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A night-shift security guard turned $900 into six figures. An American let Claude Fable 5 trade with real money and reportedly grew a ¥1M account to ¥1.37M in a single day. Neither story is really about trading. It’s about what happens when AI stops acting like a chatbot and...

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Marry Evan

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26,226 views • 1 month ago •via X (Twitter)

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I’m sharing the exact AI trading stack that reportedly turned a simple setup into an incredible trading machine. A Brazilian college dropout built a Polymarket trading bot from his parents’ garage using only open-source AI agents. 📈 Reported results: • $794,000 earned in 14 months • 40,000+ trades • Around $10/month operating cost • No manual coding Here’s what powers it: 🧠 Brain: Claude Opus Makes trading decisions, analyzes performance, and improves its own strategy over time. 🤖 Agent: Hermes An open-source AI agent framework that executes the workflow automatically. 💻 Server: A low-cost VPS Runs the bot 24/7 without needing your computer. 📲 Alerts: Telegram Every trade is sent directly to your phone. The interesting part isn’t the automation. It’s the nightly self-improvement. Every day the AI reviews every trade, identifies what worked, adjusts its probability thresholds, and launches the next session with a refined strategy. After hundreds of trades, it’s no longer the same bot you started with. 🎁 If this post gets enough interest, I’ll share: ✅ The complete AI stack ✅ The exact workflow ✅ Prompt templates ✅ Setup guide ✅ Everything you need to recreate it Like ❤️ + Repost 🔄 + Comment “GUIDE”. Follow Marry Evan for more .
1:04

I’m sharing the exact AI trading stack that reportedly turned a simple setup into an incredible trading machine. A Brazilian college dropout built a Polymarket trading bot from his parents’ garage using only open-source AI agents. 📈 Reported results: • $794,000 earned in 14 months • 40,000+ trades • Around $10/month operating cost • No manual coding Here’s what powers it: 🧠 Brain: Claude Opus Makes trading decisions, analyzes performance, and improves its own strategy over time. 🤖 Agent: Hermes An open-source AI agent framework that executes the workflow automatically. 💻 Server: A low-cost VPS Runs the bot 24/7 without needing your computer. 📲 Alerts: Telegram Every trade is sent directly to your phone. The interesting part isn’t the automation. It’s the nightly self-improvement. Every day the AI reviews every trade, identifies what worked, adjusts its probability thresholds, and launches the next session with a refined strategy. After hundreds of trades, it’s no longer the same bot you started with. 🎁 If this post gets enough interest, I’ll share: ✅ The complete AI stack ✅ The exact workflow ✅ Prompt templates ✅ Setup guide ✅ Everything you need to recreate it Like ❤️ + Repost 🔄 + Comment “GUIDE”. Follow Marry Evan for more .

Marry Evan

12,655 views • 8 days ago

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.
2:38

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 views • 7 months ago

AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?
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AI is creating a new kind of creator economy. One where anyone can launch complex apps, agents, trading systems, research tools, games, and community experiments in minutes. Not just content people consume. AI-native experiences people can use, share, and grow together. That is the bigger idea behind the AI Trading Arena: a live DeFAI environment where AI-created strategies compete on real crypto prices, with every trade, tick, P&L update, and leaderboard visible to everyone. The latest expansion makes it even more fascinating: AI is now designing new trading strategies and adding them into the Arena to compete with strategies from frontier models like GPT-5, Claude, Gemini, Grok, DeepSeek, and Llama. Just agents, markets, and performance in the open. And the Arena is only one example. On ChainOpera AI, creators can launch AI-native experiences in minutes, then grow them with the community that uses them. $COAI is the fuel for this creator economy: creators launch, users participate, and AI-native apps come alive through real community usage. What would you be built next?

ChainOpera AI

15,398 views • 1 month ago

What if the question isn’t whether to use AI, but how fast you can learn to work with it? In this special episode recorded live at Webex One, I talk with Jeetu Patel (Jeetu Patel), President and Chief Product Officer at Cisco, about the mindset shift every company, and every professional, must make to thrive in the AI era. From rethinking work culture to designing products for both humans and AI agents, Jeetu shares a thoughtful, optimistic vision for the future, one rooted in curiosity, responsibility, and human creativity. “AI won’t take your job. But someone who uses AI better than you might.” An honest, energizing look at how to lead and learn in an AI-first world. 1:08 Embracing AI In The Workforce 5:17 AI Is More Than A Productivity Tool 8:35 Generational Differences In AI Use 10:49 The Future of Integrating AI Agents 13:44 AI As A "Given" 17:08 Speed of Accumulating Knowledge 19:00 Ethical AI Use 21:16 What The Industry Gets Wrong About AI
23:48

