A bilingual guide to Thaayam, a Tamil board game

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I saw The Mahjong Guide ( https://themahjong.guide/ ) today and loved how much a good visual explanation can do for an old game. Around the same time, this HN thread about using coding assistants to revive projects you never were going to finish ( https://news.ycombinator.com/item?id=47902525 ) pushed me to actually do something with the idea. That combination sent me down a nostalgia rabbit hole. Thaayam / Dayaakattai is one of those games I played during childhood summers in Tamil Nadu with cousins, and this being summer again just brought those memories back. So I made a small bilingual guide for it, in English and Tamil. One thing this also made me realize is how strange and nice it is now to be able to build small things semi-passively with tools like Codex. You can have a little idea, keep nudging it between work or life, and a few hours later there is an actual site instead of another note sitting in a backlog. The site includes the board, stick throws, safe squares, capture/finish rules, odds, strategy notes, history, and a little Dayakattai roll simulator.

Visit product View on Hacker NewsApr 26, 2026

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

82%82% 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.

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: codex, new, visual · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

27%27% 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

17%17% 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.

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