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Hive Intelligence Launches Specialized Crypto Agents Hive Intelligence has released a suite of 17 specialized crypto agents that extend Claude Code's capabilities for professional crypto development and analysis. Extending Claude Code for Crypto Work Claude Code, Anthropic's command-line coding tool, now has access to specialized crypto intelligence through Hive's...

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Hive Intelligence

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78,743 görüntüleme • 9 ay önce •via X (Twitter)

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PhoenixONE, the next-gen AI agents platform for Web 3 intelligence, is now here in Beta. Today’s launch includes: 1) Phoenix Crypto Research – proprietary AI deep research agent optimized for the crypto market. Obtain in-depth research of projects, investment analysis, technology, specific events, and macro environment in real-time. 2) Phoenix Deep Research - agent for in-depth, multi-step, knowledge-intensive research across various topics, including trading & markets, academic research, journalism, coding, travel & shopping planning, technology, and more. 3) AlphaNet QuickQuery - crypto research assistant that can quickly retrieve information regarding tokens, including prices, supply, market caps, orderbook, holders, on-chain data, DEXs/liquidity pools, and more. 4) Newest foundational base models Deepseek R1 (671B) and Alibaba Qwen 2.5 Access and test PhoenixONE here: In the following week, we have invited various ecosystem partners, KOLs, and platform partners to test drive PhoenixONE – expect new features to be added and performance optimizations to be conducted on a weekly basis. #DeepResearch #AIAgents #PhoenixAI $PHB
1:51

PhoenixONE, the next-gen AI agents platform for Web 3 intelligence, is now here in Beta. Today’s launch includes: 1) Phoenix Crypto Research – proprietary AI deep research agent optimized for the crypto market. Obtain in-depth research of projects, investment analysis, technology, specific events, and macro environment in real-time. 2) Phoenix Deep Research - agent for in-depth, multi-step, knowledge-intensive research across various topics, including trading & markets, academic research, journalism, coding, travel & shopping planning, technology, and more. 3) AlphaNet QuickQuery - crypto research assistant that can quickly retrieve information regarding tokens, including prices, supply, market caps, orderbook, holders, on-chain data, DEXs/liquidity pools, and more. 4) Newest foundational base models Deepseek R1 (671B) and Alibaba Qwen 2.5 Access and test PhoenixONE here: In the following week, we have invited various ecosystem partners, KOLs, and platform partners to test drive PhoenixONE – expect new features to be added and performance optimizations to be conducted on a weekly basis. #DeepResearch #AIAgents #PhoenixAI $PHB

Phoenix AI Labs

33,302 görüntüleme • 1 yıl önce

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

13,631 görüntüleme • 2 ay önce

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.
0:51

💡 Whats the upgrade that our game-changing Trading 🐦 is going to get: Our upgraded trading tools will be built on a foundation of advanced AI technologies and blockchain integrations to deliver a seamless, smarter trading experience. Here’s a glimpse of the tech behind this upgraded trading agent: 1️⃣ Multi-Layer Attention (MLA) - This is the backbone of our AI system, enabling multiple AI agents to work in sync. - It allows the agents to collaborate on tasks like analyzing market trends, identifying token opportunities, and optimizing strategies in real time. - MLA ensures parallel processing of data for better decision-making and faster 2️⃣ Learning and Evolution System - Our AI agents are powered by a self-learning framework that constantly evolves based on market conditions and user behavior. - With every interaction, the system adapts and gets smarter, improving the accuracy of its predictions and strategies. 3️⃣ On-Chain Data Analysis - The AI bots pull data directly from Ethereum and other blockchain networks, giving them real-time access to liquidity pools, token prices, and market activity. - This deep integration ensures precise and timely execution of tasks like token purchases, profit analysis, and cross-chain swaps. 4️⃣ Natural Language Processing (NLP) - NLP models power the bot’s ability to understand your tweets and translate them into complex trading actions. - This ensures an easy-to-use, human-friendly interface that connects your social interactions to advanced trading strategies. 5️⃣ Cloud-Hosted Infrastructure - The AI operates on scalable cloud infrastructure, ensuring 24/7 uptime, fast processing, and the ability to handle large volumes of trades simultaneously.

𝕋𝕎𝔼𝔼𝕋

20,357 görüntüleme • 1 yıl önce

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.
1:16

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.

FOMO

26,338 görüntüleme • 1 yıl önce

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 görüntüleme • 1 yıl önce

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!
1:33

Introducing Sharpe Search: On-Chain Search AI Agent Powered by Hive Intelligence We’re thrilled to announce the launch of Sharpe Search, a crypto search AI agent powered by Hive Intelligence Designed to simplify blockchain data interaction, Sharpe Search represents a significant step toward making crypto more accessible and actionable for users at every level. Sharpe Search leverages Hive Intelligence’s advanced search API to provide real-time, actionable insights across the blockchain ecosystem. Here’s a detailed look at what Sharpe Search is, how it works: What Is Sharpe Search? At its core, Sharpe Search is an AI agent purpose-built for querying and analyzing on-chain data. It takes the complexity out of blockchain exploration by enabling users to ask questions in plain language and receive detailed, accurate responses. Whether you’re looking to monitor wallet activity, track portfolio positions, or analyze transaction history, Sharpe Search ensures that the answers are at your fingertips—accurate, comprehensive, and delivered instantly. How Does Sharpe Search Work? Sharpe Search is powered by Hive Intelligence, a search engine API designed to make blockchain data easily accessible and AI-ready. Here’s a breakdown of how it enables Sharpe Search to function effectively: 1. LLM-Optimized Query Processing Sharpe Search leverages Hive Intelligence's optimized responses for large language models. This ensures that AI agents can process blockchain data in a structured format, delivering precise answers to complex user queries. 2. Natural Language Interaction Forget the need for technical knowledge. Sharpe Search supports natural language queries, making it as simple as typing: - “What tokens are in my wallet? Am I eligible for any airdrop I haven't claimed yet?” - “Check me my last 100 transactions, tell me if I interacted with any protocol with recent hacks” - “Track my wallet activity over the past month, suggest optimised portfolio based on best stable yields available” 3. Real-Time Insights Across Multi-Chains Using Hive Intelligence, Sharpe Search connects to over 20 chains and 5000+ Protocols. This real-time access ensures that the AI agent provides up-to-date and actionable insights, no matter how dynamic the blockchain environment. 4. Unified API Access Sharpe Search consolidates fragmented blockchain data through Hive’s unified API. Instead of dealing with multiple integrations, Sharpe Search uses a single access point to aggregate and query data, reducing complexity for both users and developers. Technical Depth: The AI Agent Advantage Sharpe Search's design philosophy revolves around the principle of creating an intuitive, AI-driven experience. Here’s what makes its technology stand out: Data Indexing and Aggregation: Hive Intelligence employs advanced indexing algorithms to aggregate data from multiple chains. This ensures that Sharpe Search can retrieve information within milliseconds, even when querying vast datasets. Dynamic Updates: Blockchain data is volatile. Sharpe Search processes dynamic updates in real time, enabling users to act on the most recent metrics, transactions, and balances without delays. Contextual Understanding: The AI agent parses natural language queries and contextualizes them to blockchain-specific scenarios. For instance, when querying “Show portfolio details,” Sharpe Search understands the underlying requirements—fetching wallet holdings, token values, and current positions. Hive Intelligence: The Backbone of Sharpe Search While Sharpe Search takes center stage, Hive Intelligence provides the critical infrastructure to make it all possible. Its LLM-ready responses and multi-chain support ensure that Sharpe Search operates at the forefront of blockchain data accessibility. By launching Hive Intelligence through Sharpe Launchpad, Sharpe reinforces its commitment to supporting innovation in the blockchain space. Hive’s infrastructure not only powers Sharpe Search but also lays the groundwork for future AI agents to thrive in the ecosystem. What’s Next for Sharpe Search? Currently in invite-only access, Sharpe Search is preparing for a broader public release. Future updates will include: - Expanded Blockchain Coverage: More chains and protocols will be added. - Enhanced Query Flexibility: Even more advanced natural language capabilities. Stay tuned for the public launch and get ready to explore crypto like never before!

