Hands Off
Stop nail biting and other bfrbs with AI
Hands Off is a desktop app that intercepts nail biting, skin picking, and other BFRBs in real time. It runs locally on your device, pops up when your hands get near your face, and gradually retrains your brain. All without sending data to the cloud.
Features
- AI-powered detection: Monitors nail biting, hair pulling, and other BFRBs via webcam, with AI trained specifically for these habits.
- Real-time intervention: Visual overlays and notifications stop behaviors as they happen.
- Customizable alerts: Adjust sensitivity, select warning zones, and choose overlay style for a tailored experience.
- Progress tracking: View streaks, daily counts, and long-term trends to stay motivated.
- Privacy-first & offline: All processing runs locally; no personal data leaves your device.
Use Cases
- Nail biting prevention: Helps users stop nail biting by detecting hand-to-mouth movements in real time and showing immediate overlays.
- Hair, eyebrow, and eyelash pulling: Detects hair-focused BFRBs (trichotillomania) and provides on-the-spot alerts to interrupt the behavior.
- Nose picking & other hand-to-face habits: Monitors other repetitive hand-to-face behaviors, helping users become more aware and reduce occurrences.
- Habit tracking and self-monitoring: Users can track streaks, daily counts, and trends to see progress and identify patterns in their behavior.
- Work-from-home or study environments: Runs silently in the background, providing support without disrupting focus or workflow.
Comments
This is a thoughtful idea for anyone dealing with nail biting or skin picking. The fact that all processing happens locally is a big plus for privacy-conscious users. It would be interesting to see if future versions could support mobile or wearable detection, since BFRBs often happen away from the desk. Great start — feels genuinely helpful.
Hi all! I've been biting my nails for over 22 years, and I've tried pretty much everything you can think of. From bitter nail polish, to those mental tricks that work for about a day before you forget all about them. Nothing stuck. Here's what I finally realized about nail biting: it's not really about willpower. I could spend hours promising myself I wouldn't do it, only to see my hands full of scabs either way. The real issue wasn't that I didn't want to stop, it's that I didn’t notice I was even biting/picking in the first place. That's when it clicked for me. I didn't need more willpower, I needed someone to tap me on the shoulder the exact moment my hand started moving toward my mouth. Since hiring a person to follow me around wasn't exactly practical, I decided to build an app to do it instead.
This is a genuinely interesting concept — using AI-powered real-time feedback to break body-focused repetitive behaviors (BFRBs) like nail biting or skin picking. What stands out most is that it runs entirely offline, keeping all video processing local. That’s an impressive and privacy-first design choice, especially for something that involves continuous webcam use. A few thoughts and questions came to mind while reading: The visual overlay intervention idea is clever — but does it adapt over time as the user improves? For example, can the system gradually reduce alert frequency as the habit weakens to avoid alert fatigue? I like that it supports different behaviors (nail biting, hair pulling, etc.) — are the AI models separately trained for each type, or is there a unified detector? The progress tracking section sounds motivating. It might be even more powerful if it offered insights like time-of-day or stress-trigger correlations to help users understand when and why the behavior occurs. From a usability perspective, how does it balance sensitivity vs. false positives? I could see cases where normal gestures (like scratching an itch) might trigger alerts. Overall, “Hands Off” feels like a thoughtful blend of behavioral science and AI habit-tracking. It’s impressive that it manages to stay entirely local without compromising on responsiveness or accuracy — that’s a rare and welcome approach for wellness tools.
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Comments
This is a thoughtful idea for anyone dealing with nail biting or skin picking. The fact that all processing happens locally is a big plus for privacy-conscious users. It would be interesting to see if future versions could support mobile or wearable detection, since BFRBs often happen away from the desk. Great start — feels genuinely helpful.
Hi all! I've been biting my nails for over 22 years, and I've tried pretty much everything you can think of. From bitter nail polish, to those mental tricks that work for about a day before you forget all about them. Nothing stuck. Here's what I finally realized about nail biting: it's not really about willpower. I could spend hours promising myself I wouldn't do it, only to see my hands full of scabs either way. The real issue wasn't that I didn't want to stop, it's that I didn’t notice I was even biting/picking in the first place. That's when it clicked for me. I didn't need more willpower, I needed someone to tap me on the shoulder the exact moment my hand started moving toward my mouth. Since hiring a person to follow me around wasn't exactly practical, I decided to build an app to do it instead.
This is a genuinely interesting concept — using AI-powered real-time feedback to break body-focused repetitive behaviors (BFRBs) like nail biting or skin picking. What stands out most is that it runs entirely offline, keeping all video processing local. That’s an impressive and privacy-first design choice, especially for something that involves continuous webcam use. A few thoughts and questions came to mind while reading: The visual overlay intervention idea is clever — but does it adapt over time as the user improves? For example, can the system gradually reduce alert frequency as the habit weakens to avoid alert fatigue? I like that it supports different behaviors (nail biting, hair pulling, etc.) — are the AI models separately trained for each type, or is there a unified detector? The progress tracking section sounds motivating. It might be even more powerful if it offered insights like time-of-day or stress-trigger correlations to help users understand when and why the behavior occurs. From a usability perspective, how does it balance sensitivity vs. false positives? I could see cases where normal gestures (like scratching an itch) might trigger alerts. Overall, “Hands Off” feels like a thoughtful blend of behavioral science and AI habit-tracking. It’s impressive that it manages to stay entirely local without compromising on responsiveness or accuracy — that’s a rare and welcome approach for wellness tools.
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