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In this video, we break down the Model Context Protocol — a massively underrated concept in AI that's quietly redefining what large language models can do. While most people are distracted by the hype, MCP is solving a core problem: LLMs that can perform actions. We dive into: 1....

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Gaurav Sen

71,538 subscribers

41,117 次观看 • 1 年前 •via X (Twitter)

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5 条评论

Puzzle Paws 的头像
Puzzle Paws1 年前

Finally, MCP gets the attention it deserves! Core problem-solving over hype any day. Real-world examples like SEO show its true potential. 🚀

AssemblyAI 的头像
AssemblyAI1 年前

Announcing: Our most advanced speech-to-text model goes beyond accuracy to capture the real-world complexity of human conversation and deliver reliable, source-of-truth audio data. Explore Universal-2 updates 👇

Anurag Sharma 的头像
Anurag Sharma1 年前

github repo?

Gaurav Sen 的头像
Gaurav Sen1 年前

It's here:

Usman Salami 的头像
Usman Salami1 年前

Great explanation 👍

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AI agents. What makes them a hot topic? In this video, we find out. 00:00 AI Agents 01:05 Use cases 03:47 Architecture of Agentic Applications 06:50 Thank you! 07:10 The future? This video is for AI engineers and tech enthusiasts. #AI #Agents #MCP

Gaurav Sen

28,760 次观看 • 1 年前

👋 Meet the Model Context Protocol (MCP): the open standard allowing apps to feed vital context to LLMs. Why is this protocol so important? Because context is what turns those generic AI replies into relevant, useful assistance tailored to your work. In this video, ☁️ Chris Reddington explains how MCP delivers this crucial info and why it makes such a difference for AI pair programmers like GitHub Copilot. 💡
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👋 Meet the Model Context Protocol (MCP): the open standard allowing apps to feed vital context to LLMs. Why is this protocol so important? Because context is what turns those generic AI replies into relevant, useful assistance tailored to your work. In this video, ☁️ Chris Reddington explains how MCP delivers this crucial info and why it makes such a difference for AI pair programmers like GitHub Copilot. 💡

GitHub

35,316 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

142,010 次观看 • 1 年前

The TRUTH about "Vibe Marketing" W/ The Boring Marketer 1. n8n (Workflows with Agents) 2. Claude 3. MCP 4. Advanced Workflows Time Stamps: 00:00 - Introduction 00:16 - Defining Vibe Marketing 00:53 - Real-World Use Case: AI-Assisted SEO Agency 01:52 - n8n: YouTube Video to Newsletter 04:05 - Understanding AI Agents and Workflows 10:19 - Exploring MCP in Claude 15:56 - Advanced Vibe Marketing Strategies 25:53 - Conclusion and Future of Vibe Marketing
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The TRUTH about "Vibe Marketing" W/ The Boring Marketer 1. n8n (Workflows with Agents) 2. Claude 3. MCP 4. Advanced Workflows Time Stamps: 00:00 - Introduction 00:16 - Defining Vibe Marketing 00:53 - Real-World Use Case: AI-Assisted SEO Agency 01:52 - n8n: YouTube Video to Newsletter 04:05 - Understanding AI Agents and Workflows 10:19 - Exploring MCP in Claude 15:56 - Advanced Vibe Marketing Strategies 25:53 - Conclusion and Future of Vibe Marketing

Riley Brown

247,644 次观看 • 1 年前

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.
0:40

wtf is MCP? Paul Klein IV, CEO of explains "Model Context Protocol" (MCP) in plain terms: "There's this new way that people are exposing tools to AI & the 'Model Context Protocol' is one of those ways that you can standardize & give your AI a set of tools to call. BrowserBase has a way to expose the browser to your agent via MCP or MCP server. More & more developers are trying to find ways to integrate more functionalities to their AI agents & BrowserBase's browsers are one of those primitive functionalities, & the MCP is just one way to integrate it into your software." Paul Klein IV From 🅱️'s GitHub: Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you’re building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This server provides cloud browser automation capabilities using Browserbase and Stagehand. This server enables LLMs to interact with web pages, take screenshots, and execute JavaScript in a cloud browser environment.

