Most Kubernetes teams track pod CPU and memory. Almost none track what the cluster actually costs to run.

How we went from scaling video analysis on EKS to running autonomous coding agents in a custom agent harness, and why Kubernetes was the obvious choice.

Sometimes, you just want to run a Large Language Model (LLM)… no Jupyter notebook, no training pipeline, no fancy UI.

Zero-code log sanitization sidecar for Kubernetes. Prevents data leaks (GDPR/SOC2) by redacting PII from logs before they leave the pod.

Kubebuilder is a framework for building Kubernetes APIs using custom resource definitions (CRDs).

A single, opinionated Helm chart for deploying any Kubernetes application workload. Instead of maintaining separate charts per application, define your entire deployment through values.

Hunk is a review-first terminal diff viewer for agent-authored changesets, built on OpenTUI and Pierre diffs.

For many organizations, some form of blamelessness has become a more standard practice and blame-awareness has been gaining in popularity. However, there is an anti-pattern I have noticed as well, which I like to call superficial (or shallow) blamelessness that I think is important for people to be on the lookout for.

SREcon Chair Heinrich Hartmann on why the age of AI-assisted engineering demands a radical return to design rigor.

Eraser helps Kubernetes admins remove a list of non-running images from all Kubernetes nodes in a cluster.

openhare is an AI-powered, cross-platform desktop SQL client with multi-database support, built for everyday development, data analysis, and DBA management workflows.

CodeBurn tracks token usage, cost, and performance across 19 AI coding tools. It breaks down spending by task type, model, tool, project, and provider so you can see exactly where your budget goes.