Read-Up.io

Hacker News

Read-Up.io

https://read-up.io/ Hello everyone! I am sure you know many readers for mobile devices and PC but there are some cases which I wasn't able to achieve with the reader apps I am aware of. And I am talking about these 3 things: 1. Synchronization across all the devices you have independently on OS (ios/android/mac/win); 2. Possibility to upload already bought books for free and enjoy the reading; 3. Borrow books that you can't afford or don't want to spend money on. So, here is my little project I've started developing for myself and now would like to share with you - read-up.io. I've chosen WEB since this is the most universal platform. It's possible to upload your own pdf and epub books. And it's also possible to borrow books. Just ask other people to share it with you and voila! You can install ReadUp as PWA on your device and enjoy the native app experience (this is in the beta state now). The project is still under development but the main features are already released. Looking forward to your feedback and reviews.

Visit product View on Hacker NewsAug 17, 2019

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, 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: mac, apps · Missing: agents, macos, agent

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

42%42% 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

15%15% 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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