LiSkat: Free real-time Skat with Elo and bots

Hacker News

Skat is a ~200 year old 3-player German trick-taking card game. I built a free site to play it online, solo against bots or with other people (matchmaking and Elo). The bots are what I keep tinkering with. Card play is a linear model over hand features, with the weights tuned by self-play search. Bidding and declaring use Monte-Carlo rollouts. No signup needed to practice against the bots. It's all TypeScript (Svelte plus a small WebSocket server). GitHub: https://github.com/nic-kup/liskat

Visit product View on Hacker NewsJun 29, 2026

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3points

2comments

Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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