The Kubernetes story is one I hear often. Teams adopt K8s expecting operational simplicity, only to discover they've traded application complexity for infrastructure complexity. For a solo founder focused on content creation, maintaining a Kubernetes cluster was simply the wrong trade-off.

This article is about the patterns that actually work when you're managing real databases in Kubernetes, not hello-world demos: when your boss says "everything should be containerized" but your databases laugh in the face of ephemeral pods, and when GitOps meets a 200GB production database that absolutely cannot lose a single transaction.

Operators make life easier, but they are not always an option. In this post, I'll walk through a practical way to deploy large language models on OpenShift without relying on the OpenShift AI or NVIDIA operators. The approach uses llama.cpp as a lightweight runtime engine and runs a quantized GGUF model to enable efficient inference with minimal dependencies.

This article explains how Cosign, Kyverno, and Harbor can work together to enforce image signature verification in Kubernetes. The approach is well-suited for enterprise environments with private registries, custom TLS certificates, and restricted access to public transparency logs.

Jenkins might feel like legacy tech, but it's still powering CI/CD at thousands of companies. Instead of ripping it out, here's how to modernize it with Kubernetes — solving cost tracking, resource isolation, and scalability problems along the way.

This blog aims to provide a practical example of how core tools like Kubernetes, Terraform, and DevContainers can be combined to build a local data platform in a structured and maintainable way.

Netfence runs as a daemon on your VM/container hosts and automatically injects eBPF filter programs into cgroups and network interfaces, with a built-in DNS server that resolves allowed domains and populates the IP allowlist.

A lightweight, extensible Kubernetes Operator that probes any endpoint—HTTP/JSON, TCP, DNS, ICMP, Trino, OpenSearch, and more—and routes alerts to Slack or e-mail with a simple Custom Resource.

Argo CD Diff Preview is a tool that renders the diff between two branches in a Git repository. It is designed to render manifests generated by Argo CD, providing a clear and concise view of the changes between two branches. It operates similarly to Atlantis for Terraform, creating a plan that outlines the proposed changes.

So we built a pipeline that’s intentionally “boring”: GitHub pull requests, GitHub Actions, Kubernetes, and Argo CD — split across staging and production.

This post is a walkthrough of how a feature goes from a developer’s machine to real users, and why we made the choices we did.

How I reduced pod startup times from minutes to seconds with intelligent image preloading

If Red Hat trusts OpenShift to run the control plane for their largest infrastructure orchestrator, the same pattern should apply to your smallest.

Recently, in our daily operations, we took a deep dive into the inner workings of linkerd-destination, one of the most critical components of the Linkerd control plane.

If you've ever wanted to develop a command line client for a Kubernetes API, especially if you've considered making your client usable as a kubectl plugin, you might have wondered how to make your client feel familiar to users of kubectl. In fact, the Kubernetes project provides two libraries to help you handle kubectl-style command line arguments in Go programs: clientcmd and cli-runtime (which uses clientcmd). This article will show how to use the former.

I'm starting a series of blog posts to explore CloudNativePG (CNPG), a Kubernetes operator for PostgreSQL that automates high availability in containerized environments.

OpenKruise Agents provides best practices for managing AI agent workloads in Kubernetes. It is a sub-project of the open-source workload project OpenKruise under the Cloud Native Computing Foundation (CNCF), specifically tailored for the AI agent domain. OpenKruise Agents accelerates AI agent deployment and makes it easily accessible to both AI algorithm scientists and infrastructure engineers.

In Amazon Web Services, each instance type has a different upper limit on how many Pods it can run. For example, an m5.large instance can only run 29 Pods, but an m5.4xlarge can run up to 234. The reason is that each EC2 instance can only have a limited number of IP addresses assigned to it.

In Kubernetes clusters, GPUs are expensive. Really expensive. And when your data science team is fighting over GPU resources like it’s Black Friday at Best Buy, you know something’s broken.

That was the challenge I faced a few years ago. I implemented this solution back then, and though it’s been a while, I thought now would be a great time to share the experience.

We had a k8s cluster with 8 NVIDIA A100 GPUs, and our data scientists were… unhappy. Small model training jobs waited hours for GPU access while large distributed training jobs hogged entire GPUs at 30% utilization.

The culprit? GPU allocation granularity. Kubernetes treats GPUs as atomic resources, you either get a whole GPU or nothing. There’s no middle ground.

I needed to fix this. And the solution wasn’t what I expected.