MeetNewBooks – Book recommendations for those who read for fun

Hacker News

Hi HN, Like many projects here, this started as something I built for myself. I shared it with a few friends, and it grew from there. MeetNewBooks is a book recommendation app—a "find your next read" tool rather than a book tracking app. How it works: By Title: Enter a book you enjoyed, and we’ll suggest similar books. Depending on the book, you may see a mix of recommendations based on themes, genres, or tropes—combined with what other readers have enjoyed. Search by Keywords (Genre, Theme, Trope): Browse recommendations using filters like "epic fantasy," "coming of age," or "billionaire romances." No vectors or embeddings—just a straightforward Elasticsearch implementation. Suggestions Based on Recent Browsing: After you’ve looked at a few books, we’ll recommend others based on your browsing history. A "bulb" icon in the top right corner will show these suggestions. Personalized Recommendations (free, but requires signup): Save and rate books, and we’ll tailor suggestions as soon as we have enough books to work with. We also compare your shelves with other readers to surface books you might love. If you don’t like a recommendation, you can mark it (or the author) as "Not Interested," and we’ll refresh your list. There are no quizzes or questionnaires to fill out. And while this isn’t a book tracking app, many users have requested those features, so I’ve been gradually adding them. We also have iOS and Android apps. I’d love to hear your feedback! As a side note—after years of working on enterprise software with grumpy traders, it’s been a joy to build something for people who love reading as much as I do. Users report bugs, suggest features, and have been incredibly patient as I work through the backlog.

Visit product View on Hacker NewsFeb 20, 2025

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

92%92% 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: apps, user, new · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, 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

50%50% 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 · Strong signals: soon, users · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

21%21% 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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