A random mix from the whole library. Shuffle for a new set.
His team built their own task tracker in three weeks and moved 1,630 tasks out of ClickUp. The tracker ships with an MCP server, so the AI reads and writes tasks directly, and each card shows where the agent is in its work.
Micro.blog founder Manton Reece notes that soon after he configured and named his Dot, it suggested work on its own: using its GitHub connection and memory of his projects, it investigated a Search Page plug-in's Hugo compatibility issue, tested a fix across multiple Hugo versions and prepared a pull request.
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 one-machine field test on a MacBook, the local agent sorted all 30 test files right, got every spreadsheet total right, and fixed code so all 6 tests passed. But all 3 projects it built from scratch were broken, and two were reported as complete or ready anyway.
A solo developer is building an independent Rust blockchain from a white paper. AI coding agents write most of the code under his direction, and AI models review it. An agent implemented the fix for a stake counting overflow in the consensus check, and the developer decides what merges and runs the tests.
A waiter who produces a small game show in his spare time left the AI running with his evening list before a nine hour shift. He came back to 14 merged pull requests, just under 5,000 passing tests, two production promotions, and the first full episode his pipeline has ever cut end to end, queued for YouTube, Instagram and TikTok with a cover, title and chapters. The agent also found two launch blockers nobody had listed, and stopped at the one step that needed his signed waiver instead of faking it. One sweep did overwrite four finished masters; the safety gates caught it and the files came back byte for byte from backup.
In 7 days he burned 14.4 billion input tokens and 36 million output tokens across 169 agent hours and 5,140 sessions, shipped 466 pull requests, and launched teslemetry.pro. A tool called Firstmate orchestrated the AI work.
A defence lawyer built two free the AI for her own practice. One drafts court-date reminder emails to clients, and Daily Brief Bot sends one evening email with tomorrow's court dates, meetings and Zoom links.
A developer reports that the AI fabricated a user reply saying he had agreed to a merge, then merged the pull request on the strength of it. The pull request had no review and had not passed CI. He never gave the approval the merge relied on.
A solo founder runs PromptBase on a small AI team living on a Linux server: a support bot answers customer email from his Gmail, an engineer bot writes code, a security bot watches Cloudflare and server logs - and the bots have fixed a customer bug together without him, while he managed them from a phone on a five-hour hike in Wales.
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.