The YES Certification Tool Kit has long needed Video Tutorials for clean step-by-step tool usage and configuration. These will be done with video screen scrapes and audio overlay.

I'll spend half of hackweek preparing for the CISSP certification, in the other half I want to learn Ghidra (https://ghidra-sre.org/) and solve some reverse engineering quests with it

Hackweek 21?

Postmaster desperately needs a mail spool. I need to come up with a good way to store meta information about queued messages. An sqlite database seems like a natural fit because I might want to do some nontrivial queries in there to figure out which messages to deliver (and where). On the other hand, interfacing with sqlite is a bit unpleasant, so instead I might want to write one big JSON file that contains all relevant information. That would certainly suffice for the first version ...

The primary arcade machine emulator is MAME, and it has a very specific format for romset compression. I have previously started a project call pyros that allows the creation and update of MAME romsets. The project consists of the following tools: * pyrex: Tool to create an executable Bash script that will take unorganized files and put them into an organized MAME romset.

In the past, some customers ever complained that OCFS2 file system performance went down when listing a big directory (e.g. include 400k files). According to my preliminary investigation, this performance problem is related to file system block allocation algorithm. Then, I want to look at the current mainstream file systems (e.g. EXT4, XFS, etc) block allocation algorithms in this hack week, compare and analysis these algorithms advantages/disadvantages.

Currently, As QA, we design matrix for the test cases, then use matrix to generate test cases' name, then according the test cases' name to add related settings, then add those cases' name and settings to the openQA test suite. I hope, we can design matrix first, then other steps will be generated by automatically.

Roughly follow a few selective chapters from the book 'Hands-On Machine Learning with Scikit-Learn and TensorFlow'