Codex Learning Lab

This repository is a structured AI-assisted engineering learning lab.

It is designed to hold small, production-style software engineering challenges, each focused on a specific concept such as API design, testing strategy, state management, performance, debugging, or deployment hygiene.

Codex is used here to assist with implementation, testing, documentation, and code review. The goal is not to outsource understanding, but to accelerate deliberate practice while keeping engineering judgment front and center.

The developer remains responsible for understanding, validating, and explaining every solution in this repository. Generated output should be reviewed critically, tested thoroughly, and rewritten when needed.

Each challenge lives in its own folder and includes:

• a challenge-specific README.md
• source code in src/
• automated tests in tests/
• a REFLECTION.md document capturing the learning process

Repository Structure

.
|-- README.md
|-- CONTRIBUTING.md
|-- LEARNING_LOG.md
|-- challenges/
|-- docs/

How To Use This Repository

1. Start from challenges/000-template/.
2. Copy the template to a new numbered challenge folder.
3. Define the problem, constraints, and architecture clearly.
4. Implement the solution with Codex assistance where useful.
5. Add or refine tests until behavior is validated.
6. Record engineering decisions and lessons learned.
7. Update LEARNING_LOG.md when the challenge is complete.

Documentation Areas

• docs/codex-prompts/ stores prompt patterns, useful instructions, and experiments related to AI-assisted development.
• docs/engineering-notes/ stores design notes, trade-off writeups, and implementation references that apply across challenges.

Current Status

This repository currently contains the foundational structure and reusable challenge template only. No sample application or completed challenge has been added yet.