Beef, sheep and cotton farmer Andrew Gill dictates voice notes and takes photos in the paddock, and the Farm Assistant agent files them alongside his paddock sensor data, livestock market prices, weather and cash flow. Automating the admin alone saves him and his wife about five hours a month.
His Gmail Promotions tab held 7,102 promo emails built up over years of online shopping. He asked the AI to clear them. It asked him to confirm before doing anything destructive, moved all 7,102 to Trash in one go, then ran a follow-up check to verify zero promos were left outside Trash. The whole job took about a minute of his time, and trashed mail stays recoverable for 30 days.
Fable helped find and fix a Motor OS race he had debugged for 3 months; since July most roadmap items are fixed in about a week instead of 3 months supervised.
The AI wrote Zig tools in real use, including a wasm utility integrated into a site and reverse engineering a USB wire protocol for an industrial camera driver.
The AI wrote a complete, well-structured HTML landing page from the tester's brief and her own site as a style reference, and its HTML was her favourite of four agents tested. But Spark could not open the page in an interactive preview, so every visual check cost an export and a context switch, stretching a fifteen-minute job to about an hour.
This only partly worked. Worth knowing before you try it.
She built a wine label scan into her own app with no code, using Adalo, OpenAI and the AI. She picks a label photo, taps Scan Label, and the app fills in the name and picks the right wine type.
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.