A random mix from the whole library. Shuffle for a new set.
MMA creator Nina Marie Daniele (verified, 5.3M followers, 48K likes) set a 30-day goal to train like a pro fighter; the AI assigned an AI coach 'Drama Queen' who built Day 1-7+ programs (warm-up, 5 interval sprints with 1-min rests, tire lifts, assault bike), recommended a 40g-protein tempeh grain bowl, and tracked a low sleep score of 45. Her final interval time beat her prior best (7:42 vs 8:48).
A Meta Connect-stage carousel featuring creator Nina-Marie Daniele (Nina Drama, via someone, 2.5M followers) shows her setting a goal to 'train like a pro fighter' with her AI avatar 'Drama Queen.' A Days 1-7 video diary shows a structured plan (warm-up, interval sprints, tire lifts, assault bike), track intervals with AI breathing coaching, a recommended Tempeh Grain Bowl for 40g protein, sleep-score checks, and gym work (punching bag, battle ropes). Comments were hostile to the 30-day p
She takes a photo of every meal and her agent identifies the food, estimates calories and macros, and logs it to a CSV. When the totals ran high it caught a unit bug (grams counted as pieces) and fixed it, and the running totals show on her Home Assistant dashboard.
Commenting on the 26 use cases post, he says he has the AI building dashboards from all his Samsung Health metrics. He has one for weight, BMI and body fat percentage, another for daily steps, a third for exercise, and a separate one with gas gauge style dials showing weight loss, daily steps, exercise minutes and activity calories burned. He shared screenshots.
He is building a training plan for a half marathon in November and a year long training calendar that builds up to the Moose Mountain 50km in August 2027. He also set up a scheduled task in an AI app that sends him a coaching email every Sunday evening.
Gideon Akande (verified, 73K followers), training for two marathons while running a fitness business, named his agent 'Mini Me.' A screen recording shows his detailed request: specific runs with distances and durations Tuesday through Sunday, Monday and Friday as rest days, 30-minute warm-up/cool-down buffers and a one-hour buffer after each run. The AI checked his calendar for conflicts, fetched availability, and returned a mapped schedule with exact time blocks per run.
When every Oura MCP server turned out unmaintained, the AI suggested a simpler design - a SQLite database with a cron sync - and built it. Now he's experimenting with injecting his sleep score into his agents' startup, so they give simpler, less demanding responses on days he's not all there, down to gentler perfume samples.
A runner replaced uploading Garmin screenshots into AI with a self-hosted agent on a Mac mini that reads his Garmin data (sleep, HRV, resting heart rate, VO2 trend, laps) through MCP plus his Strava uploads: it messages him at 8am, designs the day's run and writes it to Garmin Connect, then grades each session against the prescription when it lands on Strava and decides when training load goes up. He hit his sub-20-minute 5K goal about a month later, and it saves him the hour a week he used to spend planning.
He built a personal fitness display, a website to manage his training plan, and an MCP link that lets the AI control it all. The AI adjusts the plan around his races and the weather.
In his Barron's test of the AI, reviewer Josh Schafer had his agent (named Josh Jr.) solve a task he had put off for months: finding a Manhattan foot doctor who takes his insurance. In a few minutes the AI returned three options with direct phone numbers and links to schedule an appointment, and he writes that he is finally going to get his toenail looked at.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.