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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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Python Object-Oriented Programming | I had experience with OOP prior to learning python when I was using Java. So when It came to learning python, not only were the syntax much easier than Java, but both languages all share a major similarity with each other: they both support OOP. My experience with Java made learning Python much easier because I was already familiar with the core principles of OOP. Here is more info on OOP if you’re interested: Object-Oriented Programming (OOP) in Python is a
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πŸ—‚οΈ Claude Code, decoded β€” the full project structure Most people open Claude Code and dump everything into one giant CLAUDE.md. πŸ˜… But the real power is in the structure. Here's how a clean, agent-ready repo actually looks πŸ‘‡ πŸ“„ CLAUDE.md β†’ your project's overview & config πŸ”’ .claude/ β†’ where all the magic lives πŸ“ rules/ β†’ coding conventions Claude actually follows ⚑ commands/ β†’ your own /slash workflows 🧩 skills/ β†’ auto-loaded only when relevant (keeps context light) πŸ€– agents/ β†’ specialized
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