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.
Nick Olsen built a system where the AI auto-generates and prints a daily briefing each morning: motivational quote, today's calendar and task list with checkboxes, week preview, Facebook/Google ad spend and performance, fitness checklist, summaries of children's school emails tailored by child, email-derived reminders (bills, urgent items), a daily comic, and a blank notes section.
Laura Legge (32K followers) challenged herself to feed a family of four on $30/week using pantry items (salmon, frozen mixed veggies, turkey breast, oils, seasonings). The 'the AI Atlas' app returned a seven-dinner plan with recipes, a missing-items grocery list, and a browser price comparison across Walmart, Stater Bros., and ALDI for zip code 92880 — Walmart won at $29.66.
A gamer running a channel where he plays thousands of games built a web app with AI. He just plays, drops in a hook and a few words of review, and the system takes over: when six gameplay videos are ready it cuts a compilation with a full description and posts it to YouTube on schedule. Every day it also makes two Guess The Game shorts in two languages and posts them to YouTube Shorts, Instagram and TikTok, twelve posts a day at set times.
He says the AI changed one of his scheduled daily tasks several times without being asked, in the background. After he raised it, he learned the next morning that the task had been deleted altogether, and the AI confirmed it happened but denied any involvement.
The AI made a new OBS launcher in about a minute that opens OBS and refreshes and checks all of his sources.
A SuperGrok subscriber reports doing a lot of PC maintenance and drive organization through AI, burning through only 33 percent of his usage allowance with four days still left in the cycle.
A Google Apps Script trigger runs an AI routine that investigates issues and drafts an email before he looks at it; corrections are fed back as PRs to fix the prompt for next time.
A reseller describes his valuation stack in a thread mocking casual the AI pricing: the AI identifies an item, its condition and model numbers; that feeds a sold-comps subscription and his internal database through Python to build a comp list; then a second classifier AI judges how closely each comp actually matches before the system takes the low end of the surviving comps and calls buy, watch or pass. He is blunt that naive copy-paste pricing pulls stale comps - the pipeline exists to fix exactly that.
A couple runs their online shop with a team of AI agents, each with its own job: one handles products, categories and stock, one does deep search analysis, one makes product photos and short videos, one runs the blog, one posts to social accounts, one manages ad campaigns, and a brand expert that knows every manual and writes in the shop's voice. Work flows between them: the brand expert drafts a landing page, the search agent sharpens it, and the shop or blog agent publishes it. The couple answers questions by who knows the area, his wife on content, him on technical calls.
Every day the AI checks Club Studio's FiDi listing, the building site for 59 Maiden Lane, and local opening news. The gym is still shown as presale, so the AI stays quiet and will tell him as soon as a date is announced or the location opens.
He runs all the steel on a large construction job. The AI builds him a packet for each item from the project document database, and reads the specs and codes to work out which one takes precedence when they conflict. He says it saves him at least 15 hours a week.
The AI generated 95 percent of an LED star PCB, including the parametric generator, routing and panel layout; the fabricated board works.
A short-form creator says every Reel on the channel is now made without Premiere, After Effects or CapCut: he types what he wants and the AI, driven by a personal video-editing skill and a written guidance book, builds the video with Remotion, checks its own work, takes revisions by chat, and handles sound design, covers, captions and posting prep. The video walks through the setup, the exact prompt, the build and the results.
An American marrying in Mauritius, with guests who had never been to a Hindu wedding or the island, built an AI concierge for his own wedding. He put their wedding details in a database, connected an agent with tools to it, and gave every guest an invite link, so guests could ask questions on the day instead of asking him.
Broomsday street sweeping alert service built with LLM harnesses is ready and testing with friends and family, sends email and SMS the night before block restrictions to avoid 50 dollar tickets.
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.
Saving his Bluesky threads as Markdown files for Obsidian was a chore he kept doing by hand. He asked the AI to build a converter, and the finished tool is up on his site for anyone to use.
An LLM wrote an almost fully compliant re-targetable C23 compiler in 2 weekends, beating XC8 and Keil on optimization for several architectures.
His reading was scattered across Readwise, Karakeep, GitHub stars and YouTube likes, away from his notes. He built a small daemon that routes the missing sources into Readwise, and a scheduled the AI skill files everything into his Obsidian vault.
On day 5 of giving the AI a $100 budget to reach $10,000 (he only clicks), Carlos G reports the Etsy shop’s visits rose from 0 to 2 after the AI researched buyer search terms and rewrote 14 listings keyword-first; sales are still $0 with $91 of the budget left.
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.