For people lacking context, Boeing split off and sold their division that became Spriti Aerosystems. The theory at the time was that Boeing’s core competency wasn’t building airplanes, it was managing relationships with other vendors. In particular, the actual plane manufacturing part of the company was undesirable due to perceived poor “Return on Net Assets.” The theory they pitched to shareholders was they should sell off non obviously profitable divisions so they reduced asset liability while keeping the same or better profits.

That was their explanation, of course it was a terrible idea.

Well this is a tremendous step in the wrong direction. The economic problem is the ad supported model in the first place, no matter how it’s run. This is the same thing Google does, they keep user data to themselves and sell the ad placement. So now Mozilla has the same economic incentives as Google. Unfathomably bad move.

If you read carefully this is actually very similar to the Steam news. I doubt Valve or GOG care, but generally the games are “sold” by the publisher as non transferable licenses for you to play them. So the part that matters isn’t up to them.

Note the versions, none of the results give you the official operators page for the current version, 16. They give 9, which went EOL in 2021.

Have you tried recent models? They’re not perfect no, but they can usually get you most of the way there if not all the way. If you know how to structure the problem and prompt, granted.

Them using Google indexes anonymously isn’t intending to solve the problem you think it is. It’s more about incentive structures. Google’s “free” search optimizes for ad revenue now. The API access doesn’t as much, and Kagi certainly doesn’t have an ad incentive. So privacy is a nice bonus, but the real benefit is a customer serving incentive structure.

Wonder how we should interpret the country “XD” being on the list. As far as I can tell its never been used for any real country.

A major caveat I’ve noticed some people misunderstand: it’s corporate CLAs that are problematic. The Apache Foundation also requires contributors sign a CLA, but it’s to provide a legal fail safe and a way to update to say Apache 3.0 if need be one day. Apache’s non profit, open source mission aligns with respecting the rights of contributors and the community. Corporations, on the other hand, not so much.

Codeberg is run off of donations, they have no service contract revenue. Nobody, much less a volunteer, wants to commit to a 5 or 10 year service plan like that, it’s not sustainable for a small project from a non profit.

CLAs can be abusive, but not necessarily. Apache Foundation contributors need to sign CLAs, which essentially codify in contract form the terms of the Apache 2.0 license. It’s a precaution, in case some jurisdiction doesn’t uphold the passive licensing scheme used otherwise. There’s also a relicensing clause, but that’s restricted to keeping in spirit, they can’t close the source.

I use Kagi, they provide access to all the main models in a chat interface and have a mode that feeds search engine results to them. It’s mostly replaced search engines for me. For programming work I find them very useful for using unfamiliar tools and libraries, I can ask it what I want to so and it’ll generally tell me how correctly. Importantly, the search engine mode has citations. $25 a month, but worth it.

This must be pandering to shareholders, no company in their right mind would want to compete when Meta is selling their first party headset at a giant loss.

The idea that “it’s ok cause we’d do the same” is ridiculous. There is no comparison: China is an authoritarian government and the parent company is practically an arm of the state. There are legitimate criticisms of American tech companies obviously, but they’re ultimately subject to the market and democratic governments. We shouldn’t be doing any business with authoritarians in the first place, much less inviting them to control a significant social media app in the guise of a legitimate business.

It’s not really special treatment. Generally, it’s understood that the defendant will use a bond company and put up a fraction of the cash. They are trying to balance the defendants appeal rights with not postponing the judgement forever. He’s actually tying up significantly more money this way than he would with a bond.

The actual judgement, should he lose appeal, isn’t reduced.

This is almost entirely misdirected. The success of Wikipedia is from its human structures, the technical structure is close to meaningless. To propose a serious alternative you’d have to approach it from a social direction, how are you going to build a moderation incentive structures that forces your ideal outcomes?

Federation isn’t a magic bullet for moderation, alone it creates fractal moderation problems.

So this is probably another example of Google using too blunt of instruments for AI. LLMs are very suggestible and leading questions can severely bias responses. Most people using them without knowing a lot about the field will ask “bad” questions. So it likely has instructions to avoid “which is better” and instead provide pros and cons for the user to consider themselves.

Arch for stuff I have physical access to. Nothing’s ever gone wrong, so it’s worth it for the immediate updates and consistency with my other systems. For VPS I use Debian though, occasionally the unstable/Sid branch if I really need the latest updates. There are almost always Debian images available on a VPS.

If I’m understanding this right, and this basically an API that lets you pick which app store administers an app, that could be quite helpful, not harmful. I currently have fdroid, play store, and Samsung store, and I assume they try to update apps by the fully qualified name, as multiple stores show and try to update a single app instance, sometimes with weird results.

Compression is actually a mathematical field that’s fairly well explored, and this isn’t compression. There are theoretical limits on how much you can compress data, so the data is always somewhere, either in the dictionary or the input. Trained models like these are gigantic, so even if it was perfect recall the ratio still wouldn’t be good. Lossy “compression” is another issue entirely, more of an engineering problem of determining how much data you can throw out while making acceptable compromises.

This is a classic problem for machine learning systems, sometimes called over fitting or memorization. By analogy, it’s the difference between knowing how to do multiplication vs just memorizing the times tables. With enough training data and large enough storage AI can feign higher “intelligence”, and that is demonstrably what’s going on here. It’s a spectrum as well. In theory, nearly identical recall is undesirable, and there are known ways of shifting away from that end of the spectrum. Literal AI 101 content.

Edit: I don’t mean to say that machine learning as a technique has problems, I mean that implementations of machine learning can run into these problems. And no, I wouldn’t describe these as being intelligent any more than a chess algorithm is intelligent. They just have a much more broad problem space and the natural language processing leads us to anthropomorphize it.