Why Computers Can’t Count Money
Floating Point IEEE 754 Binary representation Precision errors Programming bugs Financial software Numerical computing Decimal arithmetic Computer science fundamentals
This video explains how floating-point precision issues in early digital payment systems allowed exploited rounding errors to generate money. It covers the fundamental difference between binary (Base 2) and decimal (Base 10) representation, demonstrating why standard floating-point numbers cannot precisely represent common currency values like $0.1 or $0.2. The content is ideal for developers learning why exact decimal arithmetic is critical in financial applications.
