You've raised two absolutely critical architectural points that we're wrestling with:
Official Subtitles vs. LLM Transcription: You are 100% correct about auto-generated subs being junk. We view official subtitles as the "trusted baseline" when available (especially for major educational channels), but your experience with Gemini confirms that an optimized LLM-based transcription module is non-negotiable for niche, high-value content. We're planning to introduce an optional, higher-accuracy LLM-powered transcription feature to handle those cases where the official subs don't exist, specifically addressing the need to inject custom context (e.g., topic keywords) to improve accuracy on technical jargon.
The Automation Pipeline (RSS/RAG): This is the future. Your RSS-to-Website pipeline is exactly what turns a utility into a Research Engine. We want YTVidHub to be the first mile of that process. The challenges you mentioned—pre-processing long live stream audio—is exactly why our parallel processing architecture needs to be robust enough to handle the audio extraction and cleaning before the LLM call.
I'd be genuinely interested in learning more about your approach to pre-processing the live stream audio to remove pauses and dead air—that’s a huge performance bottleneck we’re trying to optimize. Any high-level insights you can share would be highly appreciated!