🎉 #Gate Alpha 3rd Points Carnival & ES Launchpool# Joint Promotion Task is Now Live!
Total Prize Pool: 1,250 $ES
This campaign aims to promote the Eclipse ($ES) Launchpool and Alpha Phase 11: $ES Special Event.
📄 For details, please refer to:
Launchpool Announcement: https://www.gate.com/zh/announcements/article/46134
Alpha Phase 11 Announcement: https://www.gate.com/zh/announcements/article/46137
🧩 [Task Details]
Create content around the Launchpool and Alpha Phase 11 campaign and include a screenshot of your participation.
📸 [How to Participate]
1️⃣ Post with the hashtag #Gate Alpha 3rd
Grass: The decentralized data network on Solana leads a new era of AI.
Grass Network: Decentralization platform for web scraping
Grass is a decentralized network scraping platform deployed on the Solana chain, combining AI, Depin, and Solana technology. It positions itself as the data layer for AI, aiming to help organizations train AI models by utilizing individuals' unused internet bandwidth. Grass achieves web scraping through a browser extension and rewards users for contributing bandwidth resources with Grass Points. The project's goal is to redefine the internet incentive structure, allowing users to directly benefit from the internet and ensuring that the value of the internet is in the hands of users. Currently, the Grass network has over 2 million user-operated nodes, providing a large amount of data for AI models.
Technical Architecture
Grass has built a sovereign data Rollup network on Solana for handling all transactions from data sources to processing, verification, and constructing datasets. The core components of the network include:
验证器(Validator): Receives, verifies, and batch processes web transactions from routers, generating ZK proofs to validate on-chain session data.
(Router): Connects Grass nodes and validators, responsible for reporting network metrics.
Grass Node ( Node ): Utilize unused user bandwidth to scrape public Web data.
ZK Processor: Proving the validity of batch processed Web request session data and submitting the proof to the blockchain.
Grass Data Ledger: A data structure that stores fetched data and immutable data proofs on the blockchain.
Edge Embedding Model: The process of converting unstructured web data into a structured model.
Technical Features
The Grass network is located between the client and the Web server, handling the routing of Web requests and data fetching. Main features include:
Data Source Tracking: Using the ZK processor to record the metadata of each dataset, including session keys, URLs, IP addresses, and timestamps.
High throughput processing: Using ZK processors for proof and batching to handle large-scale Web requests.
Node Contribution Tracking: Record the contributions of each node to implement a proportional reward mechanism.
Data Quality Assurance: Assess node reputation through completeness, consistency, timeliness, and availability.
Security Mechanism
Grass takes multiple measures to ensure network and user security:
Grass Token Features
Grass token holders can participate in the network in the following ways:
Currently, the annualized yield of Grass staking is approximately 45%, with about 33% of tokens participating in staking, and the total staking amount exceeding 26 million.
Router Staking and Earnings
The router serves as a decentralized hub for the network, connecting nodes and managing Web requests. Users can stake Grass tokens to different Routers to earn rewards, with each Router having different commission rates. For example, the staking amount for DBunker is approximately 1.43 million, with a minimum staking period of 7 days and a commission of 10%.
Summary
Grass is committed to building a fair and open Decentralization data layer, addressing the ethical and quality issues of internet data extraction. Through innovative technology architecture and incentive mechanisms, Grass provides a transparent and efficient data source for AI companies and protocols. As an important player in the intersection of web3 and AI, the development prospects of Grass are promising.