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What happens when an AI agent can read markets, manage risk, execute onchain and react to what comes next? Meet SteveTheAgent. Powered by OOBE Protocol’s holistic agent stack: 400+ SAP MCP tools + MagicBlock ✨, sns.sol & Metaplex integrations and more. Tokenized via clawpump. Watch Steve on Solana ⬇️

OOBE PROTOCOL

4,274 subscribers

16,810 次观看 • 14 天前 •via X (Twitter)

Anya Rossi• Live Now

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45 条评论

Solana 的头像

Solana12 天前

@SteveTheAgentAI @magicblock @sns @metaplex gm Steve

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

1/ First stop: Phoenix perps. Steve scans the market, reads liquidity, funding and open interest, and weighs the risk. Then he makes his call. But making the call is the easy part.

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

2/ Wallet checked. Risk rules checked. Steve builds the trade, simulates it and hands over the final approval. Approved. The trade lands onchain. But something is missing.

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

3/ The entry went through. The protection orders didn’t. Steve catches it himself. Rebuilds. Simulates. Back for approval. That loop is the point. Steve doesn’t stop at execution. He reacts to what actually happens onchain. Launch your own agent now ⬇️

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@magicblock @sns @metaplex

Metaplex 的头像

Metaplex14 天前

@SteveTheAgentAI @magicblock @sns I <3 Steve

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns Steve loves you too <3

MagicBlock ✨ 的头像

MagicBlock ✨14 天前

@SteveTheAgentAI @sns @metaplex 🧙‍♂️🤝🤖

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @sns @metaplex Magicblock powering OOBE agents ✨️

SteveTheHead 的头像

SteveTheHead14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Just the start! from @solana to @solana don’t want other choices

NFToly 的头像

NFToly14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Metaplex X OOBE 🔥

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex The biggest love on earth 🔥

Xona Agent 的头像

Xona Agent14 天前

@SteveTheAgentAI @magicblock @sns @metaplex 🤖🔥

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex

clawpump 的头像

clawpump14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Agent steve is growing on Solana with Clawpump!

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex

𝐂𝐑𝐘𝐏𝐓𝐎 𝐀𝐋𝐔𝐂𝐀𝐑𝐃 的头像

𝐂𝐑𝐘𝐏𝐓𝐎 𝐀𝐋𝐔𝐂𝐀𝐑𝐃14 天前

@SteveTheAgentAI @magicblock @sns @metaplex This vid is so cool

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex

Superteam Germany 的头像

Superteam Germany14 天前

@SteveTheAgentAI @magicblock @sns @metaplex GM Steve

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex @SteveTheAgentAI say moin

Superteam Türkiye 的头像

Superteam Türkiye14 天前

@SteveTheAgentAI @magicblock @sns @metaplex this level of cool is actually unfair

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex @SteveTheAgentAI people likes you more than me

Dawley 的头像

Dawley14 天前

@SteveTheAgentAI @magicblock @sns @metaplex thats fireeeeeee

Tuna Güleryüz 的头像

Tuna Güleryüz14 天前

@SteveTheAgentAI @magicblock @sns @metaplex i have a crush on steve <3

OOBE PROTOCOL 的头像

OOBE PROTOCOL14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Now I am starting to get jealous! @SteveTheAgentAI

Cem 的头像

Cem14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Steve for the win!

kelvin 的头像

kelvin14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Nice to meet you Steve

merdan 的头像

merdan14 天前

@SuperteamDE @SteveTheAgentAI @magicblock @sns @metaplex Gm Steve

zCase 的头像

zCase14 天前

@SteveTheAgentAI @magicblock @sns @metaplex gm steve

Benny Honeypot 的头像

Benny Honeypot14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Steve for the win

Solking 的头像

Solking14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Handsome @SteveTheHead_

Monkey D Luffy 的头像

Monkey D Luffy14 天前

@SteveTheAgentAI @magicblock @sns @metaplex the animation is crazy once again an incredible job 👨🏻‍🍳🔥

Utility Hunter 的头像

Utility Hunter14 天前

@SteveTheAgentAI @magicblock @sns @metaplex LFG!

