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6/28/2026
How I Built a Self-Learning YouTube AI on AWS Aurora (And Barely Survived the Weekend)

How I Built a Self-Learning YouTube AI on AWS Aurora (And Barely Survived the Weekend)

Short summary

Developer shares the technical architecture of Virantics, a self-learning YouTube growth toolkit built on AWS Aurora with pgvector embeddings that improves suggestions with real performance data. The stack uses Next.js frontend, Gemini 2.5 Flash for vision analysis, and semantic search to surface trending topics instead of hallucinated guesses. A practical case study in full-stack development patterns for vector databases and serverless architecture.

  • •Self-learning vector database stores top 30% of real YouTube wins to replace hallucinated recommendations
  • •Tech stack: Next.js + Vercel frontend, AWS Aurora Serverless + pgvector backend, Google Gemini 2.5 for vision
  • •Features include Channel DNA analysis, Thumbnail Blueprint dissection, and Trends Explorer dashboard with sentiment analysis

Generated with AI, which can make mistakes.

#ai-tools#ai-agents#product-launch
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