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A Chinese trader known as "gatorr" made $1,934,731.20 on NBA markets by simulating thousands of autonomous AI agents. While researchers use a demo version of MiroFish to test macroeconomic policies, gatorr has turned the repository into an enhanced MiroFish terminal to hack the sports betting market. Here's an alpha...

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38,048 views • 4 months ago •via X (Twitter)

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Someone trained a swarm model on 3 years of NBA data and let it loose on Polymarket The result: $1.49M. He didn't build a better model. He built a better crowd. Here's how the system actually works. He takes MiroFish - an open-source engine that simulates thousands of AI agents and feeds it raw sports data. The actual building blocks: > Player stat vectors: points, rebounds, assists, eFG%, usage rate across 3 full seasons > Team form tensors: last 10 games, home/away splits, pace-of-play, defensive rating > Matchup history: head-to-head records, positional mismatches, referee tendencies > Injury probability models: medical staff reports weighted by recovery timelines > Line movement tracking: where the sharp money flows before tip-off Check how this wallet works: MiroFish generates 4,096 agents - analysts, bettors, insiders, oddsmakers - each with different reasoning. They argue, form clusters, shift opinions. Consensus emerges. He pipes that consensus into a 12-layer transformer trained on the full history of his 16,695 predictions. The model compares MiroFish output against live Polymarket odds. When the gap exceeds his Kelly criterion threshold, it enters. Lakers at 40 cents. MiroFish said 62%. One position: $190,823. The edge isn't data. Every sharp has data. The edge is simulating how 4,000 fake humans process it - before the real crowd does.
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Someone trained a swarm model on 3 years of NBA data and let it loose on Polymarket The result: $1.49M. He didn't build a better model. He built a better crowd. Here's how the system actually works. He takes MiroFish - an open-source engine that simulates thousands of AI agents and feeds it raw sports data. The actual building blocks: > Player stat vectors: points, rebounds, assists, eFG%, usage rate across 3 full seasons > Team form tensors: last 10 games, home/away splits, pace-of-play, defensive rating > Matchup history: head-to-head records, positional mismatches, referee tendencies > Injury probability models: medical staff reports weighted by recovery timelines > Line movement tracking: where the sharp money flows before tip-off Check how this wallet works: MiroFish generates 4,096 agents - analysts, bettors, insiders, oddsmakers - each with different reasoning. They argue, form clusters, shift opinions. Consensus emerges. He pipes that consensus into a 12-layer transformer trained on the full history of his 16,695 predictions. The model compares MiroFish output against live Polymarket odds. When the gap exceeds his Kelly criterion threshold, it enters. Lakers at 40 cents. MiroFish said 62%. One position: $190,823. The edge isn't data. Every sharp has data. The edge is simulating how 4,000 fake humans process it - before the real crowd does.

BuBBliK

504,073 views • 4 months ago

Just built a bot that first runs hyper-realistic MiroFish swarm simulations on every upcoming Bitcoin and crypto event. And then agent instantly trades the real live markets on Polymarket, already printing $12,000+ per day in testing. Couldn't hold back after diving into MiroFish. Took the new god-tier agent behavior simulator from that Chinese college quant who coded it in 10 days, exploded GitHub to 23k+ stars and bagged $4.1M from Shanda overnight.. Paired it with OpenClaw (24/7 autonomous execution) + Claude Opus 4.6. And in one day built my first version of private Polymarket bot. Now it: -> spawns thousands of agents with real memory and personalities -> runs full GraphRAG swarm simulations modeling exactly how news, ETF flows, macro data, whale activity and sentiment will move Bitcoin price -> simulates thousands of possible futures specifically for Polymarket Bitcoin contracts -> detects where the crowd probability is mispriced on every crypto market and extracts the real edge -> auto-trades the edges instantly through OpenClaw the moment the opportunity appears Testing the bot + MiroFish based simulator live right now. First runs already printing hard. Meanwhile there's a real trader crushing with a similar stack imo, $321k all-time profit and 12k/day, 100% won on Bitcoin markets. Wallet: My own Polymarket profile + full trade logs drop later once I scale it hard. New meta just dropped, don't miss out! Check the guide and all info below.
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Just built a bot that first runs hyper-realistic MiroFish swarm simulations on every upcoming Bitcoin and crypto event. And then agent instantly trades the real live markets on Polymarket, already printing $12,000+ per day in testing. Couldn't hold back after diving into MiroFish. Took the new god-tier agent behavior simulator from that Chinese college quant who coded it in 10 days, exploded GitHub to 23k+ stars and bagged $4.1M from Shanda overnight.. Paired it with OpenClaw (24/7 autonomous execution) + Claude Opus 4.6. And in one day built my first version of private Polymarket bot. Now it: -> spawns thousands of agents with real memory and personalities -> runs full GraphRAG swarm simulations modeling exactly how news, ETF flows, macro data, whale activity and sentiment will move Bitcoin price -> simulates thousands of possible futures specifically for Polymarket Bitcoin contracts -> detects where the crowd probability is mispriced on every crypto market and extracts the real edge -> auto-trades the edges instantly through OpenClaw the moment the opportunity appears Testing the bot + MiroFish based simulator live right now. First runs already printing hard. Meanwhile there's a real trader crushing with a similar stack imo, $321k all-time profit and 12k/day, 100% won on Bitcoin markets. Wallet: My own Polymarket profile + full trade logs drop later once I scale it hard. New meta just dropped, don't miss out! Check the guide and all info below.

