In the newest Canary build of Windows 11 released to Windows Insider, Microsoft has not only made a number of fixes and improvements, it has also introduced changes to power settings. Other changes and additions to be found in Windows 11 Canary Build 29550.1000 are the ability to use voice typing to rename files and various performance improvements. This is far from being the end of the changes in this build, though. Some of the things Microsoft list as being changes or additions are things that we have seen before, but this is certainly not the case for everything in… [Continue Reading]

In regulated industries like finance and healthcare, "knowing what happened" is often just as critical as preventing bad things from happening. Frameworks like SOC 2 and HIPAA don't just ask you to secure your systems; they ask you to prove it. That means structured, queryable, tamper-evident records of security events: who logged in, when a token was issued, which client authenticated, and what failed.

Standard application logs aren't built for this. They're noisy, unstructured, and designed for developers to debug issues. But not for reviewing access patterns across six months of production traffic.

Duende IdentityServer ships with a structured eventing system that addresses this gap directly. Architecturally, this means a clean separation between high-volume operational logs and the dedicated, low-volume security events that form the official record.

In this post, we'll walk through how you, as a developer, can use Duende IdentityServer's events to build an audit trail that satisfies compliance requirements in Highly Regulated Industries (HRI).

Azure Local enables customers to run Azure services on customer‑owned infrastructure, bringing Azure’s consistent operations, security, and management model to on‑premises, edge, and sovereign environments. It’s a key building block for Hybrid and Sovereign Cloud strategies—especially where data residency, latency, or regulatory requirements matter. One of the most common questions I get is: How can …

The post How to Evaluate, Test, and Demo Azure Local appeared first on Thomas Maurer.

AI agents are rapidly becoming a new way to build applications. But for agents to be truly useful, they need access to the knowledge and context that helps them reason about the world they operate in.

That’s where Foundry IQ comes in.

Today we’re announcing the IQ Series: Foundry IQ, a new set of developer-focused episodes exploring how to build knowledge-centric AI systems using Foundry IQ.

The series focuses on the core ideas behind how modern AI systems work with knowledge, how they retrieve information, reason across sources, synthesize answers, and orchestrate multi-step interactions.

Instead of treating retrieval as a single step in a pipeline, Foundry IQ approaches knowledge as something that AI systems actively work with throughout the reasoning process. The IQ Series breaks down these concepts and shows how they come together when building real AI applications.

You can explore the series and all the accompanying samples here:

👉 https://aka.ms/iq-series

What is Foundry IQ?

Foundry IQ helps AI systems work with knowledge in a more structured and intentional way.

Rather than wiring retrieval logic directly into every application, developers can define knowledge bases that connect to documents, data sources, and other information systems. AI agents can then query these knowledge bases to gather the context they need to generate responses, make decisions, or complete tasks.

This model allows knowledge to be organized, reused, and combined across applications, instead of being rebuilt for each new scenario.

What's covered in the IQ Series?

The Foundry IQ episodes in the IQ Series explore the key building blocks behind knowledge-driven AI systems from how knowledge enters the system to how agents ultimately query and use it.

The series is released as three weekly episodes:

• Foundry IQ: Unlocking Knowledge for Your Agents — March 18, 2026: Introduces Foundry IQ and the core ideas behind it. The episode explains how AI agents work with knowledge and walks through the main components of the Foundry IQ that support knowledge-driven applications.

• Foundry IQ: Building the Data Pipeline with Knowledge Sources — March 25, 2026: Focuses on Knowledge Sources and how different types of content flow into Foundry IQ. It explores how systems such as SharePoint, Fabric, OneLake, Azure Blob Storage, Azure AI Search, and the web contribute information that AI systems can later retrieve and use.

• Foundry IQ: Querying the Multi-Source AI Knowledge Bases — April 1, 2026: Dives into the Knowledge Bases and how multiple knowledge sources can be organized behind a single endpoint. The episode demonstrates how AI systems query across these sources and synthesize information to answer complex questions. 

Each episode includes a short executive introduction, a tech talk exploring the topic in depth, and a visual recap with doodle summaries of the key ideas.

Alongside the episodes, the GitHub repository provides cookbooks with sample code, summary of the episodes, and additinal learning resources, so developers can explore the concepts and apply them in their own projects.

Explore the Repo

All episodes and supporting materials live in the IQ Series repository:

👉 https://aka.ms/iq-series

Inside the repository you’ll find:

• The Foundry IQ episode links
• Cookbooks for each episode
• Links to documentation and additional resources

If you're building AI agents or exploring how AI systems can work with knowledge, the IQ Series is a great place to start.

