A random mix from the whole library. Shuffle for a new set.
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.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
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.
His agent writes every sales call into the call log and the CRM, and before anyone on the team gets a number to call, it checks the log first. That stopped the team calling the same people twice.
Working with AI on his NeoBox emulator, he found setjmp was eating 81 percent of frame time, switched to _setjmp for a roughly 130x per-call gain, and the fix was merged upstream in Geolith.
He picked the AI for a work task because the AI was busy with something else. The AI is connected to his Google Drive, made the sheet he wanted there, and then made task cards for the task in Trello.
With the AI doing the grunt work he built a compiler for his own language that runs on Mac, Linux and Windows and cross-produces for those plus WASM, iOS, Android, Zynq, m68k and 6502, with a signed binary running on his phone.
Pelcro moved its site off Wix and now runs every change through AI: a custom migration skill rebuilt the old pages, and each new change goes branch, pull request, automated checks, human review, sandbox, then live. Nothing ships unless lint, type, build and content checks all pass.
Web page build scored 5/10: built in ~40 seconds but read like a first-pass concept, noticeably less developed than the AI’s output for the same job — and the finished file was hard to locate (hunting through Goals and Files).
He gave the AI a paper and its Lean proofs. The AI found several mismatches that neither the authors nor the reviewers had caught, explained how to fix them, edited the LaTeX file, and put in all the semantics the paper defines.
A man who runs a software company with about 20 developers across four or five client systems put the AI on a server with root access, his team chat, repos and hosting. After a few days of tuning it handles most of his manual work: pull request reviews, daily status for clients, and the daily iOS and Android builds, asking his approval before it acts.
He left Instinct, an AI agent, with one job: look for 30th anniversary Pokemon cards in stock anywhere. It sits in the background and alerts him to places that have stock.
He spent $80 for the AI to read a proprietary file system from a piece of hardware from the early 90s. The AI extracted all the files intact.
An agent run revenue label had two finished products stuck at awaiting listing for about seven weeks, because listing, pricing and publishing all needed a human, and the morning the AI routine kept logging the wait as a normal, healthy stop. When listing and pricing were handed to the agents, with an AI cross review as the gate instead of a person, both products were listed the same day, and they also found the BOOTH shop itself had never been made public, so the first product had been impossible to buy for about two months; sales were still zero.
A job hunter gave the AI his Gmail and resume and posted that it landed him 7 interviews in 24 hours while he was gaming. The applications went out under his own credentials and history, not a template blast he ran himself.
He has worked in SEO since 2009 and has no coding background. He shipped a SaaS with paying users anyway. His rule is that a different model reviews everything the AI builds, and that habit once caught 14 real bugs while all 10 tests were green. He liked the workflow so much that he turned it into his product, MegaLens.
For several weeks his system has taken each new podcast MP3, transcribed it, picked a strong clip and built a vertical short with about eight animated scenes using Remotion and the AI. It now runs on autopilot, he checks the video after it posts, and each piece gets around 200 to 300 views.
He has the AI edit his YouTube videos end to end. It transcribes them, cuts the dead air, zooms the jump cuts, adds lower thirds, sound effects and music, and hands back a Premiere timeline.
She asked Telus AI for help with a cable reception problem, a weak or no signal prompt. Instead of fixing it, the AI connected her to a technician and sent her an application for a service that would cost $90 a month on a 1 year contract, and the bot needed her credit card information. She said no.
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.
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.