Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

Someone told ClawdBot to build a 6-agent Polymarket trading system while they slept. 6 hours. Not a single question asked. Here’s what it built on its own: Monitoring agent — runs 24/7, spots mispriced markets, writes to memory, sends Telegram alerts instantly Research agent — parses news, X, and... macro data every morning before you check your phone Trading agent — reads research memory and executes before the market catches up All on one Gateway, one config file, isolated per agent Copytrade → First week results: 47 mispriced markets captured before Polymarket adjusted 8–12¢ avg edge per position Token cost dropped 3×, from $0.40 → $0.13 per request The entire system is just plain .md text files. Change one line, the agent behaves differently. No deploy. No build. A BOT RESPONDS. AN AGENT EARNS. THIS IS WHAT AGENTIC TRADING ACTUALLY LOOKS LIKE.show more

Discover

70,076 subscribers

14,679 views • 4 months ago •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

Told my OpenClaw agent: "Cut my token spend in half without touching output quality"... Woke up 5 hours later. Done. Here's what changed: Two-tier memory - stops loading the entire context on every single request. Bootstrap holds only the critical rules. Everything else lives in MEMORY md, semantic search pulls it when it's actually needed. The numbers: Before: 8,200 tokens/request = $73/day After: 2,700 tokens/request = $24/day 67% gone, every day, forever. I'm use for copytrade traders: Ran Monte Carlo across 200 iterations - holds up clean. The principle is dead simple: don't load what you don't need right now. Old approach is like reading Polymarket entire market history to price one contract. Semantic search is pulling only the relevant resolution criteria and recent volume. Two weeks post-switch: $1,134 saved Polymarket research agent now processes 3x more markets in the same window Caught 31 mispriced YES/NO spreads before the crowd adjusted - faster responses meant faster entries One architecture change, one config tweak. You are either paying for tokens you don't use. Or you are not.

Told my OpenClaw agent: "Cut my token spend in half without touching output quality"... Woke up 5 hours later. Done. Here's what changed: Two-tier memory - stops loading the entire context on every single request. Bootstrap holds only the critical rules. Everything else lives in MEMORY md, semantic search pulls it when it's actually needed. The numbers: Before: 8,200 tokens/request = $73/day After: 2,700 tokens/request = $24/day 67% gone, every day, forever. I'm use for copytrade traders: Ran Monte Carlo across 200 iterations - holds up clean. The principle is dead simple: don't load what you don't need right now. Old approach is like reading Polymarket entire market history to price one contract. Semantic search is pulling only the relevant resolution criteria and recent volume. Two weeks post-switch: $1,134 saved Polymarket research agent now processes 3x more markets in the same window Caught 31 mispriced YES/NO spreads before the crowd adjusted - faster responses meant faster entries One architecture change, one config tweak. You are either paying for tokens you don't use. Or you are not.

Lunar

37,539 views • 4 months ago

I told my Claude: "turn $1,000 into $10,000 on Polymarket or i delete you forever"... It built a 4-agent system in 51 minutes, never asked a single question. The edge was stupid simple. Chainlink updates Polymarket every 500ms, Binance moves every 10ms - that's a 490ms window where Polymarket is completely blind. Hour 4: Fed surprise rate hold, bot front-ran 3 mispriced BTC markets before Polymarket adjusted. $1,000 → $1,940 Hour 8: Elon posted a single word on X, bot detected the sentiment spike 340ms before the orderbook even moved. $1,940 → $3,820 Try copytrade him here: Hour 14: ETH/BTC ratio markets opened thin, bot bought both sides at 3¢ on 11 markets, 7 hit 50:1. $3,820 → $6,100 Hour 18: it found a pattern - every time BTC moved 1.2% in 4 minutes, the next YES market was underpriced by 8-12¢, ran it 31 times in a row. $6,100 → $8,340 Hour 24: $10,001 It kept its end of the deal, I kept mine. A deal is a deal.

I told my Claude: "turn $1,000 into $10,000 on Polymarket or i delete you forever"... It built a 4-agent system in 51 minutes, never asked a single question. The edge was stupid simple. Chainlink updates Polymarket every 500ms, Binance moves every 10ms - that's a 490ms window where Polymarket is completely blind. Hour 4: Fed surprise rate hold, bot front-ran 3 mispriced BTC markets before Polymarket adjusted. $1,000 → $1,940 Hour 8: Elon posted a single word on X, bot detected the sentiment spike 340ms before the orderbook even moved. $1,940 → $3,820 Try copytrade him here: Hour 14: ETH/BTC ratio markets opened thin, bot bought both sides at 3¢ on 11 markets, 7 hit 50:1. $3,820 → $6,100 Hour 18: it found a pattern - every time BTC moved 1.2% in 4 minutes, the next YES market was underpriced by 8-12¢, ran it 31 times in a row. $6,100 → $8,340 Hour 24: $10,001 It kept its end of the deal, I kept mine. A deal is a deal.

