Stories like this are sometimes more complicated than they appear. The infamous examples of $500 hammers, for example, were anti sparking hammers for working around flammables or munitions, hence requiring special materials, certification, and low production runs.

For this case, we have liquid hand soap dispensed by a pump. Pumps require a sealed vessel. Unlike commercial planes, military planes are required to anticipate prolonged operation with an unpressurized cabin. At max altitude of a C17, atmospheric pressure is only 20% of sea level. Off the shelf dispensers are unlikely to be designed to withstand that pressure difference, let alone function normally. In a high demand environment like aerospace, even apparently minor failures like an exploding soap container needs to be taken seriously due to the possibility of unexpected cascading failures. Why not use bar soap, then? Unfortunately this too has complications, like not being able to be securely mounted, liquid soaps having superior hygiene and cross contamination characteristics, and necessity for military standardized soap, sometimes designed for heavy metal, eg lead, which is likely if the cargo were munitions.

This unusual set of requirements unlikely to be seen outside the military context, so whether designed by Boeing or off the shelf the unit would likely have low quantity manufacturing runs, significantly increasing per unit costs. Combine that with the necessary certifications and the per unit costs balloon even further.

While a soap dispenser having an 80x markup seems absurd, it might be more reasonable than it seems at first glance. To be clear, there absolutely is military contractor graft. I just don’t expect even a $10,000 soap dispenser would be a substantial proportion if it even within the C17.

I recently removed in editor AI cause I noticed I was acquiring muscle memory for my brain, not thinking through the rest past the start of a snippet that would get an LLM to auto complete. I’m still using LLMs, particularly for languages and libraries I’m not familiar with, but using the artifacts editors in ChatGPT and Claude.

Given the ease of implantation of end to end encryption now, it’s a reasonable assumption that anything not e2ee is being data mined. E2ee has extensive security benefits, for example even if your data is dumped the info is still useless. So, there has to be a compelling reason to not use it.

People haven’t really changed. As always, power corrupts. When the rewards are great enough, it seems people are often enough willing to compromise their integrity.

I have LTS and zen kernels installed in addition to the default Arch one, that should prevent this yes?

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.

Funny how the DOS equivalent of ls is dir, so before the GUI folder metaphor.

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 runs great now. Most importantly, it supports extensions like ublock.

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.

Edit: I don’t mean to excuse, just explain. If anything, the implication is that Google rushed it out after attempting to slap bandaids on serious problems. OpenAI and Anthropic, for example, have talked about how alignment training and human adjustment takes a majority of the development time. Since Google is in a self described emergency mode, cutting that process short seems a likely explanation.

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.

While it sounds absurd, between travel expenses and needing to maintain residency in the state and the very expensive DC, $174,000 really doesn’t stretch very far. Instead of just paying them more, a housing and transit stipend might be prudent.

Normal people don’t, but when you get into absolutely massive enterprise archiving there’s no rival for the density and cost effectiveness. It sucks for general purpose storage, but for write once, hopefully never read use, they’re ideal.

This was true maybe 10 years ago, nowadays Linux has better driver support than Windows. Printers, networking, input devices, everything I’ve tried is plug n play with Linux, Windows you gotta driver hunt.