I don't think libraries count in terms of code. We all use code to program. Standing on the shoulder that preceded us. Using a library and a function should just count for the most part.
I don't think libraries count in terms of code. We all use code to program. Standing on the shoulder that preceded us. Using a library and a function should just count for the most part.
It's like the difference between a complete kitchen that fits in your pocket and an iPhone app that lets you order a burrito. The article suggests something like the former. A library which encapsulates 1000 lines of code into a single function call is like the latter.
Python also has some mandatory libraries if you want to do any specific. Numpy and pandas for statistical analysis make them required.
My code is concise and clean but it is because of these libraries.
I am also certain that there are exceptions.
On the other hand, there are things like Prolog. You can think of Prolog as a backtracking constraint-solving library, and then another library that parses a DSL for expressing facts and procedural constraints and feeds it to the first library. But Prolog's language isn't really a DSL, because it isn't particular to any domain: there's no closed solution-space where Prolog applies. The efficiency gains you get from Prolog's elision of proceduralized contraint-solution code can apply to any program you write. And so its value is unbounded; its ROI is certainly positive, whatever the cost was to implement it.
That's the comparison that's useful here, I think. Is this something that only solves problems in one domain? Or is this something that could be applied to (at least some little bits of) any problem you encounter?
.MODEL TINY
.CODE
CODE SEGMENT BYTE PUBLIC 'CODE'
ASSUME CS:CODE,DS:CODE
ORG 0100H
DB 'HELLO WORLD$', 0
INC DH
MOV AH,9
INT 21H
RET