Chess Twist

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Chess Twist

I continue my experiment of transposing classic games onto an irregular grid. I've previously released variants of minesweeper and checkers. And now I'd like to share my variant of Chess. Chess can be a pretty punishing game, but I thought this was nevertheless a way to make it even more challenging by making it trickier to anticipate your opponent, even though both players still have the same information. From the playtests I've done, that challenge is pretty compelling. It also seems to be more fun at 2 people, where both players keep surprising one another.

Visit product View on Hacker NewsMay 31, 2024

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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

36%36% 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

12%12% 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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