Have you ever looked at your code and thought, "Why does 0.1 + 0.2 equal 0.30000000000000004?" Do the quirks of floating-point arithmetic keep you awake at night, wondering where all those tiny fractions of a cent are going?

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Handling real numbers in computer science is a complex task, especially for fields like finance and astronomy, where precision is critical. Standard floating-point arithmetic is optimized for speed, often at the expense of exact precision. This trade-off has a particularly problematic impact on financial calculations, where even tiny rounding errors …

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