Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

We've released AI support for Semantic MediaWiki! Connect Claude, ChatGPT & other AI tools to your SMW via our open source MCP server. Let them run queries or even edit on your behalf. A game changer for Semantic MediaWiki, removing friction and easing adoption. Links below 👇

ProWikis's profile picture

Professional Wiki

3,773 subscribers

128,219 görüntüleme • 2 ay önce •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

holy sht.. DaVinci Resolve just all-in on AI you can now connect claude or chatgpt via mcp and ask it to edit and color grade your footage automatically this is the real AI filmmaking revolution
1:00

holy sht.. DaVinci Resolve just all-in on AI you can now connect claude or chatgpt via mcp and ask it to edit and color grade your footage automatically this is the real AI filmmaking revolution

el.cine

211,273 görüntüleme • 9 gün önce

How to connect Agent Wallet to your AI agent. → MCP: add our MCP server to Claude, sign in with District Pass. → CLI: install the fdx CLI, sign up or log in via email OTP, run in Claude Code. Now your AI agent doesn't just talk. It pays, swaps, and earns. Get Started →
6:03

How to connect Agent Wallet to your AI agent. → MCP: add our MCP server to Claude, sign in with District Pass. → CLI: install the fdx CLI, sign up or log in via email OTP, run in Claude Code. Now your AI agent doesn't just talk. It pays, swaps, and earns. Get Started →

Finance District

18,288 görüntüleme • 3 ay önce

We’re launching Volrix. An MCP server that lets you backtest and research trading ideas directly from Claude and Chatgpt. Connect your AI agent, describe a strategy, and run backtests across index and commodity derivatives. Link in the comments, Try it for free!
0:43

We’re launching Volrix. An MCP server that lets you backtest and research trading ideas directly from Claude and Chatgpt. Connect your AI agent, describe a strategy, and run backtests across index and commodity derivatives. Link in the comments, Try it for free!

Niraj Munot

150,733 görüntüleme • 3 ay 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

142,256 görüntüleme • 1 yıl önce

Today we’re turning your Claude subscription into a full AI app builder. Connect Claude, ChatGPT or Cursor to Zite MCP and ask for an app. It goes live with its own database, logins for your whole team, roles for who sees what. Zero Zite credits.
0:36

Today we’re turning your Claude subscription into a full AI app builder. Connect Claude, ChatGPT or Cursor to Zite MCP and ask for an app. It goes live with its own database, logins for your whole team, roles for who sees what. Zero Zite credits.

Dominic Whyte

653,797 görüntüleme • 14 gün önce

Check out the latest by akshat gada: an MCP server that gives Claude & Cursor AI agents their own crypto wallet This lets your LLM pay for APIs with stablecoins without manual approvals or juggling API keys *Built on x402 + Polygon *Open source Resources below
3:58

Check out the latest by akshat gada: an MCP server that gives Claude & Cursor AI agents their own crypto wallet This lets your LLM pay for APIs with stablecoins without manual approvals or juggling API keys *Built on x402 + Polygon *Open source Resources below

Polygon Ecosystem

15,115 görüntüleme • 10 ay önce

AI-native SwiftUI component library with full-stack recipes — connect instantly via MCP. While effects like these are challenging for Opus, Fable, or GPT to generate from scratch, providing the AI with our source code lets it effortlessly recreate and integrate them into your project.
0:29

AI-native SwiftUI component library with full-stack recipes — connect instantly via MCP. While effects like these are challenging for Opus, Fable, or GPT to generate from scratch, providing the AI with our source code lets it effortlessly recreate and integrate them into your project.

itshan

20,020 görüntüleme • 2 ay önce

Your AI can now publish all your social media posts. Introducing the Social0 MCP Server. Connect Claude, Cursor, OpenClaw, Hermes, or any MCP - compatible AI assistant and simply ask: Would love your feedback ❤️ Resources down below👇 Example usecases : • "Publish this post to LinkedIn." • "Schedule this thread for tomorrow at 9 AM." • "Show my scheduled posts." No dashboard required. Just natural language.
0:32

Your AI can now publish all your social media posts. Introducing the Social0 MCP Server. Connect Claude, Cursor, OpenClaw, Hermes, or any MCP - compatible AI assistant and simply ask: Would love your feedback ❤️ Resources down below👇 Example usecases : • "Publish this post to LinkedIn." • "Schedule this thread for tomorrow at 9 AM." • "Show my scheduled posts." No dashboard required. Just natural language.

Abhishek B R

49,601 görüntüleme • 2 ay önce

Microsoft has released a new open source AI model Phi-4-mini is surprisingly powerful for its size (3.8B!) You can use it for free locally on your Windows, MacOS or Linux laptop. Links and tutorial below
0:39

Microsoft has released a new open source AI model Phi-4-mini is surprisingly powerful for its size (3.8B!) You can use it for free locally on your Windows, MacOS or Linux laptop. Links and tutorial below

Paul Couvert

209,124 görüntüleme • 1 yıl önce

New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed!
1:32

New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed!

