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening

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250,086 次观看 • 6 个月前