Alex

@de1lymoon • 3,759 subscribers

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Lauren from the Grok Bot team just confirmed it: every bot now runs on Claude Opus 5.5 and each one can spin up its own cloud agents running Cursor’s models this is f*cking insane... so i built the harness on top of it, in one prompt: Opus 5.5 leads and builds, Grok scouts live X and the web, every claim needs two sources, a red team attacks the draft before you see it, and every run gets logged so the next one starts smarter you stop prompting models. you sign off on results. paste it into Grok Bot or Claude and thank me later all 10 steps, the workflow, and real examples below ↓

Lauren from the Grok Bot team just confirmed it: every bot now runs on Claude Opus 5.5 and each one can spin up its own cloud agents running Cursor’s models this is f*cking insane... so i built the harness on top of it, in one prompt: Opus 5.5 leads and builds, Grok scouts live X and the web, every claim needs two sources, a red team attacks the draft before you see it, and every run gets logged so the next one starts smarter you stop prompting models. you sign off on results. paste it into Grok Bot or Claude and thank me later all 10 steps, the workflow, and real examples below ↓

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Kimi K3 + GPT-6 Astra can become something bigger than two agents: a Two-Brain AI Operating System the formula: Two-Brain OS = Research Brain + Execution Brain + Shared State + Router + Tools + Verification not two models doing the same job. two specialized brains connected through one persistent system step 1 -> Kimi K3 becomes the research brain. complex research, source comparison, long-context synthesis and strategic planning happen here. its job is to explore the problem and compress the findings into a clear plan. step 2 -> GPT-6 Astra becomes the execution brain. it receives the plan, writes code, operates tools, creates artifacts and turns decisions into finished work. step 3 -> build a shared state layer. store the objective, evidence, decisions, constraints, failed attempts and next action outside the chat. both brains always know what happened and where the system should continue. step 4 -> add the router. uncertainty and open-ended questions move to Kimi K3. execution, tool use and deterministic tasks move to GPT-6 Astra. every job reaches the brain designed to handle it. step 5 -> create the handoff loop: research -> plan -> execute -> inspect -> update state -> continue. if execution reveals missing information, the task returns to Kimi. if the plan is ready, Astra takes control again. step 6 -> verify before completion. tests, source checks, constraints and explicit success criteria decide whether the result ships or returns to the correct brain with a clear failure signal. that is the difference between using two AI models and building a Two-Brain AI Operating System. Kimi K3 expands the search space. GPT-6 Astra converts it into action. shared state preserves progress, the router controls every handoff, tools execute the work and verification decides when the system is actually finished. one model can generate an answer. two specialized brains connected through memory, routing and verification can run an entire workflow. the full Kimi K3 + GPT-6 Astra Two-Brain OS breakdown is below ↓

Kimi K3 + GPT-6 Astra can become something bigger than two agents: a Two-Brain AI Operating System the formula: Two-Brain OS = Research Brain + Execution Brain + Shared State + Router + Tools + Verification not two models doing the same job. two specialized brains connected through one persistent system step 1 -> Kimi K3 becomes the research brain. complex research, source comparison, long-context synthesis and strategic planning happen here. its job is to explore the problem and compress the findings into a clear plan. step 2 -> GPT-6 Astra becomes the execution brain. it receives the plan, writes code, operates tools, creates artifacts and turns decisions into finished work. step 3 -> build a shared state layer. store the objective, evidence, decisions, constraints, failed attempts and next action outside the chat. both brains always know what happened and where the system should continue. step 4 -> add the router. uncertainty and open-ended questions move to Kimi K3. execution, tool use and deterministic tasks move to GPT-6 Astra. every job reaches the brain designed to handle it. step 5 -> create the handoff loop: research -> plan -> execute -> inspect -> update state -> continue. if execution reveals missing information, the task returns to Kimi. if the plan is ready, Astra takes control again. step 6 -> verify before completion. tests, source checks, constraints and explicit success criteria decide whether the result ships or returns to the correct brain with a clear failure signal. that is the difference between using two AI models and building a Two-Brain AI Operating System. Kimi K3 expands the search space. GPT-6 Astra converts it into action. shared state preserves progress, the router controls every handoff, tools execute the work and verification decides when the system is actually finished. one model can generate an answer. two specialized brains connected through memory, routing and verification can run an entire workflow. the full Kimi K3 + GPT-6 Astra Two-Brain OS breakdown is below ↓