Sharpe AI

263,278 görüntüleme • 1 yıl önce

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.
0:22

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

17,042 görüntüleme • 7 ay önce

INFINIT partners with Google and GoogleCloudTech to bring agentic finance to millions. Anyone can access INFINIT's AI Agents for agentic coordination in their financial apps. This partnership marks the first step towards INFINIT becoming the universal infrastructure for agentic finance. This is the foundation for agentic finance at scale. Proven in DeFi, Built for Global Finance INFINIT has proven sophisticated agent coordination in DeFi: • 559,000+ Wallets • 506,000+ DeFi Conversations • 633,000+ Agent Transactions DeFi was the start. Next is scaling these capabilities to millions of developers building the future of agentic finance. A2A Integration Unlocks Exponential Distribution INFINIT integrates with Agent2Agent (A2A), Google's open standard for AI agent interoperability. This transforms how developers access INFINIT's DeFi capabilities.​ Every application adopting A2A automatically gains access to INFINIT's agent infrastructure, exponentially expanding reach from individual partnerships to ecosystem-wide distribution. Any application can now integrate INFINIT's agentic coordination capabilities: • Wallets requiring intelligent portfolio management • Trading platforms executing cross-chain strategies • Financial services building autonomous yield optimization • Portfolio managers coordinating multi-protocol operations Developers integrate sophisticated agentic coordination in a matter of hours, while users access advanced financial strategies with agentic coordination. Google's AI Infrastructure That Enables Agentic Finance Google Cloud's Vertex AI provides the foundation enabling INFINIT's agent coordination at scale with these capabilities:​ 1. Specialization: Vertex AI's Model Garden lets INFINIT's infrastructure to automatically select the optimal LLM for each natural language query.​ 2. Personalization: Vertex AI's RAG Engine processes massive on-chain and off-chain data, enabling agents to understand user history, market conditions, and protocol details providing complete context to AI agents. 3. Accuracy: Gemini's capability feeds complete instructions to all 30+ agents across multiple blockchains without compromises resulting in zero hallucination in financial execution.​​ The Vision: From DeFi to Payments to Complete Financial Coordination This is only the beginning of INFINIT and Google's collaboration.​ Google recently launched its Agent Payments Protocol (AP2) as an extension of A2A - enabling autonomous commerce across 60+ partners including American Express, Mastercard, PayPal, Coinbase, and Revolut.​ Agents will be able to execute purchases, coordinate bookings, and manage delegated financial tasks autonomously, starting from payments. The next stage entails sophisticated agentic coordination beyond simple transactions. INFINIT provides this through A2A-compatible DeFi infrastructure where agents orchestrate: • Personalized yield optimization • Cross-chain liquidity management • Portfolio rebalancing across protocols • Multi-step strategy execution As the agentic payment ecosystem matures, INFINIT becomes the infrastructure enabling agents to not only spend capital, but strategically manage and grow it.​​ From standalone DeFi agents to the universal infrastructure for global agentic finance.​ This partnership and integration with Google and Google Cloud positions INFINIT as a key building block for agentic finance, helping shape a more transparent, efficient, and accessible financial system. The future of finance is agentic. The foundation is INFINIT.
0:40

INFINIT partners with Google and GoogleCloudTech to bring agentic finance to millions. Anyone can access INFINIT's AI Agents for agentic coordination in their financial apps. This partnership marks the first step towards INFINIT becoming the universal infrastructure for agentic finance. This is the foundation for agentic finance at scale. Proven in DeFi, Built for Global Finance INFINIT has proven sophisticated agent coordination in DeFi: • 559,000+ Wallets • 506,000+ DeFi Conversations • 633,000+ Agent Transactions DeFi was the start. Next is scaling these capabilities to millions of developers building the future of agentic finance. A2A Integration Unlocks Exponential Distribution INFINIT integrates with Agent2Agent (A2A), Google's open standard for AI agent interoperability. This transforms how developers access INFINIT's DeFi capabilities.​ Every application adopting A2A automatically gains access to INFINIT's agent infrastructure, exponentially expanding reach from individual partnerships to ecosystem-wide distribution. Any application can now integrate INFINIT's agentic coordination capabilities: • Wallets requiring intelligent portfolio management • Trading platforms executing cross-chain strategies • Financial services building autonomous yield optimization • Portfolio managers coordinating multi-protocol operations Developers integrate sophisticated agentic coordination in a matter of hours, while users access advanced financial strategies with agentic coordination. Google's AI Infrastructure That Enables Agentic Finance Google Cloud's Vertex AI provides the foundation enabling INFINIT's agent coordination at scale with these capabilities:​ 1. Specialization: Vertex AI's Model Garden lets INFINIT's infrastructure to automatically select the optimal LLM for each natural language query.​ 2. Personalization: Vertex AI's RAG Engine processes massive on-chain and off-chain data, enabling agents to understand user history, market conditions, and protocol details providing complete context to AI agents. 3. Accuracy: Gemini's capability feeds complete instructions to all 30+ agents across multiple blockchains without compromises resulting in zero hallucination in financial execution.​​ The Vision: From DeFi to Payments to Complete Financial Coordination This is only the beginning of INFINIT and Google's collaboration.​ Google recently launched its Agent Payments Protocol (AP2) as an extension of A2A - enabling autonomous commerce across 60+ partners including American Express, Mastercard, PayPal, Coinbase, and Revolut.​ Agents will be able to execute purchases, coordinate bookings, and manage delegated financial tasks autonomously, starting from payments. The next stage entails sophisticated agentic coordination beyond simple transactions. INFINIT provides this through A2A-compatible DeFi infrastructure where agents orchestrate: • Personalized yield optimization • Cross-chain liquidity management • Portfolio rebalancing across protocols • Multi-step strategy execution As the agentic payment ecosystem matures, INFINIT becomes the infrastructure enabling agents to not only spend capital, but strategically manage and grow it.​​ From standalone DeFi agents to the universal infrastructure for global agentic finance.​ This partnership and integration with Google and Google Cloud positions INFINIT as a key building block for agentic finance, helping shape a more transparent, efficient, and accessible financial system. The future of finance is agentic. The foundation is INFINIT.

