This content originally appeared on DEV Community and was authored by Michael N.
Our Obsession With Making Code “Understandable”
Humans have always struggled with translation. From carving symbols into clay to inventing alphabets, we’ve needed bridges between ideas and expression. Computers are no different. They only “speak” in binary: 1s and 0s. But since nobody wants to write 01101000 01101001 every time they mean “hi”, we invented programming languages.
Compilers and interpreters became the translators, standing between human-friendly languages and machine code. Just like a translator at the UN, they can either prepare a full translated speech in advance (compiled) or whisper line by line into the listener’s ear (interpreted). Both approaches have tradeoffs.
Where Did This Classification Even Come From?
The distinction between compiled and interpreted didn’t come out of thin air. In the 1950s, FORTRAN (Formula Translation) popularised the compiled model: code written once, translated into efficient machine instructions, then executed quickly. It was perfect for scientific calculations where speed mattered.
On the other side, Lisp and other early languages leaned more toward interpretation. They allowed programmers to test code interactively, line by line — a revolutionary idea at the time. Debugging became less of a nightmare because you didn’t need to recompile an entire program to try something new.
Back then, this divide mattered. Hardware was expensive, memory was scarce, and efficiency could make or break a project. Today, though, with more powerful machines and better tooling, the sharpness of this classification has blurred.
What We Mean by “Compiled”
When we call a language “compiled,” we mean that the source code is fully translated into machine code ahead of time. This produces an executable that runs directly on the CPU without needing the original source or an interpreter.
Performance: Fast execution, because all translation work is already done.
Error Checking: Many bugs surface during compilation.
Portability: Limited, since the binary only works on the target system.
Examples: C, C++, Rust, Go.
What We Mean by “Interpreted”
An interpreted language, on the other hand, reads and executes code line by line at runtime. The interpreter acts like a middleman every time the program runs.
Performance: Slower, because translation happens on the fly.
Error Checking: Bugs only appear at runtime.
Portability: High, since the same source can run anywhere with the right interpreter.
Examples: Python, JavaScript, Ruby, PHP
Enter the Grey Zone
Most modern languages don’t live in either extreme. They combine approaches:
Bytecode + VM: Java compiles source into portable bytecode, then executes it with the JVM.
JIT (Just-in-Time): JavaScript engines like V8 start interpreting but compile hot code into machine instructions on the fly.
AOT (Ahead-of-Time): Some environments pre-compile bytecode to native binaries for faster startup.
So, when we casually say “Python is interpreted” or “C is compiled,” we’re simplifying a much messier reality.
Introducing the Chadstrum
To make sense of this, I started thinking of it as a spectrum instead of a binary. I jokingly call it the Chadstrum:
On the far interpreted end, you’ve got languages like Bash or early Perl.
In the middle, there’s Java, C#, and modern JavaScript, which use VMs and JIT compilers.
On the far compiled end, you’ll find C, C++, Rust — where code is turned straight into machine instructions.
The point? Whether you’re coding in Bash scripts or writing kernel modules in C, you’re still a Chad. Both ends of the spectrum require skill, and both lead to powerful software.
Why This Still Matters Today
The compiled vs interpreted discussion may seem outdated, but it affects how we build and use software:
Performance: High-frequency trading software? Compiled. A quick automation script? Interpreted.
Portability: Java’s “write once, run anywhere” VM model vs. compiled binaries tied to a specific OS.
Developer Convenience: Interpreted languages let you prototype and experiment fast. Compiled ones catch errors early and scale better for performance-critical tasks.
But the bigger lesson? You don’t need to join language wars. The Chadstrum shows it’s not about being “better” — it’s about choosing the right tool for the job. At the end of the day, whether your code is compiled, interpreted, or something in between, it’s still getting the job done.
The world of programming languages isn’t black and white. It’s a messy, fascinating spectrum shaped by decades of history, performance needs, and human creativity.
This content originally appeared on DEV Community and was authored by Michael N.