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every Emperor needs a legion mine is AI Eric built me: → command center (Mac app) → trading bot (executes my strategy) → monitoring dashboard I'm not technical I'm strategic the skill isn't coding anymore it's commanding build your Legion 🦾 rule your Empire 👑

Stoic Trader

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14,329 просмотров • 8 месяцев назад •via X (Twitter)

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+$8,500 IN 30 SECONDS. THIS FUCKING GENIUS BUILT HIS OWN TRADING BOT WITH AI 🤯 He didn’t spend months learning how to code. He used an AI tutorial to build a bot that analyzes the market, finds setups, and executes trades based on his strategy. Now that tutorial is basically helping him automate his trading. Here’s how he did it ↓ → Picked one simple trading strategy → Used AI to turn the rules into a trading bot → Added entry + exit conditions → Backtested the strategy → Automated the execution → Let the bot scan for setups The key part: He didn’t ask AI to magically “make money.” He gave it a clear strategy and used AI to turn those rules into an automated system. One trade reportedly made him +$8,500 in around 30 seconds. Obviously, this doesn’t mean every trade will look like that. But the idea is powerful: You don’t necessarily need to become a programmer to start building your own trading tools. AI can help turn a trading idea into something you can actually test and automate. Here’s the full strategy + how he built the bot

+$8,500 IN 30 SECONDS. THIS FUCKING GENIUS BUILT HIS OWN TRADING BOT WITH AI 🤯 He didn’t spend months learning how to code. He used an AI tutorial to build a bot that analyzes the market, finds setups, and executes trades based on his strategy. Now that tutorial is basically helping him automate his trading. Here’s how he did it ↓ → Picked one simple trading strategy → Used AI to turn the rules into a trading bot → Added entry + exit conditions → Backtested the strategy → Automated the execution → Let the bot scan for setups The key part: He didn’t ask AI to magically “make money.” He gave it a clear strategy and used AI to turn those rules into an automated system. One trade reportedly made him +$8,500 in around 30 seconds. Obviously, this doesn’t mean every trade will look like that. But the idea is powerful: You don’t necessarily need to become a programmer to start building your own trading tools. AI can help turn a trading idea into something you can actually test and automate. Here’s the full strategy + how he built the bot

Niche trader

89,003 просмотров • 1 месяц назад

Every single person should build this crypto x AI workflow. It took me weeks to get this right, and I can't wait to show you guys what I've been building! I started vibe-coding a market-trading/arbitrage/research bot, and the build is finally almost complete. It includes: • Research dashboard for stock analysis • Trading integrations (paper/backtesting) • Risk audits • Daily snapshot • Claude integrations Been chipping away over the past couple of weeks, and can already tell this will take my research to the next level. I'll have a full guide dropping on both YouTube and here on X soon for how to build your own!

Every single person should build this crypto x AI workflow. It took me weeks to get this right, and I can't wait to show you guys what I've been building! I started vibe-coding a market-trading/arbitrage/research bot, and the build is finally almost complete. It includes: • Research dashboard for stock analysis • Trading integrations (paper/backtesting) • Risk audits • Daily snapshot • Claude integrations Been chipping away over the past couple of weeks, and can already tell this will take my research to the next level. I'll have a full guide dropping on both YouTube and here on X soon for how to build your own!

Miles Deutscher

20,674 просмотров • 4 месяцев назад

Today we launched 🌸 Arc Max 🌺 our first set of AI features that we hope level up your every day on the internet. My favorite is Ask in Page! A quick way to get answers I use whenever I'm reading dense, technical documentation. 🧵 Why did we build 20+ prototypes this summer?

Today we launched 🌸 Arc Max 🌺 our first set of AI features that we hope level up your every day on the internet. My favorite is Ask in Page! A quick way to get answers I use whenever I'm reading dense, technical documentation. 🧵 Why did we build 20+ prototypes this summer?

rosey

176,894 просмотров • 3 лет назад

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.

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.

