Grass, a depin AI web crawling project, has announced the end of its closed beta phase and outlined the requirements that users must fulfill to be eligible for an upcoming airdrop. The project announced that it will provide more details about the distribution of its token rewards, but that weighted epoch network participation would be the basis for eligibility.
AI Depin Project Grass Closes Closed Beta Phase, Prepares for Airdrop Distribution
Grass, an artificial intelligence (AI) decentralized physical infrastructure network (depin) project that seeks to solve the data poisoning problem for training large language models (LLM), has announced that it has reached the end of its closed beta. Via a post on X, Grass’ team stated that this was considered a “pivotal moment” in its history, concluding the work led in the direction of building a decentralized network powered by millions of users running crawling nodes.
The Grass team also offered insight on the upcoming airdrop that will benefit the users who have lent their internet connection and computers to the project. It confirmed that a snapshot would be taken and that the weighted network participation by each epoch would serve as the basis for the distribution of the rewards. Until now, there have been seven epochs, each one lasting one month. However, more details and the tokenomics of the airdrop will be disclosed at a later time.
Grass also opened a new bonus epoch to help users add more activity to their accounts before the reward distribution, which will be conducted using Solana network wallets tied to each user account.
Describing the future of the project’s development, the post stated:
The next stage of Grass will see a transition from building core infrastructure to supporting the development of applications that align the interests of users with the network, at scale.
This hints at the inclusion of third-party applications that will take advantage of the possibilities that a network with over a million nodes can offer to organizations seeking data to train AI agents.