Replace Vernigo?
NOT REALLY · don't botherYou can build filters, bookmarks, and an outlier score over a small set of YouTube channels, but Vernigo's product is the continuously refreshed corpus: millions of videos, channel baselines, niche-level supply and demand signals, and ranking data improved by how thousands of users interact with the library. A one-shot app can reproduce the interface and formula, not the dataset that makes the results useful.
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.
$ open in your agent (prompt prefilled, you press enter) or copy it raw
The useful result is not calculating views divided by a channel average. It is having enough current video and channel history to discover outliers and undersupplied niches before you already know where to look. Building the dashboard is straightforward; collecting, refreshing, and ranking that market-wide dataset is the product.
xthe 10M-video historical and continuously updating database
xbroad discovery beyond channels you already know
xunsaturated niche rankings across the wider YouTube market
xcommunity interaction signals from more than 2,000 users
xranking quality improved by accumulated usage data
xcoverage and freshness without managing YouTube API quotas
No mature prior art is listed for this record.
What does the Vernigo verdict mean?
The directory does not recommend replacing this one from a prompt. . Read the breakdown above.
What price does this directory record show for Vernigo?
The directory records $39/month for Pro, checked 2026-07-30. Verify the source before making a purchase decision. This reference stays outside retail Stack Math unless current matched evidence supports the comparison.
What do I lose by replacing Vernigo?
Honestly: the 10M-video historical and continuously updating database; broad discovery beyond channels you already know; unsaturated niche rankings across the wider YouTube market; community interaction signals from more than 2,000 users; ranking quality improved by accumulated usage data; coverage and freshness without managing YouTube API quotas. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Vernigo?
This record does not list mature open-source prior art. The build prompt remains available as a research starting point.