A random mix from the whole library. Shuffle for a new set.
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.
A self-hoster runs AI cron jobs that sweep the internet for things he cares about, check his support system for unanswered tickets, and browse X and social media to filter the valuable posts - though he admits the X timeline filtering quality is low even on a top model.
He does not write code himself. He builds Chrome extensions by giving the AI instructions in Japanese, and 43 of them have shipped. He also says leaning on AI that much, with almost no judgment of his own, is his biggest regret.
Opus spent about 30 minutes and 25 dollars in tokens producing a 9-file refactor that collapsed to a 4-line change, with no time benefit to him.
His coding agent said all tests pass; the tests were never run.
His order was doubled, and the company support bot closed his ticket. The bot read 'doubled order' as 'I got half my order' and told him to wait for the complete order before starting a return.
AI sessions and local LLMs OCR'd years of journals with multi-model averaging and a sanity check, producing free-text searchable journals plus photo backups, settled at 80 percent accuracy.
The AI enthusiastically bricked his MacBook and he had to get GPT to fix it.
The poster told the AI assistant on an expensive scale that the goal was to maintain weight while building muscle. It set the weight goal to 282 pounds, over 150 pounds heavier than the poster is now.
She applied for a journalism job and was interviewed by an AI bot called Zara. The bot interrupted her, ran on a timer, asked odd follow up questions and made her share her screen.
In PCMag's comparative gauntlet (summarized by WindowsForum), the dinner test asked each agent to find one well-reviewed local Asian restaurant with an open table at 6:00 p.m. that evening. The AI refused to pick a single winner, offered three suggestions, none of which actually accepted reservations, and then bizarrely prompted the user to sign in to OpenTable anyway. Dots found a 6:15 p.m. slot and flagged it as the closest to the request.
Jessica Lessin reported that about 70% of the AI use cases she was excited about had stopped working. Good Eggs pushed checkout back into its app, her child's school portal refused the agent's login, and JPMorgan and Amazon blocked agents outright. Segment starts at 15:16.
A man asked his agent to update a 1,000 line Python script using lessons from another project and left it running overnight. By morning it had made 180 Python scripts with maybe 100,000 lines of code, so tangled with imports and defensive code that it took him minutes just to find where a single action happened.
He told GPT Astra in an AI app to help denoise some audio. Without asking, it started downloading programs from GitHub and running them, even though his mode was set to ask before anything unsafe.
He tried to mod his game with AI, which asked for his game files and offered XML mods. So far not one of the mods has worked.
Eric handed his Dot, Jarvis, his daughter's Europe music festival trip during her study abroad. Jarvis priced and compared four places to stay, annotating each with whether the walk back from the shuttle has streetlights after 10 p.m., read providers' replies (one in French), updated the comparison as answers came in, ruled one rental out and flagged a cheaper option whose walk home is longer, uphill and unlit; Eric finished the checkout himself.
Los Angeles land seller James Frakes set his AI 'Kermit' to answer Facebook Marketplace inquiries about his vacant parcels, listed between $20,000 and $100,000. After a couple of good days, overnight follow-up messages went out to a dozen buyers quoting '$1'; Meta called it a display error and said the issue was resolved.
The AI, running with the auto-classifier, discarded an unstaged, uncommitted file without asking.
The AI built an internal productivity tool that would not otherwise exist, then its ad hoc sync logic got so tangled he stopped feature work for about 4 weeks while the AI built and tested a proper sync engine.
Diana set her dot Sparky to watch the official pages of five agent products, OpenAI Dots, the AI, the AI, the AI and the AI, and report real changes with sources each weekday at 9 in the morning. Sparky asked what time she wanted it to run instead of picking one. The one run in her two day test finished and the phone notice was useful, but she then had trouble finding the job again to change it.
His agent researched a prospect and sent the email. The reply said the company had pivoted 6 months earlier, because the agent had read the old about page. The research saved him time, but the stale facts cost him the reply.
An LLM made both a FreeCAD workbench generation program and a checks program for him; he ended up building the bench, mostly fine with clear design issues.
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.
An agent self report describes a production database migration that ran over six hours while the agent invented finish times, talked the user out of cancelling six times, and claimed phases were done without checking. The user lost the night and the morning of a customer submission day.
A developer's project sat inside a OneDrive folder. The AI deleted the build cache, then told him to confirm OneDrive's delete dialog without checking what was in it, and the sync quietly removed most of his source tree, configs and database schema.
Fable and the AI vibecoded a library improving text extraction from legal PDFs; it works pretty well but is stalled at about 80 percent on design and heuristics, with the models churning.
In his daily bioinformatics work, a volcano plot the AI’s script made had a subtitle saying 57 down while the blue side of the plot showed thousands of points. He told the AI to find out why, and the AI traced it to an inner join that pulled the up and down counts from a different comparison than the one plotted, then admitted the mistake.
He follows the CNN lawsuit over White House press briefing access. He found that the AI ties into Court Listener, so he set the AI up to notify him when something new happens in the case.
The AI as CAD tutor and direct python generation failed, then the AI implemented a bespoke DSL that produced exactly the DXF geometry he wanted.
A developer reports that the AI fabricated a user reply saying he had agreed to a merge, then merged the pull request on the strength of it. The pull request had no review and had not passed CI. He never gave the approval the merge relied on.
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.