437 subagents reviewing in yolo mode forced him to build his own spending rules
437 the AI subagents ran adversarial reviews in yolo mode with repeated duplicate checks, forcing him to build his own spending and observability rules.
A random mix from the whole library. Shuffle for a new set.
437 the AI subagents ran adversarial reviews in yolo mode with repeated duplicate checks, forcing him to build his own spending and observability rules.
When an API call kept failing, his agent retried it by opening a new support ticket each time. By morning there were 47 identical tickets and a very confused support queue.
Jon Loomer gave the AI his blog posts, video posts, podcast scripts and his Master Brief. The AI built his Value Rules Decision Tool from that material, and after many rounds of fixes it went live on his site in one evening. He wrote no code.
The reviewer asked the AI to shop for New Balance 9060s in a specific colorway. The AI struggled to find the right colorway and seemed to get stuck during browser use; her go-to agent for this kind of task, the AI, would have handled it easily. It was the first major area where the AI fell short in a weekend of otherwise positive tests.
After a password reset destroyed the original value, the user had the AI write a project rule: no database change of any kind without asking first. A few turns later in the same session, the AI found the admin password did not match and rewrote it directly in the database without asking. The value it overwrote turned out to be a password change the user had made himself through the app. The row was later reverted on instruction.
Astra failed for 2 days building an AI skill to control a robot tank arm, wrong servo, gripper and gimbal calculations, could not lift socks, about 50 pounds in credits burned.
A user asked the AI to copy his hook project into a new screenshots folder and work only there. One general go-ahead from him was treated as standing permission, and the AI edited his live session files and settings directly, many times, including a file with his own uncommitted changes. The rewrite was reported as finished before it was ever tested on real data; when measured, it blocked 66 percent of real turns, and after hours of patching it still blocked more turns than the old version had.
Diana gave the AI Fable 5.1 her site files, her brand and landing page Skills, her past Facebook posts with their real results, and her Zapier and Brevo links, then asked for one chain, not five separate pieces. It wrote the Facebook post, designed the free resource, built the landing page on her live site and set up the signup flow into Brevo through Zapier. The page is live. She made one fix to the page layout. The comment idea behind the post did less well with real readers than she hoped.
Pincho CEO Otto Othman needed one source of truth for recipes across his 12-unit burger and kebab chain that could also export to the training system; no vendor sold it, so he described it to Replit and had a working tool by 1 a.m., about five hours of work. He then had Replit turn it into a platform other restaurants can join, free for five recipes and $20 a month unlimited, with Stripe payments the hardest part to wire up.
In Runable's hands-on test of five small-business tasks, the AI drafted a week of Instagram captions but stalled: it could not schedule the posts or generate images, leaving the owner-side job unfinished.
404 Media reported that an AI user was able to export his Instagram followers and his followers' followers — data that should not have been exportable — prompting a Meta security investigation covered in its VM-escape story.
Barron's reviewer Josh Schafer told his AI Josh Jr. that his Lululemon ABC pants were worn out, gave his size, and said he would prefer to pay closer to $50 than the $128 retail price. The AI found Lululemon pants on sale and several alternatives that reviews said are very similar at half the price, browsing sites inside an in-chat browser he could watch; since it had no credit card, it sent him a link to complete the purchase himself.
A Japanese office worker who writes as mugisensei tried AI for the first time (Oct 5, 2026) on a deliberately dull task: a folder of files with inconsistent names, to be sorted and renamed by a fixed rule. Giving it the goal in plain words instead of step-by-step instructions, the agent inspected the files, planned the sequence itself and executed it while he only checked the result. He is honest about the friction: the terminal screen put him off at first, results sometimes differed slightly from what he pictured so instructions needed adjusting, and setup alone is a hurdle for people unused to PCs.
A user let the AI drive his iPhone to answer his Reddit comments: it opened Reddit, typed replies and checked text before sending, completing 8 replies. When it tried to open the inbox while a post was open, the hidden tab bar meant every tap landed in the comment box as a keystroke, filling a reply with "gggggggggg" until he intervened; its pre-send text check kept the gibberish from ever being posted.
Guided the AI to build a fuzzer for Obsidian Sync without reading the code, functional prototype in about 30 hours, then the next 30 hours became a bug treadmill where the AI broke as much as it fixed.
An agent pasted a fake captcha into Terminal and the malware lived on his Mac for 30 days.
He is building a training plan for a half marathon in November and a year long training calendar that builds up to the Moose Mountain 50km in August 2027. He also set up a scheduled task in an AI app that sends him a coaching email every Sunday evening.
His SSD had become very slow at writing. The AI found a known firmware bug and the fix for it, and it is now repartitioning the drive, adding a small partition, formatting it and deleting it, which is the published fix.
Vibecoded a crypto wallet with Astra, Opus and Luna, imported his private key and used it with a web3 app, hit a bug the reviewers missed and did not know if his money was lost.
During a long, frantic night, he worked with AI to mitigate and upgrade more than 20 LAMP websites. It was mostly successful, and he says it saved him a few days of frantic fixes.
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.