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Eyetem – Trade neighborhood crime information for crypto rewards

Hacker News

Eyetem – Trade neighborhood crime information for crypto rewards

We decided to completely rethink the way that neighbors organize and interact with each other and law enforcement to address crime. One of the biggest challenges that we’ve heard from our neighbors is that it can be difficult to communicate privately and safely with others about ongoing issues (e.g., identifying people, vehicles, etc. that are responsible for crimes or other issues in the neighborhood). People will often tell us that they want to help, but they are concerned about their safety and feel the hassle of getting involved is just not worth it. Other community apps today require personal information at sign-up (email, phone, etc.), so we decided to do away with that. And, with our app, people can receive crypto rewards when they share information that helps each other and law enforcement. Big picture, Eyetem is a mobile app that incentivizes neighbors to anonymously share local information that can improve their community, while also protecting their privacy. Think of it like NextDoor, Ring, or Citizen offering crypto-rewards for valuable information AND not requiring users to hand over their personal information just to chat with each other. Our focus is less on live-broadcasting crime information as it happens and more on creating the right incentives (anonymity + crypto rewards) for neighbors to help law enforcement and each other gather and reconstruct the type of information needed to solve crimes. There has been very little innovation around how law enforcement can make it easier and safer for people to get paid for sharing the types of information they are seeking. With Eyetem, we are trying something completely different and have live crime data in Washington DC, San Francisco, and Los Angeles today. We would love to get your feedback on the app. Thanks! Website: https://www.eyetem.com/ Explainer Video: https://www.youtube.com/watch?v=r9dRW1hKBWE

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Actual performance

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, video · Missing: mobile apps, ios, entrepreneurs
63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, email · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% 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 · Strong signals: chat, crypto, reward · Missing: web3, cryptocurrency, make money
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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