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The Lobste.rs invitation tree, visualized
Hacker NewsThe Lobste.rs invitation tree, visualized
Lobste.rs makes its entire invitation tree public. I scraped it and built this interactive visualization. Some interesting patterns: - The top 10 inviters are responsible for ~40% of all users. - The tree is surprisingly shallow: Most users are only 4-6 generations from the founder. Max depth is around 15. Built with Sigma.js for WebGL rendering. Works on mobile too. (I am seeking a lobste.rs invite if anyone's willing! – my email is in my profile)
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5points
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Did not reach leaderboard
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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, visual · Missing: mac, agents, macos64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: visualize, users · Missing: mobile apps, ios, personal45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue19%19% 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, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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