Discover

14,679 просмотров • 7 месяцев назад

A Polymarket bot developer mass deleted his code last week. I found his Reddit confession before it disappeared. I'm done. Not because the money stopped. Because I can't sleep anymore. He built prediction algorithms for 3 years. Made $2.1M. Then wrote 47 paragraphs explaining why he's walking away. The part that haunts me: Every time you place a bet, you're not competing with other humans. You're feeding data to systems that learned your patterns months ago. Your hesitation. Your favorite markets. The exact moment you check your phone after news breaks. We have all of it. He described the architecture: Layer 1: Sentiment scrapers reading 140,000 tweets per minute. Before a rumor becomes a headline, the position is already built. Layer 2: Exchange bridges. The bot sees BTC move on Coinbase, calculates Polymarket impact, and executes. Total latency: 80ms. Your finger hasn't touched the screen yet. Layer 3: Behavioral modeling. They know retail traders panic-sell at -15%. So they push prices to -14.8%. Wait. Then buy your shares at discount when you finally crack. The worst part? We call regular traders yield. Not competitors. Not opponents. Yield. Like crops we harvest seasonally. Stop being yield: He ended with this: The only retail traders making money are the ones who stopped trading. They just copy the wallets that copy us. I read it three times. Then I deleted my indicators. My charts. My strategy. Because there's no strategy against someone who sees your cards, knows your tells, and moves before you think. There's only one play left: Stop being yield. Start being a shadow. They spend millions on infrastructure. You spend nothing. They take the risk. You take the same entry. They do the math. You copy the answer. In Polymarket, you're either the algorithm or the agriculture. Choose. Not financial advice.

A Polymarket bot developer mass deleted his code last week. I found his Reddit confession before it disappeared. I'm done. Not because the money stopped. Because I can't sleep anymore. He built prediction algorithms for 3 years. Made $2.1M. Then wrote 47 paragraphs explaining why he's walking away. The part that haunts me: Every time you place a bet, you're not competing with other humans. You're feeding data to systems that learned your patterns months ago. Your hesitation. Your favorite markets. The exact moment you check your phone after news breaks. We have all of it. He described the architecture: Layer 1: Sentiment scrapers reading 140,000 tweets per minute. Before a rumor becomes a headline, the position is already built. Layer 2: Exchange bridges. The bot sees BTC move on Coinbase, calculates Polymarket impact, and executes. Total latency: 80ms. Your finger hasn't touched the screen yet. Layer 3: Behavioral modeling. They know retail traders panic-sell at -15%. So they push prices to -14.8%. Wait. Then buy your shares at discount when you finally crack. The worst part? We call regular traders yield. Not competitors. Not opponents. Yield. Like crops we harvest seasonally. Stop being yield: He ended with this: The only retail traders making money are the ones who stopped trading. They just copy the wallets that copy us. I read it three times. Then I deleted my indicators. My charts. My strategy. Because there's no strategy against someone who sees your cards, knows your tells, and moves before you think. There's only one play left: Stop being yield. Start being a shadow. They spend millions on infrastructure. You spend nothing. They take the risk. You take the same entry. They do the math. You copy the answer. In Polymarket, you're either the algorithm or the agriculture. Choose. Not financial advice.

Blaze

869,060 просмотров • 9 месяцев назад

One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.

One of the best finance x AI workflows I've ever built. It helped Claude print +$19,537 completely autonomously. This is my personal market Research Desk - and it took me weeks to build. Think of this as a fully local backtesting trading engine. You pick a market, a timeframe, and a strategy, and it runs that strategy against real historical price data to show you exactly how it would've performed. • It pulls real historical candles (crypto data straight from Binance, no API key needed, or your own CSV/stock data) • You pick from 400+ built-in strategies like moving-average crossover or RSI mean reversion, or bring your own logic • It shows a full interactive equity curve • Every single trade gets logged: entry, exit, P/L • It gives you the real performance metrics If you're not using AI in your trading, you're falling behind fast, and I think everyone should be building personal internal tools like this to elevate their portfolio.

