hey everyone,

I updated my open source project Hillock (AGPL-3.0) to v0.2.2: https://github.com/roandejager/Hillock

Hillock is a privacy-first memory engine written in Python that replaces vector DBs with a SQLite Knowledge Graph, Hebbian Plasticity, and Hyperdimensional Computing math.

In v0.2.0, I created TALON—a non-generative CUDA tensor pipeline (Fastcoref + MiniLM + GLiREL) that extracts knowledge graph facts in sub-seconds on a GTX 1070 GPU without waiting for LLMs to generate JSON.

It's 100% local, offline, and open source under AGPL-3.0. Would love any feedback or thoughts!

Hey Python devs,

I wanted to share a personal project I've been building in Python called Hillock (AGPL-3.0): https://github.com/roandejager/Hillock

What It Does:

Hillock gives local AI applications a privacy-first Knowledge Graph memory. Instead of vector databases that lose exact factual context, Hillock uses a decoupled SQLite Knowledge Graph, Hebbian Plasticity, and CPU-bound Hyperdimensional Computing (Vector Symbolic Architectures) written in pure Python/NumPy.

How the Python Pipeline Works:

In v0.2.0, I built TALON—a non-generative ingestion pipeline:

1. Fastcoref: Resolves pronouns across paragraphs before chunking text.
2. SentenceTransformers (all-MiniLM-L6-v2): Pre-caches a 50-predicate taxonomy and routes top candidates per sentence in <2ms.
3. GLiREL (DeBERTa-v3): Performs single-pass zero-shot matrix classification in PyTorch CUDA memory.

On my benchmark harness (evaluate_hillock_PROTO_ish.py), it processed 32 sentences in 2.1 seconds of pure GPU execution on a GTX 1070 (<1GB VRAM footprint), reaching 50% retrieval accuracy.

Source code is 100% open source under AGPL-3.0. Would love to hear your feedback on the Python architecture!

Hey everyone,

I wanted to share my open source project Hillock (AGPL-3.0): https://github.com/roandejager/Hillock

Hillock is a local-first memory engine for AI that replaces vector databases with a decoupled SQLite Knowledge Graph, Hebbian Plasticity, and Hyperdimensional Computing (VSA math).

In v0.2.0, I built TALON—a non-generative CUDA tensor pipeline combining Fastcoref, MiniLM, and GLiREL zero-shot matrix classification. It ingests documents and extracts structured SPO triples in sub-seconds on an 8GB GTX 1070 GPU.

Everything runs 100% locally and offline. Code and benchmark scripts are on GitHub. Would love your feedback!

hey r/AutoGPT,

A major pain point when giving autonomous agents long-term Knowledge Graph memory is ingestion speed. If an agent uses generative LLMs (like Llama 8B or Qwen) to extract facts, it takes 15+ minutes per PDF waiting for token-by-token JSON generation and burns GPU VRAM.

I've been building Hillock, an open-source local memory engine (AGPL-3.0): https://github.com/roandejager/Hillock

In v0.2.0, I built TALON—a non-generative CUDA tensor pipeline that bypasses generative LLMs during ingestion:

1. Fastcoref resolves pronouns across full paragraphs first (so 'She' becomes 'Marie Curie').
2. MiniLM bi-encoders filter 50+ open-domain Wikidata predicates down to the top 10 for each sentence in <2ms.
3. GLiREL does single-pass zero-shot matrix classification to pull out [Subject, Predicate, Object] triples directly in GPU memory.

Because it's pure CUDA tensor math instead of token generation, it processed 32 sentences in ~2 seconds on my GTX 1070 while using <1GB VRAM, doubling retrieval accuracy to 50%.

It's 100% local, offline, and open source under AGPL-3.0. Would love to hear your thoughts on memory for autonomous agents!

Hey agenticAI,

A big bottleneck for long-term agent memory is ingestion latency. Extracting structured Knowledge Graph facts using generative LLMs takes minutes per document waiting for token-by-token JSON generation.

