published feb 25, 2026

Turn Abandoned Twitter Bookmarks Into Something Useful with AI

intermediate

The Rundown

Set up a Perplexity research system that reads through saved articles and bookmarked content, scores each find by usefulness, and logs everything into a Google Sheet. The example topic is OpenClaw, but the same setup works for any tool, trend, or topic you want to track. Once scheduled, the system can run daily and update your sheet automatically.

Who This Is Useful For

  • Anyone with a backlog of saved articles they never get around to reading.
  • Consultants and freelancers juggling multiple tools or clients who need to stay sharp without constant scrolling.
  • Founders and operators keeping up with a fast-moving tool or space.

What You Will Build

A Perplexity research pipeline with three connected pieces: a Space with custom instructions, a daily scheduled Task, and a Google Sheet that becomes your master research list.

  • A Perplexity Space that scores and organizes research finds.
  • A scheduled Task that searches for new content automatically.
  • A Google Sheet with usefulness ratings, implementation time estimates, cost breakdowns, and recommendations for every find.
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What You Need

  • Perplexity account: free for the Comet browser, Pro or Max for scheduled tasks.
  • A blank Google Sheet. Perplexity can set up the headers.
  • Comet browser, available as a free download.

Going Further

  • Expand your sources by adding Gmail under the task’s model options so Perplexity can pull in developer newsletters and update emails.
  • Clone the setup for other tools by creating a new Space and sheet, then swapping “OpenClaw” for whatever you’re tracking.
  • Experiment with automating the bookmark scan. Scheduled Tasks start without browser access, but you can click into the thread afterward and grant access. The manual trigger is more reliable right now, but it’s worth testing.
Add a “User Usefulness Score” column at the end of your sheet so you can rate each find yourself and compare your judgment against the AI recommendation.