Checkpoints for AI Code Changes: The Feature Nobody Talks About

Everyone compares AI coding tools on model quality. Almost nobody compares them on recoverability — and that's the thing that actually saves your day.

The scenario

You ask an AI to "refactor the payment module." It touches 9 files. Six of them are fine. Three of them break something subtle that you won't notice until production.

With most tools, your options are:

• Manually figure out which of the 9 changes was wrong
• git stash and lose everything
• Revert the whole thing and start over

What checkpoints change

A checkpoint is a snapshot of your code at a specific moment — usually right before each AI action. Instead of one big "before" and one big "after," you get a timeline:

[checkpoint] before refactor
  [checkpoint] after file 1-3
    [checkpoint] after file 4-6   <- the break is here
      [checkpoint] after file 7-9

Now when something breaks, you don't debug the whole change. You rewind to the last good checkpoint and go from there.

Why this matters more with AI

Human commits are usually coherent — you commit when something works. AI changes are exploratory. The agent is guessing, and guesses are wrong sometimes. Checkpoints turn "the AI broke my code" from a disaster into a two-click fix.

The mental shift

Once you have checkpoints, you stop being afraid of letting the AI try things. You can say "go ahead, attempt the refactor" without the usual dread, because you know you can always rewind.

That fearlessness is the real productivity gain — not the typing speed.

Arcwright builds checkpoints into every AI step on Windows, and it's free to start. Whatever tool you pick, look for this feature. It's the difference between an assistant you supervise and one you're scared of.