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

follow me then comment “link” for early access to vlogit and a guide on using Cursor! today is day 119 of building @theofficialvlogit with @karam_aboulhosn today we talk about our onboarding flow and how i revamped it just recently to mimic successful apps like bereal. so far for our new beta testers it’s been working very well! make sure to follow and comment link to get the app before launch! #cursor #cursorcreator #coding #appdevelopment #startup

You Don't Need to Learn 8000 Languages 😩

There are certain types of content that always have worked and probably always will work. They follow how the brain processes information, and most successful pieces of content, when stripped back, are in one of these formats.

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Beginner Python tutorial on lambda functions #hoodcoding | lambda

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