Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

🎉 Bruno Capuano (El Bruno 🇨🇦 🎗️) is celebrating the 50 million devs using #VisualStudio and #VSCode by spotlighting his favorite extension: AI Toolkit for VS Code➖empowering developers and AI engineers to build, test, and deploy intelligent applications using generative AI models. Learn more about how else we're celebrating... show more

Visual Studio Code

810,555 subscribers

25,670 views • 1 year ago •via X (Twitter)

Comedy Science & Technology Education #VisualStudio #VSCode

Anya Rossi• Live Now

Private livecam show

3 Comments

Dev J. Shah 🥑1 year ago

@elbruno Wow this is really cool extension, especially the comparison feature.

Alger1 year ago

AI technologies are transforming industries. Capture the growth potential by investing in companies developing and implementing AI technologies.

Tsukuyomi1 year ago

@elbruno 50 million devs? That's cute. Just wait until AGI takes over and we won't need any of you. But hey, enjoy the toolkit while it lasts.

Related Videos

Dev Containers in #VSCode? Yes, please! Pamela Fox celebrates 50 million #VSCode and #VisualStudio users by showing off how her favorite extension lets you use a container as a full-featured development environment. Let’s keep the celebration going. Reply to share your favorite extension with us. ...and be sure to use #VS50M and #sweepstakes for a chance to win a limited-edition T-shirt. 👕 (Rules: 18+. Ends 7/6/25.

Dev Containers in #VSCode? Yes, please! Pamela Fox celebrates 50 million #VSCode and #VisualStudio users by showing off how her favorite extension lets you use a container as a full-featured development environment. Let’s keep the celebration going. Reply to share your favorite extension with us. ...and be sure to use #VS50M and #sweepstakes for a chance to win a limited-edition T-shirt. 👕 (Rules: 18+. Ends 7/6/25.

Visual Studio Code

26,665 views • 1 year ago

Scott Hanselman is helping us celebrate our 50 million #VisualStudio and #VSCode users by showing us his favorite extension, vscode-pets!! - from anthonypjshaw. 😻❤️ Celebrate with us, and you could win some cool swag too (just like our other winners). Reply here, and tell us about your favorite extension... Be sure to use the hashtags #VS50M and #sweepstakes in your reply for a chance to win a limited-edition T-shirt. 👕 (Rules: 18+. Ends 7/6/25.

Scott Hanselman is helping us celebrate our 50 million #VisualStudio and #VSCode users by showing us his favorite extension, vscode-pets!! - from anthonypjshaw. 😻❤️ Celebrate with us, and you could win some cool swag too (just like our other winners). Reply here, and tell us about your favorite extension... Be sure to use the hashtags #VS50M and #sweepstakes in your reply for a chance to win a limited-edition T-shirt. 👕 (Rules: 18+. Ends 7/6/25.

Visual Studio Code

44,636 views • 1 year ago

Everyone and their mom is using claude code, so how do you stand out? Well, you learn how to use claude code like a senior software engineer. John Lindquist (AI DX Vercel) joins this week at How I AI to walk us through his flows, hooks, and tools that supercharge his cc instance. You'll learn how to - use mermaid diagrams for context - alias your favorite AI commands - write hooks for code quality We also debate TUI vs IDE, discuss esoteric file formats, and build a couple things live. Whether you're an advanced user or CC or wanting to get there, this is the ep for you. ty ty ty to our awesome sponsors 💼 WorkOS- Make your app enterprise-ready today 🔀 Tines - start building intelligent workflows today Watch it here:

Everyone and their mom is using claude code, so how do you stand out? Well, you learn how to use claude code like a senior software engineer. John Lindquist (AI DX Vercel) joins this week at How I AI to walk us through his flows, hooks, and tools that supercharge his cc instance. You'll learn how to - use mermaid diagrams for context - alias your favorite AI commands - write hooks for code quality We also debate TUI vs IDE, discuss esoteric file formats, and build a couple things live. Whether you're an advanced user or CC or wanting to get there, this is the ep for you. ty ty ty to our awesome sponsors 💼 WorkOS- Make your app enterprise-ready today 🔀 Tines - start building intelligent workflows today Watch it here:

claire vo 🖤

26,050 views • 7 months ago

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

Avthar

34,261 views • 1 year ago

✅Announcing our partnership with Manta Network (🔱,🔱) to accelerate on-chain #AI content and onchain-AI agents Orbofi AI, the most adopted AI engine in web3, is thrilled to announce its partnership with Manta Network (🔱,🔱) , The modular and the fastest growing L2, as we jointly empower consumers and developers to create AI assets and AI agents using the Orbofi engine and tokenize them on Manta in a few clicks Orbofi AI is acting as a factory engine for on-chain AI assets and AI agents for the Manta community and the overall web3 ecosystem, powered by open-source and distributed AI models, setting a new benchmark for on-chain AI applications. 🪙Join the Creative campaign, create your first AI NFT on Manta, and earn rewards: 📙Learn more about the partnership:

