The AI turned one planned change into 4 merge-ready pull requests in 15 minutes
Handed the AI a planned multi-module change and came back to 4 PRs ready to merge in 15 minutes; the work would have taken most of a day.
A random mix from the whole library. Shuffle for a new set.
Handed the AI a planned multi-module change and came back to 4 PRs ready to merge in 15 minutes; the work would have taken most of a day.
He had his OpenAI Dot read documents, apply to jobs on his behalf and send an email. Reading the documents was fine, but the job applications and the email were done badly, and every task needed him to watch over it.
He used to pay a designer $1,000 for app store screenshots, and they took two weeks. Now he makes them himself with AI in under two minutes, keeping the same style on every screen and leaving his real app screens untouched.
With the AI doing the grunt work he built a compiler for his own language that runs on Mac, Linux and Windows and cross-produces for those plus WASM, iOS, Android, Zynq, m68k and 6502, with a signed binary running on his phone.
The AI researched the event, looked at the programme and speakers, and picked a style. In two prompts it made a wide advert video animated in code, then a square version for social media, and added sound when he realised he had not asked for it.
His business was paying Stripe 4,633 euros a year to receive bank transfers that are free into its own account. He had the AI go through 12 months of Revolut and Stripe charges.
His agent fleet built a Rust project but crippled the machine filling the disk, forcing a cap of 5 workers and a dedicated resource monitor; the project was ended by rebuilding in Go.
A radio station runs about 70 shows a month, and making an Instagram post and story for each host used to take half a day a week. Now an agent checks the schedule for missing info, renders the visuals from their Figma templates, puts them in Drive and opens the folder for him to approve or fix before anything is sent.
He used AI to build an analysis of his own Substack posts. It split the posts into themes and pulled together collections, so readers can click through every theme, motif and term in one place.
A builder used AI in Google AI Studio and DeepSeek to build a 19 project .NET system over about 4 and a half hours. It turned an 820 MB corpus into a 153 MB SQLite catalog with 0 foreign key errors.
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.