Hello HN, my name is Viktor. I spent 10 months going from a cell-tower climber and handyman with zero programming experience to building a production-ready RAG system that beats published scores on the tough LoCoMo long-term memory benchmark.

The Problem: Standard vector search (RAG) fails in complex, multi-hop conversations (semantic drift/hallucinations). The Solution: I built the VAC Memory System. It achieves deterministic accuracy by using a proprietary MCA-first Gate instead of relying solely on semantics. This "Gravity" logic protects core entities (like dates and names) from being missed, which is essential for multi-user, conversational agents.

Key Metrics (Tested on 100 runs):

SOTA Accuracy: 80.1% Mean Accuracy on LoCoMo.

Cost: Ultra-low, <$0.10 per million tokens (using gpt-4o-mini).

Speed: Fast, 2.5 seconds per query.

This was built in 4.5 months, using Claude in the terminal, under extreme personal pressure. This repository is proof that focus and grit can still deliver breakthrough results.

The Ask (Read by VCs): I am looking for $100k in Pre-Seed funding to formalize the business, secure the IP, and hire the first engineer to build the API layer. I am looking for partners who who back founders with deep product conviction.

Check the reproducible code and full results: https://github.com/vac-architector/VAC-Memory-System

Feedback is welcome!