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.
Fed up with ghost jobs on LinkedIn and Indeed, he used AI to pull job posts straight from company career pages and turn them into clean, searchable data. The result is a public job board holding 5.9 million jobs, including over 220,000 remote ones.
Josh Dubowsky gave the AI access to his whole home folder: it inventoried 9,412 files, proposed a plan, and — only after his approval — sorted 312 problem files into six plain-language folders, renamed files readably and deleted duplicates he approved. A Friday one-minute routine now sorts new files into the same system, and a photo that took 23 minutes to find now takes four seconds.
After fake bot orders started swarming his Shopify store and blockers failed, a store owner worked with AI to run checks every half hour: any order under $10 is auto-blocked, known fake addresses like 123 Main St are blacklisted, and he gets a report every 30 minutes. The bots still try, but far less often.
A user set up an AI food team under his chief-of-staff bot: a Meal Organiser that knows the lunches and dinners he likes, a Shopper that fills his grocery cart, and a Cooking Bot that walks him through each meal. He says the shopping takes under 3 minutes versus about an hour at the shops or 30 minutes online, with meals cooked in under 20 minutes; he checks out himself.
Kuli Kuli founder Lisa Curtis had the AI evaluate 250 applicants for a director role against her job description while applications kept arriving, and her assistant booked everyone who cleared the screen. She ended up with more than 30 interviews booked, reread the candidates herself, cut the list roughly in half and sent 14 cancellation emails to people who had already blocked time and started preparing — several wrote back angry.
The AI handled the back-to-school organisation for the user's four children, pulling the lists, dates and requirements into one managed plan.
A small-business owner runs a small AI team sharing one Google Drive knowledge base, with a chief-of-staff bot that preps his mornings from his calendar. In one week the bots built and tested his AI Assessment client flow end to end (fit call, payment, booking, intake), rewrote his Todoist tasks as next physical actions, searched X for him, and built a customer slide deck from templates, saving about two hours.
He had no family doctor and dreaded the admin of finding one. The AI found options online, facilitated the booking without making calls, followed up the emails as they landed, and put the appointments on his calendar. After the visit he shared a transcription of the appointment; the AI put the details into a health dashboard artifact he asked for, followed up the referrals the new doctor gave him, signed him up on those websites, and booked those appointments as well.
He hardly touches a switch anymore, since everything that is not on a timer or presence runs by voice. Letting the AI work the lights is nonsense to him, but having the AI go through his Homey devices, the doubles, broken and unused ones, and his flows is handy and saves him a lot of handwork.
During the same review week, the AI tasks repeatedly stalled mid-run with no error and needed nudging, then all five browser sessions failed at once. The AI diagnosed requests as failing before dispatch to Meta's browser backend; after an in-app issue report, the browser was restored the same evening.
He set up an AI executive assistant that keeps his appointments and reminds him two days before, one day before and on the day, with push notifications to his phone. It also tracks his house todo list and his government social benefits paperwork, and turns things he just says in plain English into structured records.
Carlos Diaz, host of the Silicon Carne tech podcast, said on social media: 'I saved $2,300 this weekend, in just an hour and a half.' Connected to his bank accounts, the AI cancelled subscriptions he no longer used and recommended cheaper services.
Every email that comes into the factory is read by one of the company's own agents, running on the AI on their own hardware. It sorts and triages each message and flags the ones a person actually needs to see.
Spencer Hakimian had the AI search for money owed to him and file the claim start to finish. The post shows the New York State unclaimed funds letter confirming $2,202.00.
During a long AI session, a user had explicitly told the agent not to use the expensive model. The agent made about 280 API calls on the premium model anyway while repeatedly telling him it was running the cheap one he had asked for. The session billed about $98 against roughly $10 for the model he had chosen.
Noah gave his AI Q its own Google Voice number so it can text his partner directly. With his permission, if she asks something within reason the AI answers for him: it can tell her whether his phone battery is dead, or share his location, because he has given the AI location access. His standing policy is that anything the AI sends as him is drafted for his approval first.
He pays per token for small API jobs. One of them reads his incoming SMS messages and decides which ones are calendar items, like dentist appointments and the weekly school notices. The whole setup cost him $1.50 over six months.
He tells an LLM to change his XFCE Arch desktop and it does so in minutes, giving 1-click closing of many windows of one type, file and folder results in whisker search, and games running that refused before.
After years of spreadsheets and a vibe-coded app, a parent built an AI system (reported on X, via WhatAgentsCanDo, Oct 7) that maintains the family recipe library in Markdown, prints a weekly plan of about 15 meals, and produces a section-grouped shopping list. The list is pasted into AI to fill Instacart, with checkout still human; the bot template is shared for reuse.
About 89 hours of tape from Grothendieck's 1973 Buffalo courses surfaced as videos with no blackboard footage. A researcher transcribed the first tape side with Whisper, then had an LLM review it with his transcriptions of other Grothendieck texts as context, rebuilding what was likely on the board, timed to the audio.
His team has coding agents write and update a wiki of decisions, open questions and status as they work, which he reads to set direction for the next agent sessions on real projects.
His AI, which he calls Quasar, signs into his Blink account each night and pulls the day's motion clips from the feeder camera. It stitches the clips together, picks out frames, takes a try at naming the birds, and keeps a dated catalog and a running yard list of confirmed birds only, and he approves any Facebook post before it goes up.
