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libHN - the Cocoa framework for adding HackerNews to your iOS/Mac app.

Hacker News

libHN - the Cocoa framework for adding HackerNews to your iOS/Mac app.

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Actual performance

5points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, new · Missing: agents, macos, agent
86%86% 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.
BetaListSuited for BetaList early-adopters · Missing: web3, chat, crypto
62%62% predicted probability of success on BetaList, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
59%59% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
54%54% 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
31%31% 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
27%27% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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