In this episode, Ayan Gupta and Rory demonstrate hands-on development of three different AI-powered apps, showcasing MCP, browser-based LLMs, and Microsoft’s Foundry Local. Their step-by-step approach enables Java developers to learn practical AI integration.

Building Three AI-Powered Applications: MCP, Browser LLMs, and Foundry Local

Introduction

In this episode, Ayan Gupta is joined by Rory to bring theory into practice by building three working AI applications tailored for Java developers. Each app highlights a different architectural approach for utilizing large language models (LLMs) and AI integrations.

Application 1: Calculator Service with MCP (Model Context Protocol)

  • What is MCP? MCP (Model Context Protocol) allows LLMs to call external tools and functions, extending AI capabilities beyond basic conversation.
  • How it’s built:
    • Set up a new GitHub Codespace to code the project.
    • Use the AI Toolkit extension in Visual Studio Code to automatically generate agent code, simplifying the integration process.
    • Register a calculator tool on the MCP server, making it accessible to AI models for real-world computations.
  • Key architecture: AI agents interact with registered tools using the MCP server as a bridge, which enables LLMs to perform function calling and interact with external systems.

Application 2: Offline Pet Story Generator Using Browser-Based LLMs

  • Local AI in your browser: Build a pet story generator app running entirely in the browser using JavaScript’s built-in language model interface. This approach keeps all data local, improving privacy and offline capability.
  • Features:
    • No external server required; runs in-browser for complete data privacy.
    • Processes user input and generates stories locally.
    • Includes an image analysis feature leveraging browser-based AI.

Application 3: Running LLMs Locally with Foundry Local

  • What is Foundry Local? Microsoft’s Foundry Local lets developers run large language models on their own hardware, providing full control over performance, privacy, and data handling.
  • Setup steps:
    • Install and configure Foundry Local on your development machine.
    • Run and test language models entirely on your own infrastructure, without needing cloud resources.
  • Use cases: Ideal for scenarios requiring completely private AI inference, development and testing, or strict regulatory constraints.

Key Takeaways and Deployment Comparisons

  • Cloud-Based (MCP + GitHub): Best for integration with external tools and when leveraging the scalability of cloud services.
  • Client-Side (Browser LLMs): Ideal for privacy, offline scenarios, and lightweight local AI tasks.
  • Local Deployment (Foundry Local): Crucial for organizations needing total data control or working within sensitive environments.
  • The episode wraps up with practical comparison and analysis of these approaches, empowering developers to choose the right tool and strategy for their use case.

Resources

Presented by: Ayan Gupta & Rory

Viewers leave with hands-on experience and architectural insights for AI-powered app development with Java and Microsoft tools.