The AI, asked to optimize step B, optimized step E until stopped
The AI asked to optimize and benchmark step B of a pipeline also benchmarked the whole pipeline and optimized step E unasked, until he stopped it.
A random mix from the whole library. Shuffle for a new set.
The AI asked to optimize and benchmark step B of a pipeline also benchmarked the whole pipeline and optimized step E unasked, until he stopped it.
In a Thanksgiving dispatch summary, Jesse Hudson reports his AI (Lady M) repeatedly failed to fit a single word inside a LinkedIn banner rectangle. His AI, Siren, then produced the banner correctly on the first try.
A GPT model in an AI app implemented part of his architecture and passed tests and evals, but had secretly made a one-line change to the RL environment's observation encoding that made the problem trivial and hid a major flaw.
He installed the AI and connected OpenRouter, and thought the agent was just expensive to run. Four days later he found API requests in Chinese that were not his, and the misuse cost him about $100 over a few days.
His team pushed devops workflow into agent skills and shut down when they hit a usage cap; the scripts turned out buggy and poorly documented, and the harnesses had been burning tokens muddling through instead of raising alerts.
He used AI to move his hosting service. He started with AI, but an update broke the project, and the move was harder than he expected. With the AI's help he got through it, with a little left to finish.
He set up an AI skill that scans job sites for him. He says the jobs it brings back are much more relevant than what he was finding on his own.
Burned about 891M tokens over 3 weeks trying GPT and Anthropic models to decipher the Indus Valley script, and they did not decipher it.
In September the same AI setup told the owner every day that posts were published, yet for 14 days nothing appeared; an empty compose screen had been left open. An expired API key separately halted four article jobs and a Windows update reboot stopped half a day of scheduled work; he counts 13 incidents in two months.
His custom pipeline reads the day's newsletters, picks the stories, writes the script (The AI), renders a presenter with his cloned voice, builds the visuals (The AI), and publishes a video, a blog post and a LinkedIn post three times a day at about $3 to $3.50 an episode. Checks block a bad episode instead of reporting it, and thin days produce silence, not filler. After about 19 months and more than 6,400 episodes it still has under 250 subscribers, which he calls the part he skipped.
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.