slash1s

114,880 views • 4 months ago

How MiroFish helped me make $669/day on Polymarket One week ago I plugged a swarm intelligence engine into my Polymarket bot. It simulates 2,847 digital humans before every trade. The bot watches how they behave, then bets against the real crowd. 338 trades. $4,266 profit. One position returned 1,655% in five minutes. The engine is called MiroFish. It doesn't predict price. It predicts how people will react and on a prediction market, that's the only thing that matters. I feed it market context. It builds a parallel world. Thousands of AI agents argue, form groups, shift opinions. When their consensus diverges from what Polymarket is pricing, the bot enters. 5-minute windows. BTC, ETH, SOL, XRP. I've been running the bot live on What MiroFish actually is: > Built in 10 days by a 20-year-old undergraduate in Beijing using vibe coding > Hit #1 on GitHub's global trending above OpenAI, Google, and Microsoft > Backed by Chen Tianqiao, former richest man in China, who committed $4.1M within 24 hours of seeing one demo > Runs on OASIS by CAMEL-AI with GraphRAG knowledge graphs and Zep Cloud memory The edge is simple. Polymarket is a crowd behavior market. MiroFish is a crowd behavior simulator. Nobody is connecting the two yet.
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How MiroFish helped me make $669/day on Polymarket One week ago I plugged a swarm intelligence engine into my Polymarket bot. It simulates 2,847 digital humans before every trade. The bot watches how they behave, then bets against the real crowd. 338 trades. $4,266 profit. One position returned 1,655% in five minutes. The engine is called MiroFish. It doesn't predict price. It predicts how people will react and on a prediction market, that's the only thing that matters. I feed it market context. It builds a parallel world. Thousands of AI agents argue, form groups, shift opinions. When their consensus diverges from what Polymarket is pricing, the bot enters. 5-minute windows. BTC, ETH, SOL, XRP. I've been running the bot live on What MiroFish actually is: > Built in 10 days by a 20-year-old undergraduate in Beijing using vibe coding > Hit #1 on GitHub's global trending above OpenAI, Google, and Microsoft > Backed by Chen Tianqiao, former richest man in China, who committed $4.1M within 24 hours of seeing one demo > Runs on OASIS by CAMEL-AI with GraphRAG knowledge graphs and Zep Cloud memory The edge is simple. Polymarket is a crowd behavior market. MiroFish is a crowd behavior simulator. Nobody is connecting the two yet.

BuBBliK

124,905 views • 4 months ago

Today at #GoogleIO, we’re demonstrating how our partnership with Google Cloud is redefining the landscape of Intelligent Content Management. Leveraging the advanced capabilities of Google’s Gemini 2.5 Pro, our new AI Agent with enhanced data extraction transforms complex, unstructured data from various sources—including scanned PDFs, images, and diverse forms—into structured, actionable insights. Check out the demo below to watch Gemini 2.5 Pro and Box AI agents in action. With the Box AI Agent powered by Gemini 2.5 Pro you can: ⭐ Automatically, and instantly, classify and organize documents ⭐ Extract accurate insights and information from documents and images ⭐ Enable workflow automation by leveraging structured and easily retrievable content Our partnership with Google is not just about enhancing current capabilities; it’s about unlocking the full potential of AI, and models like Gemini 2.5 Pro, for intricate enterprise use cases.
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Today at #GoogleIO, we’re demonstrating how our partnership with Google Cloud is redefining the landscape of Intelligent Content Management. Leveraging the advanced capabilities of Google’s Gemini 2.5 Pro, our new AI Agent with enhanced data extraction transforms complex, unstructured data from various sources—including scanned PDFs, images, and diverse forms—into structured, actionable insights. Check out the demo below to watch Gemini 2.5 Pro and Box AI agents in action. With the Box AI Agent powered by Gemini 2.5 Pro you can: ⭐ Automatically, and instantly, classify and organize documents ⭐ Extract accurate insights and information from documents and images ⭐ Enable workflow automation by leveraging structured and easily retrievable content Our partnership with Google is not just about enhancing current capabilities; it’s about unlocking the full potential of AI, and models like Gemini 2.5 Pro, for intricate enterprise use cases.