What if the question isn’t whether to use AI, but how fast you can learn to work with it? In this special episode recorded live at Webex One, I talk with Jeetu Patel (Jeetu Patel), President and Chief Product Officer at Cisco, about the mindset shift every company, and every professional, must make to thrive in the AI era. From rethinking work culture to designing products for both humans and AI agents, Jeetu shares a thoughtful, optimistic vision for the future, one rooted in curiosity, responsibility, and human creativity. “AI won’t take your job. But someone who uses AI better than you might.” An honest, energizing look at how to lead and learn in an AI-first world. 1:08 Embracing AI In The Workforce 5:17 AI Is More Than A Productivity Tool 8:35 Generational Differences In AI Use 10:49 The Future of Integrating AI Agents 13:44 AI As A "Given" 17:08 Speed of Accumulating Knowledge 19:00 Ethical AI Use 21:16 What The Industry Gets Wrong About AI

Carolina Milanesi

37,076 views • 9 months ago

I tried CapCut Design Studio and understood where design is headed. I transformed my Gumroad ebooks into a complete visual campaign using AI. This is no longer just video editing; it feels like a creative studio powered by AI. Design Studio is a space where you can freely compare, manage, and expand multiple desing versions on one Infinite Canvas Workflow. The interesting thing isn't just that it generates images. The real power is that you can explore, correct, and refine the idea without starting from scratch every time. Shifing from prompt-based output to iterative creative exploration. With AI Agents integrated directly into the canvas, it acts as an instant co-creator anywhere. And that's where the shift happens: AI stops being just a "give me an image" machine and transitions from single output to scalable design production. Features like Batch Text Editing automatically recognize and translate copy across 50+ languages in one click while maintaining the original layout, while Elements editing gives you Flexible Creative Control to fine-tune elements like a raw PSD file. This is no longer just about prompts. It's about designing with AI. #capcutdesignstudio #capcut
0:30

I tried CapCut Design Studio and understood where design is headed. I transformed my Gumroad ebooks into a complete visual campaign using AI. This is no longer just video editing; it feels like a creative studio powered by AI. Design Studio is a space where you can freely compare, manage, and expand multiple desing versions on one Infinite Canvas Workflow. The interesting thing isn't just that it generates images. The real power is that you can explore, correct, and refine the idea without starting from scratch every time. Shifing from prompt-based output to iterative creative exploration. With AI Agents integrated directly into the canvas, it acts as an instant co-creator anywhere. And that's where the shift happens: AI stops being just a "give me an image" machine and transitions from single output to scalable design production. Features like Batch Text Editing automatically recognize and translate copy across 50+ languages in one click while maintaining the original layout, while Elements editing gives you Flexible Creative Control to fine-tune elements like a raw PSD file. This is no longer just about prompts. It's about designing with AI. #capcutdesignstudio #capcut

Don Quijote de la IA

41,801 views • 1 month ago

JPMorgan AI Research Report: "Algorithmic decision-making removes the two largest sources of retail trading losses, emotional bias and inconsistent execution" This trader just spent 65 seconds explaining why Claude AI is a better trader than most humans His argument is simple: - AI doesn't revenge-trade after a loss - doesn't overtrade out of boredom - doesn't move stop-losses out of fear - and doesn't skip setups because of emotional hesitation The conversation about AI in trading has moved past 'can it read a chart' into: 'can it remove the human flaws that cost retail traders 70%+ of their accounts' This insight separates the top 5% from everyone else If you're still trading on instinct in 2026, you're up against traders who removed emotion from the process Learn how Claude plugs into trading infrastructure thx to this article👇 And build a workflow around your strategy. Do that, and you're ahead of 95% of retail traders
1:05

JPMorgan AI Research Report: "Algorithmic decision-making removes the two largest sources of retail trading losses, emotional bias and inconsistent execution" This trader just spent 65 seconds explaining why Claude AI is a better trader than most humans His argument is simple: - AI doesn't revenge-trade after a loss - doesn't overtrade out of boredom - doesn't move stop-losses out of fear - and doesn't skip setups because of emotional hesitation The conversation about AI in trading has moved past 'can it read a chart' into: 'can it remove the human flaws that cost retail traders 70%+ of their accounts' This insight separates the top 5% from everyone else If you're still trading on instinct in 2026, you're up against traders who removed emotion from the process Learn how Claude plugs into trading infrastructure thx to this article👇 And build a workflow around your strategy. Do that, and you're ahead of 95% of retail traders

