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Wazabi AI MCP now supports Openτensor Foundaτion subnet creation (beta). Quote burn cost → prepare subnet → sign with your wallet → live on-chain. All from Claude Cursor or any MCP-compatible AI IDE. No CLI scripts. Inviting the Bittensor community to test it out and give us feedback. Mainnet...

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Wazabi AI

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11,227 Aufrufe • vor 4 Monaten •via X (Twitter)

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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!
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Connect Claude, ChatGPT, Cursor, Perplexity, or Codex to Caffeine. The Caffeine MCP server is live. One URL. One browser sign-in. Describe what you want built from inside your AI tool - it sends the prompt straight to Caffeine. Get updates on what's being built without leaving your conversation. When it's done, ask your AI to review it. It reads the Caffeine conversation directly, so it has full context on every decision.

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We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

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Live from EthCC - Ethereum Community Conference: Introducing Cytonic AI! Build complete on-chain projects with AI agents - no coding or dev team required. From smart contracts to frontend, our agents do it all. You bring the vision. Cytonic AI builds it. Sign up for early access →

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Today we're launching Amplitude MCP MCP was made for data analytics. Ask a vague question and have an AI model iteratively query Amplitude to find you insights. Amplitude MCP exposes all of Amplitude's functionality so an agent can interact with it directly. It allows you to use Amplitude without having to know anything about Amplitude's user interface or your data taxonomy. It uses the intelligence of off the shelf AI agents, including Claude, Gemini, and Cursor, to query Amplitude data you already have to find answers. It leverages all of your existing Amplitude data and how it's used to navigate through large complex behavioral datasets. You can have conversations about your product data directly inside any AI model with MCP. It can create Amplitude dashboards and notebooks with what it finds and even make recommendations. This is the best MCP use case I've ever seen. Watching an AI agent think, reason, query Amplitude, and then repeat that process iteratively is magical. See it in action:

Spenser Skates

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three.ws

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Rohit Ghumare

32,176 Aufrufe • vor 1 Jahr

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You want to analyze any TikTok account live & in seconds with AI? We just built it. introducing the [viral. app] TikTok MCP server now, you can - get all account details of any account - see all public video statistics - create comparison tables - run engagement analysis - and much more! This is the most powerful TikTok AI on the market and you know what? We'll give out access for everyone interested for free! 🤯 Just comment "MCP" and I'll dm you the access guide!

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28,676 Aufrufe • vor 1 Jahr

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Santiago

215,882 Aufrufe • vor 1 Jahr

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I handed Claude a creator contract clause and asked for a vetted human expert to check it......without leaving the chat. Tendem now plugs into Claude, ChatGPT, Cursor and Codex via MCP. You mention Tendem, approve the quote, and the work comes back in the same conversation. The AI caught the obvious risks....uncapped liability, no kill fee, payment a client could withhold on a whim. Then a real expert does a second pass on the same clauses. AI scopes it. A human backs it. No new tab, no copy-paste. Toloka Tendem AI Live today →

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28,233 Aufrufe • vor 3 Monaten