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Crunching 1,200 Authors' Favorite Reads of 2023

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Crunching 1,200 Authors' Favorite Reads of 2023

Hi all, creator here :) I launched Shepherd.com on a Show HN in April 2021 ( https://news.ycombinator.com/item?id=26871660 ), and it has come a long way. My goal with Shepherd is to create an experience that feels like wandering your local bookstore, along with little notes from authors & experts sharing why each book is one of their all-time favorites. For 2023, I surveyed 1,200+ authors to get their three favorite reads of the year. Then I crunched that data and broke it by genre, age range, and when it was published. Publisher data is a nightmare, so some of the genres are not perfect; I am working on improving that and some of the NLP/ML that drives this. Check out their top sci-fi reads: https://shepherd.com/bboy/2023/science-fiction Or, their top nonfiction reads: https://shepherd.com/bboy/2023/nonfiction You can also zoom in on each author’s favorite 3 reads. Louise Carey - https://shepherd.com/bboy/2023/f/louise-carey Kevin Klehr - https://shepherd.com/bboy/2023/f/kevin-klehr Alice C. Hill - https://shepherd.com/bboy/2023/f/alice-c-hill Sara Ackerman - https://shepherd.com/bboy/2023/f/sara-ackerman My email is ben@shepherd.com if you want to share ideas or suggestions for 2024. Thanks, Ben P.S. I have a newsletter for readers here where I share what I am building, new features, my fav book lists: https://forauthors.shepherd.com/newsletter-for-readers

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% 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: ide, io · Missing: https docs, excited, just released
71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
51%51% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, email, notes · Missing: mac, agents, macos
30%30% predicted probability of success on Product Hunt, 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.

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