Chess960v2.com – 960×960 asymmetric chess, 600/960 rounds done

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Hi HN, I just hit a major milestone on my side project. I work a demanding day job, so I built a platform where Stockfish plays continuous self-play (2s/move to keep server load manageable) to analyze asymmetrical chess variants. In Chess960v2, White and Black independently choose any of the 960 valid setups, resulting in 921,600 possible starting combinations. I'm running a massive round-robin tournament (960 rounds total, where each setup plays against every other setup), and 600 out of 960 rounds have been completed. The goal is to mathematically find which combinations are genuinely balanced (50/50) and which give an unfair engine bias to one side due to asymmetry. I'm pretty exhausted today, so I might be slow to reply, but I'd love for you to check out the site, look at the current data, and share your thoughts. Thanks!

Visit product View on Hacker NewsSep 12, 2026

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

60%60% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

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

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

47%47% predicted probability of success on Product Hunt, 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

45%45% 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

45%45% 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

27%27% 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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