ReaderYC, a Native BlackBerry 10 Hacker News client

Hacker News

I've created a free Hacker News reader for BlackBerry 10. Currently it supports favouriting posts, searching, and reading comments and articles. It can be downloaded here from BlackBerry World: http://appworld.blackberry.com/webstore/content/34175888/ More features are coming soon, including logging in and a neat active frame. It was built using the BlackBerry Py project, and the source can be found on my Github here: https://github.com/krruzic/Reader-YC I'd love contributions, feedback or requested features for the app. Thanks for checking it out!

Visit product View on Hacker NewsAug 27, 2013

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

80%80% 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.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io, including · 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.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, using · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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

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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