osx-serial-generator VS countwords

easy way to find a valid number is too generate tons and verify them one by one on the apple website as said into the previous link of point 11 use this to generate: https://github.com/sickcodes/osx-serial-generator

You serial HAS to be invalid. There are some criteria that you should follow (it's only partially random), but all of the guides warn you that your generated serial should always result as invalid for Apple: otherwise it'd mean you just generated someone else's serial by accident. If you don't want to do it manually or following the standard guides, there are open source, safe utilities to generate one: https://github.com/sickcodes/osx-serial-generator . I can assure you that getting iServices to work is fairly easy, nowadays.

Here you go: https://github.com/sickcodes/osx-serial-generator and you also need Open Core Configurator or similar software to mount the hidden efi partition

I'm using this script to build the VMs, applying serial numbers and HW model generated with this script. The guests enroll over the hosts VPN and once they get the config profile installing machine certs I can get them on VPN directly, enroll, and start testing.

Link : https://github.com/sickcodes/osx-serial-generator

Generate Complete Sets of Serial Numbers for OS X-KVM, Docker-OS X, OpenCore\ (6 comments)

The code is relatively easy to read if you want to see: https://github.com/sickcodes/osx-serial-generator/blob/master/generate-unique-machine-values.sh

"dang, I didn't know that was 50x faster than the idiomatic way" or "hey, I didn't know that this implementation in the stdlib prioritized this over that and made this so slow, that's interesting" -- .e.g, there's some kinda neat language details to be found in something like Ben Hoyt's community word count benchmarks repo and 'simple' vs 'optimal' code: https://github.com/benhoyt/countwords

It benchmarks programs that count the total number of unique words in some input. It's not exactly equivalent to your problem, but it's similarish. All of the programs used some kind of hash map for lookups, but I contributed a program that used a trie. Its performance in my experience varies depending on the CPU interestingly enough. On my old CPU (i7-6900K) it was a little slower, but on my new cpu (i9-12900KS) it was faster.

Why not read the source code? :-)

I wrote comments explaining things: https://github.com/benhoyt/countwords/blob/8553c8f600c40a462...

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: