See what readers who loved your favorite book/author also loved to read

Hacker News

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 from 2023 to 2025. So, this covers ~15,000 books and is a high-quality vote. We wanted to keep the dataset small for now while we play with approaches. We are building a full Book DNA app that pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books (a significant challenge). You can sign up to beta test it here if you want to help me with that: https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwo... The first beta is coming out in late January, but it's pretty basic to start. Past Show HNs as we've built Shepherd: https://news.ycombinator.com/item?id=40084193 https://news.ycombinator.com/item?id=38600246 https://news.ycombinator.com/item?id=26871660 Thanks, looking forward to your comments :) Ben

Visit product View on Hacker NewsDec 29, 2025

Share card

Actual performance

134points

41comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

91%91% 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, 000, io · Missing: https docs, excited, just released

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, new · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% 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

18%18% 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

Similar products

Read your favorite book in your favorite language.57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Read your favorite book in your favorite language.

Hacker News3

Next of Kin47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A product that eases the burden of a loved one's death

Indie Hackers2communication

Make a Mixtape for a Loved One55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Make a Mixtape for a Loved One

Hacker News3

Next SF Book51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

5 SciFi Book suggestions based on the one you loved

Indie Hackers1books

Find your next book to read58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find your next book to read

Hacker News7

Librarian AI – Find your next favorite book with AI37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Librarian AI – Find your next favorite book with AI

Hacker News2

Bookology79%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The book tracker built for lifelong readers

Indie Hackers1$1,000/mobooks

Django-Readers44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Django-Readers

Hacker News3

Read Later for Hentai manga readers48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Read Later for Hentai manga readers

Hacker News3

Book Strut80%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Readers have book ideas; we have the scores.

Indie Hackers1advertising

Browse all products Analyze your launch