A community for every digital book

Hacker News

Hey everyone! I'm super happy to announce that every digital book written on Facet.ink now seamlessly evolves into a community hub where authors and readers can engage, discuss content, ask questions, and provide feedback. This new feature (we call it "Community") marks our next big stride toward cultivating a community that thrives on collaboration, inspiration, and ongoing improvement. As an author you ... * can receive feedback on the books you've written on Facet * can connect with your readers * can find inspiration for you next book * can learn from your readers * can support your readers if not everything is clear * ... As a reader you ... * can give feedback to the author of the book you're currently reading on Facet * can ask questions if things are unclear * can connect with other readers * can follow on updates of the book * ... What do you think of it? I'm super excited to receive your feedback. You can test the community feature by adding the example book from our homepage (facet.ink) to your own personal library. Thanks! Matthias

Visit product View on Hacker NewsApr 29, 2024

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

85%85% 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: new · Missing: mac, agents, macos

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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: excited, ide, io · Missing: https docs, just released, exist

47%47% 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

41%41% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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