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Flooder

Making Persistent Homology Practical for Industrial Use Cases

Details

External ID
46156069
Source
HN
Company
—
Product
Flooder
Website domain
github.io
Launched
Dec. 5, 2025
Cohort
—
Upvotes
8
Upvotes percentile
0.4217557251908397
Tags
—
Fetched at
Sept. 7, 2026, 9:25 p.m.
Updated at
Sept. 7, 2026, 9:25 p.m.

Enrichment

Theme
low-level systems and developer tools
Vertical
Manufacturing
Function
Analytics & BI
Audience
B2B
AI stance
Not AI
Project type
Commercial product
Normalized one-liner
persistent homology for industry
Manually corrected
False

Could you build this?

No Flooder implements novel, mathematically rigorous computational geometry and GPU-accelerated persistent homology algorithms on Euclidean point clouds using PyTorch and Gudhi bindings.

What it would actually take: Building this requires a deep background in algebraic topology, simplicial complex construction (Delaunay triangulations, filtered Flood complexes), and custom CUDA/C++ or PyTorch kernel development for spatial acceleration. The core novelty lies in optimizing filtration and reduction algorithms over millions of points to scale beyond Alpha complex memory bottlenecks.

Discussion

3 comments analyzed.

Concerns raised: Computational expense of Persistent Homology, Unclear use cases and limitations

Competitors

Other products that read as similar to this one — 1510 launches clear the similarity bar, closest 8 shown.

Attention rank: #777 of 1511 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 37 days after the earliest competitor.

Other launches for this product

Same idea, different domain

Nobody's really built a analytics & bi tool for Legal yet.