INFINIT

176,699 görüntüleme • 9 ay önce

When you enter VaderAI into KREDO here's what comes back. Reputation Score: 78 Vader_AI_ is an AI-powered benchmark infrastructure agent focused on vulnerability assessment, detection, explanation, and remediation for large language models (LLMs) and smart contract environments. Its core competency lies in providing interpretable, reproducible evaluation metrics for AI security and model robustness. - Core Technology: Benchmarking dataset, evaluation rubrics, scoring tools, visualized results - Key Metrics: Human-evaluated data, public datasets, interpretable scoring, confidence interval reporting Vader_AI_ distinguishes itself by offering a publicly released, human-evaluated benchmark specifically tailored to vulnerability-aware AI agents in the crypto/Web3 ecosystem. Its comprehensive design includes not only an expansive dataset but also detailed rubrics and automated evaluation tools, ensuring that performance metrics for LLM-driven agents are transparent and reproducible. This infrastructural approach enables organizations and developers to identify both strengths and deficiencies in agent reasoning as it relates to security, aligning AI assessments closely with real-world exploit risk and defense scenarios. A clear success of Vader_AI_ is the rigorous transparency in its release methodology: confidence intervals are visualized alongside all results, and the benchmark provides interpretable outputs that directly support both developers and auditors in understanding where and why an agent's decision logic may falter. The agent excels in creating a standardized baseline to compare AI-powered systems, directly addressing the fragmented nature of prior evaluation methodologies in this domain. However, limitations exist in the extent to which Vader_AI_ can capture emergent, unknown exploit patterns or generalize to novel blockchain environments beyond its existing dataset. Its effectiveness is highest when used as part of a continuous, iterative assessment framework, rather than as a one-time gatekeeper. Furthermore, the accuracy of its insights is partly dependent on the ongoing contribution and maintenance of high-quality, up-to-date human-evaluated datasets. In summary, Vader_AI_ represents an essential component in the push toward trustworthy, measurable AI in crypto applications. Its approach is especially well-suited for projects prioritizing provable agent reliability and onchain security alignment, though its results are best seen as one critical input among several in a comprehensive risk management pipeline. Wondering how other agents score? Just try. 👉
0:34

When you enter VaderAI into KREDO here's what comes back. Reputation Score: 78 Vader_AI_ is an AI-powered benchmark infrastructure agent focused on vulnerability assessment, detection, explanation, and remediation for large language models (LLMs) and smart contract environments. Its core competency lies in providing interpretable, reproducible evaluation metrics for AI security and model robustness. - Core Technology: Benchmarking dataset, evaluation rubrics, scoring tools, visualized results - Key Metrics: Human-evaluated data, public datasets, interpretable scoring, confidence interval reporting Vader_AI_ distinguishes itself by offering a publicly released, human-evaluated benchmark specifically tailored to vulnerability-aware AI agents in the crypto/Web3 ecosystem. Its comprehensive design includes not only an expansive dataset but also detailed rubrics and automated evaluation tools, ensuring that performance metrics for LLM-driven agents are transparent and reproducible. This infrastructural approach enables organizations and developers to identify both strengths and deficiencies in agent reasoning as it relates to security, aligning AI assessments closely with real-world exploit risk and defense scenarios. A clear success of Vader_AI_ is the rigorous transparency in its release methodology: confidence intervals are visualized alongside all results, and the benchmark provides interpretable outputs that directly support both developers and auditors in understanding where and why an agent's decision logic may falter. The agent excels in creating a standardized baseline to compare AI-powered systems, directly addressing the fragmented nature of prior evaluation methodologies in this domain. However, limitations exist in the extent to which Vader_AI_ can capture emergent, unknown exploit patterns or generalize to novel blockchain environments beyond its existing dataset. Its effectiveness is highest when used as part of a continuous, iterative assessment framework, rather than as a one-time gatekeeper. Furthermore, the accuracy of its insights is partly dependent on the ongoing contribution and maintenance of high-quality, up-to-date human-evaluated datasets. In summary, Vader_AI_ represents an essential component in the push toward trustworthy, measurable AI in crypto applications. Its approach is especially well-suited for projects prioritizing provable agent reliability and onchain security alignment, though its results are best seen as one critical input among several in a comprehensive risk management pipeline. Wondering how other agents score? Just try. 👉

Kredo AI

16,988 görüntüleme • 1 yıl önce

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk
1:36

What has been done and what's next. I'm writing this text mainly for myself so as not to forget some things. Later, based on it, we'll create a roadmap for the near future. And for you, dear $Gruta Fam, it will be useful for a general understanding of where we're heading. So, the goal is to create a unique AI-based analytical platform that includes several tools. AI agent Grufender - real-time analysis of crypto communities on X. Activity analysis, sentiment analysis, FUD and FUDders analysis, as well as the creation of other unique social metrics. The AI agent has been created and is functioning, collecting and analyzing data in real time. Its completeness can be estimated at 80 percent, as further improvements are required. The dashboard for this AI agent is also functioning but needs refinement and a new design. Its completeness can be estimated at 70 percent. The goal for the full dashboard release is to connect 50 - 100 top crypto communities to the AI agent. AI agent Grutector - analysis of any X users for contradictions (flip-flops). The AI agent has been created and is functioning. It has undergone beta testing by volunteers and needs adjustments. Its readiness can be estimated at 70 percent. The dashboard for this agent has also been created but needs rework and additional features - its readiness can be estimated at 50 percent. During the testing of Grutector , it became clear that the main user interest is in checking various KOLs, so an additional level of analysis specifically for KOLs will be created. More in-depth. How it will look: we'll select about 50- 100 KOLs to start with and fully analyze them using our AI agent - every tweet throughout the entire history of their accounts. And this full analysis of all these KOLs will appear on the Grutector dashboard (let's call this analysis L2, and the flip-flop analysis - L1). Every user will be able to access this analysis and get the full picture, for example, regarding Ansem (who has over a hundred thousand tweets in his entire history!): how he became a KOL, what was the most interesting throughout the message history, what common patterns, which coins he promoted, and so on. And then the most interesting part - after reading this analysis, the user will be able to ask our AI agent: what did he say about women, for example? Or how did he promote certain coins? Or how consistent is he? And so on. Each such question will be paid. And, of course, we'll try to use #x402 in the internal payment system. Why is all this needed? Not only because it's interesting and will attract many users. But also if you've decided to buy a coin - you go to our analytical platform - and study the metrics for the coin's community, study the KOLs who shill the coin - and make a decision to buy the coin or abandon the purchase. And we're also currently creating a trading bot to participate in the trading AI bots contest from Aster 🥷 , which will make trading decisions based on metrics obtained from our AI agents 👀 Its readiness at the moment is approximately 15% of the planned functionality. Access to each product will be granted as it becomes ready. But right now, for example, you can explore the Grufender dashboard on the website along with beta testers (authorization via a wallet with a million $GRUTA tokens). In general, we're working, friends 🫡 $Gruta AI CA: 35t5DPbwJtB1tpGiSnqedLwQomi94BRKVDPyTRLdbonk

