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Jensen Huang — CEO of NVIDIA, the company worth over $3.5 trillion — just set a new performance benchmark for his engineers. Not lines of code. Not features shipped. 🧠 Token spend. At the All-In Podcast, filmed on the last day of GTC 2026, he laid it out:
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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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