Original Summary

Like a lot of developers tinkering with local LLMs, I wanted a way to give my models long term memory over my personal notes and documents. Running standard RAG locally felt way too heavy. You have to spin up vector stores, burn VRAM running document parsing through an LLM, and in the end, cosine similarity still hallucinates when you ask something outside the context. As a side project, I started building Hillock to see if I could do this with SQLite and hyperdimensional vector math instead. It extracts facts in about five seconds without an LLM, stores them in SQLite, and uses a mathematical hypervector gate to block the model from answering if the facts are not in the database. The whole engine runs in under 1.2 GB of VRAM or on pure CPU. I just shipped version 0.8 with bit packed SIMD math, put up interactive documentation, and published it to PyPI via pip install hillock. It recently crossed 100 stars on GitHub which has been really exciting to see. GitHub: https://github.com/roandejager/Hillock Docs: https://hillock.mintlify.site/ Discord: https://discord.com/invite/BGUPNBcVdp   submitted by   /u/Equivalent-Flan-1590 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/8 00:08:44