1. "Nginx vs Caddy vs Traefik in 2026 — I Ran All Three Under Real Production Load" by Yusuf Seyitoğlu.
A logistics client ran ingress-nginx on EKS for four years. Fine until it wasn’t supported. They asked me to compare Nginx (F5 NIC), Caddy, and Traefik on the same cluster before picking a path forward. Same twelve microservices. Same cert issuer. Same load tests. I ran each as the edge for six weeks.
2. "From intent to enforcement: Lessons from operating Kubernetes controllers at scale" by Sri Saran Balaji Vellore Rajakumar and Jayanth Varavani, AWS.
Kubernetes controllers are what make the platform’s declarative model real. They observe the state, reconcile toward the intent, and keep doing so as the system changes beneath them. At larger scales, though, the hardest problems are no longer about writing the reconcile loop. They are about preserving correctness while caches lag, objects churn, and enforcement depends on a complete view of the world. This post distills what we learned from operating two critical controllers in Amazon EKS at scale.
3. "Shipyard: How We Built Slack’s Next-Generation EC2 Platform" by Archie Gunasekara, Slack.
Service-level deployments were tricky, infrastructure drift was inevitable, and coordinating changes across multiple layers added complexity. Containers solved this for some classes of workloads, but not everything could migrate easily. We needed a new approach—one that brought modern deployment practices like immutability, progressive rollouts, and automated safety directly to EC2 instances.
4. "Where did those 800 pods come from?" by Tibo Beijen.
Recently I merged a pull request that was like any other pull request. But things unfolded quite differently. Things didn’t break, but could have. Things shouldn’t have happened, but did. The typical unlikely combination of factors, involving Argo CD ApplicationSets and mysterious AWS EKS Fargate nodes, resulting in some head-scratching, troubleshooting and interesting take-aways.
5. "Optimizing Kubernetes pod deployments for reliability with topology spread constraints" by Andre Newman, Gremlin.
Pod distribution plays a much bigger role in reliability than you might think. Fortunately, it’s easy to control when, where, and how Kubernetes distributes pods. By adding a few lines to your manifest, you can ensure your deployments are zone-redundant and evenly scalable. The feature is called topology spread constraints, and in this blog, we’ll explain how it works in full detail.
6. "How Kubernetes probes work" by Sam Rose, ngrok.
I’m going to show you, really show you, how probes work in Kubernetes. How they can make your application more resilient, and how they can help you prevent avoidable mistakes. Like restart loops that take hours to recover from, and dropping requests during rollouts. Every interactive demo in this post uses webernetes, my partial port of Kubernetes to TypeScript.
7. "Building an AI factory on Kubernetes" by Hrittik Roy, vCluster.
An AI factory is not just a model or a cluster. It is a pool of GPUs that many teams draw from at once: one team fine-tuning, another serving inference, a third running evaluations, all on the same accelerators. NVIDIA frames it as “infrastructure for the full AI lifecycle, from data preparation through training, fine-tuning, and high-volume inference”. In an enterprise that means one fleet, many teams, and different quotas, policies, and trust boundaries layered on top. The hard question is no longer how to train a model. It is how to give every team safe, isolated access to the same expensive hardware without anyone stepping on anyone else.
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