roandejager5

3 ms·

1. 1.▲
   by roandejager5 1mo ago

   thanks, let me know what you think
2. 2.▲
   by roandejager5 1mo ago

   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 8
3. 3.▲
   Show HN: Hillock: Local neuro-symbolic memory engine in <1.2GB VRAM(github.com)

   12 points by roandejager5 1mo ago|3 comments
4. 4.▲
   Show HN: Hillock – Local neuro-symbolic memory engine in <1.2GB VRAM(github.com)

   4 points by roandejager5 1mo ago|0 comments
5. 5.▲
   by roandejager5 2mo ago

   Hi Jacob, thanks for sharing! Witchcraft looks super interesting—starring the repo now. The late interaction (ColBERT / MaxSim token-level) approach is fascinating for local search. Retaining per-token interaction matrices gives incred
6. 6.▲
   by roandejager5 2mo ago

   Hi HN, I built Hillock ( https://github.com/roandejager/Hillock ), a 100% local, offline neuro-symbolic memory engine designed to replace heavy Vector DBs and token-hungry LLM document ingestion pipelines on edge hardwar
7. 7.▲
   Hillock – Local neuro-symbolic memory engine running in <1.2GB VRAM(github.com)

   17 points by roandejager5 2mo ago|4 comments