Wall Go – browser remake of a Devil's Plan 2 mini-game

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Source code: https://github.com/schaoss/wall-go I binge-watched Netflix’s “Devil’s Plan 2” and got hooked on the “Wall Go” round. A few nights of vibe-coding later, here’s a fully playable browser version: • 7×7 board, staggered placement (1-2-2-1), 0-to-2-step moves, mandatory wall each turn. • Light / dark mode, mobile-friendly hit boxes • Simple AI (ΔReach + liberty heuristic) – beats random, not a grandmaster yet • Tech stack: Bun + Vite + React + Tailwind v4 + Zustand, deployed on GitHub Pages Looking for feedback: * is the AI too weak / unfair? * any UI quirks on touch devices? * rule misinterpretations I missed? Bug reports, feature requests, or PRs very welcome. Hope you enjoy boxing your friends into tiny corners as much as I did while coding this :)

Visit product View on Hacker NewsMay 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: tiny, coding, code · Missing: mac, agents, macos

83%83% 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

61%61% predicted probability of success on Indie Hackers, 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

40%40% 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 · Strong signals: friendly · Missing: plus, platform, intuitive

39%39% 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.

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

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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