Airdrops are a common strategy for rewarding early users and boosting adoption. But be cautious: projects offering large airdrops may face inflation issues, affecting token value long-term.
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Deep learning models use historical price data, trading volumes, sentiment analysis, and macroeconomic indicators. They analyze patterns and correlations, but given crypto's volatility, accuracy can be challenging. Comprehensive data and constant retraining are essential for reliable predictions.
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Transparency in development is vital for building trust with the community. To assess this, check if the project publishes regular updates or development progress reports—these could include GitHub repositories, detailed roadmaps, or blog posts from the team. How open are they about challenges or delays, and do they communicate openly about their next steps? Projects with transparent development processes often share detailed technical documents or hold public discussions to solicit feedback. A project that openly engages with its community and provides insight into development milestones is more likely to foster trust and long-term success.
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