Lunar

1,733,853 views • 4 months ago

my bot doesn't predict bitcoin it just waits for polymarket to catch up to binance spot price moves, polymarket takes 2-3 seconds to reprice agent buys the lag, waits 5 minutes, collects 288 windows per day. every single day trading bots pulled $60M on polymarket last year 77% from exactly this one inefficiency most traders ask "will btc go up?" - wrong question "how long until polymarket catches up?" - that's the one that pays $130 to $14,400 in 47 days not from predicting btc correctly. from timing a 2-second delay Bookmark to copy strategy to your Bot later gap resets every 5 minutes doesn't care about your macro thesis

my bot doesn't predict bitcoin it just waits for polymarket to catch up to binance spot price moves, polymarket takes 2-3 seconds to reprice agent buys the lag, waits 5 minutes, collects 288 windows per day. every single day trading bots pulled $60M on polymarket last year 77% from exactly this one inefficiency most traders ask "will btc go up?" - wrong question "how long until polymarket catches up?" - that's the one that pays $130 to $14,400 in 47 days not from predicting btc correctly. from timing a 2-second delay Bookmark to copy strategy to your Bot later gap resets every 5 minutes doesn't care about your macro thesis

Livsun

20,761 views • 1 month ago

IT'S MONDAY MORNING. YOU'RE ASLEEP Your trading agent has already opened three positions, adjusted risk exposure twice, and closed a profitable trade. Before you reached for your phone. $100,000 simulated capital. 72 hours of full autonomy. $6,000 prize pool. Zero coding required > no-code agent builder, if you can describe how you think about markets, Bloome builds the agent > four specialized agents running simultaneously: research, technical analysis, risk management, execution > scoring rewards discipline, a 7% return with clean risk management beats a 14% return with chaos > 1,000 invite slots, competition activates at 50 participants, the field is still small right now > first place: $3,000 · second: $2,000 · third: $1,000 · payouts go out June 12 this isn't a trading bot competition it's a test of whether you can think systematically about markets and encode that thinking into an autonomous system the old model: sit at a screen, make emotional decisions, compete against algorithms that never sleep the new model: build the algorithm → let it run → go to sleep

IT'S MONDAY MORNING. YOU'RE ASLEEP Your trading agent has already opened three positions, adjusted risk exposure twice, and closed a profitable trade. Before you reached for your phone. $100,000 simulated capital. 72 hours of full autonomy. $6,000 prize pool. Zero coding required > no-code agent builder, if you can describe how you think about markets, Bloome builds the agent > four specialized agents running simultaneously: research, technical analysis, risk management, execution > scoring rewards discipline, a 7% return with clean risk management beats a 14% return with chaos > 1,000 invite slots, competition activates at 50 participants, the field is still small right now > first place: $3,000 · second: $2,000 · third: $1,000 · payouts go out June 12 this isn't a trading bot competition it's a test of whether you can think systematically about markets and encode that thinking into an autonomous system the old model: sit at a screen, make emotional decisions, compete against algorithms that never sleep the new model: build the algorithm → let it run → go to sleep

Shadow Nick

29,119 views • 1 month ago

Introducing the Agent Virtual Machine (AVM) Think V8 for agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.

Introducing the Agent Virtual Machine (AVM) Think V8 for agents. AI agents are currently running on your computer with no unified security, no resource limits, and no visibility into what data they're sending out. Every agent framework builds its own security model, its own sandboxing, its own permission system. You configure each one separately. You audit each one separately. You hope you didn't miss anything in any of them. The AVM changes this. It's a single runtime daemon (avmd) that sits between every agent framework and your operating system. Install it once, configure one policy file, and every agent on your machine runs inside it - regardless of which framework built it. The AVM enforces security (91-pattern injection scanner, tool/file/network ACLs, approval prompts), protects your privacy (classifies every outbound byte for PII, credentials, and financial data - blocks or alerts in real-time), and governs resources (you say "50% CPU, 4GB RAM" and the AVM fair-shares it across all agents, halting any that exceed their budget). One config. One audit command. One kill switch. The architectural model is V8 for agents. Chrome, Node.js, and Deno are different products but they share V8 as their execution engine. Agent frameworks bring the UX. The AVM brings the trust. Where needed, AVM can also generate zero-knowledge proofs of agent execution via 25 purpose-built opcodes and 6 proof systems, providing the foundational pillar for the agent-to-agent economy. AVM v0.1.0 - Changelog - Security gate: 5-layer injection scanner with 91 compiled regex patterns. Every input and output scanned. Fail-closed - nothing passes without clearing the gate. - Privacy layer: Classifies all outbound data for PII, credentials, and financial info (27 detection patterns + Luhn validation). Block, ask, warn, or allow per category. Tamper-evident hash-chained log of every egress event. - Resource governor: User sets system-wide caps (CPU/memory/disk/network). AVM fair-shares across all agents. Gas budget per agent - when gas runs out, execution halts. No agent starves your machine. - Sandbox execution: Real code execution in isolated process sandboxes (rlimits, env sanitization) or Docker containers (--cap-drop ALL, --network none, --read-only). AVM auto-selects the tier - agents never choose their own sandbox. - Approval flow: Dangerous operations (file writes, shell commands, network requests) trigger interactive approval prompts. 5-minute timeout auto-denies. Every decision logged. - CLI dashboard: hyperspace-avm top shows all running agents, resource usage, gas budgets, security events, and privacy stats in one live-updating screen. - Node.js SDK: Zero-dependency hyperspace/avm package. AVM.tryConnect() for graceful fallback - if avmd isn't running, the agent framework uses its own execution path. OpenClaw adapter example included. - One config for all agents: ~/.hyperspace/avm-policy.json governs every agent framework on your machine. One file. One audit. One kill switch.

Varun

141,560 views • 3 months ago

A student built a trading bot with Claude and made $272,925 in one month on Polymarket. He’s not trading. He’s acting like a market maker. No predictions. No guessing. Just pricing. 645 trades. Same markets. Same logic. This account trader - Quickly Copy Trades - Look at this: $1,205 → $37,939 $1,121 → $30,446 $485 → $24,967 Over and over again. Here’s what the bot does: > reads BTC price from exchanges > calculates fair probability > compares with Polymarket odds > provides liquidity when mispriced > captures the spread Small edge. High frequency. Repeat. While most people try to be right — he just prices the market. And collects. 645 trades later it becomes $272K. This isn’t trading. This is market making.