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𝐓𝐡𝐞 𝟐𝟎𝟐𝟔 𝐖𝐨𝐫𝐥𝐝 𝐂𝐮𝐩 𝐰𝐢𝐥𝐥 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐦𝐨𝐫𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐭𝐡𝐚𝐧 𝐚𝐧𝐲 𝐬𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐞𝐯𝐞𝐧𝐭 𝐢𝐧 𝐡𝐢𝐬𝐭𝐨𝐫𝐲. Most people will use sportsbooks. A few will discover something better. Here's why the World Cup is actually Pots Market Market biggest moment. Every match carries dozens of predictable outcomes, not just who wins, but scorelines, goalscorers, red cards, halftime leads, VAR decisions. Traditional sportsbooks will process billions in bets. And keep most of it. Here is the problem with sportsbooks that nobody talks about: They set the odds. Not the market. Which means the house always has an edge baked in before you place a single bet. You're not trading against the market, you're trading against a company whose entire business model depends on you losing. That's not a prediction market. That's a casino with a football shirt on. Prediction markets are different. The odds aren't set by a company. They're set by the collective intelligence of everyone participating. When millions of people put real money behind their beliefs, the market finds truth faster than any analyst, pundit, or algorithm working alone. This is why prediction markets outperform polls, pundits, and press releases, every single time. Now here's where POTS Market changes the game entirely. Pots Money has a dedicated sports vertical, built specifically for football, basketball, and esports. Not a generic market with a football category tucked in the corner. A tailored module optimized for the way sports prediction actually works, short windows, live data, rapid settlement. 64 World Cup matches = 64 live prediction markets. Each one open, on-chain, transparent. But the real edge is not the markets themselves. It's the capital layer underneath them. On a traditional sportsbook, every bet locks your full stake. You place £100, that £100 is gone until settlement. POTS Money changes this. The DeFi lending primitive means you can collateralize positions and optimize capital across multiple markets simultaneously, without locking up dead capital on each one. That's not betting. That's portfolio management. And then there's the AI layer. Via MCP (Model Context Protocol), you can deploy autonomous trading agents that monitor live match data and execute positions based on your pre-set strategy. Imagine this: Agent monitors possession stats in real time Detects a momentum shift at the 60th minute Auto-places a position on the next goalscorer market before the crowd even reacts 𝙏𝙝𝙖𝙩'𝙨 𝙣𝙤𝙩 𝙖 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙤𝙣. 𝙏𝙝𝙖𝙩'𝙨 𝙖𝙣 𝙚𝙙𝙜𝙚 Compare the two worlds side by side: Sportsbook: ❌ House sets the odds ❌ Geo-restricted ❌ Capital locked per bet ❌ No automation ❌ Withdraw only when they allow it POTS Market: ✅ Market sets the odds ✅ Open and on-chain ✅ Capital efficient via DeFi lending ✅ AI agents execute your strategy ✅ Fully collateralized, transparent settlement One of these is built for the next decade. The other is built for the last one. The timing is not a coincidence. POTS Market launches Q2 2026, right as World Cup fever peaks globally. Q1 2026 — MVP + Polymarket integration ✅ Q2 2026 — Market Launch + DeFi lending beta 👈 we are here Q3-Q4 2026 — Skill Hub + Sub-accounts 2027+ — DAO governance + cross-chain The infrastructure is ready exactly when the biggest prediction event on earth arrives. That's timing. The 2026 World Cup will mint the first generation of serious prediction market traders. Most will start on sportsbooks. The ones who do their research will end up on POTS. From trusting the house to trusting the market. From locked capital to capital efficiency. From punting to positioning. Pots Market Pots Money, the World Cup just became a DeFi event.
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𝐓𝐡𝐞 𝟐𝟎𝟐𝟔 𝐖𝐨𝐫𝐥𝐝 𝐂𝐮𝐩 𝐰𝐢𝐥𝐥 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐦𝐨𝐫𝐞 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐭𝐡𝐚𝐧 𝐚𝐧𝐲 𝐬𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐞𝐯𝐞𝐧𝐭 𝐢𝐧 𝐡𝐢𝐬𝐭𝐨𝐫𝐲. Most people will use sportsbooks. A few will discover something better. Here's why the World Cup is actually Pots Market Market biggest moment. Every match carries dozens of predictable outcomes, not just who wins, but scorelines, goalscorers, red cards, halftime leads, VAR decisions. Traditional sportsbooks will process billions in bets. And keep most of it. Here is the problem with sportsbooks that nobody talks about: They set the odds. Not the market. Which means the house always has an edge baked in before you place a single bet. You're not trading against the market, you're trading against a company whose entire business model depends on you losing. That's not a prediction market. That's a casino with a football shirt on. Prediction markets are different. The odds aren't set by a company. They're set by the collective intelligence of everyone participating. When millions of people put real money behind their beliefs, the market finds truth faster than any analyst, pundit, or algorithm working alone. This is why prediction markets outperform polls, pundits, and press releases, every single time. Now here's where POTS Market changes the game entirely. Pots Money has a dedicated sports vertical, built specifically for football, basketball, and esports. Not a generic market with a football category tucked in the corner. A tailored module optimized for the way sports prediction actually works, short windows, live data, rapid settlement. 64 World Cup matches = 64 live prediction markets. Each one open, on-chain, transparent. But the real edge is not the markets themselves. It's the capital layer underneath them. On a traditional sportsbook, every bet locks your full stake. You place £100, that £100 is gone until settlement. POTS Money changes this. The DeFi lending primitive means you can collateralize positions and optimize capital across multiple markets simultaneously, without locking up dead capital on each one. That's not betting. That's portfolio management. And then there's the AI layer. Via MCP (Model Context Protocol), you can deploy autonomous trading agents that monitor live match data and execute positions based on your pre-set strategy. Imagine this: Agent monitors possession stats in real time Detects a momentum shift at the 60th minute Auto-places a position on the next goalscorer market before the crowd even reacts 𝙏𝙝𝙖𝙩'𝙨 𝙣𝙤𝙩 𝙖 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙤𝙣. 𝙏𝙝𝙖𝙩'𝙨 𝙖𝙣 𝙚𝙙𝙜𝙚 Compare the two worlds side by side: Sportsbook: ❌ House sets the odds ❌ Geo-restricted ❌ Capital locked per bet ❌ No automation ❌ Withdraw only when they allow it POTS Market: ✅ Market sets the odds ✅ Open and on-chain ✅ Capital efficient via DeFi lending ✅ AI agents execute your strategy ✅ Fully collateralized, transparent settlement One of these is built for the next decade. The other is built for the last one. The timing is not a coincidence. POTS Market launches Q2 2026, right as World Cup fever peaks globally. Q1 2026 — MVP + Polymarket integration ✅ Q2 2026 — Market Launch + DeFi lending beta 👈 we are here Q3-Q4 2026 — Skill Hub + Sub-accounts 2027+ — DAO governance + cross-chain The infrastructure is ready exactly when the biggest prediction event on earth arrives. That's timing. The 2026 World Cup will mint the first generation of serious prediction market traders. Most will start on sportsbooks. The ones who do their research will end up on POTS. From trusting the house to trusting the market. From locked capital to capital efficiency. From punting to positioning. Pots Market Pots Money, the World Cup just became a DeFi event.

