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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.

AI agents are quickly becoming a core part of modern development workflows, and the GitHub Copilot SDK makes it surprisingly straightforward to build your own. Instead of relying on prompt engineering alone, the SDK lets you define structured tools, give your agent explicit capabilities, and execute real code through LLM‑driven reasoning. In my latest demo, I walk through the full process of creating an agent from scratch — setting up the project, defining the agent, building custom tools, and running everything locally. You’ll see how the SDK handles tool invocation, schema validation, and natural‑language responses, all while keeping your logic deterministic and maintainable. If you're exploring agentic workflows or want to understand how Copilot can power real execution paths, this walkthrough will give you a clear, practical starting point. 🎥 Watch the full step‑by‑step video here: 👉  This is just the beginning — once you understand the pattern, you can extend your agent with...

The GitHub Copilot SDK just dropped, and it’s a game-changer for developers. You can now build your own Copilot-style AI features directly inside your apps, tools, and workflows — no more waiting for GitHub to do it for you. In my latest video, I break down exactly what the SDK is, how it works, and why it’s the future of developer productivity.  🎥 Watch now: Introducing GitHub Copilot SDK — Step-by-Step Demo If you’re serious about AI + dev tools, this is the video to start with.

Every once in a while, a new AI tool appears that doesn’t just improve on what already exists — it completely changes your expectations. That’s exactly what happened when I tested Awaz AI , a voice agent designed to handle real phone calls with natural, human‑like conversation. I’ve tried many voice systems before, and most of them sound robotic, interrupt at the wrong time, or fall apart when you ask something unexpected. But Awaz AI surprised me from the very first “hello.” The pacing, the tone, the timing — everything felt unusually natural. It didn’t rush. It didn’t freeze. It didn’t sound scripted. It actually felt like a real conversation. To make sure I wasn’t imagining it, I recorded the entire interaction. No edits. No retakes. Just a raw, real phone call between me and the Awaz AI agent. If you’re curious about how far voice AI has come — or if you simply want to hear an AI that sounds more human than most customer service lines — you should watch this demo. It’s one of the ...

AI workflows were a great starting point. They helped us build early prototypes, automate simple tasks, and experiment with LLMs. But the future of AI is not workflows - it's agents. Agents are more flexible, more intelligent, and more aligned with how real‑world tasks work. If you want to understand this shift - and learn exactly how to convert your existing workflows into agents - my video will walk you through the entire process step by step. Watch it. Learn it. Build with it. Your future AI systems will thank you.