Complete LLM Basics Guide || Large Language Model Explained (LLM) for beginners
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Fable 5.1 y el supuesto marcado de agua en textos generados por IA 🤖💧 El concepto es interesante: algunos sistemas pueden introducir patrones en la forma en que el modelo selecciona tokens, haciendo que determinadas salidas sean identificables mediante herramientas de detección. En este caso, el proceso explicado es: 🧠 Generar el contenido con Claude 🔄 Reescribirlo completamente con otro modelo ⚙️ Integrar ese proceso como una skill 📄 Obtener una nueva versión del texto Pero ojo: no basta c
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Nobody ever wrote the code that lets ChatGPT write Python. Not one line of it. 🤯 Here's how a large language model actually gets built, step by step: 1️⃣ Data. Trillions of words of books, web pages, code and forums, then cleaned: duplicates removed, spam filtered, broken text stripped. One open dataset, FineWeb, is 15 trillion tokens of web text. 2️⃣ Tokens. The model never sees words.
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3 AI Projects That Can Actually Make Your GitHub Portfolio STAND OUT in 2026 🚀 If you’re learning AI Engineering, Machine Learning, Generative AI or LLMs and wondering “What projects should I build to get a job?” start here. 👇 In this video, I break down 3 job-ready AI projects that you can build and showcase on your GitHub portfolio. You’ll learn: ✅ What to build ✅ Why each project matters ✅ Which AI/ML skills it demonstrates ✅ How to structure the project ✅ What to add to your GitHub README
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