Play Mafia Against Chat GPT

Hacker News

It's exactly what you think it is. You play the classic game of Mafia/Werewolf, where all the other players are AI opponents. I tried really hard to put a lot of effort into the UI design, to make it feel like a fun, smooth, easy flowing game. Open to and would love feedback/thoughts/suggestions, and will do my best to respond to anyone who is nice enough to take the time to comment. Stack: - Clojure + Clojurescript, Reagent/Ring/Compojure/DaisyUI I did my best to get it working on mobile, but it only works sort of well, and it's a little better suited to desktop.

Visit product View on Hacker NewsJun 27, 2024

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Actual performance

4points

4comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, open · Missing: mac, agents, macos

75%75% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

54%54% 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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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