#floatingpoint

🤯 Your computer can’t calculate 0.1 + 0.2 correctly. And a similar rounding error once contributed to the deaths of 28 soldiers. Here’s why. 💻 Computers store numbers in binary, not decimal. Just like 1/3 becomes 0.3333… forever, numbers like 0.1 have repeating binary representations. IEEE 754 floating-point numbers have limited precision, so the computer stores the closest representable value instead. That’s why in languages like Python and JavaScript: 0.1 + 0.2 !== 0.3 😳 Usually harmless. But tiny errors can become dangerous when they accumulate over time. 🚨 In 1991, a Patriot missile system in Saudi Arabia suffered a timing error after running for around 100 hours. The accumulated drift contributed to its failure to track an incoming Scud missile. The missile struck a barracks, killing 28 soldiers. 🔥 How developers avoid these problems: ✅ Use integers for money, such as cents. ✅ Use exact decimal types when appropriate, like Java’s BigDecimal or Python’s Decimal. ✅ Compare floating-point numbers using a tolerance instead of exact equality. One tiny rounding error can teach a massive lesson about software engineering. 💬 Want to learn how banks handle precision in financial systems? Comment below! #Programming #FloatingPoint #SoftwareEngineering #ComputerScience #Coding