Guardian | gift for @anyreiart I hope you like it!!! Bonus gif in a reblog ;)
BONUS: what I think Zhao Yunlan’s inner monologue was like in that moment
we're not kids anymore.

if i look back, i am lost
Today's Document

祝日 / Permanent Vacation
Alisa U Zemlji Chuda

Andulka
Jules of Nature

pixel skylines
Lint Roller? I Barely Know Her

oozey mess
Cosmic Funnies
NASA

izzy's playlists!
I'd rather be in outer space 🛸
h
YOU ARE THE REASON
let's talk about Bridgerton tea, my ask is open
almost home

roma★
sheepfilms
seen from United States
seen from United States
seen from United States

seen from Malaysia
seen from Finland

seen from United Kingdom

seen from Malaysia

seen from Italy

seen from United States
seen from Pakistan
seen from Argentina
seen from Argentina
seen from United States

seen from United States

seen from United States
seen from United States
seen from United States
seen from United States
seen from United States
seen from United States
@mariyahs-truth
Guardian | gift for @anyreiart I hope you like it!!! Bonus gif in a reblog ;)
BONUS: what I think Zhao Yunlan’s inner monologue was like in that moment
wow it's crazy how guardian only went up to episode 39 i guess we'll never know if they defeated ye zun
Art by arbor_draws.
Victor Glover (Pilot), Artemis II - April 4th 2026
Luca Marinelli at Lucca Comics & Games 2025
via @ayako_terashima
AO3'S content scraped for AI ~ AKA what is generative AI, where did your fanfictions go, and how an AI model uses them to answer prompts
Generative artificial intelligence is a cutting-edge technology whose purpose is to (surprise surprise) generate. Answers to questions, usually. And content. Articles, reviews, poems, fanfictions, and more, quickly and with originality.
It's quite interesting to use generative artificial intelligence, but it can also become quite dangerous and very unethical to use it in certain ways, especially if you don't know how it works.
With this post, I'd really like to give you a quick understanding of how these models work and what it means to “train” them.
From now on, whenever I write model, think of ChatGPT, Gemini, Bloom... or your favorite model. That is, the place where you go to generate content.
For simplicity, in this post I will talk about written content. But the same process is used to generate any type of content.
Every time you send a prompt, which is a request sent in natural language (i.e., human language), the model does not understand it.
Whether you type it in the chat or say it out loud, it needs to be translated into something understandable for the model first.
The first process that takes place is therefore tokenization: breaking the prompt down into small tokens. These tokens are small units of text, and they don't necessarily correspond to a full word.
For example, a tokenization might look like this:
Write a story
Each different color corresponds to a token, and these tokens have absolutely no meaning for the model.
The model does not understand them. It does not understand WR, it does not understand ITE, and it certainly does not understand the meaning of the word WRITE.
In fact, these tokens are immediately associated with numerical values, and each of these colored tokens actually corresponds to a series of numbers.
Write a story 12-3446-2638494-4749
Once your prompt has been tokenized in its entirety, that tokenization is used as a conceptual map to navigate within a vector database.
NOW PAY ATTENTION: A vector database is like a cube. A cubic box.
Inside this cube, the various tokens exist as floating pieces, as if gravity did not exist. The distance between one token and another within this database is measured by arrows called, indeed, vectors.
The distance between one token and another -that is, the length of this arrow- determines how likely (or unlikely) it is that those two tokens will occur consecutively in a piece of natural language discourse.
For example, suppose your prompt is this:
It happens once in a blue
Within this well-constructed vector database, let's assume that the token corresponding to ONCE (let's pretend it is associated with the number 467) is located here:
The token corresponding to IN is located here:
...more or less, because it is very likely that these two tokens in a natural language such as human speech in English will occur consecutively.
So it is very likely that somewhere in the vector database cube —in this yellow corner— are tokens corresponding to IT, HAPPENS, ONCE, IN, A, BLUE... and right next to them, there will be MOON.
Elsewhere, in a much more distant part of the vector database, is the token for CAR. Because it is very unlikely that someone would say It happens once in a blue car.
To generate the response to your prompt, the model makes a probabilistic calculation, seeing how close the tokens are and which token would be most likely to come next in human language (in this specific case, English.)
When probability is involved, there is always an element of randomness, of course, which means that the answers will not always be the same.
The response is thus generated token by token, following this path of probability arrows, optimizing the distance within the vector database.
There is no intent, only a more or less probable path.
The more times you generate a response, the more paths you encounter. If you could do this an infinite number of times, at least once the model would respond: "It happens once in a blue car!"
So it all depends on what's inside the cube, how it was built, and how much distance was put between one token and another.
Modern artificial intelligence draws from vast databases, which are normally filled with all the knowledge that humans have poured into the internet.
Not only that: the larger the vector database, the lower the chance of error. If I used only a single book as a database, the idiom "It happens once in a blue moon" might not appear, and therefore not be recognized.