Miles Deutscher

33,676 просмотров • 2 месяцев назад

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

ChainGPT

29,339 просмотров • 5 месяцев назад

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

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

sopersone

61,710 просмотров • 20 дней назад

this might actually be the new ugc method i've seen 100s of ai ugc ads lately where it's the same creator in every single one. same voice, same face, same brand colors, never drifts once what they're actually doing is running notch agents with memory they load the brand context one time. cloned voice, reference photo, brand color codes, do-not-say words, taste or they just teach it inline as they chat, and every correction becomes a saved rule then before it generates a single frame, the agent reads every relevant memory first. hard rules, reference assets, language constraints, taste preferences, all of it and it's workspace-wide, so every new ad already knows the brand before you type a word each chat isn't a re-introduction anymore. it's training your brand isn't a prompt. it's permanent context run 30-50 of those a month at a real offer and the volume does the rest punchier cut: the new method nobody's clocked yet run notch agents with memory on load the brand once. cloned voice, reference photo, color codes, do-not-say words, taste. or teach it inline and every correction saves as a rule it reads every relevant memory before it generates a single frame, workspace-wide each chat isn't a re-introduction. it's training. your brand isn't a prompt, it's permanent context run it at a real rate and the volume prints.

Sulfur

15,290 просмотров • 4 месяцев назад

I've been personally burning through billions of tokens a week for the past few months as a builder. Today I'm excited to announce Hyperagent, by Airtable. An agents platform where every session gets its own isolated, full computing environment in the cloud — no Mac Mini required. Real browser, code execution, image/video generation, data warehouse access, hundreds of integrations, and the ability to learn any new API as a skill. Deep domain expertise through skill learning. Teach the agent how your firm evaluates startups or how your team runs due diligence — now anyone on the team gets output that reflects your actual methodology, not a generic template. One-click deployment into Slack as intelligent coworkers. These aren't bots that wait to be Mentioned — they follow conversations, understand context, and act when relevant. And a command center to oversee and continuously improve your entire fleet of agents at scale. We're onboarding early users now.

I've been personally burning through billions of tokens a week for the past few months as a builder. Today I'm excited to announce Hyperagent, by Airtable. An agents platform where every session gets its own isolated, full computing environment in the cloud — no Mac Mini required. Real browser, code execution, image/video generation, data warehouse access, hundreds of integrations, and the ability to learn any new API as a skill. Deep domain expertise through skill learning. Teach the agent how your firm evaluates startups or how your team runs due diligence — now anyone on the team gets output that reflects your actual methodology, not a generic template. One-click deployment into Slack as intelligent coworkers. These aren't bots that wait to be Mentioned — they follow conversations, understand context, and act when relevant. And a command center to oversee and continuously improve your entire fleet of agents at scale. We're onboarding early users now.

Howie Liu

13,809,986 просмотров • 7 месяцев назад

A 19-year-old Japanese student allegedly turned $68 into $750,000. using an AI-powered trading bot. And he built it in just 48 hours. The crazy part? He didn’t sit in front of charts all day. His bot did the watching. 50+ markets. Live BTC data. Second-by-second scanning. Automated execution. The system looks for tiny price differences between markets — opportunities that can disappear in seconds. When humans are still checking charts… The bot has already detected the gap. When traders hesitate… The bot executes. His setup reportedly uses: → Claude Code for the trading logic → Binance API for live market data → Automated execution for trades → An iPad as a second monitoring screen First night: **$6,732 profit.** Total reported profit: **$750K+** But the real story isn't the money. It's how quickly AI is turning ideas into working systems. A few years ago, building something like this could require a team of developers. Now? One person + AI + APIs + automation. That changes everything. ⚠️ Obviously, automated trading is risky. These results aren't guaranteed and shouldn't be treated as financial advice. 💡 I’m sharing the Claude Code setup behind the concept for FREE. For the next 24 hours: 1️⃣ Comment Fable 2️⃣ Like + Repost 3️⃣ Follow Md Fahim I’ll DM you the setup. The future of trading might not be about watching charts. It might be about building better systems.

