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…
In plain words
Shepherd.com is a book discovery platform that collects reading recommendations from authors and experts. The site aggregates data from a 2023 survey of over 1,200 authors about their favorite reads, organizing recommendations by genre, age range, and publication date. Readers can browse curated lists with personal notes explaining why each book was selected, creating an experience similar to exploring a physical bookstore.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
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 [email protected] 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
Does a similar job
all alternatives →- S3Shepherd 3.0 – Like wandering the aisles of your favorite bookstore2024 · ▲102
Hi all, creator here :) - I launched Shepherd.com (https://shepherd.com/) 3 years ago on Hacker News and have added a ton since then! Here is the original Show HN (https://news.ycombinator.com/item?id=26871660). I’ve interviewed 10,000+ authors & experts to get their 5 favorite reads around different topics, themes, and moods. And I’ve connected those so that you follow your curiosity around topics, authors, books, and more. Try Art Kleiner’s favorite reads on understanding AI and its effect on people:…
- SWSee what readers who loved your favorite book/author also loved to readDec 2025 · shepherd.com · ▲134
Hi HN, Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year. Now you can enter a book/author you love and see what books readers loved who also loved that book/author. Try it here: https://shepherd.com/bboy/2025 This goes wide and doesn't try to limit itself to the genre, so you get some interesting results. What do you think? Background: I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there. This system is based on 5,000 readers voting on their 3 favorite reads…
- SCShepherd.com – Discover books in a new way, like wandering a bookstore2021 · shepherd.com · ▲313
- ISI scraped 3B Goodreads reviews to train a better recommendation modelNov 2025 · book.sv · ▲606
Hi everyone, For the past couple months I've been working on a website with two main features: - https://book.sv - put in a list of books and get recommendations on what to read next from a model trained on over a billion reviews - https://book.sv/intersect - put in a list of books and find the users on Goodreads who have read them all (if you don't want to be included in these results, you can opt-out here: https://book.sv/remove-my-data) Technical info available here: https://book.sv/how-it-works Note 1: If you only provide one or two…
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