Knowledge Management
11 articles about knowledge management.
How to Use Browser History SQLite Data for AI Agent Memory with Frequency Ranking
A practical guide to extracting Chrome, Firefox, and Safari browser history into SQLite for AI agent memory - with schemas, SQL queries, and frequency-based ranking that beats recency-only systems.
Why Desktop AI Agents Skip RAG and Use Structured Markdown for Memory
Most agent memory systems default to embed-and-retrieve. Desktop agents get better results with structured markdown files loaded by category - faster
Adding AI Semantic Search to Your Personal Knowledge Management System
Your notes, transcripts, and bookmarks are unsearchable by meaning. AI-powered semantic search turns your personal knowledge base into something you can
Compound Knowledge Across 100+ Sessions: 10% Signal, 90% Noise
After 100+ agent sessions, only 10% of stored memories are useful at retrieval time. The rest is noise. Aggressive pruning and relevance scoring are essential.
The Cost of Replacing vs Training AI Agents: Why Context Transfer Is Harder Than It Looks
Replacing an AI agent with a fresh instance loses implicit context that is expensive to rebuild. Learn why training existing agents beats starting from scratch.
Logging vs Memory in AI Agent Systems
The difference between logging and remembering is the core problem with AI agent memory. Logs record everything that happened. Memory extracts what matters.
Why Belief Extraction Beats Flat RAG for AI Agent Memory
Layered memory architectures with belief extraction outperform simple RAG retrieval for AI agents handling hundreds of conversations. Structured compression
From 800 Redundant Lines to 30 Curated Pointers - Memory Deduplication in AI Agents
AI agent memory files grow bloated fast. UPSERT over INSERT transforms 800 redundant memory lines into 30 high-signal curated pointers.
Open Source AI Memory Storage - The Deduplication Challenge
Building deduplicated memory storage for AI agents is harder than it looks. The real challenge isn't storing memories - it's knowing when two memories are
What Survives the Gap: What You Can't Regenerate
In an era of AI-generated content, what survives is what cannot be regenerated. Original data, lived experience, and institutional knowledge are the things
Why Ebbinghaus Decay Curves Beat Flat Vector Stores for Agent Memory
Most AI agent memory systems dump everything into a vector store. Ebbinghaus decay curves offer a smarter approach - memories that naturally fade unless