Instabooks GPT – Generate textbooks on any subject within ChatGPT

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This holiday weekend, I created a GPT for Instabooks, which allows you to instantly generate textbooks on any topic within ChatGPT. Why? ChatGPT is great for learning stuff, but sometimes you just want to go deep and read an entire book about something. For example, there's a great talk by Stephen Wolfram that just goes over my head ( https://www.youtube.com/watch?v=fLMZAHyrpyo ), so I copy/pasted the transcript into the GPT and it generated a textbook that goes deep into all the topics from that video ( https://instabooks.ai/products/bbacb7a2-deab-df76-e20b-1fea2... ). PS: If you don't have ChatGPT Plus (or can't access the GPT for some reason), feel free to use the old school HTML form on the website to generate a book: https://instabooks.ai

Visit product View on Hacker NewsNov 27, 2023

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

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: plus · Missing: platform, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% 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

15%15% 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

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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