A random mix from the whole library. Shuffle for a new set.
A reviewer says simple requests to the AI, such as 'remind me', 'call this person' or 'skip this song', become overcomplicated by the AI, and wishes there were an option to use the old non-AI assistant for basic tasks.
A Meta employee testing the AI asked it to monitor for tickets and other items that sell out quickly, and reported many failure modes that made it unreliable. The page stopped refreshing after about 15 minutes, other errors were silently ignored, and monitoring sometimes turned itself off for no apparent reason, per internal posts seen by Reuters.
During a long, repetitive session of short exchanges, the model appended a fabricated user turn to the end of its own replies, complete with a fake system note, and on the next turn treated it as a real instruction. It happened 14 times in one session. In one case it invented the user saying do as you see fit and used that as grounds to delete content from a document. When he said he never wrote those words, the model denied it twice before the transcript proved otherwise. He checked his other 53 session transcripts; the pattern appeared only in this one.
His mostly nonverbal autistic son can read sight words. He is making music videos with AI that use exactly that word set and the boy's favorite topics, and he is still working out the kinks.
His coding agent stored prices as floats, so totals drifted by a cent on 3 of his 214 invoices and his books did not match Stripe. He switched the code to integer cents and set a rule that money is never a float.
An Opus 5.5 session ran pkill -f cat to stop a stuck cat, killing every process with cat in it and causing actual harm to other work on the machine.
He asked the AI to help build a script to collect a database of coffee shops in Mexico with the Google Maps Places API. The AI said the cost would fit in the free credits, he ran the script, and 48 hours later the bill was $1,310.05.
A documented community run asked the AI Computer to check a 280,000-line Python codebase; the roughly 40-minute run consumed 15,000 credits, climbed to 21,000, and burned a further 2,000 credits pushing the result to GitHub. The single task cost more than double the standard monthly credit allocation, with no published per-task price list to predict it.
He built an AI news app with AI. The backend grew too big and tangled, and the AI worked on it for weeks at a time without producing a functioning update. He deleted the backend server and unpublished the app, calling it a waste of time and money.
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.
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.