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Shepherd – I asked 1,201 authors for their 3 favorite reads in 2023

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Shepherd – I asked 1,201 authors for their 3 favorite reads in 2023

Hi all, creator here :) I interviewed 1,201+ authors to ask them what their 3 favorite reads of the year were, and here are the results: https://shepherd.com/bboy/2023 Plus, you can zoom in on why the author loved each book on their pages. Julie Walker - https://shepherd.com/bboy/2023/f/julie-walker Allan Comb - https://shepherd.com/bboy/2023/f/allan-combs Louise Carey - https://shepherd.com/bboy/2023/f/louise-carey Carolyn Porter - https://shepherd.com/bboy/2023/f/carolyn-porter And if you want to look up a specific book like Leviathan Wakes (Expanse series), you can get a list of all the authors who loved it and what book lists it is on: https://shepherd.com/books-like/leviathan-wakes Or Sapiens by Yuval Noah Harari https://shepherd.com/book/sapiens We also have “books like” based on real human data, although this is fairly basic, and I am working to improve this feature in 2024. Books like Dawn Of Everything - https://shepherd.com/books-like/the-dawn-of-everything Background: 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 since then! My goal is to create an experience that feels like wandering your local bookstore, along with little notes pointing out why an author/expert picked each book as a favorite. I am purely focused on book exploration and discovery. Feel free to ask me anything :) My email is ben@shepherd.com if you want to share some ideas. Thanks, Ben P.S. I have a newsletter for readers here where I share what I am building, early access to new features, my favorite book lists, etc: https://forauthors.shepherd.com/newsletter-for-readers

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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
72%72% 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
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, email, notes · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
41%41% 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
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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