Git Alternatives for AI Coding: What Else Exists

If you’ve been coding with AI assistants, you’ve probably noticed that git doesn’t quite fit the workflow. Commits interrupt your flow, and the gaps between them are exactly when AI breaks things.

So what else exists? Is there a better alternative for AI coding?

Let’s look at the options.

The Short Answer

There’s no full replacement for git. Git is still essential for collaboration, code review, and deployment.

But there are tools that fill the gaps git leaves during AI iteration. The best approach is using git for what it’s good at and adding something else for continuous protection.

Option 1: Git Alone (With More Discipline)

The idea: Just commit more often. Before every AI operation, make a checkpoint commit.

git commit -am "before AI refactor"
# AI stuff
git commit -am "after AI refactor"

The reality: This requires discipline that breaks down when you’re in flow. You forget, you skip “just this once,” and that’s when AI breaks everything.

Verdict: Works in theory, fails in practice.

Option 2: Tool-Specific Checkpoints

Cursor Checkpoints: Cursor creates automatic restore points when you use Composer. Click “Restore” to go back.

Limitations:

• Only covers Composer, not Tab completions
• Doesn’t persist across sessions
• Reverts everything from a prompt, including manual edits you made after

Claude Code Undo: Use /undo or /undo all to revert changes.

Limitations:

• Only works within current session
• Context-dependent, may not always work
• Per-conversation only

GitHub Copilot: No built-in recovery.

Verdict: Better than nothing, but unreliable and limited to specific tools.

Option 3: VS Code Local History

VS Code (and Cursor) maintain a local history of file changes. Access via Cmd+Shift+P → “Local History.”

Limitations:

• File-by-file recovery only
• Tedious for multiple files
• Only works for files that were open
• Easy to forget it exists

Verdict: Useful backup, but not a primary protection strategy.

Option 4: Git Stash

Use git stash before risky operations:

git stash push -m "before AI experiment"
# Try something
git stash pop  # if it broke

Limitations:

• Manual, requires discipline
• Only one stash at a time (without extra commands)
• Only saves tracked files
• Easy to forget what’s stashed

Verdict: Decent for planned experiments, not for continuous protection.

Option 5: Automatic Snapshots (mrq)

mrq runs in the background and captures every file change automatically:

mrq watch

Every change is recorded with no commits or decisions required. When AI breaks something:

mrq restore abc123

Back to any point, not just where you committed.

Advantages:

• Zero effort after setup
• Works with any AI tool, any editor
• Every state recoverable
• Doesn’t interrupt flow
• Complements git rather than replacing it

Verdict: Fills the gap that git leaves.

Comparison

Option	Effort	Reliability	Coverage
Git commits	High (discipline required)	Fails when you forget	Only committed states
Cursor Checkpoints	None	Limited	Composer only
Claude /undo	None	Limited	Current session only
Local History	None	Partial	Files that were open
Git stash	Medium	Works when used	Manual, one at a time
mrq	None after setup	Always works	Every change

The Recommended Stack

Don’t think of it as “git vs alternatives.” Think of it as layers:

Layer	Tool	Purpose
Collaboration & History	Git	Commits, branches, PRs, deployment
Continuous Protection	mrq	Every change captured automatically
Emergency Fallback	Local History, Checkpoints	When you didn’t have mrq running

The workflow:

# Start session
mrq watch                    # Protection starts

# Work with AI
# Iterate freely, everything captured

# Hit a milestone
git commit -m "Feature complete"   # Clean history

# Deploy
git push                     # Normal git workflow

# Something broke?
mrq restore abc123           # Recover any state

Git handles collaboration and deployment. mrq handles protection during iteration. They complement each other.

Why Not Just Replace Git?

Git is deeply embedded in development infrastructure:

• GitHub/GitLab/Bitbucket for collaboration
• CI/CD pipelines triggered by git events
• Code review built around pull requests
• Deployment based on git branches and tags
• Team workflows everyone knows git

Replacing git would mean rebuilding everything around it. That’s not practical.

The better approach is keeping git for what it does well and adding what’s missing.

Summary

There’s no true git replacement for AI coding. Git is still essential for collaboration, history, and deployment.

What exists are tools that fill the gaps:

• Tool-specific checkpoints (Cursor, Claude) — limited but helpful
• Local History — file-by-file backup
• Automatic snapshots (mrq) — continuous protection that complements git

You don’t need to replace git. You need to add the layer of continuous protection that AI coding requires.

Related Reading

• Git for AI Coding: What’s Missing - The gap explained
• Why Git Commits Don’t Work for AI Iteration - The friction problem
• Do You Still Need Git When Using AI? - The complete picture
• Cursor Checkpoints vs Git vs mrq - Detailed comparison

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mrq adds continuous protection to your git workflow. Every change captured automatically, instant recovery when AI breaks things.