@__lauricamacho:

Lauricamacho
Lauricamacho
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Region: ES
Tuesday 23 June 2026 09:59:03 GMT
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lorena.aguilar716
yodayoda :
Por experiencia propia he llegado a esta conclusión, LOS AMIGOS NO EXISTEN, solo Dios y mi Madre.
2026-06-23 15:42:14
354
bigchaos__
bigchaos⚡ :
hermana es que vaya razón tienes
2026-06-23 12:25:51
162
shirley_vj
shirleey :
el “llama a alguien” es la prueba más REAL
2026-06-23 15:00:19
137
aracelilopzz
aracelilopzz :
Es que conseguir un solo amigo que realmente represente el verdadero significado de la amistad es muy difícil. Muchas personas confunden ser conocidos o llevarse bien con ser amigos, cuando una verdadera amistad es mucho más que eso.
2026-06-23 19:42:34
115
_caaroliinagelatina
caarool.90 :
yo lo he pasado fatal con los “grupos de amigos “
2026-06-23 23:28:03
81
anacg_18
A N A 🩵🫧 :
Literal… pues aquí nos reunimos todas las que estamos mas solas que la 1😅
2026-06-23 14:21:23
93
franxeskito91
Francesco :
mejor estar solo, que estar acompañado y sentirse solo.
2026-06-23 22:59:26
25
mariaop.op
MariaOP Op :
Mi madre decía donde hay 3 amigas una sobra
2026-06-26 04:11:17
30
intensotmax
ojr :
lo he intentado tantas veces que ya me di cuenta que estoy hecho para estar sólo,y no viene de ahora de muy pequeño ya tienes señales pero no te das cuenta hasta que madurad👍
2026-06-23 20:24:17
65
disxy04
Disxy0 :
Lo que pasa es que todos quieren buenos amigos pero no ser buenos amigos, yo ya hace tiempo que no tengo amigos, siempre lo he puesto por encima de mi, pero a mí solo me han usado por conveniencia, así que solté todo eso, para tener amigos buenos y leales también hay que serlo
2026-06-24 22:36:16
37
lissimonnn
𝐋𝐈𝐒 𝐒𝐈𝐌🍏𝐍. :
Es que total , para tener ese grupo de amigos yo me quedo sola como estoy y lo tranquila que estoy en paz y a lo mío es lo mejor que me ha pasado
2026-06-23 12:49:52
44
isacaballeroluna
Isa Caballero ✨❤️ :
pensamos igual !!!! yo también me siento diferente 😅😅😅 😘😘😘
2026-06-23 11:48:31
35
lauriicampos20
𝕷𝖆𝖚𝖗𝖎𝖎🦋 :
Como me decía mi madre desde pequeña amigos, no hay
2026-06-26 00:18:03
21
lauura_garciaa
lauura_garciaa :
Anda que no !!! Qué razón tienes
2026-06-25 12:48:15
11
pilicameros
pilicameros :
Nena yo tengo amigos sueltos, nunca grupos. Y cuando quiero viajar me voy sola en grupos de desconocidos, cuando quieras te apuntas conmigooo💗
2026-06-23 17:04:56
29
mkarma11rm
Mkarma11RM :
Los amigos y el amor no existe, solo vienen y van, y si no quieres acabar mentalmente hundido no des demasiado por nadie
2026-06-23 18:47:12
25
vengadios_ylovea
vengadios_ylovea :
Yo estoy más solo que Manolo
2026-06-25 05:57:11
10
lulu43_
lulu :
Mejor sola k mal acompañada 🤗
2026-06-23 10:17:21
22
arpiyuno
yuno gasai567 :
yo tenía un grupo de 9 amigas para salir y afortunadamente quedamos solo tres y estamos para todo. La gente buena existe, lo difícil es encontrarla
2026-06-24 07:50:27
7
gloriamh22
Gloriamh22 :
Me metí en un grupo de amigos y me sentí peor. Ignorada y lo que hablaba me ignoraban. Dure 1 hora jsjsjsj
2026-06-23 16:25:41
16
patricita157
Patricita :
soy super independiente,nunca me han gustado los grupos
2026-06-25 01:11:33
11
jenny92star
🌟Star100🌟 :
Que facto más grande no te falta razón en ninguna palabra de la que dices 💯
2026-06-23 19:23:53
14
beatrichi1988
Beatrichi :
que razon tienes!! estoy en farmasi y ni 1 amiga de toda la vida me ha comprado 😂, me puedo morir q ni se entera, no me buscan desde que no salgo.... osea no tengo amigas q venga en cualquier momento 😳
2026-06-23 19:08:58
5
