Checkpoints

Save and restore development progress with intelligent checkpoints that capture your entire development state.

Checkpoints in Byteable AI Code are intelligent snapshots of your development progress that go beyond simple file saves. They capture your conversation history, project state, AI context, and decision-making process, allowing you to resume work exactly where you left off or explore alternative approaches without losing progress.

What Checkpoints Capture

Development State
  • • File changes: All modified files and their current state
  • • Project structure: Directory layout and organization
  • • Dependencies: Package versions and configurations
  • • Environment: Development server state and settings
AI Context
  • • Conversation history: Full chat context and decisions
  • • AI memory: Learned patterns and preferences
  • • Active tasks: Current objectives and progress
  • • Mode state: Plan/Build/Debate mode context

Checkpoint Types

Automatic Checkpoints
Created automatically at key moments
Feature Complete
Major Refactor
Before Risky Changes
Session End
Manual Checkpoints
Created when you want to save progress
Milestone Reached
Experiment Start
Good State
Before Break
Shared Checkpoints
Collaborate with team members
Team Handoff
Code Review
Knowledge Transfer
Pair Programming

Key Benefits

Safe Experimentation
  • • Try risky refactoring with confidence
  • • Explore alternative implementations
  • • Test different architectural approaches
  • • Quickly rollback if experiments fail
Parallel Development
  • • Work on multiple features simultaneously
  • • Compare different solution approaches
  • • Maintain stable and experimental versions
  • • Merge successful experiments back
Team Collaboration
  • • Share complete development context
  • • Hand off work with full AI memory
  • • Collaborate on complex problems
  • • Maintain team knowledge continuity
Risk Management
  • • Never lose important progress
  • • Recover from accidental changes
  • • Maintain backup of working states
  • • Document decision-making process

How to Use Checkpoints

Creating Checkpoints
  • • Manual: Use the checkpoint button or command
  • • Automatic: Configured triggers create them automatically
  • • Named: Add descriptive names for easy identification
  • • Tagged: Organize with tags for better management
Restoring Checkpoints
  • • Full restore: Return to exact previous state
  • • Partial restore: Restore only specific components
  • • Compare: See differences between checkpoints
  • • Merge: Combine changes from multiple checkpoints
Best Practices
  • • Create checkpoints before major changes or experiments
  • • Use descriptive names that explain what was accomplished
  • • Regularly clean up old checkpoints to save storage space
  • • Share checkpoints with team members for better collaboration
  • • Use tags to organize checkpoints by feature or milestone