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Where Part 2 left us Part 2 built the context layer: AST-aware indexing with tree-sitter across 15 languages, three retrieval backends switchable via a single config field, and honest documented limit
Where Part 1 left us Part 1 covered the architecture before writing any code: the folder structure, the reasoning behind each layer, and the open questions I did not have answers to yet. The context l
Why I am building this I use Claude Code every day. For the longest time it felt like a black box. I type a prompt. Code appears. Files change. Tests run. Pull requests get written. I have no real ide
Introduction Most explanations of agent memory start at the wrong altitude. They jump straight to "use a vector database" without explaining what problem that solves, or why a vector database is somet
Introduction PageIndex, an open-source project by VectifyAI, is currently the #1 trending repository on GitHub with over 30,000 stars. Its central claim is direct and worth taking seriously: similarit
Introduction Most RAG discussions treat chunking and retrieval as separate concerns. Pick a chunk size, pick a retriever, tune k, done. What is less discussed is how much these decisions interact - sp
Introduction RAG (Retrieval-Augmented Generation) is one of those patterns that looks simple on a diagram and gets complicated fast in practice. The chunking strategy, embedding model choice, vector s
Introduction Most AI image generation workflows are open-loop: you write a prompt, you get an image, you decide if it's good. If it's not, you tweak the prompt and try again. The feedback loop runs th
Introduction The AI agent ecosystem is fragmented. Every framework — LangChain, Google ADK, CrewAI, AutoGen — has its own tool format, its own abstractions, and its own way of connecting agents to cap
