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Curv - a wearable camera

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Curv - a wearable camera

This is a hardware project that I've been working on the side. It's a wearable camera (don't roll your eyes yet) that captures events from a first person view. The difference between this and other wearable cameras is how it's worn. I used a makerbot to print the enclosure and bought a spy cam and gutted it to fit inside the enclosure. The device sits snugly on top and around your neck. It looks more like LG's around the neck bluetooth headset. Anyway, my reason for placing the camera there is that your neck experiences the least amount of shake and movement. And also because of the location and size of the device you can pack a much bigger battery. The whole motivation behind this camera is being able to passively record videos throughout your day in an unobtrusive manner. I know there are privacy issues regarding this but for me personally, I would like to capture events like while I'm traveling abroad or playing basketball. Activities where having a place to mount a traditional camera is impractical. Here are a couple videos with the prototype. What do you guys think? Shooting around bball - https://www.youtube.com/watch?v=m6IkydhtmPw Gun Range - https://www.youtube.com/watch?v=-bI0tja8JVc Random walk - https://www.youtube.com/watch?v=F2USBtarzBw Ride in a lambo - https://www.youtube.com/watch?v=8UNNZ2FINmI

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, 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
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% 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
12%12% 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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