
Leverage OpenAI Codecs as a specialized AI pair programmer for coding, offering multi-file reasoning, context management, and generating, refactoring, testing, and reviewing code across the development lifecycle.
Install codecs via npm or brew, then use the codecs CLI and the VS Code extension to manage agent modes, monitor tokens and rate limits, and connect to GitHub.
Master how CodeX manages context as short-term memory—prompts, past commands, and opened files—to generate accurate completions and prevent memory leakage.
Explore codex use cases and feature ownership, outlining API design, service logic, testing, edge cases, constraints, and deliverables through structured prompts and templates.
Explore best practices for developers, including constraints, plan of action prompts, asking for reasons, stepwise reasoning with test-driven development, showing changed files, and codecs with git flow integration.
Learn git flow with codecs by using feature branches, pull requests, and disciplined commits to protect main. See a layered dotnet refactor example featuring entity, dao, bo, and di.
Develop robust git flow by planning and implementing employee tests in a c# project, setting up in-memory entity framework test infrastructure, and validating controllers, dao, and bo.
Vibecoding is not about copying AI-generated code — it’s about working with AI the same way real engineering teams work.
In this course, you’ll learn how to build applications using OpenAI Codex as a true AI pair programmer, not as a random code generator. You’ll understand how Codex thinks,
how it maintains context, and how to control it like a disciplined junior-to-mid level engineer inside your development workflow.
Unlike traditional ChatGPT usage, Codex is designed for real software development — it understands repositories, multi-file projects, diffs, constraints, tests, and Git-based workflows. This course teaches you how to use that power correctly.
You’ll learn in this course how to:
Implement features step-by-step
Refactor legacy code safely
Generate meaningful tests
Perform AI-assisted code reviews
Maintain clean architecture and boundaries
Avoid context loss and hallucinated logic
You’ll also master advanced prompting techniques used by professional developers — defining roles, constraints, ownership, deliverables, and commit-by-commit workflows.
By the end of this course, you won’t just “use AI for coding.”
You’ll think like an engineer who collaborates with AI effectively.
This course is practical, workflow-driven, and focused on real development scenarios — exactly how modern developers are starting to build software in 2026 and beyond with confidence and realtime exposure.