From First Commit to Production Applications — a 9-course Coursera specialization that takes you from zero GitHub experience through AI-assisted development to building production applications with GitHub Copilot and AI agents.
| # | Course | Focus | Capstone | Labs |
|---|---|---|---|---|
| 1 | From Zero to Pull Request | Git workflow, branches, PRs, issues, forks, AI agents | Capstone | — |
| 2 | Security, Identity, and Access | 2FA, SSH keys, repository visibility, org policies | Capstone | — |
| 3 | Codespaces, Actions, and Ecosystem Tools | Cloud IDEs, GPU instances, CI/CD, Copilot | Capstone | — |
| 4 | Enterprise Administration Across Seven Domains | Org management, RBAC, compliance, audit logs | Capstone | — |
| 5 | Evaluating and Integrating AI Models | GitHub Models, API tokens, model comparison | Capstone | — |
| 6 | Advanced Prompt Engineering for Code | Multi-file context, slash commands, Copilot Chat | Capstone | Labs |
| 7 | AI-Augmented Testing and Refactoring | Test generation, code review, refactoring patterns | Capstone | Labs |
| 8 | Governing AI-Generated Code | Security audit, license compliance, responsible AI | Capstone | Labs |
| 9 | Production Application Capstone | End-to-end project integrating all specialization skills | Capstone | Labs |
All course code and labs live in public GitHub repositories under the paiml organization.
| Repository | Courses | Description |
|---|---|---|
| mastering-github | 1–5 | Main specialization repo — capstone projects, course structure |
| advanced-prompting-with-github-copilot | 6 | Labs for multi-turn prompting, context scaffolding, conversational AI |
| ghcp-for-systems-level-development | 7 | Labs for AI-assisted TDD, large-scale refactoring, infrastructure as code |
| responsible-ai-dev | 8 | Labs for validating AI-generated code, custom instructions, model selection |
| GitHub-Copilot-Mastery-Capstone | 9 | Capstone project — full-stack production application with Copilot |
| ruchy-docker | 9 | Reference project — Docker benchmarking (used in capstone) |
| ruchy-lambda | 9 | Reference project — AWS Lambda performance (used in capstone) |
Each course includes a hands-on capstone project that integrates all modules into a realistic scenario. Completed capstones can be shared on LinkedIn as portfolio projects. See the capstones/ directory.
git clone https://github.com/paiml/mastering-github.git
cd mastering-github
make checkmake help # Show available commands make lint # Lint markdown files make test # Validate course structure (9 courses, capstone sections) make check # Run lint + test
Each course is ~60 minutes of 3–5 minute videos organized as:
Course → Module → Lesson (3–5 videos) → Key Terms + Reflection
Every module ends with a Critical Thinking Assessment (quiz + role-play practice assignment).
- Liam Parker — Educator · Developer tools, cloud platforms, AI-assisted development
- Noah Gift — Founder, Pragmatic AI Labs · Duke University
- Alfredo Deza — Author and content creator · Python, Rust, DevOps, ML
Course content copyright Pragmatic AI Labs. Code examples are MIT licensed.