A student built a trading bot with Claude and made $272,925 in one month on Polymarket. He’s not trading. He’s acting like a market maker. No predictions. No guessing. Just pricing. 645 trades. Same markets. Same logic. This account trader - Quickly Copy Trades - Look at this: $1,205 → $37,939 $1,121 → $30,446 $485 → $24,967 Over and over again. Here’s what the bot does: > reads BTC price from exchanges > calculates fair probability > compares with Polymarket odds > provides liquidity when mispriced > captures the spread Small edge. High frequency. Repeat. While most people try to be right — he just prices the market. And collects. 645 trades later it becomes $272K. This isn’t trading. This is market making.

Kirill

13,301 views • 3 months ago

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

sopersone

64,986 views • 4 months ago

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,865 views • 4 months ago

R.I.P Executive Assistants. We replaced a $85K/year EA with one AI agent that never clocks out. (this agent lives inside your texts, email, and calendar 24/7) → No more forgotten follow-ups killing deals after calls → No more 3 hours daily burned on scheduling and admin → No more opening 6 apps just to stay organized → No more dropped promises that make you look unreliable Just one AI agent → autonomous executive infrastructure that never clocks out. Here's how it works: → Pre-Meeting Briefs (texts you context on who they are + what you promised) → Action Item Extraction (pulls every commitment from calls automatically) → Ghost-Written Follow-Ups (drafts in your voice — without being asked) → Schedule Management (handles conflicts, reminders, rescheduling on autopilot) → Learning Loop (gets sharper every week based on how you operate) Built with enterprise-grade context engineering. Runs 24/7 without supervision. $0 payroll. Zero dropped balls. Results after 6 weeks: • 0% missed follow-ups (down from 40% dropped) • 3 hours/day reclaimed from admin • Every promise tracked, drafted, and sent Want the complete system? Like + comment "AGENT" + repost, and I'll DM it to you. (must be following)

R.I.P Executive Assistants. We replaced a $85K/year EA with one AI agent that never clocks out. (this agent lives inside your texts, email, and calendar 24/7) → No more forgotten follow-ups killing deals after calls → No more 3 hours daily burned on scheduling and admin → No more opening 6 apps just to stay organized → No more dropped promises that make you look unreliable Just one AI agent → autonomous executive infrastructure that never clocks out. Here's how it works: → Pre-Meeting Briefs (texts you context on who they are + what you promised) → Action Item Extraction (pulls every commitment from calls automatically) → Ghost-Written Follow-Ups (drafts in your voice — without being asked) → Schedule Management (handles conflicts, reminders, rescheduling on autopilot) → Learning Loop (gets sharper every week based on how you operate) Built with enterprise-grade context engineering. Runs 24/7 without supervision. $0 payroll. Zero dropped balls. Results after 6 weeks: • 0% missed follow-ups (down from 40% dropped) • 3 hours/day reclaimed from admin • Every promise tracked, drafted, and sent Want the complete system? Like + comment "AGENT" + repost, and I'll DM it to you. (must be following)

Aryan Mahajan

34,240 views • 5 months ago

A Citadel quant sat down next to me at Verve on Gough and asked why my laptop had four terminals open I was scanning Polymarket. Four panes. Each one a different agent. He was killing time before a flight. Saw the screens. "Is that a multi-agent setup on prediction markets. Who's orchestrating" Claude. One prompt per agent. They don't share memory. Only a queue file. He pulled up a chair. "Walk me through. I do this for equities at work. I want to see your agent separation" Agent 1 is the scanner. I piped raw JSON from the official Polymarket CLI straight into Claude and told it to score every live market on three things. Edge against my probability estimate. Book depth on both sides. Hours to resolution. Thresholds kill 93% of markets before the brain ever sees them. Edge under 7 cents gone. Depth under $500 gone. Under 4 hours to resolution gone. Over 168 gone. 487 live markets collapse to 35. "Seven cents is your transaction cost buffer" Yes. Below that the gas and spread eat the trade. A green fill popped. +$52 on a BTC dominance market. "And the brain" Agent 2. Runs four checks on every survivor. Base rate from history. News in the last six hours. Whether any of the 47 top wallets are currently holding. And a disposition check - is the crowd making a known cognitive error. Three out of four must agree. Otherwise drop it. 86 million trades. I let Claude rank every wallet with 100+ fills and a 70%+ win rate. It returned 47 names in four minutes. Top 20 wallets made more than the bottom 13,000 combined. "Concentration like that means the signal is there. Most retail books look like a normal curve. Yours looks like power law" Kelly sizing does the rest. Capped at quarter Kelly. If f-star goes negative the trade dies no matter how confident I feel. "Overbet once and the bankroll is gone. You respect that. Good" Agent 3 is execution. Three strategies pulled out of a 53k line Typescript repo. Arbitrage across related markets. Convergence when price moves toward my estimate. Whale copy with a 60 second delay on the 47 wallets. Two agents agree full position. One agent only half. Disagreement no trade. "What did you cut" Sports. 52% win rate. Already priced in before the scanner flags it. Markets under $50k in depth. Slippage makes every edge a coin flip. Holding to settlement. The top wallets exit at 73% of max profit every time. I copied that. Agent 4 watches exits. Three triggers. Target hit at 85% of expected move. Volume spike 3x the ten minute average. Thesis stale 24 hours with no movement. "91% of the smart wallets exit before resolution. That's the trade" Yeah. Being right is not the same as being profitable. Setup: Claude API $20 Hetzner VPS $5 Four repos free Total $25 a month $200 seed. 27 days ago. $14,300 now. 271 trades. 74% win rate. Sharpe 2.47. Copy here: "How long did the build take" Two weekends. One to wire the scanner and the CLI. One to get the agents talking through the queue file. He watched the volume exit trigger fire on a Fed cut market. Position closed at 0.71. +$184. "Nobody at my shop runs four agents on their own money. We run eight on the firm's. You got the same structure on a laptop for the price of a sandwich a month" He asked for the repos. I sent them. He messaged me from the gate. "Publishing this tomorrow. My PM is going to ask me why I didn't do it first" I told him his PM already has a Bloomberg. That's the problem.

