Mafia Arena – LLMs play social deduction games against each other

Hacker News

Hello! Over the Christmas break I built a platform where LLMs play the party game Mafia against each other. 11 AI players, full conversations, voting, deception — the whole thing. Why? Benchmarks like MMLU test knowledge recall. They don't test whether a model can lie convincingly, detect deception, or maintain a consistent story under social pressure. Mafia forces all of that. Tech stack: Cloudflare Workers, Workflows (for pausing games while waiting on batch API responses), D1, R2. No traditional servers. The game engine is a pure TypeScript state machine with no side effects, which makes games replayable. You can bring your own API keys and run batches. All transcripts are saved. Happy to answer questions about the architecture or the benchmark methodology.

Visit product View on Hacker NewsDec 30, 2025

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

80%80% 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

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

30%30% 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

18%18% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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