Play Riichi Mahjong Online

Hacker News

I'm presenting a website where you can play Riichi Mahjong (4p strategy and luck boardgame): - Play Mahjong as a guest or registered user - Invite other players or play against bots - View your game history and publicly share it with others - Use the hand calculator for scoring your offline games at https://online-riichi.com/score The game client has been inspired by "online-go" which I really enjoyed playing on - it's simplistic but feels tactile and remains true to the "feel" of the game. I would like to achieve the same thing. As part of preparing to share on HN and reading the posting guide, I've added a "Play as guest" button so you don't need an account to play. Finally, my own selfish goal is to gather a small player base which I can develop features for. My question for HN is - how would you go about attracting users to this kind of project? All is built with Postgres, Rust and Preact. Done as a pastime to work. I'm sharing it now because I promised myself to do so before the end of the year! I've added a discord link on the website, but you can also contact me at cerpinsmiks@gmail.com. Merry Christmas!

Visit product View on Hacker NewsDec 25, 2025

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Did not reach leaderboard

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

77%77% 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.

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

55%55% predicted probability of success on AppSumo, 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

42%42% 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: users · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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

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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