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@marniliabellezito: People with high aura understand THIS. ✨ You attract what you want more easily when you shift your identity. Your reality shifts when you upgrade your identity. The moment you release old emotional patterns and limitations, your energy changes. You think differently. You move differently. You attract differently. Your reality elevates to match your new identity. And suddenly, life feels more effortless. You attract what you want more easily because you are the match for it. ✨ 👉🏼 Watch my full teachings on identity shifting, self-concept and how to shift your reality. Link in bio. Marni Lia Bellezito ♥️✨ #aura #selfconcept #identityshift #realityshifting

Marni Lia Bellezito
Marni Lia Bellezito
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Region: AU
Tuesday 13 October 2026 08:27:57 GMT
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Comments

rbryaneugene
RBryan Eugene :
Couldn't have said it any better. I have experienced and I can confidently attest to this 🔥🔥
2026-10-13 09:34:42
2
cathys_world
Cathy’s_world :
No limitation, positive change all the way.
2026-10-13 12:25:28
0
zeropoint307
Zeropoint :
Simple… brilliant. ☀️
2026-10-13 13:20:44
0
mejabulat3172
Alvito Steven :
happy day madam
2026-10-13 08:42:35
1
weddy875
weddy1. :
Awesome Hallelujah
2026-10-13 09:38:05
1
i.am3316
I am :
Th
2026-10-13 10:32:32
1
cesssesh254
esperanza 254 :
sure
2026-10-13 13:28:04
0
jay.arumugam
a jaey77 :
2026-10-13 14:32:43
0
luckyaayu555
Sukha Maya Lama(Aama❤️‍🩹) :
[Red heart][Red heart][Red heart]
2026-10-13 16:44:49
0
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Comment MODEL and I'll send you the complete LLM training pipeline, from raw text to a working chatbot, with code you can run. How does a model learn from billions of words and then answer a question it has never seen before? Ask it why a metal spoon feels colder than a wooden one. That exact sentence was never in training, so there is no stored answer to look up. ⚡ Teams filter huge text collections, remove duplicates, and balance general text, code, and multilingual material. That mix decides which patterns the model practices most often ⚡ A tokenizer turns every document into tokens, and each token maps to a number. The model then predicts the next token, measures how wrong it was, and the optimizer nudges its parameters slightly, batch after batch ⚡ Pretraining builds a continuation engine. Post-training on examples of useful responses and preference data turns it into an assistant that follows instructions ⚡ When you ask the question, the weights stay frozen. The model produces probabilities, a decoding rule picks one token, and the loop repeats. The answer comes from generalization, with no lookup involved A fluent answer can still be confidently wrong, because next-token prediction rewards plausible text, and plausible is not the same as true. Comment MODEL for the pipeline. Which stage did you think was doing the real work before this video? 👇 #llm #machinelearning #artificialintelligence #aiengineering #deeplearning
Comment MODEL and I'll send you the complete LLM training pipeline, from raw text to a working chatbot, with code you can run. How does a model learn from billions of words and then answer a question it has never seen before? Ask it why a metal spoon feels colder than a wooden one. That exact sentence was never in training, so there is no stored answer to look up. ⚡ Teams filter huge text collections, remove duplicates, and balance general text, code, and multilingual material. That mix decides which patterns the model practices most often ⚡ A tokenizer turns every document into tokens, and each token maps to a number. The model then predicts the next token, measures how wrong it was, and the optimizer nudges its parameters slightly, batch after batch ⚡ Pretraining builds a continuation engine. Post-training on examples of useful responses and preference data turns it into an assistant that follows instructions ⚡ When you ask the question, the weights stay frozen. The model produces probabilities, a decoding rule picks one token, and the loop repeats. The answer comes from generalization, with no lookup involved A fluent answer can still be confidently wrong, because next-token prediction rewards plausible text, and plausible is not the same as true. Comment MODEL for the pipeline. Which stage did you think was doing the real work before this video? 👇 #llm #machinelearning #artificialintelligence #aiengineering #deeplearning
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