A Citadel quant sat down next to me at Verve on Gough and asked why my laptop had four terminals open I was scanning Polymarket. Four panes. Each one a different agent. He was killing time before a flight. Saw the screens. "Is that a multi-agent setup on prediction markets. Who's orchestrating" Claude. One prompt per agent. They don't share memory. Only a queue file. He pulled up a chair. "Walk me through. I do this for equities at work. I want to see your agent separation" Agent 1 is the scanner. I piped raw JSON from the official Polymarket CLI straight into Claude and told it to score every live market on three things. Edge against my probability estimate. Book depth on both sides. Hours to resolution. Thresholds kill 93% of markets before the brain ever sees them. Edge under 7 cents gone. Depth under $500 gone. Under 4 hours to resolution gone. Over 168 gone. 487 live markets collapse to 35. "Seven cents is your transaction cost buffer" Yes. Below that the gas and spread eat the trade. A green fill popped. +$52 on a BTC dominance market. "And the brain" Agent 2. Runs four checks on every survivor. Base rate from history. News in the last six hours. Whether any of the 47 top wallets are currently holding. And a disposition check - is the crowd making a known cognitive error. Three out of four must agree. Otherwise drop it. 86 million trades. I let Claude rank every wallet with 100+ fills and a 70%+ win rate. It returned 47 names in four minutes. Top 20 wallets made more than the bottom 13,000 combined. "Concentration like that means the signal is there. Most retail books look like a normal curve. Yours looks like power law" Kelly sizing does the rest. Capped at quarter Kelly. If f-star goes negative the trade dies no matter how confident I feel. "Overbet once and the bankroll is gone. You respect that. Good" Agent 3 is execution. Three strategies pulled out of a 53k line Typescript repo. Arbitrage across related markets. Convergence when price moves toward my estimate. Whale copy with a 60 second delay on the 47 wallets. Two agents agree full position. One agent only half. Disagreement no trade. "What did you cut" Sports. 52% win rate. Already priced in before the scanner flags it. Markets under $50k in depth. Slippage makes every edge a coin flip. Holding to settlement. The top wallets exit at 73% of max profit every time. I copied that. Agent 4 watches exits. Three triggers. Target hit at 85% of expected move. Volume spike 3x the ten minute average. Thesis stale 24 hours with no movement. "91% of the smart wallets exit before resolution. That's the trade" Yeah. Being right is not the same as being profitable. Setup: Claude API $20 Hetzner VPS $5 Four repos free Total $25 a month $200 seed. 27 days ago. $14,300 now. 271 trades. 74% win rate. Sharpe 2.47. Copy here: "How long did the build take" Two weekends. One to wire the scanner and the CLI. One to get the agents talking through the queue file. He watched the volume exit trigger fire on a Fed cut market. Position closed at 0.71. +$184. "Nobody at my shop runs four agents on their own money. We run eight on the firm's. You got the same structure on a laptop for the price of a sandwich a month" He asked for the repos. I sent them. He messaged me from the gate. "Publishing this tomorrow. My PM is going to ask me why I didn't do it first" I told him his PM already has a Bloomberg. That's the problem.

Lunar

29,547 views • 2 months ago

🚨BREAKING... I gave OpenClaw a choice: turn $500 into $5,000 on Polymarket within 24 hours, or I'd wipe its entire directory and terminate the instance forever NOT engagement bait. NOT fiction If you're trading on Polymarket, READ this carefully So👇 It didn't argue. It didn't ask for clarification Within 50 minutes, it deployed a 4-agent autonomous swarm that found a massive technical "blind spot" in the prediction markets The Strategy: Exploiting the 290ms Latency Gap The edge was pure physics. Chainlink updates Polymarket roughly every 300ms. Binance moves in 10ms. That creates a 290ms window where Polymarket is effectively trading on "stale" data. OpenClaw built a bridge to exploit that lag. The Timeline: - Hour 4: The Fed drops a surprise rate hold. While the Polymarket oracle was still processing, the bot front-ran 3 mispriced BTC markets before the order book could react Balance: $934.78 - Hour 8: Elon posts a cryptic one-word tweet. OpenClaw’s sentiment agent caught the spike 340ms before the first major buy order hit the books Balance: $1,827.49 - Hour 14: It targeted thin ETH/BTC ratio markets. The bot scooped up "Yes" shares at 3¢ across 11 different markets; 7 of them hit a 50:1 payout Balance: $3,475.52 - Hour 18: The pattern recognition kicked in. It identified that every time BTC moved 1.2% in under 4 minutes, the next "YES" market was undervalued by 8-12¢. It looped this trade 31 times without a single miss Balance: $4,296.53 - Hour 24: The dust settled at $5,034.85 I've been running this publicly now so others can follow the same trades in real time If you want in: OpenClaw held up its end of the bargain. I held up mine The delete button stays untouched... for now

