A

A new kind of book recommender

Hacker News

A new kind of book recommender

Books are complicated and so are your preferences. Your specific mood and desire for what to read can change from day to day. Just because you enjoyed certain books doesn't mean you want the next one you read to be highly similar to those. Yogurrt, the book recommender I created, is different: You can select specific low-level preferences it generates on the fly. The preferences are generated based on one or more books you provide. Each run is unique. Notes - This is a solo project I made over a couple weeks part time. I learned React while making this and it's been awhile since I've done this kind of dev work - I've been a product manager for the past several years. I feel the UI is reasonably usable but unpolished - next time I'd start with a component framework instead of not doing that :) Please let me know what you think!

Visit product View on Hacker NewsAug 20, 2024

Actual performance

5points

1comments

Did not reach leaderboard

Launch Intel predictions

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

83%83% 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, notes · Missing: mac, agents, macos

73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

61%61% 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 · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

24%24% 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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