Does NEAR's price reflect network adoption? This project tests the claim against the network's own public data. It pulls six free, keyless sources (nearblocks daily chain stats; DefiLlama TVL, fees and stablecoin history), merges them into one daily table, and computes windowed growth, year-over-year deltas and price/fundamental correlations with both log-level and 7-day-change methods. Findings are reported with counter-evidence and falsifiers, not conviction: over the trailing year price roughly doubled while transactions, active accounts, fees and stablecoin float fell sharply; only DeFi TVL rose with price (r=0.858). One command (make all) refetches raw data, recomputes every number and re-renders a static, offline dashboard with inline SVG charts - no API keys, no accounts, no paid queries, no invented metrics. NEAR is the subject: the project measures what NEAR's own public data supports and where market pricing diverges from measured L1 activity, and discloses what free data cannot measure (NEAR Intents volume needs a partner JWT; BigQuery bills the querier) instead of estimating it.
Loops House