A random mix from the whole library. Shuffle for a new set.
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.
Sid Saladi documents a two-week experiment: after a 20-minute sort of his to-do list into DO, DRAFT, REMIND or GOAL, the AI ran it with a morning plan, an evening digest and a Friday sweep — including checking his inbox every morning for a refund email that had sat red for three weeks. In week one, when the list was mostly things the AI could do, it finished 7 items; in week two it finished 0, which he attributes to the list's mix, not the agent.
Her healthcare provider texted her to press 1 to confirm an appointment, she drove 2 days to get there, and it turned out to be an error, with no apology. She used three AI tools, mostly the AI in Word, to sue the provider.
Domain experts use agents via MCP to provision real environments, e.g. a 200Gi PostgreSQL with PostGIS plus a copy of a Texas layer set, a big productivity boost over provisioning web interfaces.
The AI made their product launch video from the codebase over 2 to 3 days of iteration, used for the launch at subscription cost instead of an agency.
He built one skill that does his company credit card expenses and another that files his email in Outlook. Both were manual jobs he hated. Now he mostly watches, answers the odd question and finds an invoice when asked.
Prompted an agent to build a custom LLM coding harness with session resume, done in an hour and working nearly flawlessly, plus a WebFetch tool added in 5 minutes.
LLM made human-like fuzzing sessions for his text editor; local Qwen babysits, investigates, writes repro and files ticket; bugs found after 1-2h runs.
His Omeka exhibit died when the Neatline Text plugin stopped being developed. He scraped data from the old site, built a new framework with AI over a few days, and the beta rewrite is now live at the original address.
A team ran an AI pipeline over a usability test of an investment app and saved about half a day of manual analysis. Every claim the AI made about the screen was checked against a video frame, and two claims that did not hold up were left out of the report.
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.