GamingChairModel

joined 3 years ago
[–] GamingChairModel@lemmy.world 1 points 13 hours ago (1 children)

Yeah, you're probably right. Still, if big tech's ad behavior (ads for stuff you've already bought, ads for things you'd never buy but might have looked up information about for non-consumer reasons) is any indication, I'm not actually sure they're using really sophisticated techniques here that can't be thrown off by one-off bored sessions.

[–] GamingChairModel@lemmy.world 1 points 16 hours ago

The most secure messaging system in the world won't matter if you read all your messages with a camera over your shoulder.

E2EE protects what's between the ends. But once a message is delivered to the end, the user can do whatever they want with it: screenshot or save the message, forward it to others, save it in a database open to others, etc.

[–] GamingChairModel@lemmy.world 29 points 1 day ago (6 children)

When I'm bored, I get price quotes from Lyft and Uber to the airport. I'll click around and look at the different tiers of service. Then I choose not to actually request a fare, and close the app.

I fly enough to where both apps probably have me as a regular airport customer, but that I'm price sensitive enough where I just won't use their service sometimes.

I do the same with Doordash and other food apps, where I just put together potential orders to get a sense of how much something would cost if I ordered it, and then just never order it, letting that cart expire. I'm hoping this poisons their data or adds something to the price sensitivity metrics.

I also look at airfares, hotel prices, etc., a lot. It's not for the purpose of manipulating their algorithms, as I just like to get a sense for how prices move over time. But if there's a side effect that these variable pricing algorithms take into account people who window shop and walk away, maybe I'm helping push prices down.

If they're gonna create a panopticon, we should fuck with their data.

AMP was two different things.

  1. Google defined a standard that was an approved subset of html/css/js designed to load quickly on mobile screens, with none of the extra cruft that slows down sites as experienced by phone users.
  2. Google also agreed to host AMP-compliant pages on their own ultra-fast servers. Many smaller sites, especially local news stations and local newspapers, agreed to offload the extra work by allowing Google to host their content. Those hosted pages had a whole system of referring back to the canonical URL on the actual outlet's own website (and the desktop version of the page), and had little things referring to the AMP project.

I was 100% on board with #1, but #2 was controversial and created a lot of the conflicts of interest that tore the whole thing apart. Google spun off the AMP project as an open governance model, and then it was taken over by the OpenJS foundation.

In the end, the performance gap between mobile devices and desktops narrowed, and at this point most people's phones and mobile browsers can load pages just as quickly as a laptop, so the purpose of the project itself is less important to the actual web as it exists today.

No paychecks mean no customers.

That's never been a concern with the corporate executives. The macro level stuff is someone else's problem.

We saw it in the 90's when they stopped hiring managers from within, instead relying on bringing in outside consultants, and then directly hiring the ones they liked, so that there was no longer a path between the typical employee's seat and their boss's seat.

We saw it in the 2000's when companies realized that they didn't want to train entry level employees anymore, and would rather wait for someone else to train them, and then hire midlevels. Except too many employers did that and then the pipeline of future midlevels dried up in a lot of industries that were then ill positioned to survive the tumult of the 2008-2010 recession. Then something similar played out in the 2010s in certain other industries.

It's always been buck passing. They don't care.

[–] GamingChairModel@lemmy.world 10 points 1 day ago (2 children)

He doesn't even have that. He just pretends, by misleadingly saying he has a degree from Wharton. He does have a business degree from undergrad, from the University of Pennsylvania, whose business school is called Wharton, but most people say Wharton degree to refer to an MBA from that business school. He takes advantage of the confusion by referring back to his Wharton degree, and it sometimes works.

[–] GamingChairModel@lemmy.world 10 points 2 days ago (3 children)

Google's AMP was supposed to stop this, but they never could get around the conflict of interest between "our search engine experience is improved by prioritizing fast loading results" and "our ad business needs websites to carry a ton of ads," ended up carving in enough exceptions and an anti-AMP backlash that they pretty much walked away from the whole thing.

I'm also waiting to see how Linux support for the A-series chips develops, now that the MacBook Neo has A18 Pros running MacOS. There are people working on tools to allow Asahi's m1n1 bootloader to work on the Neos. Unclear how much of that effort will ultimately be upstreamed to Asahi itself, but I'm hopeful.

