Hacker News Book Recommendations

Hacker News

After reading this Ask HN post [1] (and buying too many books), I found myself going through old posts about book recommendations. I wanted to find all books recommended in those posts and save the top ones for future book shopping sprees. This has been done before [2, 3], but I wanted to get books that were recommended, not just mentioned. I used the Algolia API [4] to get all Ask HN posts about books, reviewed them and filtered out ones that were too specific e.g best books to learn math. The comments were then sent to the Open AI API [5] to extract books that are recommended. I then grouped them, but books can be mentioned with different names (Godel, Gödel, GEB?), and GPT4 wasn't doing a good job finding similar titles in a long list (25k titles). So that part had to be done mostly manually. Finally, I added a page to browse by posts, some stats (like users who recommended the most books), and highlighted mentioned books in the comments. I hope you find a good book! Feedback is welcome :) [1] https://news.ycombinator.com/item?id=41756432 [2] https://hacker-recommended-books.vercel.app [3] https://ramiro.org/vis/hn-most-linked-books/ [4] https://hn.algolia.com/api [5] https://platform.openai.com/docs/api-reference/introduction

Visit product View on Hacker NewsOct 19, 2024

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, 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.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

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

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, openai · Missing: mac, agents, macos

26%26% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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