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Book Summaries Generated by ChatGPT

Hacker News

Book Summaries Generated by ChatGPT

Hi, I've just launched an app and wanted to know your feedback about it. Basically it's a ChatGPT based app (of course) that summarizes books, gives quotes and key ideas from books. You can think of it as an alternative to Blinkist but all the content is generated by ChatGPT. I'm a fan of reading and like to get a general idea of a book before buying it. Since I started using ChatGPT, I've tried to summarize, get key ideas and quotes from lots of books. It did an amazing job not only for popular books but also for not well known books in specific topics. After seeing the potential, decided to create a simple MVP for an app. Here’s how it works: 1. You search for a book 2. Generate summary, key ideas, quotes and actionable items using ChatGPT 3. Read and revisit key takeaways and quotes Best part is, there’s no limit to what books can be summarized unlike apps like Blinkist where there is a collection of limited pre-summarized books. Plus, there are no subscriptions. Enough talking. Check it out for yourself and share your thoughts. Would love to hear them :)

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
84%84% 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: apps, chatgpt, using · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews
57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
34%34% 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 · Strong signals: subscription · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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