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Linux

Linux is the operating system running most cloud infrastructure. Understanding it is prerequisite to operating at the server level — deployments, security hardening, performance tuning, and diagnosis.

Essential Operations​

File system navigation: ls, cd, pwd, find, tree — the basic spatial orientation.

Process management: ps, top, htop, kill, systemctl — what's running and how to control it.

Networking: curl, wget, netstat, ss, iptables, ufw — connectivity and firewall rules.

Permissions: chmod, chown, sudo, visudo — who can do what.

Disk and memory: df, du, free, lsblk — resource consumption.

Logs: journalctl, tail -f, grep — what happened and when.

The compound skill: Linux mastery is knowing which command to reach for without thinking about it. The first 50 commands cover 90% of routine operations. The rest are diagnostic tools for situations where routine operations fail.

AI-Native Shifts​

AI coding tools can generate Linux commands from natural language. This changes the learning curve but not the need for understanding. An agent that generates a destructive command at the wrong scope is more dangerous than a developer who would have paused.

The judgment layer — knowing what a command will do before executing it — remains essential until verification tooling matures significantly.

Context​

Questions​

Which Linux skill — file system mastery, process management, or networking — provides the most leverage when diagnosing a production incident under time pressure?

  • At what cloud abstraction level (VPS, container, serverless) does Linux knowledge stop being necessary and start becoming specialized?
  • How do AI coding tools change the Linux learning curve — does natural-language command generation reduce the need for command fluency or make it more important?
  • Which Linux security hardening decision has the highest impact-to-effort ratio for a small team running cloud workloads?