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Open-source AI agent flags a water spike, duplicate charge, gym fee hike, and forgotten subscription

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A developer built Napper, an offline-first AI agent that processes locally dropped CSV files of bank, electricity, step, and water data overnight. Using the open-weight TabPFN-3.5 model for forecasting and several detectors for anomalies, it produces a short morning brief delivered by text and audio. In a test with synthetic data, it identified a water usage spike of 425 liters on October 19 against an expected 52.8 liters, and a 262% electricity surge in September. It also caught a gym membership fee rising from $24.99 to $29.99, a duplicate $77.86 power charge on the same day, and a $9.99 monthly streaming subscription unused since March. The project was submitted to the Hacktoberfest Open-Source AI Challenge and uses synthetic datasets that anyone can reproduce.

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