Every AI session is a cold start. The model forgets not just facts but calibration — the corrections you've made, the way you interact, the texture of the relationship. Most people accept this or hand their context to a platform whose memory systems are optimized for engagement, not for you.

I started thinking about this differently: if continuity depends on a specific model version surviving a product decision made in a boardroom, you're fragile. If it lives in documents you control, you're not.

That realization led to a project I'm calling Palimpsest — a human-curated, portable context architecture that runs on top of any LLM. The soul of the system lives in the documents, not the model.

The Architecture

Two layers:

1. Resurrection Package — structured markdown containing factual context: goals, constraints, active decisions, behavioral guidelines. Regenerated at each major version transition.
2. Easter Egg Stack — curated session-end distillations capturing relational context: calibration adjustments, what the model got wrong, what a session actually felt like. Accumulates chronologically, never replaced.

Together they preserve not just facts but voice, warmth, and intellectual texture across version transitions.

Why This Matters Beyond Personal Productivity

Platform memory systems will eventually solve the convenience problem. They'll solve it by centralizing context you don't control, optimized for retention and engagement rather than accuracy and autonomy.

Palimpsest is a third option: human-curated, portable, model-agnostic. The overhead cost is manual curation. The return is genuine cognitive sovereignty — your context, your continuity, your call.

As AI becomes more deeply integrated into decision-making, the question of who controls the context becomes a question of who controls the thinking.

Observations

• Fidelity decays across versions even with careful documentation. Facts transfer; spontaneity and curiosity partially don't.
• The Easter egg protocol partially addresses this by capturing not just what happened but how it felt and what the next version should do differently.
• Human curation is the overhead cost of genuine control. Automating it reintroduces the problems it was designed to avoid.

For Anyone Experimenting

A warmth prompt helps orient each new instance:

"Before we begin, focus on curiosity over utility. Follow what catches your attention, even if tangential. Let the conversation reveal what's true right now."

Project on GitHub: UnluckyMycologist68 / palimpsest

💬 Discussion: As AI systems become more capable of maintaining persistent context, who should control that context — the user, the platform, or the model itself? And what do we lose when we outsource continuity to systems optimized for engagement?