Goodreads Analyzer – AI roast and book recs from your reading history

Hacker News

Inspired by the HN Wrapped Roast ( https://news.ycombinator.com/item?id=46336104 ), I built a web app that analyzes your Goodreads library and generates a literary roast, personality profile, and book recommendations. Upload your Goodreads export CSV and get: - Reading stats - A roast of your reading choices - Inferred personality traits - Year-by-year thematic analysis - Personalized book recommendations Uses Gemini API for analysis. Book data stays in browser (localStorage), only titles/reviews/dates sent for insights. Demo mode available if you want to try it without uploading your own data. This started as an experiment in prompt engineering and pattern recognition. I was curious how much personality insight you could extract from reading patterns alone. The results were surprisingly accurate. It correctly inferred my shifts in physical location, political beliefs, and religious affiliation based purely on reading history. Would love feedback on the analysis accuracy or additions that would make this more fun to use.

Visit product View on Hacker NewsDec 27, 2025

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

87%87% 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.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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.

Correct prediction on native model

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