Today we are going to look at hosting a Container in Container Apps, which is used to host a Container that has a VNC server installed, running DOSBox.
Why would you do this? Could be a few reasons, but common reasons might be looking at running legacy applications that require a GUI (Graphical User Interface) in a more modern and isolated environment, allowing for quick internal access to resources, or just for fun.
Partner Admin Link (PAL) tool is a .NET application that helps Microsoft partners link their Partner Admin Link (PAL) to all accessible Microsoft Entra tenants, enabling proper partner recognition and revenue attribution.
Partner Admin Link (PAL) enables Microsoft to identify and recognize partners who drive Azure customer success. By linking your Microsoft AI Cloud Partner Program (Associated PartnerID) to customer tenants, Microsoft can:
✅ Attribute revenue from your customers' Azure consumption to your organization ✅ Recognize your impact on customer success and business outcomes ✅ Provide insights into your partner engagement effectiveness ✅ Enable partner incentives based on customer growth
Chat Modes are predefined configurations that enable you to tailor the AI chat behavior in Visual Studio Code for specific tasks, such as asking questions, making code edits, or performing autonomous coding tasks. You can switch between chat modes at any time in the Chat view, depending on the task you want to accomplish. Chat Modes enable us to customise the response from GitHub Copilot, providing more specific tools and commands.
Very much like custom Copilot Instructions, which can work alongside the custom chatmodes, the chatmodes allow more specific structure, in this example, we will create a custom chat mode, that can assist us with writing and editing documentation for Microsoft Learn, aligned to the Microsoft Writing Style.
Microsoft recently unveiled Service Groups in Azure, now available in limited public preview - sign up for the preview and test it out! You need to have been enrolled in the public preview to provision them, either through the Portal or via the API (i.e., Terraform, Bicep, PowerShell, REST API calls, etc.).
If you’ve ever struggled with managing sprawling applications across multiple resource groups, subscriptions, and teams, Service Groups are designed with you in mind.
Where Azure Policy, Resource Graph, Tags, and Management Groups give you compliance, visibility, and hierarchy, Service Groups add an entirely new dimension: flexible, application-centric grouping of your Azure resources, without being limited by deployment boundaries.
Imagine being able to view, report on, and manage an entire application or workload, regardless of where its resources reside. Whether you’re in FinOps trying to track costs, in Ops trying to view health, or a security lead wanting to understand exposure, Service Groups give you the lens you’ve been missing.
Today we are going to look at using the Terraform AzAPI to deploy Service Groups in Azure.
The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. The architecture is straightforward: Developers can expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.
There are different types of primitives that an MCP server can expose, which extend the ability of your AI applications and clients to create, read: Resources are a core primitive in the Model Context Protocol (MCP) that allows servers to expose data and content that clients can read and use as context for LLM interactions. Prompts enable servers to define reusable prompt templates and workflows that clients can quickly surface to users and LLMs. They provide a powerful way to standardize and share everyday LLM interactions. Tools are a powerful primitive in the Model Context Protocol (MCP) that enable servers to expose executable functionality to clients. Through tools, LLMs can interact with external systems, perform computations, and take actions in the real world.
In our demo, the client will be Visual Studio Code. The client connects to an MCP server over HTTPS to call the document search tool. This tool retrieves the same content available to services like Copilot for Azure from the Microsoft Learn semantic search index. By using this index, responses can be better grounded in current documentation. For example, grounded results will surface that Azure Active Directory is now called Microsoft Entra ID and indicate which services are generally available within a specific region, based on the returned source content.