Knowledge Distillation

🚀 Summary

When you can measure what you are speaking about… you know something about it; but when you cannot measure it… your knowledge is of a meagre and unsatisfactory kind. Lord Kelvin

Remember that time you spent an hour with an AI, and in one perfect response, it solved a problem you’d been stuck on for weeks? Where is that answer now? Lost in a scroll of chat history, a fleeting moment of brilliance that vanished as quickly as it appeared. This post is about how to make that moment permanent, and turn it into an intelligence that amplifies everything you do.

🔍 Summary: Building the Engine of Understanding

This is not a finished story. It’s the beginning of one and likely the most ambitious post we’ve written yet.

We’re venturing into new ground: designing epistemic engines modular, evolving AI systems that don’t just respond to prompts, but build understanding, accumulate beliefs, and refine themselves through In-Context Learning.

In this series, we’ll construct a self-contained system separate from our core framework Stephanie that runs its own pipelines, evaluates its own beliefs, and continuously improves through repeated encounters with new data. Its core memory will be made of cartridges: scored, structured markdown artifacts distilled from documents, papers, and the web. These cartridges form a kind of belief substrate that guides the system’s judgments.