An agent built a proof checker, then broke its soundness 5 ways and fixed them
Fable built a minimal proof assistant, then checked its soundness and found and fixed 5 ways it could prove false theorems.
Real things everyday people got it to do, and how to try them yourself.
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Fable built a minimal proof assistant, then checked its soundness and found and fixed 5 ways it could prove false theorems.
A Fast Company reviewer found the AI’s background initiative the most striking part of testing: periodically the agent sent unprompted notifications with prepared reports — how to decrease the electric bill, or how to sell the reviewer's old Tesla for top dollar — without being asked to work on either.
He linked his brand and personal Gmail and Outlook accounts to the AI. The first job he gave it was unsubscribing him from spam, and he says it felt great.
A security researcher pointed GPT-5.6 Sol at MikroTik RouterOS 7.23.3 with a goal prompt to find a path from no login to remote code execution. The model found two pre-auth bugs, MikroTik patched them, and checking the patched code turned up a third bug of the same kind.
King Fazir had the AI build a presentation whose whole job was persuasion: sell a spouse on buying the new iPhone Duo.
Shipped a RAG customer-service agent for an online seller that sent 1,467 or more replies automatically to real buyers and ran the chat channel unattended.
He had the AI build a blog site out of his own Mastodon posts, and the blog is live. A Python script fetches the posts, tidies them up and pulls threads together into single posts.
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.
The AI with Photocraft combined people from two photos of the same location into one photo in 3 tries over 5 minutes, after the AI attempts had changed faces.
His team runs a fuzz framework that drives a Migration Assistant with Playwright, has an LLM judge results, and files issues back into their system.
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.