Mo

ModelKombat – Arena-style battles for coding models

Hacker News

ModelKombat – Arena-style battles for coding models

I'm Vivek, co-founder/CEO of HackerRank (YC S11); You may know us as a hiring tool for developers/companies. Over the years, we have built up deep expertise in generating programming challenges, and we are now using that to make coding models better. Our first launch is Model Kombat -- an arena where you can directly compare anonymized coding models, side by side, on real problems. * Pick an arena (Java, Python, etc.) * Each battle has 3 rounds: see the problem + two model outputs -> vote on which you’d actually prefer * Leaderboards + problem statements are updated weekly. We already have 400+ challenges live, and our goal is to evolve toward real-world, multi-file challenges and robust tooling for coding model evaluation. Try it now at: modelkombat.com We’d love your feedback.

Visit product View on Hacker NewsSep 16, 2025

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

88%88% 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

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

63%63% 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

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

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

40%40% 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

17%17% 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

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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