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Osscar – Measuring open-source growth beyond GitHub stars

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Osscar – Measuring open-source growth beyond GitHub stars

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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Product HuntOn track for Day 1 leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: organizations · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, pipe, io · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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