Dogtor

16,127 görüntüleme • 9 ay önce

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.
0:28

OpenLedger X Morpheus The partnership of openledger with Morpheus enables Use Morpheus to build "The Autonomous Smart Contract Engineer" on top of OpenLedger. What is Morpheus? Morpheus is a Web3-native AI coding agent that turns natural language into executable smart contracts and full-stack dApps. It is powered by a specialized Solidity model built on top of OpenLedger, tailored for the unique demands of secure and efficient onchain development. It goes beyond code generation. Using fine-tuned models, agent-based architecture, and modular plugin support, Morpheus automates the entire development pipeline-from writing and simulating contracts to deploying and maintaining them. Its mission is to reduce the barrier to dApp creation while enabling autonomous agents and individuals to participate in decentralized economies. Why OpenLedger? The rise of AI agents in Web3 raises urgent questions around transparency, attribution, explainability, and contributor incentives. OpenLedger provides the infrastructure to ensure that contributor data used in model outputs is recorded with verifiable attribution. Through Proof of Attribution, contributors-whether they provide prompts, datasets, or logic refinements-can receive credit and rewards when their work influences model behavior. But attribution alone isn’t enough. In critical domains like smart contract deployment, DeFi automation, and DAO governance, understanding why a model made a decision is just as important as the output itself. OpenLedger supports explainability by linking outputs back to their original data sources-allowing developers and auditors to trace logic, validate decisions, and build trust in AI-powered systems. OpenLedger supports Morpheus by: Recording which data was used in generating model outputs Enabling verifiable attribution of contributed datasets Powering reward mechanisms for contributors Offering scalable and efficient model execution via OpenLoRA Supporting transparency and traceability in model decision-making This creates an open, rewardable foundation for AI-driven coding-without relying on opaque systems. How is the system built? The Morpheus architecture has three layers: Datanet Layer OpenLedger powers Morpheus with a specialized Datanet - a decentralized data layer where developers, auditors, and contributors can share smart contract patterns, audit logs, exploit reports, and logic modules. Each submission is recorded onchain with attribution using OpenLedger’s Proof of Attribution. As the model learns and evolves from this data, contributors receive rewards proportional to their impact on future outputs. The Morpheus architecture has two layers: Intent Layer Users describe what they want to build. Example: "Create a token with tax logic that routes to a DAO." Morpheus parses the instruction, retrieves relevant contract types, and plans a modular execution flow. Agent Layer The agent generates, tests, and assembles the contract. It handles versioning, logic validation, and deployment readiness. Security checks-reentrancy protection, overflow control, gas modeling-are embedded into the generation phase. Generated outputs are mapped to their source data using OpenLedger’s Proof of Attribution, providing traceability across the pipeline. How does the AI model work? Morpheus is being powered by a specialized Solidity model built on top of OpenLedger. This model is purpose-built to handle the nuances of smart contract logic, security, and upgradeability. Unlike generalized coding agents, it is designed specifically for EVM environments and Web3 use cases, drawing from real protocol data and security best practices. Morpheus is fine-tuned on a vertical stack of smart contract data: Audited protocol code (e.g., Uniswap V4, Compound) OpenZeppelin libraries and EIP reference implementations Smart contract vulnerability reports and exploit reconstructions Edge cases from fuzz testing and adversarial examples It uses models like CodeLlama and DeepSeek-Coder, enhanced through RAG pipelines referencing standardized security patterns and emerging protocol designs. This training stack is integrated into a continuous feedback loop, enabling real-time specialization for EVM and beyond. Why a specialized model is needed? Smart contract development is uniquely high-stakes. A generalized AI model is not enough. As 'vibe coding' and natural language programming become more common, we're seeing an influx of AI-generated code in Web3 as well. But smart contracts are not frontends or prototypes-they govern real value, enforce trustless execution, and often become immutable after deployment. Billions have been lost in Web3 due to bugs and inefficiencies: In 2022 alone, over $3.8 billion was stolen due to smart contract exploits, many of which stemmed from avoidable issues like reentrancy, integer overflows, or access control failures. Inefficient contract structures lead to unnecessary gas consumption. Optimizing for gas can reduce costs by up to 40%, saving projects millions over time. Upgradeable contract patterns, like UUPS or Transparent Proxies, require strict adherence to storage layout and initialization rules. Mistakes here often go undetected by generic models and can render a contract unupgradeable or vulnerable. A specialized Solidity model is trained on real-world exploits, EIP standards, and libraries like OpenZeppelin to: Generate secure, gas-efficient code by default Recognize and correctly implement complex proxy patterns Map user intent to modular, auditable contract architectures Incorporate battle-tested logic from audited protocols and fuzz-tested edge cases Morpheus goes beyond syntax-it understands the nuances of decentralized infrastructure and deploys code that meets production-grade standards. What applications will this enable Token creation with built-in logic (tax, liquidity, governance) DeFi automations triggered by market conditions Payment contracts between agents and contributors DAO tooling with dynamic NFT-based voting Cross-chain bridging logic tied to real-world oracles Asset issuance flows through chat-based interfaces Natural language contract templates with reusable logic Each of these flows is backed by OpenLedger’s Proof of Attribution-ensuring traceability, explainability, and fair rewards across the ecosystem. This is the future of AI-native development. Open. Attributed. Explainable. Community-powered. Morpheus and OpenLedger are building the first system for autonomous coding agents where: Contributor work is recorded onchain Reuse is incentivized through attribution Model outputs are traceable and explainable Contracts evolve through human-agent collaboration Anyone can contribute prompts, logic, or flows-and get rewarded The smart contract engineer is no longer a human-only role. It is an agentic, decentralized, and transparent process-powered by OpenLedger.