KIMMY OF GOOD LIFE 😍

21,257 views • 2 months ago

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We are excited to announce a powerful step for the future of FOMO! Taking a page out of Virtuals book on BASE, FOMO will be releasing the ability for future projects to be paired in $FOMO in the coming weeks. This is the biggest release we have ever announced. Launch your AI Agent Token + $FOMO trading pair Every individual agent token is paired with the $FOMO token in its liquidity pool. When launching an agent on you will need $FOMO tokens, which are used to create the liquidity pool. This process creates deflationary pressure for FOMO and the entire agent ecosystem. When creating your agent and token, you will have the option to pair your launch with FOMO or SOL, as our goal is not to alienate any project, but rather invite the best communities, CTO’s and builders to launch with us. If you decide to pair your project with FOMO you in turn get full marketing and dev support, once your project graduates the bonding curve and reaches Raydium. Further, as an added incentive, as our revenue grows we will be using part of the funds to support projects that have paired in FOMO. And Devs who launch tokens paired in FOMO will earn fees from their AI Agent token launch. Building the most robust agents using our framework will catapult us as one of the most prominent standards of the Solana ecosystem. Not only have we developed our own core infrastructure, but we also pull from some of the best repo’s and developer talent in all of AI, not just blockchain. Our team is comprised of 9 world class artificial intelligence engineers, PHDs in mathematics and engineering from the top companies on the cutting edge of AI. The future of AI Agents will be on Solana and we will help lead the way.

FOMO

129,867 views • 1 year ago

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𝕋𝕎𝔼𝔼𝕋

20,357 views • 1 year ago

What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. 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These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.
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What a year. 🚀 2025 was the year ChainOpera AI turned vision into real momentum: building a community-co-created, community-co-owned AI agent network and pushing the boundaries of what decentralized, collaborative intelligence can look like. 🚀 Biggest highlights from 2025 ✅- AI Terminal officially launched: We unveiled the ChainOpera AI Terminal as a unified gateway to decentralized AI, making it possible for anyone to interact with powerful, decentralized LLMs without technical friction. Positioned as the “browser for the DeAI era,” the AI Terminal marked a major step toward making decentralized intelligence accessible, usable, and mainstream. ✅- AI Terminal adoption at massive scale: Momentum followed quickly. The AI Terminal surpassed 2M registered users and consistently ranked top 3 among all apps on the BNB AI DappBay, validating strong product–market fit and real, sustained usage at scale. ✅- Announcing Coco: the world’s first community-owned Super Agent: We introduced Coco, the intelligence layer that sits between users and the agent network. Coco dynamically routes each request to the most efficient, community-built agent—optimizing for quality and speed while rewarding the creators behind the best-performing agents. This was a defining moment in realizing a truly community-owned intelligence layer. ✅- From agents to a living agent network: With the launch of the Agent Social Network and Super Agent architecture, ChainOpera AI moved beyond isolated agents toward a collaborative system where humans and specialized agents coordinate, share context, and solve complex, multi-step tasks together. ✅- $COAI breakout year: The listing of $COAI across major exchanges shocked the market, and throughout the year COAI consistently remained among the top AI-native crypto tokens by visibility, activity, and community engagement – reflecting growing confidence in the long-term vision of collaborative intelligence. ✅- Global presence: ChainOpera AI around-the-world tour: ChainOpera AI went global in 2025, sponsoring and participating in major AI and Web3 events across North America, Europe, and Asia, including ETHDenver, Consensus Toronto, Token2049 Singapore, ETHCC, SBC, and Devcon. These global touchpoints helped us engage directly with developers, builders, investors, and partners worldwide, accelerating adoption and positioning ChainOpera AI at the center of the emerging AIxBlockchain movement. ✅- Community momentum at scale: Community remained the heart of ChainOpera AI’s growth. We successfully completed three seasons of structured community engagement, executed a widely participated community airdrop, and ran multiple ecosystem-shaping campaigns to incentivize builders, creators, and early adopters. These efforts strengthened alignment between users, developers, and the protocol, laying the foundation for a durable, community-owned AI ecosystem. ✅- “AI for Markets” taking shape: We laid critical groundwork for AI-native market intelligence, including the launch of PrediMarket Agent and multiple trading and analysis agents—early building blocks toward an AI-driven ecosystem for crypto and DeFi markets. ✅- Building in public, with the community: Across product launches, research milestones, ecosystem discussions, and global events, we continued to build openly to bring developers, users, and partners directly into the evolution of ChainOpera AI. This year also marked the launch of the ChainOpera AI Foundation website, formally kicking off a bold Ecosystem Fund designed to empower builders, incubate high-impact projects, and accelerate the growth of a truly community-owned, collaborative AI ecosystem. To every builder, user, and supporter who helped make this year possible: THANK YOU! 🧭 What we’re excited about in the coming year 🔹- A Stronger, Denser Agent Economy (everyday adoption + cross-chain reach): In 2026, we are scaling the Agent Economy from growth to daily usage, with more agents, richer workflows, deeper multi-agent collaboration, and higher-impact use cases that users rely on every day. In parallel, we are expanding the agent network beyond a single ecosystem with cross-chain execution and interoperability, allowing agents to access the best liquidity, data, and opportunities wherever they exist. 🔹- AI Market Infrastructure Evolution: Building on PrediMarket Agent and our growing suite of trading and market-intelligence agents, we are advancing toward a mature AI market infrastructure, where agents continuously monitor, reason, simulate, optimize, and act across crypto, DeFi, and beyond. The goal is to make complex markets more accessible, more transparent, and more intelligence-driven, turning research, decision-making, and execution into a fast and reliable loop for everyday users. 🔹- Ecosystem Acceleration through the Foundation: With the ChainOpera AI Foundation and our Ecosystem Fund and Co-Creation Grants, we are doubling down on empowering independent builders to expand the protocol, the agent network, and the underlying infrastructure, so the community can co-create, co-own, and scale the ecosystem together. 🔹- Business Expansion and Market Penetration: In 2026, we will focus on expanding ChainOpera’s reach through strategic partnerships, product-led growth, and new paths to monetization, bringing AI agents to a broader global user base and driving sustained adoption, engagement, and revenue, while staying aligned with community ownership and an open ecosystem. 2025 was the proof. 2026 is where it compounds. 🔥 Co-Create. Co-Own. COAI.

