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.
What Is LiteParse? LlamaIndex's Open-Source Document Parser for AI Agents
LiteParse is a free, GPU-free document parser from LlamaIndex that preserves spatial layout for tables and charts. Here's why it matters for AI workflows.
What Is the Context Window Limit in Claude Code? How to Manage It for Better Results
Claude Code's context window is its short-term memory. When it fills with stale content, quality drops. Here's how to keep it fresh and get consistent outputs.
What Is the Business Brain Pattern for Claude Code? How to Share Brand Context Across All Skills
The business brain pattern gives every Claude Code skill access to your tone, audience, and positioning without bloating the context window.
How to Build a Shared Business Brain for Claude Code Skills
Stop re-explaining your brand to every skill. Learn how to create a shared context layer so every Claude Code skill knows your voice, audience, and standards.
Claude 1M Token Context Window: What It Means for Long-Running Agent Tasks
Anthropic expanded Claude Opus 4.6 and Sonnet to 1 million tokens at no extra cost. Here's what that means for agents, RAG, and long workflows.
Does a 1M Token Context Window Replace RAG? What the Claude Benchmark Data Shows
Claude's 1M token window achieves 90% retrieval accuracy, but RAG is still necessary. Here's when to use each approach and why latency still matters.
Claude 1M Token Context Window: What It Means for AI Agents and Long-Running Tasks
Claude Opus 4.6 and Sonnet 4.6 now support 1M token context with 90% retrieval accuracy. Here's what that means for agents, RAG, and document workflows.
Shared Brand Context vs Context Folder: The Two Memory Layers Every AI System Needs
Understand the difference between static brand context and dynamic context folders in agentic AI systems, and why both are essential for reliable outputs.
Gemini Embedding 2 and the End of Stitched-Together Embeddings
Why Gemini Embedding 2 matters: a primer on embeddings and how a unified vector space replaces the brittle stitching of separate text, image, and audio models.
AI Memory for Professional Relationship Management: How to Never Miss a Follow-Up
Use an agent-readable database to track professional contacts, flag neglected relationships, and surface warm intro windows before they close.
How to Build an AI-Powered Job Search Dashboard with OpenBrain and Claude
Track companies, contacts, applications, and interviews in a shared database your AI agent can reason across to surface warm intros and flag expiring windows.
How to Build Visual Dashboards on Top of Your AI Memory System with Vercel
Add a human-readable interface to your OpenBrain database using Claude-generated web apps deployed free on Vercel. Both you and your agents read the same data.
Gemini Embedding 2 vs Qwen3 VL Embeddings: Which Multimodal Model Should You Use?
Compare Gemini Embedding 2 and Qwen3 VL embeddings across supported modalities, embedding dimensions, API access, and real-world search use cases.
What Is Matryoshka Representation Learning in Gemini Embedding 2?
Gemini Embedding 2 supports flexible embedding sizes from 3,072 down to 768 dimensions. Learn how Matryoshka learning works and when to use smaller embeddings.
How to Search Video Content with Gemini Embedding 2: Chunking Strategies Explained
Embed video clips in 15-30 second chunks using Gemini Embedding 2 to enable text-based search over long-form video content without transcription.
How to Build a Unified Multimodal Search System with Gemini Embedding 2 and LangChain
Use Gemini Embedding 2 with LangChain and ChromaDB to build a single search index that handles text, images, audio, video, and PDFs in one query.
What Is Gemini Embedding 2? The First Natively Multimodal Embedding Model
Gemini Embedding 2 maps text, images, video, audio, and PDFs into one shared vector space. Learn how it simplifies multimodal search and RAG pipelines.
What Is OpenBrain? The Personal AI Memory Database You Own and Control
OpenBrain is a personal Supabase database connected to any AI via MCP. Learn how it gives your agents persistent memory across Claude, ChatGPT, and OpenClaw.
How to Build a Multimodal Document Intelligence Agent with Gemini Embedding 2
Gemini Embedding 2 embeds PDFs, audio, video, and text in one vector space. Learn how to build a document search agent that retrieves across all content types.
How to Build an Image-to-Image Search System for Business Using Gemini Embedding 2
Learn how to build an image similarity search system for business use cases like roofing, real estate, or e-commerce using Gemini Embedding 2.
How to Build a Multimodal RAG Chatbot for Product Manuals with Gemini Embedding 2
Learn how to build a chatbot that searches PDFs, images, and diagrams using Gemini Embedding 2 and Pinecone — no complex pipeline required.
How to Build a Multimodal Vector Database with Gemini Embedding 2 and Pinecone
Step-by-step guide to building a multimodal vector database using Gemini Embedding 2 and Pinecone — covering text, images, video, audio, and PDFs.
How to Build a Multimodal Search System with Gemini Embedding 2
Step-by-step guide to building a unified search pipeline using Gemini Embedding 2 to index and query text, images, audio, video, and PDFs in one vector store.
Gemini Embedding 2: Variants, Dimensions, and Use Cases
A practical look at Gemini Embedding 2's variants and dimension settings, plus how teams are using it to simplify multimodal RAG and content search.