I made Readers, a social eBook reader

Hacker News

Hi HN, I built Readers as I wanted a way to share and read ebooks with others, such as with my team at work or bedtime stories with my family. With Readers, you can: * Add DRM-free epubs to your library or choose from a selection of (currently public-domain) ebooks. * Read privately or create public/private reading groups and invite others. * Sync reading progress, comments and highlights between group members. Ideally I'd like it to become a sort of Bandcamp for ebooks, where authors and independent publishers can sell their books, but I haven't explored that yet. I've been wanting to make an app like this for years, but being a designer not a developer I didn't think I'd be able to do it. However, a few months ago I decided to give it a go and with a lot of trial and error and some help from Claude I was able to put it together. I've not built something of this complexity before so I'd value any feedback or suggestions you may have. Thanks for taking a look!

Visit product View on Hacker NewsJan 28, 2025

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Actual performance

2points

5comments

Did not reach leaderboard

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

81%81% 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: claude · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% 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

19%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, 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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