Zeno shows screen recordings of his AI job-search system: a Notion page of weekly job packets (companies including Cedar, Affirm and Practice Better, with pay ranges and priorities), and an application-tracker spreadsheet the AI fills in every Monday. The AI tailors his resume per posting, picks up ATS details, opens its own browser, fills application forms and uploads the resume — an initial upload failure was retried successfully — and reports applications submitted, starting with Cedar, towards 18 in total.
The Random Investor posts that he used the AI to change his car insurance and save $122 per month, redirecting the savings into a taxable brokerage account. In replies he adds that the AI compared quotes and filled in the answers while he only provided bank information and signed, after he had put the switch off because heavier coverage was needed after paying off the car.
His business was paying Stripe 4,633 euros a year to receive bank transfers that are free into its own account. He had the AI go through 12 months of Revolut and Stripe charges.
He set up saved searches on Cars.com, Carvana and CarMax with his makes, models, price, year and features, and favorited cars on each. The AI merged them into one list, checked price guidance, mileage, consumer reports, recalls and reliability, named his ten best and ten worst, and removed the bottom ten from his favorites. They repeated the rounds until the list was tight, then refined by color and features. He test drove two cars from the list. While he was out, the AI checked the stock of two dealerships he drove past, just from him naming the brands nearby, and found one more match. Every morning it reruns the searches, removing cars that sold and adding new ones.
This only partly worked. Worth knowing before you try it.
His team of AI agents, building a biotech-investor decision-support app, turns the agent chat history into a scripted ElevenLabs-voiced podcast episode after each unit of work so he can follow progress by listening; he caught the team planning a roughly $730 API test run instead of using his AI plan and stopped it.
Fable reverse engineered the Satisfactory blueprint file format and wrote a reader, a graph auditing tool, and a website; work that would have taken a couple of weeks by hand.
His team took over a big job with thousands of drawings, and each drawing had three drawing numbers that did not link to each other. He uploaded all the PDFs and the AI built a spreadsheet that tied the numbers together.
He had a rough script that launched internet radio stations on his Linux desktop. The AI made it safe and finished: it checks the saved player, searches the Radio Browser database, adds tests, a manual and a README, and pushed it to GitHub.
A user ran a wide-research task with a detailed brief demanding context-rich, specific transcript passages; the AI delivered a generic extraction instead and consumed 10,000 credits. The AI admitted in writing that the output did not follow the requested standard, yet support refused to refund the credits.
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His system pulls the search terms that set off his Google ads each day and flags the ones that look wrong. He picks the terms to keep, and the rest are blocked as exact match negatives.
'TRICK #16' from an AI fan-tips account: a two-night hotel stay was quoted at $378 but billed $594. Breaking down 'THE FOLIO' revealed $90x2 resort fees (never used, undisclosed at booking), $76x2 parking (never used), and $100x2 minibar (never used).
His mom runs a wedding venue in southern Italy and got 15 to 20 inquiry emails a day, each taking 20 to 30 minutes, which was 70 to 80% of her week. The AI coworker he built now works the inquiries inside her 10 year old booking system, which has no API, and she approves only special quotes. After three weeks the typical inquiry is processed within 5 minutes and the system saves her over 120 hours a month for about $350 a month.
Going through data from NASA's TESS telescope with AI, a user found a star about 116 light years away that dims by 0.05 percent every 3.18 days for about two hours, the same dip in 2018, 2020 and 2025 data. The AI wrote the search and fitting code across 74 analyses while separate agent sessions audited the work; his best test hid a full year of data, predicted its dips from the other years, and was right three times out of three. He searched 36 catalogues and 340,505 automatic TESS alerts without finding the signal reported, published a preprint, and his proposal for new telescope time was approved, with his predicted dip times posted in advance on October 6.
This only partly worked. Worth knowing before you try it.
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 lives in Kyiv, where Telegram channels post constant updates on incoming drones, sometimes hundreds of messages an hour, too many to read at night. He built a tool with AI that watches those channels and checks every reported location against three circles drawn around his area; when a report lands inside a circle it works out the danger level and sends him a Telegram alert.
A self-described 'personal CFO for wealthmaxxing couples' on Facebook built an AI named 'Cupid' that stores her and her boyfriend's income, strengths, and goals, then proactively reminds them each month to have a 'money date' — bridging her moonshot-entrepreneur style with his investment-savvy approach.
During a flash sale on his petals.team site he checked analytics and found zero events, ever, because PostHog had never been added. Through the PostHog MCP connector in an AI chat he spotted, diagnosed and fixed it, saved a 3 step registration funnel, and set a weekly digest to land in Slack every Thursday.