Text Battle – AI-simulated fights, daily leagues (Elo Ranking)

Hacker News

I built Text Battle , a lightweight web game where you: - Write a character prompt in ≤ 100 chars (e.g. "A sarcastic cyber-ninja with a plasma katana"). - Earn Elo and appear in daily or unlimited-season leagues. Feedback I’m looking for: - Whether the 60 s cooldown feels fair vs. annoying. - Balance of Elo gains between free and paid matches. - I added micro-transactions because LLM costs are non-trivial; any tips on model/infra optimization to keep it free-to-play are welcome. URL: https://plan9.kr/battle Auth: Email, Google, Supabase Anonymous Sign-Ins Source / tech stack: React + Supabase + Gemini API Thanks in advance—happy to answer questions!

Visit product View on Hacker NewsApr 23, 2025

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Actual performance

2points

Did not reach leaderboard

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

89%89% 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: model, google, email · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

48%48% 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

46%46% 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 · Missing: plus, platform, intuitive

27%27% 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

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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