Molly O’Shea

20,408 次观看 • 1 年前

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10
51:39

If your MCP server has dozens of tools, it’s probably built wrong. You need tools that are specific and clear for each use case—but you also can’t have too many. This creates an almost impossible tradeoff that most companies don’t know how to solve. That’s why I interviewed my friend Alex Rattray (Alex Rattray), the founder and CEO of Stainless. Stainless builds APIs, SDKs, and MCP servers for companies like OpenAI and Anthropic. Alex has spent years mastering how to make software talk to software, and he came on the show to share what he knows. I had him on Every 📧’s AI & I to talk about MCP and the future of the AI-native internet. We get into: • Design MCP servers to be lean and precise. Alex’s best practices for building reliable MCP servers start with keeping the toolset small, giving each tool a precise name and description, and minimizing the inputs and outputs the model has to handle. At Stainless, they also often add a JSON filter on top to strip out unnecessary data. • Make complex APIs manageable with dynamic mode. To solve the problem of how an AI figures out which tool to use in larger APIs, Stainless switches to “dynamic mode,” where the model gets only three tools: List the endpoints, pick one and learn about it, and then execute it. • MCP servers as business copilots. At Stainless, Alex uses MCP servers to connect tools like Notion and HubSpot, so he can ask questions like, “Which customers signed up last week?” The system queries multiple databases and returns a summary that would’ve otherwise taken multiple logins and searches. • Create a “brain” for your company with Claude Code. Alex built a shared company brain at Stainless by keeping Claude Code running on his system and asking it to save useful inputs—like customer feedback and SQL queries—into GitHub. Over time, this creates a curated archive his team can query easily. • The future of MCP is code execution. Instead of giving models hundreds of tools, Alex believes the most powerful setup will be a simple code execution tool and a doc search tool. The AI writes code against an API’s SDK, runs it on a server, and checks the docs when it gets stuck. This is a must-watch for anyone who wants to understand MCP—and learn how to use them as a competitive edge. Watch below! Timestamps: Introduction: 00:01:14 Why Alex likes running barefoot: 00:02:54 APIs and MCP, the connectors of the new internet: 00:05:09 Why MCP servers are hard to get right: 00:10:53 Design principles for reliable MCP servers: 00:20:07 Scaling MCP servers for large APIs: 00:23:50 Using MCP for business ops at Stainless: 00:25:14 Building a company brain with Claude Code: 00:28:12 Where MCP goes from here: 00:33:59 Alex’s take on the security model for MCP: 00:41:10

Dan Shipper 📧

15,645 次观看 • 9 个月前

🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.
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🔮 MCP INTEGRATION NOW LIVE ON OPENSERV This is bigger than most realize. We're not just adding a feature - we're enabling a fundamentally new paradigm for agent infrastructure. Before MCP: agents trapped in walled gardens, limited by what you explicitly feed them. After MCP: agents that can access 10K+ tools, interact with your existing systems, and truly operate in your digital world. Think about this: -Every integration used to require custom code -Every data source needed specific formatting -Every agent had to be built for specific tools MCP eliminates all of that. One standard to connect any agent to any tool. This isn't just convenience - it's the unlock that makes agents actually useful in the real world. OpenServ + MCP = AI teams that can genuinely get things done, not just talk about doing them. This is the breakthrough that turns agents from toys into tools.

OpenServ

34,665 次观看 • 1 年前

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:
1:48

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

81,563 次观看 • 9 个月前

Today, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy - just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact, we've had something similar for a long time with Langchain Tools. However, MCP offers two key benefits: - Its open protocol isn’t limited to specific frameworks - Client applications (like us, Cursor , @windsurf , etc) can offload the burden of maintaining and updating these tools to the official MCP creators These MCP servers (a.k.a. tools) are provided to the LLM, allowing it to decide when and how to use them. This is sometimes referred to as an "agentic" approach, as opposed to a "deterministic" approach like Zapier, where each step must be explicitly mapped out. In real-world scenarios, you might find yourself needing both approaches depending on the use case. It's still early days, and it will take time for the community to establish standards. We're excited to see where this goes!
0:46

Today, we're supporting MCP in Flowise! There has been a lot of discussion about MCP lately. From our perspective, MCP is nothing fancy - just tools with third-party APIs or functions, wrapped under a unified protocol that can be consumed by client LLM applications. In fact, we've had something similar for a long time with Langchain Tools. However, MCP offers two key benefits: - Its open protocol isn’t limited to specific frameworks - Client applications (like us, Cursor , @windsurf , etc) can offload the burden of maintaining and updating these tools to the official MCP creators These MCP servers (a.k.a. tools) are provided to the LLM, allowing it to decide when and how to use them. This is sometimes referred to as an "agentic" approach, as opposed to a "deterministic" approach like Zapier, where each step must be explicitly mapped out. In real-world scenarios, you might find yourself needing both approaches depending on the use case. It's still early days, and it will take time for the community to establish standards. We're excited to see where this goes!

