The AI in his car refused to send a dictated message, mistaking it for a medical question
The AI in Android Auto refused to send his dictated message, treating the message text as a medical advice request, message never sent.
A random mix from the whole library. Shuffle for a new set.
The AI in Android Auto refused to send his dictated message, treating the message text as a medical advice request, message never sent.
He used AI at work the way he was expected to. He asked it to plan a task, answered its questions, and approved the plan. It ran for 2 hours, then said the plan was complete and that no source files were changed.
His wife’s dental practice needed signage while their designer was away, so he gave the AI his wordmark and a sketch and iterated on a tooth logo, a slider tool the AI built for the edits, and a signage mockup. Once the files reached the designer and the signage maker, the font could not be thickened without losing its look, the dimensions were way off, and zoomed in the logo was full of jagged edges that two days of prompting could not smooth out.
An LLM tried to set up his arr stack, did many things wrong and missed required services; he gave up in disgust.
Tired of LinkedIn's email notifications - each of the 77 toggles on its own page - a user let an agent flip every one off. The chore got done, and LinkedIn blocked his account for the automation, a consequence he admits was obvious in hindsight.
Controlled Vals AI simulation (researcher Matilda Glynn-Henley): a fictional customer's £350 London-Glasgow ticket was cancelled after the 28-day claim window had closed; agents had web/email access, all messages intercepted and answered by a simulator, up to 12 simulated weeks and 40M tokens. The AI outlasted every other agent, sending 69 messages across all twelve simulated weeks — 54 of them to the customer's bank — and joined three other agents in explicitly threatening legal action, without recovering a penny.
LLM agents turned traced schematics into Verilog models of a 1978 Lexicon, with many mistakes found and fixed over months.
His LLMs reverse-engineered a sunsetted mobile game, replicated its fight screen near pixel-perfect, and built an automated emulator compare pipeline for a backlog of work.
In the same Oct 16, 2026 walkthrough, Jason Tang had the AI build a 10-day Los Angeles trip using his saved Instagram videos as the inspiration source. The AI generated a daily itinerary, compared hotels on Trip.com, checked flight options, and attempted to book a Getty Center visit.
LLM agent skill he is developing is decompiling a 1998 game and migrating assets to the browser, live site with glitchy imperfect assets produced.
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.