The Problem with AI Terminals Today Every AI terminal tool works the same way: you describe what you want, the AI suggests a command, you copy it, alt-tab, paste it, run it, check the output, alt-tab back, describe the next thing... rinse and repeat. There is a cognitive cost to every context switch. When you are debugging a production issue at 2 AM, those seconds add up. WinkTerm takes a differ
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
Introduction "The best developers have always built their own tools." — The cmux Zen This is the 54th article in the "One Open Source Project a Day" series. Today, we are exploring cmux. If projects like pi-mono or Warp are redefining terminal interaction logic, cmux is building a new "physical space" for the AI Agent era. It is not just another terminal emulator; it is a highly programmable te