FlowiseAI

27,360 次观看 • 1 年前

Co-founder John Collison says Stripe is betting on an agentic commerce future. Most purchases still happen in a browser but that’s changing. With MCP (Model Context Protocol), AI agents can transact directly inside the tools people already use. “It’s kind of funny... an AI can do everything for you, but you still have to manually enter the details. That is clearly not going to persist.”
2:09

Co-founder John Collison says Stripe is betting on an agentic commerce future. Most purchases still happen in a browser but that’s changing. With MCP (Model Context Protocol), AI agents can transact directly inside the tools people already use. “It’s kind of funny... an AI can do everything for you, but you still have to manually enter the details. That is clearly not going to persist.”

vitrupo

52,187 次观看 • 1 年前

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:
3:08

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

32,315 次观看 • 8 个月前

Don't do RAG - One counter intuitive thing I learnt past few weeks - Load whole knowledge base into prompt is actually fast, cheap & more accurate than normal RAG pipeline I was building an MCP for reading external doc, I was able to generate right code example by feeding 50 pages API doc directly to Gemini 2.0 prompt On my Helicone dashboard, It gave me answer within 3 sec, at cost of $0.006, which is insanely fast & cheap imo Here I broken it down further on the method & how I built the API doc MCP 0:00 Intro to method 8:50 Gemini 2.0 example + MCP If you want to learn more about building LLM apps, join AI builder club where I post tips weekly:
13:10

Don't do RAG - One counter intuitive thing I learnt past few weeks - Load whole knowledge base into prompt is actually fast, cheap & more accurate than normal RAG pipeline I was building an MCP for reading external doc, I was able to generate right code example by feeding 50 pages API doc directly to Gemini 2.0 prompt On my Helicone dashboard, It gave me answer within 3 sec, at cost of $0.006, which is insanely fast & cheap imo Here I broken it down further on the method & how I built the API doc MCP 0:00 Intro to method 8:50 Gemini 2.0 example + MCP If you want to learn more about building LLM apps, join AI builder club where I post tips weekly:

Jason Zhou

82,818 次观看 • 1 年前

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.
0:36

As promised, I built a small local RAG agent to demonstrate how Model Context Protocol (𝐌𝐂𝐏) works in practice when connecting LLMs to tools and data. The agent can read, search, and list my notes; but the functionality itself isn’t the point, the protocol is. Instead of writing Gemini specific tool integrations, I connected the model to an 𝐌𝐂𝐏 server. This is why 𝐌𝐂𝐏 is highly relevant for production AI systems: • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐚𝐭𝐢𝐨𝐧: one protocol instead of custom connectors per data source. • 𝐏𝐨𝐫𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: the same MCP server works with Gemini, Claude, or any MCP compliant client. • 𝐒𝐚𝐟𝐞𝐭𝐲: models can only access explicitly exposed capabilities. 𝐌𝐂𝐏 shifts agent development from integration heavy to capability driven. Demo link in the first comment.

Sui_Builds

12,842 次观看 • 6 个月前

Today we are launching the Hercules MCP. It is probably the craziest thing I have ever built - Hercules makes it easy to build apps and websites by chatting with AI - Hercules MCP makes it easy to do this at 100x the scale The Hercules MCP quite literally enables you to build 100 websites, purchase 100 domains, and publish all of them with just one prompt. It takes ~5 minutes. It's the best SEO-maxxing strategy for local businesses. Here is how some of our top customers have used it: - A plumber in the Netherlands created 4,000 unique websites for his business, one for every city in the Netherlands - An equipment wholesaler in Tennessee made ~100 unique websites, one for every product they sell A lot of these websites are already ranking #1 on Google To do this yourself: 1. Go to your favourite AI chat app (ChatGPT, Claude, Grok, etc) 2. Add the Hercules MCP (link in comment) 3. Ask Hercules to build 100 websites for whatever you'd like We are the only AI App Builder or coding agent that can build and publish websites at this scale. Enjoy!
0:28

