Kubernetes offers amazing capabilities to improve compute density compared to older runtimes such as virtual machines. However, in oder to leverage the capabilities of the platform, these tend to host applications from various tenants. This introduces a strong need for properly crafted controls and well-defined compliance to ensure the tenants use the platform correctly and do not affect one another. The RBAC capabilities provided out of the box by Kubernetes are quickly insufficient to address this need. This is where policy engines such as Kubewarden come into play. In this post we will look at how Kubewarden can be leveraged to ensure correct usage of a platform, how it compares to other policy engines, and how to best adopt it.

Making my development workflow as fast as possible is a big passion of mine. From customizing my development setup to get the last inkling of efficiency out of it, to thinking how to manage notes and knowledge resources to access them as quickly as possible. With the sudden ubiquity of AI in development tools, I came to wonder how AI could help me write code faster. Being quite the skeptic when it comes to AI actually generating code for me (using tools such as Cursor or GitHub Copilot), I came to investigate AI agents which specialise in code reviews. In this blog post I will share my experience using such an agent on a real world case. I will explore where such agents shine and where they are severely lacking.

GitOps is getting adopted more and more. However, there still seems to be some confusion as to what GitOps is, how it differs from regular CI/CD pipelines, and how to best adopt it. In this post we will quickly cover what GitOps is, and the three main lessons learned from using GitOps to manage infrastructure at scale both on premise and in the cloud.

Containers are everywhere. From Kubernetes for orchestrating deployments and simplifing operations to Dev Containers for flexible yet reproducible development environments. Yet, while they are ubiquitous, images are often built sub-optimally. In this post we will be looking at a full example of a Docker build for a Python application and what best practices to consider.