securitytxt.org VS countwords

Lemme guess, Well-known Security?

As in `http://mydomain.example/.well-known/security.txt ?

https://securitytxt.org/

If you see at the end of attack carefully, you'll see /.well-known/security.txt which already explained by securitytxt.org. That means, there's an either non-profit or for-profit organization that proactively find your site's vulnerability and trying to help you. For the for-profit organizations, usually they just give you information what vulnerability but you have to pay in order to get the solution.

I would laugh if I saw this in a security.txt file.. make it happen.

as defined on https://securitytxt.org/ “When security risks in web services are discovered by independent security researchers who understand the severity of the risk, they often lack the channels to disclose them properly. As a result, security issues may be left unreported. security.txt defines a standard to help organizations define the process for security researchers to disclose security vulnerabilities securely.”

There are quite a lot of websites that implement the security. txt already. See https://securitytxt.org for more information about that

There are additional optional fields available for encryption, acknowledgements, canonical references, and more. You can find out more about this at the official security.txt website.

here it is: https://securitytxt.org/

My own code example of a drastic speed up (~25%) simply replacing a couple of for loops with iters: https://github.com/benhoyt/countwords/pull/115

And yet Go was faster than Rust in a simple app that count words: https://benhoyt.com/writings/count-words/

> but the performance profiles & characteristics that we must know about in order to make a choice on which tool to use. And it shouldn't be that each user has to figure it out on their own, dig into PR's or whatever.

That's an interesting take – I like the idea of a catalog of standard tasks with implementations in several languages as well as their performance characteristics. I suppose Rosetta Code gets the ball rolling with this, but it's missing some performance metrics. It reminds me of [Ben Hoyt's piece](https://benhoyt.com/writings/count-words/) on counting unique words in the KJV Bible in different languages.

This article shows that map lookups can be optimized by using the (unintuitive) pattern:

I further improved the efficiency of the optimized go implementation by about 10% https://github.com/benhoyt/countwords/pull/113

There is a parallel.go in the repo, but it's much more complicated (5x as long) and doesn't perform as well as the optimized single-threaded version. https://github.com/benhoyt/countwords/blob/master/parallel.go

A more recent comparison found Gforth took 6 times as long as C, and found LuaJit took 2.5 times as long as C.