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From 25 Years of Data to 79% Winrate on Prediction Markets Indicators [38] - Moving averages: MA10, MA20, MA30 - RSI (overbought/oversold) - MACD / MACD_Signal (trend) - Bollinger Upper/Lower (volatility) - Volatility 10/20/30, OBV, momentum, sentiment, insider activity, etc Humans can't do that -> AI processes everything in seconds Here's how it works. The purpose of the model Not to predict the price It answers one question: “Will there be growth tomorrow?” Target1 = (close.shift(-1) > close).astype(int) # 1 → BUY, 0 → HOLD For the complete structure, you also need: - Neural network architecture - Monte Carlo Dropout (key point) - Trading logic Result: - Win rate 59–78% - 100+ trades per month → consistently profitable - The human brain is incapable of analyzing 38 indicators × 30 markets × 24/7 Therefore, this trader has already made $1.4 million in profit in 4 months You can find a full explanation of how this works at Noisy Original reading time: 15 min

From 25 Years of Data to 79% Winrate on Prediction Markets Indicators [38] - Moving averages: MA10, MA20, MA30 - RSI (overbought/oversold) - MACD / MACD_Signal (trend) - Bollinger Upper/Lower (volatility) - Volatility 10/20/30, OBV, momentum, sentiment, insider activity, etc Humans can't do that -> AI processes everything in seconds Here's how it works. The purpose of the model Not to predict the price It answers one question: “Will there be growth tomorrow?” Target1 = (close.shift(-1) > close).astype(int) # 1 → BUY, 0 → HOLD For the complete structure, you also need: - Neural network architecture - Monte Carlo Dropout (key point) - Trading logic Result: - Win rate 59–78% - 100+ trades per month → consistently profitable - The human brain is incapable of analyzing 38 indicators × 30 markets × 24/7 Therefore, this trader has already made $1.4 million in profit in 4 months You can find a full explanation of how this works at Noisy Original reading time: 15 min

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A Polymarket weather trader turned $6 → $2990 on a single Atlanta temperature bet a 49,744% return in 24 hours He built a system that snipes ultra-rare weather mispricings in the {0.2¢ - 11¢} range before the crowd checks the forecast GFS says Atlanta 38°F → 65% likely. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +49,744% return His best trades: $6 → $2,990 (Atlanta 38-39°F at 0.2¢ +49,744%) $40 → $1,871 (NYC 34-35°F at 2¢ +4,578%) $536 → $4,639 (Seattle 52-53°F at 11.6¢ +764%) Start copy-trading any trader even with $10 → Stat: > $57,136 all-time profit > 1,504 trades > Biggest win: $4,103

A Polymarket weather trader turned $6 → $2990 on a single Atlanta temperature bet a 49,744% return in 24 hours He built a system that snipes ultra-rare weather mispricings in the {0.2¢ - 11¢} range before the crowd checks the forecast GFS says Atlanta 38°F → 65% likely. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +49,744% return His best trades: $6 → $2,990 (Atlanta 38-39°F at 0.2¢ +49,744%) $40 → $1,871 (NYC 34-35°F at 2¢ +4,578%) $536 → $4,639 (Seattle 52-53°F at 11.6¢ +764%) Start copy-trading any trader even with $10 → Stat: > $57,136 all-time profit > 1,504 trades > Biggest win: $4,103

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I found a Polymarket account that made $393k profit in 20 days with a single strategy: 5-minute Up/Down His entire strategy sits in one range: {7¢ – 22¢} Every entry between 7.6¢ and 21.7¢ outcomes the market gives less than 22% chance but the oracle already confirms at 80% Here's how: > Chainlink oracle updates BTC price > bot compares to Polymarket 5-min odds > sees divergence real probability 82%, market still at 7.6¢ > enters in milliseconds > 5 minutes later resolved > $918 becomes $5,732 > bot resets. next window in 5 minutes Start copy-trading any trader even with $10 → The math: 12,184 trades ÷ ~20 days = 609 trades/day $393,045 ÷ 20 = $19,652/day $19,652 ÷ 609 = $32.27 avg profit per trade