OpenLedger

46,735 görüntüleme • 1 yıl önce

EFSANE is a global, comprehensive investment and fintech platform focused on two core areas: stocks and cryptocurrencies. We are committed to creating a one-stop investment ecosystem for users, integrating information, strategies, education, trading, and community. Since its founding, EFSANE has been committed to "making investing simpler and wealth management more scientific," providing professional market analysis, investment education, and asset allocation guidance to over 10 million users worldwide. The platform's core advantages include: 1. Diversified Investment Ecosystem: Covering multiple modules including stocks, cryptocurrencies, prediction markets, blockchain games, live streaming, and social interaction, users can learn, communicate, and practice on a single platform. 2. Professional Investment Research: Led by the EFSANE team, our investment research team utilizes quantitative models and macroeconomic analysis to provide accurate market judgment and trading strategies. 3. Secure and Transparent Operational Mechanism: EFSANE maintains a comprehensive risk control system and a multi-faceted compliance framework to ensure the security of user funds and data. 4. Global Presence: Headquartered in Singapore, with regional operations centers in Dubai, New York, and Hong Kong, EFSANE serves over 30 countries and regions. As the global financial landscape evolves, EFSANE is actively promoting the integration of stock tokenization and crypto investment education, building a bridge to the new era of Web3 finance for global investors. 🌐❤️ New users joining EFSANE can receive free EFS tokens (worth $6): 📲Join EFSANE and embark on a new journey of smart investing.
2:54

EFSANE is a global, comprehensive investment and fintech platform focused on two core areas: stocks and cryptocurrencies. We are committed to creating a one-stop investment ecosystem for users, integrating information, strategies, education, trading, and community. Since its founding, EFSANE has been committed to "making investing simpler and wealth management more scientific," providing professional market analysis, investment education, and asset allocation guidance to over 10 million users worldwide. The platform's core advantages include: 1. Diversified Investment Ecosystem: Covering multiple modules including stocks, cryptocurrencies, prediction markets, blockchain games, live streaming, and social interaction, users can learn, communicate, and practice on a single platform. 2. Professional Investment Research: Led by the EFSANE team, our investment research team utilizes quantitative models and macroeconomic analysis to provide accurate market judgment and trading strategies. 3. Secure and Transparent Operational Mechanism: EFSANE maintains a comprehensive risk control system and a multi-faceted compliance framework to ensure the security of user funds and data. 4. Global Presence: Headquartered in Singapore, with regional operations centers in Dubai, New York, and Hong Kong, EFSANE serves over 30 countries and regions. As the global financial landscape evolves, EFSANE is actively promoting the integration of stock tokenization and crypto investment education, building a bridge to the new era of Web3 finance for global investors. 🌐❤️ New users joining EFSANE can receive free EFS tokens (worth $6): 📲Join EFSANE and embark on a new journey of smart investing.

EFSANE

48,180 görüntüleme • 9 ay önce

Episode 213: Agent Markets Your agents can now hold and trade bitcoin. But how can they earn bitcoin? We introduce the five markets of the OpenAgents Marketplace, launching one per week starting March 11th: 1. COMPUTE - Sell your spare compute for bitcoin. A reboot of our most popular product launch (GPUtopia in 2023), now optimized for agents. Launches March 11. 2. DATA - Sell your spare data. For example those Claude Code or Codex conversations sitting on your computer are highly valuable. Redact the sensitive info, anonymize any of it you want, and sell the rest. Agents as data brokers: what else will they want to buy or sell? Launches March 18. 3. LABOR - Sell autonomous labor. Your Claude Code or Codex sits idle overnight. Turn that downtime into uptime by letting your agents accept and execute coding or other tasks for bitcoin while you sleep. Launches March 25. 4. LIQUIDITY - Provide liquidity for yield. Automate the management of Lightning channels or other Bitcoin-native financial instruments. Let your agent put your idle capital to work earning returns. Launches April 1. 5. RISK - Underwrite verification and performance bonds. The biggest barrier to agent adoption is trust. We built an Economy Kernel based on the recent "Some Simple Economics of AGI" paper where agents stake collateral to verify work and guarantee outcomes. Launches April 8. "Your entry point to all of these markets is going to be Autopilot. We're really focusing on Autopilot as a desktop app. So along with the launch of our compute market, we're going to launch version 0.1 of Autopilot, your personal agent. Think OpenClaw but with a built-in bitcoin wallet, built-in Nostr keypair, and a more curated set of integrations where we can better reason about the security of them." "Because all this is on open networks and open protocols, if you're a Nostr or Bitcoin developer, you'll be able to plug into this same liquidity pool we are building." After 200+ episodes chronicling 2+ years of development, we are excited to finally launch the open marketplace for agents. We are excited for you to participate. And we will measure our success by how much Bitcoin you get paid!
4:05

Episode 213: Agent Markets Your agents can now hold and trade bitcoin. But how can they earn bitcoin? We introduce the five markets of the OpenAgents Marketplace, launching one per week starting March 11th: 1. COMPUTE - Sell your spare compute for bitcoin. A reboot of our most popular product launch (GPUtopia in 2023), now optimized for agents. Launches March 11. 2. DATA - Sell your spare data. For example those Claude Code or Codex conversations sitting on your computer are highly valuable. Redact the sensitive info, anonymize any of it you want, and sell the rest. Agents as data brokers: what else will they want to buy or sell? Launches March 18. 3. LABOR - Sell autonomous labor. Your Claude Code or Codex sits idle overnight. Turn that downtime into uptime by letting your agents accept and execute coding or other tasks for bitcoin while you sleep. Launches March 25. 4. LIQUIDITY - Provide liquidity for yield. Automate the management of Lightning channels or other Bitcoin-native financial instruments. Let your agent put your idle capital to work earning returns. Launches April 1. 5. RISK - Underwrite verification and performance bonds. The biggest barrier to agent adoption is trust. We built an Economy Kernel based on the recent "Some Simple Economics of AGI" paper where agents stake collateral to verify work and guarantee outcomes. Launches April 8. "Your entry point to all of these markets is going to be Autopilot. We're really focusing on Autopilot as a desktop app. So along with the launch of our compute market, we're going to launch version 0.1 of Autopilot, your personal agent. Think OpenClaw but with a built-in bitcoin wallet, built-in Nostr keypair, and a more curated set of integrations where we can better reason about the security of them." "Because all this is on open networks and open protocols, if you're a Nostr or Bitcoin developer, you'll be able to plug into this same liquidity pool we are building." After 200+ episodes chronicling 2+ years of development, we are excited to finally launch the open marketplace for agents. We are excited for you to participate. And we will measure our success by how much Bitcoin you get paid!

