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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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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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G-Stack just open-sourced by Y Combinator’s CEO is revolutionizing Claude Code operations with 23 tools that automate your entire dev and release workflow. Get the exact system from top startup accelerator Gary Tan, and turn your Claude Code into a smart, fully managed ops team with one command. #gstack #ycombinator #claudecode #startuptech #automationtools
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AI agents default to generating code. Ponytail makes your agent ask whether it should. Before writing anything, the skill forces it to reason like the laziest senior developer in the room — the one who knows the best code is the code you never wrote. Can this be done by deleting something? Reusing what exists? Not building it at all? Less code, less surface area, fewer bugs. The senior-engineer instinct, bottled into a skill. #github #opensource
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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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