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I made a book shortener with LLMs

Hacker News

I made a book shortener with LLMs

This project shortens books or long texts using an LLM (Large Language Model). Overview The LLM Book Shortener is a tool designed to condense long texts or books into shorter, more manageable versions. By leveraging Large Language Models such as OpenAI's GPT-4o or others, this tool ensures that the essence of the original text is preserved while reducing its length significantly. The primary goal is to facilitate faster reading and comprehension, making it ideal for students, researchers, and avid readers. Motivation I wrote this to read significantly more and it worked. Results test@MacBook-Pro llm-book-shortener % wc -c waldenrewrite.txt 231074 waldenrewrite.txt test@MacBook-Pro llm-book-shortener % wc -c waldenprogress.txt 636980 waldenprogress.txt The rewritten version is approximately ~36% from the original. That's a significant reduction. URL: https://github.com/sturza/llm-book-shortener

Visit productView on Hacker NewsJun 19, 2024
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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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: ide, io · Missing: https docs, excited, just released
60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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