dev_hugepages

Potentially dumb question, but could you expand on how you use LLMs as writing critics? Do you just ask them to flag bad writing? I'm asking because I don't think i'm skilled in writing.

Anthropic currently seems under AI psychosis/mania so I wouldn't use it as an argument of authority

This is somewhat funny given that not that long ago they were doing an article instigating fears of what Claude could do, and one of them was a researchers screening venom peptides along other things Perhaps he could have worked on a similar subject? Who knows.

My guess is that it would mean "creating new markets"

But if you look at the image, the image doesn't look like an actual new yorker cartoon; it has the tells of the RL process (notice how it puts random text everywhere for example?)

If you use local models and actually train them on an artist's output, it will look extremely close to that artist's work and will also match the signature.

The image generator shouldn't exhibit the signature problem after it's been reinforcement learning trained.

I think the OpenAI people have trouble with monitoring their model's output because the model has a ton of quirks that shouldn't be there

This isn't a human you are asking your question to.

From https://newpipe.net/blog/pinned/newpipe-and-online-advertisi...:

> In summary, it can be said that sponsoring is a good approach. It is fair and non-discriminating to all users, preserves their privacy, lets creators select their partners themselves, and provides them with a larger share of the revenue. Even though there are some disadvantages, it is more or less beneficial for all sides. [...] Ethical advertising, for example through sponsorship, should therefore not be touched by us. This would turn our key selling proposition from being privacy-friendly to being a gatekeeper for advertising. And this is not what NewPipe ever has been or wants to be. We’ve always stressed that NewPipe’s main goal is to protect the users by maintaining their privacy, not to, e.g., provide features a user would otherwise have to pay for, for free. Regarding SponsorBlock: after a lot of discussion within the team, we decided not to support this system. As stated earlier, we don’t consider advertising in general problematic. It’s the unethical, privacy-invasive systems that are very common today which we consider evil. Sponsorship provides a mostly ethical way of advertising.

If that makes you feel better, the guy who vibecoded an app in one sitting will probably have 0 clue on how the code works, the app itself will look like the average claude eyesore and the docs will be unreadable by humans without stimulants

"Computer, make a meme site where people can upload files so I can reach the top of hackernews. Make no mistakes."

If i'm not mistaken, this already exists, and the assembly language here is called PTX

https://llvm.org/docs/NVPTXUsage.html

This is called an oomkiller. The kernel has one but it kicks in very late and the kernel prefers to do page trashing instead of killing processes.

systemd-oomd should be integrated in systemd, you can configure it to your liking and see if it improves your problem.

To be fair the internet also had huge amounts of slop before the advent of chatGPT

This is a quirk of the last gpt image model (gpt-image-2). It put this sort of high frequency noise on all of the image especially if it's in a "drawn" style. There is often lots of other tells that this model in particular generated it.

Image models somewhat watermarking the image in a way that's very easily identifiable by a human seems present in all the image models of the big labs, since DALL-E 3 on OpenAI's side and the first nano banana on Google's side. I have no idea what they did to reach this and why they don't try to fix it.

I'm unsure why you're using an LLM to generate images. Don't we already have models (some made by the same company) that do this?

I do AI assisted images for fun and browsing their art I can't see any tell of AI or even tracing on AI; people became paranoid and jump to conclusion without any substance and I find it sad that people who put a lot of time and effort into their craft are getting knee jerks comments like this.

> I was put off by the overly dramatic presentation. It gets tiring that the author apparently finds this more exciting than I do, and writes like it's enthralling.

That's one of the main tells that AI wrote this. All the stylistic tics that people usually point out combine to make the writing seem more important than it is.

I heard there's a way to arrange code such that the compiler can autobectorize easier. I wonder if there's a way to do that here?

Would probably have to pass `-C target-cpu=native` to cargo so that llvm is allowed to use AVX512.

Hey, could you share the prompt you're using for "teacher mode"?

We're going to need more than a "I knew a guy" here

What parameter would you advise for min_p?

You can't treat it with opioid agonist therapy (buprenorphine, methadone, ...) to suppress the withdrawals like you would with most other cases of opioid dependency?

Because it sounds like the average LinkedIn poster and we don't want it to invade spaces outside LinkedIn.

He makes a point, though: bug bounties exist to incentivize people to find and report bugs to a company. We talk about white, gray, and black hats, roughly based on their level of ethics. For black hats – and some gray hats – money is one of the big reasons they look for vulnerabilities.

They are different drugs in the sense that methamphetamine will cause more serotonin release and will act longer than amphetamine, but they will both cause increase in blood pressure, increase heart rate and increase internal temperature. The rest of your comment is right though.

I read your blog and I can't agree, i've installed arch Linux before and only had to read the arch wiki page on it and it worked fine with none of the issues you described.

Same thing with gentoo, that I am daily driving now.

I sure had hickups but I managed to solve them by sitting and reading the manuals/wikis of the distros directly, or the programs I was dealing with. Maybe the problem is that you read an obscure forum to get your answer? I don't know.

Yes, most things are better (or at least too different) than they were 15-20 years ago.

Then, you are obsolete in the AI age.

Predicting a word is the final objective, as in the output of the model is a probability distribution of the next token. However, choosing the right token is more complicated than just regurgitating the training data (and you won't encounter an exact example in the training data, so you need to interpolate). This makes the model learn abstract representation of things that it is able to manipulate before outputting this back into token. RL also complicates this because the "fitness" is now some arbitrary metric computed over an entire sequence of tokens.