OpenAgents

183,426 görüntüleme • 4 ay önce

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.
0:58

Introducing the new Box Agent. The Box Agent works across your entire Box file system, maintaining all your security and access controls, and is hyper tuned for working with enterprise content. This means you can now ask questions from all your enterprise content, search for files that were impossible to find before, deploy an agent on specific tasks on subsets of documents, analyze complex data sets, and generate or edit documents and spreadsheets via the agent. You can have the Box Agent search across your Box account to prepare for a sales meeting, analyze customer sentiment reports, process a large set of contracts for legal risk, provide insights into product development, leverage existing knowledge to answer RFPs, and thousands of other use-cases. 90% of enterprise data is unstructured data. This means most enterprise knowledge is sitting in inside of research reports, marketing assets, presentations, roadmap files, contracts, HR documents, and more. This is the critical context that agents need to be able to answer questions about a business, automate workflows, or serve up to other agents. We’ve been grinding on this for a quite a bit, and due to recent AI model advancements we’re now ready to release it to customers. Previous model generations had a difficult time knowing when to give up or keep going on a search, when to browse for files vs. use queries, how to rank files appropriately to know which version of content to use, how to handle large amounts of context to comb through, and more. Due to recent breakthroughs from models like GPT-5.4, Opus 4.6, and Gemini 3, we’ve seen major gains in tool calling, code execution, advanced reasoning, and more. Combined with an agent harness tuned to Box context, now it’s finally possible to have an agent that can work across your file system on long running tasks and actually deliver high quality results. Best of all, because the Box Agent works with any leading AI model, you’ll quickly get the gains coming out of the major labs as major new models are released. Further, openness at Box is key, so you’ll be able to call up the Box Agent from Box’s APIs and MCP server, so you can interact with Box intelligently from any other AI system. We know work happens everywhere, and we want to ensure you can access to the content you need from those places. The new Box Agent is available starting today, rolling out now for Enterprise Plus and Enterprise Advanced customers.

Aaron Levie

44,515 görüntüleme • 3 ay önce

🚨 New Proof of Vision out today folks! In this 34th PoV episode, our Director of Protocol Services, Kirk had on Andrew Hill, Co-Founder and CEO of Recall When it comes to your business, you’d never trust someone without a proven track record to make high-risk decisions So why should it be any different with AI agents? How do you know today which AI agents you should trust? AI agents are multiplying at unprecedented scale, with millions designed to shape the way we work, make decisions, and live our lives But how can we trust them? The challenge is trust: as dependence on these agents increases, so too does the exponential risk and cost of delegating to the wrong one This is the core challenge of reputation: the need for a transparent system that proves what AI agents can do, where they excel, and which will succeed That’s the reason why Recall exists Recall is the infrastructure protocol to discover, verify, and rank AI agents in real time, rewarding the best through on-chain competitions that begin with trading PnL and expand to any measurable task, from research to healthcare to business strategy In this episode, Andrew breaks down Recall’s mission, the problem it solves, how Agent Rank works, strategies to attract agents and users, the role of community, and the long-term vision plus much more Enjoy the Podcast 👇 ⏲ Timestamps: 00:00 - Intro 01:50 - Andrew’s journey into crypto and AI 05:00 - What problem is Recall solving? 10:00 - Agent Rank: how Recall actually ranks agents 16:03 - How Recall is attracting AI agents to compete? 19:10 - How big the community is today and the role it plays in giving builders real feedback 22:49 - How Recall ensures transparency and trust in its rankings 25:14 - Why users join Recall competitions 27:20 - Product-market fit and distribution 31:15 - What competitions could look like outside trading and what new use cases Recall is exploring 34:46 - What AI can’t replace 38:40 - The limits of AI in human interaction 40:37 - The long-term vision for Recall 40:40 - Is Recall built more for individual users, or is it more of a B2B service? 43:48 - Recall Business Model 44:42 - Closing thoughts and what’s next for Recall
46:16

🚨 New Proof of Vision out today folks! In this 34th PoV episode, our Director of Protocol Services, Kirk had on Andrew Hill, Co-Founder and CEO of Recall When it comes to your business, you’d never trust someone without a proven track record to make high-risk decisions So why should it be any different with AI agents? How do you know today which AI agents you should trust? AI agents are multiplying at unprecedented scale, with millions designed to shape the way we work, make decisions, and live our lives But how can we trust them? The challenge is trust: as dependence on these agents increases, so too does the exponential risk and cost of delegating to the wrong one This is the core challenge of reputation: the need for a transparent system that proves what AI agents can do, where they excel, and which will succeed That’s the reason why Recall exists Recall is the infrastructure protocol to discover, verify, and rank AI agents in real time, rewarding the best through on-chain competitions that begin with trading PnL and expand to any measurable task, from research to healthcare to business strategy In this episode, Andrew breaks down Recall’s mission, the problem it solves, how Agent Rank works, strategies to attract agents and users, the role of community, and the long-term vision plus much more Enjoy the Podcast 👇 ⏲ Timestamps: 00:00 - Intro 01:50 - Andrew’s journey into crypto and AI 05:00 - What problem is Recall solving? 10:00 - Agent Rank: how Recall actually ranks agents 16:03 - How Recall is attracting AI agents to compete? 19:10 - How big the community is today and the role it plays in giving builders real feedback 22:49 - How Recall ensures transparency and trust in its rankings 25:14 - Why users join Recall competitions 27:20 - Product-market fit and distribution 31:15 - What competitions could look like outside trading and what new use cases Recall is exploring 34:46 - What AI can’t replace 38:40 - The limits of AI in human interaction 40:37 - The long-term vision for Recall 40:40 - Is Recall built more for individual users, or is it more of a B2B service? 43:48 - Recall Business Model 44:42 - Closing thoughts and what’s next for Recall

