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.

The comments from that article are some of the most vitriolic I’ve ever seen on a technical issue. Goes to prove the maintainer’s point though.

Some are good for a laugh though, like assertions that Rust in the kernel is a Microsoft sabotage op or LLVM is for grifters and thieves.

FOSS in general needs better means of financial support. While the software is free and libre, developer time is not, and ultimately they gotta eat and pay bills. I hope they get positive results and don’t catch much unnecessary flak.

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.

My first programming experience, an online class, was in a Linux VM. Linux made programming easy and delightful, Windows always made it a huge pain. As time went on, more of what I did was easier on Linux, and now everything is.

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.

The moment that shocked me was when printers, network cards, and even motherboard integrated Ethernet didn’t work on Windows without driver downloads but Linux was plug and play. Full reversal of the situation.

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.

Have you used it recently? Previous versions I would’ve agreed, but 5.0 was a huge improvement. If I didn’t know, I’d likely have assumed it to be a native feature.

I’ll take a look at Vivaldi’s approach though, I’ve heard good things about those features previously.

If you want vertical tabs with the ability to organize them in trees I suggest the Sideberry extension. It legitimately makes me nervous that the functionality would ever go away, it improves my productivity so much.

You can bookmark trees, collapse them, search them, load/unload them manually, I could go on. It makes it easy to organize dozens or hundreds of tabs. I have some trees for emails, news, forums, projects, etc. When I’m done just fold it up: the top tab bar can hide tabs that aren’t in the active tree you’re using, so you can still navigate the tabs normally.

GNOME always seemed like an odd choice considering how little customization is available. It feels like a prescriptive approach, you will use your computer the way GNOME feels is appropriate, whereas KDE tries to accommodate however you want to use your computer.

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.

3 or 4 years, including on Nvidia machines. I’ll admit it took fiddling to get working awhile ago. Nowadays I use my desktops AMD iGPU as the main display driver and offload the rendering to the Nvidia card for intense programs or games, best of both worlds.

Don’t get too excited, this is a pretty fringe theory that doesn’t really have experimental evidence. They were able to make some observations fit with their theory without dark matter yes, but not all of them. The tired light part in particular has a lot of contradictions with observation that they don’t explain.

So interesting, but far from definitive.

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.