I found a Polymarket account that made $393k profit in 20 days with a single strategy: 5-minute Up/Down His entire strategy sits in one range: {7¢ – 22¢} Every entry between 7.6¢ and 21.7¢ outcomes the market gives less than 22% chance but the oracle already confirms at 80% Here's how: > Chainlink oracle updates BTC price > bot compares to Polymarket 5-min odds > sees divergence real probability 82%, market still at 7.6¢ > enters in milliseconds > 5 minutes later resolved > $918 becomes $5,732 > bot resets. next window in 5 minutes Start copy-trading any trader even with $10 → The math: 12,184 trades ÷ ~20 days = 609 trades/day $393,045 ÷ 20 = $19,652/day $19,652 ÷ 609 = $32.27 avg profit per trade

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HondaCivic turned $37 → $15,182 on a Hong Kong temperature bet at 0.2¢ $63,788 P&L. 4,689 trades. Biggest single win: $15,144 He finds the mispricing. He doesn't care if it's at 0.2¢ or 99.9¢ How it works: Most weather quants pick one tick range and camp it. He picks a city, then asks: where is this market wrong? Follow his profile: Sometimes the answer is "0.2¢ on a tail nobody priced" buy and wait for a 500× resolve Sometimes the answer is "99.9¢ on a market that's already resolved in your favor but the order book is asleep" buy a dollar's worth of certainty for 99.9¢ and pocket the difference 4,000 times His winning trades cover the full price spectrum: $37 → $15,182 (Hong Kong 15°C YES at 0.2¢, +40,511%) $3,675 → $5,337 (London 12°C YES at 99.9¢, +45%) $3,703 → $8,759 (London 18°C NO at 42.2¢, +136%) $580 → $2,270 (NYC 56-57°F YES at 16.8¢, +291%) Notice the spread: 0.2¢, 16.8¢, 42.2¢, 99.9¢. Every tick on the order book has a setup He doesn't have a specialty. He has a scanner 4 cities. 4 different price ranges. One simple rule: find the gap, size the bet

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How to build your own weather trading bot Every weather trader making money on Polymarket follows the same 7 steps. Free data. Simple formula. Repeat daily Traders making $20k-$180k all started here free APIs, basic math the crowd ignores, and patience Here's the playbook: Step 1: Pick 2-3 cities Don't try to trade everything. Start with NYC, Chicago, and one international city. Learn how the forecast behaves for those exact locations. Fewer cities = faster feedback loop Start copy-trading any trader even with $10 → Step 2: Pull free forecast data Open-Meteo API free, no API key, updates every 6 hours. NOAA official US government forecasts, 85-90% accurate at 1-2 days. Windy / Ventusky, visual model viewers to sanity-check your data These are the same data sources the top weather traders use. All free. All public Step 3: Turn forecasts into probabilities The market shows 8-12 temperature buckets (25°C, 26°C, 27°C). Your job: calculate the real probability for each bucket based on the forecast Simple method: > take the forecast daily max > assume ±1.5°C error range > simulate 50,000 scenarios > count how many land in each bucket > that's your probability per bucket

How to build your own weather trading bot Every weather trader making money on Polymarket follows the same 7 steps. Free data. Simple formula. Repeat daily Traders making $20k-$180k all started here free APIs, basic math the crowd ignores, and patience Here's the playbook: Step 1: Pick 2-3 cities Don't try to trade everything. Start with NYC, Chicago, and one international city. Learn how the forecast behaves for those exact locations. Fewer cities = faster feedback loop Start copy-trading any trader even with $10 → Step 2: Pull free forecast data Open-Meteo API free, no API key, updates every 6 hours. NOAA official US government forecasts, 85-90% accurate at 1-2 days. Windy / Ventusky, visual model viewers to sanity-check your data These are the same data sources the top weather traders use. All free. All public Step 3: Turn forecasts into probabilities The market shows 8-12 temperature buckets (25°C, 26°C, 27°C). Your job: calculate the real probability for each bucket based on the forecast Simple method: > take the forecast daily max > assume ±1.5°C error range > simulate 50,000 scenarios > count how many land in each bucket > that's your probability per bucket

