So here we are…

That’s why they have miniscule user bases. Ain’t nobody gaming on nouveau, bruh

Thanks, I hate it

Still pretty rapey vibes

What in the actual fuck is this? This looks like a rape scene from a movie.

Here’s their plan:

1. Claim open investigations to not release certain files
2. Stall for the holidays
3. When someone calls yet another referendum or forces testimony in Congress again…stall
4. Someone in Congress finally admits the files released are not complete because they have seen the the unredacted versions
5. Stall again

They will ratchet up all the bullshit pain they are inflicting on Americans through ICE as much as they possibly can in this time, and try and force Representatives to back off any further action until they relent.

Which means it’s going to be bullshit, doctored files, or the same things we already have.

Did it…not have that already? I swear it did, but honestly I thought Exchange was dead long ago.

From your own linked paper:

To design a neural long-term memory module, we need a model that can encode the abstraction of the past history into its parameters. An example of this can be LLMs that are shown to be memorizing their training data [98, 96, 61]. Therefore, a simple idea is to train a neural network and expect it to memorize its training data. Memorization, however, has almost always been known as an undesirable phenomena in neural networks as it limits the model generalization [7], causes privacy concerns [98], and so results in poor performance at test time. Moreover, the memorization of the training data might not be helpful at test time, in which the data might be out-of-distribution. We argue that, we need an online meta-model that learns how to memorize/forget the data at test time. In this setup, the model is learning a function that is capable of memorization, but it is not overfitting to the training data, resulting in a better generalization at test time.

Literally what I just said. This is specifically addressing the problem I mentioned, and goes on further to exacting specificity on why it does not exist in production tools for the general public (it’ll never make money, and it’s slow, honestly). In fact, there is a minor argument later on that developing a separate supporting system negates even referring to the outcome as an LLM, and the supported referenced papers linked at the bottom dig even deeper into the exact thing I mentioned on the limitations of said models used in this way.

It most certainly did not…because it can’t.

You find me a model that can take multiple disparate pieces of information and combine them into a new idea not fed with a pre-selected pattern, and I’ll eat my hat. The very basis of how these models operates is in complete opposition of you thinking it can spontaneously have a new and novel idea. New…that’s what novel means.

I can pointlessly link you to papers, blogs from researchers explaining, or just asking one of these things for yourself, but you’re not going to listen, which is on you for intentionally deciding to remain ignorant to how they function.

Here’s Terrence Kim describing how they set it up using GRPO: https://www.terrencekim.net/2025/10/scaling-llms-for-next-generation-single.html

And then another researcher describing what actually took place: https://joshuaberkowitz.us/blog/news-1/googles-cell2sentence-c2s-scale-27b-ai-is-accelerating-cancer-therapy-discovery-1498

So you can obviously see…not novel ideation. They fed it a bunch of trained data, and it correctly used the different pattern alignment to say “If it works this way otherwise, it should work this way with this example.”

Sure, it’s not something humans had gotten to get, but that’s the entire point of the tool. Good for the progress, certainly, but that’s it’s job. It didn’t come up with some new idea about anything because it works from the data it’s given, and the logic boundaries of the tasks it’s set to run. It’s not doing anything super special here, just very efficiently.

Nah, I’m just not going to write a novel on Lemmy, ma dude.

I’m not even spouting anything that’s not readily available information anyway. This is all well known, hence everybody calling out the bubble.

🤦🤦🤦 No…it really isn’t:

Teams at Yale are now exploring the mechanism uncovered here and testing additional AI-generated predictions in other immune contexts.

Not only is there no validation, they have only begun even looking at it.

Again: LLMs can’t make novel ideas. This is PR, and because you’re unfamiliar with how any of it works, you assume MAGIC.

Like every other bullshit PR release of it’s kind, this is simply a model being fed a ton of data and running through thousands of millions of iterative segments testing outcomes of various combinations of things that would take humans years to do. It’s not that it is intelligent or making “discoveries”, it’s just moving really fast.

You feed it 10² combinations of amino acids, and it’s eventually going to find new chains needed for protein folding. The thing you’re missing there is:

1. all the logic programmed by humans
2. The data collected and sanitized by humans
3. The task groups set by humans
4. The output validated by humans

It’s a tool for moving fast though data, a.k.a. A REALLY FAST SORTING MECHANISM

Nothing at any stage if developed, is novel output, or validated by any models, because…they can’t do that.

I sure do. Knowledge, and being in the space for a decade.

Here’s a fun one: go ask your LLM why it can’t create novel ideas, it’ll tell you right away 🤣🤣🤣🤣

LLMs have ZERO intentional logic that allow it to even comprehend an idea, let alone craft a new one and create relationships between others.

I can already tell from your tone you’re mostly driven by bullshit PR hype from people like Sam Altman , and are an “AI” fanboy, so I won’t waste my time arguing with you. You’re in love with human-made logic loops and datasets, bruh. There is not now, nor was there ever, a way for any of it to become some supreme being of ideas and knowledge as you’ve been pitched. It’s super fast sorting from static data. That’s it.

You’re drunk on Kool-Aid, kiddo.

Animal brains have pliable neuron networks and synapses to build and persist new relationships between things. LLMs do not. This is why they can’t have novel or spontaneous ideation. They don’t “learn” anything, no matter what Sam Altman is pitching you.

Now…if someone develops this ability, then they might be able to move more towards that…which is the point of this article and why the guy is leaving to start his own project doing this thing.

So you sort of sarcastically answered your own stupid question 🤌

Lol 🤣 I’m SO EMBARRASSED. You’re totally right and understand these things better than me after reading a GOOGLE BLOG ABOUT THEIR PRODUCT.

I’ll never speak to this topic again since I’ve clearly been bested with your knowledge from a Google Blog.

LLMs are just fast sorting and probability, they have no way to ever develop novel ideas or comprehension.

The system he’s talking about is more about using NNL, which builds new relationships to things that persist. It’s deferential relationship learning and data path building. Doesn’t exist yet, so if he has some ideas, it may be interesting. Also more likely to be the thing that kills all human.

His presidential run was kind of a disaster. I’m pretty sure being known as a billionaire isn’t going to help any either.

KRunner is the launcher that is responsible for launching all things by keyboard, yes. You can’t edit system entries easily without patching some stuff in.

Here’s the list of various runners: https://userbase.kde.org/Plasma_application_launchers

The piece of software you’re asking about is KRunner, but I don’t think editing existing entries is supported. It’s possible, sure, but it’s probably a bigger mess than you’d like to deal with.

I would just make a shortcut to the shutdown action and let it populate in the results, then just use that to trigger a shutdown. Or I suppose you could make a quick keyboard shortcut.