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Announcing Databutton MCP – Vibe code tools for your AI assistant. (Resources in replies) Why did we build Databutton MCP? The Databutton AI agent can build full-stack applications with React front ends and Python back ends. This means you can create fully tailored tools both for people (UIs) and...

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Databutton

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42,531 次观看 • 1 年前 •via X (Twitter)

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MightyBot1 年前

🧠 Unified Search. Smarter Meetings. Effortless CRM. MightyBot is your AI agent platform for seamless workflows—record meetings, automate CRM updates, and find answers across apps in seconds. 🌟 Focus on what matters. We'll handle the grind.

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Announcing Great AI Agents, need great tools. Now, you can vibecode MCP-ready tools to power any AI agent.
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Announcing Great AI Agents, need great tools. Now, you can vibecode MCP-ready tools to power any AI agent.

BuildShip

119,697 次观看 • 1 年前

In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:
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In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:

DeepLearning.AI

23,829 次观看 • 1 年前

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!
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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!

Andrew Ng

141,930 次观看 • 1 年前

AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.
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AI Agents can now connect to 100,000+ APIs instantly. Postman's AI Agent builder can turn any API into an MCP server in seconds without writing a single line of Code. You can use it in Claude, Cursor, Windsurf and other AI tools.

Shubham Saboo

79,628 次观看 • 1 年前

🔥🔥 Introducing BuildKit 2.0 🔥🔥 The shadcn for AI tools.→ Build AI tools + MCPs in minutes → Customize with a single prompt → Works with any SDK - AI SDK, , #mcp Stop waiting for integrations. Start creating them. 🚀 Out now.
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🔥🔥 Introducing BuildKit 2.0 🔥🔥 The shadcn for AI tools.→ Build AI tools + MCPs in minutes → Customize with a single prompt → Works with any SDK - AI SDK, , #mcp Stop waiting for integrations. Start creating them. 🚀 Out now.

Moe

14,587 次观看 • 9 个月前

ANNOUNCING: Uno Platform Studio 2.0 - with AI! ✅MCP ✅App MCP ✅Hot Design Agent (Preview) - NO CREDIT LIMITS Be in control and build cross-platform .NET applications with Code + Designer + Agent. #dotNETConf #AI
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ANNOUNCING: Uno Platform Studio 2.0 - with AI! ✅MCP ✅App MCP ✅Hot Design Agent (Preview) - NO CREDIT LIMITS Be in control and build cross-platform .NET applications with Code + Designer + Agent. #dotNETConf #AI

Uno Platform

34,959 次观看 • 6 个月前

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Akshay 🚀

115,825 次观看 • 10 个月前

5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.
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5 huge AI agents, MCP, and LLM updates today: 1. China just dropped an opensource Deep Research multi-agent framework with MCP support. It connects AI Agents with specialized tools for tasks like web search, crawling, and Python code execution. 100% opensource.

Unwind AI

79,540 次观看 • 1 年前

Excited to announce my new short course: Building Agentic Applications with Replit Agent and n8n. With AI this capable, I believe anyone can become a builder. The stack I use here will teach you how to rapidly build agentic apps with no-code tools.
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elvis

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Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!

Bindu Reddy

13,598 次观看 • 1 年前

Satya Nadella says we’ve entered the era of autonomous AI agents. "You know intelligence has been commoditized when CEOs can start vibe coding." From agent factories to MCP support, code review bots, and multi-agent systems, Microsoft is turning dev tools into a full AI platform.
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Satya Nadella says we’ve entered the era of autonomous AI agents. "You know intelligence has been commoditized when CEOs can start vibe coding." From agent factories to MCP support, code review bots, and multi-agent systems, Microsoft is turning dev tools into a full AI platform.

vitrupo

25,899 次观看 • 1 年前

Grammarly + Coda: A Game-Changing Merger @jason & alex 🏴‍☠️🇺🇸🇺🇦 sit down with Coda's Shishir to discuss the merger with Grammarly and their shared vision for the future. Why Merge? Initially, the companies seemed different, but after sharing vision memos, it became clear they were headed toward the same goal: a user-centered AI platform combining applications and agents. Key Vision: AI Superhighway • Grammarly: Known for its AI agent helping 40M+ daily users improve writing across 500K+ apps. • Coda: Renowned for creating workflows, integrations, and tools to supercharge productivity. • Together: They aim to extend Grammarly’s AI agent to provide contextual insights while leveraging Coda’s integrations for dynamic workflows. The merger aims to redefine productivity by creating a home for AI applications and enhancing agents to assist users seamlessly across tools. How do you see this merger shaping the future of AI-powered productivity tools?
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Grammarly + Coda: A Game-Changing Merger @jason & alex 🏴‍☠️🇺🇸🇺🇦 sit down with Coda's Shishir to discuss the merger with Grammarly and their shared vision for the future. Why Merge? Initially, the companies seemed different, but after sharing vision memos, it became clear they were headed toward the same goal: a user-centered AI platform combining applications and agents. Key Vision: AI Superhighway • Grammarly: Known for its AI agent helping 40M+ daily users improve writing across 500K+ apps. • Coda: Renowned for creating workflows, integrations, and tools to supercharge productivity. • Together: They aim to extend Grammarly’s AI agent to provide contextual insights while leveraging Coda’s integrations for dynamic workflows. The merger aims to redefine productivity by creating a home for AI applications and enhancing agents to assist users seamlessly across tools. How do you see this merger shaping the future of AI-powered productivity tools?

