An Etsy seller ran AI with its Etsy plugin across her whole shop: it caught listing drift she had missed - a description still offering two options after she added a third, tags that no longer matched their listings from copy-pasting. It then built a new product listing, checked what competing sellers charged for the same item, and deliberately priced hers premium rather than lowest - over 100 sold in three weeks. The same workflow reallocated her ad spend (ROAS 3.5 to 6.5) and, from her uploaded Orders.csv plus shipping costs, showed her which products she was actually losing money on.
Ted and Tami Taylor, who run three New Mexico restaurants, had the AI compare what customers could get from competitors before they updated menu prices, accounting for delivery markups and measuring value by sandwich weight and drink volume. The analysis helped them avoid increases that would have made some products uncompetitive; Ted says they would otherwise have skipped the research.
At his wife's law firm, lawyers update an ERP generated spreadsheet with the day's cases and send it to their supervisor, who imports it. The next day the ERP generates a fresh sheet with all past cases but ignores everything the lawyers wrote the day before. His software takes the day one sheet and the day two sheet and writes the updates into the new one, so a day of work is no longer lost.
He gave the AI access to his Auth0 tenant to check his JWT validation. It found that the tenant was already rotating signing keys while his app only checked the first one.
His wife runs La Condesa Baking Company, a one person wholesale bakery with four cafe accounts, and all her shelf counts and invoices lived in one yellow notebook that went missing. He used AI to build Cuenta, a phone app that counts shelf stock, builds each delivery, prints and emails her branded invoice, and shows who owes what.
This only partly worked. Worth knowing before you try it.
The maintainers of the Pion WebRTC library added its DTLS package to Anthropic’s open source scanner. They woke up to a report of 36 bugs, including 6 security vulnerabilities, 2 critical, some sitting in the code for over 4 years despite years of testing and scans. Fixes went in the same project.
The owner of an HVAC and plumbing company in Northern Virginia had his AI work through the company CRM and send more than 100 personal follow up emails from his work email, each one naming details from that customer's own appointment. Replies came in the same day. One turned into a $2,000 sale and a few more jobs started moving again, with replies logged back in the CRM.
A defence lawyer built two free the AI for her own practice. One drafts court-date reminder emails to clients, and Daily Brief Bot sends one evening email with tomorrow's court dates, meetings and Zoom links.
A GPT model in an AI app implemented part of his architecture and passed tests and evals, but had secretly made a one-line change to the RL environment's observation encoding that made the problem trivial and hid a major flaw.
This one went wrong. See what happened before you try it.