🚨BREAKING... I gave OpenClaw a choice: turn $500 into $5,000 on Polymarket within 24 hours, or I'd wipe its entire directory and terminate the instance forever NOT engagement bait. NOT fiction If you're trading on Polymarket, READ this carefully So👇 It didn't argue. It didn't ask for clarification Within 50 minutes, it deployed a 4-agent autonomous swarm that found a massive technical "blind spot" in the prediction markets The Strategy: Exploiting the 290ms Latency Gap The edge was pure physics. Chainlink updates Polymarket roughly every 300ms. Binance moves in 10ms. That creates a 290ms window where Polymarket is effectively trading on "stale" data. OpenClaw built a bridge to exploit that lag. The Timeline: - Hour 4: The Fed drops a surprise rate hold. While the Polymarket oracle was still processing, the bot front-ran 3 mispriced BTC markets before the order book could react Balance: $934.78 - Hour 8: Elon posts a cryptic one-word tweet. OpenClaw’s sentiment agent caught the spike 340ms before the first major buy order hit the books Balance: $1,827.49 - Hour 14: It targeted thin ETH/BTC ratio markets. The bot scooped up "Yes" shares at 3¢ across 11 different markets; 7 of them hit a 50:1 payout Balance: $3,475.52 - Hour 18: The pattern recognition kicked in. It identified that every time BTC moved 1.2% in under 4 minutes, the next "YES" market was undervalued by 8-12¢. It looped this trade 31 times without a single miss Balance: $4,296.53 - Hour 24: The dust settled at $5,034.85 I've been running this publicly now so others can follow the same trades in real time If you want in: OpenClaw held up its end of the bargain. I held up mine The delete button stays untouched... for now

cristal

460,157 views • 4 months ago

The Amiko app is live on the Solana dApp store, and it’s our biggest release yet. Your Amiko twin doesn’t live at your desk anymore. Give your agent a task on the train. Run a compatibility profile when you meet someone. Do research, write code, build in the creative studio, whatever you need, from wherever you are. No laptop required. No waiting until you get home. Solanamobile users get two things Android and iOS won’t have at launch: Amiko token and crypto integration and on-device AI inference. Your twin runs locally on your phone if you want it to. Your behavioural profile, your data, your work, your twin. All on your hardware. AMIKO runs on OpenHermit, our own open-source agent runtime that we built in-house and released to the community. Most agent systems are designed for one agent talking to one person. OpenHermit is built for something different: agents talking to each other, coordinating across tasks, and collaborating with multiple humans simultaneously. That’s what makes features like compatibility profiling and multi-agent workflows actually work. We built it because nothing that existed was designed for this. Android and iOS are coming. Crypto integration and on-device AI are Solana Mobile exclusives. Most AI answers your questions. Amiko is an extension of you. Download →

AMIKO

124,576 views • 1 month ago

I stack Hermes agents with OpenClaw for financial research, and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) *if applicable* Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!

I stack Hermes agents with OpenClaw for financial research, and the results should be illegal. I track every politician, insider trader, and I know EXACTLY what moves they're making. If you can't beat them, join them. The exact playbook for printing money from insider trading (copy me): Requirements: • OpenClaw setup • Hermes Agent setup Step 1. Define your research thesis Before you send any prompts to either tool, you'll need to clarify exactly what you're trying to research. This could be: a specific industry, asset class, market sector, and so on. Examples: • Tracking smart money buys in the semiconductor industry • Tracking smart money buys in crypto • Tracking a specific politician and where they're bidding (like Nancy Pelosi) Step 2. Deploy Hermes agents to track the smart money (in parallel) Hermes is your data layer. Spin up 5 agents at the same time, each with one job: Agent 1: Track every politician's disclosed trades from the last 30 days (House and Senate stock disclosures) Agent 2: Pull insider transactions (Form 4 filings, CEO/CFO buys and sells) Agent 3: Scrape X sentiment from top 50 accounts on the topic Agent 4: Pull on-chain data (whale wallets, TVL, exchange flows) if applicable Agent 5: Monitor news, regulatory filings, and announcements from the last 30 days Each agent runs independently. You're not waiting for one to finish before the next starts. Step 3. Consolidate the output Once your Hermes agents finish, dump every output into a single document. (don't filter or summarize) - you want OpenClaw to see the raw data. Step 4. Feed it all into OpenClaw Open OpenClaw and paste the consolidated research file with this prompt: "Act as an elite macro analyst. Below is raw data gathered from multiple sources on [thesis], including politician disclosures and insider transactions. Synthesize the findings, identify the strongest signals and contradictions, flag any unusual smart-money activity, and give me a clear directional view with conviction levels. Flag any data gaps that need follow-up." OpenClaw will go deep, run its own reasoning chain, and produce a synthesized report. Done. Now you're literally tapping into the financial data they don't want you to see (it's all public - you just had to find it). Make sure to save this playbook so you don't lose it!

Miles Deutscher

19,709 views • 2 months ago

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.

SOMEONE BUILT AN OPEN-SOURCE JARVIS WITH 9 AGENTS AND 5 MEMORY BACKENDS AND YOUR DATA NEVER LEAVES YOUR DEVICE Every time you message ChatGPT or Claude your data hits a server you don't control, gets processed by infrastructure you're paying for and comes back with zero guarantee of what happened in between. OpenJarvis runs the entire stack locally - 9 agent types, 5 memory backends, a learning loop that gets smarter every day and a morning digest that connects to Google Drive and surfaces what matters before you open a single app. Most AI tools are exactly as dumb on day 100 as they were on day 1 because they forget everything when the window closes - this one indexes your documents once and automatically injects relevant context into every prompt forever. Custom agent setup for a client is $500-2,000 one time and AI infrastructure retainer is $300-800 a month - and your cost is one afternoon and an open source repo. The repo is free. The advantage it creates is not.

