QuizKnit, an open source quiz creator

Hacker News

QuizKnit is an open source quiz creator to create quizzes by pasting in text or selecting from a large range of historical categories. Started as a very simple app for me to play around with OpenAI’s API last year then morphed into a portfolio project during my job search earlier this year. Now happily employed so decided to open source it. Right now, a user can create a quiz, take a quiz, save it and share the quiz with other people using a URL. You can try out the full working application at https://quizknit.com Github Links: Frontend: https://github.com/jibolash/quizknit-react , Backend: https://github.com/jibolash/quizknit-api

Visit product View on Hacker NewsJun 30, 2025

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

82%82% 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.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, openai, using · 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 · Missing: mobile apps, ios, personal

43%43% 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

35%35% 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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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