Phase I: Testnet

As a pioneering force in the Web3 domain, the CREDIA LAYER ecosystem represents an infrastructural evolution tailored to the ethos of decentralization and user empowerment. By integrating DEPIN and AI, it aims to revolutionize credit scoring and computing resource management, resolving the perennial challenges faced by traditional credit systems. As part of our commitment to innovation and user-centricity, we are excited to introduce the Phase 1 Testnet Airdrop program within the CREDIA LAYER ecosystem.

The Phase 1 Testnet Airdrop program is designed to incentivize active participation in decentralized trading and provide users with a novel pathway to earn credit points within our innovative framework. By engaging in Swap & Stake transactions & Community tasks, users will have the opportunity to accumulate credit points, thus contributing to the ecosystem's growth and development.

1. Credit Points Linked Airdrop Rewards: Airdrop rewards will be distributed based on users' credit point levels. This mechanism aims to reward users who contribute significantly to the platform's growth, thereby fostering a vibrant and engaged community.

2. Credit Point Tiers: Multiple credit point tiers will be established to ensure fairness and provide additional incentives. As users reach new credit point tiers, they will unlock higher-tier airdrop rewards, encouraging continued participation and contribution.

3. Unified Distribution Timing: A unified timing for reward distribution will be determined based on the completion of specified activities, including computational leasing, DeFi transactions, and Swaps, along with reaching the corresponding credit point tier. This approach promotes sustained engagement and activity within the ecosystem.

4. Reward Calculation and Distribution: Rewards are automatically calculated using our AI-powered credit scoring system. Utilizing smart contracts ensures accurate and fair distribution, fostering trust. Our AI analyzes extensive datasets, including transaction histories and user behaviors, to compute current and predictive credit scores, providing a nuanced reflection of users' creditworthiness.