Hillock – Local, brain-inspired AI memory using SQLite and HDC

Developer Roan Dejager released Hillock, a local AI memory system that combines SQLite, Hebbian plasticity, and hyperdimensional computing to provide lightweight, brain-inspired memory for offline chatbots. In a benchmark using a tiny Qwen 1.5B model, Hillock achieved 30% retrieval accuracy and 30% gate accuracy, demonstrating the challenges of running complex memory systems on small models. The project is an experimental prototype not intended for production use.

Hi This is Hillock , which is basically a local, personal memory system I've been hacking on because standard vector databases always felt way too heavy and complicated just to run a quick, offline chatbot on my own computer. Heads up: This project is very much a work in progress, and honestly, it isn't all that. It's just a fun personal experiment I'm working on to see if we can use brain-inspired math to make local AI memory better. It is definitely not a finished, production-ready product, so expect some clunky parts and weird bugs. I put this prototype through a massive, highly rigorous 30-sentence scientific benchmark with complex sentence structures, deep distractors, and tricky "hard negative" queries. Running a tiny local Qwen 1.5B model, here is how it did: Retrieval Accuracy : 30.0% It retrieved the correct facts for some of the highly complex queries, but the tiny model missed others during extraction . Gate Accuracy : 30.0% It successfully blocked many unanswerable/hallucinatory queries, though some leaks occurred due to tiny model extraction errors . For a more detailed technical breakdown of these metrics and why running a tiny 1.5B model on complex grammar is actually quite hard, check out the Benchmark section at the bottom. Here is a quick look at how data moves through the system: Raw Text / PDFs │ ▼ Parallel Ingestor Ollama Qwen2 │ │ ▼ ▼ SQLite Graph Hebbian Memory │ │ └─────┬──────┘ ▼ VSA/HDC Reservoir ──► Gating Controller Hillock Note: This ASCII diagram was made with AI, so it might not be 100% correct or perfectly aligned, but it shows the general idea of how things connect. Basically, it splits the work into a few different layers: - 💾 SQLite Graph : Stores the permanent, hard facts as simple triples like Marie Curie - born in - Poland so the system has a solid ground truth. - ⚡ Hebbian Plasticity : Dynamically tracks which entities are being talked about in the chat and strengthens the connections between them, like a simple digital synapse. - 🌀 Hyperdimensional Computing HDC : Uses a 10,000-dimensional vector that constantly updates with conversational history, which helps the system resolve pronouns like "he" or "she" and decide when to block a query to prevent hallucinations. If you actually want to try running this clunky prototype, it is highly recommended to set up a clean Python virtual environment so you do not mess up your global packages. You will also need Ollama https://ollama.com/ installed and running locally. git clone https://github.com/roandejager/Hillock.git cd Hillock Create the environment python -m venv .venv Activate it Windows .venv\Scripts\activate Activate it Mac/Linux source .venv/bin/activate pip install -r requirements.txt ollama pull qwen2:1.5b python main.py Inside the console, you can use these commands: /ingest filepath — Index a local .txt or .pdf file. /mode strict/balanced/conversational — Change how conversational the AI is. /reset — Wipe the SQLite database and reset the HDC memory space. Here is the exact diagnostic output from the upgraded, highly rigorous evaluation script evaluate hillock PROTO ish.py : -------------------------------------------------- Extraction Precision : 10.6% Correctly structured factual nodes Extraction Recall : 22.7% Completeness of indexed relations Retrieval Accuracy : 30.0% Factual accuracy on answerable queries Gate Accuracy : 30.0% Hallucination defense rate -------------------------------------------------- The 10.6% Extraction Precision & 22.7% Recall : We pushed the evaluation set to a massive 30 complex, multi-subject sentences spanning Quantum Physics, Computer Science, Space Exploration, and Philosophy. A tiny 1.5B parameter model qwen2:1.5b is simply too small to parse this much dense text without getting confused. It hallucinated relationships like James Watson - discovered - double-helix model of DNA or Grace Hopper - became a pioneer - developed the first compiler . The "Newton / Galileo / Aristotle" Blocks : Because the 1.5B model failed to parse their clean relations during the parallel ingestion phase, those questions were safely blocked during step 2 resulting in correct blocks for unanswerable ones but false blocks for answerable ones . The "Edison / Feynman" Leaks : Because the 1.5B model extracted noisy relations during ingestion like Heinrich Hertz - born in - Hamburg, Germany , when asked about unmentioned things like who Hertz collaborated with , the gate opened on the birth fact, resulting in "leaks" under the strict test suite. Vector Normalization : The retriever matching itself is mathematically highly stable. By keeping all candidate facts strictly bound to exactly 3 unique components Subject, Object, and best-matching Predicate word , we prevent shorter facts from having artificially higher similarity scores. config.py — Holds all the hyperparameters HDC dimensions, decay rates, etc. . database.py — The SQLite interface for symbolic fact storage. ingestor.py — Spawns parallel worker threads to chunk and parse documents. plasticity.py — Tracks Hebbian co-activation weights betwee