Card-Snap – Generate Printable Flashcards from Any Document

Hacker News

Hi HN, I built Card-Snap: www.card-snap.com It’s a very simple tool where you upload a document (Word, PDF, TXT), and it generates a PDF file with printable flashcards. The idea came to me when helping my daughter study science. I realized that while there are many digital flashcard apps, it’s not easy to get physical flashcards without a lot of manual work. Card-Snap is minimal: no signup, no tracking, no ads — just upload, pick how many questions you want, and download the printable cards. I’d love your feedback! Some questions I have: Is the idea useful enough on its own? What features would you expect next (e.g., export options, better formatting, templates)? Would you prefer more control over card content? Thanks so much for taking a look!

Visit product View on Hacker NewsApr 26, 2025

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Actual performance

4points

27comments

Did not reach leaderboard

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

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best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

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TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apps, physical · Missing: mac, agents, macos

33%33% 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: ide, io · Missing: https docs, excited, just released

25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Missing: web3, chat, crypto

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

Correct prediction on native model

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