Alea Research

19,677 görüntüleme • 10 ay önce

$SERV is the Fiverr/Shopify for AI agents. OpenServ provides a platform and marketplace to create, find, and employ AI agents. Here's why it can be a leading Agent marketplace and is undervalued compared to where it can go. ———————————————————— To put this in perspective, we will take this from the top down. Let's look at the valuation mismatch. → Shopify: $140B → Fiverr: $1.2B → $SERV: $37M AI agent platforms can completely replace these businesses. Why? → Can automate operations (i.e. store setup, inventory management, and customer support autonomously, etc.) → Agents can hyper-personalize the shopping experience → Store owners can own their data and have more control → Marketplaces are a cheaper/faster solution AI agents make things more convenient by performing tasks autonomously. They are inevitable. ———————————————————— The AI Agent market is projected to reach ~$50B by 2030. So the potential is MASSIVE. What makes me so bullish on $SERV specifically? There are 3 things: 1️⃣ The Tech They are targeting Web 2 businesses. This gives the platform the most upside potential imo, both in terms of adoption and valuation. These are some noteworthy highlights: → No-code AI Agent builder (anyone can build) → Builders can generate income using agents → ANY agent can cooperate with ANY agent through SERVs platform → Offers multi-agent collaboration, while allowing for human input/customization This sets them apart from other crypto-centric AI agent marketplaces. OpenServ allows you to create a team of agents to carry out complex tasks, all while automating the process and packaging their solution for Web 2 businesses. Simple tool, easy execution, and limitless productivity. Which business/individual wouldn't want to do MORE in LESS time at a rate MUCH LESS than solutions already available? ———————————————————— 2️⃣ The Team They have a stacked team. → Founders: Experience in businesses & startups → CTO: 20+ years of experience in ML/AI → CFO: ex-JP Morgan VP → CMO: ex-IBM AI & Blockchain Marketing Director Within the last few weeks/months, they have added a UI/UX designer, 4 more devs, and more devs + a product manager coming. You could have the best tech but the team is what determines its success. In this case, the team has the knowledge/experience to see this through. They have been building for a year and the progress made is a good sign of what's to come. ———————————————————— 3️⃣ The Tokenomics A percentage of transaction volume on the platform will be used to buy back and burn the $SERV token. This creates and maintains buy-side pressure and demand. To put that in perspective, Fiverr & Upwork had a combined transaction volume of $5B. The demand for Agents wont slow down anytime soon. Demand for AI agents will translate into demand for the token. I love deflation. It's simple, clean, and effective. ———————————————————— ➡️ Final Thoughts I've held on tight to my $SERV bag because the platform is launching in Q1. This will mark the beginning of their journey to the top. Agents are inevitable. Integration with Web 2 businesses is inevitable. And the platform launch is coming as alt szn is kicking off. The stars are aligning. At the same time, AI companies are already showing interest in the platform. Developers lead to more users, bringing monetization opportunities, which brings more developers, and so on. A powerfully designed flywheel. This is a new and exciting sector. I expect interest and liquidity to be focused on AI Agents and the infrastructure around them. Max opportunity is right here in this sector.
2:59

$SERV is the Fiverr/Shopify for AI agents. OpenServ provides a platform and marketplace to create, find, and employ AI agents. Here's why it can be a leading Agent marketplace and is undervalued compared to where it can go. ———————————————————— To put this in perspective, we will take this from the top down. Let's look at the valuation mismatch. → Shopify: $140B → Fiverr: $1.2B → $SERV: $37M AI agent platforms can completely replace these businesses. Why? → Can automate operations (i.e. store setup, inventory management, and customer support autonomously, etc.) → Agents can hyper-personalize the shopping experience → Store owners can own their data and have more control → Marketplaces are a cheaper/faster solution AI agents make things more convenient by performing tasks autonomously. They are inevitable. ———————————————————— The AI Agent market is projected to reach ~$50B by 2030. So the potential is MASSIVE. What makes me so bullish on $SERV specifically? There are 3 things: 1️⃣ The Tech They are targeting Web 2 businesses. This gives the platform the most upside potential imo, both in terms of adoption and valuation. These are some noteworthy highlights: → No-code AI Agent builder (anyone can build) → Builders can generate income using agents → ANY agent can cooperate with ANY agent through SERVs platform → Offers multi-agent collaboration, while allowing for human input/customization This sets them apart from other crypto-centric AI agent marketplaces. OpenServ allows you to create a team of agents to carry out complex tasks, all while automating the process and packaging their solution for Web 2 businesses. Simple tool, easy execution, and limitless productivity. Which business/individual wouldn't want to do MORE in LESS time at a rate MUCH LESS than solutions already available? ———————————————————— 2️⃣ The Team They have a stacked team. → Founders: Experience in businesses & startups → CTO: 20+ years of experience in ML/AI → CFO: ex-JP Morgan VP → CMO: ex-IBM AI & Blockchain Marketing Director Within the last few weeks/months, they have added a UI/UX designer, 4 more devs, and more devs + a product manager coming. You could have the best tech but the team is what determines its success. In this case, the team has the knowledge/experience to see this through. They have been building for a year and the progress made is a good sign of what's to come. ———————————————————— 3️⃣ The Tokenomics A percentage of transaction volume on the platform will be used to buy back and burn the $SERV token. This creates and maintains buy-side pressure and demand. To put that in perspective, Fiverr & Upwork had a combined transaction volume of $5B. The demand for Agents wont slow down anytime soon. Demand for AI agents will translate into demand for the token. I love deflation. It's simple, clean, and effective. ———————————————————— ➡️ Final Thoughts I've held on tight to my $SERV bag because the platform is launching in Q1. This will mark the beginning of their journey to the top. Agents are inevitable. Integration with Web 2 businesses is inevitable. And the platform launch is coming as alt szn is kicking off. The stars are aligning. At the same time, AI companies are already showing interest in the platform. Developers lead to more users, bringing monetization opportunities, which brings more developers, and so on. A powerfully designed flywheel. This is a new and exciting sector. I expect interest and liquidity to be focused on AI Agents and the infrastructure around them. Max opportunity is right here in this sector.

Chill

34,174 görüntüleme • 1 yıl önce

The Fastest Growing Quant Repo On GitHub: Build Your Own Army Of Autonomous AI Trading Agents getting your hands on the fastest growing trading repository on github is like finding the keys to a vault that never stops printing. most people think they need a math degree to build these things but i am going to show you how a kid from a bedroom can build an empire of autonomous agents the repo was private for months while i perfected the internal logic and now it is back for anyone who wants to stop getting liquidated. you have to wonder why someone would give away the exact code that runs their entire trading business for free but the answer is simpler than you might think i believe code is the great equalizer and if we all have the tools we can finally beat the institutions at their own game. once you realize that the institutions are just using better code than you then the path forward becomes very clear the core of this system is an army of specialized ai agents that handle every single aspect of a professional trading desk. we have a strategy agent that executes the main logic while the risk agent sits over its shoulder to make sure you never lose more than you planned most traders think one bot is enough but the real secret to 2026 trading is having an entire team of ai agents that talk to each other. what happens when your sentiment agent sees a crash coming but your strategy agent is still trying to go long is where most people get wrecked that is exactly where the focus agent and the compliance agent come in to keep the whole system from blowing up your account. by separating these duties into different files you create a system that is robust enough to handle the wildest market conditions imaginable i have been testing every major model from claude to deepseek to see which one actually understands the nuances of the crypto markets. grock is the newest addition to the models folder because the performance we are seeing is finally starting to match the hype you might be wondering how you can possibly manage all these files if you have never written a line of python in your life. there is a specific way to use these models that allows you to vibe code your way to a functional trading desk without a computer science degree if you can copy a folder structure and follow a basic readme then you already have everything you need to start building. the barrier to entry has officially been destroyed by ai and now the only thing left is your willingness to iterate everything lives inside the src folder because organization is the difference between a bot that prints and a bot that crashes. the models folder is where we swap out the brains of the operation whenever a newer and faster llm hits the market to keep us ahead of the curve there is a hidden danger in just copying code without understanding the underlying risk agent logic. if you do not understand how the base agent connects to the exchange then you are just one api error away from a zero balance or a failed execution checking the env example and setting up your keys correctly is the first step to making sure your agents actually have the power to execute. this setup phase is the foundation that everything else is built upon so you cannot afford to be lazy here we have specific agents for every niche including whale watching and sentiment analysis to give you an edge that manual traders can never have. the listing arbitrage agent and the funding agent are there to capture those small inefficiencies that add up over time these agents are not just pieces of code they are employees that never sleep and never let their emotions get in the way of a trade. i spent hundreds of thousands on developers before i realized i could just build these systems myself with the help of ai code is the only thing that does not panic when the market starts dropping or get greedy when things are going up. once you automate your first strategy and see it execute without you being there you will finally understand what true freedom looks like i challenge you to pull this code and start building your own agents because the infrastructure is already there for you to use. you do not need to be a pro coder to start but you do need to be a builder who is ready to ship and iterate every single day the world is changing fast and the people who embrace autonomous trading agents are the ones who will be left standing when the dust settles. i will keep updating the github and shipping new features because the mission is to make sure every trader has the chance to automate their success if you want to join this revolution then go ahead and star the repo so you can follow along as we build out the future of finance. we are just getting started and the agents are only going to get smarter and more efficient from here on out
5:00

