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.
Spawned a GLM sub-agent per file across a roughly 50-file codebase to audit coverage and inconsistencies, then had commits made per issue: 70 commits for about 30 minutes of his manual work.
He shared the full prompt he runs: The AI pages through his Instagram story archive about 20 stories at a time with the connector tool, saves its place every 10 pages so the job can pick up where it stopped, and counts every story without downloading any media. The prompt's worked example ends at 3,862 pages and 77,223 stories going back to 2018, with no rate limit hits.
Shipped a RAG customer-service agent for an online seller that sent 1,467 or more replies automatically to real buyers and ran the chat channel unattended.
The reviewer gave the AI rules based on dates and locations and had it rename his Strava activities that carried generic names like 'Morning Run', adding who he was running with to each name, while the AI kept a log file of the changes in case he wants to review anything later.
He blogs on his own and set up an 8 member virtual publishing desk in Antigravity, with agents reviewing each other and strict gates before anything goes live. The desk worked through his backlog of more than 50 drafts.
He drafts in an AI app, and the AI itself sorts each piece, tags it, and moves it word for word into his Obsidian vault using a skill he built. The filing runs as part of how he writes.
Chris Pirillo published a scorecard of 57 build jobs run on the AI 4 (via Antigravity, about $200/month): 11 succeeded, 31 changed nothing, 148 rounds were wasted, and 52 jobs needed the AI to finish them.
He gave GPT a security audit skill that checks his machine against newly published vulnerabilities, patches what it can, and warns him about the rest. He runs it whenever new CVEs land, and says the patches need care because a fix can cause as much trouble as the flaw.
Railroad CSX embedded Chessie, an AI Studio agent, in its ShipCSX portal used daily by 7,000 customers. Shoppers ask in natural language to track a shipment or get FAQ answers; in the first 45 days over 1,000 customers used it across 4,000+ conversations and kept coming back.
The AI fixed a long-running Tailscale problem by lowering the MTU.
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.