Hate to be that guy, but which model works without fail for any task that ffmpeg can do?

"Writing working commands first try for every single ffmpeg feature that exists" is the highest bar I've ever heard of, I love it. I'm gonna start listing it as a requirement on job postings. Like an ffmpeg speedrun.

Obligatory xkcd: https://xkcd.com/1168/.

To be fair `tar` is quite easy to use once you understand the grammar of the options.

Yes and every failure of a product turns into a support ticket.

I don't think there's a single human on or outside of this planet that can meet that requirement, but Claude has been pretty good to me. It's certainly a much better starting point than pouring over docs and SO posts.

In my experience you still get a lot of stuff that used to work or stuff that it just makes up.

This doesnt exist in reality so in one sense, you could challenge the relevance

I think in the non LLM world though you at least have the trail of documentation you can unwind once you're in a bind. I don't care for prompt-a-mole fighting.

I know I struggled on getting a good command to “simply” make the videos from my Z8 smaller (in file size).

Usually the color was wrong and I don’t care enough to learn about colorspaces to figure out how to fix it and it’s utterly insane how difficult it is even with LLMs.

Just reencode it as is but a little more lossy. Is that so hard?

Handbrake may be a better option for you. I find that for some tasks it’s not only simpler but straight up works better than FFmpeg.

https://handbrake.fr/docs/en/latest/cli/cli-options.html

GPT-4

Fair enough. If you remember what you were testing with, I'd love to try it again to see if things are better now.

You have a fair point. Some LLMs are better at some tasks, and prompts can make a difference no doubt.

Perhaps at some point there will be a triage LLM to slurp up the problem and then decide which secondary LLM is most optimal for that query, and some tertiary LLMs that execute and evaluate it in a virtual machine, etc.

Maybe someday

Oh I talked to some guys who started a company that does that. This was at an AI meetup in SF last year. They were mainly focused on making $/token cheaper by directing easy/dumb queries to smaller dumber models, but it also increases output quality because some models are just better at certain things. I'm sure all the big companies already have implementations of this by now even if they don't use it everywhere

I was suggesting optimizing for answer quality, but optimizing for cost might be useful too I suppose for "business innovation" purposes.

Yes they are called routers. One is https://withmartian.com/