Holdy logged the AI into his medical provider's portal and had it pull his itemized bills and question every line. It found several cases of double billing, asked for discounts, and has saved him over $4,000 so far.
The founder of the Gym Commitment app connected the AI to it through a custom MCP server so the AI works as the trainer inside the app. In a real test, a user described his goal and schedule, the AI asked up to 12 questions, searched 1,872 exercises, and pushed a 4 day plan to his phone in about 5 minutes, only after he approved it.
He spent 10 minutes booking a Covid vaccine appointment through the CVS AI assistant, got off work and drove to the store, and staff told him they did not have the vaccine and had not had it.
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A triathlete uses a Garmin MCP through AI Desktop - forked because the stock version couldn't build his workout types - so the AI creates and schedules his swim, bike, run, and gym sessions, pushing them to his Garmin calendar, which syncs straight to the watch on his wrist.
Each week he drops three files into a folder: an Apple Health export, his Peloton data, and a spreadsheet of extras. The AI runs the numbers through 19 metrics, flags shifts like cycling power dipping after a hard hike, and updates a dashboard he checks to adjust his training.
The AI handled the user's medication refill end to end: messaging the doctor through the portal, getting the approval email, and placing the pharmacy order.
A man set the AI to pull each week's workouts from training providers he used to pay for, since they post the sessions free but late. The AI checks his calendar for when he can train, offers him two options a day, then records what he actually did and how it felt, and uses that history to scale future sessions. A few weeks in it is saving him 20 dollars a month in subscriptions.
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.
A 14 person dental practice on Daniel Island had $612,000 of diagnosed treatment that patients had never booked, and the staff's own AIs (six the AI, four the AI, two the AI) now read each day's patient notes every evening, offer open slots to the longest waiting patients and book on a yes. Of the $612,000, $188,000 of work is done and $241,000 more is booked, and same month bookings rose from 38 percent to 71 percent.
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.
This only partly worked. Worth knowing before you try it.
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.
Aggie Lal (verified, 1.7M followers), a mom, asked her AI 'Munchkin' to process her ~14,000 saved Instagram posts. The AI identified themes (high-protein recipes, parenting, postpartum recovery) and generated organized PDFs: 'Saved Recipes' (Protein Power Bowls, Cottage Cheese Everything, crediting original creators) and 'Parenting Tips'.
Vo asked the AI to set up a gentle sleep-training goal for her eight-and-a-half-month-old, noting the family room-shares in a small house. The AI created a gentle action plan with automated reminders tailored to that constraint.
Amy, 58, who never wrote code, built Wally on the AI for her brother after laryngectomy left him voiceless: one tap speaks urgent phrases like 'I am having trouble breathing,' plus daily needs and family phrases; it also pulls live golf scores, hosts family messages and a medical ID page. He used it daily until his death in August 2026.
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.
A Stanford doctor gave the AI his own genome file and a 180 word prompt based on the framework his team published in 2009. In about 30 minutes for about $5, the AI matched the main findings of that team’s work, flagged genes that affect how he responds to some medicines, and warned that his file did not have enough data for disease risk scores.
The AI reviewed roughly 1,000 pages of the user's medical records spanning eight years and surfaced the relevant tests and medications for the user's AFib care team.
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.
Pavuluri Gopikrishna had the AI build a photo-based calorie tracker, then cancelled his paid calorie-tracking subscription because the agent-built one replaced it. The build-to-cancel pattern shows the AI replacing a recurring SaaS cost.
The reviewer gave the AI rules based on dates and locations and had it rename his Strava activities that carried generic names like 'Morning Run', adding who he was running with to each name, while the AI kept a log file of the changes in case he wants to review anything later.
Same reel as the recipes row: Aggie Lal's AI agent 'Munchkin' summarized her ~14,000 saved Instagram posts and generated a second PDF, 'Parenting Tips', covering postpartum recovery, mom fitness, and breastfeeding nutrition.
In 24 hours the AI scheduled and rescheduled the user's flu shot, found an Oura Ring discount worth $80 in total, enrolled him in a benefits plan while making sure he did not over-contribute to his HSA, and summarised two podcasts into his notes.
Lulu (mom, holding her young child on camera) set up an AI avatar named 'CB' in an AI app: she shared her weight (237 lbs) and goal (180 lbs), answered questions about frequency (4-5 days/week) and location (home/mix), and got a personalized Mon-Fri strength and cardio plan. She logs 'Workout complete' and gets confirmations back.
