Knowledge-Management

An LLM Wiki fails when old facts remain plausible, contradictions become polished, and generated summaries drift from their sources.

The dominant model for publishing knowledge online has not changed much since the early 2000s: write something, polish it, publish it, move on.

Most engineering notes are written once and forgotten. You capture something in a debugging session, paste it somewhere, and find it two years later with no context for why it mattered.

Organizing notes by topic sounds logical until you have notes on PostgreSQL in five different folders and cannot find the one that matters for today’s problem.

AI is not replacing knowledge management; it is changing the shape of it for both individuals and teams.

Developers do not usually suffer from a lack of information. We suffer from too much of it.

Most modern knowledge systems optimize retrieval, and that is understandable. Search is visible, easy to demo, and feels magical when it works. Type a question, get an answer.

The premise is simple: compiled knowledge is more reusable than retrieved fragments. RAG became the default answer to a straightforward question - how do I give an LLM access to external knowledge?

PKM, RAG, wikis, AI memory systems, and now practical AI-assisted workflows are often discussed as if they solve the same problem. They do not. They all deal with knowledge, but they operate at different layers:

Personal knowledge management spans Obsidian, Logseq, DokuWiki, Zettelkasten, and PARA — the right choice depends on whether you want a local note graph, a self-hosted wiki, or an outliner-driven workflow.

Here’s a detailed breakdown of Obsidian as a powerful tool for personal knowledge management (PKM), explaining its architecture, features, strengths, and how it supports modern knowledge workflows.

Here is an overview of Personal Knowledge Management, it’s goals, methods, and software systems that we can use on this wonderful day in July 2025.

Dokuwiki is a selfhosted wikipedia which can be hosted easily on premises and not requiring any databases. I was running it dockerised, on my pet kubernetes cluster.