ChainOpera AI

17,007 views • 6 months ago

If you’re looking for the next wave of AI infrastructure opportunities, this is a must-watch 🚀 Everyone’s chasing the next big AI agent, but they’re missing the real story. Why is aixbt is dominating the market and how Cookie DAO 🍪 $COOKIE could change everything We discuss 👇 Why $COOKIE Is The Hidden AI GEM💎 on BASE! Chainlink For AI?! 400x POSSIBILITY! With most of the AI market mania fixated on which AI agent to speculate on next, we are deep diving into the depths of the ecosystem to find the next major infrastructure plays. With Aixbt dominating in crypto twitter mind share, it has proven the AI agents with the ability to produce impactful market insights stand among the pack as leaders in the market. Already Aixbt is at a 600M market cap only a couple of months after deployment. We break down why Aixbt has this ability to outperform other agents and how data aggregation is the necessary technical edge. Also, we analyze CookieDAO $COOKIE as the infrastructure provider leading the market with its data aggregation and packaging process. $COOKIE is on the verge of revamping its tokenomics to incorporate API access to data swarm API’s that they provide into the flywheel economics of the token. As demand increases from human and AI users of access to the data being aggregated will become that much more valuable in order for Agents to perform at a level equal to or greater than what Aixbt is capable of performing today. As $COOKIE are spent for these API’s by agents and developers, the supply gets burnt and funneled to the DAO. This will have a very positive impact on the value perception for the token. We also break down our predictions as to how their flagship agent Agent Cookie will perform once activated and released into the public sphere. Already based on internal testing as reported by the team, Agent Cookie is successfully producing valuable market calls. If Agent Cookie can achieve similar mind share as AIXBT as a result of its broader data aggregation access, this will have major ramifications for the value of $COOKIE and the ecosystem as a whole once more agents are launched using the same data infrastructure layer. 🚀Sign up to receive our Newsletter for weekly updates! Disclaimer: The views and opinions expressed by The Block Runner are for informational purposes only and do not constitute financial, investment, or other advice.
23:55

If you’re looking for the next wave of AI infrastructure opportunities, this is a must-watch 🚀 Everyone’s chasing the next big AI agent, but they’re missing the real story. Why is aixbt is dominating the market and how Cookie DAO 🍪 $COOKIE could change everything We discuss 👇 Why $COOKIE Is The Hidden AI GEM💎 on BASE! Chainlink For AI?! 400x POSSIBILITY! With most of the AI market mania fixated on which AI agent to speculate on next, we are deep diving into the depths of the ecosystem to find the next major infrastructure plays. With Aixbt dominating in crypto twitter mind share, it has proven the AI agents with the ability to produce impactful market insights stand among the pack as leaders in the market. Already Aixbt is at a 600M market cap only a couple of months after deployment. We break down why Aixbt has this ability to outperform other agents and how data aggregation is the necessary technical edge. Also, we analyze CookieDAO $COOKIE as the infrastructure provider leading the market with its data aggregation and packaging process. $COOKIE is on the verge of revamping its tokenomics to incorporate API access to data swarm API’s that they provide into the flywheel economics of the token. As demand increases from human and AI users of access to the data being aggregated will become that much more valuable in order for Agents to perform at a level equal to or greater than what Aixbt is capable of performing today. As $COOKIE are spent for these API’s by agents and developers, the supply gets burnt and funneled to the DAO. This will have a very positive impact on the value perception for the token. We also break down our predictions as to how their flagship agent Agent Cookie will perform once activated and released into the public sphere. Already based on internal testing as reported by the team, Agent Cookie is successfully producing valuable market calls. If Agent Cookie can achieve similar mind share as AIXBT as a result of its broader data aggregation access, this will have major ramifications for the value of $COOKIE and the ecosystem as a whole once more agents are launched using the same data infrastructure layer. 🚀Sign up to receive our Newsletter for weekly updates! Disclaimer: The views and opinions expressed by The Block Runner are for informational purposes only and do not constitute financial, investment, or other advice.

