I built a tool that generates quizzes from documents using LLMs

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Hey everyone! I recently built this little side project that takes any document you upload and turns it into practice quizzes using LLMs to generate the questions: https://www.cuiz-ai.com Being a backend engineer, I have always struggled to finish my side projects due to my awful frontend skills. Now, with the progress of AI coding tools, I had no more excuses. 100% of the frontend, landing page and even the logo was made using a combination of Cursor, ChatGPT and Claude, but it's always been under my supervision and never blindly accepted the proposed changes. The backend and infra was handled by myself. The tool itself is pretty straightforward: upload any PDF/DOC, get quiz questions generated by an LLM, export them if you want, and come back to your previous quizzes anytime. I got this idea when I was studying from a pdf for my UK citizenship. I know there are similar tools around, but it was a good opportunity for me to build something end to end! I'd love to know what you think or what you might use this for. Any feedback is super appreciated!

Visit product View on Hacker NewsMar 29, 2025

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

86%86% 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: cursor, claude, chatgpt · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

31%31% 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: way · Missing: mobile apps, ios, personal

29%29% 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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