A random mix from the whole library. Shuffle for a new set.
A Low Code Studio creator gave the AI a single prompt describing a Compliance Tracker app; the AI designed the data structure, created the SharePoint list with columns and data types, and wrote the YAML for all four screens. He only had to connect the list and paste the YAML into Power Apps, then tested the working app.
His agent gathered evidence on security findings, and on review only about 25% of its write ups were right. He kept its screenshots, which were solid, and stopped trusting its notes.
AI bots manage his startup finance, payroll, meeting prep and social media, with a primary bot auditing and optimising the other bots weekly.
Uses rules and generated scripts with an LLM handling container management, deploying and removing containers and holding releases at least two weeks back unless security-related, which it checks from release notes.
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 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.
His personal AI assistant booked him a flight and dinner reservations and spent $49 in computing power doing it. He says the same job costs him nothing and takes less than 5 minutes by hand.
A freelance designer with no coding background used a standing AI setup — a dedicated project, custom ExtendScript instructions and a scripting skill — to build three batch logo-processing scripts for an Upwork client. The client paid $640, left a five-star rating and became a repeat client.
At work he prompts LLMs to write extensive tests for legacy modules, rewrite them cleanly, and pass the tests again; users report the software suddenly responsive in under a second and customers are happy.
Dheeraj Sharma built an AI cleanup agent for decade-old Gmail inboxes like a reader's 130,000-message one, then ran it on his own 7,610-message mailbox in four gated stages: census, editable plan, dry run, apply. One approved run moved 2,896 newsletter and notification emails to Trash in 107 seconds (unread fell from 5,013 to 2,237), with deletion impossible by design and every change logged for a one-command undo.
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.