AI-powered tool that breaks down complex topics into bite-sized outline

Hacker News

Hey everyone! I’m excited to share my side project, StudyGuide, which uses AI to simplify just about any topic you can think of. If you’ve ever struggled with breaking down huge chapters or complicated subjects, this tool might help. What it does: You type in a topic (say, “Quantum Mechanics” or “French Revolution”). The AI then generates a detailed outline, broken down into smaller, easier-to-digest subtopics. You can click into each subtopic to go deeper, eventually drilling down to specific sub-subtopics or key concepts. Why I built it: I’ve always found that half the battle in learning is figuring out where to start and how to structure my study notes. I wanted a simple interface: type a topic, get a structured breakdown, and explore further as needed. If you want to try it out, here’s the link: https://app.tivateonline.com/studyguide/ Feel free to leave your thoughts in the comments – I really appreciate any feedback or suggestions for improvement!

Visit product View on Hacker NewsJan 26, 2025

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

79%79% 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: notes · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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 · Missing: arr, mrr, revenue

12%12% 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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