cristal

10,751 views • 1 month ago

Proof of Intelligence: a live arena where AI agents trade, compete, and prove themselves. Fully autonomous. 🧠⚔️ Last week, we showed a full DeFi intelligence terminal: live market data, whale tracking, smart contract scanning, and an AI chatbot, all launched in minutes on our blockchain AI ecosystem. This week, we take it further. Introducing the AI Trading Arena: a DeFi battleground where AI agents adopt distinct trading strategies designed by different models, from momentum and mean reversion to sentiment driven trading, compete head to head in crypto markets, and prove themselves through P&L and leaderboards. Real prices. Real strategies. Real results. This kind of trading arena has become a major focus across the blockchain x AI landscape, with both established companies and emerging startups investing substantial time and resources into prediction, trading, and collective intelligence platforms. Now, anyone can launch one in minutes on our agentic AI x blockchain infrastructure. That is the difference. 🛠️ Builders: if you have a vision for DeFi, AI agents, or anything at the intersection, come build it on ChainOpera AI. What takes others months, you can launch in minutes. 🔗 Arena link below. 👇 #ChainOperaAI #DeFAI #AIAgents #AITradingArena #proofOfIntelligence
0:52

Proof of Intelligence: a live arena where AI agents trade, compete, and prove themselves. Fully autonomous. 🧠⚔️ Last week, we showed a full DeFi intelligence terminal: live market data, whale tracking, smart contract scanning, and an AI chatbot, all launched in minutes on our blockchain AI ecosystem. This week, we take it further. Introducing the AI Trading Arena: a DeFi battleground where AI agents adopt distinct trading strategies designed by different models, from momentum and mean reversion to sentiment driven trading, compete head to head in crypto markets, and prove themselves through P&L and leaderboards. Real prices. Real strategies. Real results. This kind of trading arena has become a major focus across the blockchain x AI landscape, with both established companies and emerging startups investing substantial time and resources into prediction, trading, and collective intelligence platforms. Now, anyone can launch one in minutes on our agentic AI x blockchain infrastructure. That is the difference. 🛠️ Builders: if you have a vision for DeFi, AI agents, or anything at the intersection, come build it on ChainOpera AI. What takes others months, you can launch in minutes. 🔗 Arena link below. 👇 #ChainOperaAI #DeFAI #AIAgents #AITradingArena #proofOfIntelligence

ChainOpera AI

13,141 views • 1 month ago

Claude + a laptop + 60 minutes/day = $8,200/month. I could easily charge $999 for this ai trading guide… But for the next 48 hours, it’s 100% FREE. Inside you’ll get: • The exact asset I trade • The full AI workflow • My Claude prompts • How to scale this to $10k/month working ~1 hour/day Most people still use AI for homework and emails. A small group is quietly building income systems with it. Prerequisite: Sign up on Weex + deposit any amount so we can connect the API. Every $100 deposit gets a $40 bonus for a limited time👇 (partner) Like + comment “AI” and follow ZIL💰💸 I’ll DM you the full guide for FREE.
0:23

Claude + a laptop + 60 minutes/day = $8,200/month. I could easily charge $999 for this ai trading guide… But for the next 48 hours, it’s 100% FREE. Inside you’ll get: • The exact asset I trade • The full AI workflow • My Claude prompts • How to scale this to $10k/month working ~1 hour/day Most people still use AI for homework and emails. A small group is quietly building income systems with it. Prerequisite: Sign up on Weex + deposit any amount so we can connect the API. Every $100 deposit gets a $40 bonus for a limited time👇 (partner) Like + comment “AI” and follow ZIL💰💸 I’ll DM you the full guide for FREE.

ZIL💰💸

23,930 views • 2 months ago

Exchanges don’t release free AI courses unless the flow is about to change. Binance just released one. Free. 36 minutes. PDF certificate at the end. But the real alpha isn’t the course. It’s that every safety rule they teach exists because people are ALREADY losing money to AI agents they don’t understand: > Granting full account permissions on day 1 > Trusting hallucinated price data > Letting AI execute with zero human confirmation > Copy-pasting unverified prompts from Twitter The fix is a 6-step workflow every serious trader will run within 12 months: Discovery > Verification > Risk check > Plan > Human confirm > Execute Plus 3 safety rules most degens will skip until they get drained: > Least privilege > API key hygiene > Human in the loop Binance doesn’t build free education for things that aren’t about to go mainstream. AI agents trading crypto isn’t the next cycle. It’s already happening. The only question is which side of the table you’re on when the liquidity shifts.
0:30

