Linux: Erase/clean MBR(partition table included) of a (pen)drive/block device using dd command

MBR size is (in bytes): 446 + 64 + 2 = 512

446 bytes - Bootstrap code.
64 bytes - Partition table.
2 bytes - Signature.

So
# dd if=/dev/zero of=/dev/sdb bs=512 count=1
cleans everything in the MBR.

Dynamic programming Vs Divide and Conquer

Dynamic programming is a method for solving a bigger problem by breaking it down into smaller subproblems, solving each of those subproblems only once and storing their solutions. Next time the same subproblem occurs, instead of recomputing its solution, we simply look up the previously stored solution, hence saving computation time at the expense of some amount storage space. Each of the subproblem's solution is indexed/hashed in some way, normally based on the value of its input parameters, so as to facilitate lookup. The technique of storing solutions to subproblems instead of recomputing them is called "memoization". It is an optimization technique.

Divide and Conquer works in these steps.
Divide: Divide the main problem into small subproblems.
Conquer: Solve those subproblems recursively.
Combine: Combine results of subproblems to compute result of main problem.

Dynamic Programming is similar to Divide and Conquer when it comes to dividing bigger problems into smaller subproblems. But here, each subproblem is solved only once. In Divide and Conquer, the subproblems are independent of each other while in case of Dynamic Programming, subproblems are not independent of each other. Solution of one subproblem may be required to solve another subproblem. That is why in Dynamic programming solutions to subproblems are remembered. We store the result of subproblems in a table so that we don't have to compute the result of a subproblem again and again.

Divide and Conquer example: Merge Sort
Dynamic Programming example: Tower of Hanoi, Nth Fibonacci number
function fib(n)
if n <= 1 return n
return fib(n − 1) + fib(n − 2)

Jon Bentley on code correctness.

"If it doesn't have to be correct, I can make it as fast as you'd like it to be" - Jon Bentley

Big Omega, Big Theta and Big Omicron.

In code complexity measure,

Big Omega gives us asymptotic lower bound function g(n).
Big Theta gives us asymptotically tight bound function g(n), i.e. both lower and upper bound being c1.g(n) and c2.g(n) respectively.
Big Omicron gives us asymptotic upper bound function g(n).

Most of the time we talk about Big Oh. That is actually Big Omicron.
[The character O is the upper-case Greek letter Omicron, not English letter O]

And surprisingly, when we say, complexity of a code is O(N^2) etc,
we really mean Big Theta! We not only mean, N^2 being the upper bound,
but also we mean, N^2 is the lower bound too. Isn't it?

Ignorance is bliss...

"Where ignorance is bliss, 'tis folly to be wise." -Thomas Gray

Static:Dynamic and Strong:Weak Typing.

Static languages = statically typed language = type is known from program text.
Dynamic languages = dynamically typed language = type is known when program runs.

But, dynamic typing != weak typing.
And static typing != strong typing.

Strong typing = Variables have specific data types. Once you assign a variable to something, it is bound to that type.
Weak typing = Variables are not strictly bound to a given data type. Variable's value can be interpreted differently.

Static typing can also be weak, as in C.
Static typing can also be strong, as in Java.

Java is statically typed and strongly typed.
C is statically typed but weakly typed.
Python is dynamically typed and strongly typed.
PHP is dynamically typed but weakly typed.

A good read: https://prateekvjoshi.com/2014/10/03/static-vs-dynamic-typing/

Dynamically typed languages and maintenance of large code bases.

Dynamically typed languages make it more difficult to maintain large code bases.
Mostly because errors that are caught by a statically typed languages are now not available for free.
Programmer must write test cases to ensure correctness.

A good read: https://softwareengineering.stackexchange.com/questions/221615/why-do-dynamic-languages-make-it-more-difficult-to-maintain-large-codebases