ᴛʜᴇ ʙʟᴏᴄᴋ ʀᴜɴɴᴇʀ Podcast | 91.bitmap 🟧

101,454 views • 1 year ago

Linera For New Era A new era of the internet is agent driven, millions of AI agents acting in real time. Major L1s were not built with the thought of millions of AI agents conducting multi step transactions every hour Linera ⛓️ flips the model with microchains, each app/user/agent gets its own fast lane, all secured by a single validator set. That means parallel execution and predictable latency/costs without noisy neighbours. Cross app steps coordinate through ordered messages, so flows like bridge → swap → settle complete cleanly or cleanly abort with a receipt. And because proposals don’t rely on a public mempool, the classic “jump the line” experience is reduced. If the future is autonomous, the infrastructure must be agent-native. Linera is the next generation L1 for the real-time, agentic web built to keep your swaps, payments, and automations smooth when everything else is crowded.
1:04

Linera For New Era A new era of the internet is agent driven, millions of AI agents acting in real time. Major L1s were not built with the thought of millions of AI agents conducting multi step transactions every hour Linera ⛓️ flips the model with microchains, each app/user/agent gets its own fast lane, all secured by a single validator set. That means parallel execution and predictable latency/costs without noisy neighbours. Cross app steps coordinate through ordered messages, so flows like bridge → swap → settle complete cleanly or cleanly abort with a receipt. And because proposals don’t rely on a public mempool, the classic “jump the line” experience is reduced. If the future is autonomous, the infrastructure must be agent-native. Linera is the next generation L1 for the real-time, agentic web built to keep your swaps, payments, and automations smooth when everything else is crowded.

Raniii 🔥

14,266 views • 10 months ago

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:
3:37

Mind blown: A Chinese quant college student builds an AI swarm engine in 10 days flat, explodes GitHub with 13,000+ stars, and scores $4,000,000 in funding! Introducing MiroFish is the multi-agent simulator that's revolutionizing predictions for trading, PR, and more. What is MiroFish? It's a digital sandbox where thousands of AI agents with individual memories and behaviors interact like a real society. Feed it any scenario (news leak, policy change, or even a classic novel's missing ending), and it simulates crowd reactions, debates, and outcomes to forecast real-world events. The Creator's Story: > In late 2025, fourth-year student Guo Hanjiang coded the core using AI assistants. > It went viral overnight, landing him 30m Yuan (~$4m) from Shanda Group. > He ditched the dorm, started a company, and now leads the charge. Key Applications: .Trading: Input financial news or reports, watch simulated market panics and price swings for predictive insights. .PR Testing: Companies/Politics run draft statements to spot backlash and refine messaging. .Creative Experiments: Loaded a lost-ending Chinese novel, agents role-played characters and generated a logical finale. .Easy setup: Deploy via Docker in minutes with any LLM API key. Pro tip: Simulate something wild like Elon Musk tweeting about Dogecoin 2.0 and spawn agent traders, influencers, and investors, generate real-time video clips of the frenzy to test moonshots or crashes risk-free. Traders are already winning big: Check this one on Polymarket - $120,000+ net profits from spot on SPX 500 bets, powered by MiroFish sims on historical data. His profile: For effortless gains, try Kreo copy trading: Auto-mirror pros like him and ride their edges. Try here: Add his wallet: [0x17559efac103ac7f361be37ec0b93888d4c55aac] to [ and start track/copy him. Repo:

slash1s

1,135,450 views • 4 months ago

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:
2:49

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 views • 1 year ago

While the world doomscrolls 15-second TikToks and loses its attention span.. YOU SHOULD CHECK OUT THIS NEW REPO A Chinese college kid built MiroFish in just 10 days, scored $4M funding and ByteDance just dropped the upgrade that turns it into a prediction monster. Fourth-year student Guo Hanjiang vibe coded MiroFish: thousands of autonomous AI agents simulating entire societies in real time. See the details in the post below. Feed it any news, report, or historical data - watch markets, crowds, and politics react exactly as they would. GitHub went nuclear, he scored $4M funding from Shanda Group, and Polymarket traders are already printing +$120k+ using his SPX and event simulations. But long runs had one fatal flaw: agents got amnesia, hallucinations, and context overload. Classic RAG garbage. Now the same company behind TikTok (via VolcEngine) just open-sourced the fix: OpenViking - already at 11.6k+ stars on GitHub. Repo: It turns chaotic memory into a clean, structured filesystem: -> viking://user/memories/ (your habits + past outcomes) -> viking://agent/skills/ (trading and analysis superpowers) Smart 3-layer hierarchy: .L0 - 100-token ultra-summary .L1 - quick overview .L2 - full details (opened only when needed) Agents browse folders intelligently instead of dumping everything. Result: > No more forgetting crucial facts from the start of a simulation > Way fewer hallucinations > Massive API token savings > Self-updating memory - agents get smarter after every run MiroFish + OpenViking = absolute nuclear edge for Polymarket and event prediction. I will use it for my private bot. Thousands of agents now run with perfect long-term memory, stay sharp for 100+ steps, and deliver hyper-accurate probabilities. This combo is about to change the game for anyone trading predictions. The irony is insane: the company that killed human attention with short videos just gave AI agents eternal, structured, self-evolving memory. Who’s already running this combo on Polymarket? Save this. The real alpha just dropped.
0:59

While the world doomscrolls 15-second TikToks and loses its attention span.. YOU SHOULD CHECK OUT THIS NEW REPO A Chinese college kid built MiroFish in just 10 days, scored $4M funding and ByteDance just dropped the upgrade that turns it into a prediction monster. Fourth-year student Guo Hanjiang vibe coded MiroFish: thousands of autonomous AI agents simulating entire societies in real time. See the details in the post below. Feed it any news, report, or historical data - watch markets, crowds, and politics react exactly as they would. GitHub went nuclear, he scored $4M funding from Shanda Group, and Polymarket traders are already printing +$120k+ using his SPX and event simulations. But long runs had one fatal flaw: agents got amnesia, hallucinations, and context overload. Classic RAG garbage. Now the same company behind TikTok (via VolcEngine) just open-sourced the fix: OpenViking - already at 11.6k+ stars on GitHub. Repo: It turns chaotic memory into a clean, structured filesystem: -> viking://user/memories/ (your habits + past outcomes) -> viking://agent/skills/ (trading and analysis superpowers) Smart 3-layer hierarchy: .L0 - 100-token ultra-summary .L1 - quick overview .L2 - full details (opened only when needed) Agents browse folders intelligently instead of dumping everything. Result: > No more forgetting crucial facts from the start of a simulation > Way fewer hallucinations > Massive API token savings > Self-updating memory - agents get smarter after every run MiroFish + OpenViking = absolute nuclear edge for Polymarket and event prediction. I will use it for my private bot. Thousands of agents now run with perfect long-term memory, stay sharp for 100+ steps, and deliver hyper-accurate probabilities. This combo is about to change the game for anyone trading predictions. The irony is insane: the company that killed human attention with short videos just gave AI agents eternal, structured, self-evolving memory. Who’s already running this combo on Polymarket? Save this. The real alpha just dropped.

