Wreflecto – word puzzle inspired by the ancient SATOR Square

Hacker News

Wreflecto is a word square puzzle where every row and every column must form a valid word. Read more about ancient word squares here - https://en.wikipedia.org/wiki/Sator_Square Unlike crossword or wordle, there are many possible solutions. Any valid word combination works. 4x4 (easy) on weekdays. 5x5 (hard) on weekends. Two modes - mirror ( https://wreflecto.com/mirror ), and cross ( https://wreflecto.com/cross ). Daily puzzle is free on web. Built using vanilla JS/HTML/CSS, everything runs in the browser, no backend, no ads, no account/login. Also turned it into an iOS game with both daily and on-demand unlimited play offline: https://apps.apple.com/us/app/id6751056949 Please let me know if you try it.

Visit product View on Hacker NewsApr 21, 2026

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

84%84% 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.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% 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

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Product Hunt, 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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