The last couple of years have been a wild ride. The biggest parts of the conversation around AI for most of that time have been dominated by absurd levels of hype. To go along with the cringe levels of hype, a lot of people have felt the pain of dealing with the results of rushed and forced AI implementation.

As a result the pushback against AI is loud and passionate. A lot of people are pissed, for good reasons.

Because of that it would be understandable for people casually watching from a distance to get the impression that AI is mostly an investor fueled shitshow with very little real value.

The first part of the sentiment is true, it's definitely a shitshow. Big companies are FOMOing hard, everyone is shoehorning AI into everything they can in hopes of capturing some of that hype money. It feels like crypto, or Web 3.0. The result is a mess and we're nowhere near peak mess yet.

Meanwhile in software engineering the conversation is extremely polarized. There is a large, but shrinking, contingent of people who are absolutely sure that AI is something like a scam. It only looks like a valid tool and in reality it creates more problems than it solves. And until recently that was largely true. The reason that contingent is shrinking, though, is that the latest generation of SOTA models are an undeniable step change. Every day countless developers try using AI for something that it's actually good at and they have the, as yet nameless but novel, realization that "holy shit this changes everything". It's just like every other revolutionary tech tool, you have to know how to use it, and when not to use it.

The reason I bring up software engineering is that code is deterministic. You can objectively measure the results. The incredible language fluency of LLMs can't gloss over code issues. It either identified the bug or it didn't. It either wrote a thorough, valid test or it didn't. It's either good code or it isn't. And here's the thing: It is. Not automatically, or in all cases, and definitely not without careful management and scaffolding. But used well it is undeniably a game changing tool.

But it's not just game changing in software. As in software if it's used badly, or for the wrong things, it's more trouble than it's worth. But used well it's remarkable. I'll give you an example:

A friend was recently using AI to help create the necessary documents for a state government certification process for his business. If you've ever worked with government you've already imagined the mountain of forms, policies and other documentation that were required. I got involved because he ran into some issues getting the AI to deliver.

Going through his session the thing that blew my mind was how little prompting it took to get most of the way there. He essentially said "I need help with X application process for X certification" and then he pasted in a block of relevant requirements from the state. The LLM agent then immediately knew what to do, which documents would be required and which regulations were relevant. It then proceeded to run him through a short Q and A to get the necessary specifics for his business and then it just did it. The entire stack of required documentation was done in under an hour versus the days it would have taken him to do it himself. It didn't require detailed instructions or .md files or MCP servers or artifacts, it just did it.

And he's familiar with this process, he has the expertise to look at the resulting documents and say "yeah this is exactly what the state is looking for". It's not surprising that the model had a lot of government documentation in its training data, it shouldn't even really be mind blowing at this point how effective it was, but it blew my mind anyway. Probably because not having to deal with boring, repetitive paperwork is a miraculous thing from my perspective.

This kind of win is now available in a lot of areas of work and business. It's not hype, it's objectively verifiable utility.

This is not to say that it's not still a mess. I could write an overly long essay on the dangers of AI in software, business and to society at large. We thought social media was bad, that the digital revolution happened too fast for society to adapt... AI is a whole new category of problematic. One that's happening far faster than anything else has. There's no precedent.

But my public service message is this: Don't let the passionate hatred of AI give you the wrong idea: There is real value there. I don't mean this is a FOMO way, you don't have to "use AI or get left behind". The truth is that 6 months from now the combination of new generations of models and improved tooling, scaffolding and workflows will likely make the current iteration of AI look quaint by comparison. There's no rush to figure out a technology that's advancing and changing this quickly because most of what you learn right now will be about solving problems that will be solved by default in the near future.

That being said, AI is the biggest technological leap since the beginning of the public, consumer facing, internet. And I was there for that. Like the internet it will prove to be both good and bad, corporate consolidation will make the bad worse. And, like the internet, the people who are saying it's not revolutionary are going to look silly in the context of history.

I say this from the perspective of someone who has spent the past year casually (and in recent months intensively) learning how to use AI in practical ways, with quantifiable results, both in my own projects and to help other people solve problems in various domains. If I were to distill my career into one concept, it would be: solving problems. So I feel like I'm in a position to speak about problem solving technology with expertise. If you have a use for LLM powered AI, you'll be surprised how useful it is.

EDIT: As one user pointed out, this is not about Apple Music the streaming platform, this is about basically itunes but itunes no longer technically exists as an application.

So a little background: my father just died and a big part of his life was listening to music, for most of his life he's been building themed compilations of songs he liked using whatever medium was available, magnetic reel tapes in the '60s and '70s, then cassette tapes, then CDs, and of course playlists for the last 20 or so years. Now my mother and I would like to back up and save a lot of that work as those compilations have a lot of sentimental value and are pretty unique. There's lots of old obscure rhythm and blues and soul songs that you aren't really going to come across anywhere else. However, it's pretty much all locked into Apple Music, which isn't really a problem in the here and now, because we all have tended to use macs since my mother adopted them in the '80s or '90s. However, we don't really want that data just locked into a private ecosystem that has been getting more and more restricted and where we have less and less control.

So I'm looking for a way to keep those playlists intact and export them out of Apple Music in a playable format and into a less locked in system to then back them up. Most of the music should be DRM free as a lot of it would have been taken off of CDs probably as MP3 files, though a lot of that would've happened 15+ years ago.

Does anyone have any ideas about the best way to do that? I seem to be able to manually export each one into a .txt file but of course it's not really playable sound files. My tech skills are pretty limited, I have about an average amount of knowledge or even slightly more for someone my age (30s) who grew up around computers and the internet but I grew up after it necessary to have basic coding skills to use computers so my experience doing even basic coding or running scripts is pretty much nil. Any ideas would be appreciated.

Edit: it’s version 1.0.6.10

Hey there! I'm trying to repurpose a Raspberry Pi that's been collecting dust for a few years, and I'm a bit out of my depth with systems/networking, so I'm hoping for some guidance on what I did wrong (or what I could've done better).

The story is: I upgraded my PC and had an old SSD lying around, and I also had a Raspberry Pi that I never really had time to toy with. I figured I could combine both and make a small family “drive” where everyone can upload photos/videos/documents and keep them in one place at home.

Then I realized the Pi sits right behind the TV, next to the router since the Ethernet cable is short. So I thought: if it's already there, maybe it could also be a media player. The idea was: upload videos and store them on the SSD then play them on the TV via Kodi.

What’s going wrong is that Nextcloud uploads are painfully slow, even short videos take ages, and movies are basically impossible. On top of that, once files are there, Kodi playback is choppy/laggy.

I'm not sure what the real bottleneck is. Nextcloud was already "kinda slow" before Kodi. I don't know if this is Docker overhead/volume configuration, the Pi just being overloaded, Nextcloud background work (previews/scanning/etc.), or the SSD adapter to USB C limiting speeds.

If you have ideas, I'd really appreciate pointers on where to start diagnosing, and what the "sane" architecture is here (even if the answer is "don't do both on one Pi").

TL;DR: Tried to reuse an old SSD and a Raspberry Pi to make a family Nextcloud drive, then added Kodi because the Pi is behind the TV. Nextcloud uploads are extremely slow and Kodi playback is laggy. Not sure if it's Docker, Nextcloud tuning, USB/SSD adapter, or just too much for a small device. Looking for beginner-friendly troubleshooting steps and/or a better setup plan.