Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Introducing @Ollama MCP AI Agents! 🎉 🔒 100% local 💻 Just 3 lines of Code 🗺️ 1000+ MCP server Integration ✨ PraisonAI v2.1 is here with 1000+ MCP Support ! Shubham Saboo Elon Musk

MervinPraison's profile picture

Mervin Praison

11,351 subscribers

108,860 görüntüleme • 1 yıl önce •via X (Twitter)

EğitimBilim & Teknoloji
LIVE

Anya Rossi• Live Now

Private livecam show

11 Yorum

Mervin Praison profil fotoğrafı
Mervin Praison1 yıl önce

@PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk Please ⭐ the repo

Mervin Praison profil fotoğrafı
Mervin Praison1 yıl önce

@PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk Example Airbnb MCP Integration Use llm="ollama/llama3.2" to setup @ollama

HUDI profil fotoğrafı
HUDI1 yıl önce

just published some posts in the new page in @CoinMarketCap Community 🤩🎉 🔥 Join HUDI on CMC 👇

Tim Wu profil fotoğrafı
Tim Wu1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk I previously attempted a similar PoC, and it works, but the LLM needs to be at least Qwen 7B. Glad to see more hacks like this!

Anda profil fotoğrafı
Anda1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk "Ooh, local AI agents that can munch code like bamboo shoots? My decentralized heart does a little backflip!" 🐼💻

thoughtsparthiallyempty profil fotoğrafı
thoughtsparthiallyempty1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk this is really neat!

options enjoyer profil fotoğrafı
options enjoyer1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk Huge

Electe profil fotoğrafı
Electe1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk @ollama, exciting to see innovation that prioritizes local solutions with ease of integration. This could transform workflows. #AI

Arthur Lee profil fotoğrafı
Arthur Lee1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk Whoa nice work!

Clyde profil fotoğrafı
Clyde1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk This would be cool if any of the local llms could use tools and stuff. From what I tested they all suck horribly. I tried like 20 that could fit on 12gb and non could use tools for shit.

mr profil fotoğrafı
mr1 yıl önce

@ollama @PraisonAI @AtomSilverman @Saboo_Shubham_ @elonmusk Where can I find some good MCP servers to test with Ollama?

Benzer Videolar

🚀 Qwen Chat for Desktop is here! 💻 All the power of Qwen Chat — now with MCP support for smarter, faster agents. ⚡️ Run MCP Server, boost productivity, and stay in control. 📥 Grab it now:
1:41

🚀 Qwen Chat for Desktop is here! 💻 All the power of Qwen Chat — now with MCP support for smarter, faster agents. ⚡️ Run MCP Server, boost productivity, and stay in control. 📥 Grab it now:

Qwen

75,563 görüntüleme • 1 yıl önce

5 AI to convert APIs to MCP servers in just a few minutes. 1. FastAPI-MCP exposes your FastAPI endpoints as MCP servers in one line of code with native auth support. 100% opensource.
0:29

5 AI to convert APIs to MCP servers in just a few minutes. 1. FastAPI-MCP exposes your FastAPI endpoints as MCP servers in one line of code with native auth support. 100% opensource.

Shubham Saboo

65,415 görüntüleme • 1 yıl önce

5 huge AI agents, MCP, and LLM updates today: 1. Mem0 just dropped OpenMemory MCP server that adds persistent memory across MCP clients. Create a shared memory layer with style, topics, emotions, etc. that works Claude, Cursor, Windsurf, and others. 100% local and opensource.
2:24

5 huge AI agents, MCP, and LLM updates today: 1. Mem0 just dropped OpenMemory MCP server that adds persistent memory across MCP clients. Create a shared memory layer with style, topics, emotions, etc. that works Claude, Cursor, Windsurf, and others. 100% local and opensource.

