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Most AI systems can read your documents — but very few can understand them. Traditional RAG pulls text chunks and hopes the answer makes sense. It works for simple questions, but the moment you ask something relational —  Who works with whom? How are these teams connected? Why does this project matter? — it breaks. Graph RAG fixes that by turning your documents into a knowledge graph. People become nodes. Teams become nodes. Projects, locations, responsibilities — all nodes. And the relationships between them become edges. Suddenly, your data isn’t flat anymore. It’s connected. Graph RAG doesn’t just retrieve text. It retrieves meaning. It understands relationships, clusters related entities into communities, and uses LLM ‑generated summaries to explain what each group represents. So when you ask a complex question, the system doesn’t guess — it reasons. If you want to see this visually — with examples, and a full breakdown of how the pipeline works — I’ve created a detailed vide...

Are you trying to build an app with Claude AI but not getting the results you want? Your prompts might be the problem — not Claude . Introducing App Brief Wizard — a simple, offline HTML tool that helps you generate precise, structured Claude prompts for any app idea. Answer 5 quick questions → get a tailored prompt → instantly better UI and app concepts. No installs. No accounts. No learning curve. Just better prompts — instantly. 🎯 Perfect for:   Freelancers • Designers • Product Managers • Entrepreneurs • AI Builders • Claude Users 💡 Why it works:   App Brief Wizard helps Claude understand your app’s purpose, audience, and design personality — so your outputs look professional, not generic. 🔥 Early Bird Offer:   First few copies are just $5 (use code FIRST10) After that, it’s $9 — grab yours before it’s gone! 👉 Get App Brief Wizard here: App Brief

If you’re anything like me, you probably spend a big part of your day typing — emails, stories, books, blogs, notes, messages, ideas, reminders… it never ends. And honestly, it gets tiring. Our hands get tired. Our minds get tired. And sometimes, typing just slows us down. That’s when I realized something simple:   Talking is easier than typing. We speak faster, more naturally, and with less effort. But here’s the problem — most voice‑to‑text tools send your voice to the cloud.   Some store it.  Some analyze it.   Some require monthly subscriptions.   And many people don’t feel comfortable with that.  So, I decided to build something different. Meet Speakly — A Private, Offline Voice‑to‑Text App for Windows Speakly is a small, simple tool that lets you talk , and your computer types everything for you .   But the best part is what it doesn’t  do: It doesn’t send your voice to the internet   It doesn’t store your...

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.