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I’m excited to share my most awaited video, where I break down how to build an MCP server completely from scratch using FastAPI —with no SDKs, no decorators, and absolutely no hidden magic.  This tutorial walks through the real MCP protocol step‑by‑step. You’ll see how the manifest, tools list, execution flow, and even streaming responses come together using pure Python. It’s a simple, transparent way to understand MCP at its core. 🎥 Watch the full video: 

Over the past few weeks, I’ve been exploring how to build practical, privacy‑first agentic AI workflows that run entirely on a local machine. In my latest project, I combined GitHub Copilot SDK with Foundry Local to create a fully offline agent capable of choosing and executing tools intelligently — without relying on any cloud model. In this demo, I walk through how I built: - A Foundry Local LLM tool for on‑device inference - Three lightweight Python tools  - A router prompt that lets Copilot SDK decide which tool to invoke - A clean async loop that ties everything together The result is a flexible, extensible agent that can reason, select tools, and produce polished answers — all running locally. If you’re interested in agent design, local LLMs, or practical orchestration patterns, this walkthrough will give you a clear, end‑to‑end example you can adapt to your own projects. 🎥 Watch the full video here:

If you’ve ever wanted to run an LLM directly on your laptop without relying on the cloud, this new video is for you. I just released a hands‑on walkthrough of Foundry Local, where I show you exactly how to download an AI model to your machine and use it completely offline. In the video, I break down both methods developers use: - CLI workflow — install Foundry Local, pull a model, and run inference offline - Python SDK workflow — load the model in your code and build real offline AI features Whether you're a developer, an AI enthusiast, or someone who wants more privacy and zero token costs, this tutorial will help you get started in minutes.

Let’s be honest: most AI demos feel like magic tricks.  You type something, it replies. Cool!  But what happens when you want your AI to actually do something?  Like read a file, s ummarize a document, c all an API, r un a script, t rigger a workflow, etc. That’s where the GitHub Copilot SDK and MCP servers come in. They let you build real AI agents — ones that can reason, call tools, and interact with your environment like a tiny software teammate. In my latest video, I show you how to wire up local and remote MCP servers into a GitHub Copilot SDK agent. You’ll learn: - How MCP works  - How to build your own MCP server in Python - How to plug it into your agent - How to mix local and remote tools like a pro 👉 Watch the full walkthrough here If you’ve ever wanted to build an AI agent that feels like a real part of your stack — not just a chatbot — this is the video to watch. Let me know what you build after watching. I’m curious.