cryptonewsz.com
Pastel Network recently announced establishing a partnership with Parity Technologies. The collaboration will see Pastel Network bringing permanent storage and NFT security to Polkadot.
As the builders behind Substrate and Polkadot, Parity Technologies is a well-known name in the industry. The partnership is a huge step for the entire NFT community, and it shows that the constant demand for verifiability, security, and reliability is being addressed with Pastel’s modern framework.
The collaboration means Polkadot will access Parity’s Web3 protocols, which can be easily integrated across Layer 1s and Layer 2s dApps. Parity will work with Polkadot’s L1 parachains and DeFi & NFT apps built on the network.
This will allow the venture to integrate:-
- Sense, Near-Duplicate NFT Detection: It uses Deep Learning technology to assess the relative rareness of non-fungible tokens. It helps detect scams, copyright infringement, and counterfeits, providing certification of authenticity.
- Cascade, NFT Data, and Metadata Storage: It is a permanent, distributed redundant storage protocol. It prevents centralized points of error, 404 errors and IPFS pinning, and monthly subscription maintenance.
Since these protocols are lightweight with interoperability for Web3 Open APIs, Polkadot members can access them securely and distributedly.
In the long run, the platforms will collaborate to create trustless and decentralized interoperability using a communication bridge. Pastel Network can reach trustless communication with Polkadot using Substrate instance due to its non-parachain blockchain nature.
The Substrate instance can be deployed to the Polkadot network using a common-good community-operate or public utility parachain. In the beginning, Pastel’s network of SuperNodes (high-compute validators) can act as a federation of off-chain agents.
Such scale of interoperability allows Parity and Pastel to bring decentralized NFT security and permanence to the ecosystem. Given the platforms’ global success, their collaboration will render similar results.