A random mix from the whole library. Shuffle for a new set.
A local publisher’s the AI research, write and sell the ads for a Thursday paper read by 6,000 neighbours. The bots cover the whole production loop of a real local news product, not just the writing.
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 built a recording app that screencasted the interactive diagrams in his blog article and added titles, turning the article into a published video, though it took many hours.
Via the Slide Factory review, a developer on OpenAI's forum reported testing a .NET 10 application on a Dot's cloud computer: dependencies restored, the backend compiled and 193 backend tests passed. The full stack would not run because the Debian 13 environment had no sudo and Docker could not reach the host socket.
His company runs an agentic software factory handling low-level chore tickets and merging about a third of their PRs, with no slop downtime incident he is aware of.
His company cuts and sews shirts in America from materials bought from many vendors, shared across 10 to 20 shirt styles. Working out the true cost of a finished shirt by hand was a mess. Now an AI automation does the full calculation in about 10 minutes, exact, and publishes the cost to Shopify.
A marketer trained his AI setup on everything about his company and what performs; it now writes the script, creates an avatar, generates the video and b-roll, and edits it together with captions and a CTA end card - and the ads perform better than when he made them manually.
The same side business owner had the AI redo his website and take over its search work. The AI logs into his Google insights and handles the weekly optimisation for the words he wants to rank for. After two weeks the site sat at the bottom of the first page for those words, on an old Weebly site he expected to hold it back.
Diana asked her dot Sparky to turn a real meeting record into decisions, owners, dates and a follow up message, and told it not to invent commitments and not to send anything yet. It invented nothing, but the follow up came back as plain chat text, and she had to ask twice before it made a send ready draft. When she told it to send, it sent at once.
Set up the Figma MCP with AI to match web pages exactly to Figma designs across about 40 pages, where feeding screenshots had done poorly, and it did what he needed.
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.