Debategle – ranked 1v1 debates judged by an LLM

Hacker News

I built Debategle, a platform for live 1v1 debates. You get matched with a random opponent (ranked matching is based on topics you’re interested in, casual matches are just open rooms), you each argue your side, and an LLM judges who made the better case. Stack: FastAPI backend, Clerk for auth, Elo-style rating system for ranked matches. The obvious objection is LLM-as-judge reliability, I don’t think it’s perfect, but I’ve found it’s decent at scoring argument structure and rebuttals rather than just rewarding confident-sounding text. Curious what people think breaks it, I’m sure there are ways to game the judging that I haven’t found yet.

Visit product View on Hacker NewsJun 30, 2026

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

72%72% 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

52%52% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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