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Beakdown – a game inspired by Joust/Skirmish

Hacker News

Beakdown – a game inspired by Joust/Skirmish

I used to play Joust with my little brother on the BBC Micro many moons ago and have fond memories of it. So when I was experimenting with Claude Opus 5 it was a nice inspiration for this game. Did a little research and found there aren't many games out there that just work in a browser without ads, loading screens or account sign-ups. So I've put this directly on the landing page with no noise in the way - it works on any browser on any device and loads instantly. If it doesn't work at all, please tell me. And also let me know if you think it needs music - I went without it to begin with but don't know if that's what people would prefer or not. Would also love to know how far you get, my personal best is 199,000. Thanks for your interest!

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Actual performance

11points
19comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude · Missing: mac, agents, macos
87%87% 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
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released
49%49% 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
47%47% 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
15%15% 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.

Incorrect prediction on native model

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