A bookshelf you can share as a link

Hacker News

I read on my Kindle and I travel a lot, so I don't have a physical bookshelf, and I miss that. When folks ask me what I've read or what I recommend, I send a few lines or a messy Notion. To solve that for me, and for the other digital book lovers out there, I built this app, a free forever easy and beautiful way to share what you've read, what you've watched and what you recommmend others. Think Goodreads minus the bloat and the corporate! You can easily search for books, add them in bulk, select covers for them, and then share the link with others. It also has a shelf for movies! Let me know if you are a book lover or a movie lover and you find this useful! It's also the first time I publish a project, so appreciate the feedback.

Visit product View on Hacker NewsJul 31, 2026

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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.

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

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

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% 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

16%16% 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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