Watch the episodes and explore the cookbooks! We’re excited to see what you build and welcome your feedback & ideas as the series evolves.

If you’re learning to build AI agents, you’ve probably hit a familiar wall: your agent can generate text, but it doesn’t actually know anything about your data. It can’t look up your documents, search across your files, or pull facts from multiple sources to answer a real question.

That’s the gap Foundry IQ fills. It gives your AI agents structured access to knowledge, so they can retrieve, reason over, and synthesize information from real data sources instead of relying on what’s baked into the model.

Why Should You Care?

As a student or early-career developer, understanding how AI systems work with external knowledge is one of the most valuable skills you can build right now. Retrieval-Augmented Generation (RAG), knowledge bases, and multi-source querying are at the core of every production AI application, from customer support bots to research assistants to enterprise copilots.

Foundry IQ gives you a hands-on way to learn these patterns without having to build all the plumbing yourself. You define knowledge bases, connect data sources, and let your agents query them. The concepts you learn here transfer directly to real-world AI engineering roles.

What is Foundry IQ?

Foundry IQ is a service within Azure AI Foundry that lets you create knowledge bases, collections of connected data sources that your AI agents can query through a single endpoint.

Instead of writing custom retrieval logic for every app you build, you:

1. Define knowledge sources — connect documents, data stores, or web content (SharePoint, Azure Blob Storage, Azure AI Search, Fabric OneLake, and more).
2. Organize them into a knowledge base — group multiple sources behind one queryable endpoint.
3. Query from your agent — your AI agent calls the knowledge base to get the context it needs before generating a response.

This approach means the knowledge layer is reusable. Build it once, and any agent or app in your project can tap into it.

The IQ Series: A Three-Part Learning Path

The IQ Series is a set of three weekly episodes that walk you through Foundry IQ from concept to code. Each episode includes a tech talk, visual doodle summaries, and a companion cookbook with sample code you can run yourself.

👉 Get started: https://aka.ms/iq-series

Episode 1: Unlocking Knowledge for Your Agents (March 18, 2026)

Start here. This episode introduces the core architecture of Foundry IQ and explains how AI agents interact with knowledge. You’ll learn what knowledge bases are, why they matter, and how the key components fit together.

What you’ll learn:

• The difference between model knowledge and retrieved knowledge
• How Foundry IQ structures the retrieval layer
• The building blocks: knowledge sources, knowledge bases, and agent queries

Episode 2: Building the Data Pipeline with Knowledge Sources (March 25, 2026)

This episode goes deeper into knowledge sources, the connectors that bring data into Foundry IQ. You’ll see how different content types flow into the system and how to wire up sources from services you may already be using.

What you’ll learn:

• How to connect sources like Azure Blob Storage, Azure AI Search, SharePoint, Fabric OneLake, and the web
• How content is ingested and indexed for retrieval
• Patterns for combining multiple source types

Episode 3: Querying Multi-Source Knowledge Bases (April 1, 2026)

The final episode shows you how to bring it all together. You’ll learn how agents query across multiple knowledge sources through a single knowledge base endpoint and how to synthesize answers from diverse data.

What you’ll learn:

• How to query a knowledge base from your agent code
• How retrieval works across multiple connected sources
• Techniques for synthesizing information to answer complex questions

Get Hands-On with the Cookbooks

Every episode comes with a companion cookbook in the GitHub repo, complete with sample code you can clone, run, and modify. This is the fastest way to go from watching to building.

👉 Explore the repo: https://aka.ms/iq-series

Inside you’ll find:

• Episode links — watch the tech talks and doodle recaps
• Cookbooks — step-by-step code samples for each episode
• Documentation links — official Foundry IQ docs and additional learning resources

What to Build Next

Once you’ve worked through the series, try applying what you’ve learned:

• Study assistant — connect your course materials as knowledge sources and build an agent that can answer questions across all your notes and readings.
• Project documentation bot — index your team’s project docs and READMEs into a knowledge base so everyone can query them naturally.
• Research synthesizer — connect multiple data sources (papers, web content, datasets) and build an agent that can cross-reference and summarize findings.

Start Learning

The IQ Series is designed to take you from zero to building knowledge-driven AI agents. Watch the episodes, run the cookbooks, and start experimenting with your own knowledge bases.

👉 https://aka.ms/iq-series

1168. This week, we look at the word "leprechaun" and its surprisingly wild origin story involving shoemaking, ancient Rome, and wolf-men. Then we look at the word "equinox": its Chaucer connection, the newer word "equilux," and why the first point of Aries is actually in Pisces now (and headed for Aquarius).

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