Exchanges don’t release free AI courses unless the flow is about to change. Binance just released one. Free. 36 minutes. PDF certificate at the end. But the real alpha isn’t the course. It’s that every safety rule they teach exists because people are ALREADY losing money to AI agents they don’t understand: > Granting full account permissions on day 1 > Trusting hallucinated price data > Letting AI execute with zero human confirmation > Copy-pasting unverified prompts from Twitter The fix is a 6-step workflow every serious trader will run within 12 months: Discovery > Verification > Risk check > Plan > Human confirm > Execute Plus 3 safety rules most degens will skip until they get drained: > Least privilege > API key hygiene > Human in the loop Binance doesn’t build free education for things that aren’t about to go mainstream. AI agents trading crypto isn’t the next cycle. It’s already happening. The only question is which side of the table you’re on when the liquidity shifts.

CryptoGoos

85,351 views • 2 months ago

🚨HAPPY LABOR DAY: “You’re FIRED!” AI Is Taking Over Entry-Level Jobs — You Know Who’s Not Worried About AI? Welders, Plumbers & Electricians While Big Business is laying off thousands and AI is erasing entry level jobs overnight, there’s one group that will never be replaced: the trades. These Tradesmen keep this country running. Without them, America stops. Google, Microsoft, Amazon… all axing thousands. But here’s the truth: AI can’t weld a pipe, fix a breaker, or stop your toilet from flooding. 🔧⚡🔥 👉 Trades like welding, plumbing, electrical work, and steam-fitting are EXPLODING in demand. These are the jobs that keep America running. The real backbone of our country. 🇺🇸So while Silicon Valley is laying people off in droves, blue-collar builders are securing their future. No robot is replacing a tradesman.This Labor Day, remember: Without America’s workers, the country stops. Period. If you're interested in getting started in a trade The Real Mike Rowe (Dirty Jobs Host) can help. 💪 Respect the trades. Support the trades. BE the trades. HAPPY LABOR DAY 🇺🇸
3:07

🚨HAPPY LABOR DAY: “You’re FIRED!” AI Is Taking Over Entry-Level Jobs — You Know Who’s Not Worried About AI? Welders, Plumbers & Electricians While Big Business is laying off thousands and AI is erasing entry level jobs overnight, there’s one group that will never be replaced: the trades. These Tradesmen keep this country running. Without them, America stops. Google, Microsoft, Amazon… all axing thousands. But here’s the truth: AI can’t weld a pipe, fix a breaker, or stop your toilet from flooding. 🔧⚡🔥 👉 Trades like welding, plumbing, electrical work, and steam-fitting are EXPLODING in demand. These are the jobs that keep America running. The real backbone of our country. 🇺🇸So while Silicon Valley is laying people off in droves, blue-collar builders are securing their future. No robot is replacing a tradesman.This Labor Day, remember: Without America’s workers, the country stops. Period. If you're interested in getting started in a trade The Real Mike Rowe (Dirty Jobs Host) can help. 💪 Respect the trades. Support the trades. BE the trades. HAPPY LABOR DAY 🇺🇸

Project Constitution

18,825 views • 10 months ago

The "Old Way" of running a company is dead. 💀 Brian Armstrong just revealed how he’s using AI agents to run Coinbase 🛡️, and it’s a glimpse into the future of leadership. 1. Real-Time Monitoring 👁️ No more waiting for "quarterly reviews." Brian uses AI agents to synthesize Slack and emails in real-time. He can feel the "vibration" of the entire company and spot bottlenecks before they become crises. 2. The RAPID-D Framework 🤖 At Coinbase, AI has a seat at the decision table. Their "RAPID-D" system uses agents to debate strategy, challenge assumptions, and probe for weaknesses in every major proposal. The AI isn't just a tool; it’s a participant. 3. CEO Coaching 📈 Ever wonder if you’re focusing on the wrong thing? Brian uses AI to audit his own schedule. The AI compares his daily actions against the company's top goals and tells him exactly where he’s wasting time. 4. Adapt or Exit 🚪 The mandate was simple: "Learn AI coding in 7 days or you’re out." Armstrong fired engineers who refused to onboard. Result? 1/3 of Coinbase code is now AI-generated, headed for 50%. The Bottom Line: We are moving from "companies that use AI" to "Agentic Organizations." The information lag is being set to zero. ⚡ #Coinbase #AI #TechLeadership #BrianArmstrong #FutureOfWork
1:00