Cronometer raised its price, so he had the AI build a full clone with food search and nutrition data from the USDA database. It took about 20 minutes and he uses it instead of paying.
He gave the AI his home address and asked for 5K and 10K loops from his house using OpenStreetMap data. It worked for 27 minutes and gave him both routes as a map he could view and as GPX and GeoJSON files he could download.
X poster someone had the AI do doctor's-office admin in about 5 minutes: it emailed the doctor's office, left a voicemail, filled out new-patient paperwork, and created a calendar entry.
Her AI logs meals from photos, tracks workouts, and produced a Week 1 report with average calories, protein, activity days, deficit and pace; it pings her three times a day for water and generates weekly and monthly reports.
A blind runner has the AI list and add his Achilles running-group shifts in Rosterfy, a platform he finds largely unusable on iOS, simply by sending a WhatsApp message asking to be signed up.
Her mom normally needs a care service to take off her compression stockings. With the AI she found a way her mom can do it alone in an emergency, her mom tried it and it worked, and her brother no longer has to come over when the care service fails.
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.
Booking swimming lessons for his son sat on a man's task list, pulled from his email by the AI. The AI watched lesson availability on its own and booked a suitable lesson as soon as one became available.
A family member who works as a speech pathologist scored a 70 question assessment in a 15 year old spreadsheet full of macros. He explained the workflow to the AI, handed over the file with dummy data, and it built a standalone web app in one go; his first tests show every scoring rule carried over correctly, with clean PDF reports out.
This only partly worked. Worth knowing before you try it.
He said he built his setup with AI and local models. He told the agent to use a GitHub tool to download his Garmin data automatically and store it in a local SQLite database, and the data includes sleep and HRV, not just workouts.
His gym opens class bookings at 6am the day before and the popular classes fill fast, so he used to set an alarm for 6 just to book. Now an agent does the booking for him every morning and he sleeps until 9.
Dan Peguine's the AI watched his fully-booked doctor's cancellation queue and booked a same-day slot when an opening appeared. The case reached X as a quote-post by Meta executive Alexandr Wang, so the amplifier is promotional even though the underlying report is a user's own.
She has been using AI on her health insurance claims, even though she is wary of sharing health info. It found a lot of mismanaged claims that Cigna owes her money for.
A person opened their medical site and launched the AI browser extension. It downloaded every medical claim for the year and built an Excel index, then logged into HealthEquity, checked the EOBs for duplicates and submitted them as FSA expenses. The submissions went through as the person typed the post.
Lianna Torres (down 70+ lbs, still working toward her goals) asked her agent 'Clover' for a personalized workout at 5'3"/188 lbs targeting stomach fat and overall toning. The app asked about injuries, then generated 'Day 1: Full-Body Foundations' (bodyweight squats, incline push-ups), which she followed on her TV at home.
Crawford Collins (verified, 1.1M followers), a busy new dad, personalized the AI as 'Atlas' (fox avatar) and used voice commands while caring for his infant son. Atlas generated an efficient 4-day-a-week workout plan synced to his calendar, found and ordered fragrance-free size-3 diapers (approved on screen), and when grandma was watching the baby, found a date-night sushi restaurant (Yasu Beverly Hills, Sushi Note) which he booked.
The AI logs into his five patient portals, pulls every appointment into one place, and checks them against his calendar each week. The morning after a visit it explains the after visit summary in plain words and lists the follow ups he owes.
The AI reads the reviewer's Strava trend data, which is normally shown as charts a screen reader cannot use, summarizes it in words, and correlates it with his data from the Apple Health app.
Since the AI got access to his Strava through an MCP, it has become his running coach. He says his progress is clear and he may reach 150 km of running in one week, but the sessions are so strict he feels like he is doing maths.
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).
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.
Simmons asked László to learn his Zone 2 method from his training community and analyse three months of pace and heart-rate data ahead of the New York City Marathon. Finding Garmin's API needed a developer account, the Dot signed up for Intervals.icu on its own, pulled his runs, built a dashboard called Steady and set up post-run messages.
He got an $8,000 medical bill. The AI worked out different billing codes he could ask the hospital to use when it resubmitted the claim to his insurance, and the bill came down to $1,800.