Загрузка видео...

Не удалось загрузить видео

На главную

MCP clearly has demand, But monetizing it has been tricky. I figured out the easiest way to build & monetize a Paid MCP server: 1. mcp-remote package 2. Stripe agent tool kit 3. Cloudflare mcp auth 4. Helicone to track LLM usage I've made a step by step video...

42,728 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля Saurabh Patel
Saurabh Patel1 год назад

@stripe @Cloudflare @helicone_ai Use the to bring mcp to chatgpt, gemini, google ai studio and many more directly in Browser. Check it out: This is insane 🔥🔥

Фото профиля Saïd Aitmbarek
Saïd Aitmbarek1 год назад

@stripe @Cloudflare @helicone_ai wow really cool, looking for ways to monetize mine for @microlaunchhq will have a look at AI builders club, we should launch you on Microlaunch mate

Фото профиля Arindam Majumder 𝕏
Arindam Majumder 𝕏1 год назад

@stripe @Cloudflare @helicone_ai Interesting

Фото профиля C12s
C12s1 год назад

@stripe @Cloudflare @helicone_ai Pretty dope, but I'd pick @langfuse for the traces/tracking of the LLM's!

Фото профиля Outcydaz
Outcydaz1 год назад

@stripe @Cloudflare @helicone_ai Dope!

Фото профиля Calvin Mercer
Calvin Mercer1 год назад

@stripe @Cloudflare @helicone_ai My man Jason 💪 ❤️

Фото профиля UK Apollo Group
UK Apollo Group1 год назад

@stripe @Cloudflare @helicone_ai Time to turn those MCP dreams into dollar signs 💸

Фото профиля achraf cheham
achraf cheham1 год назад

@stripe @Cloudflare @helicone_ai May the crypto gods be ever in your favor 🙌

Фото профиля Dharmik Jagodana
Dharmik Jagodana1 год назад

@stripe @Cloudflare @helicone_ai I’m also adding @Imejis_io to the list. Let’s see what happens; maybe I can monetise it too.

Фото профиля josh
josh1 год назад

@stripe @Cloudflare @helicone_ai Don't forget the secret ingredient: unicorn magic! 🦄💫

Похожие видео

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 просмотров • 1 год назад