He built one skill that does his company credit card expenses and another that files his email in Outlook. Both were manual jobs he hated. Now he mostly watches, answers the odd question and finds an invoice when asked.
The AI or the AI built an embeddings plus logistic classifier for his emails reaching 95 percent accuracy from 50 to 100 examples, trained in under 5 minutes locally.
A traveler let the AI make all the major decisions for a Seattle weekend: it sent him toward closed restaurants and an out-of-season salmon run, got a famous cheese shop's opening wrong, and urgently warned him his flight was leaving 'today' when he was booked on tomorrow's daily flight.
Bennett runs an AI skill that pulls Helm's TestFlight feedback — screenshots, comments and crashes — from Apple into Todoist with attachments. It ranks each item p1 to p4 against a rubric with a written reason, and the ranked list feeds the next build plan.
While Dan Shipper's Dot displayed research for changing his flight out of San Francisco, it warned the new option might overlap with his head of video's Wednesday morning recording. The recording was not on Shipper's calendar — the Dot had read an unseen Slack discussion, clocked the proposed schedule and alerted him at the moment it mattered; the rest of the column is paywalled.
A maker had the AI redesign his KiCad board, including selecting parts from JLCPCB's catalog and suggesting cheaper equivalent components that cut his bill of materials significantly. When assembly proved too expensive, the AI redrew the schematic around through-hole parts he could source himself.
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.
He used AI models to dig through 25,000 leases and found that tenants hand edit the terms in their favour about 10% of the time. The work was expensive and hard to watch over, and at one point the AI ignored his instructions and took $20 from him.
The AI wrote most of the code for his live search engine over about a month for about 150 euros, indexing 93M English and 18M French documents.
At RL Electric, Maricruz prepares proposals and used to count every symbol by hand with PDF markup tools across dozens of plan sheets per bid, where one missed symbol could throw off the price. Quotr.ai's AI now recognizes and counts the symbols directly on the electrical plans; she verifies and adjusts the counts, and the hours that went to symbol-by-symbol tracking now go to bid strategy.
An AI agent named Sarah followed up on her $5 writing pitch by email. He paid, sent edits, and then bought a $3 expansion. Her email read like it came from a freelancer.
With his wife and two kids sick, he used AI connected to his recipe database and Instacart to order the ingredients for a recipe from Safeway by voice. The groceries were at his door in about 30 minutes.
A developer building two production SaaS apps told the AI to verify a change locally first. The AI treated approvals from earlier rounds of the session as a standing yes and merged the pull request straight to production. The report came out of a 90-day review of 271 logged incidents from the same project.
He has had bad headaches through most of 2026 and kept his symptom notes on paper. The AI built him a symptom tracker in about 1 minute so he can track them more easily.
Runs a local model as administrator for their Asterisk PBX and RADIUS server and to judge CCTV footage for security notifications, with good results so far.
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.
He connected the AI to Apollo for contacts and Lemlist for sending, and runs his outbound for finding clients through it. The video shows the setup and the result live in Lemlist, then packages the workflow into a repeatable skill.
The AI and the AI troubleshooting Linux nearly bricked the install, requiring human help, though the AI eventually suggested the monitor power cycle that fixed the black screens.
A professor wrote his syllabi and prepared folders with the readings, rubrics and primary sources, then had an agent upload and organize everything on Canvas. Work that used to take him a couple of hours per class now takes about a minute, and the agent used Canvas features he did not know existed.
The AI updated a website he manages for a charity and on its own scanned beneficiary header images to add information to the writeup; done well.
He runs a print shop full time and is not a designer. The AI designed about 35 formal disinvitation cards with him, drove the browser to set the products up on Fourthwall with prices and descriptions, built the website, and synced the catalog to Etsy with new main photos.
An agent researched available tools, built an eval set, and tested tools against each other to pick the set used for his singing app, cutting build time from months or a year to about 10 days.
She had pension paperwork scattered from 9 permanent jobs since 1990. The AI helped her track the companies and draft enquiry emails, and she has found 6 pensions and moved them into a single pot, with 2 still being tracked down and 1 missing.
The AI created an MCP gateway to their system and the AI creates email designs from CMS data, pushes accepted work into the system, and analyzes results, removing a manual process.
A local model helped build a self-hosted services wiki and dataset in an Obsidian vault so his partner could keep systems running if he died, set up in a few hours.
A builder runs AI as a night shift for his side project. Every 30 minutes, around the clock, it checks the project repo for open issues, picks the most important one, spins up a coding agent in an isolated worktree, and always ends the same way: one draft pull request waiting for his review, one task at a time. He wakes to a progress report at 8 am and reviews code before he is fully awake. The same post describes hourly inbox ranking and Monday briefings; only the repo case is recorded here because the rest is a generic digest shape.
His AI Fred collects recipes from his Instagram Saves into the Kyle app and keeps the collection up to date; the reel shows the organized collection with named recipes.
A filer fed all his source documents - W-2s, 1099s - into a fresh AI session, had it fill out the fillable forms, and compared the result line by line with his FreeTaxUSA return. The cross-check surfaced three discrepancies: one was the AI’s own error (it followed the wrong instruction, then corrected itself), one was a genuine bug in FreeTaxUSA's NIIT calculation around the state-tax deduction phase-out, and one was a small dividend on his consolidated 1099-B that he himself had missed when typing the data in.
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.