Instead of littering the forum with another post I tought I might ask here.
I´ve been suggested to buy a power amp (Hypex Ncore 252 or more powerful) and a bluesound Node (2024) to simplify my setup and future proof it. I´ve been asking ChatGpt a lot of questions about this, and it warns me about "digital volume damping". As I´ve interpretated that means that when I reduce the volume (spl) on the node the digital signal weakens which in turn reduces the sound quality? I like to listen at various spl´s from low 40-50 to around a 100 db (when in that mode).
Is this true?
Well, if it's true it's a useless truth...
Ideally you'd want a power amp that is just powerful enough to drive your speakers to as loud as you'd want to hear them, including enough headroom to not degrade the sound. It doesn't matter whether you use analog attenuation or digital.
Assuming digital and especially 24-bit, there is so much headroom that degradation from dialing bits back won't make an audible difference.
For instance, how much lower level are you going to listen at, while still being able to hear the music adequately enough to tell if there is degradation? You're not going to listen at 96 dB lower, for instance, leaving you with essentially 8-bit audio (ideally—in reality the low bits are relatively high-noise), because you'll scarcely hear the slightest sound from your speakers. More likely, you'll want something closer to a 30 dB range at most, giving up 5 bits of your 24-bit audio. That leave 19-bits, but you won't even hear the bottom bits of that, you'd only be able to hear 10-12 bits max at that reduced volume, so you've still got a crazy amount of untapped resolution left. (Using the same scenario, 16-bit audio still holds up pretty well.)
As others have said, don't count on chatGPT and the like for anything other than an outline to consider. Artificial Intelligence is like artificial turf—it sounds intelligent from a distance, but beware of thinking it is able to tap into a greater truth.
Example #1: A well-known Stanford professor, PhD in electrical engineering, posted one of those brain-fooler images on social media, that looks like it shows different colored balls. In discussion, he commented that chatGPT revealed that all balls were in fact gray. I enlarged the image and pointed out that they were not even close to gray, but a mixture of colors, the main tint being golden. He replied that, he didn't understand how chatGPT, given the actual RGB values to examine, thought it was gray. But that's the thing—charGPT simply recognized the image, then regurgitated what other people said about it. It had the RGB, knows what gray is, but didn't have the intelligence to check for itself.
Example #2: Saw this recently. Someone wanted to test whether chatGPT could get fooled by a paradox. The first test was giving it a paradox involving an alligator. ChatGPT said that it couldn't answer the question, because it was a paradox. The guy wanted to see if it really knew, or had just found information on the puzzle. The second test involved choosing 1 or 2, with the conditions stated. The AI chose #2 as the solution. The guy pointed out the problem with choosing #2, the AI noted that was true and changed its answer to #1. So the guy pointed out the problem with choosing #1, and the AI again noted he was correct and changed it to #2.
The moral of the story is, AI is really good at presenting solutions that look like truth, but may not be truth. My concern with AI is that it will get better and better to the point it's a viable substitute for truth, in very complicated matters that are tough for humans. But at that point, good luck convincing people that it's wrong in a particular case, because people will have accepted that it does better than a human.
(For fun, I here's an image I made using actual gray balls, simulating the trick...)