The AI found $200 of overbilling on my medical procedure
The AI went over his medical procedure bill and found $200 of overbilling. The procedure had been charged as bilateral twice over, as if he had 4 sides instead of 2.
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The AI went over his medical procedure bill and found $200 of overbilling. The procedure had been charged as bilateral twice over, as if he had 4 sides instead of 2.
He gave the AI a CSV of old macro tracking data and asked it to add the data into the folder where he tracks it. The AI misread the spreadsheet, decided the new data contradicted what was there, and overwrote his existing data without asking.
The AI books the user's tennis club clinics and pays for them, keeping the user's regular sessions in place without weekly admin.
He sends his RingConn 3 wearable data to his AI email address. It sends back a dashboard and tips to improve his health.
While training on his Wattbike, he wondered if the AI Fable could crack the workout file format. It reverse engineered the format in under 10 minutes, and half an hour later there was a Python and JavaScript library plus a static viewer app.
Built a macros tracker an agent can log meals into via MCP, so he can say log my normal breakfast.
He dictated his training to the AI: the exercises, sets, and weights. The AI added a warmup, rest breaks, and a rule for when to add weight, then made it into a page he opens on his phone. It took about 15 minutes and he installed nothing.
He downloaded his Apple Watch Fitness app data and used AI to make visualizations from it. One chart shows his short rides to work, grocery stores, the doctor and the dentist.
He set up an AI project as his weight loss coach with access to his Apple Health and Google Drive, and he logs everything he eats with it. It found him the cheapest tirzepatide, which he ordered. Six months on he is down from 301 to 190 pounds and runs a 5k a day.
Since March a lifter has run his training through an AI setup he built for himself: it imports workouts from Strong and Hevy and health data from Apple Health, builds and audits his program, and sets each week's targets with a reason. He learned not to let the model do the maths after early versions gave different answers on different days, so every number now comes from small tested scripts and the AI explains the result. Safety rules live in code too: a medical check before any program, pain flags that keep exercises off an injured area, and weekly targets that freeze once accepted. He has since shared it as an open source plugin.
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