Draft posts from rough notes in your own style

A marketer used Cursor to turn raw ideas into newsletters, LinkedIn posts, and threads that matched his past writing.

Done with Cursorยท Advanced

What they did
Harshit Jain keeps rough thoughts in a text file inside a Cursor workspace. Python scripts then pull in his earlier LinkedIn writing, scrape posts from three marketing leaders with Selenium, and read newsletter feeds so the agent has examples to learn from. From there, it produces a newsletter, LinkedIn posts, and a Twitter thread in local folders that are pushed to Git for his review and edits.
How it went
He said one newsletter was generated from a single lazy note. No numbers were shared for time saved or audience response, and the claim that readers could not tell the difference was not measured.
Tips
This setup depends on browser automation for LinkedIn scraping, so it suits someone comfortable running Python and Selenium scripts. It works best when you feed it both messy source notes and a few examples of your own writing.

Try it yourself with Cursor

Iโ€™m using Cursor. Learn my writing style from these examples: [paste links, files, or text samples]. Then turn these raw notes into a draft [newsletter / LinkedIn post / thread]: [paste notes]. Match my tone, pacing, and structure as closely as you can, and show me the draft in a local file or editor. If you think it should be sent, published, deleted, or otherwise finalized, stop and ask for my approval first.

Fill in the [brackets]. Agents can make mistakes, so review anything before it is sent, paid or deleted.

Read the original on newsletter.qback.ai โ†—

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