C4

C4 – Connect Four Game

Hacker News

C4 – Connect Four Game

You can play against computer (a simple AI, not that easy to beat), or against other human offline or online. This project started back in 2013 and I first submitted to HN back in 2015: https://news.ycombinator.com/item?id=8886897 Over the years, I've made lots of code changes (several code architecture revamps, online mode, dropping old browsers), but the AI part remains relatively the same: it's using minimax algorithm to "think" several steps ahead, all calculations are run in your browser. So I'd like to know what you think. What do you like about it and what features/improvements do you think is missing. Thanks!

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Actual performance

2points
1comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
79%79% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: computer, new, using · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
58%58% 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 · Missing: mobile apps, ios, personal
48%48% 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
45%45% 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
17%17% 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.

Incorrect prediction on native model

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