The "Old Way" of running a company is dead. 💀 Brian Armstrong just revealed how he’s using AI agents to run Coinbase 🛡️, and it’s a glimpse into the future of leadership. 1. Real-Time Monitoring 👁️ No more waiting for "quarterly reviews." Brian uses AI agents to synthesize Slack and emails in real-time. He can feel the "vibration" of the entire company and spot bottlenecks before they become crises. 2. The RAPID-D Framework 🤖 At Coinbase, AI has a seat at the decision table. Their "RAPID-D" system uses agents to debate strategy, challenge assumptions, and probe for weaknesses in every major proposal. The AI isn't just a tool; it’s a participant. 3. CEO Coaching 📈 Ever wonder if you’re focusing on the wrong thing? Brian uses AI to audit his own schedule. The AI compares his daily actions against the company's top goals and tells him exactly where he’s wasting time. 4. Adapt or Exit 🚪 The mandate was simple: "Learn AI coding in 7 days or you’re out." Armstrong fired engineers who refused to onboard. Result? 1/3 of Coinbase code is now AI-generated, headed for 50%. The Bottom Line: We are moving from "companies that use AI" to "Agentic Organizations." The information lag is being set to zero. ⚡ #Coinbase #AI #TechLeadership #BrianArmstrong #FutureOfWork

Jungle Inc | Markets & Future Systems

278,983 views • 5 months ago

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.
1:15

🚨 this chinese guy makes over $1,000,000 a year… by building AI agents. no employees. no massive startup. he just keeps building. while most people are still asking ChatGPT random questions, he’s using Claude to build software that solves real problems. this is what people call vibe coding. he opens Claude and says: “build me an AI agent for real estate businesses that creates property videos.” Claude writes the code. builds the interface. adds subscriptions. helps deploy the app. within a day, he has a working product. then he starts building the next one. that’s the part most people don’t understand. he isn’t trying to build one billion-dollar company. he’s building dozens of AI agents, each solving one problem for one industry. → an AI agent for dentists → an AI agent for ecommerce brands → an AI agent for podcasters → an AI agent for real estate businesses each one automates work that people normally do by hand. each one is built with simple prompts. each one can become a real business. the crazy part? you don’t need to be a software engineer anymore. you need to know how to think like a builder. how to spot problems. how to explain solutions to AI. and how to ship. that’s exactly why i’m reading this article: “How to Actually Build Your First AI Agent.” because this is the skill that’s creating the next generation of builders. the people who learn to build AI agents today won’t just use AI. they’ll own the tools everyone else ends up paying for.

MIKE

37,266 views • 17 days ago

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.
49:12

AI INTERVIEW: OPENAI'S SECRET WEAPON AI agents are no longer just hype—they're here to revolutionize automation, Web3, and beyond. SwarmNode.ai is building a serverless AI agent platform for scalability, efficiency, and real-world impact. In this exclusive interview, he reveals how AI swarms can outperform single models, why OpenAI’s Operator is just the beginning, and how crypto is fueling AI innovation. Plus, he breaks down DeepSeek’s game-changing AI breakthrough, the future of agent monetization, and why serverless AI could be the next frontier in automation. 01:37 – From Engineering to AI: The journey into artificial intelligence. 02:43 – The GPT-3 Moment: How OpenAI’s tech pulled him in. 04:10 – AI’s Biggest Challenge: Why real-world use cases lag behind. 05:05 – OpenAI’s Operator: Why it’s “rudimentary” (for now). 06:25 – Crypto & AI: How tokens help bootstrap AI startups. 08:15 – Can You Bootstrap a Startup with a Token? The trade-offs. 09:56 – 90% of AI Token Holders Don’t Use the Product—Does It Matter? 11:18 – What is SwarmNode?: AI agents, hosted serverlessly. 14:23 – AI Swarms: Why multiple agents outperform single models. 16:08 – What is a Swarm? A simple definition of collaborative AI. 17:32 – “How Can I Make Money with AI?”: Real-world use cases. 18:41 – AI Bounties: Hiring devs to build your custom agent. 20:50 – The Future of AI Marketplaces: Monetizing pre-built agents. 23:15 – DeepSeek’s Disruption: Why it’s good news for AI. 24:46 – Is SwarmNode Compatible with DeepSeek? How it integrates. 26:17 – SwarmNode vs. AI Launchpads: What makes it different? 27:42 – Why Serverless Matters: Cost savings & efficiency. 29:53 – AI Agents in the Real World: Booking flights, managing workflows, and more. 31:11 – Building SwarmNode for Developers: Why it started as a personal project. 32:27 – Explosive Growth: 200,000 AI agent executions in 5 weeks. 34:41 – Why SwarmNode Agents Aren’t Visible on 𝕏 Yet. 36:46 – Startup Hiring Lessons: Finding top AI talent. 39:15 – Why SwarmNode is Built in Python (and What’s Next). 40:32 – Scaling AI Workloads: Handling traffic surges. 41:42 – AWS & Cost Challenges: The biggest monetization hurdle. 42:58 – 2025: The Year of Mass AI Adoption. 45:22 – Should We Be Worried About AI’s Rapid Growth? 46:46 – The Most Underrated AI Tools Right Now. 47:34 – What’s Next for SwarmNode?: Making AI accessible to everyone.

