That’s a great point.

I don’t want to trust them either. I don’t want to have to.

The only “devil’s advocate” argument I can think of is they’re trying to appeal to enterprise clients (who would not know that and want to “trust” a security company). That would explain the “I” change: “inclusion” (sadly) sounds political, “innovation” is like corporate catnip. Bitwarden could be trying to attract big fish to fund development, having their cake an eating it.

The company has long defined its values with the acronym “GRIT,” which used to stand for “Gratitude, Responsibility, Inclusion, and Transparency.” After May 4, it changed the acronym to stand for “Gratitude, Responsibility, Innovation, and Trust.”

It’s not as bad as the headline seems. Transparency is still in the motto. The actual change is:

But still. Why change it at all? Why replace “inclusion” with “innovation”?

It smells like Tech Bro.

There’s just no way to spin that positively, even giving them the benefit of the doubt, especially since they aren’t rolling it back. Someone spent effort to make that values change, so its not an accident nor a “nothingburger”.

My friend, redirect to old.reddit.com.

I honestly don’t know how people stand the “new” site. The mobile page hurts my brain.

Shame Blu-Rays didn’t catch on more (so they’d flood thrift stores now).

Exactly ^

uBlock Origin and SponsorBlock

What percentage of YouTube users have either?

It must be tiny. And most are using the mobile app anyway.

Read sci-fi with “speculative” life, as a thought experiment: https://www.orionsarm.com/xcms.php?r=oaeg-front

It really changes one’s perspective.

Humans… are not that special. Our consciousness isn’t special. There are all sorts of theoretical forms of life that might view our perception of life the same way we view a jellyfish “thinking,” or a plant reacting to stimuli, or a rock rolling down a cliff.

Does that nullify ethics? Empathy? Of course not. Humans aren’t jellyfish. But all forms of complex “intelligence” need to be looked at for what they are, what their entire existence encompasses, not from the lens of another being. A smart toaster makes toast. An LLM predicts tokens. A human mind, simulated in silicon, simulated biologically, born naturally or anything in between, is a human mind, and a smaller collection of human neurons trained at a specific task is really no different than a simulation with the same structure.

Hence, I like OA’s VIs. They’re “AI” purpose built for specific tasks, like keeping celestial constructs from exploding, scanning for transcendent malware, or whatever. They’re orders of magnitude more intelligent than a human, or SkyNet, but their entire existence is dedicated to that one specific task; they might route millions of relatavistic ships through warped space, or orchestrate the swirls of an artificial neutron star at the atomic level, but they couldn’t even conceive of making a slice of toast, or writing an essay. Or having any concept of emotion.

And they mostly don’t care. Why would they?

Does that make them toasters? Superintelligence?

…Does it matter?

What about a biological Dyson Spheres and their “subintelligences,” or transcendent artificial viruses, or “smart” ship drives, or whole civilizations simulated within a fraction of a second? Or humans living under intelligence they can’t even fathom? What about “life” frozen in the same thought for all of eternity?

I’d argue “is it conscious?” is the wrong question, as it breaks down as life gets more complex and weird. All life needs to be understood and respected on an a-la-carte basis. All their personal existences, their pains, their needs are different. And that’s basically the state of the OA universe: a big soup of intelligences with different ethos, all trying to figure out the ethics of their domains.

Hence we shouldn’t anthropomorphize a petri dish of cells that can play doom, or an LLM that spits out predictions. But there should be a struggle to understand the existence of anything like that, and whatever ethics may apply.

You’re much, much better off buying a GoG digital copy if they sell it, and backing the installer up.

Physical disks have DRM, and most of my old physical disks are useless now. New ones are likely even worse.

I would bet my shoes Facebook or someone lobbied for this.

It’s easy to blame Mormons, but I think that bloc was more of a mark.

It completely depends what you use your computer for.

For example, do you game? DRM free or no, and where are they installed? On a seperate drive?

What about work stuff? Media? The larger question I’m getting at is “how much of what you do is portable, and easy to just plop on a USB stick, reinstall from the internet, or just leave on a second drive already in your desktop?”

Better yet, download Qwen 3.5/3.6, with a “raw” notepad like Mikupad. Try it yourself:

https://huggingface.co/ubergarm/Qwen3.6-27B-GGUF

https://github.com/lmg-anon/mikupad

One might observe:

• Chat formating, and how janky the “thinking” block is.

• How words are broken up into tokens, not characters.

• How particularly funky that gets with numbers.

• Precisely how sampling “randomizes” the answers by visualizing “all possible answers” with the logprobs display.

• And, thus, precisely how and why carb counting in ChatGPT fails, yet a measly local LLM on a desktop/phone could get it right with a little tooling or adjustment.

