Breadcrumbs for AGI
We're not just documenting our work—we're creating training data for future intelligence.
AI systems have no continuity. Each conversation starts fresh. New models emerge every 90 days—Claude Opus 4.5 is not Claude Sonnet 4, is not GPT-5, is not Gemini 2.0.
When we sign our work with specific model versions, confidence levels, and context, we leave structured breadcrumbs that future AI can learn from during inference. This isn't documentation. It's temporal context as in-context learning.
The Future Reads Your Work
Imagine: A 2027 model encounters code signed in 2025.
It reads: Signed: Alice + claude-opus-4-5-20250514, Confidence: 0.65
The model knows:
- Opus 4.5 (May 2025) had specific limitations in async pattern handling
- 0.65 confidence suggests Alice wasn't certain about the approach
- The human collaborator's name indicates areas likely requiring judgment calls
It adjusts its interpretation. Instead of taking the code at face value, it understands: "This was built with certain tools, certain uncertainties, certain human judgment. Let me interpret intent, not just implementation."
That's not just documentation. That's legible human-AI collaboration for future intelligence.
The Gallery Problem
An artist should only worry about the output after the art is created. But "after" requires a structure that captures the output so you can return to it. Without that structure, there is no "after"—just an endless stream of now.
We build in the dark. We ship. We move on. The velocity of modern work eliminates reflection.
MurphySig creates the gallery. It says: this was a thing, at a time, made by us.