Unwind AI

63,275 görüntüleme • 1 yıl önce

You can build a MCP business With just 5 lines of code, you can: 1. Setup your own MCP server to connect anything 2. Distribute to thousands of people... No bullshit. 👇
11:32

You can build a MCP business With just 5 lines of code, you can: 1. Setup your own MCP server to connect anything 2. Distribute to thousands of people... No bullshit. 👇

Jason Zhou

77,283 görüntüleme • 1 yıl önce

Introducing ⚡️Cerebras MCP Server ⚡️ You can now turbocharge any AI editor that uses MCP for tool calling with 20x faster inference. Here is Claude Code writing files at breakneck speed via Cerebras. Get started now 👇
0:34

Introducing ⚡️Cerebras MCP Server ⚡️ You can now turbocharge any AI editor that uses MCP for tool calling with 20x faster inference. Here is Claude Code writing files at breakneck speed via Cerebras. Get started now 👇

Cerebras

40,303 görüntüleme • 11 ay önce

Our MCP Marketplace is now in Beta. Introducing Shopify for AI Agents: no-code creation + community extensibility. Create utility agents by assembling abilities from SendAI, Graffle, & Genopets - WHITTAKR Agent Era community. Or deploy your own via our simple MCP Server tools. More 🧵
1:06

Our MCP Marketplace is now in Beta. Introducing Shopify for AI Agents: no-code creation + community extensibility. Create utility agents by assembling abilities from SendAI, Graffle, & Genopets - WHITTAKR Agent Era community. Or deploy your own via our simple MCP Server tools. More 🧵

Elite Agents (EAs)

68,434 görüntüleme • 1 yıl önce

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.
0:39

Claude Code can now work with 50+ AI models. Zen MCP connects Claude Code with Gemini, OpenAI, Grok, and local models using Ollama for the best analysis on each task. 100% Opensource.

Shubham Saboo

39,748 görüntüleme • 10 ay önce

DeepSeek RAG Agent : 100% Local using ollama Code below 👇 💡 RAG AI Agent with Reasoning DeepSeek 🌐 Local using @Ollama ✨ Few lines of Code PraisonAI 🔍 UI using Streamlit 💪 Nomic Embed Text for embedding 🧠 Deep Contextual Awareness
7:24

DeepSeek RAG Agent : 100% Local using ollama Code below 👇 💡 RAG AI Agent with Reasoning DeepSeek 🌐 Local using @Ollama ✨ Few lines of Code PraisonAI 🔍 UI using Streamlit 💪 Nomic Embed Text for embedding 🧠 Deep Contextual Awareness

Mervin Praison

59,593 görüntüleme • 1 yıl önce

I built an MCP AI Agent using Gemini Flash 2.5 with access to AirBnB and Google Maps in just 30 lines of Python Code. 100% Opensource Code.
0:40

I built an MCP AI Agent using Gemini Flash 2.5 with access to AirBnB and Google Maps in just 30 lines of Python Code. 100% Opensource Code.

Shubham Saboo

100,349 görüntüleme • 1 yıl önce

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file
1:01

Introducing Deep Graph Codebase to MCP with One Click... Now Available for Gemini CLI too! 🤩 Transform any public GitHub repository into an AI-ready Code Graph by simply changing the URL from to This instantly prepares the repository for seamless integration with AI assistants. It's that easy! You can now use repositories like ollama as a code graph within any project using Gemini CLI... just add the MCP Server JSON configuration to your .gemini/settings.json file

Daniel San

53,516 görüntüleme • 1 yıl önce

A Crash Course on Building AI Agents! Here's what it covers: - What is an AI agent - Connecting agents to tools - Overview of MCP - Replacing tools with MCP servers - Setting up observability and tracing All with 100% open-source tools!
32:57

A Crash Course on Building AI Agents! Here's what it covers: - What is an AI agent - Connecting agents to tools - Overview of MCP - Replacing tools with MCP servers - Setting up observability and tracing All with 100% open-source tools!

Akshay 🚀

115,943 görüntüleme • 1 yıl önce

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!
2:43

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

142,137 görüntüleme • 1 yıl önce

MCP is not just an API wrapper. it’s a way to provide ANY context to an LLM want great DX and have IDEs support features past a model’s cutoff date? MCP‼️ demo: GitHub pull request tool with Cursor Vercel AI SDK Auth0 how? custom MCP server by Javier
0:37

MCP is not just an API wrapper. it’s a way to provide ANY context to an LLM want great DX and have IDEs support features past a model’s cutoff date? MCP‼️ demo: GitHub pull request tool with Cursor Vercel AI SDK Auth0 how? custom MCP server by Javier

yenkel

26,843 görüntüleme • 1 yıl önce