Let your body be the gamepad using a webcam

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Hi HN, I made a keyboard emulator that tracks your body pose and emits the key presses accordingly. My main motivation was to make my kids move more on rainy days. I already made a couple of mini-games using the body pose [1-3], and those were definitely fun to make and play! However, once the kids learned the tricks, they got bored. I could only produce that much content myself, and soon realized that I lack time for churning out games. Finally, I decided to tap into an endless pool of PC games, preferrably simple and less addictive ones like the Gameboy and DOS games. This project has been also inspired by [4], which has been featured on HN recently. [1]: https://github.com/mristin/pop-that-balloon-desktop [2]: https://github.com/mristin/cactusss-desktop [3]: https://github.com/mristin/ski-leu-desktop/ [4]: https://github.com/everythingishacked/Semaphore Edit: clarified title; layout of references

Visit product View on Hacker NewsApr 16, 2023

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Hacker NewsStrong engagement from HN community · Strong signals: 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.

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

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Product Hunt, 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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