Hillock: Local neuro-symbolic memory engine in <1.2GB VRAM
Details
- External ID
- 49501209
- Source
- HN
- Company
- —
- Product
- Hillock: Local neuro-symbolic memory engine in <1.2GB VRAM
- Website domain
- github.com
- Launched
- Aug. 30, 2026
- Cohort
- —
- Upvotes
- 12
- Upvotes percentile
- 0.6364247311827957
- Tags
- —
- Fetched at
- Sept. 10, 2026, 5:31 a.m.
- Updated at
- Sept. 10, 2026, 5:31 a.m.
Enrichment
- Theme
- systems tools and desktop utilities
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Hobby / open-source project
- Normalized one-liner
- local neuro-symbolic memory engine
- Manually corrected
- False
Could you build this?
No Designing a local neuro-symbolic memory engine running under tight VRAM constraints (<1.2GB) requires low-level machine learning model optimization, custom quantizations, and formal knowledge representation logic.
What it would actually take: The architecture involves a tiny quantized transformer or custom embedding model paired with a symbolic graph reasoner (such as a Datalog or RDF-like inference engine) optimized to run simultaneously in under 1.2GB of GPU memory using C++/CUDA or Rust with ONNX Runtime/GGML. The hard challenge is maintaining symbolic logical consistency and low-latency inference while operating within strict VRAM limits, preventing memory fragmentation, and fusing vector similarity with formal logic rules. This requires expertise in machine learning systems optimization, CUDA programming, and neuro-symbolic AI research.
Discussion
3 comments analyzed.
Competitors mentioned: Vector RAG / dense vector databases, Standard LLM extraction models (8B+), ColBERT
Concerns raised: Retrieval quality bounded by extraction recall, Extraction misses during ingestion cause false refusals, Benchmark accuracy relatively modest (54.5% answerable retrieval)
Competitors
Other products that read as similar to this one — 1599 launches clear the similarity bar, closest 8 shown.
Attention rank: #601 of 1600 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 303 days after the earliest competitor.
- TinyOS · hn · 2026-04-03 · 102 upvotes · similarity 0.68
- Jev-Mem · github · 2026-09-20 · 72 upvotes · similarity 0.67
- Hebbs · hn · 2026-03-09 · 6 upvotes · similarity 0.64
- ANBC · github · 2026-09-18 · 11 upvotes · similarity 0.64
- Memctl v0.1.0 Open source shared persistent memory for AI coding agents · hn · 2026-03-01 · 7 upvotes · similarity 0.64
- genesis-memory · github · 2026-09-10 · 15 upvotes · similarity 0.63
- Nønos · hn · 2025-12-23 · 9 upvotes · similarity 0.63
- Shodh– AI memory that learns from use, no LLM calls, single Rust binary · hn · 2026-02-28 · 6 upvotes · similarity 0.63
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a model & infra tool for Fintech yet.