This project offers an interactive, AI-driven game designed to teach Docker, Linux, networking, and Kubernetes. It targets engineers and power users seeking a hands-on, engaging alternative to traditional courses, providing practical experience through simulated real-world DevOps scenarios within a narrative framework.

How It Works

The core approach utilizes an AI editor (e.g., Claude Code, Cursor) as the game engine. Users clone the repository, open it in their chosen AI editor, and initiate the game by typing "let's play." The AI, embodying a mentor named Sarah, guides users through lessons and challenges. It reads game files and interacts with the user's terminal to verify Docker and Kubernetes configurations, simulating real-world DevOps tasks within a fictional chaotic coffee startup.

Quick Start & Requirements

• Primary install: Clone the repository (git clone https://github.com/ericboy0224/learn-docker-and-k8s.git) and navigate into the directory (cd learn-docker-and-k8s).
• Prerequisites: Docker, Docker Compose v2, and a compatible AI editor (Claude Code, Cursor, Windsurf, Copilot, Cline, Codex CLI, Gemini CLI, etc.). kubectl and kind are optional for advanced chapters (6-7).
• Setup: The AI editor handles the game logic; no server setup is required.
• Links: GitHub Repository

Highlighted Details

• AI as game engine: Prompt files serve as game code, eliminating the need for a separate server.
• Story-driven learning: Features characters and a narrative set in a chaotic coffee startup, making learning engaging.
• Real environments: Utilizes actual Docker and Kubernetes on the user's machine for practical experience.
• Challenge mode: AI provides progressive hints rather than direct answers, promoting problem-solving.
• Cross-learning: Integrates Linux fundamentals and networking concepts naturally within Docker/K8s contexts.
• Safe sandbox: All resources are prefixed with learn- for easy cleanup and to prevent host damage.

Maintenance & Community

This is an open-source project welcoming contributions for new challenges, chapters, translations, bug fixes, and platform support. No specific community channels (e.g., Discord, Slack) are listed.

Licensing & Compatibility

The project is licensed under the MIT License, which is permissive for commercial use and closed-source linking.

Limitations & Caveats

The primary dependency is a compatible AI editor, which may be a barrier for some users. While Docker and Compose are core requirements, full Kubernetes functionality requires optional kubectl and kind installations for later chapters. The learning experience is heavily dependent on the user's interaction with the AI editor.