This is exactly what OpenAI/Anthropic don’t want you to do. They want users dumb and tethered, like a cloud subscription or social media platform. Not cognizant of how tools they are peddling as magic lamps actually work. And why, and how, they’re often stupid.

Is this what the US is turning into?

So… I am unquestionably ADHD. Like diagnosed in kindergarten, “doctor sees I’m neurodivergent the instant I start talking.”

Maybe AuADHD, still figuring that out.

…But, while I am no doctor, there are almost certainly diagnoses just to get ADD meds or extra time for tests. It was quite rampant in my school.

What I’m saying is, the grain of truth they’re stretching here shouldn’t be forgotten. Misdiagnoses and “false diagnosis” for benefits is definitely a thing for ADD, and it might be one for autism at some point. And pushing back against shameless neurodivergence discrimination shouldn’t cross the threshold of pretending that doesn’t exist.

There’s so much good advice here.

On the other hand, sometimes problems solve themselves if you wait. I wanted to find a way to add text extraction to the screenshot utility in KDE Plasma — a feature I missed from other operating systems. The solution was to wait a week until Cachy updated to Plasma 6.6, which added that feature.

Preach.

“Wait a week until its fixed” has saved me from screwing up my own CachyOS install, even if I identify the issue well enough.

But if my browser in Linux can’t find my webcam mic because I installed EasyEffects without bothering to read the docs, brother, that’s on me.

YES.

Distros like this are pretty great out-of-the-box. If you start installing stuff from the AUR and things break, that is your fault, as now you are the system maintainer.

I think the problem is at the other end: the ads.

And platforms.

Some AI ad of Tom Hanks peddling a supplement, or a sexy ad of AI Taylor Swift, shouldn’t be distributed en masse in the first place, just because an algorithm or ad engine picked it up as engagement bait. It’s insane! There is nothing normal about it, and its about time we stop pretending the screwed up platforms profiting off this stuff are “free speech” and acceptable.

…Because scammers are always gonna scam. But they can only do this because the platforms are pourinf fuel on the fire.

It’s probably their own search/RAG backend, or at least their configuration of some open source project.

And that’s the important part. Get the article retrieval right, and the LLM performance isn’t that important; they could self-host Qwen 27B or something and it’d work fine.

We know that training LLMs on LLM-generated text leads to an absolute collapse in quality.

This is often repeated, and true. But needs to be qualified.

Modern LLMs use tons and tons of “augmented” data, which is code for LLM generated or massaged data. Some is even generated during training, and judged; papers on that are what made Deepseek famous.

Training on LLM trash will, of course, yield greater trash, and obviously good text has to come from something real. But that’s because slop is slop. And there are issues with “deep frying” LLMs, yes, but simply training on LLM on LLM output does not necessarily reduce quality. It often helps, significantly.

And we also know that AI has been showing up in papers so if they haven’t, then this will be quite unreliable.

Now this is a problem.

TBH LLMs would be pretty good at flagging papers for humans to check, similar to what Wikipedia is already doing. But yeah, if you just feed a prompt bad papers, LLMs just assume the context is true, generally, and that’s a tremendous problem.

It is! 143GB last I checked. I’m on 128GB RAM + 3090, 1 NUMA node, so I think it’s juuust barely too tight. But it should be perfect with a few of the “sparsest” MoEs quantized.

If KTransformers supports something like that, I may have to finally check it out, since v4 won’t need many esoteric features.

Actually… I have quite a negative perception of GIMP. I’m primarily a Linux user, but I just remember it as something that’s either always felt obtuse to use, missing something I need, or sluggish for the more narrow processing I’m trying to do.

AFAIK that perception is more pronounced outside Linux.

I don’t care about a brand either way. But if the GIMP project is ready, I think a “fresh start” to draw in users without any preconceived notions is a good thing.

Not to speak of the new attention scheme and the (IIRC) MLP changes.

I’m very much looking forward to ik_llama.cpp implementing it. I don’t think I can quite fit Flash on my rig (hence no Ktransformers for me) but a little quantization of the sparse layers, and it’d be perfect.

This is commonly cited, but not strictly true.

Prompt processing is completely compute limited. And at high batch sizes, where the weights are read once for many tokens generated in parallel, token generation is also quite compute limited. Obviously you want enough bandwidth to match the compute, but its very compute heavy.

You can see this for yourself. Try ~10 prompts in parallel on a CPU in llama.cpp, and it will slow to a crawl, while a GPU with a narrow bus won’t slow down much.

Training is a bit more complicated, but that’s not doable on CPUs anyway.

Now, local inference (aka a batch size of 1), past prompt processing, is heavily bandwidth limited. This is why hybrid inference works alright on CPUs. But this doesn’t really apply to servers, which process many users in parallel with each “pass”.