Hi everyone,

I've been hacking on a local personal memory system called Hillock. Honestly, it's very much a work in progress and it isn't some flawless breakthrough, but I wanted to see if we could build a lightweight, completely offline memory layer for local LLMs without the overhead of running a heavy neural vector database or wasting precious VRAM.

The project is named after the biological Axon Hillock—the exact gatekeeper region of a human neuron that sums up incoming electrical charges and decides whether to fire (open the gate) or remain silent (block).

How the architecture works:

• The Ground Truth (SQLite): Stores hard facts as simple database triples (Subject-Predicate-Object) so the system has a solid symbolic foundation.
• The Synapses (Hebbian Plasticity): Tracks which concepts co-occur during a conversation to dynamically build gradient-free associative weights.
• The Context (Hyperdimensional Computing): Maintains a 10,000-dimensional leaky context vector that rolls, binds, and accumulates history. This helps the system resolve pronouns (like "he/she") and decide when to block a query to prevent hallucinations.

The Honest Benchmarks (Yes, it breaks!)

I wrote a tough, 30-sentence scientific benchmark with complex sentence structures and hard negatives (like asking what Einstein discovered when the text only mentions Curie discovering radioactivity and Einstein working with her).

Running Qwen 1.5B locally on my computer, here is how it actually did:

• Extraction Precision: 10.6%
• Extraction Recall: 22.7%
• Retrieval Accuracy: 30.0%
• Gate Accuracy: 30.0%

Why are these scores low? Because a tiny 1.5B model completely trips over complex English grammar during ingestion (it gets confused and creates weird predicates). However, the actual HDC vector-matching itself is incredibly stable. I enforce a Constant-Component-Count of exactly 3 components per fact, which balances the vector norms and keeps retrieval highly reliable once the facts are actually in the database.

It is fully open-source (under the AGPL-3.0 copyleft license) and designed to run entirely offline on consumer hardware.

If anyone is interested in VSAs, neuro-symbolic AI, or has feedback on the HDC context-binding math, I'd love for you to check it out!

GitHub Repository: https://github.com/roandejager/Hillock