# How to Vibe Code Your Own Vernigo (and Stop Paying for It) > Find Inspiration for your next VIRAL Video Idea! - Site: https://vernigo.com - Category: Creator Research & Curation SaaS - Platforms: Web app - Verdict: **Solid side project** (62/100 vibecodeable) - Estimated effort: 2-3 weekends of focused building ## Verdict Build a personal subset with AI tools in a couple of weekends, but the data pipeline required to keep 1M+ outlier videos fresh is an operational chore. The frontend UI, bookmarking collections, and filtering layout are trivial to scaffold with AI coding agents in an afternoon. However, Vernigo's true value lies in its background data engine—constantly querying the YouTube Data API, tracking channel baselines, and computing view multipliers. If you mock the database with a curated JSON export or a modest subset of your favorite niches, you can easily build a satisfying personal research dashboard. If you expect a self-updating index of a million live records, you will spend more time maintaining cron jobs and proxy rotation than writing code. ### What you can't replicate - The proprietary background scraping pipeline monitoring millions of YouTube channels - The active creator community and brand equity ## What it does A web-based research and curation platform indexing over one million YouTube outlier videos cross-referenced by category, view multiples, duration, and age. ### Core features - Google OAuth authentication - Outlier video directory grid with thumbnail previews - Multi-parameter filtering (category, view multiples, duration, age) - Search across indexed video metadata - Private collections and bookmarking folders - Stripe recurring subscription billing integration ## The business ### Pricing - Free Tier: Free — Browsing and entry-level access gated behind account registration. - Paid Plan: Hidden / Paid — Full access to outlier video libraries, filters, and collections. Founded 2025. Team size: 1-2. ## The hard parts - Continuous data ingestion pipeline polling YouTube Data API for millions of channels - Computing historical channel baseline view counts to flag statistical outliers - Database indexing and query performance over 1M+ dynamic video records with complex filters ## How to vibe code Vernigo ### Prerequisites - Node.js (free): Runtime environment for Next.js and TypeScript tooling. - GitHub (free): Code repository hosting and deployment integration. ### Recommended AI tools - Claude Code: Best-in-class terminal coding agent for scaffolding the full Next.js stack, database schema, and API ingestion scripts. - Cursor: Excellent AI code editor for fine-tuning UI grids, filter components, and Tailwind styling. ### Stack - Frontend: Next.js with Tailwind CSS and shadcn/ui - Backend: Next.js App Router API Routes - Database: Turso (SQLite at the edge) - Auth: better-auth - Payments: Stripe (optional for personal clone) - Other: Vercel AI SDK ### Hosting - Cloudflare (Hosting the Next.js frontend and edge API routes): $0-5/mo ### Build guide 1. **Scaffold Project and Database Schema** — Initialize the Next.js project with Tailwind CSS, configure Turso SQLite connection, and define the database schema for videos, categories, and user collections. ``` Create a new Next.js 16 project with TypeScript and Tailwind CSS. Configure Turso (libSQL) as the database client. Write a migration script for a schema containing tables: 'videos' (id, youtube_id, title, channel_title, thumbnail_url, view_count, baseline_views, outlier_multiplier, duration_seconds, published_at, category_id), 'categories' (id, name), 'collections' (id, user_id, name), and 'collection_items' (collection_id, video_id). Ensure strict typing and robust error handling on database connection failures. ``` 2. **Implement Authentication and User Sessions** — Set up better-auth with Google OAuth and email login to secure personal access and private collections. ``` Integrate better-auth into the Next.js app supporting Google OAuth and email/password login. Connect better-auth tables to the Turso database. Create protected API middleware and client-side auth context hooks so users can log in, view their session state, and manage private collection folders. ``` 3. **Build the Outlier Video Explorer UI** — Develop the core dashboard grid displaying video cards with thumbnail previews, view multiples (e.g., 3.6x), view counts, and publication age. ``` Build a responsive dashboard page in Next.js using Tailwind CSS and shadcn/ui primitives. The page should feature a clean grid layout of video cards mirroring Vernigo's aesthetic. Each card must display the video thumbnail, title, channel name, formatted view count, publication age, and a prominent badge showing the outlier multiplier (e.g. '3.6x'). Include loading skeletons and pagination or infinite scroll. ``` 4. **Add Multi-Parameter Filtering and Search** — Implement instant client/server filtering by category, view multiplier ranges, video duration, and full-text title search. ``` Implement a comprehensive filtering and search sidebar for the video explorer dashboard. Users should be able to filter by category dropdown, minimum outlier multiplier slider (e.g., 2x to 50x+), duration ranges (Shorts, Medium, Long), and search by keywords matching video titles or channel names. Write optimized SQL queries for Turso that handle these intersecting filters efficiently with proper indexes. ``` 5. **Implement Private Collections and Bookmarking** — Enable users to save reference videos into custom private folders and manage their saved inspiration library. ``` Build a collections management feature allowing authenticated users to create custom folders, save/unsave videos from the dashboard cards directly into folders, and view a dedicated '/collections' page where they can organize and review their saved viral video ideas. Ensure optimistic UI updates when toggling bookmarks. ``` 6. **Mock Data Seeding Script or YouTube Ingestion Cron** — Create a seed script or lightweight cron job to populate initial outlier records so the clone is immediately usable. ``` Write a TypeScript seeding script that populates the Turso database with realistic sample outlier video records across various categories (gaming, finance, tech, lifestyle) complete with calculated outlier multipliers and high-res thumbnails. Also draft a stubbed background ingestion service structure that outlines how a script would fetch trending videos from the YouTube Data API and compute channel medians. ``` ### Cost vs paying **Starting costs (one-time):** - Domain name (optional): $12/yr - Total: ~$12 one-time **Ongoing costs (monthly):** - Cloudflare / Turso hosting: $0-5/mo - Total: ~$0-5/mo - Paying for the SaaS instead: Hidden / Paid subscription - Build time: 12-18 hours - AI tool credits: $20 (Claude Pro or Cursor Pro) - Break-even: Building is for personal learning and custom workflow tuning rather than direct cost savings. ## Sources - [Vernigo Official Landing Page](https://vernigo.com)