Attention, acceleration, market-quality, and sustainability research for trending assets.
Learn what an AI altcoin scanner can rank, where model errors arise, and how to validate signals with transparent features, backtests, confidence checks, and human review.
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A research framework for separating durable attention from short-lived hype using search interest, volume, liquidity, catalysts, persistence, and cross-chain context.
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A chart-scanning framework for separating durable breakouts from thin spikes using OHLC data, volume, volatility, relative strength, liquidity, and multi-timeframe confirmation.
Read the guide →Start with a defined universe, combine independent signals, and record why each candidate survives or fails the scan.