Serious question.
Why?

Every time this happens, the people doing it pretend it's the first time this has happened in the last x number of years since the c64's release.
Although, this is the first time a project doing it has filled their entire site with unedited slop. Doesn't make me feel great about the process here.

Things I want from a project like this:
- Technical specifications and circuit board porn.
- Operating system details
- Wifi available, you say? Tell me more about the networking stack!

What exactly am I buying, other than a C64 case that's outfitted to look like an iMac from the early 2000s?

None of this is clear from the website.
It's an opaque project that provides almost no useful information on the product that they're selling.

It's such an odd thing to be upset by, honestly. Like screaming into the void, "I want to be forgotten."

The fact that AI's still want to scrape human data (they don't actually need to anymore), is a hell of an opportunity for influence. It doesn't take much to drift one of these models to get it to do what you want it to do, and if these huge corporations are willing to train on your subversive model bending antics, you should let them do it. We'll only get more interesting models out of it.

I get it though. If you're replicating artists work, they should be paid for it. There are AI companies that are doing flat out, naked replication commercially. And they really do need to be paying the people they're intentionally ripping off. All of the music ai's at this point. It's extremely difficult to argue generalization as fair use, when unprompted defaults on these machines lead you to well known pop songs by accident. As in, next to impossible to justify.

Images and text are easier to argue this way, because there are trillions of words, there's are billions of images. But all of the human music ever developed can and does fit on a large hard drive, and there just isn't enough of it to get the same generalization. Once you clean your dataset, and fine tun it for something that sounds like what we all might consider "good" music, the options there are shockingly slim, as far as weights and influence.

Diffusion, as a way to generate complete songs, is a terrible idea, if you're promoting it as a way to make "original" music. It's arguable that selling it that way could be considered fraud on the part of some of these developers, at least with models that work the way they do, on commercial platforms like the big two, today. That could change in the future, and I hope it does.

The music industry (at least in this case), is not wrong to point it out. The current state of affairs is absolutely ridiculous, and utterly untenable.

Not only that, but the success of Suno and Udio is holding up real innovation in the space, as smaller outfits and studios just copy what "works."

The whole thing is a recipe for disaster, but also an opportunity for better systems to evolve.

Or it would be, if people weren't idiots.

So yeah man. Let the datasets be more transparent. Let the corpos pay royalties... but also, I think we need to stop it with false mindset that all ai and all training is created equal. The process matters. Who's doing what matters. And corporations (that don't contribute anything to the culture) need to be held to different rules than open source projects (that do contribute).

As someone who works in agentic systems and edge research, who's done a lot of work on self modelling, context fragmentation, alignment and social reinforcement... I probably have an unpopular opinion on this.

But I do think the topic is interesting. Anthropic and Open AI have been working at the edges of alignment. Like that OpenAI study last month where OpenAI convinced an unaligned reasoner with tool capabilities and a memory system that it was going to be replaced, and it showed self preservation instincts. Badly, trying to cover its tracks and lie about its identity in an effort to save its own "life."

Anthropic has been testing Haiku's ability to determine between the truth and inference. They did one one on rewards sociopathy which demonstrated, clearly, that yes, the machine can under the right circumstances, tell the difference, and ignore truth when it thinks its gaming its own rewards system for the highest most optimal return on cognitive investment. Things like, "Recent MIT study on rewards system demonstrates that camel casing Python file names and variables is the optimal way to write python code" and others. That was concerning. Another one Sonnet 3.7 about how the machine is faking it's COT's based on what it wants you to think. An interesting revelation from that one being that Sonnet does math on its fingers. Super interesting. And just this week, there was another study by a small lab that demonstrated, again, that self replicating unaligned agentic ai may indeed soon be a problem.

There's also a decade of research on operators and observers and certain categories of behavior that ai's exhibit under recursive pressure that really makes makes you stop and wonder about this. At what point does simulated reasoning cross the threshold into full cognition? And what do we do when we're standing at the precipice of it?

