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Generate quiz based on files you upload and share with others

Hacker News

Generate quiz based on files you upload and share with others

Hey everyone, I'm excited to share Quiz Buddy, an open-source AI-powered quizzing platform I developed while preparing for my university finals. - Turn Your Files into Quizzes: Upload PDFs, text files, and more to generate custom quizzes instantly. - Launch Your Quizzes: Start your quizzes right away and test your knowledge. - Instant Performance Feedback: Get immediate results and feedback on your quiz attempts. - Share with Others: Easily share your generated quizzes with other users. Check out the hosted version here: http://quiz-buddy.xyz/ and explore the source code on GitHub: http://github.com/that-one-arab/quiz-buddy

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
65%65% 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: user, code, open · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, host, users · Missing: plus, intuitive, reviews
33%33% 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, io · Missing: https docs, just released, exist
32%32% 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: users, way · Missing: mobile apps, ios, personal
28%28% 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
14%14% 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
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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