Every run starts with a clear research topic. Use a short sentence that tells the Humyn ML engine exactly what to prioritize—think “onboarding friction for AI teams” or “why creators migrate off Platform X.” This keeps clustering and sentiment analysis pointed at the signal you care about.
List the keywords and phrases Humyn should listen for. Mix explicit product terms with intent-led language (“upgrade flow,” “pricing jump,” “workflow automation”). You can add or edit the list anytime before you launch a run.
Paste one or many Reddit URLs (plus other supported sources) into the feed. Humyn will fetch full threads, comments, and context automatically. Batch pasting works great when you have a backlog from research sprints or community callouts.
Hit Run analysis to kick off ingestion. Humyn spins up a processing run, shows real-time progress, and marks the job as 100% processed once everything is clustered and tagged. From there you can jump straight into Explore, Metrics, or Chat with Hue to work with the new data.
