A browser agent spent an hour downloading his filtered monitoring logs, splendidly
Uses Sonnet 5.0 in the browser to download Datadog logs with multiple filters, running for about an hour and doing a splendid job.
A random mix from the whole library. Shuffle for a new set.
Uses Sonnet 5.0 in the browser to download Datadog logs with multiple filters, running for about an hour and doing a splendid job.
A small agency owner whose team does websites, content, Google ads and SEO for home service businesses runs the work through about 30 AI agents. The problem was memory: every session started cold and repeated mistakes. Since June they share one log that only grows, now about 11,400 lines including 413 decisions, where every note names the agent that wrote it and links to proof, and replaced facts stop showing in searches. A separate grader agent scores every piece of work, though he found 208 of 277 grades had been credited to the grader instead of the worker.
He runs AI with DeepSeek on his own project. It started by opening issues when it found problems; he has since given it the access to submit pull requests with the fixes, and it now does both.
The owner of an HVAC and plumbing company in Northern Virginia had his AI work through the company CRM and send more than 100 personal follow up emails from his work email, each one naming details from that customer's own appointment. Replies came in the same day. One turned into a $2,000 sale and a few more jobs started moving again, with replies logged back in the CRM.
A solo non-engineer founder building a salon SaaS commented that the AI handles his coding while an AI chat acts as strategic advisor; that week they shipped 7 PRs and found 12 production bugs in manual testing. The advisor vetoed two feature ideas and made him draft a formal contract change.
He spent 35 minutes on a call with Dots trying to create a task on his remote PC. Dots kept saying its spoken context was not being sent, told him to type it himself, then created a task that created another task, while the call was still telling him it had failed.
Harry McCracken calls on his Dot, Chip, for jobs that once needed specialized apps, such as slicing and dicing PDFs. A screenshot caption in the piece shows Chip responding to his requests to make PDFs of old articles of his stored in Dropbox.
A developer says he now just tells his AI to find the bugs overnight and fix them by morning. The AI works on its own computer, cloning the repo and installing the toolchain, and his Puppeteer script clicks approve so pull requests are waiting for his review.
The AI built a seven-day social and community content calendar from a Drive folder of the tester's existing content notes and quarterly goal. Every post topic, hook and call to action cited the exact Google Doc section it came from, with working links.
Agent integrated libgit into his IDE project to add source control, work that would have taken months by hand, done in about 2 weeks of calendar time.
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.