Most CPUs implement a strategy called "race-to-sleep," where it throws all the resources it has at a computing task, to finish it as fast as possible, so that it can hurry back to a low power sleep mode.

For that reason, optimizing speed often improves battery life.

Technically these are all System in Package (SiP) rather than systems on a chip (SoCs). It's not cost effective to waste silicon area on things like memory and storage in the latest and greatest TSMC nodes, so they have several different silicon dies that they connect together using advanced packaging techniques, but where each silicon die in that package might come from a different foundry/process.

[–] GamingChairModel@lemmy.world 50 points 3 days ago

Not exactly, the bootpicker just won't show partitions that are not flagged as bootable. This is a departure from previous versions, that would check to see whether partitions were bootable without looking at the flag, and took a little bit of investigating to figure out this undocumented behavior, but it is an easy fix:

https://asahilinux.org/2026/06/progress-report-7-1/

[–] GamingChairModel@lemmy.world 1 points 3 days ago (1 children)

Somewhere along the line someone is going to have a password that is their favorite sports team followed by their anniversary. Someone will reuse the same password over and over including a site with exploitable security flaws.

None of this is new, and none of this has destroyed the feasibility of electronic payments. AI has nothing to do with any of it.

The ability to conduct fraud, hack websites, and hijack accounts has always been around. The systems we've built will always assume that occurs on a regular basis, and are resilient against needing to address this reality. How does any of this prevent electronic payments from being processed?

 

In recent years, I've seen people use projectors at night to protest policies: projecting slogans, images, etc., using a normal projector against the wall of the target of a protest (embassies, government buildings, prisons/detainment facilities, corporate headquarters, etc.).

Here's an example, but I've seen lots of other examples. And even outside of protest, people have been doing ridiculous things with 3D projection, like this guy animating a hotel cuck chair (SFW).

This got me to thinking: if the image recognition of Flock and similar cameras is just an AI image recognition function (using ImageNet or something like that) being run on captured video, what's stopping people from projecting false data onto streets and sidewalks in a way that tricks the Flock camera into recording false people/vehicles in places where there was no person/vehicle? Including fake license plates, etc.?

Seems like an attack vector for some kind of denial of service attack, like putting fake faces and license plates all over.

Is there anyone doing this? Would it be possible, at least at night, for certain camera locations and angles?

 

(Note: McSweeney's is a satirical publication)

 

I've read some of Ed Zitron's long posts on why the AI industry is a bubble that will never be profitable (and will bring down a lot of companies and investors), and one of the recurring themes is that the AI companies are trying to capture growing market share in an industry where their marginal profits are still negative, and that any increase in revenue necessarily increases their costs of providing their services.

But some of the comments in various HackerNews threads are dismissive, saying that each new generation of models makes the cost of inference lower, so that with sufficient customer volume, the companies running the models can make enough profit on inference to make up for the staggering up-front capital expenditures it took to build out the data centers, train their models, etc.

It's all pretty confusing to me. So for those of you who are familiar with the industry, I have several questions:

  1. Is the cost of running any given pretrained model going down, for specific models? Are there hardware and software improvements that make it cheaper to run those models, despite the model itself not changing?
  2. Is the cost of performing a particular task at a particular quality level going down, through releases of newer models of similar performance (i.e., a smaller model of the current generation performing similarly to a bigger model of the previous generation, such that the cost is now cheaper)?
  3. Is the cost of running the largest flagship frontier models going down for any given task? Or does running the cutting edge show-off tasks keep increasing in cost, but where the companies argue that the improvement in performance is worth the cost increase?

I suspect that the reason why the discussion around this is so muddled online is because the answers are different depending on which of the 3 questions is meant by "is running an AI model getting cheaper over time?" And the data isn't easy to synthesize because each model has different token prices and different number of tokens per query.

But I wanted to hear from people who are knowledgeable about these topics.

 

Curious what everyone else is doing with all the files that are generated by photography as a hobby/interest/profession. What's your working setup, how do you share with others, and how are you backing things up?

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