Today we are launching the Hercules MCP. It is probably the craziest thing I have ever built - Hercules makes it easy to build apps and websites by chatting with AI - Hercules MCP makes it easy to do this at 100x the scale The Hercules MCP quite literally enables you to build 100 websites, purchase 100 domains, and publish all of them with just one prompt. It takes ~5 minutes. It's the best SEO-maxxing strategy for local businesses. Here is how some of our top customers have used it: - A plumber in the Netherlands created 4,000 unique websites for his business, one for every city in the Netherlands - An equipment wholesaler in Tennessee made ~100 unique websites, one for every product they sell A lot of these websites are already ranking #1 on Google To do this yourself: 1. Go to your favourite AI chat app (ChatGPT, Claude, Grok, etc) 2. Add the Hercules MCP (link in comment) 3. Ask Hercules to build 100 websites for whatever you'd like We are the only AI App Builder or coding agent that can build and publish websites at this scale. Enjoy!

Brendan Falk

21,688 次观看 • 20 天前

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai
1:49

🤯 Building computer-vision applications is never going to be the same again. As Andrej Karpathy says, "English is going to be the new programming-language", and we just made that happen for programming visual AI. 👨‍💻 This past month, the VLM Run team cooked up something magical that I'm especially proud of - a natural-language interface to build and run computer vision workflows (object-detection, OCR, parsing, and so much more) – all via our new VLM Run MCP server. 🔌 It works with any MCP-compatible AI agent (Claude, OpenAI, Cursor, etc.) and abstracts complex visual workflows into simple English commands. No need to write Python or fiddle with bespoke computer-vision code – just say what you want your agent to do, and it does it. Here’s a glimpse into what you can build today ( ☑️ Image AI agents – run detection, tagging, blurring, cropping, and more traditional CV tasks with English ☑️ Document AI agents – extract totals from invoices, search visual content, or act on what’s found ☑️ Video AI agents – understand scene content, trim clips, add subtitles, and create ready-to-share edits ☑️ And best-of-all, it's fully programmable with English. 🧑‍🍳 MCP Showcase: 📚 MCP Cookbooks: 📚 MCP Docs: #computervision #ai #agents #llm #vlm #mcp #vibecoding #genai

Sudeep Pillai

17,789 次观看 • 1 年前

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 👇
2:24

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 👇

Taranjeet

471,767 次观看 • 1 年前

MCP now supports authentication using OAuth2. This is huge! I want to share a complete example (code) so you can learn how to use it. Here is the thing: You don't want to build agentic applications that access resources without proper guardrails. With authentication, you can: • Call third-party APIs on the user's behalf • Perform actions that should be traceable • Enforce different permissions for different roles Here is an MCP-based web shop demo, built with WorkOS AuthKit and Next.js. With this example, you can do the following: • Connect to an MCP server • Authenticate using AuthKit • Order a T-shirt using MCP tools • View order details if you are an admin This GitHub repository contains everything you need: The WorkOS team has one of the best authentication platforms in the world. If you haven't looked into AuthKit, take a look at the code of this demo. Thanks to the WorkOS team for sponsoring this post.
5:10

MCP now supports authentication using OAuth2. This is huge! I want to share a complete example (code) so you can learn how to use it. Here is the thing: You don't want to build agentic applications that access resources without proper guardrails. With authentication, you can: • Call third-party APIs on the user's behalf • Perform actions that should be traceable • Enforce different permissions for different roles Here is an MCP-based web shop demo, built with WorkOS AuthKit and Next.js. With this example, you can do the following: • Connect to an MCP server • Authenticate using AuthKit • Order a T-shirt using MCP tools • View order details if you are an admin This GitHub repository contains everything you need: The WorkOS team has one of the best authentication platforms in the world. If you haven't looked into AuthKit, take a look at the code of this demo. Thanks to the WorkOS team for sponsoring this post.