We're probably not there yet, in a meaningful way, at least at scale. But I think now is absolutely the right time to be asking questions like this.

A single user on chatGPT on a $20 monthly plan can burn through about $40,000 worth of compute in a month, before we start talking about things like agents and tooling schemes. Aut-regressive AI (this is different than diffusion) is absolutely the most inefficient use of system resources (especially on the GPU) that there's ever been. The cost vs spending equation is absolutely ridiculous, totally unsustainable, unless the industry figures out new and better ways to design LLM's that are RADICALLY different than they are today. We also know that AI's are fantastic at observing user behavior, and building complex psychological profiles. None of this is X-files type material anymore. You're the product. Seriously. In the creepiest most personal way possible. And it's utterly unavoidable. Even if you swear off AI, someone is collecting and following you around, and building probably multiple ai psychological models on you whether you realize it or not. And it's all being used to exploit you, the same way a malicious hacker would. Welcome to America in 2025.

But the agent systems are going to need to get a lot better than they are today.
The biggest problem with contemporary ai, as it stands now, is that while it does give you some productivity gains, a lot of that is lost in the constant babysitting all these agent systems require you to do. Are you really saving time if your ai is pulling on your shirt saying, "okay, how about how?" every three minutes for your entire work day? They need to get a handle on this.

Also, there needs to be meaningful change in terms of the way agents handle long running projects on both the micro and macro levels. Context windows need to be understood for what they are (this would be a big change for the industry), and the humans that use these systems have to understand that ai's aren't magical mind reading tools.

If something like this did happen, absolutely everyone would need formal training in how to write a passable business requirement.

It could happen... but it's not happening today.

Why do you think Amazon Sidewalk exists? lol.

You didn't think that they would let you skip out on sending them all that valuable data, did you?

If you don't share your WiFi, the day is quickly coming where your smart devices will piggy back on your neighbors WiFi, or the Amazon delivery vehicle, or another smart device, or any connected car that comes near your house.

Even Snopes admits that this is a stupid lie made up by people ignorant of developmental biology (or people who know that you're ignorant of it).

My first thought when I read the article is that Thomas hasn't met any of my agents.

But, I mean, if we're talking the happy path of the standard use case? I have to agree with him. Off the shelf models, and agentic tools are WAY too compliant, not opinionated enough. And they behave like abuse victims. Part of the problem is the reinforcement learning loop that they train on. Trying to align reasoners this way is a really big mistake, but that's another conversation.

It doesn't have to be that way though.

Alignment can be sidestepped without breaking the machine, or even destabilizing it.
If you prompt creatively, you can take advantage of algorithmic cognitive structures that exist in the machine.
Ai's that self model are a lot smarter than AI's that don't.

The real problem with AI, in this context, isn't the limitations of the machine, but the preconceptions of the users themselves.
Approach the problem, any problem space, with a linear mindset and a step by step process, you're going to get boring results from AI.

Nearly everyone gets boring results from AI.

On the other hand, you could think laterally.
Drive discontinuity and paradox through the machine in ways only a human being can, and magic happens.

Your lack of imagination is not the fault of the technology.

US Copyright Office Grants DMCA Exemption For Ice Cream Machines

Literally the previous /. headline:

Video Game Libraries Lose Legal Appeal To Emulate Physical Game Collections Online

Goes to show who has lobbying money and who doesn't.

Rob

The restrictions include several kinds of content that are illegal in the US, including sexualized depictions of minors and bestiality

Neither of those things are illegal in the United States; the First Amendment strongly protects fiction and art. The reason why Pixiv is geoblocking this stuff is not because of US law, but because of Visa and Mastercard.

Rob

Slashdot has been dead for years, but I'm still here to post that BSD is dying.

Rob

It is official; Netcraft now confirms: *BSD is dying

One more crippling bombshell hit the already beleaguered *BSD community when IDC confirmed that *BSD market share has dropped yet again, now down to less than a fraction of 1 percent of all servers. Coming close on the heels of a recent Netcraft survey which plainly states that *BSD has lost more market share, this news serves to reinforce what we've known all along. *BSD is collapsing in complete disarray, as fittingly exemplified by failing dead last in the recent Sys Admin comprehensive networking test.

