Shepherd 3.0 – Like wandering the aisles of your favorite bookstore

Hacker News

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: https://shepherd.com/best-books/understanding-ai-and-its-eff... Under each book, you can click “What is this book about?” to explore different topics and genres that interest you. I am working on adding themes and other fun connections. Or you can explore things like... Places to explore if you like the book Sapiens: https://shepherd.com/search/book/1504 S. B. Divya on her favorite realistic near-future science fiction: https://shepherd.com/best-books/realistic-near-future-scienc... Places to explore if you like hard sci-fi: https://shepherd.com/search/shelf/12622 Places to explore if you like Stephen King: https://shepherd.com/search/author/4826 Azby Brown’s favorite books on Japanese carpentry and construction: https://shepherd.com/best-books/japanese-carpentry-and-const... Malayna Evan’s favorite reads on badass women who left a mark on the ancient world: https://shepherd.com/best-books/badass-women-who-left-a-mark... Shepherd is bootstrapped, and I’ve got many reader features coming soon! I have a newsletter about building the project and early access to new features here: https://forauthors.shepherd.com/newsletter-for-readers What do we use to build this? Python, Django, Heroku, Postgre, Cloudflare, NLP/ML for Wikipedia topic IDs via Wikifier ( https://wikifier.org ), Nielsen’s book API database (publisher data + Library of Congress data), and Cloudinary. My email is ben@shepherd.com if you want to share ideas or suggestions :) Thanks, Ben

Visit product View on Hacker NewsApr 19, 2024

Share card

Actual performance

102points

40comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

86%86% 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: hacker news, ide, 000 · Missing: https docs, excited, just released

73%73% 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: ios · Missing: mobile apps, personal, entrepreneurs

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

50%50% 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 · Missing: mac, agents, macos

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · Missing: arr, mrr, revenue

13%13% 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

How bloated is your favorite app?38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

How bloated is your favorite app?

Hacker News39

AppLounge56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

You can select your favorite app from here

Indie Hackers1$10/moapis

Notifier – Keep track of your favorite dependencies38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Notifier – Keep track of your favorite dependencies

Hacker News5

Jvm, like rvm, but for Java46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Jvm, like rvm, but for Java

Hacker News2

Kinda like posterous?43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kinda like posterous?

Hacker News53

"I'm Like..." - Texting With More Feeling47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

"I'm Like..." - Texting With More Feeling

Hacker News1

Succinctly manipulate dom-like trees from java50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Succinctly manipulate dom-like trees from java

Hacker News2

Spritz like bookmarklet37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Spritz like bookmarklet

Hacker News8

Endorfyn: Everything You Like59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Endorfyn: Everything You Like

Hacker News14

More Like This Bookmarklet33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.