OpenSoccer – open-source soccer game in browser in 128 prompts

Hacker News

I built an open-source soccer arcade game clone with Modelence in 128 prompts, inspired by the 2026 World Cup and EA FC (aka FIFA). As far as I searched, there was nothing else you could play in browser, so I decided to give it a go, even though Modelence is primarily made for SaaS and not games. This is as far as I could get. Recorded the progress, so you can see it going from just two circles to an actual 11 vs 11 players with "proper" dynamics: https://youtu.be/RO1I3ML8iPk Link to the live game: https://worldcup2026.modelence.ai GitHub repo: https://github.com/modelence/open-soccer Features: - Similar controls to EA FC / FIFA on PC - All 48 World Cup 2026 teams with their real kits and players - All players have their actual skill scores mapped (pace, dribbling, shooting, passing, defending, physicality), so faster players actually run faster, etc. Not sure if it's worth adding online mode, slightly bigger commitment - let me know if you'd ever play it.

Visit product View on Hacker NewsJun 23, 2026

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

69%69% 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.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, physical, open · Missing: mac, agents, macos

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

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.

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

39%39% 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 · Strong signals: saas · Missing: arr, mrr, revenue

19%19% 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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