Artificial Intelligence is everywhere. But meaningful results are not. We reached a point where access is no longer the problem. Tools are abundant. Content is abundant. “AI-powered” workflows are everywhere. Even so, most professionals are still thinking and operating at the same level as before. Something is off. The issue is not the technology itself. It is how it is being used. Right now, most
The Problem Most engineers deploy to Kubernetes by clicking buttons in a UI. I built Archnet — a fully automated Internal Developer Platform What is an Internal Developer Platform? An IDP is the infrastructure layer that sits between your code How code gets deployed How secrets are managed How the system monitors itself How failures get detected and fixed Most companies pay Humanitec or Backsta
We had ArgoCD running perfectly. Every deployment was reconciled from Git. Drift detection worked. Rollbacks were one-click. Our GitOps setup was clean. Developers still couldn't provision a staging environment without pinging the platform team. That gap — between "GitOps in place" and "developers can actually self-serve" — is where most platform engineering teams get stuck. GitOps solves a real p