An agent proposed and profiled its own kernel optimisations to a 10x faster kernel
Agent autonomously proposed and tested CUDA kernel optimizations and profiled them with Nsight by itself, ending with a kernel an order of magnitude faster.
A random mix from the whole library. Shuffle for a new set.
Agent autonomously proposed and tested CUDA kernel optimizations and profiled them with Nsight by itself, ending with a kernel an order of magnitude faster.
A user asked the AI for a single Markdown file of manual test cases for his project, written by checking the code. Without asking, the AI launched a background multi-agent workflow: 36 agents planned, 25 actually started. It ran until his usage was gone, the agents failed, and the session ended. No verifier finished and no file was ever written to his project. The AI later admitted one pass would have done the job.
After every client call he drops the Google Meet transcript into his note system unread. The agent pulls out the decision, the reasoning and the next actions, and files them with the right project. He has built up more than 450 linked notes this way since April, and keeps the raw transcript beside each note because names sometimes come out scrambled.
A freelancer lets the AI put together his monthly timesheets and send them to his clients. Once a client signs a timesheet off, the AI creates the invoice for it. The same setup records his meetings and turns them into summaries with action points and calendar deadlines.
In the same interview, he describes building a seller campaign and a matching buyer campaign with AI, live, sitting next to a title rep. The campaigns feed the intake stack that his AI follow-up then works.
Kalena Waters-Gardner ('HOW TO USE MUSE EP. 2') says one week of the AI benefited her business.
Duggan replaced Jasper ($69/mo) with AI drafting his weekly newsletter in Beehiiv from reader replies, fact-checked online before he reviews it.
After hackers hit their research site meta-analysis.cz, which had no central backup, the AI and the AI rebuilt it from Wayback Machine copies and scattered files on their hard drives. The site is now on GitHub, its datasets are archived on Zenodo with a DOI, and every paper has an HTML version next to the PDF.
Analyzer agents audited and trimmed skill files, cutting about 18 percent then about 600 more lines, and extracting bash into scripts turned up bugs.
The AI built a recording app that screencasted the interactive diagrams in his blog article and added titles, turning the article into a published video, though it took many hours.
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.