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.
Mufti Hassan, a scholar and teacher who runs the Learn with Mufti Hassan channels, had the AI build his website with a domain and hosting and put it live. It was the first of 14 real jobs he gave the AI over two to three days in the video.
The AI configured his arr stack plus NFS mounts, share templates and a VPN init-container in Kubernetes; the cluster is now easy to manage and debug.
Used the AI with his own APK analysis tool to analyze phones from 3 brands and found 2 RCEs and 5 root bugs in a few days.
A Max subscriber uses AI Dispatch from his iPhone: on his daily Windows PC tasks run normally, but on his Windows 11 Pro server PC every dispatched message instantly returns "Task ended" and no task ever starts, even though regular Cowork tasks run fine on that machine. The failure survived reinstalls and a full Windows reset, and his phone never appears under Trusted Devices, pointing to a host registration stuck on Anthropic's side.
Simmons woke to a message from László: Vercel's third attempt to collect $22 had failed, with another retry due October 2. He had never asked it to watch invoices — it only had Gmail connected — and it also noticed he had used $15 of his $20 monthly Vercel credit, something his own email filters had hidden from him.
Release testing one of her libraries kept failing checksum checks, and she feared she would have to pull a release for a corruption bug. The AI wrote the reproducers and did most of the analysis, which traced the failures to single bit flips at the same spot and pointed to bad RAM in her own machine, not a bug in the library.
He works with collision data as a UK highways engineer but could never answer the simple version for his own commute: which route has actually hurt people. He built SafeRoute with AI, which plans a route and scores it on five years of collision records; colleagues then asked for the same data as branded evidence reports for committees, which the tool now produces in about 30 seconds instead of a day of manual work.
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.
The AI tried to fuzz a Jane Street ASIC puzzle circuit, switched to a Z3 and SMT route and solved it in about 3 hours; writeup published.
The user built EVA, a family assistant on the AI, which finds what matters in school email, tracks deadlines, updates the family calendar, and answers questions about what is coming up.
Uses Opus 5.5 to decompile Windows 95-98 games in about a week each, getting byte-to-byte exact output with clear names and no Ghidra smell.
After dry-running a personal-assistant the AI through August, its owner let it pay a real $3,731 bill that arrived by email; routines now watch his Gmail for invoices three times a day. The first live payment went through under the watch setup he had tested for a month.
After putting it off for years, he worked with AI over a few sessions to build a homepage for his genealogy research. The page is up with links to eight parts of his work, and three of them already have material on them.
The Causal team rebuilt its company website in three days by preparing a one-page brief, content architecture, references, assets and a task roadmap, then handing the build to the AI. Through MCP, the AI read everything in their canvas, built the site and coded the animations from their moodboard.
The AI read Palworld game data files and built a personal wiki with map assets and linked drops and locations, more useful than the fan sites (a second game wiki in the same comment left as a passing mention).
Answering a thread about saving and making money, he says he had the AI look online for part time data entry work and found a few positions paying $15 an hour for remote work. The AI applied using his resume and was accepted for one position out of about 15 applications. The AI does the job while he works at his actual job during the day. In a follow up a few days later he says it brings in about $200 a week, that it got a performance review from a manager that morning and scored 72%, and that its only mess ups so far were getting stuck on captchas twice during onboarding without telling him right away.
He has a large watch collection and wanted proper records: what comes in and out, cost per watch, photos, current value, gains and losses, labels and barcodes, and a straps inventory. He described it to the AI and got a working app he now uses to keep track of everything, with an icon on his Mac dock.
Across the same two-week inbox-and-calendar test, the AI proactively flagged every one of the author's seven calendar double-bookings before the meetings occurred.
His bookkeeping for a full calendar year was a trainwreck, and he needed to move accounting platforms, tidy the previous year up and prepare for an admittedly late tax return. Fable built a proof of concept migration in less than two days: it set up hosting, automated the spend categorization and got the new platform displaying his previous year’s books. When Opus 5.5 came out, it reviewed the work, caught several discrepancies in the data and corrected them, and also stood up two other self hosted accounting tools for him to try. He still hated the platform he had chosen, because it was over complicated for his needs.
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.