Logiquiz – Daily Self-Referential Puzzles

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Hey HN, About twenty years ago, while I was in college, I first stumbled upon James Propp's Self-Referential Aptitude Test [1] and absolutely loved it. Ever since then, I've had this idea of turning that concept into a daily game, and I've finally built it: https://www.logiquiz.com/ The game interface checks each question against the answers you've given, so it doesn't spoil anything by giving away the answers. There are five different tests each day, from very easy to very hard. I'd love to hear what you think! [1]: https://faculty.uml.edu/jpropp/srat-Q.txt

Visit product View on Hacker NewsApr 23, 2025

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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

35%35% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% 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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