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@chloebarker_5: Grateful to be pregnant with our sweet boy but man it’s been tough this 4th time around. #pregnancyjokes #pregnancyhumor #pregnant #pregnantlife #pregnancy
Chloe Barker
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Region: US
Wednesday 27 May 2026 03:27:56 GMT
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Tina Marquez :
😂😂😂
2026-06-04 05:09:33
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Simple basket recipe with breads, dates, olives and lentil paste Cathedrals 4 flatbreads or pieces of rustic bread 1 cup dates 1/2 cup green and black olives 1 cup cooked lentils 1 olive oil 1 teaspoon lemon juice 1 pinch of salt 1 pinch of cumin, ham Fresh herbs to finish Lighting mode Cook the lentils in water until soft. Drain well and place in a bowl. Knead the lentils with olive oil, lemon, salt and cumin. Mix until a rustic paste forms. Heat the breads quickly in a skillet or low oven. Place the breads in a simple basket. Add the dates on the side. Add the olives in a small bowl or directly to the basket. Place the lentil paste in a small clay or ceramic jar. Finish with fresh herbs. Serve as a delicious, shareable and welcoming meal.
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. "Unbelievable" becomes 4 pieces: Un · bel · iev · able. Each piece becomes a number, and each number becomes a vector of hundreds of numbers. 3️⃣ The transformer. Stacks of layers, and inside each one, attention. In "the programmer fixed the server because it crashed", a real GPT-2 attention head sends 54% of "it"'s attention straight to "server". 4️⃣ Pre-training. One task, repeated trillions of times: predict the next token. Guess wrong, measure how wrong, nudge the weights a tiny bit downhill. Meta trained Llama 3 on up to 16,000 GPUs. 5️⃣ Post-training. A pre-trained model is just a very powerful autocomplete. Fine-tuning on good examples, human feedback and checkable rewards (did the math match? did the code pass?) turns it into an assistant. 6️⃣ Serving. A 405-billion-parameter model needs about 810 GB just for its weights. One GPU holds 80. So it gets split, compressed and batched so millions of people can use it at once. Every example in the video comes from a real model: the tokens, the attention, even the loss landscape. The weird part? Writing code, explaining physics, translating French: none of it was programmed. It all emerges from one tiny objective, repeated at enormous scale: predict what comes next, and get slightly less wrong every time. Which step surprised you most? Drop the number 👇 Save this for the next time someone calls AI "just autocomplete." #llm #artificialintelligence #machinelearning #chatgpt #techexplained
What have you done 😂💔
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