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genpark-singular-value-decomposition-svd-truncated-skill

Power iteration Truncated SVD for low-rank matrix approximation and latent embeddings

Details

External ID
1392345272
Source
GITHUB
Company
—
Product
genpark-singular-value-decomposition-svd-truncated-skill
Website domain
github.com
Launched
Sept. 28, 2026
Cohort
—
Upvotes
7
Upvotes percentile
0.05976172175249808
Tags
agent-skills, data-science, dimensionality-reduction, latent-semantic-analysis, linear-algebra, low-rank-approximation, matrix-factorization, mcp, python-standard-library, singular-value-decomposition, svd
Fetched at
Oct. 1, 2026, 1:02 a.m.
Updated at
Oct. 1, 2026, 1:02 a.m.

Enrichment

Theme
scientific computing and research algorithms
Vertical
Horizontal
Function
Data infrastructure
Audience
Developer
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
truncated svd algorithm for low-rank matrix approximation
Manually corrected
False

Could you build this?

Yes The power iteration algorithm for truncated SVD is a standard, compact numerical routine easily implemented using NumPy or standard linear algebra libraries within an MCP skill.

Competitors

Other products that read as similar to this one — 846 launches clear the similarity bar, closest 8 shown.

Attention rank: #685 of 847 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 332 days after the earliest competitor.

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