A 19-year-old Japanese student allegedly turned $68 into $750,000. using an AI-powered trading bot. And he built it in just 48 hours. The crazy part? He didn’t sit in front of charts all day. His bot did the watching. 50+ markets. Live BTC data. Second-by-second scanning. Automated execution. The system looks for tiny price differences between markets — opportunities that can disappear in seconds. When humans are still checking charts… The bot has already detected the gap. When traders hesitate… The bot executes. His setup reportedly uses: → Claude Code for the trading logic → Binance API for live market data → Automated execution for trades → An iPad as a second monitoring screen First night: $6,732 profit. Total reported profit: $750K+ But the real story isn't the money. It's how quickly AI is turning ideas into working systems. A few years ago, building something like this could require a team of developers. Now? One person + AI + APIs + automation. That changes everything. ⚠️ Obviously, automated trading is risky. These results aren't guaranteed and shouldn't be treated as financial advice. 💡 I’m sharing the Claude Code setup behind the concept for FREE. For the next 24 hours: 1️⃣ Comment Fable 2️⃣ Like + Repost 3️⃣ Follow Md Fahim I’ll DM you the setup. The future of trading might not be about watching charts. It might be about building better systems.

Md Fahim

55,354 просмотров • 1 месяц назад

I just built a Google Ads Builder in Claude Code that turns one URL into a complete, launch-ready campaign 🤯 Point it at your website and it builds the whole Google Search campaign — keywords, ad groups, every headline, the negative list — structured the way a good paid-search manager would. All inside Claude Code. Perfect for DTC brands and agencies who need Search live but don't have days to build it right. If you're staring at a blank Google Ads account, researching keywords one browser tab at a time, writing 15 headlines per ad group to a 30-character limit, guessing at a negative list while budget quietly leaks on junk clicks... This builds the entire campaign from a single input — your homepage URL: → Reads your site and works out what you sell and who for → Groups keywords into tight, high-intent ad groups → Writes every Responsive Search Ad — 15 headlines, 4 descriptions, to Google's exact limits → Builds a negative-keyword list so you stop paying for junk clicks → Adds sitelinks, callouts, match types, bidding, and a budget split → Exports a Google Ads Editor CSV you import in one click No blank-account paralysis. No keyword rabbit holes. No character-counting 60 headlines by hand. What you get: → A launch-ready campaign from just your URL → Tight ad groups built for Quality Score, not a keyword dump → Every ad written to Google's limits and policy → A one-click CSV import plus a full campaign dashboard Built 100% in Claude Code. No API keys, no Google Ads login. And I'm not sending a playbook this time — I'm giving the whole skill away for free. The actual file. Install it and build your own campaign in 5 minutes. Want the skill? > Like this post > Comment "BUILD" And I'll send it over (must be following so I can DM)

I just built a Google Ads Builder in Claude Code that turns one URL into a complete, launch-ready campaign 🤯 Point it at your website and it builds the whole Google Search campaign — keywords, ad groups, every headline, the negative list — structured the way a good paid-search manager would. All inside Claude Code. Perfect for DTC brands and agencies who need Search live but don't have days to build it right. If you're staring at a blank Google Ads account, researching keywords one browser tab at a time, writing 15 headlines per ad group to a 30-character limit, guessing at a negative list while budget quietly leaks on junk clicks... This builds the entire campaign from a single input — your homepage URL: → Reads your site and works out what you sell and who for → Groups keywords into tight, high-intent ad groups → Writes every Responsive Search Ad — 15 headlines, 4 descriptions, to Google's exact limits → Builds a negative-keyword list so you stop paying for junk clicks → Adds sitelinks, callouts, match types, bidding, and a budget split → Exports a Google Ads Editor CSV you import in one click No blank-account paralysis. No keyword rabbit holes. No character-counting 60 headlines by hand. What you get: → A launch-ready campaign from just your URL → Tight ad groups built for Quality Score, not a keyword dump → Every ad written to Google's limits and policy → A one-click CSV import plus a full campaign dashboard Built 100% in Claude Code. No API keys, no Google Ads login. And I'm not sending a playbook this time — I'm giving the whole skill away for free. The actual file. Install it and build your own campaign in 5 minutes. Want the skill? > Like this post > Comment "BUILD" And I'll send it over (must be following so I can DM)