Andrew Ng

62,497 görüntüleme • 10 ay önce

English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.
3:27

English is the most powerful language in the world. MongoDB just added support for MCP, and literally anyone can now query data without knowing anything about databases. If you know English, you are all set. I recorded a quick video to show you how I connect to a sample Airbnb database and run different queries: • How many listings? • How many listings in the US? • What's the list of countries in these listings? It takes 10 seconds to configure Claude, Cursor, WindSurf, or any other AI assistant to connect to MongoDB's MCP Server. By the way, the server supports much more than queries: • You can explore your collections • You can fetch schema information • You can generate app code snippets • You can manage your databases • You can handle cluster/user/admin tasks Here is a link to the MCP server GitHub repository: This is huge for developers. It's even bigger for non-developers. Thanks to the MongoDB team for collaborating with me on this post.

Santiago

44,855 görüntüleme • 1 yıl önce

What even is OpenBot? It’s a free, open-source Grok Bot alternative that runs locally on your machine. Create AI teammates, give them jobs, and let them work for you.
0:46

What even is OpenBot? It’s a free, open-source Grok Bot alternative that runs locally on your machine. Create AI teammates, give them jobs, and let them work for you.

OpenBot

269,585 görüntüleme • 21 gün önce

Agentic Trading is live for all customers. Connect any AI agent through the Robinhood MCP server, fund a dedicated agentic account, and let it research, trade, and rebalance on your terms. Watch our VP of Product, Abhishek Fatehpuria, walk you through it 👇
6:16

Agentic Trading is live for all customers. Connect any AI agent through the Robinhood MCP server, fund a dedicated agentic account, and let it research, trade, and rebalance on your terms. Watch our VP of Product, Abhishek Fatehpuria, walk you through it 👇

Robinhood

4,505,400 görüntüleme • 3 ay önce

we built this thing called Palmier > it's like a video editor but for ai and open source > generate ai footage using leading models, mix it with your real footage, and connect your Claude to edit videos for you > we used it to make 15+ cinematic yc launch videos > it's free to try today, no waitlist
3:09

we built this thing called Palmier > it's like a video editor but for ai and open source > generate ai footage using leading models, mix it with your real footage, and connect your Claude to edit videos for you > we used it to make 15+ cinematic yc launch videos > it's free to try today, no waitlist

Marcos Rico Peng

16,614 görüntüleme • 3 ay önce

ComfyUI MCP server - A project from Pixelle AI Shoutout to Pixelle-MCP - an amazing open-source project that bridges ComfyUI with LLMs through the MCP protocol. Zero-code, talk to your LLM to iterate ComfyUI workflow! Highlights: - Full TISV (Text, Image, Sound/Speech, Video) support - Flexible deployment options - Easy setup with Docker or one-click scripts - Custom workflow tools in just a few steps 🧵👇
1:00

ComfyUI MCP server - A project from Pixelle AI Shoutout to Pixelle-MCP - an amazing open-source project that bridges ComfyUI with LLMs through the MCP protocol. Zero-code, talk to your LLM to iterate ComfyUI workflow! Highlights: - Full TISV (Text, Image, Sound/Speech, Video) support - Flexible deployment options - Easy setup with Docker or one-click scripts - Custom workflow tools in just a few steps 🧵👇

ComfyUI

27,949 görüntüleme • 1 yıl önce

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X
0:42

X just made itself plug straight into AI tools with MCP servers. Your agents can now search and post on X directly. Not only read about it. Grok Build, Cursor, Claude, VS Code. All in. What you get: * X MCP; search posts, trends, bookmarks, even publish Articles * Docs MCP; API docs inside your workflow, no tab switching Setup is straightforward: * Go to create an X app * Enable OAuth 2.0; required for authentication * Add redirect URI; * Copy your CLIENT_ID and CLIENT_SECRET * Plug those into your MCP client or config * Run the setup commands; connect X MCP and Docs MCP * Log in once via browser; approve access * Verify; run grok mcp doctor xapi and grok mcp list Log in once. Done. Your workflow now includes X itself. X

Mario Nawfal

59,976 görüntüleme • 2 ay önce

🚨 SOMEONE JUST TURNED AN LLM INTO AN AUTONOMOUS RED TEAM Cyberstrike is an open source AI security agent built to automate penetration testing from your terminal. Plug in your Claude, GPT, or other LLM subscription and you get: → 13+ specialized security agents → 7,600+ security skills → 120+ OWASP test cases → 150+ AI providers → 5,300+ models → 56+ built in tools → 176+ MCP tools Instead of one AI trying to do everything, it splits security work across specialized agents. This is where AI powered pentesting gets seriously interesting. Use it only on systems you own or have explicit permission to test. REPO BELOW
0:35

🚨 SOMEONE JUST TURNED AN LLM INTO AN AUTONOMOUS RED TEAM Cyberstrike is an open source AI security agent built to automate penetration testing from your terminal. Plug in your Claude, GPT, or other LLM subscription and you get: → 13+ specialized security agents → 7,600+ security skills → 120+ OWASP test cases → 150+ AI providers → 5,300+ models → 56+ built in tools → 176+ MCP tools Instead of one AI trying to do everything, it splits security work across specialized agents. This is where AI powered pentesting gets seriously interesting. Use it only on systems you own or have explicit permission to test. REPO BELOW

MAX

17,029 görüntüleme • 13 gün önce