With the continuous development of the Web3 ecosystem, the competition to participate in Airdrop activities is becoming increasingly fierce. Relying solely on simple interactions can no longer meet the current demands. Now, successfully participating in Airdrops requires not only understanding data and querying data but also being able to accurately analyze which projects may issue coin, to whom, and how to distribute.

Recently, a ZK (zero-knowledge proof) data tool called Lagrange has attracted widespread attention in the industry. It provides Web3 participants with unprecedented data analysis capabilities.

Let us illustrate the powerful functionality of Lagrange with a specific example: suppose there is an Airdrop project announcing "tokens will only be distributed to old addresses with multi-chain interactions." Traditional methods may require checking interaction records on each blockchain one by one, or writing complex scripts to scrape data, and even constantly monitoring social media for project clues. These methods are not only time-consuming but also inefficient.

By using Lagrange's cross-chain ZK query mechanism, users can generate a complete full-chain interaction profile in just a few minutes and quickly verify the authenticity of the data. This greatly improves the efficiency and accuracy of participating in Airdrop.

Lagrange is essentially a "cross-chain data proof generator". Ordinary users can accomplish tasks that previously required complex technical means by calling its provided data API, plugins, or future user-facing product interfaces.

The application scenarios of Lagrange are very extensive, for example:

1. Quickly query addresses that are active on multiple blockchains, which helps to accurately locate the address patterns favored by project parties.

2. Analyze whether the old address is active in areas such as EigenLayer, L2 staking, or NFT minting, in order to identify the characteristics of "veteran players" and provide references for participating in the Airdrop.

3. In the future, AI agents may use Lagrange for identity verification and credit prediction, further promoting the development of the Web3 ecosystem.

With tools like Lagrange emerging continuously, the data analysis capabilities of the Web3 ecosystem are undergoing a qualitative leap. This not only changes the strategies of participants but also provides project parties with more accurate user profiles, promising to drive the entire industry towards a more mature and efficient direction.