Git and version control

Protect your work with repositories, commits and a recovery path before AI makes larger changes.

What this step is for.

Git records snapshots of project history. A repository is the project history; a commit is a named checkpoint; a branch can isolate work. AI assistance is much safer when every meaningful change can be compared and reverted.

Keep the scope small enough that you can inspect the result yourself. AI can accelerate the work, but it should not erase the distinction between a suggestion, a changed file, a successful build and a verified product.

Take one concrete action.

Initialize Git in your practice project, make a clean baseline commit, change one file, inspect the diff and commit the verified change. Then practice restoring the baseline in a disposable copy.

Turn the idea into evidence.

Do the action in a disposable or backed-up workspace first. Write down what you expected to happen, what actually happened and what you changed when the result differed. This small habit becomes increasingly important as your AI tools gain access to more files and commands.

Do not move on until this is true.

You can show two commits and explain how to return to the earlier one without guessing which files changed.

Why we use this principle.

Nyfir Studios treats generated output and verified output as different states. Development work on local AI, Android software and bookkeeping workflows has repeatedly shown that recoverable state, explicit tests and clear product status are more useful than simply producing more output.