Terminal-based group chat app with real-time WebSocket messaging, file sharing, themes, and admin tools — built with Go and Bubble Tea.

Self-hosted task management that combines the simplicity of personal with the power of professional project organization. Built for individuals and teams who value privacy, control, and efficiency.

Have you heard of Kubernetes (also known as k8s)? Until a few months back, I knew it existed and that it was like infrastructure’s holy grail. It has to cover the basics, like auto-scaling and load balancing or automated rollbacks… And then there are millions of tools to build on top of it.

As we recently migrated our deployment from AWS Elastic Container Service (ECS) to AWS Elastic Kubernetes Service (EKS; managed Kubernetes cluster), I wanted to share some tips. It also feels nice to do that on the 10th anniversary of “Kubernetes: The Future of Cloud Hosting” MeteorHack’s blog post.

Please keep in mind that a Kubernetes cluster is an extremely complex beast, and I’m pretty far from being able to explain all the “whys” you may have. Our amazing DevOps Engineer managed to make it work, and I’m really happy with the current setup. Both because the app performs better at a lower cost and because I learned a lot along the way.

Kubermatic Kubernetes Platform is in an open source project to centrally manage the global automation of thousands of Kubernetes clusters across multicloud, on-prem and edge with unparalleled density and resilience.

This article will teach you how to continuously promote application releases between environments on Kubernetes using the GitOps approach.

Most guides to the finer aspects of managing databases like Postgres are… not great. The Postgres documentation is well-written, but it has too much information for most developers. On the other hand, most online Postgres optimization guides are essentially a repeated version of: “Run this command. Got it? Cool.” This should provide you with a relatively brief introduction to Postgres tuning, focusing on the most important knobs, while also describing how these knobs relate to Postgres’s overall functioning and internals.

We're using AI to build an agentic product that works collaboratively with responders to improve incident investigations and resolve incidents faster. A bold claim, I know, and I think pretty impressive to land the word “agentic” so early on—I promise it’s the last time I use it.

After six months of digging into this, I’m convinced: AI in incident response won’t just be helpful—it’ll be essential. As more software is built with, and increasingly by, AI, responders will have less and less context about the systems they’re operating. That shrinking understanding—combined with the ever-growing volume of software—only increases the need for tools that can assist.

Done right, there's a huge upside in this approach too—faster incident resolution, reduced customer impact, and less cognitive burden on the folks putting out the fires.

But with more automation comes a new shape of risk—much of which is captured in Lisanne Bainbridge’s 1983 paper, Ironies of automation. In the paper, Bainbridge explains that automation meant to help can paradoxically make things harder. As routine tasks get automated, human skills fade from lack of practice, so when the system fails (and they will!), responders are left underprepared and out of context.

Working in tech companies, I’m yet to see these risks materialise seriously, but there are definite elements of truth here. Count the number of Kubernetes incidents where operators have no idea what’s happening and you’ll get the gist.

In this post I'm going to demonstrate 2 reasons I will be avoiding auto-increment fields in Postgres and MySQL in future. I'm going to prefer using UUID fields unless I have a very good reason not to.