Cryptoshare vs MAZE

We are a Peer to Peer Decentralized Credit System that runs on Blockchain and Cryptocurrency instead of Fiat Currency.

Cryptoshare Credit allows people to Borrow and Lend based on Accumulated Cryptoshare Credits (not race, gender, height, salary, and unnecessary factors that have been traditionally used to exclude Billions of people in the world from participating in the worldwide financial system).

Current Credit Applications have a required credit score and you achieve that credit score and sometimes still get denied. Decentralized Credit eliminates all bias, thanks to Blockchain technology because it is 100% based on your accumulated Credit Grade, NOTHING ELSE!

It actually works quite simply. We have eliminated Credit Scores and replaced them with Credit Grades. Your Credit Grade is based on the amount of Cryptoshare Credit in your wallet. You can purchase Cryptoshare Credit with Cryptocurrency. The more Cryptoshare Credit you have the higher your Credit Grade will be. Our Decentralized Credit System is 100% Anonymous between Lenders and Borrowers. This means if you have a Good Credit Grade you will automatically be approved for a loan (no more having to deal with middleman to get a loan).

Crypto$hareCredit will allow digital and physical assets to be used as collateral to give borrowers the opportunity to receive funding.

-Users can also borrow money using physical assets (car titles, jewelry, and etc) as collateral using Blockchain Smart Lockers as custodians within the app.

Location: Watertown, United States

MAZE - connecting people in geographic micro-communities around the world

MAZE aims to connect people in geographic micro-communities around the world that share the same values, beliefs and interests, people that belong to the same tribe, so to speak.

MAZE is a revolutionary, new model for discovering like-minded people in close physical proximity. This is done through a mobile platform that uses an Internet of Things (IoT) ecosystem of location-aware devices, including Bluetooth Low Energy sensors, and a proprietary matching algorithm that learns from the user’s interactions using machine learning.