why Sanskrit is the best language for computer applications. computer in Sanskrit. computer applications in Sanskrit.
Computer in the Sanskrit Language
In computer science, there are multiple systems that have been developed which have what is called high-performance semantics, meaning that they are more efficient than other systems. One of the best examples is how a language is used as a high-performance language in a sequence programming system computer in the Sanskrit language. This system defines a language called Gkontu Sāgri, or Unix Syntax, a recursive, dynamic, high-performance system that works by using natural language semantics.
Sanskrit is the best language for computers. why natural language?
You can imagine that a sentence in natural language has to be parsed first. That is where the initial language is interpreted by a “parser”, which gives you back the actual code and semantics. That is how natural languages work. There are also meta-languages that are used as language extensions. The syntax extension is called high-level programming language (HPL), and the semantics is a different set of semantics. This is where the reasoning about which language is a better language to use can be given, and a list of languages can be written.
the best language for computer applications is Sanskrit
If you can write a more efficient language with better semantics, then it is the best, but if you can write a more efficient language with better semantics, and it is the best language to implement a good high-performance system, then it is the best language to use. This is what is generally called the best language for computer applications, the best language to write software. What is the best language for computer programming is defined by the best combination of semantics, performance, and efficiency.
Sanskrit is more efficient than the traditional languages
What happens if you only use the syntax extension and semantics, if you don’t use the semantics but only the syntax, and if you don’t use the performance but only the performance?
Then you can make an arbitrary subset of languages, such that the language extensions are always one set, and the semantics is another set. By assigning certain things, the semantics can be made arbitrarily fast, and the performance can be just a bit slower. By doing that, you can write a language that is fast to evaluate, and whose execution is much more efficient than the traditional languages, but which has some of the same performance as those languages. And that is very efficient.
But this requires that you have an engine to process and implement these semantics in the computer, and it also requires that you have an engine that can implement these semantics in the computer.
This will not be the best solution to solving the complexity problem. We can’t use algorithms to implement the semantics, because those algorithms are not necessarily efficient.
The algorithm to implement the semantics may only give the right answer but it takes so much time to do it that the complexity of this algorithm can be much larger than the complexity of the semantics. By giving an algorithm to implement the semantics, we make that process very inefficient.
we want the semantics to be stored in a computer, and this is the main reason why Sanskrit has better semantics for computer applications than any other language.
You can achieve great performance by using a computer-based implementation. Such a solution is called a high-performance virtual machine (HVM). The semantics is stored in computer memory, and the semantics is executed. The advantage is that the system is very fast because only that one piece of code is required to implement the semantics.