Built an end-to-end autonomous pipeline that generates and publishes short-form video content daily, with zero manual intervention from data ingestion to social publishing. The system pulls real-time trend signals (Reddit API, Google Trends RSS), scores them for engagement potential, and feeds the highest-scoring angle into an LLM pipeline (Groq/LLaMA-3) constrained to output structured JSON: hooks, paced scripts, visual cues, and captions. That JSON drives a React + Remotion rendering pipeline that programmatically builds and exports a 9:16 video, which is then uploaded to Cloudinary and auto-published via the Meta Graph API. Key engineering decisions: Structured-output prompting to make LLM responses reliably parseable by downstream code, not just human-readable Decoupled architecture, so data scoring, generation, rendering, and publishing run as independent stages that can fail or retry without breaking the pipeline Fully code-driven video rendering (React/TypeScript via Remotion) instead of manual editing, enabling daily scale Automated via GitHub Actions for unattended daily runs Stack: Python, React, TypeScript, Remotion, Groq API (LLaMA-3), Reddit API, Google Trends, Meta Graph API, Cloudinary, GitHub Actions