A Hacker News Reader Built in React Native

Hacker News

Hello! Over the past few months, I've been working on a side project to gain an understanding of the React Native workflow. I decided to create a Hacker News Reader app. Coming from an iOS background, I gotta say that this has been an amazing ride and I'm looking forward to creating more apps in RN. I'm planning on open sourcing my implementation over the next few days, but I felt like sharing what I've done, first. :) All feedback/comments are welcome and I'm also open to answering any questions. Let me know what you think! iOS: https://itunes.apple.com/app/hacker-buzz/id1292825792?mt=8 Android: https://play.google.com/store/apps/details?id=com.hackerbuzz

Visit product View on Hacker NewsOct 13, 2017

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, month · 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

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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