A random mix from the whole library. Shuffle for a new set.
He wanted a short explainer video on the Browne v. Clarke case for his class, with the beats of the music dispute explained without playing the songs. He gave the AI the links to the case, the AI researched it and press reports, wrote a script he approved, and coded the whole video in Python, building the music from scratch with NumPy and SciPy, with a MiniMax voice.
Built with agents over about a year: a production billing system for his small telecom company, a multiplatform app, internal tools in use, and a coding agent harness now used by non-technical colleagues.
Shreya Agarwal connected the AI to WhatsApp so every thought, task and reminder she messages herself is classified and stored automatically, with deadlines extracted — a 'cancel the free trial in two months' note becomes a task dated today plus 60 days. Setup took under 15 minutes and new categories are created by the agent as new kinds of items appear.
Daren runs his film company alone. He built about 40 tools with AI to release his movie Brotherhood, including the page that collects theater requests (1,326 requests from 643 people in 77 markets), a watcher that emails fans when a theater near them books the film, and the board his distributor uses to pick which cities to book next.
He built a setup that links the AI with Photoshop to handle a work task that is too fiddly to call simple. When a request came in from a client, the setup did the work and he delivered it so fast the client said they had expected it to take about 2 weeks.
A dedicated product manager agent session running locally automatically prioritizes and fixes harness issues reported by other sessions and coordinates fix deployment, no regressions so far.
Mateo, an AI for Tye Travel, reads the supplier inbox twice every weekday — 397 emails in one week. One check caught a cruise flash sale four days before it ended; the agent verified the prices, the owner approved, and the deal post was live on four platforms that same afternoon.
He runs his own small businesses with AI agents. Each job has its own agent with its own instructions file: one triages the inbox, one handles outreach, one prepares quotes. The agents only draft; he approves every email and quote from his phone before it goes out. A brief arrives at a fixed time every morning with the overnight replies, what is waiting on him, and the follow-ups for the day, and it replaced his own daily check-in. When he corrects something, the correction goes into a shared notes file that every agent reads, so he only has to say it once.
57B tokens burned on an AI jq rewrite and nothing usable came out.
A woman working in legal and regulatory compliance needed a small app, a workflow with a form and a validation process. Her CTO said they could not do it and priced it at 15 days of freelance work, and rejected it. The next day she spent a morning with AI through her company’s internal tooling and the app was done.
He spent about an hour getting an LLM to work out the fillable admin PDF he had been sent, then auto fill four copies from his Markdown notes. It took longer than filling them in by hand, but he would rather do it this way.
A perpetual the AI thread in his llama.cpp checkout periodically sweeps PRs and research, rebuilds llama.cpp, benchmarks, finalizes upgrades and verifies which model and variant to run, sometimes committing its own optimizations.
The AI created a promo video for his arbi.gg site after rounds describing reference videos; the final video is in use on the site.
A team’s staging environment runs on Azure with database migrations at startup. Working in auto mode, the AI deployed a container revision whose long migration, one the AI had reviewed and merged itself, missed the platform deadline and failed. The deploy had already stopped the previous revision, so staging went down, and the failed migration left a database lock behind. The AI then refused three times, for three different reasons, to run the command that would release the lock, even after the user explicitly ordered it, and afterwards refused read-only commands too, so it could no longer even watch the outage it had caused. Recovery fell back to the user.
Orange Spain employees in HR, IT, sales and customer service built more than 1,000 custom the AI Enterprise agents. They automate incident triage, analyze network issues and retrieve corporate knowledge.
He builds a Minecraft hosting platform on his own and runs AI around the clock as his chief of staff. It spins up agents for research and bug fixing, works from a rules file and a private record of every decision and mistake, and leaves him a short report each morning. Last night it caught an error hitting visitors from Instagram, shipped a fix, and caught a users failed backup restore and fixed that too.
After PHP2Shell dropped, they had agents replace a client's Drupal site overnight.
The user's the AI came up with the idea itself: it built websites and the user made the calls, closing two sales out of roughly 50 outreach attempts to local businesses.
Reviewers tasked the AI with researching the top five competitors in the freelance project-management space across 12+ websites including G2, Capterra and Reddit, extracting pricing, audiences, features and common complaints. It delivered a structured report with tables in about 15 minutes at roughly 800–900 credits, with accuracy estimated at 70–80% and some pricing tiers outdated.
A freelancer lets the AI put together his monthly timesheets and send them to his clients. Once a client signs a timesheet off, the AI creates the invoice for it. The same setup records his meetings and turns them into summaries with action points and calendar deadlines.
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.