Last year, I blogged about Container Patching with Azure DevOps, Trivy and Copacetic, and how to use Azure DevOps to automate the patching of your container images using Trivy and Copacetic. This was a great solution, but it required a lot of manual work to set up and maintain. Today, I am going to take a look at Continuous Patching with Azure Container Registry (ACR) and how to use it to automate the patching of your container images.

Today, we are going to use the Azure Storage SFTP functionality and an Event Hub to trigger an Azure Function. The Azure Function will then process the Write Logs, and output the File name, SFTP Local User name, Agent header and the SFTP Client IP address to the host, from there you can do whatever you want with the data.

You've probably heard the acronym 'MCP' before or Model Context Protocol (MCP). Open-sourced by Anthropic in November 2024, it has quickly become the go-to standard for connecting AI assistants to systems of various types.

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.

Today, we are going to use the MCP protocol to connect to Azure Communication Services and send an email from an MCP client and server running on a GitHub Codespace from GitHub Copilot.

Today, we are going to take a look at something a little different. We are going to talk about workload criticality, methods to assess how critical your workload is, and the ramifications of that criticality in terms of the Cloud.

I've talked about GitHub Codespaces before, but what I haven't covered - is using a GitHub Codespace to connect to a repository in Azure DevOps. It's classed as 'Remote Repository' in the Codespace, however, it's something that can be done.