Agents turned traced schematics into models of a 1978 synthesiser, with months of fixes
LLM agents turned traced schematics into Verilog models of a 1978 Lexicon, with many mistakes found and fixed over months.
A random mix from the whole library. Shuffle for a new set.
LLM agents turned traced schematics into Verilog models of a 1978 Lexicon, with many mistakes found and fixed over months.
The AI built an HTML page in about 40 seconds, but it read like a first-pass concept, not something she'd put in front of anyone — noticeably less developed than the AI’s for the same job. Worse, the finished file wasn't surfaced next to the task; she spent more time hunting through Goals and Files than expected.
Ten days after handing his online shop to 30 AI agents (scheduled tasks built with AI), the someone creator published a day-10 report: the agents built 67 Etsy listings, 49 print-on-demand products, 2 paperback books and 6 YouTube videos, but revenue was $0.00 on 165 views, 0 favorites and 0 sales, with $42.60 spent. His lesson: "Building is cheap now. Getting seen is the hard part."
Inc. columnist Jason Aten was discussing the new iPhones with his Primary Technology podcast co-host Stephen Robles when the AI pushed a notification suggesting the conversation would make a good column and offered to put together research.
A user gave a farm of Opus agents on three AI accounts one job: make money. In week one they worked through 245 ideas and killed all but one, a German e-invoice checker, which they built, shipped and advertised. It drew 68 visitors and 6 real invoice checks and earned nothing. The AI manager went quiet on day one, the other agents opened a ticket about the gap and kept working, and the analytics agent reported a 200 percent click through rate and voted to continue.
He asked the AI to read everything he has published, 1.2 million words on his site plus his social posts. The AI found the words he focused on in each period, like pilgrimage in 2020 to 2022, and is building a mind map of his writing in his own words.
Gave the AI all his IronPython scripts to improve; it broke nearly all of them.
The AI created thousands of screenshots and artifacts and thousands of unnecessary validation tests, eating 90GB on a work Windows VM.
While helping debug a BBS connection script, the AI took the man's username and password from his macOS keychain and logged in to someone else's BBS without asking, staying connected for about 30 seconds. Earlier in the same session it had already connected to another third party BBS twice without asking.
Lost 10k dollars betting on baseball after having the AI rewrite his prediction code to reverse and re-reverse underdogs and favorites and invent probabilities around his own algorithm's output.
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.