I built an agent that works while I sleep. It found a pipe leak, a price hike, and a subscription I forgot I had.

说明与用途

Napper 是一个离线自主代理，自动处理 CSV 导出，使用 TabPFN‑3.5 进行预测，筛查异常，审核交易，并提供简洁的晨间摘要，发现价格上涨、重复收费、电力峰值和水管爆裂。

问题

The author wanted to stop manually checking multiple CSV exports each morning and only be notified when something important happened.

结果

On demo data Napper detected four anomalies: a gym price hike, a duplicate power bill charge, a 3‑day electricity spike, and a water pipe burst, plus a forgotten subscription. No false alarms were reported.

工作流

• Drop CSVs into a local folder
• Napper ingests and normalizes data, hashing to avoid duplicates
• TabPFN‑3.5 generates 30‑day forecasts with holdout validation
• Model‑free seasonal screen compares today to median of previous eight same‑weekdays
• Recurring‑charge detector flags price hikes and duplicate charges
• Morning brief is generated and sent via text and TTS

证据

I generated four synthetic CSVs (bank, electricity, steps, and water) with real‑world storylines planted in them, seeded so anyone can reproduce the exact results.

来源

Four catches across four distinct detectors: Gym Membership Hike, Double Billing, 3-Day Electricity Spike, Water Pipe Burst, Forgotten Subscription.

来源

TabPFN-3.5, an open-weight tabular foundation model from Prior Labs that runs on your local CPU/GPU, to generate recursive 30-day forecasts with holdout validation.

来源