AI Memory & Knowledge Bases
Persistent memory and knowledge bases for AI agents — Karpathy's LLM wiki, OpenBrain, second brain setups, self-evolving Claude Code memory, three-layer memory architectures, RAG patterns, vector databases, embeddings strategy.
Claude Code Memory Systems Explained: Which One Should You Use?
From claude.md to semantic vector search to cross-tool databases — six levels of Claude Code memory compared so you can pick the right one for your workflow.
Karpathy's AI Wiki vs Structured Databases: Which Memory System Is Right for You?
Karpathy's wiki compiles knowledge at write time. Structured databases query at runtime. Here's when to use each and how to combine both approaches.
How to Build a Persistent Memory System for Claude Code Agents
Learn the four-layer memory framework — agent instructions, brand context, agent context, and project memory — that makes Claude Code agents smarter over time.
What Is the Gemini Notebooks Feature? How It Compares to Claude Projects and ChatGPT
Gemini Notebooks gives paid users a dedicated workspace with custom instructions, notebook memory, and NotebookLM sync. Here's how it stacks up against rivals.
Build a Personal Knowledge Base: 70x Faster Than RAG
A practical guide to building a personal LLM Wiki — directory structure, agent read/write patterns, and why it runs up to 70x more efficiently than RAG.
Where RAG Breaks Down: The Karpathy LLM Wiki Alternative
Chunking, retrieval drift, embedding mismatches — the hidden ways RAG fails. Here's why Karpathy's plain-text LLM Wiki avoids them entirely for agent knowledge.
Gemini Notebooks vs Claude Projects vs ChatGPT Memory: Which AI Workspace Wins?
Google's new Notebooks feature brings organized AI workspaces to Gemini. Compare it to Claude Projects and ChatGPT memory to find the best fit.
What Is the Gemini Notebooks Feature? How It Compares to Claude Projects and ChatGPT Memory
Gemini Notebooks organizes chats, files, and custom instructions in one space and syncs with NotebookLM. Here's how it stacks up against competitors.
What Is the Karpathy LLM Wiki Pattern? How to Build a Personal Knowledge Base With Claude
Andrej Karpathy's LLM Wiki uses plain text files instead of vector databases and is reportedly 70x more efficient than RAG. Here's how to build one.
LLM Wiki vs RAG: A Decision Framework for AI Knowledge Bases
Decide between an LLM Wiki and a RAG pipeline using accuracy, cost, and complexity. A side-by-side framework for picking the right knowledge architecture.
What Is Andrej Karpathy's LLM Knowledge Base? The Compiler Analogy for AI Memory
Karpathy's LLM knowledge base treats raw articles as source code and a wiki as the compiled executable. Learn the architecture and how to build your own.
What Is Agent Memory Infrastructure? How Mem0 Beats OpenAI's Built-In Memory by 26%
Mem0 uses a hybrid graph, vector, and key-value store to outperform OpenAI's memory on accuracy, latency, and token usage. Here's how it works.
What Is Andrej Karpathy's LLM Knowledge Base Architecture? The Compiler Analogy Explained
Karpathy's LLM knowledge base treats raw articles like source code and compiles them into a queryable wiki. Here's the full architecture breakdown.
What Is the LLM Knowledge Base Index File? How Agents Navigate Without Vector Search
Karpathy's LLM wiki uses an index.md file as a navigation map so agents can find information without semantic search or vector databases.
LLM Wiki vs RAG for Internal Codebase Memory: Which Approach Should You Use?
Karpathy's wiki approach uses markdown and an index file instead of vector databases. Here's when each method works best for agent memory systems.
What Is Andrej Karpathy's LLM Wiki? How to Build a Personal Knowledge Base With Claude Code
Karpathy's LLM wiki turns raw documents into a structured markdown knowledge base Claude can query. Here's how to set it up in 5 minutes with Obsidian.
Karpathy's LLM Wiki: 95% Less Token Use Than RAG
Andrej Karpathy's LLM wiki approach cuts token use by up to 95% on small knowledge bases. Here's how it works and where it beats a traditional RAG pipeline.
How to Build an AI Second Brain That Evolves Over Time with Claude Code and Obsidian
Learn the full architecture for a self-improving AI second brain: memory layers, heartbeat scheduling, skills management, and multi-client support.
Claude Code Source Leak: The Three-Layer Memory Architecture and What It Means for Builders
The Claude Code source leak revealed a self-healing memory system using memory.md as a pointer index. Here's what it means for building your own AI agents.
How to Build an AI Second Brain with Claude Code and Obsidian
Learn how to build a personal AI second brain using Claude Code and Obsidian that learns from every session and automates your daily business tasks.
How Context Compounding Works in Claude Code (And How to Stop It)
Every Claude Code message re-reads your entire conversation history. Learn why token costs compound exponentially and how to manage it effectively.
Agentic RAG vs File Search: When to Use Each in Your AI Agent Workflow
File search beats traditional RAG for small corpora, but semantic search still wins for large knowledge bases. Here's how to choose the right approach.
What Is the Context Window in Claude Code? How to Manage It for Consistent Results
Claude's context window is its short-term memory with a hard limit. When it fills with stale data, quality drops. Here's how to keep it fresh and focused.
What Is Chroma Context-1? The Specialized RAG Model That Beats Frontier Models
Chroma Context-1 is a 20B parameter model trained specifically for retrieval tasks. It beats GPT-5.4 on search benchmarks at a fraction of the cost.