Git is a graph of commits. Once you see that, everything — merges, rebases, worktrees, cherry-picks — follows from the same mental model. This course builds that model from scratch and applies it to the AI project workflows that actually matter in 2026.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on git, github, and version control you can find — even without producing a single minute of custom video.
This course is built by people who ship production git, systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
The commit graph, objects (blobs, trees, commits), refs, HEAD, and the index. Once you understand these 5 things, every git command is just graph manipulation.
init, clone, add, commit, push, and pull. The workflow that covers 80% of daily Git usage. The .gitignore patterns that prevent committing secrets and build artifacts.
Branch creation, fast-forward vs 3-way merges, rebase vs merge decisions, conflict resolution, and the pull request workflow that teams use.
Actions for CI/CD, Copilot integration, Issues and Projects, repository settings, branch protection rules, and the GitHub API.
Git LFS for model weights, git worktrees for parallel agent experiments, .gitattributes for handling large binary files, and the commit conventions that make AI experiment tracking useful.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
The complete Git workflow from init to pull request. Covers the commit graph mental model that makes everything else click.
Branch creation, merge strategies, rebase vs merge, and conflict resolution with visual examples.
Building automated test and deployment pipelines with GitHub Actions.
How git worktrees enable multiple working directories on the same repository. The feature that makes parallel agent development possible.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
The reference implementation. Reading the commit-graph and pack-objects source teaches you what Git is actually doing when operations are slow.
The official collection of .gitignore templates for every language and framework. Day 2 .gitignore setup starts here.
Large File Storage for Git. Used in Day 5 for model weights. The pointer file format source explains what gets committed vs stored remotely.
Official CI/CD workflow templates. The Python and Node.js workflows are the starting points for Day 4 automation.
Merge conflicts are just overlapping edits in the commit graph. Once you understand the graph, conflicts become routine instead of terrifying.
Git worktrees let you run multiple agent experiments on the same repo simultaneously. Day 5 covers this pattern in depth.
SVN, Mercurial, or nothing — the mental model shift to distributed version control is the hard part. This course makes it explicit.
The 2-day in-person Precision AI Academy bootcamp covers Git, GitHub, and version control hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
Reserve Your Seat