@cocky.bob: #dax #burgermusic #tompearl #julia #juliaconner

Cocky Bob

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Thursday 17 September 2026 08:00:37 GMT

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⛧ 𝕁𝕦𝕕𝕘𝕖𝕞𝕖𝕟𝕥 ⛧ :

I require context for the background

2026-09-17 19:24:27

41

𒉭 :

Who is that?

2026-09-17 15:39:12

6

markedsheep :

2026-09-20 12:15:48

2

Clipz :

2026-09-20 05:12:52

1

ollie ✈️ :

prime julia connor=💀💀💀

2026-09-20 04:40:23

5

so :

Mrbeat is this the new dreamybull

2026-09-17 17:22:37

9

☣︎☣︎✩✩𝓝𝓸𝓪𝓱✩✩☣︎☣︎ :

What I’m bout to do in the shower

2026-09-20 22:30:35

1

Camden🇮🇩 :

@5liotK @Lahna 🍌

2026-09-17 16:32:03

3

cam :

@Dominic @Christian @️zen.

2026-09-17 21:05:10

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How do language models turn words into numbers they can actually work with? 🧠 That's where text embeddings come in. Here's the intuition: 🔹 Language models split text into tokens and represent each token as a vector — a list of numbers. 🔹 Similar words tend to have similar vectors, so they end up close together in the embedding space. 🔹 Embeddings can capture more than similarity — differences between vectors can also represent relationships between words. 🔹 For example, in classic word embeddings, the relationship between “man” and “woman” is roughly reflected in the relationship between “king” and “queen.” 🔹 Real embeddings can have hundreds or thousands of dimensions, even though we often visualize them in just 2D or 3D. But there's an important distinction: 🔹 Classic word embeddings give a word a single learned vector. 🔹 Modern LLMs use contextual representations, so the representation of a token can change depending on the words around it — allowing “bank” to represent a river bank in one context and a financial institution in another. The big picture: A language model turns tokens into vectors, and those vectors encode patterns and relationships learned from text. #TextEmbeddings #NLP #LLMs #MachineLearning #AI

How do language models turn words into numbers they can actually work with? 🧠 That's where text embeddings come in. Here's the intuition: 🔹 Language models split text into tokens and represent each token as a vector — a list of numbers. 🔹 Similar words tend to have similar vectors, so they end up close together in the embedding space. 🔹 Embeddings can capture more than similarity — differences between vectors can also represent relationships between words. 🔹 For example, in classic word embeddings, the relationship between “man” and “woman” is roughly reflected in the relationship between “king” and “queen.” 🔹 Real embeddings can have hundreds or thousands of dimensions, even though we often visualize them in just 2D or 3D. But there's an important distinction: 🔹 Classic word embeddings give a word a single learned vector. 🔹 Modern LLMs use contextual representations, so the representation of a token can change depending on the words around it — allowing “bank” to represent a river bank in one context and a financial institution in another. The big picture: A language model turns tokens into vectors, and those vectors encode patterns and relationships learned from text. #TextEmbeddings #NLP #LLMs #MachineLearning #AI

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