DRK consensus is upgraded from Proof-of-Stake of ETH 2.0 into Proof-of-Honor (PoH), following the footsteps of other multi-billion-dollar projects such as TON by Telegram and Quorum by JP Morgan. The rationale behind our choice of ETH 2.0 (over EOS, TRON, Stellar, NEO, etc.) is because ETH 2.0 is optimized for utilities and supported by the largest and strongest communities of developers around the world. We further upgraded the PoS consensus of ETH 2.0 into Proof-of-Honor (PoH), which uses HodgeRank to rank nodes based on the amount of Stakes and amount of Transactions to encourage DApp developers to serve as many users and generate as many transactions as possible. In a comparison, it is much more advanced than Proof-of-Importance (PoI) of NEM, which uses NCDawareRank.

PoH also adopts an advanced incentive system to eliminate all cheating & slacking miners to preserve the longevity of the blockchain network, and adopts a smart free-gas policy called “GasStation” which is more advanced than Tron’s to make blockchain transactions cheaper while evenly anti-spam and incentivizing to miners.

A privacy protocol on DRK called DaRK was invented as the first in the world to have scripting tools to support smart contracts and provide “privacy on demand” (instead of “privacy by default”), bringing blockchain closer to mass adoption. It combines the best of Aztec protocol, CryptoNote, BulletProof, and more. DaRK is a powerful weapon for our Smart Contract 2.0, making it more advanced than Mimblewimble and Zk-snark (which only accommodate native privacy coins without any scripting tools to write smart contracts).

Draken

OTCBTC Token

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Splinterlands is a digital, collectible card game based on blockchain technology and inspired by games like Magic the Gathering and Hearthstone, where players build a collection of cards with different attributes and battle other players in skills-based matches. It is a game where players can buy, sell and trade their digital assets just as they would with physical trading cards games such as Magic the Gathering, Pokémon and Yu-Gi-Oh!

Splinterlands was created as players lost the ability to truly own their assets (or trading cards) once games went digital. With blockchain technology, it allows players to own and trade their digital assets freely once again. It also allows the game to have true scarcity and transparency, which means all cards on Splinterlands have a verifiable supply and historical record.

Players battle other players by choosing ranked or practice battles. With ranked battles, players can increase their rating. There is no downside to playing ranked battles as a beginner since players with a rating below 100 do not lose ranking points. Battles take place between players of similar strength. When matched up with an opponent, both players choose a set of six monster cards and one summoner card for the battle to destroy all their opponent’s monster cards and win the battle.

Although the game is free-to-play, players need to purchase the summoner’s spellbook for $10 if they want to unlock the game in full. With the summoner’s spellbook, players get access to their account keys and unlock the ability to earn Dark Energy Crystals, which can be redeemed for real currency.

Splinterlands started in May 2018 and launched Splintershards (SPS) – its governance token for the SPS decentralized autonomous organization (DAO) at the end of July 2021. It concluded a $3.6M private token sale for 6.6% of the SPS token supply. After reaching 100,000 players in its first three years, it added another 50,000 in just over a month from June 2021. Splinterland plans to build out its game in the latter half of 2021, with oracle integration planned for Q1 2022.

Splintershards

YUKI

Future1coin

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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.