But if the cube contained all the books ever written by humanity, everything would change, because the idiom would appear many more times, and it would be very likely for those tokens to occur close together.
Huggingface has done this.
It took a relatively empty cube (let's say filled with common language, and likely many idioms, dictionaries, poetry...) and poured all of the AO3 fanfictions it could reach into it.
Now imagine someone asking a model based on Huggingface’s cube to write a story.
To simplify: if they ask for humor, we’ll end up in the area where funny jokes or humor tags are most likely. If they ask for romance, we’ll end up where the word kiss is most frequent.
And if we’re super lucky, the model might follow a path that brings it to some amazing line a particular author wrote, and it will echo it back word for word.
(Remember the infinite monkeys typing? One of them eventually writes all of Shakespeare, purely by chance!)
Once you know this, you’ll understand why AI can never truly generate content on the level of a human who chooses their words.
You’ll understand why it rarely uses specific words, why it stays vague, and why it leans on the most common metaphors and scenes. And you'll understand why the more content you generate, the more it seems to "learn."
It doesn't learn. It moves around tokens based on what you ask, how you ask it, and how it tokenizes your prompt.
Know that I despise generative AI when it's used for creativity. I despise that they stole something from a fandom, something that works just like a gift culture, to make money off of it.
But there is only one way we can fight back: by not using it to generate creative stuff.
You can resist by refusing the model's casual output, by using only and exclusively your intent, your personal choice of words, knowing that you and only you decided them.
No randomness involved.
Let me leave you with one last thought.
Imagine a person coming for advice, who has no idea that behind a language model there is just a huge cube of floating tokens predicting the next likely word.
Imagine someone fragile (emotionally, spiritually...) who begins to believe that the model is sentient. Who has a growing feeling that this model understands, comprehends, when in reality it approaches and reorganizes its way around tokens in a cube based on what it is told.
A fragile person begins to empathize, to feel connected to the model.
They ask important questions. They base their relationships, their life, everything, on conversations generated by a model that merely rearranges tokens based on probability.
And for people who don't know how it works, and because natural language usually does have feeling, the illusion that the model feels is very strong.
There’s an even greater danger: with enough random generations (and oh, the humanity whole generates much), the model takes an unlikely path once in a while. It ends up at the other end of the cube, it hallucinates.
Errors and inaccuracies caused by language models are called hallucinations precisely because they are presented as if they were facts, with the same conviction.
People who have become so emotionally attached to these conversations, seeing the language model as a guru, a deity, a psychologist, will do what the language model tells them to do or follow its advice.
Someone might follow a hallucinated piece of advice.
Obviously, models are developed with safeguards; fences the model can't jump over. They won't tell you certain things, they won't tell you to do terrible things.
Yet, there are people basing major life decisions on conversations generated purely by probability.
Generated by putting tokens together, on a probabilistic basis.
Think about it.
Need something to read while ao3 is in the ICU?
Here is a link to the old X Files fic archive, Gossamer.
Gossamer Project: X-Files Fan Fiction Archive
“I just quit smoking. I need to have something in my mouth.” - actual quote from Zhao Yunlan, Guardian (镇魂) Episode 1.
Do you recognize this TV theme song? #324
I know this and can name the series
I know this but can't name the series
I might know this
I've never heard this
"you should rest soon."
I apologise again for what happened last time. As long as you promise to help my friend, I'll do anything you ask. Fine. Kneel. What did you say? Kneel down. Go kneel down outside.
Whumptober 2025 No. 21 - Kneeling
ZYL: "Excuse me, Professor, have you ever been arrested?"
Shen Wei: "Yes."
ZYL: "What? Wait, I was building up to a "you're so hot it should be illegal" joke, but now I want to know."
Shen Wei: "I was arrested on suspicion of murder. The local chief thought I was a serial killer."
ZYL: "That doesn't COUNT, I didn't even book you!"
Me, naive: Oh that’s so funny, fans calling a Harper’s Bazaar China photoshoot “the wedding photos,” probably because they always make everyone look so good.
NOPE
Damn.
Bless you Harper’s Bazaar, you treat us so good.
Also I assume this one is photoshopped to include the flowers but honestly seems like it could go either way.
So a while ago Bai Yu did a podcast on Lizhi where he discussed his character in Guardian and read some lines from the novel. I did my best to make translations and put together an Eng subbed video.
The audio comes from lizhifm here
There’s another video of Bai Yu reading lines from the novel, which @naanima posted here
"I waited 5 years." - Luo Binghe and Mo Ran
"I waited 16 years." - Lan Wangji
"I waited 800 years." - Hua Cheng
"..." - Shen Wei
Don't you think of lying to me. What did you do to heal my eyes? You used the Longevity Dial. You shared your life force with me. You used your strength to fix the damage to me. Didn't you? GUARDIAN | episode twenty-three
I need more of this, @queercrowley on Twitter.