genpark-locality-sensitive-hashing-lsh-cosine-index-skill
Random hyperplane Locality-Sensitive Hashing (LSH) for sub-linear cosine similarity candidate generation
Get picks like this daily. Today's launch numbers, the top launches, AI and tech news, and a daily opportunity pick, straight to your inbox.
Want to reach these readers? Sponsor an issue.
GITHUB · launched Sep 29, 2026 · 7 upvotes (top 95%) · 0 views
Niche: specialized algorithms and computing libraries · 230 launches, 4 in the last 7 days ▼ -73% · Explore this niche →
Category and tags
- Horizontal
- Search & retrieval
- Developer
- Not AI
- Hobby / open-source project
- agent-skills
- ann-search
- cosine-similarity
- developer-tools
- hashing
- hyperplane-hashing
- +5 more
Visit website ↗ Source listing ↗
More like this: all 230 launches in specialized algorithms and computing libraries →
Could you build this?
Yes Random hyperplane Locality-Sensitive Hashing (LSH) for cosine similarity is a standard, mathematically straightforward nearest-neighbor algorithm that can easily be generated via vibe coding.
Competitors
Other products that read as similar to this one : 2495 launches clear the similarity bar, closest 8 shown.
Attention rank: #2302 of 2496 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 334 days after the earliest competitor.
- genpark-consistent-hashing-virtual-nodes-skill · github · 2026-09-10 · 7 upvotes · similarity 0.65
- utreexo-utreexo · github · 2026-11-06 · 29 upvotes · similarity 0.60
- invariant-twins · github · 2026-10-28 · 15 upvotes · similarity 0.58
- genpark-consistent-hashing-virtual-nodes-ring-skill · github · 2026-09-09 · 8 upvotes · similarity 0.57
- ST-Pruner · github · 2026-10-22 · 20 upvotes · similarity 0.56
- toks · github · 2026-10-05 · 93 upvotes · similarity 0.56
- advanced-hash-calculator · github · 2026-11-10 · 102 upvotes · similarity 0.56
- polyxor · github · 2026-09-26 · 53 upvotes · similarity 0.55
The niche, labels, vibe code rating and competitor comparison on this page are analysis by Nicheloom. If you use them, please credit nicheloom.com. How you may use our data.