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Show HN: Clarifytube –Turn any YouTube video into a full article

https://clarifytube.com/article/this-might-be-bigger-than-deepseek?id=xLFkqYOUN24&l=en
2•lfgtavora•4h ago

Comments

lfgtavora•4h ago
Hi HN! I was trying to keep up with two complex topics: investing (deep dives into the S&P 500) and the constantly changing laws around my Portuguese citizenship. I quickly realized the most up-to-date and nuanced information wasn't on news portals or Medium articles anymore—it was being discussed in long, detailed YouTube videos by lawyers and financial analysts. As someone who reads much faster than they watch, I was stuck. I needed a way to "read" these videos efficiently.

I started with a simple script for my own use, and it worked so well that I decided to turn it into a proper web app for everyone facing the same problem.

How it Works: The core idea was to make it as frictionless as possible. You can just take a YouTube URL and replace youtube.com with clarifytube.com. The server then fetches the video's transcript, parses it, and structures it into a clean, readable article.

Example: Original Video: https://www.youtube.com/watch?v=xLFkqYOUN24 Clarifytube Article: https://clarifytube.com/watch?v=xLFkqYOUN24&l=en

The "Why" (Technical Diferentiator): I tried existing AI summarizers, but I found them unreliable. They often "hallucinate" by injecting information not present in the video or oversimplify to the point of losing critical nuance. My goal was different: to create a faithful representation of the source content. Clarifytube doesn't try to interpret or summarize; it structures the full transcript into a complete article. This way, the user maintains all the original context and can decide for themselves what is important.

Tech Stack: It's built with [mention your stack, e.g., Next.js, Vercel, Python for the backend processing, and PostgreSQL]. I'm happy to answer any questions about the architecture.

This is still very much a work-in-progress, and the HN community's feedback would be invaluable. I'm here to listen and answer any questions you might have. Thanks for checking it out.

pheeney•3h ago
Is this open source? Or would you be willing to share the prompt of how you accomplished the faithful representation of content? I’m curious what approach you took that sets it apart from the tools that you felt had failed. I didn’t watch the original video but the article looks well formatted.

Prompt Injection in LLM-Driven Systems

https://blog.gopenai.com/prompt-injection-in-llm-driven-systems-how-a-single-sentence-can-wipe-data-or-get-a-paper-f885e97ed0fc
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