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Comment by jonwinstanley | Hacker News Reader

What does it mean by chat with a transcript?

I.e. what are the kind of things I can ask and get value from?

First, I would say that reading is faster than watching. Therefore, it is more time-efficient to read a YouTube video, especially if it covers technical content or interesting ideas. Additionally, you can ask follow-up questions about the content, and since it's in an OAI conversation, you can leverage the "intelligence" of the model to help you understand the parts that you find difficult. Sometimes, I watch technical YouTube videos and wish I had a written version; so here it is.

This is an interesting example, it feels different than watching the ~12min video. https://chatgpt.com/share/66e9eaff-248c-8009-9761-d848d97881...

Nothing, it means nothing, like most of this "AI" hype nonsense.

They copy paste text transcripts into an Llm and have it generate more text based on its training and prompt data. You can't "chat" with a text document of course.

I’m not sure I follow. Can you explain ‘you can’t chat with a text document’ because you clearly can.

Is anyone even chomping at the bit to hear a pedant explain how "chatting with a text document" isn't the most precise way to phrase this concept that we all understand?

chatting with a bot about a text document.

chatting about a text document

Chatting with a text document implies it has AI or magical abilities.

You wouldn't say you are chatting with your dog if you are talking to your wife about your dog.

IRC is just multiplayer Notepad.

Chat with the document means chat about that document with an LLM who has “read” it.

It can be useful; it's not hype nonsense.

Ahh ok.

So rather than watch the video or read the transcript you just ask the one thing you want to know.

Could it take you to the moment in the video that is useful too?

You could ask it for a couple of verbatim sentences from the transcript that are most related to what you are interested in, then find the timestamp for that text. (There could be UI for this.)

Another solution would be to skip the LLM prompting part altogether and

1. break the transcript into short sections

2. create embeddings from them and remember the timestamps for each

3. embed your query (what are you interested in)

4. calculate the closest embedding in the transcript to your query

5. return the original timestamp

That's a good idea. However, I believe the challenging part lies in first reconstructing the short utterances into coherent, meaningful paragraphs.

Currently, with the API [1], you can retrieve a JSON with timestamps. The main issue, though, is how to parse the text effectively into meaningful sentences, and then add the timestamps at the beginning of the paragraph. WIP.

[1]: https://textube.olivares.cl/watch?v=9iqn1HhFJ6c&format=JSON

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