Mike Futia

61,123 просмотров • 3 месяцев назад

$🚨 I TURNED OPUS 5.5 INTO A 24/7 TRADING AGENT Opus 5.5 surprised me. In my trading setup, it is outperforming what I was getting from Fable 5.1 - at a fraction of the cost. I wanted an AI system that could research a strategy, build it, challenge it, test it, deploy it and keep watching it after I go to sleep. Here is the architecture: 1. REASONING ENGINE Opus 5.5 sits at the center of the stack. Long-horizon reasoning, large context, code execution, computer use. The ability to work through complex quantitative problems without collapsing into one giant prompt. 2. 10-AGENT RESEARCH TEAM AgentKit turns Opus 5.5 into a 10-agent specialist team running through Claude Code: Conductor Backend Architect Test Engineer Code Reviewer Data Engineer Debugger Frontend Infra / DevOps Performance & Observability Security Same model. Ten different disciplines. 3. GATED WORKFLOW The agent cannot simply decide to build something and ship it. Every project moves through: Brainstorm → Architecture → Plan → Build → Review → Ship Operator approval sits between the stages. Nothing gets built before the spec is approved. 4. QUANTITATIVE CORE The system is not built around generic indicators. The production stack includes: - Ornstein-Uhlenbeck for statistical arbitrage - Avellaneda-Stoikov for market making - Hawkes processes for order-flow dynamics - Heston for volatility modeling Different market problems. Different mathematical frameworks. 5. PRODUCTION ARCHITECTURE Every strategy wraps into six independent layers: Data → Signal → Decision → Risk → Execution → Monitoring That separation matters. A broken feed should not become a fake signal. A valid signal should not automatically become an order. A disconnected WebSocket should not quietly kill the strategy at 2 AM. 6. RISK ENGINE Kelly-based sizing. 5% drawdown kill switch. Delta-neutral hedging where required. Execution constraints. Intent blotter before orders. The numbers are not theoretical. An earlier chatbot trading script managed to blow through 12% in one session. That changed the architecture completely. 7. VALIDATION BEFORE CAPITAL A backtest looking good is not enough. The strategy has to clear the gates: - Sharpe > 1.5 - Drawdown 55% - t-stat > 2.0 - 5 years of walk-forward validation Fail one? Back to research. No capital. 8. OBSERVABILITY The agent does not just produce trades. It produces a machine-readable decision trail: - Instrument - Strategy - Market regime - Signal - Confidence - Sharpe - Drawdown - Action window - Kelly-sized position Every decision can be traced back through the stack. 9. FINAL INTERFACE Telegram becomes the control surface. My phone gets the final output instead of thousands of logs: - What is trading - Why it is trading - How large the position is - What invalidates the thesis - When the opportunity expires Opus 5.5 is outperforming Fable 5.1 in my setup - at about 40% of the cost. That changes what is economically possible when you are running agents 24/7. Turns out, the hardest part of AI trading wasn't the AI. It was everything around it: Data, validation, risk, execution, monitoring. Build those layers properly, and one model can operate like an entire quant desk. This is only version one. The next build gets posted here first. Follow and turn on notifications.$

