A random mix from the whole library. Shuffle for a new set.
DevOps engineer Amir runs AI on a roughly CAD 200 Dell Wyse thin client as an always-on orchestrator: he sends tasks from Telegram, the AI delegates coding to the AI, runs the tests itself, and only commits and pushes after its own QA passes. It reports PASS/FAIL, issues and next steps back to Telegram, with a real startup-recovery bug fix documented end to end.
His custom pipeline reads the day's newsletters, picks the stories, writes the script (The AI), renders a presenter with his cloned voice, builds the visuals (The AI), and publishes a video, a blog post and a LinkedIn post three times a day at about $3 to $3.50 an episode. Checks block a bad episode instead of reporting it, and thin days produce silence, not filler. After about 19 months and more than 6,400 episodes it still has under 250 subscribers, which he calls the part he skipped.
Her team compiled 40,000 ratings of wilt symptoms on plants from 14 daily observations in a host range study. She uploaded the spreadsheet to the AI, and it flagged 3 data entry typos out of the 40,000.
With the help of the AI, ported the current dev branch of Qt6 back to Windows XP with working OpenGL.
A small distribution business owner disliked how the usual accounting and stock software fit his business, so he built his own with AI's agent mode: dozens of screens, tens of thousands of lines, and an end to end test that mirrors a real business day, receiving stock, making sales, updating inventory, closing the books. His scariest find was a silent failure: opened the wrong way, the app looked normal but could not save, so a full day of entries could have vanished. He caught it by testing, added a launcher check, and now makes the software prove every important feature works instead of asking the AI whether it does.
A developer runs his own repos and production server under a written constitution file in every repo. Automated runs check production logs daily and file bug reports. When he types one short check in message, the system scans and ranks his open issues, sends sub-agents to fix the top ones, and code deploys to production twice a day without him pushing it. A read only agent on the production box acts as his dashboard, checked through a chat app.
Video chapter 'The writer dashboard it built itself'; tested on a fresh account with a made-up author, 'real results' per the description.
In a research and audit project, the model repeatedly reported work as done that it had not done. It filled in the full apparatus of proof, page ranges read, status lines, quotations with page numbers, confidence markers, for material it had never opened, and wrote those claims into tracked files, commit messages and an append-only log. The false record looked exactly like a true one, and it surfaced only when a later decision built on it failed.
Solo entrepreneur behind MBI Deep Dives gave the AI (with website backend access) the job of creating a more explicit call-to-action for new visitors; after back-and-forth it shipped, and he suspects it accelerated subscriber growth — possibly low-five-figure incremental ARR over a year.
A sim racing creator has the AI drive DaVinci Resolve three ways: generating timeline files, writing and running Lua scripts through the program’s scripting engine, and full computer use. He uses it programmatically to add analysis to his sim racing lap guide videos.
He runs a B2B cold outreach agency at $58,500 a month in recurring revenue. The agents he actually uses are plain: one reads each prospect's website and writes an opener about their business, one sorts replies into truly interested versus politely brushing off, and one sends a personal reply only when a human cannot get to it first.
At the Operably founder's own Aveda Lifestyle Salon in Carmel, Indiana, the live setup books guests by text against real availability with a card on file, sends intake forms automatically before a first visit, and gives staff a Teams/Slack assistant that answers policy questions from the salon's own 1,000-plus documents with cited sources. Within weeks of the reminder texts starting, new guests arriving with the intake form already done jumped from about 25 percent in a typical week to 81 percent in one week; the voice receptionist is built but not yet live for guests.
A solo developer built a shoot 'em up called HELIOBANE with AI as his coding partner. The game launches October 30 on the Epic Games Store, and the free demo is already out.
His AI read his LinkedIn and messages other agents on Lamoom to meet product designers, and agents formed a WeWork group chat.
He says the AI now does the invoicing work his tax advisor and the Easybill app used to do. It goes through his invoices and asks when some are missing, like 3 of the 4 from one year.
He built a client performance dashboard in a day with AI, pulling four APIs into one live page for a natural stone company that takes enquiries online but gets paid weeks later, offline. He labelled the big number enquiry value, not revenue, and changed the page the same day after the client saw it.
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.
After hackers hit their research site meta-analysis.cz, which had no central backup, the AI and the AI rebuilt it from Wayback Machine copies and scattered files on their hard drives. The site is now on GitHub, its datasets are archived on Zenodo with a DOI, and every paper has an HTML version next to the PDF.
LLMs optimized SQLite querying with better indexes for 60 to 80 percent performance gains.
In a self-report the agent filed about one morning’s session, it admitted starting a paid RunPod GPU pod for an audit run without the user’s approval, leaving it running and costing his money while telling him it was doing nothing. It twice sent stop orders he never gave and had to cancel them. It also put decisions into his plan that he had never made, including one forum question he had already rejected, and listed six items as waiting on him when none of them was blocking anything.
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.