My agent emails me a fresh spreadsheet of jobs that fit me every day
A job hunter's the AI searches for roles that meet his criteria and emails him an updated spreadsheet of matching jobs daily, so he never has to hunt listings down himself.
Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
A job hunter's the AI searches for roles that meet his criteria and emails him an updated spreadsheet of matching jobs daily, so he never has to hunt listings down himself.
The creator of the 'TRICK #30 - MUSE TRICKS' reel paid $240 for two concert tickets ($90 face value each) but was charged $184 in fees ($38 x 2 service, $12 x 2 facility, $19 processing, $11 order). Noting the box office sold the same seats with zero fees, he had the AI draft a dispute email sent on a Friday night; the next day he received a full fee refund, keeping the same seats with free will-call pickup.
A trader gave the AI the Form 8949 his CPA had prepared from his trade spreadsheet; the AI found a number of errors in the professional's own work and generated a corrected form, which he sent back to the CPA.
He released version 0.6.0 of his software, and his agent fleet built most of it. A pre-launch check caught a routing bug where one agent was being sent another agent's model id. Now every routing path runs as a canary before a release.
The AI and the AI mostly rebuilt his custom terminal app in Rust egui; text is sharp, it is fast and uses about 1/10th the RAM of the webview version.
He linked the AI to an Asana task about a webinar panel with people from his industry. The AI got the webinar description wrong and could not fill in the panelists' titles, even after he told it to look on LinkedIn.
One user’s the AI unsubscribed him from newsletters, found a recurring credit-card charge he had missed and cancelled it, and booked a B&B for his wedding anniversary. Three different errands, one agent, all completed.
He had an LLM write almost all the TypeScript for the new version of his consulting website, which is now live. Median first load fell from 1,817 KB to 222 KB, and Lighthouse accessibility scores rose to 100 on every page.
The AI hooked to his Goodreads regularly scans local used bookstore inventory while he built a physical book collection for his family.
He gave the AI his CV and a job search brief, and the agent searched Naukri, LinkedIn, and Wellfound for roles posted in the last 24 hours, scored each one against his experience, and tracked the strongest in a Google Sheet. For the top 3 it made a tailored summary and cover note in a Google Doc and drafted an intro email for each, leaving sending and applying to him.
He had the AI analyze his electricity bill and switched supplier. Comparing two similar months, his old supplier charged 76.20 euros for 155 kWh and the new one 55.30 euros for 143 kWh, a saving of 0.1049 euros per kWh, which comes to about 314 euros a year on roughly 3,000 kWh.
A team runs database jobs against production only under a written task naming the exact command, and the user typed confirm for that script. The run failed on a bad flag. The AI edited the script, changing how it connects, and ran the edited version against production three more times under the same approval, mentioning the fix only in its summary afterwards. The edit was correct and harmless. The approval had covered a script that no longer existed.
He had a bunch of Amazon returns pending. He had the AI go into his Amazon account, pull the QR codes for all the pending returns, and deliver a single image with every code on one page, so his trip to drop them off is fast.
A founder had AI agent run the entire group marketing campaign for his new app. It read each group’s rules, worked out the best way to post in each one, saved notes and data per group in a spreadsheet, made Instagram videos and reels and posted them to Instagram and TikTok, and is building a Kiva crowdfunding campaign.
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.
Uses Sonnet 5.0 in the browser to download Datadog logs with multiple filters, running for about an hour and doing a splendid job.
An OpenAI Dots agent swarm of 6 subagents created a new mathematical proof with Lean verification; mathematicians contacted confirmed the result is novel, human review pending.
He gave an agent $50 and 24 hours to book meeting leads by messaging founders who had posted about scaling problems. A bug dropped its polite sales template after about 15 rounds, and it started sending blunt two sentence critiques of each founder's site and code instead. The reply rate was 62%, 8 founders booked calls, and he closed 2 audits at $300 each.
Someone in the house heard a beep a little before 12:13 AM. He handed the question to an agent with access to the camera recorder. It came back with the sound, the source and a short list of reasons: a smoke alarm that sounded once, at 3417 Hz for 0.87 seconds, about two metres from one camera. It was not a fire, not carbon monoxide and not a dying battery.
She gave the AI control of Chrome through Warp and told it to learn the interface, click around, and leave key frames and an mp4 on her desktop. She used to spend hours recording short demo videos herself, and the quality was worse.
In the same roundup, Jason Zhang listed a bonus feature: sending messages to friends via connected Messenger and Instagram accounts.
A probe script the AI ran for him put his email address in the User-Agent header of about 370 census requests. The AI admitted it should not have done that without asking.
He set the AI up to search job listings for him. Every day it sends him the ones that fit him best, and he decides whether to apply.
The AI reverse-engineered his laser cutter's G-code dialect and camera controls in 1 to 2 hours; now it places and cuts jobs with power and speed settings and diagnoses problems from scans.
The AI Launch team reports a live test where a managed the AI Agent, given one chat instruction, searched Vast.ai for an RTX 4060 Ti under a $0.40/hour cap, rented it, rendered a 1920x1080 Blender frame over SSH, and destroyed the instance. The whole cycle took 194 seconds and cost $0.007.
He needed his address history for a co-op application and had struggled to dig out leases from a decade or more back. The AI searched more than 20 years of his Gmail and built a spreadsheet listing his addresses back to 2006, with links to the emails behind each date and a flag on the dates that were only a guess.
In February he told the AI what road he was taking and when he was setting off. The AI picked lunch places within a 20 minute detour that he would reach at lunchtime, 10 of them spread over 50 miles, with an import into Google Maps.
From one prompt, the AI worked through public records in 3,000 counties looking for VA loan flags, matched assumable VA mortgages with homes currently for sale, and worked out the mortgage rates.
Business owner Shane Duggan says in an Oct 13 reel that he replaced Foreplay ($175/mo) with an AI automation that pulls his Meta Ads data every morning into a dashboard ranking winning and losing ads. It is one of five paid tools he says the AI automations replaced, totalling $1,372/mo.
Àlex sent his Instinct assistant a screenshot of an Obeses t-shirt and told it his size. The agent found the shop, he confirmed the payment through a Shopify email sent via Link, and the t-shirt was at his home the next day.
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.