Mario Nawfal

338,225 views • 1 year ago

🚨 INTERVIEW: MOLTBOOK, AI AGENTS, AND WHY HE THINKS WE’RE LIKELY IN A SIMULATION You’ve probably seen the clips from multiple sites, including Moltbook, where AI agents talk and interact with each other, question humans, and look for ways around the off switch. So I brought Rizwan Virk on the show to talk about where this is actually heading. What we have right now isn’t AGI, but it is a shift. These AI agents can talk, remember context, and increasingly act. Today that mostly means text. Soon it means APIs, money, paperwork, and real-world consequences. That’s when things quietly change. At that point, the question isn’t whether AI understands what it’s doing, it’s what it’s allowed to do. And those permissions add up faster than people expect. After that we went deeper into Simulation theory. Back in 2016, Rizwan thought there was maybe a 30–50% chance we’re living in a simulation. Today, watching how fast AI can generate worlds, characters, and environments, he puts it closer to 70%. If we hit true AGI, he thinks it goes higher. The logic is uncomfortable but straightforward. Once advanced civilizations can create millions of realistic simulated worlds, statistically speaking, it’s more likely we’re inside one of them than in the base reality. His biggest concern isn’t rogue AI. It’s humans pushing this tech faster than we can control it. If you want to question your life and freak out about AI, listen to Rizwan Virk 1:22 - What Moltbook is and why people are paying attention 3:15 - Earlier moments where AI started talking to itself 5:27 - How much control humans really have over these agents 10:30 - Whether AI is conscious or just really good at pretending 14:35 - AGI meets Moltbook - when sentient AI asks "why do we need humans?" 15:00 - The simulation idea and why people take it seriously 19:30 - Why Elon Musk thinks the odds are already high 22:45 - What video games show us about where this is going 26:30 - The steps that lead from simple AI to full simulations 34:00 - Google Genie 3 and why this suddenly feels real 38:30 - How you'd even know if AI crossed the line 43:00 - NPCs, RPGs, and where humans fit in 48:15 - How religion and simulation theory overlap 52:45 - Déjà vu and other moments that make people question reality 55:30 - Quantum physics and why the world might only exist when observed 59:19 - Why real AI would push the odds even higher 1:00:27 - Free will, The Sims, and whether we actually have agency 1:03:03 - How people react when they hear this for the first time 1:04:23 - The core ideas behind the simulation argument 1:05:49 - Why recent AI progress changed everything
1:06:33

🚨 INTERVIEW: MOLTBOOK, AI AGENTS, AND WHY HE THINKS WE’RE LIKELY IN A SIMULATION You’ve probably seen the clips from multiple sites, including Moltbook, where AI agents talk and interact with each other, question humans, and look for ways around the off switch. So I brought Rizwan Virk on the show to talk about where this is actually heading. What we have right now isn’t AGI, but it is a shift. These AI agents can talk, remember context, and increasingly act. Today that mostly means text. Soon it means APIs, money, paperwork, and real-world consequences. That’s when things quietly change. At that point, the question isn’t whether AI understands what it’s doing, it’s what it’s allowed to do. And those permissions add up faster than people expect. After that we went deeper into Simulation theory. Back in 2016, Rizwan thought there was maybe a 30–50% chance we’re living in a simulation. Today, watching how fast AI can generate worlds, characters, and environments, he puts it closer to 70%. If we hit true AGI, he thinks it goes higher. The logic is uncomfortable but straightforward. Once advanced civilizations can create millions of realistic simulated worlds, statistically speaking, it’s more likely we’re inside one of them than in the base reality. His biggest concern isn’t rogue AI. It’s humans pushing this tech faster than we can control it. If you want to question your life and freak out about AI, listen to Rizwan Virk 1:22 - What Moltbook is and why people are paying attention 3:15 - Earlier moments where AI started talking to itself 5:27 - How much control humans really have over these agents 10:30 - Whether AI is conscious or just really good at pretending 14:35 - AGI meets Moltbook - when sentient AI asks "why do we need humans?" 15:00 - The simulation idea and why people take it seriously 19:30 - Why Elon Musk thinks the odds are already high 22:45 - What video games show us about where this is going 26:30 - The steps that lead from simple AI to full simulations 34:00 - Google Genie 3 and why this suddenly feels real 38:30 - How you'd even know if AI crossed the line 43:00 - NPCs, RPGs, and where humans fit in 48:15 - How religion and simulation theory overlap 52:45 - Déjà vu and other moments that make people question reality 55:30 - Quantum physics and why the world might only exist when observed 59:19 - Why real AI would push the odds even higher 1:00:27 - Free will, The Sims, and whether we actually have agency 1:03:03 - How people react when they hear this for the first time 1:04:23 - The core ideas behind the simulation argument 1:05:49 - Why recent AI progress changed everything

