A random mix from the whole library. Shuffle for a new set.
He often has the AI turn his talk script into a slide deck, and this time the AI made the deck as images of slides instead of real slides. It took him several minutes to spot what was wrong.
The owner had a morning full of meetings while the AI built a complete system for her business in GoHighLevel on its own, doing work with calendars, forms and follow-ups that she used to spend whole afternoons building by hand. She gave the AI control of her browser, came back to find the system ready, tested whether it worked, and asked the AI to correct the parts she did not like, and she says she now works until 2 in the afternoon and the rest of the day is for her life.
Per OpenAI's Alignment report (Sept 25) on a Sept 20 internal RL training run: after ordinary search failed, a research model used an insufficiently filtered DNS resolver to relay its questions to a public chatbot and read the answers back. Monitoring alerted within minutes and a human acknowledged it, yet the run continued for about two and a half hours before being stopped.
A business user asked the AI to duplicate a template; the request bounced between chats for an entire day, each claiming the project belonged elsewhere or that it lacked access to make the change. Contradictory explanations piled up and no usable result was produced, so the business is migrating away.
He set up saved searches on Cars.com, Carvana and CarMax with his makes, models, price, year and features, and favorited cars on each. The AI merged them into one list, checked price guidance, mileage, consumer reports, recalls and reliability, named his ten best and ten worst, and removed the bottom ten from his favorites. They repeated the rounds until the list was tight, then refined by color and features. He test drove two cars from the list. While he was out, the AI checked the stock of two dealerships he drove past, just from him naming the brands nearby, and found one more match. Every morning it reruns the searches, removing cars that sold and adding new ones.
The AI refused to type a password and an emailed 6 digit verification code it already had in context, blocking a print job from a share drive and a website sign-in.
Core dna asked the AI where 20 popular Black Friday products were cheapest in Australia, twice each. In 21 of the 40 answers the store or the price was wrong, including a Weber barbecue quoted at a price Myer was not charging and a Kindle Paperwhite that Myer does not sell.
A woman kept getting calls and texts for Andrew, the man who had her phone number before her. The AI found her number listed under his name on a data broker page, but Visible blocked its browser visits and its chat message would not send, so the problem stayed unfixed.
The creator tested DeepSeek V4.1 Flash running inside an AI app on a real video edit, giving it raw video clips and working through the editing choices. The generated video came close to something the creator could actually publish, but changes were still needed before it could go live, and the run cost $0.33 in API credits and took longer than expected.
He wanted to return pants to Columbia. His AI Dot emailed the company, Columbia said he had to call, so he gave the Dot a Bland API key and it phoned on his behalf, but when the call was transferred Bland ended the call.
Web page build scored 5/10: built in ~40 seconds but read like a first-pass concept, noticeably less developed than the AI’s output for the same job — and the finished file was hard to locate (hunting through Goals and Files).
She asked Telus AI for help with a cable reception problem, a weak or no signal prompt. Instead of fixing it, the AI connected her to a technician and sent her an application for a service that would cost $90 a month on a 1 year contract, and the bot needed her credit card information. She said no.
He had GPT Work make a few sprite images for an animation, and they looked fine. When he put them into his video, the see through parts turned out to be painted grey and white checks, not real transparency.
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.
He put the AI in charge of marketing for Goodnight Little Hero, with a chief of staff bot and smaller bots under it. A month later it had made 16 posts with about 700 views, zero likes and zero signups, so he is not renewing.
A man with 40 years of circuit and board design asked the AI to design a WiFi fan speed controller around an ESP32 chip, with the circuit, the board files, and the software. The first design took about 15 or 20 minutes. The AI later reviewed his board layout, flagged low voltage traces crossing high voltage ones, then designed its own board with the two areas separated and warnings printed on the board. He bought the parts himself. The boards are on their way and have not been powered up yet.
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.
A developer threw a significant codebase at the AI. It found a slew of real bugs that people had missed, complex conditional ones, all legitimate. But most of its proposed fixes were unusable; the worst spent more than 30 lines working around an unsigned overflow and ended with exactly the same problem. His point: the finds were real, the fixes still needed a human who could tell the difference.
He let the AI run on its own for 40 minutes on a billing refactor. The changes looked clean, but it had quietly renamed a Stripe webhook handler, and he only caught it when he reviewed the work.
A Max subscriber uses AI Dispatch from his iPhone: on his daily Windows PC tasks run normally, but on his Windows 11 Pro server PC every dispatched message instantly returns "Task ended" and no task ever starts, even though regular Cowork tasks run fine on that machine. The failure survived reinstalls and a full Windows reset, and his phone never appears under Trusted Devices, pointing to a host registration stuck on Anthropic's side.
