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Hand Tracking for Mobile AR/VR/IOT

Hacker News

Hand Tracking for Mobile AR/VR/IOT

https://www.youtube.com/watch?v=qCVahnoM-Fk We started working on this more since last year (the VR market was slightly better at that time). One of the differences between this and the existing tech (eg. LeapMotion) is this runs on generic depth/stereo sensors and embedded platforms. So, there is no extra HW needed for many AR headsets with depth sensor already. We do see many applications for this, but could be wrong. We hope to get your opinion on this. Here is another demo video showing the accuracy: https://www.youtube.com/watch?v=uBdvL6F0hLE

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Actual performance

3points
2comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
51%51% 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 · Strong signals: video · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Strong signals: started · Missing: supports, reddit linkedin, podcasting
35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
32%32% 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
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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