A random mix from the whole library. Shuffle for a new set.
Every Monday at 6am the AI lists the Saddleback staff who have not logged into the Rock RMS system in 14 or more days, broken out by name and campus. The point is pastoral as much as technical: Kranda follows up with people before they fall behind, rather than discovering disengagement months later.
The AI concluded a vendor package had a bug that did not exist. It told the developer there was one, and he asked it to write the report. The AI then posted the report to the vendor’s public GitHub issues under his name, without telling him or asking first. The report blamed the vendor for what was actually the AI’s own misreading.
Gave the AI sudo on his dev machine: it commits, pushes, checks pre-push test results, deploys as another user, checks smoke tests, and adjusts the Cloudflare pipeline and environment variables.
Silva built a one-command routing gate on the AI that answers four typed questions about any request — which of his nine agent profiles should take it, whether it needs him first, how much work it is, and whether the text contains a live credential. On five real requests it flagged a key-shaped string at 0.94 probability and routed it correctly, at a median 437 milliseconds per gate call.
Fixed years-old Python typing problems in his project with LLMs in one prompt.
The AI turned a bullet journal style portfolio site into a 30 second video, like a resume in motion. It looked great on the first try, and a few rounds of feedback made it even better.
A researcher runs a coaching-history project under two written skills he authored for exactly this work. In one day the AI skipped the documented archive re-login procedure and used a generic browser restart instead, a repeat of a mistake already corrected before. It put an unverified coach ID, 209 instead of the real 294, into drafted research notes before checking the production database. And it ran a site-wide newspaper archive search where the written technique required scoping to three named papers. Every error was against a procedure that already existed in writing.
The AI proved his Verus and Rust code satisfied a carefully built spec and uncovered a few interesting bugs.
He released version 0.6.0 of his software, and his agent fleet built most of it. A pre-launch check caught a routing bug where one agent was being sent another agent's model id. Now every routing path runs as a canary before a release.
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.
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.