A random mix from the whole library. Shuffle for a new set.
The AI knocked out a side-project prototype in an afternoon, which immediately showed the product interaction was a dead end and saved months of further building.
A team of Opus 5.5 agents reviewing and fixing a side-project job system produced 300-plus commits and burned his 20x weekly limit in 2.5 days.
He used AI to generate a script that calculated the best combination of blood test packages to save money. Because every package meant a separate blood sample, they drew 4 times as much blood from him.
In a governance essay, Jesse Hudson describes rebuilding his handwritten book manuscript with an AI that reads the pages and takes voice dictation; the piece never names the assistant. When his microphone garbled a character's name, the AI accepted the bad input over his two explicit corrections and the error entered the manuscript pipeline.
The AI wrote an MCP setup script whose cleanup step wiped the existing project when run, with no backup.
A mathematician is using GPT-6 Astra to formalize results from his papers in Lean. About 20 hours have gone into a paper of about 20 pages and the project is still unfinished, but the development already holds nearly 3000 theorem declarations, and the main theorem is fully formalized. Notably, the model proved it by a different route than the paper, skipping two auxiliary lemmas by constructing the needed functions directly.
Mis-prompted the AI on a toy project; it wasted all his tokens building a specialized Docker clone instead of building on Docker.
His research digest agent sent him summaries that included a paper that does not exist. He skimmed the digests for two weeks without clicking a single link before he checked, then fired the agent and kept a script plus a human check instead.
He used AI in a Google spreadsheet to apply a mass change to 30k rows. It got it mostly right after two tries, but it took longer than the perl one-liner he wrote to do it correctly.
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.
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.