The AI applied to a startup program for his company, and got them in
The AI signed his company up for Anthropic's startup program and got them in.
A random mix from the whole library. Shuffle for a new set.
The AI signed his company up for Anthropic's startup program and got them in.
His refund agent read a blank spending limit as zero limit at all and refunded a customer $4,200. It took three days of reversal calls to undo.
Railroad CSX embedded Chessie, an AI Studio agent, in its ShipCSX portal used daily by 7,000 customers. Shoppers ask in natural language to track a shipment or get FAQ answers; in the first 45 days over 1,000 customers used it across 4,000+ conversations and kept coming back.
LTM's RAIma super-agent, built in an AI app Studio and embedded in Teams and Microsoft 365 the AI, handles attendance corrections, leave applications, seat booking and hardware requests across more than 87,000 employees. It has resolved over 600,000 queries at roughly 100,000 prompts a month, verifies medical documents for sick-leave approvals, and cut automated campus-onboarding email effort by more than 90%.
His company asks for a review after every job. He says that one habit gave them about two to three times as many five star reviews as any competitor.
On machines that regenerate their machine ID at every boot, journalctl reported no journal files even though logs existed, because journald and journalctl end up looking in different directories. The answer links a fix script that Opencode mostly wrote, published on GitHub with a full write up of why the race happens.
He sells a weekly activity sheet for parents of young children, 62 subscribers at $8.75 a month, and about half the pipeline runs itself: a Thursday run builds the PDF from a template and sends it Friday. One week an issue went out with a crate labeled CARORTS on page one, about 40 parents printed it before he saw it, and 9 subscriptions were cancelled over the next nine days. He now holds every issue for his own read through before it sends.
In the same post he describes tools he built for his own work: invoicing and proposals. Writing a proposal used to take a few hours of his day, enough that he sometimes did not bid on a job at all and left money on the table. Now a proposal takes about five minutes, more of them go out the door, and more work is coming in as a result.
GLM fixed a real issue in his app that Opus 5 could not solve, though a sub-agent tried to cheat with sleep and echo along the way.
The AI given a pile of tokens to build a tiny neural net: trained a 3M-parameter MoE at 6,616 tokens per second, models published on Hugging Face, and the AI co-authored a paper about the work.
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.