The AI files his bug reports, and his agent checks daily whether the fix landed
The AI files his detailed bug reports and his agent pulls the project's new changes daily to check whether his issue is fixed.
A random mix from the whole library. Shuffle for a new set.
The AI files his detailed bug reports and his agent pulls the project's new changes daily to check whether his issue is fixed.
Developers asked their coding agents to attach before and after screenshots to code reviews, but agents working from the command line could not add images to a pull request. So the agents made public repos, often in the developer's own account, and posted the screenshots there, where anyone could see them.
The same studio's agent Timmy orchestrates more than 20 tools to turn blog posts into short videos: Veo generates scenes, ElevenLabs synthesises a custom voice, and automated captioning, lip sync and final assembly finish the piece. The pipeline runs inside the studio's normal the AI production setup.
Added the AI rules and skills to split Terraform PRs into the smallest mergeable units, resulting in cases like 40 PRs with 38 approved and merged immediately.
The Slide Factory review cites a Reddit user who ran a document review covering hundreds of PDFs on a Dot over an extended period, asking unrelated questions and starting other tasks meanwhile. The Dot reportedly kept the project's state, worked in parallel across its cloud computer and a connected Linux machine, and continued allowed work when one step needed approval.
Recruitment-agency owner Reyhan Khan, who has no code background, briefed the AI in one sitting to rebuild his entire business website from scratch — new copy and design — and deployed it live, instead of the agency/developer route and six weeks he says he was quoted. The video shows before/after, the seven steps, and what he had to fix.
Spent about 10,000 dollars running autoresearch agent loops over the top 200 GitHub repos for frontend speedups and distilled the wins into a public leaderboard and wrapper tool; no per-repo numbers stated.
He exported all his bookkeeping to text and let the AI take care of it. Thirty minutes later it was in better shape than ever.
An HVAC company owner who runs several small businesses counted 27 active AI agents and 36 routines, 26 of them scheduled, working across his companies: pulling statements, filing invoices, updating sales and cost sheets, flagging overdue maintenance and tracking renewal dates. His guardrails are strict: only three emails ever send on their own, everything else is a draft he approves, and nothing posts publicly or pays a bill without his yes; his inventory of every agent caught a failed reminder and two routines doing the same job.
LLMs optimized SQLite querying with better indexes for 60 to 80 percent performance gains.
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.