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17,169 次观看 • 1 年前

Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to Comet's Opik library Here is the video, free for you to watch.
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Build your first AI agent + MCP Server in Python. Here is everything you need to build your first AI agent in less than 20 minutes. About the code you'll see here: 1. I used Google ADK with Gemini Flash to power the agent 2. The agent connects to an MCP server 3. It also uses two custom tools to do its work 4. You can see everything the agent does thanks to Comet's Opik library Here is the video, free for you to watch.

Santiago

56,549 次观看 • 1 年前

Our co-founder Pratyush Kumar highlighted Sarvam's "contrarian bet" of being a full-stack AI platform. That means we build every layer: the models, how they run (inference), how agents work with each other (orchestration), and the AI applications people use. This lets us build AI that actually works for India's unique context and diverse language needs.
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Our co-founder Pratyush Kumar highlighted Sarvam's "contrarian bet" of being a full-stack AI platform. That means we build every layer: the models, how they run (inference), how agents work with each other (orchestration), and the AI applications people use. This lets us build AI that actually works for India's unique context and diverse language needs.

Sarvam AI

16,310 次观看 • 5 个月前

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
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New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

80,860 次观看 • 8 个月前

📣 Today, I’m excited to walk you through Unity’s NEW AI offering, Meta MCP Extensions, and agentic tools to demonstrate how we can 𝗕𝘂𝗶𝗹𝗱 𝗔 𝗙𝘂𝗹𝗹 𝗩𝗥/𝗩𝗥 𝗚𝗮𝗺𝗲 from start to finish using these AI tools in a practical way. 🎥 Full video available at: 📌 Here’s what I’m covering today: - Unity VR project setup (with OpenXR plugins) - Installing the Unity AI Assistant and demos - Configuring Unity MCP + Meta MCP Extensions - Configuring Claude Code & MCPs - Building a VR/MR Basketball Game with the Unity AI Assistant, Claude Agent, and external Claude Code CLI - A lot of iteration with Claude Code + the Meta XR Simulator 💡Also, it’s been a while since I've posted a new video, and I’m genuinely excited to be back, especially with a topic like this that I know many devs have been waiting for.
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📣 Today, I’m excited to walk you through Unity’s NEW AI offering, Meta MCP Extensions, and agentic tools to demonstrate how we can 𝗕𝘂𝗶𝗹𝗱 𝗔 𝗙𝘂𝗹𝗹 𝗩𝗥/𝗩𝗥 𝗚𝗮𝗺𝗲 from start to finish using these AI tools in a practical way. 🎥 Full video available at: 📌 Here’s what I’m covering today: - Unity VR project setup (with OpenXR plugins) - Installing the Unity AI Assistant and demos - Configuring Unity MCP + Meta MCP Extensions - Configuring Claude Code & MCPs - Building a VR/MR Basketball Game with the Unity AI Assistant, Claude Agent, and external Claude Code CLI - A lot of iteration with Claude Code + the Meta XR Simulator 💡Also, it’s been a while since I've posted a new video, and I’m genuinely excited to be back, especially with a topic like this that I know many devs have been waiting for.

Dilmer 🔜 AWE

24,521 次观看 • 1 个月前

AI is here, but the tools are still missing. That’s your opportunity. AI agents need web data extractors, automation workflows, MCP servers, and API wrappers. Build them. Publish on Apify. $1M Challenge prize pool. 🌄 Start building →
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AI is here, but the tools are still missing. That’s your opportunity. AI agents need web data extractors, automation workflows, MCP servers, and API wrappers. Build them. Publish on Apify. $1M Challenge prize pool. 🌄 Start building →

Apify

14,071 次观看 • 7 个月前

Explore the dart_mcp package, a powerful framework for building Model Context Protocol (MCP) servers and clients in Dart, enabling you to connect AI models to resources, tools, and create custom workflows.
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Explore the dart_mcp package, a powerful framework for building Model Context Protocol (MCP) servers and clients in Dart, enabling you to connect AI models to resources, tools, and create custom workflows.

Flutter

23,056 次观看 • 1 个月前

Databutton Fullstack AI Agents are INSANE (FREE!) 🤯
12:48

Databutton Fullstack AI Agents are INSANE (FREE!) 🤯

Julian Goldie SEO

19,912 次观看 • 1 年前

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.
4:10

Building AI agents and tools is getting ridiculously easy! Here is a pretty impressive FREE and Open-Source AI agent builder that runs on your computer. It's a desktop app you can install on Mac or Windows. Link: • Free • 100% no-code: You can type what you want, and the tool will build everything for you. • You can use local models like DeepSeek or Qwen, or connect to cloud models like GPT-4o, Claude, or Gemini with your API key. • You can turn the tools you build into MCP Servers. I recorded a quick video to show you how easy it is to build something from scratch.

Santiago

56,815 次观看 • 1 年前