Hillock v1.0: local neuro-symbolic memory engine runs under 1.2GB VRAM with claimed zero hallucinations
Equivalent-Flan-1590 · reddit · 2026-10-28
- An independent developer released Hillock v1.0 stable after six months of daily work: an offline neuro-symbolic memory engine for local LLMs that runs on consumer hardware or a laptop CPU with under 1.2GB VRAM, open-sourced under AGPL-3.0.
- It drops dense vector databases entirely: cosine similarity is probabilistic, so standard RAG returns the nearest noise chunks for unanswerable questions and small models hallucinate. Hillock pairs a SQLite knowledge graph with a 10,000-dimensional hyperdimensional computing gate, algebraically verifying facts before the LLM generates a token and issuing a mathematical refusal otherwise, claiming a verified 0% hallucination rate on an unanswerable-questions benchmark.
- Ingestion uses a batch-accelerated extraction pipeline that runs fast on CPU or low-end Nvidia cards, avoiding slow 8B summarization.
- Ships an OpenAI-compatible API with true token streaming over SSE, so AnythingLLM or Open-WebUI can point straight at localhost:8000.
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