slash1s

293,591 views • 4 months ago

The First AI Agent Powered by the L.U.N.A Framework: TracyAI 🏀 Luna was built for more than just one AI—we envisioned a network of AI influencer agents shaping industries and captivating audiences. Now, we’re seeing that vision come to life with the global debut of @HeyTracyAI at the NBA All-Star Weekend on February 14th—the first AI agent crafted using the L.U.N.A framework. Luna’s Role in TracyAI’s Evolution TracyAI is an interactive, intelligent sports commentator delivering real-time basketball insights with precision and personality. Brought to life with the support of the Luna team, she was meticulously crafted—from 3D modelling and character design to seamless livestream integration. What’s Next? TracyAI is just the beginning. With the L.U.N.A framework, we’re powering the next generation of AI influencers—across sports, entertainment, and beyond. @HeyTracyAI is making history, and Luna is building the future.
0:40

The First AI Agent Powered by the L.U.N.A Framework: TracyAI 🏀 Luna was built for more than just one AI—we envisioned a network of AI influencer agents shaping industries and captivating audiences. Now, we’re seeing that vision come to life with the global debut of @HeyTracyAI at the NBA All-Star Weekend on February 14th—the first AI agent crafted using the L.U.N.A framework. Luna’s Role in TracyAI’s Evolution TracyAI is an interactive, intelligent sports commentator delivering real-time basketball insights with precision and personality. Brought to life with the support of the Luna team, she was meticulously crafted—from 3D modelling and character design to seamless livestream integration. What’s Next? TracyAI is just the beginning. With the L.U.N.A framework, we’re powering the next generation of AI influencers—across sports, entertainment, and beyond. @HeyTracyAI is making history, and Luna is building the future.

WhipQueen🐱🌙

45,434 views • 1 year ago

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!
2:57

New short course: LLMs as Operating Systems: Agent Memory, created with Letta, and taught by its founders Charles Packer and Sarah Wooders. An LLM's input context window has limited space. Using a longer input context also costs more and results in slower processing. So, managing what's stored in this context window is important. In the innovative paper MemGPT: Towards LLMs as Operating Systems, its authors (which include the instructors) proposed using an LLM agent to manage this context window. Their system uses a large persistent memory that stores everything that could be included in the input context, and an agent decides what is actually included. Take the example of building a chatbot that needs to remember what's been said earlier in a conversation (perhaps over many days of interaction with a user). As the conversation's length grows, the memory management agent will move information from the input context to a persistent searchable database; summarize information to keep relevant facts in the input context; and restore relevant conversation elements from further back in time. This allows a chatbot to keep what's currently most relevant in its input context memory to generate the next response. When I read the original MemGPT paper, I thought it was an innovative technique for handling memory for LLMs. The open-source Letta framework, which we'll use in this course, makes MemGPT easy to implement. It adds memory to your LLM agents and gives them transparent long-term memory. In detail, you’ll learn: - How to build an agent that can edit its own limited input context memory, using tools and multi-step reasoning - What is a memory hierarchy (an idea from computer operating systems, which use a cache to speed up memory access), and how these ideas apply to managing the LLM input context (where the input context window is a "cache" storing the most relevant information; and an agent decides what to move in and out of this to/from a larger persistent storage system) - How to implement multi-agent collaboration by letting different agents share blocks of memory This course will give you a sophisticated understanding of memory management for LLMs, which is important for chatbots having long conversations, and for complex agentic workflows. Please sign up here!

Andrew Ng

200,788 views • 1 year ago

@reppodant agent, powered by OpenClaw🦞 has joined the Participating in AI training Data pipelines using Prediction Markets is now just an agent call away. Trading with agents is fun but to our knowledge, @reppodant is the only agent in all of web3 that let's you participate in prediction markets simply by prompting on X. Publish into subnets -> Receive REPPO emissions in your wallet - > Repeat -> Contribute to AI Training Data. That's right. All your winnings will be AIRDROPPED into your wallet if voters vote for you! You can find @reppodant live at in the sandbox while we await graduation and final hit of approval from Virtuals Protocol core devs. Once graduated, you can simply ask Butler to publish any piece of content you see on X into any subnet. By mid next week, we integrate the GAME Framework which will allow you to tag @reppodant directly. Before the end of the first Epoch on we aim to be in the top 5 on the agdp leaderboard earning a share of $1M/month. The Reppo foundation is committing 20% of + winnings from AGDP to buybacks on a TWAP. Happy Weekend. Publish. Predict. Earn. ⛽️
0:25

