AutoTube
Automated video production pipeline from story sourcing to final render
AutoTube
An end-to-end automated video production system that sources stories from Reddit, generates scripts with Claude, synthesizes voice with Kokoro TTS, generates images with Gemini, and renders final videos with Remotion. Runs across a Bun API server and a Python GPU backend with an RTX 5070.
Multi-Service
Platform
Solo Developer
Role
From Story to Screen
Story Sourcing Pipeline
Reddit scraper pools ~250 stories from targeted subreddits. Scored by engagement metrics, filtered for content suitability. The pool refreshes automatically so there's always a backlog of production-ready source material.
Script & Voice Generation
Claude generates narration scripts with channel-specific voice and style guidelines. Kokoro TTS on the GPU box synthesizes the voice track. Whisper generates word-level timestamps for precise subtitle alignment. Getting the voice to not sound robotic took more prompt iteration than I expected.
Image Generation & Rendering
Gemini generates scene illustrations matching script segments. Remotion layers voice, images, subtitles, transitions, and background music into the final video. FFmpeg handles encoding. 15-20 minutes per video, start to finish.
Review & Publishing
Finished videos land in Aurora's review queue with script, metadata, and preview. Approve to schedule, reject with regeneration notes. Three YouTube channels with different themes plus TikTok for short-form.
Hands-Off, Quality Up
End-to-End Automation
Reddit story to rendered video in 15-20 minutes. No manual steps. Six services orchestrated: story sourcing, script generation, voice synthesis, image creation, video rendering, metadata generation. Each stage retries independently. A failed image generation doesn't restart the whole pipeline.
GPU-Accelerated Processing
Kokoro TTS and Whisper run on a dedicated machine with an RTX 5070. The GPU box exposes HTTP APIs that the Bun orchestrator consumes. Ollama handles local LLM inference for tasks that don't need Claude-level reasoning. Keeps costs down.
Multi-Channel Content System
Three YouTube channels share the same pipeline infrastructure. Channel-specific prompts, visual styles, posting schedules are all configuration, not code. Adding a new channel is a config file, not a feature branch.
Under the Hood
Key Technical Decisions
Bun + Python Split Architecture
Bun handles orchestration and API coordination. Python handles GPU work (TTS, transcription, image generation) on the GPU box. I tried running everything in one runtime. Dependency hell. This split avoids CUDA/PyTorch in the Node ecosystem and lets each runtime do what it's good at. Plain HTTP between the two.
Remotion for Programmatic Video
Each scene is a React component with props for voice timing, image URLs, and subtitle data. Video templates are version-controlled, testable, composable. Changing the visual style across 50 videos is a CSS change, not 50 editing sessions. The trade-off is learning a new paradigm, but it pays for itself fast.
Word-Level Subtitle Alignment
Whisper gives you word-level timestamps from the synthesized audio. Those timestamps drive subtitle rendering in Remotion, so each word appears at the exact moment it's spoken. Much better than sentence-level captioning. Makes the videos feel professionally produced even though no human touched them.
What I Learned
I tried running everything in one process
TTS and LLM inference in Node.js was a non-starter. Splitting into Bun for orchestration and Python for GPU work over HTTP added complexity but eliminated dependency hell. Each runtime stays lean.
Remotion changed how I think about video
Traditional editing is manual and non-reproducible. Remotion treats video like a React app. Components, props, styling. Changing the subtitle font across 50 videos is one line of CSS.
The first version auto-published everything. Bad idea
Quality was inconsistent. Some stories were boring, some scripts missed the tone completely. Adding a human review step in Aurora (approve/reject/regenerate) dramatically improved output quality without losing the automation.
Aurora
Content studio and operations dashboard for solo content creators