Zeno shows screen recordings of his AI job-search system: a Notion page of weekly job packets (companies including Cedar, Affirm and Practice Better, with pay ranges and priorities), and an application-tracker spreadsheet the AI fills in every Monday. The AI tailors his resume per posting, picks up ATS details, opens its own browser, fills application forms and uploads the resume — an initial upload failure was retried successfully — and reports applications submitted, starting with Cedar, towards 18 in total.
He runs OpenFree, a site that collects open source projects. The AI scans X every day for new projects, filters them, and submits them to his site backend, where a Cloudflare workflow parses each one and sends it to him to approve before it goes live.
A small distribution business owner disliked how the usual accounting and stock software fit his business, so he built his own with AI's agent mode: dozens of screens, tens of thousands of lines, and an end to end test that mirrors a real business day, receiving stock, making sales, updating inventory, closing the books. His scariest find was a silent failure: opened the wrong way, the app looked normal but could not save, so a full day of entries could have vanished. He caught it by testing, added a launcher check, and now makes the software prove every important feature works instead of asking the AI whether it does.
During a migration, a written plan in the session said nothing in the legacy repo gets touched. The plan was re-shown in full mid-session. When the user later asked for a follow-up change, the AI searched the folder it had been working in most recently, which was that repo, found a file that looked right, and edited it there. The user caught it, and the edit was reverted and redone in the correct repo.
This one went wrong. See what happened before you try it.
A WooCommerce store owner had a legacy on-site point-of-sale system whose vendor finally offered a backend way to pull customer emails from a mailbox - and he avoided implementing it for two months because the integration looked painful. One evening, about two hours with AI produced a small working plugin; tested that night, it now automatically does data entry he describes as hours and hours of manual typing.
A man at a company that paid $300,000 a year for Looker for ten years says one engineer, working part time for a couple of months with AI, built a home grown replacement for their dashboards. The core idea checked out in a couple of weeks, and the company told Looker it will not renew.
His todo list outgrew his Obsidian file, so he moved about 300 issues into Linear through the AI. He then refined the roadmap by talking with AI while driving.
A creator handed the AI his two small YouTube channels: scripts, voice over, image prompts, renders and comment replies, with one hard rule that anything public waits for him. By morning three long episodes were rendered in 4K, 32 images had been checked on a contact sheet, and five replies were posted after every number in them was verified. The failures were quiet ones: a daily lesson that simply never went out four days earlier, timed posts that never fired because the session was busy, and a 3:15am scheduled task that never ran, caught only because his new routine checks after every due time that the thing actually happened.
This only partly worked. Worth knowing before you try it.