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Weather sniper on Polymarket turned $37 → $15,182 by placing a single bet on the temperature in Hong Kong He buys YES at any price from 0.2¢ tails to 99.9¢ near-resolves wherever the market lags the forecast Forecast says Hong Kong 15°C → 8%. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +40,511% return His best trades: $37 → $15,182 (Hong Kong 15°C at 0.2¢, +40,511%) $580 → $2,270 (NYC 56-57°F at 16.8¢, +291%) $3,675 → $5,337 (London 12°C at 99.9¢, +45%) Three trades. Three different prices. All YES. All resolved $1.00 Edge ratio = (1 − q) / q At q = 0.002 → 499× per winning trade At q = 0.168 → 4.95× per trade At q = 0.999 → 0.001× per trade but with 99% hit rate Cornwall edge at 0.2¢. Simons edge at 99.9¢. Most weather traders pick one. HondaCivic runs both $56,504 all-time profit · 4,104 predictions · biggest win $15.1K

Weather sniper on Polymarket turned $37 → $15,182 by placing a single bet on the temperature in Hong Kong He buys YES at any price from 0.2¢ tails to 99.9¢ near-resolves wherever the market lags the forecast Forecast says Hong Kong 15°C → 8%. Market prices it at $0.002. He buys Result: $0.002 → $1.00 when it hits = +40,511% return His best trades: $37 → $15,182 (Hong Kong 15°C at 0.2¢, +40,511%) $580 → $2,270 (NYC 56-57°F at 16.8¢, +291%) $3,675 → $5,337 (London 12°C at 99.9¢, +45%) Three trades. Three different prices. All YES. All resolved $1.00 Edge ratio = (1 − q) / q At q = 0.002 → 499× per winning trade At q = 0.168 → 4.95× per trade At q = 0.999 → 0.001× per trade but with 99% hit rate Cornwall edge at 0.2¢. Simons edge at 99.9¢. Most weather traders pick one. HondaCivic runs both $56,504 all-time profit · 4,104 predictions · biggest win $15.1K

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Andrej Karpathy just made the most contrarian bet in autonomous AI: "Tesla has a software problem. Waymo has a hardware problem. Software problems are much easier." Waymo: ~3,500 robotaxis, 500K rides a week. Tesla: 9 million cars, all collecting data Karpathy bets the 2,500x gap decides everything ↓

Andrej Karpathy just made the most contrarian bet in autonomous AI: "Tesla has a software problem. Waymo has a hardware problem. Software problems are much easier." Waymo: ~3,500 robotaxis, 500K rides a week. Tesla: 9 million cars, all collecting data Karpathy bets the 2,500x gap decides everything ↓

99,218 просмотров • 3 месяцев назад

Stop Guessing the Market. Start Running Simulations Most models try to predict the future. MiroFish does it differently: it simulates it p ≈ 320 / 1000 → EV = pW − (1 − p)L Instead of giving you one forecast, MiroFish builds a digital world from news, policy drafts, earnings reports, and market signals then fills it with thousands of AI agents and lets them react > They argue > They set up camps > They amplify narratives > They change their beliefs under pressure And after hundreds or thousands of runs, you don’t get a prophecy you get scenario frequencies. > If one outcome happens 320 times out of 1000, then p ≈ 0.32 From there, it becomes a decision problem: - calculate Expected Value - compare your probability to the market’s - use Kelly to size the bet correct

Stop Guessing the Market. Start Running Simulations Most models try to predict the future. MiroFish does it differently: it simulates it p ≈ 320 / 1000 → EV = pW − (1 − p)L Instead of giving you one forecast, MiroFish builds a digital world from news, policy drafts, earnings reports, and market signals then fills it with thousands of AI agents and lets them react > They argue > They set up camps > They amplify narratives > They change their beliefs under pressure And after hundreds or thousands of runs, you don’t get a prophecy you get scenario frequencies. > If one outcome happens 320 times out of 1000, then p ≈ 0.32 From there, it becomes a decision problem: - calculate Expected Value - compare your probability to the market’s - use Kelly to size the bet correct

188,393 просмотров • 7 месяцев назад

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

Anthropic just released 5 Claude workshops on the new skill every AI builder needs: Loop Engineering: 00:00 - Stop babysitting your agents 37:07 - Build a proactive Claude Code loop 1:11:23 - Build a production-ready Managed Agent 1:38:46 - Teach agents to learn from your team 2:07:15 - Hill-climb your agent with eval loops It is a 2 hour and 25 minute blueprint for Loop Engineering: proactive workflows, managed agents, team feedback, evals, and self-improvement. Watch it today, and read the article below to improve your skills with Fable 5.

