Solana Blockchain

by Solana Labs

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Algorand

by Algorand

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Zilliqa

by Zilliqa

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Solana Blockchain

by Solana Labs

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Algorand

by Algorand

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Zilliqa

by Zilliqa

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What problem does this service solve?

Solana allows developers to create DApps with faster performance by separating the consensus process from the recording of transactions.Algorand's goal is to create a blockchain with faster transaction times and a more inclusive staking mechanism to achieve consensus.Zilliqa is building a scalable development platform based on sharding.

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Company Description

The Solana blockchain uses an innovative Proof of History mechanism to confirm transactions that are recorded on the network. Solana is designed to separate the consensus process from the flow of transactions. This is done with a recursive, verifiable delay function that hashes incoming transactions, and recording information as a function of time. Every node on the Solana blockchain is assigned a cryptographic clock that enables the network to determine the time and ordering of transactions, without having to wait for consensus from other nodes. By separating the process of transaction verification from their recording, a higher throughput is achieved without sacrificing network security.

Algorand is a Boston based company that is developing a blockchain network with a Pure Proof of Stake (PPoS) consensus mechanism. Algorand's decentralized platform is designed to enable faster transaction times and remove some of the technical barriers of creating scalable DApps. Algorand was founded by Silvio Micali, a pioneer in the field of cryptography.

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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