Writing about durable knowledge architecture, maintenance, and file-based systems.
View All TagsA simple index.md is a strong starting point for an LLM wiki.
It is readable, portable, and easy to maintain when the corpus is still small.
But after a point, file listing stops being enough.
The obvious use of an AI research assistant is summarization. Give it a document, get the bullet points, move on. That is useful, but it is not the real job.
The real job is continuity. A good research assistant should remember what has already been decided, keep track of open threads, notice when new material conflicts with old assumptions, and help turn scattered inputs into durable output.
A private second brain is useful because it remembers what the human mind drops. It stores sources, decisions, notes, half-formed ideas, and the connective tissue between projects. But if it only stores, it becomes a prettier archive. The real leverage appears when the system has a public edge: a place where private thinking is forced to become clear enough for someone else to read.
That public edge does not mean publishing everything. In fact, the opposite is usually healthier. A good second brain should keep raw notes private, protect unfinished thinking, and avoid turning every captured source into content. But some ideas need pressure. They need to be compressed, argued, structured, and exposed to reality.
An LLM wiki does not become reliable just because the model is good at answering questions.
It becomes more reliable when the assistant follows a written schema: clear page types, naming rules, update steps, and logging habits.
That is the difference between a clever chatbot and a knowledge maintainer.
You do not need a database-heavy stack to start an LLM wiki.
For a small or medium knowledge base, markdown files plus Git are often enough to get the important part right first: durable, inspectable knowledge that can keep improving over time.