It’s fun to build things like this but if you want to nourish a user base you need to fully understand the landscape of similar tools and then explain your differentiating value. This is /particularly/ important for security related tools.
Specifically you should compare and contrast to tools like SOPS, Ansible Vault, pass, etc.
This is an unnecessary optimization, particularly for the article's use case (small files that are read immediately after being written). Just use /tmp. The linux buffer cache is more than performant enough for casual usage and, indeed, most heavy usage too. It's far too easy to clog up memory with forgotten files by defaulting to /dev/shm, for instance, and you potentially also take memory away from the rest of the system until the next reboot.
For the author's purposes, any benefit is just placebo.
There absolutely are times where /dev/shm is what you want, but it requires understanding nuances and tradeoffs (e.g. you are already thinking a lot about the memory management going on, including potentially swap).
Woohoo, one of the highlights of this time of year. I had to do mine from an eastbound flight over the pacific. This has become a fun tradition not just for me personally but for many friends, colleagues, and fellow HNers. Big props once again to wastl and his helper elves for making this!
I encourage anyone who gets value from this to donate to support it if they can. It is a passion project but nonetheless comes with real costs.
Architecture matters because while deep learning can conceivably fit a curve with a single, huge layer (in theory... Universal approximation theorem), the amount of compute and data needed to get there is prohibitive. Having a good architecture means the theoretical possibility of deep learning finding the right N dimensional curve becomes a practical reality.
Another thing about the architecture is we inherently bias it with the way we structure the data. For instance, take a dataset of (car) traffic patterns. If you only track the date as a feature, you miss that some events follow not just the day-of-year pattern but also holiday patterns. You could learn this with deep learning with enough data, but if we bake it into the dataset, you can build a model on it _much_ simpler and faster.
So, architecture matters. Data/feature representation matters.
But not all things you might do with a dotfile (or, more generally, per-user customization) are just replacing files. Things like cronjobs, brew installs, `defaults` in MacOS, etc. Viewing dotfile-based customization as strictly files to obliterate with pre-existing files is needlessly myopic.
For this broader problem, there are other more complete solutions that are more robust and flexible. Personally I like dotbot (https://github.com/anishathalye/dotbot) as a balance between power and simplicity, particularly when managing files across multiple OS homedirs (e.g. linux server, macos laptop).
Specifically you should compare and contrast to tools like SOPS, Ansible Vault, pass, etc.