If you are learning how to get an application from your machine into Azure without stitching together every deployment step by hand, Azure Developer CLI, usually shortened to  azd , is one of the mos...

The promise and the gap Your organization has invested in an AI agent. You ask it: "Prepare a summary of Q3 revenue by region, including year-over-year trends and top product lines." The agent fi...

The Model Context Protocol (MCP) gives AI agents a standard way to call external tools, but things get more complicated when those tools need to know who the user is. In this post, I’ll show how to b...

Hi there! I'm the creator of oBeaver. This project started from a pretty simple desire: I wanted to run large language models on my own computer. No data sent to the cloud. No API keys. No p...

A hands-on guide to building an on-device AI support agent using Context-Augmented Generation, JavaScript, and Foundry Local. You have probably heard the AI pitch: "just call our API." But what h...

Agents League brought together developers from around the world to build AI agents using Microsoft's developer tools. With 100+ submissions across three tracks, choosing winners was genuinely difficu...

A developer's guide to building an offline, mobile-responsive AI support agent using Retrieval-Augmented Generation, the Foundry Local SDK, and JavaScript. Imagine you are a gas field engineer ...

How to build a 100% offline, AI-powered interview preparation tool using Microsoft Foundry Local, Retrieval-Augmented Generation, and nothing but JavaScript. Foundry Local 100% Offline RAG + TF-I...

Why developers need a fast lane from research → prototypes AI engineering has a speed problem, but it is not a shortage of announcements. The hard part is turning research into a useful prototype b...

Introduction Retrieval-Augmented Generation (RAG) has become a widely adopted architecture for building AI applications that combine Large Language Models (LLMs) with external knowledge sources. ...