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I built a Replit app for something i constantly do with LLMs - I always ask Gemini about books on a certain topic, then ask the llm to give me a summary of the book its chapters - this helps me: 1. keep the llm grounded as a real expert (instead of saying pretend to be an engineering expert, give it a distributed systems book and then ask opinions on it) 2. helps me discover and buy books! Once i go through book concepts and chat with llm about it, i'm more convinced to buy it and dig deeper on low level on it So i built an app through Replit on it, try it on https://teach-me.replit.app/ Cheers!

Visit product View on Hacker NewsMar 29, 2026

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

63%63% 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.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: gemini · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, 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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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