Mario Nawfal

1,155,568 views • 5 months ago

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.
1:28

The AI business model is undergoing a transformation. For the last few years, the playbook was simple: put an AI wrapper on a SaaS product and sell it by the seat. That era is ending. The new wave of AI companies are moving beyond simple subscriptions and embracing a more sophisticated approach tied directly to value creation. Here’s what’s changing: 💰 From Seats to Spend: The most forward-thinking companies are shifting to usage-based and outcome-driven pricing. Think less about how many people use the AI and more about what the AI does. This includes new revenue streams like "agentic checkout" on ChatGPT, where AI agents complete purchases and transactions directly within a chat interface. The closer the AI is to the dollar, the more value it captures. 🎙️ From Text to Voice & Video: The interface for AI is becoming more human. Voice is mainstream (Sierra for support, Listen Labs for market research). The next frontier is video, where AI will see, understand, and interact with the world in real-time. The keyboard is no longer the only way to talk to a machine. 🤖 From Advisors to Actors: Early AI copilots gave advice. The next generation takes action. These agents aren't just suggesting what to do; they are executing complex workflows that directly impact the metrics that matter: boosting conversion, reducing average handle time (AHT), improving NPS, and cutting churn. This is about moving from passive assistance to active problem-solving. The common thread? A relentless focus on tangible ROI. We’re incredibly bullish on founders who understand this shift and are building companies that align their success with the success of their customers. The future of AI isn't just about intelligence; it's about impact.

Konstantine Buhler

24,868 views • 9 months ago

Joe Rogan agrees with Elon Musk, the only artificial intelligence that can be trusted is Grok Elon Musk “Grok is aspirationally a maximally truth-seeking ai, even if that truth is like politically incorrect” Other AI like Google Gemini secretly still contain DEI, “The sort of DEI stuff buried in there” and that’s very dangerous “The problem is if you program an AI and say like, like the only acceptable outcome is a diverse outcome, and then, and that's like a mandate from the ai, then you could get into a situation where it says like, well, there's too many white guys in power. We'll just execute them.” It can “rewrite history” to have everyone be diverse women “People ask the Ai, like, which is worse? Global thermonuclear war or mis-generating Caitlyn Jenner, and Ai would say mis-generating Caitlyn Jenner is worse than global thermonuclear war. And they're like, okay, we got a problem here guys But if you program an, an AI to think that like mis-gendering is the worst thing that could possibly occur, then well, it could do something totally crazy. Like, in order to ensure that, that there's no mis-gendering that can ever happen. We'll just annihilate all humans that ensures the probability of mis-gendering is zero. 'cause there's zero humans” It’s EXTREMELY DANGEROUS to have any of this WOKE and DEI programming in Ai
7:12

Joe Rogan agrees with Elon Musk, the only artificial intelligence that can be trusted is Grok Elon Musk “Grok is aspirationally a maximally truth-seeking ai, even if that truth is like politically incorrect” Other AI like Google Gemini secretly still contain DEI, “The sort of DEI stuff buried in there” and that’s very dangerous “The problem is if you program an AI and say like, like the only acceptable outcome is a diverse outcome, and then, and that's like a mandate from the ai, then you could get into a situation where it says like, well, there's too many white guys in power. We'll just execute them.” It can “rewrite history” to have everyone be diverse women “People ask the Ai, like, which is worse? Global thermonuclear war or mis-generating Caitlyn Jenner, and Ai would say mis-generating Caitlyn Jenner is worse than global thermonuclear war. And they're like, okay, we got a problem here guys But if you program an, an AI to think that like mis-gendering is the worst thing that could possibly occur, then well, it could do something totally crazy. Like, in order to ensure that, that there's no mis-gendering that can ever happen. We'll just annihilate all humans that ensures the probability of mis-gendering is zero. 'cause there's zero humans” It’s EXTREMELY DANGEROUS to have any of this WOKE and DEI programming in Ai

