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Osscar – Measuring open-source growth beyond GitHub stars

Osscar – Measuring open-source growth beyond GitHub stars

by aleciffo·Apr 28, 2026·5 points·0 comments

AI Analysis

MidSlick

Nice dashboard, but just another metrics site in a sea of OSS analytics.

Strengths
  • Log-minmax normalization prevents massive repos from skewing emerging project scores.
  • Combines three distinct signals: stars, contributors, and npm/PyPI/Cargo downloads.
Weaknesses
  • VC-backed leaderboard feels more like marketing for the fund than a community tool.
  • Excluding personal accounts limits utility for individual developers tracking growth.
Category
Target Audience

Open source maintainers and VC investors

Similar To

OSS Insight · Star-history · LibHunt

Post Description

OSSCAR is a quarterly ranking of the fastest-growing open-source GitHub organizations. It's a joint project between Supabase and >commit, a VC focused on open-source.

A few notes about the methodology: we focus on organization accounts (excluding personal accounts and forks), split them into two divisions by starting star count so smaller and larger ecosystems are evaluated among peers, measure growth across three signals (GitHub stars, contributors, and package downloads from npm, PyPI, and Cargo), normalize each signal within its division using a log–minmax transform, and combine the resulting scores into a single composite via the L² norm. Organizations are then ranked by this composite within their division. Full methodology is on the site.

This is v1 of the ranking, so there’s a lot of room for improvement. In future versions we want to add other package managers, new signals and refine the methodology. Things we'd love opinions on:

1. What do you think about the signals we are using? Which important ones are we missing? 2. Is log-minmax a robust scaler in this setting, or is there something better? 3. Is L^2 norm the right aggregation strategy for sparse, heterogeneous signals?

Everything's open: the website, the data and the scoring pipeline. We'd love your feedback on the methodology, the rankings, signals we're missing, or anything that looks wrong. Issues and PRs very welcome.

Repo: https://github.com/commitvc/osscar

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