🚨 I TURNED OPUS 5.5 INTO A 24/7 TRADING AGENT Opus 5.5 surprised me. In my trading setup, it is outperforming what I was getting from Fable 5.1 - at a fraction of the cost. I wanted an AI system that could research a strategy, build it, challenge it, test it, deploy it and keep watching it after I go to sleep. Here is the architecture: 1. REASONING ENGINE Opus 5.5 sits at the center of the stack. Long-horizon reasoning, large context, code execution, computer use. The ability to work through complex quantitative problems without collapsing into one giant prompt. 2. 10-AGENT RESEARCH TEAM AgentKit turns Opus 5.5 into a 10-agent specialist team running through Claude Code: Conductor Backend Architect Test Engineer Code Reviewer Data Engineer Debugger Frontend Infra / DevOps Performance & Observability Security Same model. Ten different disciplines. 3. GATED WORKFLOW The agent cannot simply decide to build something and ship it. Every project moves through: Brainstorm → Architecture → Plan → Build → Review → Ship Operator approval sits between the stages. Nothing gets built before the spec is approved. 4. QUANTITATIVE CORE The system is not built around generic indicators. The production stack includes: - Ornstein-Uhlenbeck for statistical arbitrage - Avellaneda-Stoikov for market making - Hawkes processes for order-flow dynamics - Heston for volatility modeling Different market problems. Different mathematical frameworks. 5. PRODUCTION ARCHITECTURE Every strategy wraps into six independent layers: Data → Signal → Decision → Risk → Execution → Monitoring That separation matters. A broken feed should not become a fake signal. A valid signal should not automatically become an order. A disconnected WebSocket should not quietly kill the strategy at 2 AM. 6. RISK ENGINE Kelly-based sizing. 5% drawdown kill switch. Delta-neutral hedging where required. Execution constraints. Intent blotter before orders. The numbers are not theoretical. An earlier chatbot trading script managed to blow through 12% in one session. That changed the architecture completely. 7. VALIDATION BEFORE CAPITAL A backtest looking good is not enough. The strategy has to clear the gates: - Sharpe > 1.5 - Drawdown 55% - t-stat > 2.0 - 5 years of walk-forward validation Fail one? Back to research. No capital. 8. OBSERVABILITY The agent does not just produce trades. It produces a machine-readable decision trail: - Instrument - Strategy - Market regime - Signal - Confidence - Sharpe - Drawdown - Action window - Kelly-sized position Every decision can be traced back through the stack. 9. FINAL INTERFACE Telegram becomes the control surface. My phone gets the final output instead of thousands of logs: - What is trading - Why it is trading - How large the position is - What invalidates the thesis - When the opportunity expires Opus 5.5 is outperforming Fable 5.1 in my setup - at about 40% of the cost. That changes what is economically possible when you are running agents 24/7. Turns out, the hardest part of AI trading wasn't the AI. It was everything around it: Data, validation, risk, execution, monitoring. Build those layers properly, and one model can operate like an entire quant desk. This is only version one. The next build gets posted here first. Follow and turn on notifications.

Nonzee

12,574 просмотров • 21 дней назад

This app uses AirDrop to send files from your Android phone to your Macbook! Yes, it actually uses AirDrop. That means you don't have to install ANYTHING on your Mac to send files from your Android phone! Here's a video of a Galaxy Z Flip 5 AirDropping a file to a Macbook running macOS Ventura 13.5.1. (Thanks to u/FragmentedChicken for testing this app for me and sharing the video!) A few months ago, Twitter user @Linus13499209 brought an app called WarpShare to my attention. WarpShare is an app made by the developers of MoKee, an AOSP-based custom ROM that was popular in China. Since MoKee wasn't as popular outside of China, it seems the existence of their WarpShare app slipped under the radar. I was skeptical about whether it would work at all. Grishka, the developer of NearDrop, an open source port of Google's Nearby Share to macOS, told me that they were under the assumption that AirDrop requires the use of AWDL (Apple Wireless Direct Link, Apple's proprietary WiFi-based protocol) to communicate both ways. However, it seems that AWDL is only required for your Android phone to be discoverable by your Mac (ie. to send files from your Mac to your Android phone) but not the other way around. Because of this, though, WarpShare only supports sending files from Android to Mac but not vice versa. Your Mac also needs to have AirDrop discoverability set to "everyone" for this to work, as "contacts-only" requires Apple-signed certificates. Plus, it also doesn't support sending files from Android to iPhones or iPads, even when "everyone" mode is enabled. Still, if you find other Android --> Mac file sharing options to be lackluster, give WarpShare a try! The fact that it works at all is incredible, which is why I'm sharing this news here. If you want to download WarpShare on your Android device, you'll need to compile the app from its source code. If you're a Patron/X subscriber, however, I will share my compiled APK with you. WarpShare source code:

