I’m giving some reasons why turning on or off location services at the OS level doesn’t appreciably change battery life.

You can turn off higher level location services at the OS level, but at the radio level the cellular network will always need a precise enough location to handle tower handoffs and timing issues between the tower and phone, as well as modern beam forming techniques where the tower “aims” the signal at the phone. The simple act of the phone communicating with a specific tower tells the phone where it is (sometimes with surprisingly high precision).

911/emergency services also use more low level location techniques, but I’m pretty sure those functions don’t get called unless you dial an emergency number.

It’s not feasible for a mass market consumer product like Starlink.

Why not? That’s a service designed to serve millions of simultaneous users from nearly 10,000 satellites. These systems have to be designed to be at least somewhat resistant to unintentional interference, which means it is usually quite resistant to intentional jamming.

Any modern RF protocol is going to use multiple frequencies, timing slots, and physical locations in three dimensional space.

And so the reports out of Iran is that Starlink service is degraded in places but not fully blocked. It’s a cat and mouse game out there.

I’d think that there are practical limits to jamming. After all, jamming doesn’t just make radio impossible, it just makes the transmitter and receiver need to get closer together (so that their signal strength in that shorter distance is strong enough to overcome the jamming from further away). Most receivers filter out the frequencies they’re not looking for, so any jammer will need to actually be hitting that receiver with that specific frequency. And many modern antenna arrays rely on beamforming techniques less susceptible to unintentional interference or intentional jamming that is coming from a different direction than where it’s looking. Even less modern antennas can be heavily directional based on the physical design.

If you’re trying to jam a city block, with a 100m radius, of any and all frequencies that radios use, that’s gonna take some serious power. Which will require cooling equipment if you want to keep it on continuously.

If you’re trying to jam an entire city, though, that just might not be practical to hit literally every frequency that a satellite might be using.

I don’t know enough about the actual power and equipment requirements, but it seems like blocking satellite communications between satellites you don’t control and transceivers scattered throughout a large territory is more difficult than you’re making it sound.

90GB of both RAM+NAND combined. I’m guessing most of it is actual persistent storage for all the stuff the infotainment system uses (including imagery and offline map data for GPS, which is probably a big one), rather than actual memory in the sense of desktop computing.

Everything else that you said seems to fit the general thesis that they’re making a lot more money selling to AI companies.

If those reasons were still true but the memory companies stood to not make as much money on those deals, I guarantee the memory manufacturers wouldn’t have taken the deal. They only care about money, and the other reasons you list are just the mechanisms for making more money.

It’s a very common complaint among people administering websites. This particular AI poisoning service seems to be directed at those people.

So maybe it’s not the majority of complaints about AI, but it’s a significant portion of the complaints about AI from site administrators.

The Fediverse is designed specifically to publish its data for others to use in an open manner.

Sure, and if the AI companies want to configure their crawlers to actually use APIs and ActivityPub to efficiently scrape that data, great. Problem is that there’s been crawlers that have done things very inefficiently (whether by malice, ignorance, or misconfiguration) and scrape the HTML of sites repeatedly, driving up some hosting costs and effectively DOSing some of the sites.

If you put Honeypot URLs in the mix and keep out polite bots with robots.txt and keep out humans by hiding those links, you can serve poisoned responses only to the URLs that nobody should be visiting and not worry too much about collateral damage to legitimate visitors.

What’s crazy is that they aren’t just doing this because they make more money with AI.

No, they really are making more money by selling whole wafers rather than packaging and soldering onto DIMMs. The AI companies are throwing so much money at this that it’s just much more profitable for the memory companies to sell directly to them.

That’s why “bullshit,” as defined by Harry Frankfurt, is so useful for describing LLMs.

A lie is a false statement that the speaker knows to be false. But bullshit is a statement made by a speaker who doesn’t care if it’s true or false.

If I am reading this correctly, anyone who wants to use this service can just configure their HTTP server to act as the man in the middle of the request, so that the crawler sees your URL but is retrieving poison fountain content from the poison fountain service.

If so, that means the crawlers wouldn’t be able to filter by URL because the actual handler that responds to the HTTP request doesn’t ever see the canonical URL of the poison fountain.

In other words, the handler is “self hosted” at its own URL while the stream itself comes from the same URL that the crawler never sees.

In terms of usage of AI, I’m thinking “doing something a million people already know how to do” is probably on more secure footing than trying to go out and pioneer something new. When you’re in the realm of copying and maybe remixing things for which there are lots of examples and lots of documentation (presumably in the training data), I’d bet large language models stay within a normal framework.

The hot concept around the late 2000’s and early 2010’s was crowdsourcing: leveraging the expertise of volunteers to build consensus. Quora, Stack Overflow, Reddit, and similar sites came up in that time frame where people would freely lend their expertise on a platform because that platform had a pretty good rule set for encouraging that kind of collaboration and consensus building.

Monetizing that goodwill didn’t just ruin the look and feel of the sites: it permanently altered people’s willingness to participate in those communities. Some, of course, don’t mind contributing. But many do choose to sit things out when they see the whole arrangement as enriching an undeserving middleman.

Most Android phones with always on have a grayscale screen that is mostly black. But iPhones introduced always on with 1Hz screens and still show a less saturated, less bright version of the color wallpaper on the lock screen.

Joke’s on him, I’m putting my website at 305.domain.tld.

It’s actually pretty funny to think about other AI scrapers ingesting this nonsense into the training data for future models, too, where the last line isn’t enough to get the model to discard the earlier false text.

On phones and tablets, variable refresh rates make an “always on” display feasible in terms of battery budget, where you can have something like a lock screen turned on at all times without burning through too much power.

On laptops, this might open up some possibilities of the lock screen or some kind of static or slideshow screensaver staying on longer while idle, before turning off the display.

While we’re at it, I never understood why the convention for domain name wasn’t left to right tld, domain, subdomain. Most significant on left is how we do almost everything else, including numbers and ISO 8601 dates.

It’s a fancy marketing term for when AI confidently does something in error.

How can the AI be confident?

We anthropomorphize the behaviors of these technologies to analogize their outputs to other phenomena observed in humans. In many cases, the analogy helps people decide how to respond to the technology itself, and that class of error.

Describing things in terms of “hallucinations” tell users that the output shouldn’t always be trusted, regardless of how “confident” the technology seems.

Its a bad idea because ai doesnt “know” in the same way humans do.

Does that matter? From the user’s perspective, it’s a black box that takes inputs and produces outputs. The epistemology of what knowledge actually means is kinda irrelevant to the decisions of how to design that interface and decide what types of input are favored and which are disfavored.

It’s a big ol matrix with millions of parameters, some of which are directly controlled by the people who design and maintain the model. Yes, those parameters can be manipulated to be more or less agreeable.

I’d argue that the current state of these models is way too deferential to the user, where it places too much weight on agreement with the user input, even when that input contradicts a bunch of the other parameters.

Internal to the model is still a method of combining things it has seen to identify a consensus among what it has seen before, tying together certain tokens that actually do correspond to real words that carry real semantic meaning. It’s just that current models obey the user a bit too much to overcome a real consensus, or will manufacture consensus where none exists.

I don’t see why someone designing an LLM can’t manipulate the parameters to be less deferential to the claims, or even the instructions, given by the user.