How I “Vibe-Coded” a Self-Healing Vector Engine for MongoDB in One Weekend

As a software engineer with nearly a decade of experience, I’ve spent thousands of hours manually typing every semicolon and crafting every database schema. But last weekend, I decided to try something radical. I wanted to see if I could build a production-ready, enterprise-grade SDK by focusing purely on vision, architecture, and testing, while letting AI handle the heavy lifting of syntax. The result? @manasdb/core is now live on NPM. The Problem: RAG is Brittle and Expensive If you’ve built Retrieval-Augmented Generation (RAG) applications with MongoDB, you’ve likely hit these three walls: The Context Gap: Traditional vector search returns isolated sentences. When you feed these fragments to an LLM, it loses the “big picture,” leading to hallucinations. The “Dimension Crash”: Switch your embedding model from OpenAI to Gemini, and your entire Vector Index crashes because of a dimension mismatch. The PII Leak: Sensitive data (emails, credit cards) accidentally hitting external LLM API