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How LLMs Are Trained?  Went in caveman mode again 🤣 hope it was useful! In the Reel, I explained it in the simplest way: Data = examples the model learns from Tokens = text broken into smaller units Weights = learned numbers inside the network Pretraining = learning to predict the next token Instruction tuning = learning how to respond to requests RLHF = learning which responses humans prefer Now the slightly more technical version: An LLM starts with a large dataset containing text, code, and other carefully selected sources. Before training, the data is cleaned, filtered, and deduplicated. The text is broken into tokens, which are converted into numbers the model can process. At first, the Transformer’s weights are mostly random. During pretraining, the model predicts the next token, measures how wrong the prediction was, and updates those weights. Predict. Measure error. Update. Repeat. After this happens across massive datasets using many GPUs, a base model is created. But a base model mainly knows how to continue text. It is not automatically a helpful chat assistant. That is where post-training begins. Instruction tuning teaches the model how to follow requests using examples of good prompts and responses. Then preference-training methods such as RLHF use human feedback to guide the model toward responses people consider more helpful, safer, and better aligned. So the Reel’s simple version is actually the core idea: Data provides examples. Pretraining teaches prediction. Instruction tuning teaches chat-assistant behaviour. Preference training teaches preferred behaviour. Evaluation checks quality. An LLM is not born smart. It becomes useful through data, optimization, post-training, evaluation, and a huge amount of compute. Very expensive baby indeed😅 . . . [ #LLM Large Language Models, How LLMs Are Trained, LLM Training, Transformer, Pretraining, Post Training, Instruction Tuning, #RLHF ,Reinforcement Learning from Human Feedback, Preference Tuning, AI Alignment, Tokenization, Tokens, Model Weights, Neural Networks, Base Model, Chat Assistant, Generative AI, Deep Learning, #MachineLearning #AI Engineering, LLM Engineering, MLOps]
How LLMs Are Trained? Went in caveman mode again 🤣 hope it was useful! In the Reel, I explained it in the simplest way: Data = examples the model learns from Tokens = text broken into smaller units Weights = learned numbers inside the network Pretraining = learning to predict the next token Instruction tuning = learning how to respond to requests RLHF = learning which responses humans prefer Now the slightly more technical version: An LLM starts with a large dataset containing text, code, and other carefully selected sources. Before training, the data is cleaned, filtered, and deduplicated. The text is broken into tokens, which are converted into numbers the model can process. At first, the Transformer’s weights are mostly random. During pretraining, the model predicts the next token, measures how wrong the prediction was, and updates those weights. Predict. Measure error. Update. Repeat. After this happens across massive datasets using many GPUs, a base model is created. But a base model mainly knows how to continue text. It is not automatically a helpful chat assistant. That is where post-training begins. Instruction tuning teaches the model how to follow requests using examples of good prompts and responses. Then preference-training methods such as RLHF use human feedback to guide the model toward responses people consider more helpful, safer, and better aligned. So the Reel’s simple version is actually the core idea: Data provides examples. Pretraining teaches prediction. Instruction tuning teaches chat-assistant behaviour. Preference training teaches preferred behaviour. Evaluation checks quality. An LLM is not born smart. It becomes useful through data, optimization, post-training, evaluation, and a huge amount of compute. Very expensive baby indeed😅 . . . [ #LLM Large Language Models, How LLMs Are Trained, LLM Training, Transformer, Pretraining, Post Training, Instruction Tuning, #RLHF ,Reinforcement Learning from Human Feedback, Preference Tuning, AI Alignment, Tokenization, Tokens, Model Weights, Neural Networks, Base Model, Chat Assistant, Generative AI, Deep Learning, #MachineLearning #AI Engineering, LLM Engineering, MLOps]

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