• In this theater session, Software Engineer Eleanor Boyd walks through building a custom MCP (Model Context Protocol) server in Visual Studio Code using GitHub Copilot and Python. She walks through a real use case, fetching PyPI package data, and shows how to connect your tool to Copilot’s Agent Mode for powerful AI-assisted coding.

    #MCP #ModelContextProtocol #VisualStudioCode #GitHubCopilot #Python #PyPI #AIAssistedCoding #AgentMode #SoftwareEngineering #CodeDevelopment #AItools #Codegen #CodeCompletion #Tabnine #AmazonCodeWhisperer #JetBrainsAIAssistant

    https://youtu.be/SYcQXozpb_E?si=8QgX0-7lr3WsA0ZJ
    In this theater session, Software Engineer Eleanor Boyd walks through building a custom MCP (Model Context Protocol) server in Visual Studio Code using GitHub Copilot and Python. She walks through a real use case, fetching PyPI package data, and shows how to connect your tool to Copilot’s Agent Mode for powerful AI-assisted coding. #MCP #ModelContextProtocol #VisualStudioCode #GitHubCopilot #Python #PyPI #AIAssistedCoding #AgentMode #SoftwareEngineering #CodeDevelopment #AItools #Codegen #CodeCompletion #Tabnine #AmazonCodeWhisperer #JetBrainsAIAssistant https://youtu.be/SYcQXozpb_E?si=8QgX0-7lr3WsA0ZJ
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  • Unlock the Power of Model Context Protocol (MCP) with Sampling!

    In the latest installment of the MCP crash course, we dive into sampling, one of the most powerful features of the protocol. This innovative approach allows servers to delegate tasks back to the client's LLM, enabling a seamless integration of AI functionalities without overwhelming server resources.

    Key Highlights:
    Bidirectional Architecture: While LLM clients typically invoke server logic, sampling lets servers request input from the client’s AI model, creating a more efficient workflow.

    Cost Efficiency: By transferring the burden of AI computation to the client, it reduces server load and associated costs.

    Flexibility & Scalability: Clients can choose their preferred models for different requests, which enhances application scalability and user customization.

    By implementing sampling, developers can build better, more responsive AI-driven applications. Discover how to optimize your MCP servers and improve the overall performance with practical examples and best practices in the full article!

    #AI #MachineLearning #TechInnovation #ModelContextProtocol #MCP #Sampling #ServerSideLogic #LLM #OpenSource #DevCommunity

    https://www.dailydoseofds.com/model-context-protocol-crash-course-part-5/
    🚀 Unlock the Power of Model Context Protocol (MCP) with Sampling! 🌟 In the latest installment of the MCP crash course, we dive into sampling, one of the most powerful features of the protocol. This innovative approach allows servers to delegate tasks back to the client's LLM, enabling a seamless integration of AI functionalities without overwhelming server resources. Key Highlights: Bidirectional Architecture: While LLM clients typically invoke server logic, sampling lets servers request input from the client’s AI model, creating a more efficient workflow. Cost Efficiency: By transferring the burden of AI computation to the client, it reduces server load and associated costs. Flexibility & Scalability: Clients can choose their preferred models for different requests, which enhances application scalability and user customization. By implementing sampling, developers can build better, more responsive AI-driven applications. Discover how to optimize your MCP servers and improve the overall performance with practical examples and best practices in the full article! #AI #MachineLearning #TechInnovation #ModelContextProtocol #MCP #Sampling #ServerSideLogic #LLM #OpenSource #DevCommunity https://www.dailydoseofds.com/model-context-protocol-crash-course-part-5/
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