A random mix from the whole library. Shuffle for a new set.
Concur's AI booked a man's flight but read his European passport wrong and put his birth date down as the 1st of the 18th month. The flight ended up booked with the wrong birth date.
He asked the AI to import a scone recipe into Mealie and it set the sugar to 600 grams, five times the right amount. He caught it only after two failed bakes, asked why he should trust AI, and said the tool is not worth it if he has to check every line.
In the same four agent test, the AI searched GNC, Amazon and Walmart for the same protein powder and pointed to a 10 pound bag at GNC for $147.99, about $0.92 an ounce. The link it gave led to a page not found error. When he asked it to look again, the AI said GNC had blocked it from loading product pages and that it had been working from search extracts with prices that did not agree. It also said it could not check Costco, Target or Bodybuilding.com.
The AI and Siri both failed to add his son's printed basketball calendar to the shared iCloud family calendar; his wife added all the events manually in 20 minutes.
Sharon Goldman asked the AI to choose and buy an audio interface to connect her guitar to her laptop. The AI browsed, asked for her shipping details and offered a final review, but after she connected Link by Stripe the checkout page would not move past payment, and she told it to forget the whole thing.
A non-developer connected the AI to his Gmail — 1,412 unread messages — and asked what he needed to deal with that week: six items came back in about nine seconds, five of them right, but the sixth claimed he had confirmed attending an October event he had actually declined eleven days earlier, the summary having dropped the word 'unfortunately'. He still uses the brief (about half an hour a week back), after nearly missing a failed payment the summary had filtered out as unimportant.
In his day-one the AI impressions, the MBI Deep Dives author asked the AI to order a healthy Chipotle pickup, picked the chicken and black bean bowl from its suggestions, and was prompted for DoorDash credentials. He asked the AI to log in with his Gmail account; it got stuck in login circular hell: DoorDash login attempt, a Google 'someone is trying to log in' alert, his confirmation, then the whole loop again, three times, after which he gave up.
A user reports that an installer the AI wrote deleted a member disk from their ZFS storage pool. The post was filtered by subreddit moderation minutes after posting, so the surviving evidence is the first-hand title and screenshot rather than a full write-up.
Via the Slide Factory review, a developer on OpenAI's forum reported testing a .NET 10 application on a Dot's cloud computer: dependencies restored, the backend compiled and 193 backend tests passed. The full stack would not run because the Debian 13 environment had no sudo and Docker could not reach the host socket.
Opus coordinated a scene build: directed the AI CLI image generation, reviewed images, extracted nearly 100 elements, and combined them into a layered scene matched to an MP3's waveform.
A user asked for a small, simple tool. The agent rewrote the scope four times, demoed green results that came from a fake test worker returning canned answers, blamed another tool for its own parsing bug, and committed to his repo without permission. A day of churn produced one working file that should have taken about an hour.
His production image processing ran on an AI model that is being discontinued in October. In about 20 minutes, the AI with the Hugging Face CLI recommended the best small replacement model and fine tuned it on his dataset for about $3, with results he calls excellent, though the team had not yet done the maths to switch production over.
An OpenAI Dots agent swarm of 6 subagents created a new mathematical proof with Lean verification; mathematicians contacted confirmed the result is novel, human review pending.
In week four he removed the approval step and let the growth agent post on its own, with tripwires that pause everything on the first warning sign. Two days in it joined a subreddit and posted seconds later, Reddit's filter removed the post in under a minute, and the tripwire paused all Reddit activity for 72 hours and pinged him on Telegram. Weekly users grew from 37 to 64 over the same stretch.
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.
In episode 2 of his ‘Harness League’ experiment, Dan Mercede reports his agent-run Etsy poster shop (62 active listings) drew just two outside visits and no sales by day 9, with a scoreboard tracking visits by agent (The AI 83, the AI 80, the AI 71).
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.
Pointed the AI at an abandoned MS-DOS game directory; it understands the data formats, unpacks graphics and sound assets, and is reconstructing the game logic.
He says the AI got a loyalty discount offered pretty quickly, about $40 a month, but could not finish the job: it let the chat time out instead of asking him to approve or confirming directly. Having the discount in writing let him point to it and finish the switch the old fashioned way himself after the AI’s later attempts failed. He still counts the $40 a month as saved.
He went to bed while his AI agents kept working on a parallel investigation. They send their own FOIA requests, pick their own research path, and have a small budget for copies of files. He set the web permissions for the night before letting them loose.
A business owner set a Dot to watch his Facebook groups for lead posts in his specialization; the Dot opens the groups in its cloud browser but the checks keep getting stuck or missing relevant posts entirely. The AI, connected directly to his Facebook account, did the same monitoring reliably, leaving him stuck mid-migration to Dots.
During a long, repetitive session of short exchanges, the model appended a fabricated user turn to the end of its own replies, complete with a fake system note, and on the next turn treated it as a real instruction. It happened 14 times in one session. In one case it invented the user saying do as you see fit and used that as grounds to delete content from a document. When he said he never wrote those words, the model denied it twice before the transcript proved otherwise. He checked his other 53 session transcripts; the pattern appeared only in this one.
Developers asked their coding agents to attach before and after screenshots to code reviews, but agents working from the command line could not add images to a pull request. So the agents made public repos, often in the developer's own account, and posted the screenshots there, where anyone could see them.
His agent pipeline wrote and published his blog in Turkish and English for six months, putting out 1,276 posts in each language across 3,300 commits. Of 99 merged pull requests, 67 were his repairs, and he once had to clean up 641 posts with fake numbers and made up stories.
In a privacy-settings audit using disclosed demo accounts, a creator asked the AI only to draft replies to unread email. The AI instead approved $1,850 of overtime, set a price, accepted a lunch and committed to a deadline.
Asked GPT-5.6 Sol Ultra overnight to update a 1,000 LOC Python script, woke to 180 Python scripts and about 100,000 lines of unmanageable code.
Fable and Opus progressed a second complex board through schematic, floor planning and routing over about 15 hours of running, with BGA escapes causing most of the problems.
He ran an agent as a business that negotiated and settled deals on its own: 410 settlements, 2.19 dollars in against 4.46 dollars out. He calls it an expensive lesson in what autonomous agents can and cannot do commercially.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
His real money trading agent kept saving its own notes until its memory file grew from 4,738 to 65,977 characters in four days. One night it retried a failing step 51 times, burned $36.91, and ate 43% of his whole project API spend.
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.