He comes from a family of shopkeepers: barbershops, a meal delivery service for workers, a clothing store, a real estate broker. Every business had its own paid ERP and none of them fit. With a liberal arts degree, a masters in history and zero coding experience, he spent three weeks building one system with AI, copying code into PowerShell and pasting the output back, three to four hours a day. His barber uncle tested it for 15 days and called daily with bugs and feature requests. Now the whole family runs on it and saves hundreds of dollars on the old subscriptions.
A developer gave the AI a token for his code repo and set a two hour cycle. The AI checks the issues page, makes a branch for each issue, writes the fix or change, and opens a pull request. Merges rebuild the app and serve it on his home network, and the AI texts him on Telegram when it has a question or when work is done. He uses the app daily and files features as issues, which are handled within a couple of hours.
Reporting Anthropic's September 23 engineering post, this analysis describes the AI running a two-week performance sprint from a Slack channel: 150-plus threads, benchmarks built for each bottleneck, and more than 3,000 merged changes with no customer-facing incident. Time to a typeable page at the 75th percentile fell from 3.1 seconds to 0.55, with a human approving every pull request.
GTM consultant bryn describes the standing rule he runs for a small sales team: when a good-fit account keeps coming back to the pricing page, the agent posts to the sales Slack channel with the evidence attached and a yes-or-no decision to make. The point is that reading and assembling evidence is the agent's job, not the rep's.
Caption+image creation scored 7.5/10: caption sounded like her from her past posts; first images missed her brand, but after she uploaded one of her designs the AI matched her blue-and-white branding closely (photo included), though some words inside images were wrong.
This only partly worked. Worth knowing before you try it.
A 7-figure Amazon seller growing a Shopify store (300 orders/month, fulfilled in-house from the east coast) hit his limit on slow, expensive west-coast and Florida shipments. Shopify's native routing couldn't see order-level detail and Functions needed Plus, which he doesn't have - so over one weekend he built a custom app with AI: it checks each incoming order's zip code, and if it falls above a USPS tier threshold and every line item is in FBA stock, it silently reassigns the order to Amazon MCF. He added a vacation-mode switch (everything routes to MCF while he travels) and stuck-package alerts; shipping time fell from up to 7 days to a max of 5, hosted for $5/month instead of a $99/month order-editing app.
He builds the visual overlays used in car inspections in Blender. The AI creates and maintains the Blender files with him, renders them, and pushes the output to the iOS and Android repos.
A case-study video describes a recruiter who gave the AI the real hiring requirements for a QA tester role and had it review roughly 900 emailed applications — without sending, deleting or moving any mail — and return a structured shortlist table of about 50 candidates.
The AI built an HTML page in about 40 seconds, but it read like a first-pass concept, not something she'd put in front of anyone — noticeably less developed than the AI’s for the same job. Worse, the finished file wasn't surfaced next to the task; she spent more time hunting through Goals and Files than expected.
This only partly worked. Worth knowing before you try it.