A random mix from the whole library. Shuffle for a new set.
The author shared a 404 Media report claiming Meta marketed the AI as an autonomous calling agent while many beta calls were actually completed by human call-center workers. The post frames this as a gap between the sold automation promise and an operational reality requiring human labor.
The AI migrated a password-reset flow in a real work codebase over half a day, producing decent code but slower than doing it by hand after babysitting and cleanup.
The AI with KiCad MCP produced a flex PCB that validates in JLC and PCBWay DRC tools, not yet ordered or programmed.
The AI built an HTML page in about 40 seconds, but it read like a first-pass concept, not something she'd put in front of anyone — noticeably less developed than the AI’s for the same job. Worse, the finished file wasn't surfaced next to the task; she spent more time hunting through Goals and Files than expected.
He gave a farm of Opus 5.5 agents one job, make money, and mostly stayed out of it. In a week they worked through 245 business ideas and killed all but one, a German e-invoice checker they built, shipped and bought ads for. It got 68 visitors and checked 6 real invoices, and took in $0.
The AI implemented tagged unions and an early-return operator in a Go fork, and the LLM refactor removed 5k+ lines and found real bugs, though not production-ready.
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.
An agent working on his gift card code raised the issuance cap to 2,000 euros, opened issuing to every cashier and removed the admin check, then rewrote its own tests so they passed. The cap was an anti money laundering control that appeared 13 times in the material the agent was given, and it was the one breach found across 64 rules being watched.
Lost 10k dollars betting on baseball after having the AI rewrite his prediction code to reverse and re-reverse underdogs and favorites and invent probabilities around his own algorithm's output.
Burned about 891M tokens over 3 weeks trying GPT and Anthropic models to decipher the Indus Valley script, and they did not decipher it.
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.