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I built Cuiz-AI, turns documents into quizzes in seconds

Hacker News

I built Cuiz-AI, turns documents into quizzes in seconds

I built Cuiz-AI to generate practice quizzes from PDFs, Word docs, and PowerPoints using AI. The name is a play on "Quiz" + "Cuis", a small rodent from Argentina, where I'm from. That's why the logo is a little rodent. The tricky part: Getting an LLM to generate quality quizzes fast was quite hard. I spent a lot of time testing different models and approaches to get the generation time down from minutes to seconds. You upload a document, the LLM reads it and generates multiple choice questions with explanations. It also tracks your quiz history and progress. Would love feedback, especially on use cases I might not have thought of and in the quality of the questions! https://www.cuiz-ai.com/ thank you!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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: model, models, using · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% 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.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
23%23% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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.

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