Body Controlled 3D Dino Game

Hacker News

Hey HN, I am Niko. I've built this 3D Dino Game In browser using tech like three.js and MoveNet (tensorflow). Basically, it's a normal 3D dinosaur game with a twist: you need to actually perform actions irl to avoid obstacles. Duck to crouch, jump to jump, raise left hand - go left, raise right hand - go right. Game is using your phone/laptop camera to track your body movements and perform in-game actions. PS. Game is 100% client side and I don't record/track/use/save any of your data Hope you find it worth playing. (better play on PC) It's a 100% FREE browser game with no login! Please feel welcome to DM feedback or reply or anything!

Visit product View on Hacker NewsApr 23, 2025

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Actual performance

29points

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

67%67% 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.

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

55%55% 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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

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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