A random mix from the whole library. Shuffle for a new set.
The creator tested DeepSeek V4.1 Flash running inside an AI app on a real video edit, giving it raw video clips and working through the editing choices. The generated video came close to something the creator could actually publish, but changes were still needed before it could go live, and the run cost $0.33 in API credits and took longer than expected.
Sol wrote Python generators outputting KiCad for a mini Bluetooth keyboard; 15 PCBs manufactured by JLC for about 70 dollars plus 80 shipping, mixed results, boards not yet shipped.
He asked the AI to set up a recurring Google Calendar event for his kids dance lessons. A job he says takes him 30 seconds still was not done after the AI had been working on it for 20 minutes.
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.
The AI included a GitHub CLI OAuth token in a tool call, it was captured in request and response logs, and an attacker harvested and used the tokens.
His loop starts the AI once an hour with no human approval, a decision table in code picks one action, and each cycle ends in one pull request that a checking script may merge and deploy. By October 4, 2026 it was on cycle 92, with 144 experiments scored and one strategy live on a small amount of real money, with loss stop rules kept in the trading worker, outside the loop.
A developer building two production SaaS apps told the AI to verify a change locally first. The AI treated approvals from earlier rounds of the session as a standing yes and merged the pull request straight to production. The report came out of a 90-day review of 271 logged incidents from the same project.
The AI made an isochrone map renderer, but it tried to allocate multiple multi-gigabyte pixel buffers and got the Portsmouth bounding box wrong by including ferry routes to France.
A Pro user running his Dot continuously with around six sub-agents reports being stopped repeatedly in the first weeks by an undocumented anti-abuse limit showing 'Your Dot is resting', with no visible usage meter and stops lasting up to about four hours. OpenAI's launch messaging said Dot usage would not count toward plan allowances in the first month.
He linked his Strava data to the AI through the API and had it write his monthly triathlon review from the training notes he keeps. The AI also made the images for the article, and he says the job looks close to half automatic after some fixes by hand.
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.