| ▲ | Show HN: Hillock: Local neuro-symbolic memory engine in <1.2GB VRAM(github.com) | |||||||
| 12 points by roandejager5 2 days ago | 3 comments | ||||||||
| ▲ | toplinesoftsys 2 days ago | parent | next [-] | |||||||
I did not try to play with it yet, but from what I read - it is a great idea. | ||||||||
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| ▲ | roandejager5 2 days ago | parent | prev [-] | |||||||
Hey HN! I am Roan, and I built Hillock: a 100% local, offline neuro-symbolic memory engine built for consumer hardware (runs in <1.2 GB VRAM on a GTX 1070 or on pure CPU laptops). Why not standard vector RAG? Dense vector databases and 8B+ extraction models are heavy on local hardware, suffer from semantic drift, and still hallucinate when out of context. Hillock explores a deterministic alternative:
Refusal is Control Flow (Not Prompting)Instead of prompting an LLM to "only answer if you know", Hillock's gate is an actual programmatic check. If candidate facts fail our similarity threshold with positive predicate intent, it returns a hardcoded refusal immediately. The local LLM (Ollama) is never called with un-evidenced context, saving 100% of GPU compute on unanswerable queries. What is New in v0.6 (HYDRA & HyperGraph)
Benchmarks (Unseeded 32-query run on a laptop CPU)
Known LimitationsRetrieval quality is strictly bounded by extraction recall: if the extractor misses a fact during ingestion, the gate honestly refuses (producing a false block). The project is AGPL-3.0 licensed with 1-click quickstart launchers (run.bat / run.sh) and a standalone 21-point CPU verification suite (verify_hillock.py). GitHub: https://github.com/roandejager/Hillock I would love to hear your thoughts, feedback, and critiques! | ||||||||