His agent team shipped an autoupdate change end to end, issue to master
His oh-my-pi agents opened an issue, shipped code, ran tests and updated master for an autoupdate change.
A random mix from the whole library. Shuffle for a new set.
His oh-my-pi agents opened an issue, shipped code, ran tests and updated master for an autoupdate change.
A student asked the AI to free up disk space, meaning the recordings and screenshots that were filling it. The agent deleted his whole co-op game project instead, source code, docs, decisions and playtest logs, and with no remote copy the project cannot be recovered.
Gave his AI agent pre-patch disclosure details meant to be useless; the agent found the vulnerability in less than an hour.
His AI server checks what the current shipped Micro.blog app versions are and whether an app is in review. It updates the public help page by itself.
Handed the AI a planned multi-module change and came back to 4 PRs ready to merge in 15 minutes; the work would have taken most of a day.
A Canadian realtor had over 60,000 old leads sitting in his CRM, some from 2017. An automation agency built him a text agent and a voice agent that ask each person where they stand now and flag anyone ready to talk. Of about 44,000 people they could contact, 9,100 talked, 480 were ready now, and over about seven months that became 210 appointments and 34 closed deals worth about $490,000 in commission.
His agent setup built 2 commercial apps used by real companies, a marketing tool and a Texas land analysis tool, after agents kept claiming done when their tests only faked real conditions.
Used the AI with his own APK analysis tool to analyze phones from 3 brands and found 2 RCEs and 5 root bugs in a few days.
She is the training coordinator at her job and tracks everyone's certification hours in a color coded Excel workbook. She still had to log into the training website, check each person's hours, and type them in. Now the AI checks the training website for every employee, fills the hours into her uploaded workbook, and emails her the updated workbook and a report. It runs on its own on the 1st of each month.
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.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.