# LLM Wiki v2 — Comprehensive Summary

## Source

https://gist.github.com/rohitg00/2067ab416f7bbe447c1977edaaa681e2
Author: rohitg00
Forked from: karpathy/llm-wiki.md
Last active: 2026-04-13

## Overview

A pattern for building personal knowledge bases using LLMs, extending Karpathy's original LLM Wiki idea with lessons from building agentmemory. Addresses what breaks at scale, what's missing, and what separates a useful wiki from one that rots.

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## What the Original Gets Right

> **Stop re-deriving, start compiling.** RAG retrieves and forgets. A wiki accumulates and compounds.

- Three-layer architecture works: raw sources → wiki → schema
- Basic operations (ingest, query, lint) cover the basics

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## Missing Layer: Memory Lifecycle

### Confidence Scoring
Every fact should carry a confidence score indicating:
- How many sources support it
- How recently it was confirmed
- Whether anything contradicts it

### Supersession
When new information contradicts existing claims:
- Old claim explicitly superseded, not just noted
- Linked and timestamped
- Old version preserved but marked stale

### Forgetting
- Wikis that never forget become noisy
- Implement a retention curve based on Ebbinghaus's forgetting curve
- Architecture decisions decay slowly. Transient bugs decay fast.

### Consolidation Tiers

| Tier | Description | Characteristics |
|------|-------------|------------------|
| Working memory | Recent observations | Not yet processed |
| Episodic memory | Session summaries | Compressed from raw |
| Semantic memory | Cross-session facts | Consolidated from episodes |
| Procedural memory | Workflows and patterns | Extracted from repeated semantics |

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## Beyond Flat Pages: Knowledge Graph

### Entity Extraction
Extract structured entities: People, projects, libraries, concepts, files, decisions

### Typed Relationships
Not all connections are equal: uses, depends_on, contradicts, caused, fixed, supersedes

### Graph Traversal for Queries
Instead of keyword search: walk outward through typed edges to find all related nodes.

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## Search That Actually Scales

### When index.md Breaks
Works up to ~100-200 pages. Beyond that, becomes too long for LLM.

### Hybrid Search Architecture

| Stream | Catches | Method |
|--------|---------|--------|
| BM25 | Exact terms | Keyword matching |
| Vector search | Semantic similarity | Embeddings |
| Graph traversal | Structural connections | Entity-aware relationship walking |

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## Automation: Event-Driven Operations

| Event | Action |
|-------|--------|
| On new source | Auto-ingest, extract entities, update graph, update index |
| On session start | Load relevant context based on recent activity |
| On session end | Compress session into observations, file insights |
| On query | Check if answer is worth filing back (quality score > threshold) |
| On memory write | Check for contradictions, trigger supersession |
| On schedule | Periodic lint, consolidation, retention decay |

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## Quality and Self-Correction

### Score Everything
Every piece of LLM-generated content gets a quality score based on structure, citations, wikilink density, length, and fact consistency.