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EyesOff – Alerts you when someone peeps at your screen

Hacker News

EyesOff – Alerts you when someone peeps at your screen

Hey HN, this is Yusuf. I've built a privacy focused macOS app which makes use of a locally running neural network (YuNet), to notify you if other people are looking at your screen. YuNet runs fully on-device with no data leaving your computer. The app utilises a 230kb facial detection model, which takes images from your webcam and checks for any faces entering the viewing field of your webcam. If the number of faces exceeds the threshold an alert will be shown. Built with Python + PyQt, the YuNet code comes from OpenCV. Currently it's a macOS app only, however I will be widening access to windows devices soon. Link + Source code: https://www.eyesoff.app I'd love your feedback on the app, I look forward to reading your comments on thoughts and future directions you'd like to see!

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4points
5comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor
88%88% 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
75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
67%67% 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
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
25%25% 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
17%17% 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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