"treat 'em like cattle, not pets".
This was, and continues to be, how many look at Kubernetes Pods and microservice-based architecture. It makes a lot of sense for objects
AI is only as good as the information you provide it. Aside from the general hallucinations and wild outcomes we sometimes see from LLMs, the general gist of an Agent not performing as
Having an Agent run, whether it's on your local system (e.g - Claude Code) or in a k8s cluster, is now table stakes. Everyone from engineers to people in other professions,
Where does a person come into an AI workflow today? Is it during code creation? PRs? Quantifying business output? This is the question that every organization using AI is trying to figure out
