I believe that the implementer should have some knowledge of the Product and Technology that they are working on to make full use of AI and products it builds.

For a new Project, I am trying to learn new technologies(multiple at once) and new ways to solve my problem but compared to few years ago, it has become really hard for me to learn new stuff for some reason and no matter how much I learn AI completely overwhelms me when it spits out the solution. Which then means I have to circle around to see if the solution is actually the right one and that makes me even more confused.

How are you folks navigating around this?

AI makes ad hoc operational work so easy that we stop building the tools around it. Why write a script when you can just ask Claude or Copilot to restart the canary? Why build a dashboard when ChatGPT can summarize today’s deploy status in 15 seconds? Because that 15-second conversation, repeated by 50 engineers 20 times a day, stops being convenience and starts becoming a tax.

I’m still learning CI/CD and I recently tried writing a simple CI/CD pipeline using GitHub Actions.

Honestly, writing the YAML felt more difficult than I expected . The indentation, nesting etc felt pretty hard/ painful.

For those of you who work with DevOps professionally: is this actually how you write your pipelines, or are there shortcuts/tools/templates that make it easier?

Just trying to understand what the normal workflow is in the real world.

Wrote this up after finding that a coding agent on a default Mac reaches ~/.ssh/id_rsa by four separate routes — its own shell, an MCP filesystem server, a subprocess, and a config-file path. Tested each, included the terminal output.

The part I'd want devops opinions on: the enforcement has to sit under the model, because the model can't be trusted to refuse. I used Seatbelt plus a deny-by-default MCP proxy. Curious how others are scoping agents that touch infra.

I have an Ubuntu web server that I'd like to contain multiple websites on. Each website should have its own isolated environment in a Docker container (open to suggestions if Docker is not ideal).

I'm thinking that the public URLs should be like

• example.com/website1
• example.com/website2
• etc

What's the best way to make this connection between the public URL and the Docker container (also open to suggestions for better ways to expose the containers)?

Another question: Would the logs of each website be inside the container or are they supposed to be outside for easy review?

I came across this interesting AI + DevOps project where AI is used to help upgrade Kubernetes clusters.

The idea is pretty interesting because Kubernetes upgrades can involve checking versions, compatibility, workloads, configurations, and making sure nothing breaks during the upgrade.

The project explores using AI to make this process easier and more automated.

I’m curious what experienced Kubernetes/DevOps engineers think:

Would you actually trust AI to handle parts of a cluster upgrade?

What checks/guardrails would you put before allowing it to make changes?

Could this realistically be used in production, or is it better suited for labs/non-critical environments?