10Number Converter
A number base (radix) is a system for representing numbers. Beyond the decimal (base-10) system we use daily, computer science frequently uses binary (base-2), octal (base-8), and hexadecimal (base-16). Binary is the fundamental data unit in computing, hexadecimal is used for memory addresses, color codes, and MAC addresses, and octal is commonly seen in Unix file permission settings. This tool supports real-time conversion between all four bases, using JavaScript's BigInt for precise handling of very large numbers.
How to Use
- 1Enter a Number in Any Field
Type a number in any of the four fields (decimal, binary, octal, or hexadecimal), and the other three fields will update automatically.
- 2View Conversion Results
The entered value is instantly converted to all other bases. Invalid input will display an error message.
- 3Copy Results
Use the copy button next to each field to copy the converted value to your clipboard.
Tips
- 💡Converting hex color codes (e.g., FF5733) to decimal helps understand how RGB values work under the hood.
- 💡Converting Unix file permissions (e.g., 755, 644) to binary makes it easy to see each permission bit (read/write/execute).
- 💡BigInt support ensures precise conversion of 64-bit and larger numbers without overflow.
- 💡Useful for debugging when you need to convert memory addresses or byte values between number bases.
FAQ
- Q. Where are binary, octal, and hexadecimal numbers used?
- A. Binary (base 2) is used internally by all computers. Octal (base 8) appears in Unix file permissions (chmod). Hexadecimal (base 16) is used for color codes, memory addresses, and byte-level data.
- Q. How are negative numbers represented in different bases?
- A. Computers represent negative integers using two's complement. The tool shows the signed representation based on your input, helping you understand how the CPU stores negative values.
- Q. Can floating-point numbers be converted?
- A. The integer portion uses standard base conversion. Note that many decimal fractions (like 0.1) cannot be represented exactly in binary, which is why floating-point arithmetic can produce surprising results.