Cursor expands newsletter notes with content research

Harshit Jain describes a Cursor workflow that expanded raw notes into the newsletter announcing the workflow itself. He saved his thinking in a text file, then used local scripts and external research to prepare a content package in his writing style.

The setup combines Python content analysis, Selenium browser automation for LinkedIn and newsletter feed parsing. It gathers marketing material, examines content structures and uses more than thirty-two of Jain’s own LinkedIn posts as style context. The source describes generated newsletter, LinkedIn and Twitter outputs stored in organized folders and pushed to Git.

For the published example, Jain says a short thought about avoiding newsletter writing became the piece readers were viewing. He could edit and refine generated material before publication. The account supplies a concrete output and the collection-to-writing sequence, but does not show an accuracy evaluation, an originality assessment or measured engagement attributable to the system.

The claimed ability to sound exactly like the author is a subjective judgment. The useful result is a repeatable research and drafting workspace that produced a published example. It should not be read as evidence that an agent automatically published every package or that predictions about engagement were validated.

Prompt
Original prompt

Source: newsletter.qback.ai · Harshit Jain