Linux Systems Programming (AI-Accelerated)

Course Description

Formerly "Linux Systems Programming."

EMBD.X420 is an industry-paced Linux systems programming course built for practicing engineers in the Embedded Devices track. Students learn the Linux/POSIX programming model by implementing real tools and services, and by building one integrated project end-to-end over the full 10-week run.

The course project is a custom shell that evolves from a minimal command runner into an AI-enhanced developer tool ("AiShell"). Students use Linux system APIs to implement features week-by-week (files, processes, pipes, signals, threads, sockets) and also implement the surrounding engineering work that makes systems software usable in production: packaging, structured documentation, stable CLI interfaces, and automation.

Naming used in course materials:

• Project/system name: AiShell
• Reference command/binary name used in examples: aishell

A defining characteristic of EMBD.X420 is that AI is not optional. AI-assisted development is required and explicitly taught as a professional workflow to increase productivity, expand scope, and accelerate learning of advanced topics that are difficult to cover formally in a short course (for example grammar-driven parsing and security concepts). Later in the course, students also build for AI by exposing structured command metadata and agent-oriented interfaces (MCP-style patterns) so the evolving shell can be safely and usefully driven via a natural-language interface.

Development is performed on a Linux desktop environment. Students may optionally run or deploy the resulting tools on an embedded Linux target (for example a Raspberry Pi or other embedded Linux device).

Learning Outcomes   
At the conclusion of the course, you should be able to

• Implement and explain core Linux/POSIX system programming concepts across file I/O, processes, IPC, signals, threads, and sockets.
• Design a clean command-line tool interface with consistent help, stable flags, and predictable error behavior.
• Build an integrated shell that executes built-in tools and external programs, including pipelines and redirection.
• Package and document a suite of tools with structured metadata and generated documentation.
• Apply an AI-assisted workflow responsibly: planning, implementing, refactoring, debugging, and verifying systems code under time constraints.
• Expose structured tool metadata and agent-facing interfaces so an AI assistant can safely discover capabilities and propose actions.