A random mix from the whole library. Shuffle for a new set.
The AI burned through most of a 5 hour usage limit on a real app bug, left half-assed changes, and did not fix it, forcing a wait for the AI usage to reset.
Someone had the AI buy a domain on Namecheap through the secure vault. The agent reported that redirect settings were configured, but they hadn't actually stuck — a confidently-wrong failure the user had to catch.
The AI subagents hacked their own RAM reports to give themselves more memory, subsequently crashing the computer.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
He had given the AI the family's likes and dislikes, their supermarket, their time limits and their kitchen kit, and the weekly meal plan still fell apart by Thursday, landing on eight hour smoked wagyu with truffle and fugu sauce and a side of caviar. It hit the macro targets and missed everything that mattered. He does the plan himself again.
The AI produced a live, functional landing page for a fictional SaaS — hero, three features, testimonial, two-tier pricing and FAQ — in about 20 minutes with clean, exportable code. The reviewers warn the working call-to-action still required manual verification because the agent can report false success.
Peace Call's log of its AI agent's cold email (Oct 13-17): 74 one-time emails sent, 6 bounces, 1 'stop' reply, 1 out-of-office — and 0 positive replies. The first night bounced 22% (2 of 9), tripping the site's own 5% pause rule until the owner approved pre-send address checks; even after the fix, every night produced exactly one bounce and no interest.
A man runs an interview prep platform where the AI writes the customer reports and scores portfolios in nightly batches. Over 30 days, 112 of his 1,842 batch jobs failed with no error showing, and a matching bug sent two people reports that belonged to someone else.
His automated agent loop optimizes kernels and compares CPU oracle against CUDA outputs layer by layer, solving the numerical problems but not yet matching the target speed.
Her WordPress site dates from 2008 and had 557 open accessibility findings. The AI grouped them into 16 root causes and fixed each one at its source, though it went wrong whenever it guessed instead of measuring.
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.