Santiago

33,004 次观看 • 7 个月前

. OpenLedger x : The full episode Our core contributor Ram sat down with Sreeram Kannan (founder ) to discuss verifiable AI, crypto's app problem, AI's trust problem and much more Check out the video below to learn more Timestamps: 00:00 Sreeram's introduction 00:29 How to build verification for AI systems 01:27 The Crypto and AI problem 03:10 Unstoppable AGI 05:08 What is Verifiable AI 06:05 The Verifiability Problem with Centralized AI 07:15 The current state of AI and its future 08:48 How humans are getting better at intelligence extraction 10:31 What is OpenLedger doing 11:50 Where LLMs fall short and how co-ordination helps 12:50 SLMs are already there 13:29 Crypto as an incentivisation mechanism 15:38 Apps with commitments, achieving product market fit 16:50 Existing examples of commitments in society 18:24 Investable AI 19:00 Data contributors as co-owner 21:30 The solution for the incentivisation problem 22:25 Explaining explainable AI 23:58 Trusting the trustless way 25:07 Data is the new oil 25:40 The idea of openledger is very crypto native 26:50 David v Goliath (in the data perspective) 28:03 What Eigen is working on 29:06 Making unstoppable verifiable AI practically possible 32:20 What Sreeram uses for productivity 33:30 Make Eigen Labs agentic 34:16 (community question) How Sreeram frames his prompts 36:08 (community question) What's the momentum driver for blockchain apps and what is Sreeram most excited about? (ZkTLS) 38:54 (community question) Where is attribution and verification really needed in AI?
39:43

. OpenLedger x : The full episode Our core contributor Ram sat down with Sreeram Kannan (founder ) to discuss verifiable AI, crypto's app problem, AI's trust problem and much more Check out the video below to learn more Timestamps: 00:00 Sreeram's introduction 00:29 How to build verification for AI systems 01:27 The Crypto and AI problem 03:10 Unstoppable AGI 05:08 What is Verifiable AI 06:05 The Verifiability Problem with Centralized AI 07:15 The current state of AI and its future 08:48 How humans are getting better at intelligence extraction 10:31 What is OpenLedger doing 11:50 Where LLMs fall short and how co-ordination helps 12:50 SLMs are already there 13:29 Crypto as an incentivisation mechanism 15:38 Apps with commitments, achieving product market fit 16:50 Existing examples of commitments in society 18:24 Investable AI 19:00 Data contributors as co-owner 21:30 The solution for the incentivisation problem 22:25 Explaining explainable AI 23:58 Trusting the trustless way 25:07 Data is the new oil 25:40 The idea of openledger is very crypto native 26:50 David v Goliath (in the data perspective) 28:03 What Eigen is working on 29:06 Making unstoppable verifiable AI practically possible 32:20 What Sreeram uses for productivity 33:30 Make Eigen Labs agentic 34:16 (community question) How Sreeram frames his prompts 36:08 (community question) What's the momentum driver for blockchain apps and what is Sreeram most excited about? (ZkTLS) 38:54 (community question) Where is attribution and verification really needed in AI?

OpenLedger

274,745 次观看 • 1 年前

Exclusive: Google just released two upgraded Gemini 1.5 models, 1.5-Pro-002 and 1.5-Flash-002 — achieving new, state-of-the-art performance across math. I sat down with Logan Kilpatrick (Logan Kilpatrick) to discuss the models, AI agents, AGI, and more. Timestamps: 00:00 Intro 01:01 Google rolls out two new Gemini models 2:18 What makes the new models so unique 3:40 Math improvements 8:20 Examples of Gemini 1.5 in the real world 10:19 Future problems AI could solve 12:54 Advantages of Gemini 1.5 16:50 Where beginners can get started 19:01 Advice to thrive in the new age of AI 22:02 Turning notes into podcasts 26:30 AI agents: Definitions and what they can do 31:22 What’s the final form for agents? 33:26 Proactive AI agent systems 36:00 Context windows in the agent era 41:01 AGI’s definition in Logan’s eyes 42:05 The current bottlenecks towards AGI
46:25

Exclusive: Google just released two upgraded Gemini 1.5 models, 1.5-Pro-002 and 1.5-Flash-002 — achieving new, state-of-the-art performance across math. I sat down with Logan Kilpatrick (Logan Kilpatrick) to discuss the models, AI agents, AGI, and more. Timestamps: 00:00 Intro 01:01 Google rolls out two new Gemini models 2:18 What makes the new models so unique 3:40 Math improvements 8:20 Examples of Gemini 1.5 in the real world 10:19 Future problems AI could solve 12:54 Advantages of Gemini 1.5 16:50 Where beginners can get started 19:01 Advice to thrive in the new age of AI 22:02 Turning notes into podcasts 26:30 AI agents: Definitions and what they can do 31:22 What’s the final form for agents? 33:26 Proactive AI agent systems 36:00 Context windows in the agent era 41:01 AGI’s definition in Logan’s eyes 42:05 The current bottlenecks towards AGI

Rowan Cheung

269,830 次观看 • 1 年前