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I don’t think their system architecture is a secret. This kind of stuff is regularly discussed in interviews.

Cool read but I don’t see the link to technology.

Downvote me if you want, but some of their points actually made sense to me. For example:

11. Our society has grown too eager to hasten, and is often gleeful at, the demise of its enemies. The vanquishing of an opponent is a moment to pause, not rejoice.

In a vacuum / without any additional context, they are right. Society - in general - has grown rather polarised IMO.

until it’s revealed a horse is transphobic

The premise of umamusume is that horses from our world get reincarnated as horse girls in the world of umamusume.

Yes, even the male horses.

“Can you” as in “are you able to” or “should you”?

Anyway, yes on both counts personally. It’s like reviewing resumes with identifiers removed.

Otherwise, one would be judging the content with preconceived bias. IMO it’s a slippery slope to, and belong in the same subset as, so many other identity-related issues in society e.g. tribalism, identity-based politics, discriminition based on identities like race, etc

What’s the alternative for a not-so-wealthy country in the short term?

Nature’s wine bottle openers

Literally cockscrews 😩😭

The way I see it, the vast majority of people with pokemons treat them as house pets (e.g. Poketoon: The Slugma-Powered Home). Only a small percentage actually use them in battles. A way smaller percentage actively abuse pokemons (i.e. the villains)

I find that the trick - or at least the idea - isn’t to go out of the way to avoid such political discussions; it is to seek out spaces with some niche interest. Discussions would then naturally gravitate around the topic of interest, and political discussion usually doesn’t make sense in such a space.

An example topic is anime. Most anime are not political in nature, so commenters in ani.social don’t have the chance to go off-topic and discuss politics.

To use an analogy, let’s say you are a picky eater. You are given some food (think soup, salad, etc), but one of its ingredients is something you don’t like. Rather than try to pick out that one ingredient you dislike bit by bit (which can be tedious), how about adding condiments / dressing / etc to the dish? Enough of those and the taste would change, with the newly-added tastes having overpowered the one you dislike.

In terms of implementation, you basically have to curate your feed somehow. On Lemmy, the straightforward way to do so is to subscribe to topics / communities you are interested in, and participate in just those.

Off the top of my head / all-time popular:

• Avatar (the blue alien one)
• Interstellar
• The Dark Knight trilogy
• Lord of the Rings trilogy

To satisfy my inner little boy:

• Ip Man
• Transformers
• Pacific Rim

Animation:

• Frozen
• How to train your dragon trilogy

Yeah I’m a basic bitch

I know. It’s just that the phrasing is kinda weird.

The article’s title is:

Iran’s women’s team decline to sing national anthem before Asian Cup tie

The post title made it sound like they are disrespecting South Korea or something…

Super Smash Bros Ultimate. The final smashes are all the same. I miss the final smashes of Super Smash Bros Brawl.

Disclaimer: I am honestly a layman in this field. I may get a bunch of stuff wrong, but am happy to learn from experts. Feel free to point mistakes out and destroy me in the replies.

Simplifying and phrasing my understanding, an LLM works like - Given a prompt: Write a program to check if input is an odd number (converts the prompt to embedding), then the LLM plays a dice game/probability game of: given prompt, then generate a set of new tokens.

This feels like an oversimplification. Unfortunately, I can’t think of a good analogy without anthromorphosising LLMs.

IMO this anime scene works well enough as an analogy at a super high level: anime_irl

“Comprehending what other people is saying is one step” - encoder

“Thinking about how to answer is one more step” - working with the feature representation

“Putting the things that popped into my mind into words is another step” - decoder

Now my question is, how are the current LLM’s are able to parse through a bunch of search results and play the above dice game?

By current LLMs, I am going to assume that you are not referring to the raw models, but platforms like ChatGPT, Perplexity, etc with UIs for you to interact with the underlying models.

There are fundamentally two different problems here: searching the web for answers, and putting the answers into words.

Like at times it reads through say 10 URLs and generate results, how are they able to achieve this?

If I ask you: “What is the colour of fire engines?”, I imagine you would answer “Red”, sometimes “Yellow”, off the top of your head.

What if I ask you “What are the 10 longest rivers in the world”? I believe you won’t be able to give me an answer right away. What you can do is a web search, find the answer, then present the results to me. You can give it to me in 10 short bullets points, or you can come up with an essay with paragraphs describing each river.

You probably got my point by now, but to make it explicit: finding an answer and putting it into words are two different processes. They are independent of each other, so the final text output can be as long or as short as need be.

For these LLM platforms, when the model “doesn’t know” the answer, they probably have a subroutine that searches the web, then feed the answer to the underlying model. The model then packages the search results into readable form - in words instead of vectors - to you.

What’s the engineering behind generating such huge verbose of texts?

Sorry but I can’t think of a good answer to this at the moment; leaving it to others for now - unless I managed to think of something good.

Cause I always argue about the theoretical limitations of LLM, but now that these “agents” are able to manage huge verbose of text I dont seem to have a good argument. So what exactly is happening? And what is the limit of AI non theortical limit of AI?

Same for this question.

Hope the partial answer helps; tried my best to ELI5.

Just to reiterate, this is a conspiracy theory. I wouldn’t put too much weight into it.

After a quick search, the official reason is that ani.social is full of CSAM. Which is outright false.

My conspiracy theory is that those Chinese agents hate the growing soft power of Japan through anime.

I started with lemmy.world, the biggest generalist instance, for an easy starting point. I thought I would be interacting in programming and anime threads.

In the end, I end up discussing mostly weeb stuff, so ani.social looks more appropriate.

Also, I don’t know the specifics, but if ani.social triggered lemmy.ml enough for lemmy.ml to defederate from ani.social, it must be doing something right.

In my experience, the instance and community moderators of ani.social are based. I barely see any drama unlike some other instances. I’d say I chose well.

The world is ran by a small group of “elites”.