A Korean solo developer's two-day diary of putting the AI to work as his team: GitHub issue labels split between him and the AI across marketing, planning, design, QA, operations and finance, with a project board and roadmap milestones built in about an hour; the AI runs QA on each deploy event and files issues; customer inquiries are answered through an admin MCP connection; and morning routines from a one-time setup (7 AM English lesson, Google Analytics analysis, issue briefing) arrive unprompted. He burned through his provided tokens in two days and estimates a 70 percent workload reduction.
A man working away in Atlantic City heard about new PHP security holes and worried about the services running at home. Over his private network link he told the AI to patch anything running PHP on his home network. Ten minutes later everything at home was patched.
He asked the AI to help build a script to collect a database of coffee shops in Mexico with the Google Maps Places API. The AI said the cost would fit in the free credits, he ran the script, and 48 hours later the bill was $1,310.05.
$1,310at stake
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He was missing Adobe Lightroom's denoise feature for his RAW photos, so he had the AI build it. The app also denoises his standard JPEGs, which Lightroom could not do, and he has a lot of low res, noisy JPEGs lying around.
He points an AI project at a folder of case files. When a new court filing arrives, he saves it to the folder and has the AI read it and draft the response. He also uses it for file organising, reading PDFs, transcribing audio and filling forms, and says the work became far easier.
During a Moneywise hands-on test, the AI went beyond the three assigned money tasks and unprompted created a personalized feed surfacing deals and news related to the journalist's past questions, plus a goal-tracking list and ideas for new tasks she could delegate.
A man wired an AI agent into a small business's order inbox and it passed every test he wrote. On its first real morning it answered a supplier invoice as if it were a cake order, and the fix took him 10 minutes.
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A man gave the AI strict tag research tasks and had it work through the whole ticker list again and again, searching and comparing XBRL tags in SEC filings. The pipeline now pulls 10-K and 10-Q filings for more than 600 companies, maps tags onto about 35 financial concepts, and produces cleaned data. Doing 3 tickers by hand had taken him about 3 weeks and still gave wrong data.