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I built Bibliou – a platform for book lovers to read, share and connect

Hacker News

I built Bibliou – a platform for book lovers to read, share and connect

After 5 months of coding and tweaking, I’m excited to introduce Bibliou, a social media platform dedicated to book enthusiasts. With Bibliou, you can: -Read and upload books -Share what you've read and discover new reads from others -Connect with fellow book lovers, make friends, and see what they’re reading -Download books from the community It's a place where bookworms can geek out about their favorite reads, follow others’ reading journeys, and build meaningful connections over shared literary interests. I’d love to get feedback on the platform, and if you’re into books, feel free to check it out!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% 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, coding · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
62%62% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
41%41% 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: month · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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