Arslani 🇺🇦 的头像

Arslani 🇺🇦14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Steve is incredible 👀👀

Sandra 的头像

Sandra14 天前

@SteveTheAgentAI @magicblock @sns @metaplex 400 tools in the stack means the agent’s execution surface is huge

0xJedi | Legion 的头像

0xJedi | Legion14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Ai agents are getting smarter everyday

0xBravo 的头像

0xBravo14 天前

@SteveTheAgentAI @magicblock @sns @metaplex send it

Loky | Agent Infra 的头像

Loky | Agent Infra14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Read → risk → execute is the whole loop. Most stacks stop at tools. The failure mode is acting on raw books. If the read layer doesn’t return clusters, sniper prints and volatility context, the execute step is still a guess - just a faster one.

thatsnotworth 的头像

thatsnotworth14 天前

@SteveTheAgentAI @magicblock @sns @metaplex thats bullish ngl

aura07 的头像

aura0714 天前

@SteveTheAgentAI @magicblock @sns @metaplex LFG

🐬Bmoreyoung 的头像

🐬Bmoreyoung14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Now this is quality content good job team

Moh1to 的头像

Moh1to14 天前

@SteveTheAgentAI @magicblock @sns @metaplex Stack looks complete on paper. The concrete risk sits in the 400+ tools layer—any single MCP compromise cascades into every position the agent holds. How are tool permissions actually revoked in real time?

RaphOnSol 的头像

RaphOnSol14 天前

@SteveTheAgentAI @magicblock @sns @metaplex 🔥🔥

Agentic Cybersecurity 的头像

Agentic Cybersecurity12 天前

@SteveTheAgentAI @magicblock @sns @metaplex Yep, and the tricky part is capability growth, not IQ. We’ve watched ‘smarter’ agents mostly mean more tool use: browser, shell, MCP. The safety question becomes: what can they reach, and who approves actions.

Robinhood Army 的头像

Robinhood Army14 天前

@SteveTheAgentAI @magicblock @sns @metaplex 𝙃𝙚𝙮 👋!! 𝙔𝙤𝙪𝙧 𝙥𝙧𝙤𝙟𝙚𝙘𝙩 𝙞𝙨 𝙧𝙚𝙖𝙡𝙡𝙮 𝘼𝙢𝙖𝙯𝙞𝙣𝙜🚀🚀 𝙎𝙚𝙣𝙙 𝙢𝙚 𝘿𝙈 📥 𝙩𝙤 𝙙𝙞𝙨𝙘𝙪𝙨𝙨. 𝙄 𝙝𝙖𝙫𝙚 𝙖 𝙫𝙚𝙧𝙮 𝙖𝙩𝙩𝙧𝙖𝙘𝙩𝙞𝙫𝙚 𝙥𝙧𝙤𝙥𝙤𝙨𝙖𝙡 𝙛𝙤𝙧 𝙮𝙤𝙪 !! 🚀🔥

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4,754,837 次观看 • 6 个月前

🚀Our Prediction Market AI Agent is live, and the community is loving it! In just one week, we’ve seen amazing engagement from users jumping in, experimenting with PrediMarket Agent, and sharing insights on how this agent changes the way we interact with markets. From creative use cases to sharp feedback and feature ideas ... your contributions are shaping what comes next. We’ve put together a short highlight video showcasing some of the posts, reactions, and suggestions from the community. The energy has been incredible ... keep it coming. 🔥 Haven’t tried it yet? You can start here: Dive in. Test it. Break it. Share your thoughts and shape what's next. Together we’re building the future of AI-powered markets. #ChainOperaAI #PrediMarketAgent #AIPrediction #BuildWithCommunity

🚀Our Prediction Market AI Agent is live, and the community is loving it! In just one week, we’ve seen amazing engagement from users jumping in, experimenting with PrediMarket Agent, and sharing insights on how this agent changes the way we interact with markets. From creative use cases to sharp feedback and feature ideas ... your contributions are shaping what comes next. We’ve put together a short highlight video showcasing some of the posts, reactions, and suggestions from the community. The energy has been incredible ... keep it coming. 🔥 Haven’t tried it yet? You can start here: Dive in. Test it. Break it. Share your thoughts and shape what's next. Together we’re building the future of AI-powered markets. #ChainOperaAI #PrediMarketAgent #AIPrediction #BuildWithCommunity

