I remember watching a video from Mischa where he said that one should apply for DevOps related roles even if you’re a beginner. the market rewards motion more than mastery you don’t wait to become “ready” for devops but instead you just start simulating readiness some things I picked up to start building this path: - rename yourself on LinkedIn to Aspiring DevOps Engineer - then begin to assemble visible proof of intent - small, sharp projects that make you legible to the algorithm I also made a small list to consider based on watching Mischa: Linux: learn to speak to metal directly. cd, ls, grep, chmod. control. destroy. rebuild. Python: the duct tape of automation lol? Git: the memory palace of collaboration CI/CD: Jenkins, GitHub Actions, GitLab CI, CircleCI, Travis CI. Docker: ship universes in boxes. make your app portable. Kubernetes; learn to command swarms. chaos becomes choreography. Cloud: pick a god (AWS, Azure, GCP) and learn compute, storage, networking Infrastructure as Code: Terraform or Ansible. Monitoring: Prometheus, Grafana, ELK Stack. Networking: the invisible bones of the internet: TCP/IP, DNS, Firewalls. On RESUME building: recently i also learned about the “Why, How, What” approach behind strong resumes. not just simply listing what you did, but why it mattered, and how you pulled it off. I understood that the trick is to write your bullets like micro case studies in leverage. - What: the action, starting with a verb. - How: the method or tool, the mechanism of impact. - Why: the consequence, the measurable delta in the system. example: "Automated deployment pipelines using GitHub Actions and Docker, reducing release time by 30%." QUESTION: Is this how you write it? I'm wondering: for a complete beginner writing about small personal projects, is something like this even relevant? or how would you write it if you were documenting your early devops work, before the metrics, before the job title?

Home labs teach you a lot of things, but miss some other things. What if there were "community labs", focusing on "home lab" blindspots? Would you participate? Like navigating legacy systems, coordinating across dysfunctional teams, managing hundreds of interdependent resources, debugging production incidents under pressure, making architectural trade-offs with incomplete information, technical debt, and human factors.

Throughout my career, I've witnessed so many instances of technical debt, neglected configuration, overengineering, that using tech debt as a content topic to grow on social media is genuinely one of the strategies I'm considering. I'd like to hear your most frustrating stories to see if they match any of mine. The concept: take a technical debt horror story, create a lab environment that will reproduce that scenario, and build videos/articles on how to resolve them, and why someone should even care — especially non-technical decisionmakers to whom these issues are even less obvious, but can be proven methodically to have a huge financial impact long-term.

While we execute this command: kubectl create deployment nginx --image=nginx We are not just creating a container, we are telling Kubernetes to create a Deployment, which ensures Pods running Nginx exist and stay running. The sequence is: kubectl does not create Pods directly, It sends a request to the Kubernetes API server (running on the control-plane node). Then the API server validates the request. Kubernetes creates a Deployment resource in etcd. The Deployment automatically creates a ReplicaSet. The ReplicaSet instructs the scheduler (control plane) to assign the Pod to a worker node.

I'd would love to see what kind of homelabs have been built by this community, whether bare-metal or virtualized! What is your hardware stack? What is your software stack? Got any pictures? What about Git repos?