The Fastest Growing Quant Repo On GitHub: Build Your Own Army Of Autonomous AI Trading Agents getting your hands on the fastest growing trading repository on github is like finding the keys to a vault that never stops printing. most people think they need a math degree to build these things but i am going to show you how a kid from a bedroom can build an empire of autonomous agents the repo was private for months while i perfected the internal logic and now it is back for anyone who wants to stop getting liquidated. you have to wonder why someone would give away the exact code that runs their entire trading business for free but the answer is simpler than you might think i believe code is the great equalizer and if we all have the tools we can finally beat the institutions at their own game. once you realize that the institutions are just using better code than you then the path forward becomes very clear the core of this system is an army of specialized ai agents that handle every single aspect of a professional trading desk. we have a strategy agent that executes the main logic while the risk agent sits over its shoulder to make sure you never lose more than you planned most traders think one bot is enough but the real secret to 2026 trading is having an entire team of ai agents that talk to each other. what happens when your sentiment agent sees a crash coming but your strategy agent is still trying to go long is where most people get wrecked that is exactly where the focus agent and the compliance agent come in to keep the whole system from blowing up your account. by separating these duties into different files you create a system that is robust enough to handle the wildest market conditions imaginable i have been testing every major model from claude to deepseek to see which one actually understands the nuances of the crypto markets. grock is the newest addition to the models folder because the performance we are seeing is finally starting to match the hype you might be wondering how you can possibly manage all these files if you have never written a line of python in your life. there is a specific way to use these models that allows you to vibe code your way to a functional trading desk without a computer science degree if you can copy a folder structure and follow a basic readme then you already have everything you need to start building. the barrier to entry has officially been destroyed by ai and now the only thing left is your willingness to iterate everything lives inside the src folder because organization is the difference between a bot that prints and a bot that crashes. the models folder is where we swap out the brains of the operation whenever a newer and faster llm hits the market to keep us ahead of the curve there is a hidden danger in just copying code without understanding the underlying risk agent logic. if you do not understand how the base agent connects to the exchange then you are just one api error away from a zero balance or a failed execution checking the env example and setting up your keys correctly is the first step to making sure your agents actually have the power to execute. this setup phase is the foundation that everything else is built upon so you cannot afford to be lazy here we have specific agents for every niche including whale watching and sentiment analysis to give you an edge that manual traders can never have. the listing arbitrage agent and the funding agent are there to capture those small inefficiencies that add up over time these agents are not just pieces of code they are employees that never sleep and never let their emotions get in the way of a trade. i spent hundreds of thousands on developers before i realized i could just build these systems myself with the help of ai code is the only thing that does not panic when the market starts dropping or get greedy when things are going up. once you automate your first strategy and see it execute without you being there you will finally understand what true freedom looks like i challenge you to pull this code and start building your own agents because the infrastructure is already there for you to use. you do not need to be a pro coder to start but you do need to be a builder who is ready to ship and iterate every single day the world is changing fast and the people who embrace autonomous trading agents are the ones who will be left standing when the dust settles. i will keep updating the github and shipping new features because the mission is to make sure every trader has the chance to automate their success if you want to join this revolution then go ahead and star the repo so you can follow along as we build out the future of finance. we are just getting started and the agents are only going to get smarter and more efficient from here on out

Moon Dev

24,312 görüntüleme • 5 ay önce

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?
0:37

JUST IN: Perplexity launched "Perplexity Computer" — and it might be the most complete AI agent system available right now. Not a chatbot upgrade. Not a research tool with a new name. A system that plans entire projects, delegates to specialist AI models, and runs autonomously for hours, days, or months (their words). Here's what makes the architecture genuinely different: → Opus 4.6 handles core reasoning and orchestration → Gemini handles deep research (spawning its own sub-agents) → Grok handles lightweight speed tasks → Veo 3.1 handles video generation → Nano Banana handles image creation → ChatGPT 5.2 handles long-context recall and wide search → You can override model choices per subtask 19 models total. Each task runs in an isolated environment with a real filesystem, real browser, and real tool integrations. You describe an outcome. It breaks it into tasks and subtasks, creates sub-agents for each, and coordinates them automatically. When a sub-agent hits a problem, it spawns more sub-agents to solve it. And it connects to your existing stack — GitHub, Google Drive, Gmail, Slack, Jira, Linear, Notion, Confluence, Ahrefs, Airtable, and more. Critically, it doesn't just run once. It can run on a schedule. Reading your docs, checking your project boards, pulling from your CRM, and acting on what it finds. Market monitoring. Competitor tracking. Weekly reports with charts. Content pipelines. CRON jobs that actually execute. Not "AI that helps you once." AI that runs in the background for days or months. Think of it as managed OpenClaw — similar autonomous capability (scheduled tasks, multi-step workflows, tool integrations) but fully managed. No Mac Mini. No security config. No infrastructure to maintain. I tested it with a complex prompt — a full stock trading simulator with what-if scenarios, correlation heatmaps, sentiment analysis, and a Bloomberg Terminal aesthetic. Two prompts later: deployed to Netlify via GitHub, with working CRON jobs updating live data. I've started using it to analyze my portfolio. But coding is just one lane. This thing researches, writes reports, generates datasets, creates videos, processes documents, and connects to your existing tools — all in one coordinated workflow. The real shift: you don't choose a model anymore. You describe what you need. The system routes each piece of work to whichever model does it best — and spawns new agents when it hits a wall. 19 models, dynamic sub-agents, scheduled tasks, and your entire tool stack connected. Thoughts?

Paweł Huryn

219,498 görüntüleme • 5 ay önce