Hacker News clone for books

Hacker News

I love books! I had once come across a great list of book recommendations on the LessWrong website ("The Best Textbooks on Every Subject"). But when I had stumbled upon the list a couple of years ago the last comments and recommendations on it were from 2011. And similarly I found that most recommendations on the web suffer from this form of time decay. What I wanted was a site that had high quality recommendations similar to the LessWrong list that would feel fresh and current. Amazon was overwhelming for me since it has a gazillion books on each subject and the "Thanks! Arrived in good condition!" type of reviews drive the signal-to-noise ratio down. So I decided to scratch my own itch with this project. It's a bare bones MVP, shamelessly copying HN's look-and-feel (even on mobile!) but Im hoping it will get used for quality comments and recommendations on books. Would love to get some feedback. Thanks! marai2books@gmail.com or http://twitter.com/vivalabooks1 http://www.vivalabooks.com

Visit product View on Hacker NewsOct 1, 2015

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

66%66% 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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: reviews · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

11%11% 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.

Correct prediction on native model

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