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.
SPLIT 1 of 1 taken: Fable wrote a Python program that solved a hard board routing problem for his hardware product (firmware diagnosis episode in same comment skipped, analysis only).
A man set up his agent setup so the AI could ping him on Telegram when it needed hands. Working on hardware together, he got a message every 5 to 15 minutes telling him to move a resistor or patch cable in a set way, and following the instructions they got the hardware working the way he wanted.
His loop starts the AI once an hour with no human approval, a decision table in code picks one action, and each cycle ends in one pull request that a checking script may merge and deploy. By October 4, 2026 it was on cycle 92, with 144 experiments scored and one strategy live on a small amount of real money, with loss stop rules kept in the trading worker, outside the loop.
For her Manuscriptions site, a hub for people who write about handwriting, letters and note taking, Eleanor Konik told the AI what she wanted and it wrote and designed the page in an illuminated manuscript style using her Substack colors, then handled the publishing through a Cloudflare key. Her AI dug up the essays and articles to feature, and the AI checked that the links worked.
LTM's RAIma super-agent, built in an AI app Studio and embedded in Teams and Microsoft 365 the AI, handles attendance corrections, leave applications, seat booking and hardware requests across more than 87,000 employees. It has resolved over 600,000 queries at roughly 100,000 prompts a month, verifies medical documents for sick-leave approvals, and cut automated campus-onboarding email effort by more than 90%.
A Threads user shared that the AI automatically negotiated their internet bill down, saving $30 without any manual work on their part. Replies were mixed between enthusiasm and privacy concerns about sharing financial info with Meta.
A business ran a seven-agent the AI support desk that cleared 2,384 tickets with a 91% resolve rate for $11.20 in usage, with billing questions and furious customers fenced off and flagged for a human. The angry and money-touching tickets never reached the bots’ send button.
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.
Independent musician someone lists three career assists from the someone agent: an automatic playlist pitcher that researched genre-matched Spotify playlists and sent cold emails resulting in a placement, an EPK refresh that auto-checked streaming stats and compiled clickable social links, and data-driven venue pitching across new cities. No compensation from Meta disclosed; referral code IH600W shared.
He says he cannot code, but he used AI to produce a working replacement for Lightroom for his own photo work. It works well enough that he ditched his Adobe subscription.
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.