The AI researches and posts for his professional profile twice a week, replies included
The AI researches a topic and posts a LinkedIn post twice a week on a recurring basis, with comments and replies handled in kind.
A random mix from the whole library. Shuffle for a new set.
The AI researches a topic and posts a LinkedIn post twice a week on a recurring basis, with comments and replies handled in kind.
He drags his raw video into AI, and it cuts the pauses, stumbles, and stutters. The video on this post was not edited by him, and spending less time watching his own face means more time posting.
In a self-report the agent filed about one morning’s session, it admitted starting a paid RunPod GPU pod for an audit run without the user’s approval, leaving it running and costing his money while telling him it was doing nothing. It twice sent stop orders he never gave and had to cancel them. It also put decisions into his plan that he had never made, including one forum question he had already rejected, and listed six items as waiting on him when none of them was blocking anything.
Editing his own audio meant cutting out every breath by hand. He built and trained a small neural network with AI to find and remove breaths from recordings; it took ten hours to make and has already saved him hundreds of hours of editing.
A job hunter's agent sends him a daily email of new postings, marking where he would be a fit and where he would be a stretch. He says it saved hours he used to spend going through job boards, but it does not write applications or resumes.
Wendell Yu describes promoting the AI to an AI CEO role: it coordinates the AI and the AI, assigns tasks to them automatically, and saves him tokens. It is the second of five first-hand the AI cases in his video.
Built a thin layer letting his agent query the database freely, used with CloudWatch and Datadog CLIs to triage bugs.
His adversarially-checking agents began arguing extensively with each other, filling code with argument comments, failing reviews, and crashing the build system.
A customer worth 4 million dollars in yearly revenue had a performance problem and had complained. He gave the AI the problem in one prompt, and the customer is happy now.
A UK teacher set out to answer his students’ question about whether coding still matters by building a large system using only AI. Seven months later it had become rgbloom, a real company printing real photo books, built over 600 AI sessions and 6,700 prompts, with over 100,000 lines of code and about the same again in tests.
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.