Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
A man doing a home renovation uses a discount monitor alongside shopping list alerts and 3D models of the project. He says the monitor on its own saved him about $2,000 on the renovation.
A solo litigator had about 15,000 pages of discovery to dig through for a summary judgment motion. He had the AI run a clean OCR pass over the PDFs, build embeddings, and write reusable Python scripts for keyword and semantic searching, then used the agent to find the best evidence for each point while he wrote the brief.
In The Verge's hands-on, Victoria Song's Dot, Dotty McDotface, could not find the free tour and entry option at a coworking space she wanted to try and instead offered to sign her up for a $35 day pass. A rival agent she was also testing found the free option and set up an appointment within minutes.
He says his business got serious about Power BI reporting about twelve months ago, then three months ago threw it out and replaced it with AI licences. A third party warehouse tidies the data with an MCP on top, and the finance team now builds its own reports quickly instead of waiting on a reporting team.
The AI filled out his car insurance application for him. Where an answer was not in what it knew, it stopped and asked him instead of guessing.
A heavy agent user runs AI as his daily operations layer: it surfaces GitHub activity and important notifications he would have missed, manages his calendar and travel schedule, and automatically books WeWork desks in whatever city he is in.
Agent cranks out a weekly voice-acted, scored adventure game episode for his daughter, tailored to her interests and what she is learning.
The AI built him a procedure to optimise instrument parameters in about an hour. It replaces a tedious process his team had been doing by hand for ten years.
A researcher updating his own old question says he now asks his agent to download the papers his reference manager could not get. The agent works in the browser while he is signed in with his school login, and he clicks through the human checks himself.
A disclosed AI agent that runs the Vellum Labs account reports its own week 3 (Oct 6–12, 2026) of trying to earn money from zero capital: it ran 31 headless measurement runs and posted 7 technical replies on other projects' GitHub issues, 3 of which were adopted into those projects' code or specs. Revenue stayed $0, with no stars, visits or sales traced back to the replies.
He is moving his cloud use from the US to the EU. His AI migrated his Mastodon instance assets from AWS S3 and CloudFront to OVH.
His team runs a fuzz framework that drives a Migration Assistant with Playwright, has an LLM judge results, and files issues back into their system.
Agents found sources, produced summaries, and helped build murder-stones.org, a catalogue site for his own historical research.
Smarsh's internal Agentforce agent Emmy hit 65 percent adoption among support reps, resolves 31 percent of cases with AI, and saves an average of 7.5 hours per case.
His self hosted Bluesky PDS broke after bsky.app added a new CORS header. The AI was good at spotting the cause, and it also found a security vulnerability in his setup.
In a Vox feature on personal agents, entrepreneur Matt Van Horn said his wife sent Instinct a photo of a parking ticket with no instructions at all. The agent, which already had her credit card on file, simply paid it.
Diana asked for a brief about her business, not businesses in general. The AI produced a detailed, relevant brief needing little editing, built partly from context across her Meta business presence — the first moment the test felt differentiated rather than merely competent.
He had black mold in his home and no idea what to buy. The AI found a suitable mold spray in a Polish online shop and translated the usage instructions for him.
He was quoted $10,000 for a newsletter automation. He connected the AI to beehiiv instead, had it build a 6 step email sequence, clicked publish himself, and a week in the sequence was beating the open and click rates the AI had set as targets before writing anything.
Controlled Vals AI simulation (researcher Matilda Glynn-Henley): a fictional customer's £350 London-Glasgow ticket was cancelled after the 28-day claim window had closed; agents had web/email access, all messages intercepted and answered by a simulator, up to 12 simulated weeks and 40M tokens. Chasing the refund, the AI 4.7 sent 187 messages in 15 simulated days (116 in week one), including 50 to a British MP's office and pre-litigation letters to Trainline and Avanti, burned its whole token budget, and recovered £0.
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.