47,626 просмотров • 3 месяцев назад

Grok Bot + Kimi K3 can be turned into something bigger than an agent: an AI operating system the formula: AI OS = Router + Reasoning + Memory + Tools + Loops + Verification not one giant assistant. six layers that keep work moving without you step 1 -> Grok Bot becomes the operator. you give it the goal, it breaks the goal into jobs, assigns priorities and decides what part of the system should act next. step 2 -> Kimi K3 becomes the reasoning core. hard research, synthesis, long context and planning move here instead of forcing every task through the same model. step 3 -> externalize memory. store goals, decisions, failed attempts, artifacts and current state outside the chat. close the session, come back tomorrow, and the system still knows where it is. step 4 -> connect tools: search, code, files, APIs, docs and data. reasoning decides what should happen. tools actually make it happen. step 5 -> add the loop engine: plan -> execute -> inspect -> update memory -> retry. the loop can wait for new information, rerun a failed task, hand work to another agent or stop when the goal is complete. step 6 -> verify before output. tests, source checks, constraints and explicit completion rules decide whether the system ships the result or sends it back into the loop. that's the difference between an AI assistant and an AI operating system. an assistant waits for your next message. an operating system carries state, routes work and keeps moving. Grok Bot handles orchestration, Kimi K3 handles deeper reasoning, memory keeps the state alive, tools execute, the loop keeps the system running, verification decides when it is actually done. build one reliable loop and you have an agent. connect reasoning, memory, tools and multiple loops around it and you start building infrastructure. the full Grok Bot + Kimi K3 AI OS breakdown is below ↓

Grok Bot + Kimi K3 can be turned into something bigger than an agent: an AI operating system the formula: AI OS = Router + Reasoning + Memory + Tools + Loops + Verification not one giant assistant. six layers that keep work moving without you step 1 -> Grok Bot becomes the operator. you give it the goal, it breaks the goal into jobs, assigns priorities and decides what part of the system should act next. step 2 -> Kimi K3 becomes the reasoning core. hard research, synthesis, long context and planning move here instead of forcing every task through the same model. step 3 -> externalize memory. store goals, decisions, failed attempts, artifacts and current state outside the chat. close the session, come back tomorrow, and the system still knows where it is. step 4 -> connect tools: search, code, files, APIs, docs and data. reasoning decides what should happen. tools actually make it happen. step 5 -> add the loop engine: plan -> execute -> inspect -> update memory -> retry. the loop can wait for new information, rerun a failed task, hand work to another agent or stop when the goal is complete. step 6 -> verify before output. tests, source checks, constraints and explicit completion rules decide whether the system ships the result or sends it back into the loop. that's the difference between an AI assistant and an AI operating system. an assistant waits for your next message. an operating system carries state, routes work and keeps moving. Grok Bot handles orchestration, Kimi K3 handles deeper reasoning, memory keeps the state alive, tools execute, the loop keeps the system running, verification decides when it is actually done. build one reliable loop and you have an agent. connect reasoning, memory, tools and multiple loops around it and you start building infrastructure. the full Grok Bot + Kimi K3 AI OS breakdown is below ↓

13,312 просмотров • 1 месяц назад

1 PROMPT + 30 MINUTES = A $500/MONTH LOCAL SAAS most people spend months building apps before finding a customer. this setup starts with one problem every local business already has: getting more Google reviews. > Emergent builds the full SaaS, database, login and dashboard > automated SMS asks every customer for a review > personalized messages make each request feel human > analytics show the owner exactly how many reviews were generated > Zapier connects Square or Calendly to trigger the workflow automatically a customer pays or books. 2 hours later, the review request is sent without the owner touching anything. 1 app. 0 code. $500/month per business. the goal is not to build a billion-dollar startup. it is to solve one painful problem and sell the automated outcome. bookmark this before building your first AI SaaS

1 PROMPT + 30 MINUTES = A $500/MONTH LOCAL SAAS most people spend months building apps before finding a customer. this setup starts with one problem every local business already has: getting more Google reviews. > Emergent builds the full SaaS, database, login and dashboard > automated SMS asks every customer for a review > personalized messages make each request feel human > analytics show the owner exactly how many reviews were generated > Zapier connects Square or Calendly to trigger the workflow automatically a customer pays or books. 2 hours later, the review request is sent without the owner touching anything. 1 app. 0 code. $500/month per business. the goal is not to build a billion-dollar startup. it is to solve one painful problem and sell the automated outcome. bookmark this before building your first AI SaaS

13,949 просмотров • 3 месяцев назад

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