This app uses AirDrop to send files from your Android phone to your Macbook! Yes, it actually uses AirDrop. That means you don't have to install ANYTHING on your Mac to send files from your Android phone! Here's a video of a Galaxy Z Flip 5 AirDropping a file to a Macbook running macOS Ventura 13.5.1. (Thanks to u/FragmentedChicken for testing this app for me and sharing the video!) A few months ago, Twitter user @Linus13499209 brought an app called WarpShare to my attention. WarpShare is an app made by the developers of MoKee, an AOSP-based custom ROM that was popular in China. Since MoKee wasn't as popular outside of China, it seems the existence of their WarpShare app slipped under the radar. I was skeptical about whether it would work at all. Grishka, the developer of NearDrop, an open source port of Google's Nearby Share to macOS, told me that they were under the assumption that AirDrop requires the use of AWDL (Apple Wireless Direct Link, Apple's proprietary WiFi-based protocol) to communicate both ways. However, it seems that AWDL is only required for your Android phone to be discoverable by your Mac (ie. to send files from your Mac to your Android phone) but not the other way around. Because of this, though, WarpShare only supports sending files from Android to Mac but not vice versa. Your Mac also needs to have AirDrop discoverability set to "everyone" for this to work, as "contacts-only" requires Apple-signed certificates. Plus, it also doesn't support sending files from Android to iPhones or iPads, even when "everyone" mode is enabled. Still, if you find other Android --> Mac file sharing options to be lackluster, give WarpShare a try! The fact that it works at all is incredible, which is why I'm sharing this news here. If you want to download WarpShare on your Android device, you'll need to compile the app from its source code. If you're a Patron/X subscriber, however, I will share my compiled APK with you. WarpShare source code:

Mishaal Rahman

1,290,809 просмотров • 3 лет назад

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 running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is 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.

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 running 24/7 — spots mispriced markets, writes to memory, pings Telegram instantly > Research agent parsing news, X, and macro data every morning before you check your phone > Trading agent reading the research memory and acting before the market catches up > All of it running on one Gateway, one config file, isolated per agent First week results: - 47 mispriced markets caught before Polymarket adjusted - 8-12c avg entry edge per position - Token cost dropped 3x, from $0.40 to $0.13 per request The whole system is 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.

0xMarioNawfal

80,450 просмотров • 7 месяцев назад

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.

Miles Deutscher

56,625 просмотров • 2 месяцев назад

I'm very proud to launch and introduce my design studio: Antitype. I've been convinced for several years that design will be the work of my life, and this studio is the starting point. A few days ago, a well-known designer publicly announced he was quitting design, overwhelmed by what AI is now capable of producing. A lot of designers share that feeling right now. I don't share it at all. I actually believe it's never been more important to create, to innovate on your product, your interfaces, your art direction. Differentiation has become key for ambitious companies looking to escape the standardisation that AI can generate. That's exactly what Antitype is built on: turning brand and product into a competitive advantage for those who want to stand out. This studio is a solid foundation for what I want to build long-term, and I'm really excited to share it with you!

Sébastien Deconinck

16,858 просмотров • 2 месяцев назад

Proudly Me! I'm gay. Yes, I'm gay, and there is absolutely nothing wrong with that. Being gay is one part of who I am, but it doesn't define the limits of my dreams, my character, my kindness, my strength, or my worth. I deserve to love, to be loved, to build a life, to have a family, to feel safe, and to exist openly without shame or apology. To every gay man, lesbian, bisexual, transgender person, queer person, and anyone still finding the courage to understand themselves: you are not broken, you are not a mistake, and you do not have to become someone else to deserve respect. Some of us grew up afraid to say these words. Some were bullied, rejected, judged, or taught to hide. Others are still living in places or families where being open can be difficult or dangerous. Wherever you are on your journey, remember that your life has value and your story matters. I'm gay. Yes. And I'm proud of the person I've become. May we continue creating a world where nobody has to whisper who they are, where love isn't treated as something shameful, and where everyone can live authentically, freely, and with dignity.

Proudly Me! I'm gay. Yes, I'm gay, and there is absolutely nothing wrong with that. Being gay is one part of who I am, but it doesn't define the limits of my dreams, my character, my kindness, my strength, or my worth. I deserve to love, to be loved, to build a life, to have a family, to feel safe, and to exist openly without shame or apology. To every gay man, lesbian, bisexual, transgender person, queer person, and anyone still finding the courage to understand themselves: you are not broken, you are not a mistake, and you do not have to become someone else to deserve respect. Some of us grew up afraid to say these words. Some were bullied, rejected, judged, or taught to hide. Others are still living in places or families where being open can be difficult or dangerous. Wherever you are on your journey, remember that your life has value and your story matters. I'm gay. Yes. And I'm proud of the person I've become. May we continue creating a world where nobody has to whisper who they are, where love isn't treated as something shameful, and where everyone can live authentically, freely, and with dignity.

Urban

15,287 просмотров • 1 месяц назад