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.
Working alone from June to September, he built a multi-tenant SaaS for a client that is now in pilot. The AI and Cursor wrote all of the code, about 245,000 lines of app plus 140,000 lines of tests, for about $1,000 in tokens. Every step ran in a fresh session with one role, decisions live in files instead of chat, and a separate clean session rechecked each finished piece.
After Amazon blocked automated login windows, a user built a small HTTP API on a machine they control. The AI SSHes in and calls http://127.0.0.1:8792 to search Amazon and add items to a personal or Prime Business cart (no checkout).
A man used AI to build a full runbook of how his Linux desktop is set up, covering installed software, configs, hacks and workarounds. He then used the runbook to move to a new laptop in one afternoon, and the runbook now keeps itself up to date.
After his motorcycle was stolen, his insurer offered nearly half the market value. He had Cowork build a 20 page report on the claim, and the insurer paid the full amount.
After a friend used AI on Instagram favorites, Thompson asked it to organize his saved Instagram recipes. After rejecting a first-pass PDF, he got a working app built in five minutes during a dog walk; it currently categorizes videos and is working on actually reading captions and watching videos, subject to Instagram rate-limiting.
A man used AI to lower his internet bill by $60 a month. He says it was free, took 30 minutes in the background, and needed very little from him.
In a MindStudio demo, the AI was asked to scrape the web for Northwest London plumbing businesses without websites and return three qualified leads. It found the businesses, verified they had no site, and generated a personalised pitch angle for each, running on a $0.24/hour CPU instance.
A man created a virtual credit card in Robinhood for his AI. He had the agent buy his tickets to a jazz show that night.
Andy Bennett built Helm, a Mac, iPhone and iPad job tracker with CV tailoring, with AI writing all of the code and most of the design across 641 commits and 21 plans. The first working Mac version landed in about eight hours of commits on September 11; both apps are now in TestFlight beta.
He linked his brand and personal Gmail and Outlook accounts to the AI. The first job he gave it was unsubscribing him from spam, and he says it felt great.
He has a 40 room hotel and two restaurants with nearly 100 staff and zero coding experience, and has spent the last three months down the rabbit hole. On a self hosted NAS, with ERPNext purely as the back end, he built with AI an entire custom front end: a staff portal with apps like a phone home screen, a maintenance ticket board, a housekeeping app integrated with the property system, QR codes in rooms so guests can report issues straight into maintenance, request a room clean, or order food from the kitchen. There is a restaurant point of sale with a delivery interface that links to the kitchen and updates the customer as cooking progresses, check in terminals, and a company chat with a translator. Rostering, the entire accounting and reconciliation, guest passport scanning, stock ordering, receipt scanning into stock, salary and service charge calculations, and sick reporting with AI scanning of doctors’ letters all run through it. Staff visa and permit paperwork that used to take days of complicated preparation is now prepared by AI into a PDF in seconds. About 140,000 lines of unique code, not counting ERPNext.
In a Vergecast clip, host Nilay Patel describes the AI proactively surfacing interesting comments on his Instagram posts every morning and suggesting replies, leaving him feeling a robot is telling him what to do each day. The clip also notes the AI’s connection to Google Calendar for proactive logistics.
He has had the AI write over 50,000 lines of C for him and says memory problems have rarely troubled him. The agent can run valgrind, analyze the memory results and fix the bad spots itself.
The AI generated 95 percent of an LED star PCB, including the parametric generator, routing and panel layout; the fabricated board works.
He set up the AI with five helpers for his YouTube work. One scouts what is new every Monday, one helps write scripts, one makes all his thumbnails, one updates his personal brand GitHub repo, and one helps him engage on X.
A researcher used AI and the AI Fable 5.1 to search for examples, write and run verification code, develop and check the proofs, and draft the paper for a triangulation problem. The result settles the value at 5 for every n from 9 up, with coordinates and edge lists published in a repository. He checked the argument himself; the paper is not yet refereed.
Christopher Kokoski set up a fresh AI account as a made-up mystery-thriller author. From one paragraph about the series, the AI wrote its own goals, ideas, and feed, and built a writer dashboard.
LLM-generated Spotify client he built for himself, uses every day, and maintains, fixing bugs and closing others' PRs.
Corina Noriega (130K followers), a busy mom, nutrition coach, and trainer, demonstrated the AI’s assistant 'Kai' (cartoon dog). She entered family of three, $50 grocery budget, fridge/freezer contents (chicken tenders, beef), training schedule, and macro goals; Kai generated a detailed PDF plan covering Friday Sept 18 to Thursday Sept 24 with grab-and-go breakfasts, prepped dinners, a $38.43 shopping list, training windows, and recipes.
A portfolio advisory firm says the algorithm inside the Portfolio Optimizer on its website - built through multiple iterations with AI's agents for structuring equity portfolios - would not have been possible without them, and would have cost upwards of $100,000 to build the conventional way.
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.