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Why do system programming languages care so much about variable size, while languages like JavaScript completely ignore it? In this video, we explore what fixed-size types really mean, why they exist, and how they directly impact memory usage, performance, and execution speed. You’ll understand why languages like C and Rust make you choose between u8, u32, or u64, and what trade-offs dynamic languages silently make on your behalf. We’ll uncover how numbers are stored in memory, why extra metadata is required in dynamically typed languages, and how that hidden cost affects both RAM consumption and CPU performance. More importantly, you’ll see why compilers in system languages can generate lean, highly optimized machine code when they know the exact size and type of data at compile time. This video is for you if: You’re moving from JavaScript/Python to C or Rust You’ve heard “low-level languages are faster” and want to know why You want a deeper mental model of memory and performance You’re curious how real-world systems are optimized Once you understand this, you’ll never look at variables the same way again. 🔑 Keywords : system programming, memory management, fixed size types, variable size in programming, rust vs javascript, c vs javascript, static vs dynamic typing, low level programming, how memory works, binary representation, bits and bytes, u8 u32 u64, integer sizes, why rust is fast, why c is fast, javascript memory model, dynamic typing overhead, compiler optimizations, machine code generation, programming fundamentals, software performance, systems programming concepts, memory optimization, programming languages comparison 👉 Subscribe: @FullStackTau