You and me are great examples of that. We are both extremely stupid and yet we can speak.

This seems like a glib one liner but I do think it is profoundly insightful as to how some people approach thinking about LLMs.

It is almost like there is hardwiring in our brains that makes us instinctively correlate language generation with intelligence and people cannot separate the two.

It would be like if for the first calculators ever produced instead of responding with 8 to the input 4 + 4 = printed out "Great question! The answer to your question is 7.98" and that resulted in a slew of people proclaiming the arrival of AGI (or, more seriously, the ELIZA Effect is a thing).

Agreed. I have a bunch of little command-line apps that I use 0.3 to 3 times a year* and I'm never going to memorize the commands or syntax for those. I'll be happy to remember the names of these tools, so I can actually find them on my own computer.

* - Just a few days ago I used ImageMagick for the first time in at least three years. I downloaded it just to find that I already had it installed.

There is no universe where I would like to spend brain power on learning ffmpeg commands by heart.

The thing about ffmpeg is there's no substitute for learning. It's pretty common that something simple like "ff convert" simply doesn't work and you have to learn about resolution, color space, profiles, or container formats. An LLM can help but earlier this year I spent a lot of time looking at these sorts of edge cases, and I can easily make any LLM wildly hallucinate by asking questions about how to use ffmpeg to handle particular files.

For the kinds of things you’d need to reach for an LLM, there’s no way to trust that it actually generated what you actually asked for. You could ask it to write a bunch of tests, but you still need to read the tests.

It isn’t fair to say “since I don’t read the source of the libraries I install that are written by humans, I don’t need to read the output of an llm; it’s a higher level of abstraction” for two reasons:

1. Most Libraries worth using have already been proven by being used in actual projects. If you can see that a project has lots of bug fixes, you know it’s better than raw code. Most bugs don’t show up unless code gets put through its paces.

2. Actual humans have actual problems that they’re willing to solve to a high degree of fidelity. This is essentially saying that humans have both a massive context window and an even more massive ability to prioritize important things that are implicit. LLMs can’t prioritize like humans because they don’t have experiences.

I don’t because I trust the process to get the artifacts. Why? Because it’s easy to replicate and verify. Just like how proof works in math.

You can’t verify LLM’s output. And thus, any form of trust is faith, not rational logic.

Thanks, but no luck. I tested it on a 3 second video, and got a 6 second video. I.e. it bounced 1 time, not 5 times.

Maybe this should be an AI reasoning test.

Here is what eventually worked, iirc (10 bounces):

  ffmpeg -i input.mkv -filter_complex "split=2[fwd][rev_in]; [rev_in]reverse[rev]; [fwd][rev]concat=n=2,split=10[p1][p2][p3][p4][p5][p6][p7][p8][p9][p10]; [p1][p2][p3][p4][p5][p6][p7][p8][p9][p10]concat=n=10[outv]" -map "[outv]" -an output.mkv

That's a deficiency with this particular implementation, not an inherent disadvantage to the method

But you only need to find the correct tool once and mark it in some way. Aka write a wrapper script, jot down some notes. You are acting like you’re forced to use the cli each time.

I know everybody uses a subscription for these things, but doesn't it at least feel expensive to use an LLM like this? Like turning on the oven to heat up a single slice of pizza.