Common Goals for the Minimum Continuity Layer™ (MCL)

• Keep project context from dissolving
MCL preserves decisions and working assumptions across sessions so small projects do not reset to zero.

• Reduce repetitive explanation
Carry forward prior definitions, goals, and constraints without re-entering them.

• Stabilize emerging work
MCL provides just enough structure to keep ideas coherent as they develop.

• Work across tools without lock-in
Your continuity persists even as models or interfaces change.

• Build habits that scale
The same structure that supports small projects can support larger ones later.

MCL is designed as scaffolding.
It provides structure without complexity.
When work grows in scope, the continuity already in place can expand with it.

Designed to fit your existing tools

• No new interface to learn. Looks just like the AI interface(s) you already use.
  
  

• Avoid lock-in to a single AI
  Not tied to a single AI — your work remains usable even as tools change.
  
  

• Works alongside the AI tools and agents you already use

• MCL preserves continuity across your existing AI workflows rather than replacing them.
  

• MCL creates a new structural entry point into AI-assisted work.
  Once continuity is established, new forms of scaffolding become possible.
  

• The accompanying guide illustrates one example: coursework scaffolding that persists across sessions and evolves over time.