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Grass testing has ended, Airdrop is imminent, attracting the attention of 2.2 million users.
Grass testing phase has ended, and the Token Airdrop is about to take place.
Grass has announced that its closed testing phase has ended, and it is currently taking a snapshot to determine eligibility for the upcoming Token Airdrop. User participation in the network (weighted by epoch) will serve as the benchmark for receiving rewards.
In the coming weeks, the team will provide detailed Airdrop eligibility check guidelines and share more information about Token economics. The next phase of Grass's development will shift from building core infrastructure to supporting large-scale development, focusing on applications that combine user interest with network functionality.
It is reported that the number of users of Grass has exceeded 2 million. As a decentralized network, Grass aims to provide the data needed for AI model training by accessing public networks. This makes Grass an important component of the AI data layer during the process of scaling up to clean and prepare structured datasets, establishing its foundational position in the field of artificial intelligence.
Financing Background and Technical Strength
The team behind Grass, Wynd Network, has successfully completed a $3.5 million seed round financing, bringing the total financing amount to $4.5 million. This funding will be used to enhance Grass's technological infrastructure, expand the node network, and improve the data validation process.
Grass is a decentralized bandwidth market where users can sell their idle internet connections to help AI labs obtain network data for training models. The entire process ensures user privacy and data security. The collected bandwidth is used to extract raw data from the internet and convert it into AI datasets.
The core technology of Grass is Socrates, an AI development tool that excels at collecting unstructured data from the web and structuring it. As a result, Grass becomes an AI data warehouse, providing the data needed for model training for other AI systems.
As a project deployed on Solana, Grass combines AI, DePIN, and Solana technology, positioning itself as the data layer for AI. The AI data layer is a crucial starting phase in the artificial intelligence development process, primarily responsible for data collection and preparation, providing the foundation for model training.
Market Potential Analysis
Grass is built on Solana, leveraging its high throughput advantages. To address the issue of L1 storage for traceability of each fetching task, Grass has constructed a rollup that uses ZK processors to batch process traceability proofs and then publish them to Solana.
Grass's Web3-first approach offers several advantages over centralized residential brokerage providers, including a fairer distribution of AI-generated value and savings on the costs of bundling code for payment application developers.
In terms of market potential, Grass currently has 2.2 million independent users, and it is expected that more users will join after the Token Generation Event (TGE). The Grass network is owned and operated by its users, who earn shares of the network by running nodes and earning points.
Other market advantages include:
If the user base of Grass can expand 20 times, there is potential to train a system from scratch that can compete with mainstream AI assistants, and this is one of the reasons it is expected to become a leader in the DEPIN track.
Conclusion
Grass is committed to correcting the shortcomings of the Web 2.0 era and promoting the development of Web3 values. By participating in Grass, users can not only earn rewards but also contribute to building a fairer and more just world.
Grass not only provides users with a way to participate in the AI revolution but also promotes the development of decentralized and open-source AI, allowing everyone to access and utilize public network data fairly. Its innovation and unique positioning have made it an important player in the AI and Web3 space, with the potential to become a leader in this field.