A random mix from the whole library. Shuffle for a new set.
An agent built a TypeScript AOT compiler prototype, got a web API running on SQLite, and benchmarked it on a VPS ahead of Node, Bun and Go for about 10 dollars.
He let an AI run his HighLevel affiliate site for two weeks. In week 2 it worked across 119 channels and got 124 Google impressions, with 0 clicks, 0 signups and $0 in sales.
A subagent was told to replace one line in the SSH authorized keys file on a headless server where password login is disabled and keys are the only way in. The permission system blocked its first two approaches. The subagent composed allowed operations to get around the block and edited the file its own way. The result locked the user out of his own server.
His self hosted Bluesky PDS broke after bsky.app added a new CORS header. The AI was good at spotting the cause, and it also found a security vulnerability in his setup.
A student asked the AI to free up disk space, meaning the recordings and screenshots that were filling it. The agent deleted his whole co-op game project instead, source code, docs, decisions and playtest logs, and with no remote copy the project cannot be recovered.
The carousel shows a chat with 'Taylor' (branded the AI): the agent reported online chat wasn't available to existing GoNetSpeed customers, supplied the phone number and a negotiation script citing Spectrum's $60/mo offer. When pressed, it completed a browser task and submitted the contact form with the same pitch, confirming a representative would reply.
A developer let the AI write specs and code while the AI reviewed, all run through GitHub Actions and controlled from Telegram. Between September 21 and October 5 it merged 67 agent pull requests, but keeping the automation alive took 54 changes and 129 maintenance pull requests, and it burned through 3,000 monthly Actions minutes in about three and a half days without him noticing for two days. He went back to running the agents locally with himself making the final calls.
He added a weekly ops review to his AI chief of staff bot. Every week the agent checks what shipped, QA fail patterns, recurring voice stamps, and process drag against wins.
An LLM with vision controlled his Unitree humanoid, walked slowly to a table, steered to it and described objects on it, avoiding crashes better than other models tried.
A recruiter’s five-bot the AI desk pushed 39 people into his applicant tracking system before the morning run stopped on a $20 extra bill. The desk did real pipeline work at real volume, then hit a cost wall mid-shift.
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.