Production-grade AI platform converting 2-hour videos into viral shorts. Built on a distributed architecture utilizing Inngest, Modal GPUs, and DeepSeek v3 for automated video pipelines.
Manually scrubbing through a 2-hour podcast to find viral moments, extracting clips, detecting active speakers, and burning in subtitles takes creators hours of tedious work.
From an engineering perspective, processing massive video files (up to 500MB) requires complex orchestration. Synchronous HTTP requests timeout. FFmpeg chokes standard CPUs. And running LLMs + Whisper concurrently requires serious, scalable GPU infrastructure.
ViralRot decouples the frontend from the heavy lifting. Next.js handles the UI and tRPC APIs, instantly offloading processing to Inngest background jobs. Inngest orchestrates the pipeline, passing tasks to FastAPI workers.
The heavy compute runs on Modal GPU workers: a custom fine-tuned Whisper model handles Hinglish transcription, DeepSeek v3 reasons about viral moments alongside LR-ASD (Active Speaker Detection), and FFmpeg handles the final compositing and subtitle burn-in.
An event-driven orchestration layer ensures the Next.js client remains non-blocking while heavy video and AI workloads are executed on distributed GPU clusters.