Open-Source H-Index

Hacker News

Open-Source H-Index

Have you heard of Hirsch index for academic researchers and publications? We adapted the algorithm for open source contributors. Fun project! Open Source Hirsch Index is equal to N if a developer has at least N projects, each with at least N commits from the developer, and each project has at least N stars. Hirsch ≥ 27 is like winning a Nobel Prize—only a few hundred people reach this level. Hirsch ≥ 13 puts you in the top 10,000, which is on par with earning an Olympic medal. Hirsch ≥ 8 places you in the top 100,000, where you’ll find people making a serious impact in tech—think billionaires and CTOs of unicorn companies. Hirsch ≥ 3 lands you in the top 1 million, the marathoners of the industry.

Visit product View on Hacker NewsOct 29, 2024

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

75%75% 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, 000, io · Missing: https docs, excited, just released

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

56%56% 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.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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.

Incorrect prediction on native model

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