I really don't understand the "LLM as therapy" angle. There's no way people using these services understand what is happening underneath. So wouldn't this just be textbook fraud then? Surely they're making claims that they're not able to deliver. I have no problem with LLM technology and occasionally find it useful, I have a problem with grifters.

The cloud is just someone else’s computer.

So is there a way to fill my social media with endless markov chains without: Spamming other users. Just sticking them all in some dedicated channel that would allow them to be easily filtered out.

Same, especially when searching technical or niche topics. Since there aren't a ton of results specific to the topic, mostly semi-related results will appear in the first page or two of a regular (non-Gemini) Google search, just due to the higher popularity of those webpages compared to the relevant webpages. Even the relevant webpages will have lots of non-relevant or semi-relevant information surrounding the answer I'm looking for. I don't know enough about it to be sure, but Gemini is probably just scraping a handful of websites on the first page, and since most of those are only semi-related, the resulting summary is a classic example of garbage in, garbage out. I also think there's probably something in the code that looks for information that is shared across multiple sources and prioritizing that over something that's only on one particular page (possibly the sole result with the information you need). Then, it phrases the summary as a direct answer to your query, misrepresenting the actual information on the pages they scraped. At least Gemini gives sources, I guess. The thing that gets on my nerves the most is how often I see people quote the summary as proof of something without checking the sources. It was bad before the rollout of Gemini, but at least back then Google was mostly scraping text and presenting it with little modification, along with a direct link to the webpage. Now, it's an LLM generating text phrased as a direct answer to a question (that was also AI-generated from your search query) using AI-summarized data points scraped from multiple webpages. It's obfuscating the source material further, but I also can't help but feel like it exposes a little of the behind-the-scenes fuckery Google has been doing for years before Gemini. How it bastardizes your query by interpreting it into a question, and then prioritizes homogeneous results that agree on the "answer" to your "question". For years they've been doing this to a certain extent, they just didn't share how they interpreted your query.

Worse. Office.com now takes me to m365.cloud.microsoft which as of today now takes me to a fucking Copilot chat window. Ofc no way to disable it because gee why would anyone want to do that?

It’s funny how the article asks the question, but completely fails to answer it. About 15 years ago, Nvidia discovered there was a demand for compute in datacenters that could be met with powerful GPU’s, and they were quick to respond to it, and they had the resources to focus on it strongly, because of their huge success and high profitability in the GPU market. AMD also saw the market, and wanted to pursue it, but just over a decade ago where it began to clearly show the high potential for profitability, AMD was near bankrupt, and was very hard pressed to finance developments on GPU and compute in datacenters. AMD really tried the best they could, and was moderately successful from a technology perspective, but Nvidia already had a head start, and the proprietary development system CUDA was already an established standard that was very hard to penetrate. Intel simply fumbled the ball from start to finish. After a decade of trying to push ARM down from having the mobile crown by far, investing billions or actually the equivalent of ARM’s total revenue. They never managed to catch up to ARM despite they had the better production process at the time. This was the main focus of Intel, and Intel believed that GPU would never be more than a niche product. So when intel tried to compete on compute for datacenters, they tried to do it with X86 chips, One of their most bold efforts was to build a monstrosity of a cluster of Celeron chips, which of course performed laughably bad compared to Nvidia! Because as it turns out, the way forward at least for now, is indeed the massively parralel compute capability of a GPU, which Nvidia has refined for decades, only with (inferior) competition from AMD. But despite the lack of competition, Nvidia did not slow down, in fact with increased profits, they only grew bolder in their efforts. Making it even harder to catch up. Now AMD has had more money to compete for a while, and they do have some decent compute units, but Nvidia remains ahead and the CUDA problem is still there, so for AMD to really compete with Nvidia, they have to be better to attract customers. That’s a very tall order against Nvidia that simply seems to never stop progressing. So the only other option for AMD is to sell a bit cheaper. Which I suppose they have to. AMD and Intel were the obvious competitors, everybody else is coming from even further behind. But if I had to make a bet, it would be on Huawei. Huawei has some crazy good developers, and Trump is basically forcing them to figure it out themselves, because he is blocking Huawei and China in general from using both AMD and Nvidia AI chips. And the chips will probably be made by Chinese SMIC, because they are also prevented from using advanced production in the west, most notably TSMC. China will prevail, because it’s become a national project, of both prestige and necessity, and they have a massive talent mass and resources, so nothing can stop it now. IMO USA would clearly have been better off allowing China to use American chips. Now China will soon compete directly on both production and design too.

I'm a big fan of the manta "Make your designs as simple as possible and no simpler". Pointless complexity drives me nuts, but others take it too far and remove functionality by making things too minimal. It doesn't help that a lot of businesses optimize for people who make changes, so the positive feedback loop is change for the sake of change rather than improving the product.