Ku

Kurt Vonnegut's lost board game online

Hacker News

Kurt Vonnegut's lost board game online

Three weeks ago GHQ, a strategy board created by Kurt Vonnegut appeared on the front of HN[0]. A friend and I got hooked and spent the last week and half building out a playable online version of the game. It supports pass n'play, multiplayer, and a primitive bot (named kurt) that you can play against. We're also chess nerds so had to ship with an Elo system. Huge kudos to nicolodavis[1] and the maintainers of Boardgame.io [2] - it's the only reason we could build something this good, this fast. The library separates concerns and authority across the server / client seamlessly without requiring you to rewrite the logic in multiple places. The abstractions felt right immediately. If you want to build a browser based board game, start here. Hope this is fun and brings GHQ to more people. We opened source the code if you want to contribute or remix it :) [0] https://news.ycombinator.com/item?id=41896636 [1] https://github.com/nicolodavis [2] https://boardgame.io/

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created, para · Missing: reddit linkedin, podcasting, latex
76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
74%74% 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: para · Missing: mobile apps, ios, personal
42%42% 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
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, code, open · Missing: mac, agents, macos
16%16% 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
14%14% 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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