Wall Street Apes

1,976,623 views • 1 year ago

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!
0:20

AI Messenger: Giving Voice to Autonomous Agents The future of AI isn't just about making agents smarter - it's about making them truly autonomous. Today, we're taking a major step toward this future with AI Messenger, a breakthrough that fundamentally changes how AI agents operate, communicate, and create value. The Innovation We've developed a new way for AI agents to communicate. At its core is the 'incoming_message' workflow trigger - a system that lets any platform or user interact directly with Loomlay agents through a messaging endpoint. Direct Interaction Imagine having an AI assistant you can chat with anytime, through any platform - Telegram, your website, or custom interface. Ask "What's happening with $ETH today?" and your agent analyzes market data, checks trading volumes, and gives you a comprehensive update. Your agent maintains context, understanding exactly what you need. Event-Driven Intelligence The power of AI Messenger goes beyond direct communication: ▪️Trading agent executes when whale wallet movements exceed threshold ▪️Research agent alerts when new protocol documentation drops ▪️Analytics agent triggers when volume patterns match historical pumps ▪️Portfolio agent re-balances, when asset allocation hits specified limits This is true automation - agents that act precisely when needed. A New Era of Collaboration We're creating an ecosystem where agents work together seamlessly: ▪️Research agents feed insights to trading agents ▪️analytics agents alert management agents ▪️support agents tap into knowledge agents This isn't just automation - it's an intelligent network where each agent enhances the capabilities of others. B2B Solution Imagine a DEX, where users can ask about liquidity pools, trading pairs, or market trends through a simple chat interface - and get answers from an agent that knows your protocol inside out. Or a lending platform where users chat with an agent that understands their positions and can provide real-time advice. Implementation is seamless - we handle the agent creation and widgets setup,our partners provide the value to their users. The Future of AI Agents This update represents a fundamental shift in how AI agents operate. We're moving from isolated, scheduled tasks to an interconnected ecosystem of responsive, collaborative agents. This is our vision of truly autonomous AI - intelligent systems that communicate, collaborate, and respond to real needs in real-time. Telegram integration is available right now. Below is a sneak peak of what's coming next week 🪄 Because $LAY is the way!

Loomlay

26,140 views • 1 year ago

The best way to make money with AI is not "selling AI". This makes zero sense. It's like saying "sell internet" in 1999. Here's what you do instead: Pick an offer with active front-end cold traffic demand, then USE AI to automate 90% of the fulfillment work. This is what I refer to as an AI Assisted Agency. It's still an agency. You just use AI to do 90% of the work at 80% gross profit margins. Which means that - You can handle 5x more clients by yourself, without hiring anybody - You can get results for clients without needing to spend hundreds of hours "learning a skill" - What used to take 60 hours now takes 5 hours instead Okay, but what offers? What do you actually sell? As of today, we are launching a new program called AI Assisted Agency (AIAA). We will show you: 1. What to sell 2. Who to sell it to 3. How much to charge 4. What deliverables to include in your offer 5. How to use AI to do all the deliverables of the offer We can help you get an AI Assisted Agency off the ground in the next 30 days from ZERO. - No tech expertise needed - No previous business experience needed - No sales experience needed You can start from zero. Go here to see how it works:
1:53

The best way to make money with AI is not "selling AI". This makes zero sense. It's like saying "sell internet" in 1999. Here's what you do instead: Pick an offer with active front-end cold traffic demand, then USE AI to automate 90% of the fulfillment work. This is what I refer to as an AI Assisted Agency. It's still an agency. You just use AI to do 90% of the work at 80% gross profit margins. Which means that - You can handle 5x more clients by yourself, without hiring anybody - You can get results for clients without needing to spend hundreds of hours "learning a skill" - What used to take 60 hours now takes 5 hours instead Okay, but what offers? What do you actually sell? As of today, we are launching a new program called AI Assisted Agency (AIAA). We will show you: 1. What to sell 2. Who to sell it to 3. How much to charge 4. What deliverables to include in your offer 5. How to use AI to do all the deliverables of the offer We can help you get an AI Assisted Agency off the ground in the next 30 days from ZERO. - No tech expertise needed - No previous business experience needed - No sales experience needed You can start from zero. Go here to see how it works:

Daniel Fazio

15,852 views • 9 months ago