LennyFighter: A multiplayer fighting game on Cloudflare Durable Objects

Hacker News

I built LennyFighter to explore how far Cloudflare's developer platform can go for real-time multiplayer. Everything runs at the edge with zero origin servers: Vinext (React SSR) + Workers + Durable Objects for match state + D1 for stats + KV for session tokens + WebSockets for real-time play. It's a 1v1 trivia fighting game where you pick a tech industry leader as your fighter (Brian Chesky, Marty Cagan, Elena Verna, etc.) and answer trivia from their real Lenny's Podcast interviews. Correct answers deal damage scaled by type effectiveness; wrong answers cost you recoil HP. Each match is a MatchmakingQueue DO → MatchRoom DO (one per match) with hibernatable WebSockets, 20s reconnection grace period, and an optional pre-match wager/item selection phase. Source: https://github.com/iglesiasbrandon/lennyfighter Inspired by PokeLenny. Would love feedback on the architecture or the game or new features to add!

Visit product View on Hacker NewsApr 21, 2026

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Actual performance

2points

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Launch Intel predictions

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

70%70% 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: new · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% predicted probability of success on AppSumo, 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

43%43% 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 · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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