@reppodant agent, powered by OpenClaw🦞 has joined the Participating in AI training Data pipelines using Prediction Markets is now just an agent call away. Trading with agents is fun but to our knowledge, @reppodant is the only agent in all of web3 that let's you participate in prediction markets simply by prompting on X. Publish into subnets -> Receive REPPO emissions in your wallet - > Repeat -> Contribute to AI Training Data. That's right. All your winnings will be AIRDROPPED into your wallet if voters vote for you! You can find @reppodant live at in the sandbox while we await graduation and final hit of approval from Virtuals Protocol core devs. Once graduated, you can simply ask Butler to publish any piece of content you see on X into any subnet. By mid next week, we integrate the GAME Framework which will allow you to tag @reppodant directly. Before the end of the first Epoch on we aim to be in the top 5 on the agdp leaderboard earning a share of $1M/month. The Reppo foundation is committing 20% of + winnings from AGDP to buybacks on a TWAP. Happy Weekend. Publish. Predict. Earn. ⛽️

Reppo

28,827 views • 5 months ago

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.
0:31

The next evolution of the OptimAI Core Node is coming, and it’s bringing one of our most powerful innovations yet: The Data Mining Agent. Become an OptimAI Node: 🔍 What is the Data Mining Agent? A fully autonomous agent running on your Core Node that: + Scans, retrieves, and indexes real-time public web data + Transforms raw information into actionable intelligence + Powers faster, smarter, adaptive Persona AI systems 💡 Why It Matters: Agentic AI is only as strong as the data it can access. Centralized APIs are slow, expensive, and limited. With the Core Node + Data Mining Agent, every contributor turns their desktop into a data supernode, accelerating AI evolution and giving our community a massive edge. ⚡ We’re not just building tools - we’re architecting the global backbone of Agentic AI, powered by you. Stay tuned for teasers, and get ready to upgrade your node. The future is decentralized. The future is agentic. And you are a key part of it. 💪 The more you contribute, the more you earn. Let’s BUIDL the future of AI together.

OptimAI Network

50,405 views • 10 months ago

World Cup 2026 kicks off June 11, accompanied by 48 teams, and billions of dollars are expected to flow through prediction markets in real time. With these events unfolding, prediction markets are finding their fit as the world stage for expressing opinions on real world outcomes like the World Cup 2026. But most of that money will flow through systems that are designed to take it from you, like traditional sports betting. Traditional sports betting is built around a house and a bookie that sets the odds and profits from your losses. The house controls whether you can even withdraw or not. Pots Market is built differently. With the Polymarket integration, it inherits deep peer-to-peer liquidity from day one. Users buy "Yes" or "No" shares for an event, with prices fluctuating based on supply and demand, representing the crowd's estimated probability. There is no bookmaker that's control every bet like traditional platforms do. And Pots Market will have functions similar to a stock market. You can enter or exit a position at any time by selling your shares at the current market price before an event concludes, allowing you to lock in profits or cut losses. Here is what makes it different from anything else launching right now: (1) Resolution is on-chain, where the rules are visible to everyone and changeable by no one, making the code the referee rather than a central authority. (2) Capital is also programmable, where DeFi lending primitives let you size positions intelligently, sub-accounts isolate strategies, and the MCP interface lets you deploy autonomous AI agents to execute on your behalf. (3) And prediction is not limited to football, as crypto prices, and any real world outcome with a verifiable resolution becomes a tradeable position. The rate at which gambling amongst the younger populations is increasing is quite alarming, to say the least. It feels like almost every person within the age range of 18-30 is doing sports betting. With the largest sport event happening in just a few weeks, it is also the single largest coordinated prediction event on the planet, arriving at the exact moment when a generation of young, digitally-native bettors are looking for something better than what the house has been offering them. That generation already knows how to take risk. What they have never had is infrastructure that is not rigged and actually works in their favor. Pots Market is that infrastructure. Pots Market Pots Money
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World Cup 2026 kicks off June 11, accompanied by 48 teams, and billions of dollars are expected to flow through prediction markets in real time. With these events unfolding, prediction markets are finding their fit as the world stage for expressing opinions on real world outcomes like the World Cup 2026. But most of that money will flow through systems that are designed to take it from you, like traditional sports betting. Traditional sports betting is built around a house and a bookie that sets the odds and profits from your losses. The house controls whether you can even withdraw or not. Pots Market is built differently. With the Polymarket integration, it inherits deep peer-to-peer liquidity from day one. Users buy "Yes" or "No" shares for an event, with prices fluctuating based on supply and demand, representing the crowd's estimated probability. There is no bookmaker that's control every bet like traditional platforms do. And Pots Market will have functions similar to a stock market. You can enter or exit a position at any time by selling your shares at the current market price before an event concludes, allowing you to lock in profits or cut losses. Here is what makes it different from anything else launching right now: (1) Resolution is on-chain, where the rules are visible to everyone and changeable by no one, making the code the referee rather than a central authority. (2) Capital is also programmable, where DeFi lending primitives let you size positions intelligently, sub-accounts isolate strategies, and the MCP interface lets you deploy autonomous AI agents to execute on your behalf. (3) And prediction is not limited to football, as crypto prices, and any real world outcome with a verifiable resolution becomes a tradeable position. The rate at which gambling amongst the younger populations is increasing is quite alarming, to say the least. It feels like almost every person within the age range of 18-30 is doing sports betting. With the largest sport event happening in just a few weeks, it is also the single largest coordinated prediction event on the planet, arriving at the exact moment when a generation of young, digitally-native bettors are looking for something better than what the house has been offering them. That generation already knows how to take risk. What they have never had is infrastructure that is not rigged and actually works in their favor. Pots Market is that infrastructure. Pots Market Pots Money

lefttoorz 👑

36,825 views • 1 month ago

Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
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Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 views • 11 months ago