Evolution of Top-10 Coins in CoinMarketCap’s History (2013-2023)

BlackCoin

BNB Smart Chain (BEP20)

BinaryX

PancakeSwap

Trust Wallet Token

SafePal

Beldex

Ontology Gas

MOBOX

Meta Games Coin

MetFi

Lista DAO

Open Campus

H2O DAO

BakeryToken

OwlDAO

King of Legends

RGAMES

Liza Coin

Laika AI

Graviocoin

Pascal

Peercoin, also known as PPC or Peer-to-Peer Coin, was created by Sunny King and his team and launched in August 2012. It is the first blockchain to implement Proof-of-Stake consensus.

The primary inspiration behind the creation of Peercoin was to address a number of perceived shortcomings of Bitcoin, including energy efficiency, security and sustainability, decentralization and long-term viability.

Essentially it was designed as an enhanced replacement for Bitcoin. As such, it was originally forked from Bitcoin and uses the same UTXO style blockchain. However, Peercoin's code was modified to introduce Proof-of-Stake as its primary consensus protocol. The next section explains how this makes for a better blockchain.

Given that Peercoin uses most of the same code, it can easily port new features from Bitcoin and utilize any of the supporting infrastructure, including tech advancements like Taproot or Lightning Network. Peercoin is the only Proof-of-Stake blockchain that uses modern Bitcoin code, making it the perfect drop-in replacement.

Peercoin also has a dedicated community, is actively developed, fairly distributed, has low barriers of participation, can do everything that Bitcoin does, minus the 13 TWh of energy usage, and it's not a scam.

Peercoin

Ycash

X-CASH

SmartCash

Pillar

Beacon

QuadrantProtocol

Validity

Litecoin Cash

WaykiChain

0xBitcoin

Tachyon Protocol

Vertcoin

BitKan

CyberVein's big-data solution is based on the PISR (private, interlink, secure, robust) distributed database, database operations are stored on CyberVein blockchain network which runs on a Proof-of-Contribution (PoC) consensus mechanism. The solution is focusing on enterprise-level "blockchain + big-data" customized services.

CVT is the native utility token that is used for:

Storage payment: The data owner pays the corresponding storage fee based on the file size and bandwidth consumed in such a process.

Computing power payment: Payments from user to owner for usage, and remuneration for software developers is going to be exclusively conducted in CVT.

Data exchange: On CyberVein's federated learning platform, the data acquirer trades the data at a price contracted between the two parties for distributed modeling applications.

CROSS NFT issuance payment: Dapp businesses are obligated to pay for Storage and Data Exchange in light of network resources occupied, and may set up CyberVein token as a means of payment to issue NFT or conduct business in accordance to their desired business model.

CyberVein tools include:

PISR Consortium database, creates better data management: through safe and reliable virtual machine operation and maintenance of user data, data is efficiently aggregated for upstream data, and data traceability and tracking are realized to ensure the reliability of data sources and outputs, and achieve cross-level, cross-departmental data sharing to improve the timeliness, diversity, stock and density of data to realize the commercial value of data, and truly solve the problems in data flow.

DAG storage chain, creates safer data storage, with better efficiency, no block confirmation, low transaction fees, and also eliminates miner, supports asynchronous verification and parallel processing of each node, the more nodes, the faster the speed, the stronger the scalability, which leads to better scalability. The database backup on DAG storage chain provides an extra layer of security for businesses.

Cytrix, allows GPU to be shared. Nodes can contribute part of their GPU to the network, in return for compensation through the Proof of Contribution mechanism. The contributed GPU can aid enterprises or individuals in data modeling with federated learning.

Federated Learning Platform, enables data to be monetized. Federated Learning is a distributed machine learning workflow. The datasets are first trained locally, and the data of all parties is kept locally, without revealing privacy or violating regulations.  Individual terminals return trained models to the server to create a shared model. Effectively protect data privacy and security, solve the problem of information silo, and thus realize the value of data. The platform will also provide algorithms to support data modeling operations by enterprises and individuals.