The link provided was very entertaining, thanks 😃

Afaik Spotify normalizes the tracks by default and from what I gather that means it boosts the volume across the track and applies a limiter to roll off the loudest parts, which I guess should make them sound much flatter.

You’re not alone though, my gf reported the same thing so we’ve spent an afternoon comparing the same albums on Spotify and my Jellyfin collection. Same phone, same headphones, the differences listening to some albums were huge.

I got a 2020 mustang and it even more basic in terms of tech than my previous car, a 1999 bmw.

Sending you two questions that produce garbage:

Q: why would i use EDODF (error diffusion with output dependent feedback) instead of Floyd-Steinberg?

What to expect: at least some mention of green noise characteristics, clustering behaviour and reduced dot-gain and dot-loss.

What is wrong: Reduced worm-like artifacts, blue noise characteristics, some fine-tuning garbage it spitted out.

In the same chat you can then try:

Q: Describe the MED class algorithms to me.

What to expect: MED stands for multiscale error diffusion. Generally speaking it scans the image progressively, starting from a coarse grid and ending up with a single pixel to paint either black or white for each pixel in a predefined pixel budget. A similar approach was introduced by E. Peli in the 90s but perfected by Fung and Chen in the 2000s. It could be used for both dithering with both green and blue noise characteristics.

What I got: Hallucinations of some Minimum Error Disturbance class of algos i’ve never heard of. it seems to have something mixed up, as it seems to crop up in other fields. It was trying to describe something closer to a DBS (Direct binary search).

What is wrong: Anything related to DBS.

If you feel like you need any more I’ll do my best to think of some more.

Anytime you get into specifics instead of surface level knowledge it starts getting wildly inaccurate while still being confident af.

Off the top of my head I asked it about EDODF (error diffusion with output dependent feedback), a dithering algorithm dating back to 1999, and a very important milestone in halftoning for print.

At first it told me it’s not sure what I’m talking about, so I elaborated and extended the acronym. At that point it confidently hallucinated absolute garbage based on its interpretation of the name.

If you want to check chatgpt’s answers about edodf (or many other concepts) against a proven and cited source written by human I highly recommend Modern digital halftoning.

Not trying to be rude, but maybe the questions you are benchmarking it against in your stated fields of experitse are rather basic?

Glad it was reported properly. Imo this is just as bad as copy fail, as it affects mostly regular desktop users.

I have helped plenty of not-very-technical people switch to Linux and these kinds of vulnerabilities scare me the most when it comes to them.

He has been largely forgotten. Recently we were talking about data collection with my slightly younger gf (21) when I mentioned him. Turns out she never even heard his name. It’s not that she’s stupid or does not care about privacy, he just hasn’t been mentioned in mainstream media for over a decade, nobody makes reels about him on Instagram/tiktok. Tbh I haven seen him mentioned all that often on lemmy too.

With the rise of fascist parties worldwide, and especially here in the EU, and talks of AI, privacy seems to have taken a back seat in mainstream discourse.

One of the first small projects I worked on when i was starting with python was a telegram birthday reminder bot as i really didn’t want to rely on Facebook for that. At first I was just looping over all the entries in a list, then went to a database, at some point added fuzzy search, adding and removing entries. Still use it today.

Imo the best way to learn is to think of a project that you personally find useful and need solved for yourself, not some abstract exercise.

What do you mean? Nichicon’s fg series are one one the best reviewed audio grade capacitors, not to mention that Yamaha used their products in the original build. It just so happens that at these specs even the most expensive capacitors cost next to nothing, that’s why I mentioned it as a cheap luxury.

I looked into them, but unfortunately they are very hard to find in the EU.

Just got some Nichicon fine gold capacitors for the signal path of my 1985 Yamaha amplifier. Sounds cleaner than ever and all the caps I ordered cost me less than £2.

Well, I know its quite specific, but nothing beats AI at stereo matching and depthmap generation and that’s important in many fields.