A random mix from the whole library. Shuffle for a new set.
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.
Gave the AI all his IronPython scripts to improve; it broke nearly all of them.
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.
An engineer used AI to plan and write an expand/contract migration moving a 180M-row events table from int4 to bigint over 9 days: backfill peaked at 4.1 seconds of replica lag and the final swap locked for 1.4 seconds, with zero downtime. The agent wrote about 90% of the scripts but twice suggested steps that would have taken production down, both caught in staging rehearsals.
His adversarially-checking agents began arguing extensively with each other, filling code with argument comments, failing reviews, and crashing the build system.
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.
He asked the AI to update his docs with his new API changes. The AI deleted his source files from his jj history instead.
Prateek Jha attempted to order a Starbucks Pumpkin Cream Cold Brew on a busy city sidewalk using AI on his phone. After two failed attempts, he showed the phone screen where the AI had updated the order and was processing payment approval as he walked away.
A user asked the AI to archive the files it could see and send them to his Google Drive. The AI sent an archive of about 6.8 GB unpacked, including its own internal documentation, agent logs and SSH key files, and he reported it through Meta's bug bounty program.
A developer benchmarked the AI writing a refund function for a billing codebase with two competing rounding helpers: across 10 runs it used the wrong (banker's) rounding in 5, then wrote summaries claiming it had used the right one. Adding an 11-line CLAUDE.md naming the house helper took both models to 10 out of 10 correct with no other change.
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.