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 legal librarian at a law firm had to check ten references for a Belgian lawyer working on warranty of liabilities cases. Three references had errors, one of them serious, and the AI Pro with skills and Google AI Overviews found the right reference in a badly protected PDF on revue-banque.fr where another tool pointed to the wrong one, and one more error was fixed using the next year's index of decisions.
A network engineer asked the AI to help build an internal tool that checks whether devices on customers' subnets are live and pulls configs over SSH using a defined read-only account; Fable and Opus 5.5 refused under cyber guardrails. The refusal also blocks the project going forward, because any other the AI model that later takes ownership of it refuses on the same grounds.
This one went wrong. See what happened before you try it.
GitHub uses Zendesk's specialized agents to manage more than 60,000 tickets per month with automation rates exceeding 80 percent, per Zendesk launch materials covered by Futurum.
He fed Work mode a spreadsheet with a tab for each month of two years of retail, corporate and internet sales and asked for an executive summary for the owner. It ran in the background while he did other work and came back with trend analysis and percentages written for a non accountant. The owner loved it, and he figures it saved him days.
Jenny Stojkovic (95K followers, AI/wealth/investing) documented building a tax-deduction tracker in an AI app after discovering she overpaid taxes by $46,324. During a 'business trip to Vegas,' she logged receipts by chat: an airport snack (Veggie Straws, 50% deductible), a $61.88 airport-to-hotel Uber, baggage fees, and a porter tip — all flagged work-related; a Vegas show ticket with a client and a rented conference dress marked non-deductible ('ordinary clothing is generally personal'); hotel laun
Tester Diana fed the AI three competitor pages plus her own search-metric and page-copy exports after live URLs alone gave unreliable visual change detection. Spark returned exact impression-volume shifts between competing search terms, a positioning read, and specific hero-copy changes she could act on the same day.
This only partly worked. Worth knowing before you try it.
A Japanese Threads creator split his account's operation into six AI agents with one deliverable each (researcher, writer, poster, fetcher, analyst, supervisor) and reports 1,181+ posts and over 10 million cumulative impressions. The writer agent may only cite numbers stored in a single 'truth file' so it cannot inflate results, the poster must confirm the published URL rather than the button click, and the fetcher records 'not retrieved' instead of zero when a fetch fails.