Summarize a YouTuber's latest video into a Google Doc daily
A Make scenario runs at 9 AM, finds a YouTuber's newest video, has Hugging Face summarize it, and appends the summary to a Google Doc.
An educator's Dot checks public documents for changes, sorts them by impact, and verifies an article's notation against linked pages, offering revision candidates only.
An educator's Dot checks public documents for changes, sorts them by impact, and verifies an article's notation against linked pages, offering revision candidates only.
The author sets up a dot, an always-on OpenAI agent with its own cloud computer. He proposes two roles for it. One is reading changes in specified public documents and listing which teaching materials they affect, with source URLs. The other is checking a draft's notation against linked pages. His sample request limits it to revision proposals. It must not overwrite, publish, send or pay, and must mark unconfirmed items as unconfirmed. A person decides.
The revision-candidate and draft-check roles are only proposed, not run. He finished setup and used the dot for a few days. He says it contacts him on its own and flags Drive and Gmail items.
Connecting Drive or email hands over that data, and disconnecting does not erase what the dot already learned, so start with public documents and a narrow connection scope.
Every [week] check these public documents for changes: [list of links, e.g. standards, syllabi, regulations]. Sort the changes by how much they affect my teaching materials on [topic], and check that the notation in [my article or handout] still matches the linked pages. Give me a ranked list of revision candidates with the passage and source link; only suggest, do not edit my files.
A Make scenario runs at 9 AM, finds a YouTuber's newest video, has Hugging Face summarize it, and appends the summary to a Google Doc.
An n8n workflow scrapes Reddit posts and comments via Bright Data, extracts concerns and themes with Gemini, and writes reports to Notion.
A user left a Gemini agent running overnight to analyze 170k LLM logs with the llm CLI and a free model, and it finished the job despite limits.