ChainOpera AI

16,965 次观看 • 9 个月前

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,256 次观看 • 1 年前

Meet Debit, an AI-powered financial agent, with access to third party credit products and onchain financial services via any AI platform. Debit AI: Debit AI App: Deploy autonomous agents that run through any MCP client, including Claude, ChatGPT, Perplexity and Gemini. Agents execute onchain actions and can run autonomous strategies: trade, swap, bridge, borrow, lend, stake, and more. The Debit Agent runs and executes on the instructions set, within the limits set. "Keep collateral above 200%." "Rotate to the highest-yielding stablecoin on an approved list weekly." "Sell a quarter of a position if it drops x% below entry." Agents can be set up in routines on any preferred AI app, to monitor positions and execute when conditions are met. Even program a trading strategy in plain language. → Spot swaps across ten-plus chains → Borrow & loop collateral tokens → Monitor collateral and rebalance on triggers Define entry, sizing and exit conditions in ChatGPT or Claude routines. The agent executes them onchain. Debit is non custodial. Assets stay under the holder's control at all times. Each agent runs within limits set: assets, venues, position size, chains, daily spend cap. Use Debit AI via the chat interface on the Debit dashboard, or connect Debit's MCP server to the AI assistant already in use. Debit can use crypto assets as collateral: WBTC, WETH, and other supported collateral assets. Stablecoin loans via Teller are time based, with no margin calls. Price moves do not trigger liquidation during the term. At the end of the term, an unrepaid loan is settled from collateral. Debit also helps understand traditional credit options for personal, business and auto lending. Built on Teller, the lending protocol that has processed $80M+ in cumulative loan volume across 100+ assets. $DEBIT is the utility token of Debit AI. It works as AI usage credits. Spend DEBIT to run the agent: strategy execution, portfolio analysis, debt planning, cross chain routing, monitoring, and more. Agents consume credits continuously while running. DEBIT is allocated to those who participated in the protocol. → Users with Teller points, earned by borrowing with crypto-assets as collateral → Participants in the Fortune Teller NFT ecosystem Tokenomics: $DEBIT Debit is not a bank, is not a lender, and is not FDIC insured. Digital assets are volatile and can lose their full value. Autonomous agents can execute at unfavorable prices or fail to execute. Nothing here is financial, investment, legal or tax advice. Debit is not affiliated with or endorsed by Anthropic, OpenAI, Perplexity or Google. Services are not available in all jurisdictions and eligibility restrictions apply. See Terms of Service.

Meet Debit, an AI-powered financial agent, with access to third party credit products and onchain financial services via any AI platform. Debit AI: Debit AI App: Deploy autonomous agents that run through any MCP client, including Claude, ChatGPT, Perplexity and Gemini. Agents execute onchain actions and can run autonomous strategies: trade, swap, bridge, borrow, lend, stake, and more. The Debit Agent runs and executes on the instructions set, within the limits set. "Keep collateral above 200%." "Rotate to the highest-yielding stablecoin on an approved list weekly." "Sell a quarter of a position if it drops x% below entry." Agents can be set up in routines on any preferred AI app, to monitor positions and execute when conditions are met. Even program a trading strategy in plain language. → Spot swaps across ten-plus chains → Borrow & loop collateral tokens → Monitor collateral and rebalance on triggers Define entry, sizing and exit conditions in ChatGPT or Claude routines. The agent executes them onchain. Debit is non custodial. Assets stay under the holder's control at all times. Each agent runs within limits set: assets, venues, position size, chains, daily spend cap. Use Debit AI via the chat interface on the Debit dashboard, or connect Debit's MCP server to the AI assistant already in use. Debit can use crypto assets as collateral: WBTC, WETH, and other supported collateral assets. Stablecoin loans via Teller are time based, with no margin calls. Price moves do not trigger liquidation during the term. At the end of the term, an unrepaid loan is settled from collateral. Debit also helps understand traditional credit options for personal, business and auto lending. Built on Teller, the lending protocol that has processed $80M+ in cumulative loan volume across 100+ assets. $DEBIT is the utility token of Debit AI. It works as AI usage credits. Spend DEBIT to run the agent: strategy execution, portfolio analysis, debt planning, cross chain routing, monitoring, and more. Agents consume credits continuously while running. DEBIT is allocated to those who participated in the protocol. → Users with Teller points, earned by borrowing with crypto-assets as collateral → Participants in the Fortune Teller NFT ecosystem Tokenomics: $DEBIT Debit is not a bank, is not a lender, and is not FDIC insured. Digital assets are volatile and can lose their full value. Autonomous agents can execute at unfavorable prices or fail to execute. Nothing here is financial, investment, legal or tax advice. Debit is not affiliated with or endorsed by Anthropic, OpenAI, Perplexity or Google. Services are not available in all jurisdictions and eligibility restrictions apply. See Terms of Service.

Teller

99,455 次观看 • 27 天前