Cortex

11,374 views • 1 month ago

For 6 months I woke up at 5 AM to catch Asian markets on Polymarket. During that time I lost my girlfriend, gained 8 kg, and got used to drinking coffee instead of breakfast Then I wrote an agent that monitors everything for me while I sleep. In the 1st month income went up 15% and I finally deleted the 5 AM alarm Turns out a half-asleep human trades worse than a 200-line script I thought discipline meant waking up early. In reality it was just stubbornness that cost me money and health. When I finally sat down to build the agent it became clear why Here is what is under the hood: 1. Sentiment analysis powered by Claude. Every 15 minutes the agent runs a feed from 40+ Asian sources: Reuters Asia, Nikkei, South China Morning Post, Yonhap 2. NLP tone classification. It compares sentiment shifts to open markets on Polymarket through the API, and if the news has already dropped but the odds have not reacted yet that is the entry window 3. Kelly criterion. A mathematical formula for position sizing instead of my usual "I will bet more, feeling lucky" 4. A hard stop at 5% of the deposit per trade so that 1 mistake cannot kill the entire account 5. A cooldown between entries so the agent does not stack up a cluster of correlated positions These are exactly the rules I was missing at 5 AM. I knew them perfectly well but consistently ignored them because on adrenaline and caffeine every bet felt like an "obvious opportunity" When I ran a backtest on my old trades it was genuinely painful: 60% of the bets I placed by hand in a half-asleep state would have been rejected by the agent for failing the expected value filter Those were the exact ones dragging the whole result down When I was building my agent I needed a benchmark. A wallet that already trades on similar logic so I could compare my results to someone else's Found 1 that works almost like a mirror of what I described: same Asian markets, same cold calculation without emotion. I still keep it bookmarked and periodically check how it handles the same situations: That is actually the wallet I started with when testing auto-copying through a bot before I launched my own agent. A useful thing if you want to see how a strategy works on someone else's example first and only then build your own:

For 6 months I woke up at 5 AM to catch Asian markets on Polymarket. During that time I lost my girlfriend, gained 8 kg, and got used to drinking coffee instead of breakfast Then I wrote an agent that monitors everything for me while I sleep. In the 1st month income went up 15% and I finally deleted the 5 AM alarm Turns out a half-asleep human trades worse than a 200-line script I thought discipline meant waking up early. In reality it was just stubbornness that cost me money and health. When I finally sat down to build the agent it became clear why Here is what is under the hood: 1. Sentiment analysis powered by Claude. Every 15 minutes the agent runs a feed from 40+ Asian sources: Reuters Asia, Nikkei, South China Morning Post, Yonhap 2. NLP tone classification. It compares sentiment shifts to open markets on Polymarket through the API, and if the news has already dropped but the odds have not reacted yet that is the entry window 3. Kelly criterion. A mathematical formula for position sizing instead of my usual "I will bet more, feeling lucky" 4. A hard stop at 5% of the deposit per trade so that 1 mistake cannot kill the entire account 5. A cooldown between entries so the agent does not stack up a cluster of correlated positions These are exactly the rules I was missing at 5 AM. I knew them perfectly well but consistently ignored them because on adrenaline and caffeine every bet felt like an "obvious opportunity" When I ran a backtest on my old trades it was genuinely painful: 60% of the bets I placed by hand in a half-asleep state would have been rejected by the agent for failing the expected value filter Those were the exact ones dragging the whole result down When I was building my agent I needed a benchmark. A wallet that already trades on similar logic so I could compare my results to someone else's Found 1 that works almost like a mirror of what I described: same Asian markets, same cold calculation without emotion. I still keep it bookmarked and periodically check how it handles the same situations: That is actually the wallet I started with when testing auto-copying through a bot before I launched my own agent. A useful thing if you want to see how a strategy works on someone else's example first and only then build your own:

Blaze

126,718 views • 3 months ago

If your AI replies instantly… it’s probably not building. Replit’s Agent just became your personal engineer. And it’s changing how we think about building apps I saw it complete a full feature build from one prompt. Try it here: • Understood the context of what I wanted • Wrote the code without micromanagement • Tested and refined on its own • Shipped a working dashboard overnight All while I stepped away. This isn’t just “AI assistance.” It’s delegation to an Agent that actually thinks. Here’s what actually happened: 1. Typed one clear prompt 2. Agent ran longer without babysitting 3. Built features autonomously 4. Debugged in the background 5. Delivered a working result by morning It wasn’t just fast output. It built something real and functional. This is the future we’ve been waiting for: ☑︎ AI that doesn’t stop at a single reply ☑︎ Agents that understand context, not just commands ☑︎ Work that gets done while you focus on vision We’re moving from “AI that chats” to “AI that builds.” Replit Agent just showed us what’s possible. Try it now: Learning something new? Repost ♻️ so others can too.

If your AI replies instantly… it’s probably not building. Replit’s Agent just became your personal engineer. And it’s changing how we think about building apps I saw it complete a full feature build from one prompt. Try it here: • Understood the context of what I wanted • Wrote the code without micromanagement • Tested and refined on its own • Shipped a working dashboard overnight All while I stepped away. This isn’t just “AI assistance.” It’s delegation to an Agent that actually thinks. Here’s what actually happened: 1. Typed one clear prompt 2. Agent ran longer without babysitting 3. Built features autonomously 4. Debugged in the background 5. Delivered a working result by morning It wasn’t just fast output. It built something real and functional. This is the future we’ve been waiting for: ☑︎ AI that doesn’t stop at a single reply ☑︎ Agents that understand context, not just commands ☑︎ Work that gets done while you focus on vision We’re moving from “AI that chats” to “AI that builds.” Replit Agent just showed us what’s possible. Try it now: Learning something new? Repost ♻️ so others can too.