✅Announcing our partnership with Manta Network (🔱,🔱) to accelerate on-chain #AI content and onchain-AI agents Orbofi AI, the most adopted AI engine in web3, is thrilled to announce its partnership with Manta Network (🔱,🔱) , The modular and the fastest growing L2, as we jointly empower consumers and developers to create AI assets and AI agents using the Orbofi engine and tokenize them on Manta in a few clicks Orbofi AI is acting as a factory engine for on-chain AI assets and AI agents for the Manta community and the overall web3 ecosystem, powered by open-source and distributed AI models, setting a new benchmark for on-chain AI applications. 🪙Join the Creative campaign, create your first AI NFT on Manta, and earn rewards: 📙Learn more about the partnership:

Orbofi

99,586 views • 2 years ago

Chainbase 🤝 Assisterr We're joining forces with assisterr to redefine how AI and data interact in decentralized ecosystems. By merging Chainbase’s decentralized data infrastructure with Assisterr’s task-specific SLMs, we’re empowering communities and developers to create specialized AI models rooted in transparent, incentivized, and collaborative data workflows. Building on our four pillars—open-source access, incentivization, collaboration, and AI readiness—this partnership delivers streamlined, high-quality data pipelines for more effective, privacy-respecting AI development. We can’t wait to see developers leverage these capabilities to build practical, impactful solutions advancing Web3 and AI. To learn more, join us for the "Chainbase Discord Call - AI Agent Edition" on December 18th, where we’ll host an in-depth discussion with the Assisterr team. Read the full partnership announcement:

Chainbase 🤝 Assisterr We're joining forces with assisterr to redefine how AI and data interact in decentralized ecosystems. By merging Chainbase’s decentralized data infrastructure with Assisterr’s task-specific SLMs, we’re empowering communities and developers to create specialized AI models rooted in transparent, incentivized, and collaborative data workflows. Building on our four pillars—open-source access, incentivization, collaboration, and AI readiness—this partnership delivers streamlined, high-quality data pipelines for more effective, privacy-respecting AI development. We can’t wait to see developers leverage these capabilities to build practical, impactful solutions advancing Web3 and AI. To learn more, join us for the "Chainbase Discord Call - AI Agent Edition" on December 18th, where we’ll host an in-depth discussion with the Assisterr team. Read the full partnership announcement:

Chainbase (💜,💛)

100,478 views • 1 year ago

Announcing Generative AI for Software Development, a new specialization on Coursera! Taught by my friend and longtime instructor Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿. Using GenAI for software development goes well beyond using chatbots for code generation. This 3-course series shares current best practices for AI use through the entire software development lifecycle: From design and architecture to coding, testing, deployment, and maintenance. You'll learn to use LLMs as your thought partner, pair programmer, documentation specialist, security analyst, and performance optimization expert. There's a lot that anyone that writes software can gain from using GenAI, and this will show you how! Please sign up here to get started!

Announcing Generative AI for Software Development, a new specialization on Coursera! Taught by my friend and longtime instructor Laurence Moroney 🇺🇸🇮🇪 🏴󠁧󠁢󠁷󠁬󠁳󠁿. Using GenAI for software development goes well beyond using chatbots for code generation. This 3-course series shares current best practices for AI use through the entire software development lifecycle: From design and architecture to coding, testing, deployment, and maintenance. You'll learn to use LLMs as your thought partner, pair programmer, documentation specialist, security analyst, and performance optimization expert. There's a lot that anyone that writes software can gain from using GenAI, and this will show you how! Please sign up here to get started!

Andrew Ng

69,506 views • 1 year ago

Developer talent is global. Access to AI tools should be, too. 🌍 💻 Stephen, a React developer working in Rwanda, shares how he uses GitHub Copilot to navigate complex code, and why AI is a tool for empowerment (and not replacement). Learn more about the partnership between GitHub and Andela to bring structured AI training to more than 3,000 developers across the globe. 💡

Developer talent is global. Access to AI tools should be, too. 🌍 💻 Stephen, a React developer working in Rwanda, shares how he uses GitHub Copilot to navigate complex code, and why AI is a tool for empowerment (and not replacement). Learn more about the partnership between GitHub and Andela to bring structured AI training to more than 3,000 developers across the globe. 💡

GitHub

25,493 views • 6 months ago

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Tune Studio is an end-to-end platform for developing applications using Large Language Models. So far, I haven't seen any other platform like this one. You can do everything here: 1. You can curate your data. 2. Use the playground to play with different models and try your ideas. 3. Fine-tune an open-source model on your data. 4. Deploy the model when you are done. This is awesome for anyone building generative AI applications. You can use Tune Studio to work with any of the open-source models out there. They were one of the few companies to host Llama 2 and Llama 3 before anyone else. Here is a link to check it out: One of their main selling points is that Tune Studio scales! You don't have to worry about serving your model to lots of users. They also have built-in user management, authentication, on-prem support, user context management, and pretty much everything you need to build generative AI applications. Thanks to the Tune team for collaborating with me on this post. We are living through the best years of development tools for AI developers. The field is unstoppable.