I've been building Hillock, an open-source local memory engine (AGPL-3.0): https://github.com/roandejager/Hillock

In v0.2.0, I created a non-generative tensor pipeline (TALON) for agent memory:

1. Fastcoref resolves pronouns across full paragraphs first.
2. MiniLM bi-encoders route top 10 dynamic Wikidata predicates in <2ms.
3. GLiREL zero-shot matrix classification extracts [Subject, Predicate, Object] triples directly in GPU memory.

It processed 32 sentences in ~2 seconds on a GTX 1070 (<1GB VRAM footprint), doubling retrieval accuracy to 50%.

Everything runs 100% locally and offline. Would love to hear how others are handling structured long-term memory for agents!

hey OpenSourceAI,

I've been building Hillock (AGPL-3.0), a privacy-first local memory engine in Python: https://github.com/roandejager/Hillock

Instead of vector databases that suffer from semantic hallucinations, Hillock uses a decoupled SQLite Knowledge Graph, Hebbian Plasticity, and CPU-bound Hyperdimensional Computing (HDC math) for hallucination defense.

In v0.2.0, I built TALON—a non-generative tensor pipeline using Fastcoref, MiniLM, and GLiREL. It extracts structured facts in sub-seconds on a consumer GTX 1070 GPU without token generation or cloud APIs.

It's 100% local, offline, and open source under AGPL-3.0. Would love any feedback or thoughts!

hey r/ollama,

I've been building a personal open source project called Hillock (AGPL-3.0) to give local LLMs a fast, privacy-first Knowledge Graph memory: https://github.com/roandejager/Hillock

A big pain point I kept running into when ingesting documents alongside local Ollama models was speed. Having an 8B LLM extract facts token-by-token takes 15+ minutes per PDF and hogs GPU VRAM.

In v0.2.0, I created TALON—a non-generative tensor pipeline using Fastcoref, MiniLM, and GLiREL zero-shot matrix classification. It runs pure CUDA tensor math to extract structured facts in ~2 seconds on my GTX 1070 without touching Ollama's VRAM allocation during ingestion.

It's 100% offline and open source under AGPL-3.0. Would love to hear your thoughts or feedback if you're building local RAG/memory setups!

hey LangChain community,

I've been working on a personal open source project called Hillock for a while now, and I just released v0.2.2.

Standard vector DB memory has two major drawbacks in production: semantic hallucinations on exact factual lookups, and slow/expensive LLM extraction during document ingestion.

Hillock replaces vector DBs with a decoupled SQLite Knowledge Graph, Hebbian Plasticity, and Hyperdimensional Computing (VSA math) for hallucination defense.

In v0.2.2, I built TALON—a non-generative tensor pipeline using Fastcoref, MiniLM, and GLiREL. It extracts structured knowledge graph facts in sub-seconds on an 8GB GTX 1070 GPU without generating a single text token.

It's 100% local, offline, and open source under AGPL-3.0. I put the GitHub link in the comments below, would love your feedback and thoughts!

Hey everyone,

A big bottleneck for long-term AI agent memory is ingestion speed. If an agent tries to extract structured Knowledge Graph facts using generative LLMs (like Llama 8B or Qwen), it takes 15+ minutes per document waiting for token-by-token JSON generation.

I've been building Hillock, an open-source local memory engine in Python. In v0.2.2, I built a non-generative tensor pipeline (TALON) that bypasses generative LLMs during ingestion:

1. Fastcoref resolves pronouns across full paragraphs first (so 'She' becomes 'Marie Curie').
2. MiniLM filters 50+ open-domain Wikidata predicates down to the top 10 for each sentence in <2ms.
3. GLiREL does single-pass zero-shot matrix classification to pull out [Subject, Predicate, Object] triples directly in GPU memory.

Because it uses pure tensor math instead of token generation, it processed 32 sentences in ~2 seconds on a GTX 1070 while using <1GB VRAM, doubling retrieval accuracy to 50%.

I've put the GitHub link in the comments below! Would love to hear your thoughts on non-generative extraction for agent memory.