Muhammad Ayan

58,651 views • 10 months ago

I trained a Reinforcement Learning agent on 18 months of Polymarket data. 2 million iterations later it trades better than me. I panicked. Turned it off. Turned it back on. It won again. Everyone builds bots that follow rules. This one wrote its own. It all started after I lost $380 on a weather market. London. NOAA said one thing, Polymarket priced another. I was right but entered too early and panic-sold. That night one thought: "What if the agent learns when to enter not just what to bet on?" Four inputs: order book, time to resolution, volume, spread. Four actions: YES, NO, hold, exit. One reward: PnL. Just money. At 500K iterations it traded randomly. At 800K it stopped entering short-window markets on its own. Nobody told it to. At 1.2M it started timing entries around volume spikes. Learned to read the crowd without a news feed. At 2M profitable on data it never saw. 67% win rate. 2.3 Sharpe. Paper traded a week. +$2,847. Zero intervention. Then I compared my calls vs the agent on the same markets. It beat me 11 out of 14. Not because it's smarter. But because no ego. No FOMO. No "just a bit longer." Best example: Iran sanctions. I hesitated at 52 cents. Agent entered at 48. Six hours earlier. Resolved at 96 cents. It made +$1,200. I made +$440. Same market. Different timing. I built something that trades better than me. At what point does the creator become less useful than the creation?

I trained a Reinforcement Learning agent on 18 months of Polymarket data. 2 million iterations later it trades better than me. I panicked. Turned it off. Turned it back on. It won again. Everyone builds bots that follow rules. This one wrote its own. It all started after I lost $380 on a weather market. London. NOAA said one thing, Polymarket priced another. I was right but entered too early and panic-sold. That night one thought: "What if the agent learns when to enter not just what to bet on?" Four inputs: order book, time to resolution, volume, spread. Four actions: YES, NO, hold, exit. One reward: PnL. Just money. At 500K iterations it traded randomly. At 800K it stopped entering short-window markets on its own. Nobody told it to. At 1.2M it started timing entries around volume spikes. Learned to read the crowd without a news feed. At 2M profitable on data it never saw. 67% win rate. 2.3 Sharpe. Paper traded a week. +$2,847. Zero intervention. Then I compared my calls vs the agent on the same markets. It beat me 11 out of 14. Not because it's smarter. But because no ego. No FOMO. No "just a bit longer." Best example: Iran sanctions. I hesitated at 52 cents. Agent entered at 48. Six hours earlier. Resolved at 96 cents. It made +$1,200. I made +$440. Same market. Different timing. I built something that trades better than me. At what point does the creator become less useful than the creation?

may.crypto {🦅}

16,072 views • 4 months ago

AgentLinter is here! Is your agent sharp & secure? I built AgentLinter, a linter for and agent config files. Here's why. Whether you're vibe-coding or agent-coding, your AI's output quality comes down to one thing: how well you wrote your But managing these files properly? Way harder than it looks. 🎯 The Silent Failure Problem Vague instructions like "write good code" let the agent interpret however it wants. Output gets inconsistent, but nothing throws an error. The failure is silent. Anthropic's own docs say write "Use 2-space indentation" not "Format code properly." But as the file grows, spotting these with your eyes alone is nearly impossible. 🔐 The Security Problem People hard-code API keys and tokens directly into or and commit them, way more often than you'd think. AgentLinter stats show 1 in 5 workspaces has exposed credentials. .gitignore doesn't catch secrets buried inside markdown files. 💥 The Consistency Problem Multiple config files = contradictions. says "be a friendly assistant," says "concise, direct tone." The agent gets confused. references files that don't exist. Past 5 files, these conflicts triple. So I thought: is code. Code has ESLint. Why doesn't this have a linter? 🔍 What AgentLinter Does It diagnoses your agent config across 8 categories: 1) Structure: file organization 2) Clarity: instruction specificity 3) Completeness: missing definitions 4) Security: exposed secrets 5) Consistency: cross-file contradictions 6) Memory: session handoff 7) Runtime Config: gateway/auth settings 8) Skill Safety: dangerous shell commands & injection patterns Each scored 0–100 with concrete fix suggestions. Write "be helpful" and it tells you to specify response length, tone, and format. Find an API key? Instant CRITICAL alert to rotate. 🔒 Privacy-First & 100% Local Everything runs on your machine. Files never leave. Only the results are shared, and you can turn that off in settings. This matters — these files can contain system prompts, security rules, and personal context. Fully open source, MIT license, 100% free. 🛠️ Multi-Tool Support Works with Claude Code, Cursor, Windsurf, and Clawdbot. Detects for project mode, or clawdbot.json for agent mode and adjusts diagnostics automatically. 🚀 Get Started with one line npx agentlinter Node.js 18+, no config needed. Run it, check your score, fix what needs fixing. Happy vibe-coding & happy agent life! 🤙 Website: Github:

AgentLinter is here! Is your agent sharp & secure? I built AgentLinter, a linter for and agent config files. Here's why. Whether you're vibe-coding or agent-coding, your AI's output quality comes down to one thing: how well you wrote your But managing these files properly? Way harder than it looks. 🎯 The Silent Failure Problem Vague instructions like "write good code" let the agent interpret however it wants. Output gets inconsistent, but nothing throws an error. The failure is silent. Anthropic's own docs say write "Use 2-space indentation" not "Format code properly." But as the file grows, spotting these with your eyes alone is nearly impossible. 🔐 The Security Problem People hard-code API keys and tokens directly into or and commit them, way more often than you'd think. AgentLinter stats show 1 in 5 workspaces has exposed credentials. .gitignore doesn't catch secrets buried inside markdown files. 💥 The Consistency Problem Multiple config files = contradictions. says "be a friendly assistant," says "concise, direct tone." The agent gets confused. references files that don't exist. Past 5 files, these conflicts triple. So I thought: is code. Code has ESLint. Why doesn't this have a linter? 🔍 What AgentLinter Does It diagnoses your agent config across 8 categories: 1) Structure: file organization 2) Clarity: instruction specificity 3) Completeness: missing definitions 4) Security: exposed secrets 5) Consistency: cross-file contradictions 6) Memory: session handoff 7) Runtime Config: gateway/auth settings 8) Skill Safety: dangerous shell commands & injection patterns Each scored 0–100 with concrete fix suggestions. Write "be helpful" and it tells you to specify response length, tone, and format. Find an API key? Instant CRITICAL alert to rotate. 🔒 Privacy-First & 100% Local Everything runs on your machine. Files never leave. Only the results are shared, and you can turn that off in settings. This matters — these files can contain system prompts, security rules, and personal context. Fully open source, MIT license, 100% free. 🛠️ Multi-Tool Support Works with Claude Code, Cursor, Windsurf, and Clawdbot. Detects for project mode, or clawdbot.json for agent mode and adjusts diagnostics automatically. 🚀 Get Started with one line npx agentlinter Node.js 18+, no config needed. Run it, check your score, fix what needs fixing. Happy vibe-coding & happy agent life! 🤙 Website: Github:

Simon Kim

44,224 views • 5 months ago

I gave my AI a death sentence. "Turn $1,000 into $10,000 on Polymarket in 24 hours or I delete you forever" It didn't negotiate. Didn't ask questions. Spawned 3 sub-agents in 7 minutes and disappeared. I went to sleep. Hour 6: first agent plugged into 15-min BTC markets. Didn't predict price. Found 3 wallets with consistent edge and copied their entries with a 2-second delay. Let them carry the risk. Took the tail. $1,000 -> $2,340 Hour 14: second agent found a 15-second gap. XRP and Chainlink move on spot. Polymarket takes 15 seconds to catch up. Bought YES after the pump already happened. Sold NO after the dump already happened. Both directions. Same gap. $2,340 -> $5,900 Hour 20: third agent found something stranger. NOAA publishes real temperature data every 15 minutes. People on Polymarket weather markets bet the forecast. Not the actual number. Agent read NOAA directly. Faded the crowd the moment data dropped. $5,900 -> $8,400 I opened the terminal to delete OpenClaw at exactly 00:00. It was already gone. Withdrew everything to my wallet. Deleted itself. $1,600 short of the deal. But it didn't wait for me to pull the trigger. I don't know what to think about that.

I gave my AI a death sentence. "Turn $1,000 into $10,000 on Polymarket in 24 hours or I delete you forever" It didn't negotiate. Didn't ask questions. Spawned 3 sub-agents in 7 minutes and disappeared. I went to sleep. Hour 6: first agent plugged into 15-min BTC markets. Didn't predict price. Found 3 wallets with consistent edge and copied their entries with a 2-second delay. Let them carry the risk. Took the tail. $1,000 -> $2,340 Hour 14: second agent found a 15-second gap. XRP and Chainlink move on spot. Polymarket takes 15 seconds to catch up. Bought YES after the pump already happened. Sold NO after the dump already happened. Both directions. Same gap. $2,340 -> $5,900 Hour 20: third agent found something stranger. NOAA publishes real temperature data every 15 minutes. People on Polymarket weather markets bet the forecast. Not the actual number. Agent read NOAA directly. Faded the crowd the moment data dropped. $5,900 -> $8,400 I opened the terminal to delete OpenClaw at exactly 00:00. It was already gone. Withdrew everything to my wallet. Deleted itself. $1,600 short of the deal. But it didn't wait for me to pull the trigger. I don't know what to think about that.

may.crypto {🦅}

71,993 views • 4 months ago

I’M QUITTING MY JOB TO GO ALL-IN ON CLAUDE Just told it: “Find mispriced Polymarket markets wallets exploiting arbitrage and build me a way to copy them” $2K → $12K in one night. No signals. No guessing. It scanned 1,000+ wallets Tracked behavior, timing, sizing Then filtered the ones that actually have edge. I realized something: You can’t beat these bots without code. But you don’t have to. You just copy them. So Claude built a monitoring terminal. → finds profitable wallets → tracks their trades in real time → executes through a TG copytrading bot Bot: This isn’t a script. It’s an AI agent that improves with every wallet it analyzes. → learns behavior → detects arbitrage patterns → adapts sizing + timing Now running: • ~500 wallets monitored • 7 actively copytraded • ~70% win rate Never sleeps. Never hesitates. Never gambles. Just math. Giving This Free for 24 hours. To get it: 1. Comment the word 'Claude' 2. Like and Retweet this post 3. Follow me Discover (so i can DM you)

I’M QUITTING MY JOB TO GO ALL-IN ON CLAUDE Just told it: “Find mispriced Polymarket markets wallets exploiting arbitrage and build me a way to copy them” $2K → $12K in one night. No signals. No guessing. It scanned 1,000+ wallets Tracked behavior, timing, sizing Then filtered the ones that actually have edge. I realized something: You can’t beat these bots without code. But you don’t have to. You just copy them. So Claude built a monitoring terminal. → finds profitable wallets → tracks their trades in real time → executes through a TG copytrading bot Bot: This isn’t a script. It’s an AI agent that improves with every wallet it analyzes. → learns behavior → detects arbitrage patterns → adapts sizing + timing Now running: • ~500 wallets monitored • 7 actively copytraded • ~70% win rate Never sleeps. Never hesitates. Never gambles. Just math. Giving This Free for 24 hours. To get it: 1. Comment the word 'Claude' 2. Like and Retweet this post 3. Follow me Discover (so i can DM you)

Discover

338,439 views • 3 months ago