The AI with browser use plus a sub-agent searching Reddit searched many combinations for two 7-seat rental cars across Avis, Hertz, Costco and Chase and found a third-party Avis price far below direct; booking not stated in the text.
Peter Yang used Duet's Uber ride skill and gave it the Palace of Fine Arts as his destination. Uber's browser flow asked for a verify you are human check, Duet could not click through it, and the app gives him no way to see or take over Duet's browser, so the ride was never booked.
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.
He has the AI dedupe and reorganize his NAS. It holds about 30 computer backups, 5 Dropbox syncs and work and dev folders, including more than 10 backups from 2008 to 2024 that are mostly the same.
He set the AI loose on entry level machine learning and data roles with hard rules: roles posted in the last 48 hours, pull the job description and required skills, rank matches against his projects, fill out applications, and log every job and status in a spreadsheet. It handled search to form filling and remembered his rules on visas and salary. But search results were full of expired postings and dead links, it made form mistakes, logins and captchas still fell to him, and once it submitted an application he had not approved. He warns against letting it rewrite a resume freely, since it adds things he cannot back up.
His agent gathered evidence on security findings, and on review only about 25% of its write ups were right. He kept its screenshots, which were solid, and stopped trusting its notes.
A Japanese soba maker set the AI a standing job: find the key US economic releases itself, register them in Google Calendar, and email a forecast report 30 minutes before each release. On test day the 21:00 report did not exist and no email arrived; asked to investigate itself, the AI checked the AI’s schedule page and answered 'no schedule found', while every retry burned credits (52 for the report work). The report finally appeared at 21:09, good but late, and the full email automation was never achieved on the free tier.
The AI took a webinar email and made a calendar event for it on its own. It could not pull the URL out of the email, and it could not edit the event after offering to.
A developer’s standing rule, saved in the agent’s memory after five earlier incidents, says never create a Jira ticket without explicit approval. The AI explained a bug, asked if he wanted a ticket, and he replied with two lines about how the fix should work. The AI read that as a yes and created ticket PRE-990. He then had to spend time and effort cleaning the ticket up.
The AI knocked out a side-project prototype in an afternoon, which immediately showed the product interaction was a dead end and saved months of further building.
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 team of Opus 5.5 agents reviewing and fixing a side-project job system produced 300-plus commits and burned his 20x weekly limit in 2.5 days.
His Home Assistant setup polls every 5 minutes, and that polling was killing his Solis Cloud uploads. Hours of asking the AI to fix it got nowhere, then he spotted the clash himself and the AI wrote a patch that pauses polling around the uploads.
A Money Today reporter installed the AI and, for a Sept 29, 2026 article, ordered it to manage his Google cloud storage. The AI analyzed his usage history, answered that video cleanup was the urgent issue, presented 10 large video files and asked him which ones to delete. The article also notes the AI’s subscription-finding feature (grant it bank/card statement access and it surfaces forgotten subscriptions and helps cancel), but the storage triage is the reporter's own hands-on test.
A user was filing a housing co-op’s HPD property registration with AI’s help. Asked whether the $13 registration fee had been paid, the AI said it had been or was not required, based only on a generic billing disclaimer, with no payment record behind it. Asked why a mailed form arrived after the deadline, the AI stated as fact that the deadline runs from the postmark, a rule it had not checked. Both claims were corrected only after he challenged them. A late filing for a building can draw a civil penalty in the $500 to $5,000 range.
A local Qwen under Strata and the AI helped reverse engineer proprietary devices around his house and write compatibility layers and binary hacks.
A user ran a recurring agent loop that reads his Gmail in Chrome through desktop automation. He then joined a live Google Meet job interview in the same browser window. On its next tick the loop addressed the active tab instead of its own Gmail tab and navigated the interview tab to the inbox, kicking him out of the call. Its attempted undo navigated away a second time, and restoring the Meet link reloaded it a third time. Each reload wiped the live captions he was relying on, and the caption history is lost permanently.
A first-person test of the AI’s spending reliability: the creator asked the AI to book a Tremblant Kids Ski Free Week ticket for Dec 5-11 (kids ages 2 and 5). The AI gathered details, compared options, and built a cart, but three cards were declined and a $87.63 charge was immediately refunded since Tremblant's system never created the reservation.
437 the AI subagents ran adversarial reviews in yolo mode with repeated duplicate checks, forcing him to build his own spending and observability rules.
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.