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Project Analysis - The analysis suggests that Monad's actions may have concluded the testnet airdrop phase, prompting a review of EclipseFND [1] - The report highlights a case where data analysis was used to advise against participating in the "Eclipse" project, which was considered a potential scam [1] - The core argument is that in the AI era, effective data analysis requires reliable data sources, appropriate tools, and professional domain knowledge to uncover insights and validate assumptions [1] Data Interpretation & Validation - The analysis points out that many were drawn to Eclipse based on the "data point" of low participation, but this was based on a misleading Dune panel [1] - The Dune panel data, showing a small number of addresses (around 3 thousand) with over 0.1 ETH interacting with Eclipse, was based on an outdated cross-chain bridge address [1] - The report emphasizes the importance of examining data trends (e.g., "recent 7/30 day data growth") to identify inconsistencies and avoid relying solely on static statistics [1] AI & Human Expertise - While AI simplifies data analysis by enabling quick query writing and visualization, it can't replace human judgment in assessing data source reliability and operational context [1] - The analysis argues that AI cannot replace "business common sense," such as knowing when a project changes its cross-chain bridge address [1] - The report concludes that success in data analysis, especially in the AI era, requires a combination of technical skills, domain expertise, and critical thinking [1] Project Outcome - Eclipse experienced a large-scale layoff and CEO change after its TGE, signaling the project's end [2] - Eclipse's official account showcased a K-line chart of its price plummeting, indicating a complete failure and potential exploitation of investors [2]