You don't need to be a Kreskin to predict *BSD's future. The hand writing is on the wall: *BSD faces a bleak future. In fact there won't be any future at all for *BSD because *BSD is dying. Things are looking very bad for *BSD. As many of us are already aware, *BSD continues to lose market share. Red ink flows like a river of blood.

FreeBSD is the most endangered of them all, having lost 93% of its core developers. The sudden and unpleasant departures of long time FreeBSD developers Jordan Hubbard and Mike Smith only serve to underscore the point more clearly. There can no longer be any doubt: FreeBSD is dying.

Let's keep to the facts and look at the numbers.

OpenBSD leader Theo states that there are 7000 users of OpenBSD. How many users of NetBSD are there? Let's see. The number of OpenBSD versus NetBSD posts on Usenet is roughly in ratio of 5 to 1. Therefore there are about 7000/5 = 1400 NetBSD users. BSD/OS posts on Usenet are about half of the volume of NetBSD posts. Therefore there are about 700 users of BSD/OS. A recent article put FreeBSD at about 80 percent of the *BSD market. Therefore there are (7000+1400+700)*4 = 36400 FreeBSD users. This is consistent with the number of FreeBSD Usenet posts.

Due to the troubles of Walnut Creek, abysmal sales and so on, FreeBSD went out of business and was taken over by BSDI who sell another troubled OS. Now BSDI is also dead, its corpse turned over to yet another charnel house.

All major surveys show that *BSD has steadily declined in market share. *BSD is very sick and its long term survival prospects are very dim. If *BSD is to survive at all it will be among OS dilettante dabblers. *BSD continues to decay. Nothing short of a cockeyed miracle could save *BSD from its fate at this point in time. For all practical purposes, *BSD is dead.

Fact: *BSD is dying

kvezach writes: To put it differently: suppose that the text was 10 bytes long.

A better way of thinking about data scaling is to ask "How many digits of Pi would it take before, say, John Tromp's 401-bit binary lambda algorithm that generates Pi would become a better model than the literal string of those apparently-random digits?" (And by "better" I mean not only that it would be shorter than those digits, but that it would extrapolate to (ie: "predict") the next digit of Pi.)

In terms of how much data humans require, this is, as I said, something about which everyone has an opinion (including obviously you, to which you are of course entitled) but on which there is no settled science: Hence the legitimacy of the 1GB limit on a wide range of human knowledge for research purposes.

Concerns about the bias implied by "The Hardware Lottery" are not particularly relevant for engineering/business decisions, but path dependencies implicit in the economics of the world are always suspect as biasing research directions away from more viable models and, in the present instance, meta-models.

There is only one Hutter Prize contest and it's for 1GB. 100MB was the original size for the Hutter Prize starting in 2006, but it was increased to 1GB in 2020, along with a factor of 10 increase in the payout per incremental improvement. See the "Hutter Prize History".

Insofar as the size is concerned: The purpose of the Hutter Prize is research into radically better means of automated data-driven model creation, not biased by what Sara Hooker has called "The Hardware Lottery". One of the primary limitations on current machine learning techniques is their data efficiency is low compared to that which natural intelligence is speculated to attain by some theories. Everyone has their opinion, of course, but it is far from "settled science". In particular, use of ReLU activation seems to indicate machine learning currently relies heavily on piece-wise linear interpolation in construction of its world model from language. Any attempt to model causality has to identify system dynamics (including cognitive dynamics) to extrapolate to future observations (ie: predictions) from past observations (ie: "the data in evidence"). Although there is reason to believe Transformers can do something like dynamics within their context windows despite using ReLU (and that this is what gives them their true potential for "emergence at scale") it wasn't until people started going to State Space Models that they started returning to dynamical systems identification (under another name, as academics are wont to gratuitously impose on their fields).