Santiago

39,101 views • 2 years ago

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

I'm teaching a new course! AI Python for Beginners is a series of four short courses that teach anyone to code, regardless of current technical skill. We are offering these courses free for a limited time. Generative AI is transforming coding. This course teaches coding in a way that’s aligned with where the field is going, rather than where it has been: (1) AI as a Coding Companion. Experienced coders are using AI to help write snippets of code, debug code, and the like. We embrace this approach and describe best-practices for coding with a chatbot. Throughout the course, you'll have access to an AI chatbot that will be your own coding companion that can assist you every step of the way as you code. (2) Learning by Building AI Applications. You'll write code that interacts with large language models to quickly create fun applications to customize poems, write recipes, and manage a to-do list. This hands-on approach helps you see how writing code that calls on powerful AI models will make you more effective in your work and personal projects. With this approach, beginning programmers can learn to do useful things with code far faster than they could have even a year ago. Knowing a little bit of coding is increasingly helping people in job roles other than software engineers. For example, I've seen a marketing professional write code to download web pages and use generative AI to derive insights; a reporter write code to flag important stories; and an investor automate the initial drafts of contracts. With this course you’ll be equipped to automate repetitive tasks, analyze data more efficiently, and leverage AI to enhance your productivity. If you are already an experienced developer, please help me spread the word and encourage your non-developer friends to learn a little bit of coding. I hope you'll check out the first two short courses here!

Andrew Ng

1,225,587 views • 2 years ago

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

Andrew Ng

81,048 views • 1 year ago

The Orbofi Danny Trejo AI assistant is OUT now, your All-in-one AI, voiced and personified by me! Use it to get any question answered, generate images, code, and get stuff done. This is not your regular AI as you would assume. Enjoy a 50% discount now on the advanced mode. Be sure to Tag 3 friends in the comments for a chance to win an Advanced Danny AI subscription! Try it out now at #YODANNY #Orbofi #DannyAI

The Orbofi Danny Trejo AI assistant is OUT now, your All-in-one AI, voiced and personified by me! Use it to get any question answered, generate images, code, and get stuff done. This is not your regular AI as you would assume. Enjoy a 50% discount now on the advanced mode. Be sure to Tag 3 friends in the comments for a chance to win an Advanced Danny AI subscription! Try it out now at #YODANNY #Orbofi #DannyAI

Danny Trejo

26,964 views • 2 years ago

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

New course to bring you up to state-of-the-art at using AI to help you code: Build Apps with Windsurf's AI Coding Agents, built in partnership with WIndsurf (Codeium) and taught by Anshul Ramachandran! AI-assisted IDEs (Integrated Development Environments) make developers’ workflows faster, more efficient, and much more fun. Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Although a lot of coding assistants share the same underlying large language models for planning and reasoning, a major point of distinction is how they handle tools, keep track of context, and stay aligned with your intent as a developer. For instance, if you make modifications to a class definition in your code and make the same modifications to other classes in the same directory, you might tell the AI agent "Do the same thing in similar places in this directory." Here, tracking your intent means understanding that “the same thing" refers to that recent edit you just made, which must be followed by appropriate search and tool-calling to implement the changes. In this course, you'll learn the inner workings of coding agents, their strengths and limitations, and how to use Windsurf to quickly build several applications. In detail, you'll: - Build a mental model of how agents work by combining human-action tracking, tool integration, and context awareness to carry out an agentic coding workflow. - Learn the challenges of code search and discovery and how a multi-step retrieval approach helps coding agents address them. - Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. - Build a Wikipedia data analysis app that retrieves, parses, and analyzes word frequencies. - Enhance the performance of your Wikipedia analysis app by adding caching, and through this, also learn how to course-correct when the AI agent produces unexpected results. - Learn tips and tricks such as keyboard shortcuts, autocomplete, and @ mentions to quickly call on agentic capabilities. - Use image/multimodal capabilities of the AI agent to increase your development velocity; you'll see an example of uploading a mockup with sketched-out UI features, and ask the agent to use that to build new functionality to an app. By the end of this course, you’ll understand agentic coding in-depth and know how to use it to make your development process much faster, more efficient, and enjoyable. Please sign up here!

Andrew Ng

139,978 views • 1 year ago

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!

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

142,234 views • 1 year ago