Hot take: what most people call AI (large language and diffusion models) is, in fact, part of peak capitalism:

• relies on ill gotten gains (training data obtained without permission, payment or licensing)
• aims to remove human workers from the workforce within a system that (for many) requires them to work because capitalism has removed the bulk of social safety netting
• currently has no real route to profit at any reasonable price point
• speculative at best
• reinforces the concentration of power amongst a few tech firms
• will likely also result in regulatory capture with the large firms getting legislation passed that only they can provide “AI” safely

I could go on but hopefully that’s adequate as a PoV.

“AI” is just one of cherries on top of late stage capitalism that embodies the worst of all it.

So I don’t disagree - but felt compelled to share.

What is success here? The few founders and VC get filthy rich as the larger population dumps their money into Discord stock while the users and teams with limited foresight, who’ve moved their communities to discord, suffer?

I mean yeah I guess that’s the success Cory Doctorow warns us about again and again.

But that’s not my definition of success.

For context I’ve been on the receiving end of an IPO and the founders and investors made out like bandits while a fair number of employees were stuck holding the bags thanks to lock-ins, dilution and over priced shares.

So maybe we’re kinda staring at two sides of the same coin. Because yeah, you’re not misrepresentin my point.

But wait there’s a deeper point I’ve been trying to make.

You’re right that I am also saying it’s all bullshit - even when it’s “right”. And the fact we’d consider artificially generated, completely made up text libellous indicates to me that we (as a larger society) have failed to understand how these tools work. If anyone takes what they say to be factual they are mistaken.

If our feelings are hurt because a “make shit up machine” makes shit up… well we’re holding the phone wrong.

My point is that we’ve been led to believe they are something more concrete, more exact, more stable, much more factual than they are — and that is worth challenging and holding these companies to account for. i hope cases like these are a forcing function for that.

That’s it. Hopefully my PoV is clearer (not saying it’s right).

Ok hear me out: the output is all made up. In that context everything is acceptable as it’s just a reflection of the whole of the inputs.

Again, I think this stems from a misunderstanding of these systems. They’re not like a search engine (though, again, the companies would like you to believe that).

We can find the output offensive, off putting, gross , etc. but there is no real right and wrong with LLMs the way they are now. There is only statistical probability that a) we’ll understand the output and b) it approximates some currently held truth.

Put another way; LLMs convincingly imitate language - and therefore also convincing imitate facts. But it’s all facsimile.

Really?

I read your reply as saying the output is (can be) libellous - which it cannot be because it is not based on a dataset which resolves to anything absolute.

Maybe we’re just missing each other - struggling to parse each others’ output. ;)

Surely you jest because it’s so clearly not if you understand how LLMs work (at the core it’s a statistic model - and therefore all approximation to a varying degree).

But great can come out of this case if it gets far enough.

Imagine the ilk of OpenAI, Google, Anthropic, XAI, etc. being forced to admit that an LLM can’t actually do anything but generate approximations of language. That these models (again LLMs in particular) produce approximations of language that are so good they’re often indistinguishable from the versions our brains approximate.

But at the core they cannot produce facts because the way they are made includes artificially injected randomness layered on-top of mathematically encoded values that merely get expressed as tiny pieces of language (tokens) - ones that happen to be close to each other in a massively multidimensional vector space.

TLDR - they’d be forced to admit the emperor has no clothes and that’s a win for everyone (except maybe this one guy).

Also it’s worth noting I use LLMs for work almost daily and have studied them quite a bit. I’m not a hater on the tech. Only the capitalists trying to force it down everyone’s throat in such a way that we blindly adopt it for everything.

It’s all hallucinations.

Some (many) just happen to be very close to factual.

It’s sad to see that the marketing of these tools has been so effective that few realize how they work and what they do.

Emotionally.

But that’s exactly my point - we here in this bubble prefer Jellyfin - but it’s not ready for mass adoption. Even plex is a drop in the bucket.

I see Jellyfin suggested as an alternative to Plex here. I hope it is one day.

At the moment it’s nowhere close.

I’ve been running Jellyfin side-by-side Plex for two years and it’s still not a viable replacement for anyone but me. Parents, my partner, none of the possible solutions for them come anywhere near close to the usability of Plex and its ecosystem of apps for various devices.

That will likely change because plex is getting worse every day and folks can contribute their own solutions to the playback issues. With plex it’s more noise, more useless features. So one gets better (Jellyfin) and one gets worse (Plex).

But at the moment it really isn’t close for most folks who are familiar with the slickness of commercial apps.

Even from the administrative side, Jellyfin takes massively more system resources and it doesn’t reliably work with all my files.

Again, Jellyfin will get there it’s just not a drop in replacement for most folks yet.

And for context I started my DIY streaming / hosting life with a first gen Apple TV (pretty much a Mac mini with component video outs) that eventually got XBMC and then Boxee installed on it. I even have the forksaken Boxee box.

We use NGINX’s 444 on every LLM crawler we see.

Caddy has a similar “close connection” option called “abort” as part of the static response.

HAProxy has the “silent-drop” option which also closes the TCP connection silently.

I’ve found crawling attempts end more quickly using this option - especially attacks - but my sample size is relatively small.

Edit: we do this because too often we’ve seen them ignore robots.txt. They believe all data is theirs. I do not.

Mother would be proud.

Awesome. Thanks.

I hope I’m wrong but it looks like they disabled the ability to do opt into the program.

I cannot join my X1C to the program currently. It just errors out or leaves me in a CloudFlare “are you human” loop.

I’ve had good luck with Garuda after nearly two decades on Ubuntu and its derivatives.

So much so that I moved my work os to it, despite the gaming bent.

If I’m not mistaken Gamers Nexus showed this largely to be true. Even nipping at the bud of the 4070 on occasion.

B580 is a great deal. I’m hoping they release a B770 24GB and that Linux support for Battlemage is excellent by then.

That’s not very nice.

To garbage.

I mean, at least in a pinch you can burn some garbage to stay warm. Its going to suck but not as much as actions.

I agree.

As someone who uses a number of LLMs often as a pair programmer / sounding board - they’re incredibly useful if you have a very clear idea of your goal and also a solid idea of the architectural patterns you’re going after because they’re so often flatly wrong or suggest solutions that are wildly inefficient/inappropriate for larger projects/applications.

The more context you can provide the better it does but it still falls over often - suggesting courses of action or solutions that are completely hallucinated.

The one thing that’s consistently true is that the better I can describe my goals the better the response tends to be.

The best part about using them is that, for the most challenging work, I find that forcing myself to clearly explain my problem and goals in writing often leads me to a solution without ever having to submit a request.

There’s something about trying to clearly explain the problem to someone who “doesn’t know the space” that’s been helpful to finding the solution.

It’s odd that I had to rediscover this in such a visceral way after a previous life as a tech product person.

Also have white led Keychrons. Both are K2s.

Loved them at first, overtime I’ve grown to strongly dislike the wasted key for the white backlight but they’re still great keyboards - especially for the price.

I was so impressed with Garuda that I adopted it for my primary workstation OS even though I’m using the “gaming edition”.