Getting Married Young and Still Achieving Dreams
0:13
TikToklegally_mary
Getting Married Young and Still Achieving Dreams
Proof that early marriage and motherhood don't stop success—law school graduation and goals ahead. #LLM #lawyer #graduation #hijabi #firstgeneration Legally Mary 🎀(@legally_mary). Forever - Explicit Version - Drake & Kanye West & Lil Wayne & Eminem. I am proof that getting married young and having kids does not debilitate you. God willing ...
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Large Language Model Applications
Every Large Language Model Explained in 17 Minutes!
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How LLMs Work? | How Large Language Models Work | What Are LLMs? | LLMs Explained | Simplilearn
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TikTokllm_anchor
Siempre que le preguntas a Claude si tu idea es buena, te dice que sí. Aunque sea mala. El LLM Council lo arregla: lanza 5 asesores con perspectivas opuestas, se revisan entre sí, y un juez te da el veredicto y el paso a seguir. El skill y la guía están en mi comunidad de WhatsApp. Link en mi perfil. #ClaudeCode #IA #Claude #Anthropic
0:40
Siempre que le preguntas a Claude si tu idea es buena, te dice que sí. Aunque sea mala. El LLM Council lo arregla: lanza 5 asesores con perspectivas opuestas, se revisan entre sí, y un juez te da el veredicto y el paso a seguir. El skill y la guía están en mi comunidad de WhatsApp. Link en mi perfil. #ClaudeCode #IA #Claude #Anthropic
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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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Personal Example of the 'Car Wash Problem' with LLM Claims
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Caveman mode once again! This time for Mother of All AI papers: Attention is All you need 🙌 In the Reel, I explained it in the simplest way: Transformer = architecture behind modern LLMs Attention = heart of the Transformer Query = what this word is looking for Key = what each word represents Value = information each word passes forward Multi-head attention = many ways to look at context Positional encoding = word order information Now the slightly more technical version: Modern LLMs like ChatG
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An LLM made Mitchell Hashimoto's renderer 10x faster. His handwritten version was 100x faster. Charlie Marsh (founder of Astral, now at OpenAI) says this is the trap: if you're not thinking from first principles about how fast something *should* be, you'll celebrate a 10x win and never find the 100x solution sitting right there. #softwareengineering #AIcoding #devtoolsofthefuture
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