ChainLink

by SmartContract

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FLO

by FLO

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Zilliqa

by Zilliqa

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ChainLink

by SmartContract

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FLO

by FLO

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Zilliqa

by Zilliqa

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What problem does this service solve?

ChainLink wants to solve the connectivity problem facing smart decentralized applications. By connecting smart contracts to external data resources, many potential use cases for blockchain can be opened up.FLO's blockchain allows users to record short comments or notes to its transactions.Zilliqa is building a scalable development platform based on sharding.

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Company Description

ChainLink is developing the LINK Network, which will enable connected smart contracts. LINK will provide smart contracts with a variety of external data and access to APIs. ChainLink's goal is to solve the connectivity problem that plagues smart contract based systems, and discourages mass adoption. Many decentralized applications are unable to connect with important external data sources due to limitations involving their consensus protocols. The LINK network wants to connect their smart contracts with external systems and APIs by using secure middleware.

FLO is a Proof-of-Work blockchain with a metadata layer called floData. It enables users to easily record notes to its blocks, and can be used to create DApps. One of FLO's central features is the Open Index Protocol, which standardizes cryptographic ownership on open networks. The founders of FLO are also working on Alexandria; an open-source standard for users to publish and distribute original content. FLO's source code is based on that of Bitcoin and Litecoin. It is designed to enable fast transactions times.

Zilliqa is an innovative blockchain platform that is designed to be more scalable as the network grows. Zilliqa's platform is based on the concept of sharding; which involves breaking a large network of computers into smaller decentralized networks.
Zilliqa's goal is to increase transaction rates as its network expands. The platform is tailored towards enabling secure data-driven decentralized apps that are designed to meet the scaling requirements of machine learning and financial algorithms.

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