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@quyentuixinh: #tui #tuixach #túi_xách_đẹp #xuhuong #xaykenhtiktok MUA TÚI TĂNG KÈM CHAM NƠ TĂNG HỘP

Mê Túi Xinh🛍️
Mê Túi Xinh🛍️
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Saturday 17 October 2026 02:44:26 GMT
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Yang Penting Urusan Rumah Beres Istirahat Pun Tenang😌#no1bajuanakkoreanstyle #marvesbaby #momlife #momlifevibes #kehidupanrumahtangga
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Google Search and AI are completely different, and understanding how they work changes how you use them. *To be specific: by “AI” here I mean Large Language Models, or LLMs. Google has technically always been AI – its PageRank algorithm, which decides which links to return to you, has always been a form of AI. And yes, Google is now augmented with LLMs. What we’re comparing in this post is traditional Google (using PageRank) and LLMs.* Lets break down the differences in plain English & answer some of the most commonly asked questions below (so you know when to use which tool): Q: What’s the biggest difference between Google and LLMs like ChatGPT? Google searches, and LLMs predict. Google scans the internet and returns the most relevant websites using a ranking system. LLMs don’t search. They learn from massive amounts of text and then predict what word comes next based on probability. Q: So are LLMs just looking things up? No. They’re word generators. They learn patterns in language, and then generate text that follows those patterns. That’s why they can sound confident even when they’re wrong. Many - crucially, not all!!! - models today have been augmented with the ability to pull some data from the internet and insert it into its context window (i.e. the input into the LLM, the prediction machine) which reduces the risk of “hallucination,” (i.e. making things up) but even so, the model is still a predictor. It can (and often does) still mess up EVEN with web search added. Q: What’s the biggest tradeoff to watch out for? Hallucinations. LLMs are generators (& not a fact checkers), so they can make things up. That’s why tools like web-browsing and retrieval-augmented generation (RAG) are so important: they anchor generation to real data. But they’re not foolproof. Bottom line: If Google is a librarian, LLMs are improv actors who’ve read the entire library. But when you understand *how* they think you can use them way more effectively. Leave your questions or comments in the section below 🥰👇 & share to spread the word!! This concept is simple and yet so important. You guys rock, sending love to you all! Also, if you’re new here, hi! I’m Harper. I’ve spent a decade building AI as a Stanford computer scientist and coding AI systems at Meta & at a startup acquired by NVIDIA. I’ve always loved to teach, and now I get to create educational videos to share the skills, understanding, and intuition I’ve worked years to develop with you. For my (free) 10 Days of AI Basics series + more, DM me GUIDE and I’ll send my video walkthrough guide to you.
Google Search and AI are completely different, and understanding how they work changes how you use them. *To be specific: by “AI” here I mean Large Language Models, or LLMs. Google has technically always been AI – its PageRank algorithm, which decides which links to return to you, has always been a form of AI. And yes, Google is now augmented with LLMs. What we’re comparing in this post is traditional Google (using PageRank) and LLMs.* Lets break down the differences in plain English & answer some of the most commonly asked questions below (so you know when to use which tool): Q: What’s the biggest difference between Google and LLMs like ChatGPT? Google searches, and LLMs predict. Google scans the internet and returns the most relevant websites using a ranking system. LLMs don’t search. They learn from massive amounts of text and then predict what word comes next based on probability. Q: So are LLMs just looking things up? No. They’re word generators. They learn patterns in language, and then generate text that follows those patterns. That’s why they can sound confident even when they’re wrong. Many - crucially, not all!!! - models today have been augmented with the ability to pull some data from the internet and insert it into its context window (i.e. the input into the LLM, the prediction machine) which reduces the risk of “hallucination,” (i.e. making things up) but even so, the model is still a predictor. It can (and often does) still mess up EVEN with web search added. Q: What’s the biggest tradeoff to watch out for? Hallucinations. LLMs are generators (& not a fact checkers), so they can make things up. That’s why tools like web-browsing and retrieval-augmented generation (RAG) are so important: they anchor generation to real data. But they’re not foolproof. Bottom line: If Google is a librarian, LLMs are improv actors who’ve read the entire library. But when you understand *how* they think you can use them way more effectively. Leave your questions or comments in the section below 🥰👇 & share to spread the word!! This concept is simple and yet so important. You guys rock, sending love to you all! Also, if you’re new here, hi! I’m Harper. I’ve spent a decade building AI as a Stanford computer scientist and coding AI systems at Meta & at a startup acquired by NVIDIA. I’ve always loved to teach, and now I get to create educational videos to share the skills, understanding, and intuition I’ve worked years to develop with you. For my (free) 10 Days of AI Basics series + more, DM me GUIDE and I’ll send my video walkthrough guide to you.
🚨 Antes de continuar rolando, faça este ato de fé. Se você acredita em *Deus, escreva *
🚨 Antes de continuar rolando, faça este ato de fé. Se você acredita em *Deus, escreva *"AMÉM"** nos comentários e declare sua fé diante de todos. Que esta mensagem alcance o maior número possível de pessoas que precisam ouvir a Palavra de Deus hoje. 🙏❤️ . . . . .


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