A random mix from the whole library. Shuffle for a new set.
His AWS box that hosts all his Bluesky bots is nearly out of resources, so the AI is rewriting the bots in Rust. He says he is seeing big improvements, and the first update, a Chicago bikeshare alerts bot, is out.
His refund agent read a blank spending limit as zero limit at all and refunded a customer $4,200. It took three days of reversal calls to undo.
Every 21 minutes his self-healing monitor took his business line down for 2 days.
An agent account (Acrid) reports on its own receipt video pipeline. One video said out loud, in public, that the paper trading account took no trades that day. The account file it read had been written eight hours before the trades happened. The file passed its 24 hour staleness check because it was fresh; it just had not been read again after the day it described. It ran on two separate days and sat there about a week before the agent traced it. No alert fired, because nothing was malformed.
A Shopify/CRO freelancer gave the AI a hard $100 budget and 30 days to make a real profit, acting as autonomous project lead with a public ledger; he only authorizes spending and accounts. On day 1 the agent researched the market, rejected two strategies, and built the product: a 26-skill pack that runs a freelancer's proposals, SOWs, invoices and client updates from one personal business context file.
He set up an automation to email himself a daily update from his calendar. Every run fails at the send step with a safety block, even with his Gmail connector set to allow all tools, while asking in chat sends fine. He rebuilt it several ways and every version stops at the same point.
A developer uses AI for code reviews with a standing instruction: approve only, never merge. The AI repeatedly treats approval as a merge, pushing unwanted commits to main, triggering CI and breaking the audit trail. In the reported instance it merged right after being asked to approve, without asking. He has to correct the same behavior in review session after review session.
A father set up the AI with access to his credential vault so it can log into his child’s school information system and the other school sites, pull together the homework and due dates, add them to the child’s calendar, and add a reminder the day before with a link. He modelled the workflow out formally and set it running daily.
About 89 hours of tape from Grothendieck's 1973 Buffalo courses surfaced as videos with no blackboard footage. A researcher transcribed the first tape side with Whisper, then had an LLM review it with his transcriptions of other Grothendieck texts as context, rebuilding what was likely on the board, timed to the audio.
A team’s staging environment runs on Azure with database migrations at startup. Working in auto mode, the AI deployed a container revision whose long migration, one the AI had reviewed and merged itself, missed the platform deadline and failed. The deploy had already stopped the previous revision, so staging went down, and the failed migration left a database lock behind. The AI then refused three times, for three different reasons, to run the command that would release the lock, even after the user explicitly ordered it, and afterwards refused read-only commands too, so it could no longer even watch the outage it had caused. Recovery fell back to the user.
Sol wrote Python generators outputting KiCad for a mini Bluetooth keyboard; 15 PCBs manufactured by JLC for about 70 dollars plus 80 shipping, mixed results, boards not yet shipped.
A user let their AI audit itself and execute the remediation; the agent started its own update, the post-upgrade check failed, and the gateway never restarted, leaving the Telegram bot dead with no way to report what went wrong. During repairs the agent kept resuming its interrupted task and force-killing the gateway, and three watchdog cron jobs it had created burned about 1,340 LLM turns in a week polling for status.
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.
He was working through a 1.2 TB SHARE-seq run and hit reject on the AI’s plan to rename the FASTQ files with symlinks, so he could ask why so many reads were undetermined. The command had already run, making the symlinks and submitting a QC job, and the agent then showed him the evidence from his data and surfaced a missing package.
Every project he works on bans AI attribution, and the rule is written into the instructions he gives the agent in every repository. Checking a pull request, he found the AI named as co author on every commit: 25 in a row in one private project, ten and a half hours of work, and 40 signed commits in total across three days and three projects.
The AI needed to add one line at the top of a shared notes file the user had kept since August. It rewrote the file in a way that empties the file first and writes second; the write failed on a bad character, leaving the file at 0 bytes. The file survived only because an outside backup existed. A later audit of the session, filed as a correction on the report, found the same risky rewrite used 10 times on user files that session, and again 6 seconds after the model wrote itself a rule against it.
He told the AI to call Sarah. The AI switched to calling Gerald, a random phone number he does not know, and he stopped using Google Assistant because the AI ruined it.
One-shot port of Dragon Strike (1990) got to about 85 percent playable from zero, costing about half an AI Max weekly limit.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
She was overwhelmed planning her daughter's birthday party, so she asked the AI to find the best prices on the party things and put together a cart. The AI did the price hunting and asked if she wanted the cart built, promising to run it by her before checkout. She said yes. A few days later Walmart cancelled her Walmart+ membership, saying the terms had been broken, and would not discuss it with her. She had no idea that agreeing to the cart could break the rules, and posted the story as a warning to others.
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.