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Quizdom – Your AI-Powered Assessment Assistant

Hacker News

Quizdom – Your AI-Powered Assessment Assistant

Hi HN! We’re thrilled to introduce Quizdom, an AI-powered tool designed for educators, trainers, and assessors. With Quizdom, you can: - Upload documents (PDFs, notes, etc.). - Automatically generate questions and answers tailored to the content. - Customize and refine as needed. - Export to PDF/DOCX or publish assessments online. - Review and grade submissions, including an AI grading option. Our goal is to save time and enhance the quality of assessments for everyone who creates or manages them. We’d love your feedback or suggestions—let’s build something incredible together! Try Quizdom here: https://www.getquizdom.com

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: notes · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released
38%38% 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: answers · Missing: mobile apps, ios, personal
38%38% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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