Tiao, A two-player turn-based board game

Hacker News

Hi HN, I built this digital version of Tiao, a two-player turn based strategy board game. Think Checkers meets Go. It's free, runs in the browser, has multiplayer, AI, over the board mode and a lot of other neat things. The source is on GitHub (AGPL). The game was originally designed by my friend Andreas Edmeier. He created the rules and has been playtesting and refining the game design for years. I built the website for it. The core in about 2 weeks using TypeScript, Next.js, Express, Websockets, and MongoDB. Fully dockerized, deployed on a Hetzner VPS with Coolify. Authentication with better-auth. Real-time gameplay, ELO matchmaking, OpenPanel analytics, and a fully functional achievements system. Play it: https://playtiao.com Source: https://github.com/trebeljahr/tiao Happy to answer questions about the tech, the game design, or anything else. My hope is that more people will play this game because I think it is genuinely fun and would be cool to one day see people play this on a Go board or on their phones/computers. Have a good one.

Visit product View on Hacker NewsApr 26, 2026

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

66%66% 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: 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.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% 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

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

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

16%16% 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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