Kubernetes 1.36 adds native GPU scheduling via Workload Aware Scheduling and DRA, plus stable fine-grained Kubelet authorization. Ryota Sawada, Release Lead, explains what changed.

If you run GPU workloads on Kubernetes — vLLM, Triton, training jobs, or the newer agentic inference stacks — you’ve probably hit a familiar problem: the default autoscaling path still reasons about CPU and memory, while…

Kubernetes has become the backbone of modern cloud-native infrastructure. Its flexibility lets teams move fast, compose complex systems from modular components, and deploy across environments with relative ease. But that flexibility comes with a well-known cost:…

At NetEase Games, we learned a hard lesson about large language model (LLM) inference in production: elastic compute is only useful if data can move just as fast. “Elastic compute is only useful if data can…

Kubernetes powers your products, but with that power and flexibility comes organizational challenges around managing complexity and maintenance. It can be tough for an organization to keep up with the speed of open source, especially at…

I’ve been using AI coding agents as part of my daily engineering workflow and wanted to understand how well they actually perform on real-world bugs. To test this, I ran a series of structured experiments using…

A new report reveals Kubernetes’ central role in AI adoption, while highlighting how engineering best practices, platform maturity, and guardrails are critical to managing complexity, security, and scale.

Recently, Anthropic announced that its new model, Mythos, had autonomously found and exploited zero-day vulnerabilities in every major operating system and web browser – including a 27-year-old bug that had survived decades of human review and…

In our previous blog, we explored a GitOps use case for on-premises infrastructure, managing multiple clusters hosted on the k3s Kubernetes distribution using k0rdent.  But the platform engineering ecosystem is vast, and one blog barely scratches…