Should you build your own Vernigo?

Build me a personal YouTube outlier research tool inspired by Vernigo. Requirements:

- Django + PostgreSQL, one self-hosted web app; Google login is out of scope,
  use a single admin password from .env.
- Let me add YouTube channel IDs manually or import them from a CSV seed list.
- Pull each channel's recent videos through the official YouTube Data API and
  store title, thumbnail, views, duration, publish date, and channel stats.
- Calculate an outlier multiplier as video views divided by the average views
  of that channel's previous five videos available in the database.
- A searchable video grid with filters for multiplier, views, subscribers,
  duration, publish date, category, and channel age; sortable by multiplier,
  views, or newest.
- Bookmark folders: create, rename, and delete folders, and save or remove
  videos without duplicating them.
- A niche page that groups imported channels by a manually assigned niche and
  ranks niches using median views per video divided by videos published.
- Run refresh jobs with Celery + Redis once per day, respect API quota errors,
  and show the last successful refresh time for every channel.
- Store all secrets in .env; include Docker Compose for Django, PostgreSQL,
  Redis, Celery worker, and scheduler.
- Out of scope: crawling all of YouTube, a 10M-video corpus, community ranking
  signals, automatic niche classification, and claims that this finds the
  best opportunities market-wide. It only analyzes the channels I seed.
- README: YouTube API setup, quota limits, CSV format, calculation details,
  backup steps, and the limits of a small personal dataset.