Share Description
This course, created by IT automation expert Adam Bertram, is designed to help developers master complex, production-grade AI development workflows. While basic AI assistants work well for simple autocomplete suggestions, handling large-scale development such as parallel feature building, deep research, GUI testing, and repetitive team tasks carries significant risk if managed ad hoc. This practical course teaches you how to leverage OpenAI Codex's most powerful capabilities to isolate development risk and scale engineering output. Through hands-on walkthroughs, you will learn how to isolate experimental code changes using Git Worktrees, orchestrate parallel subagents for automated research and code synthesis, utilize Computer Use for GUI-bound testing, and package proven engineering patterns into reusable, project-level skills.
Topics This Course Covers
- The Codex Worktree Lifecycle: Managing parallel development tasks by isolating code changes, running experiments safely, and making clean handoffs using disposable or permanent Git Worktrees.
- Subagent Orchestration: Spawning general and custom subagents in Codex to run parallel developer research, analyze complex codebases, and synthesize multi-agent findings.
- Computer Use for GUI Testing: Leveraging Computer Use capabilities to execute GUI-dependent coding tasks, conduct frontend visual QA, and inspect UI evidence automatically.
- Creating Project-Level Skills: Packaging repeatable coding workflows into custom Codex skills to standardize development practices within a repository.
- Cross-Project Skill Sharing: Modularizing, reference-splitting, and sharing custom skills across multiple projects and engineering teams for long-term productivity.
Who Will Be Benefited Taking This Course
- Software Engineers & Full-Stack Developers: Programmers who want to move beyond basic one-shot prompts and direct autonomous subagents to handle complex, multi-file code tasks.
- Tech Leads & Senior Architects: Engineering leaders responsible for standardizing team workflows, creating shared coding skills, and managing parallel feature development safely.
- QA & Frontend Automation Engineers: Professionals looking to automate visual UI inspection, GUI-dependent testing, and end-to-end quality assurance using Computer Use capabilities.
- DevOps Engineers & Technical Consultants: System administrators and consultants looking to automate repetitive IT workflows and build scalable, agentic automation pipelines.
Why Take This Course
Handling parallel code changes, deep codebase research, and visual UI testing manually creates significant developer overhead and context-switching fatigue. Taking this targeted course equips you with the advanced skills necessary to transform OpenAI Codex from a simple code assistant into a full-fledged agent orchestration tool. By learning to combine Git Worktrees with subagent swarms, Computer Use QA, and custom skill libraries, you can run risky experiments safely without polluting main branches or sacrificing code quality. Whether you want to scale your individual developer output or build standardized, reusable AI workflows across your engineering team, this practical course provides the exact strategies needed to manage complex software projects with confidence.
