Thanks for the comment! Yes this is meant more for your personal projects than for using in existing projects

The idea behind needing a password to get a password, totally understand, my main goal was to have local encrypted storage, the nice thing about this implementation is that you can have all your env files saved and shared in your git repo for all devs to have access to, but only can decrypt it if given the master password shared elsewhere (keeper, vault etc) so you don’t have to load all values from a vault, just the master

100% though this doesn’t cover a large range of usage, hence the name “simple” haha, wouldn’t be opposed to expanding but I think it covers my proposed use cases as-is

I have the snap installed, for what it’s worth it’s pretty painless AS LONG AS YOU DON’T WANT TO DO ANYTHING SILLY

I’ve found it nearly impossible to alter the base behaviour and have it not entirely break, so if nextcloud out of the box does exactly what you want, go ahead and install it via snap…

I predict that on docker you’re going to have a bad time if you can’t give it host network mode and try to just forward ports

That said, docker >>>> VM in my books

The article doesn’t address it, maybe someone here can… what does “consumed” mean? Where does the water go after it’s used to cool? Surely it’s reusable, right?

Yes that’s a good comment for an FAQ cause I get it a lot and it’s a very good question haha. The reason I use it is for image size, the base nvidia devel image is needed for a lot of compilation during python package installation and is huge, so instead I use conda, transfer it to the nvidia-runtime image which is… also pretty big, but it saves several GB of space so it’s a worthwhile hack :)

but yes avoiding CUDA messes on my bare machine is definitely my biggest motivation

lollms-webui is the jankiest of the images, but that one’s newish to the scene and I’m working with the dev a bit to get it nicer (main current problem is the requirement for CLI prompts which he’ll be removing) Koboldcpp and text-gen are in a good place though, happy with how those are running

I need a citation for that for sure, I know until very recently all software updates were non-OTA, meaning you had to drive to a dealership to get the software applied, which means dealers were hesitant to issue them, that could all be incorrect now and it’s certainly incorrect for some of them, I’m positive there are car companies that put out OTA like tesla, i just don’t know who they are

that last edit you added is probably the worst part, because it takes away from how solid Toyota and others are because it ruins the entire metric, Toyota is likely crushing it, and entirely possible Tesla is actually really really bad, but without the RIGHT metrics we can’t actually draw any good conclusions, it’s not just bad for tesla but for the whole market

Yeah there’s definitely been some aggregious recall issues, but the problem is the stats include minor things that only required a quick OTA, so it skews the numbers awkwardly and means we can’t properly judge the real problems they had

If they separated the numbers, we might see that either Tesla has very few real recalls, Tesla actually does have a lot of real recalls but also happens to have software ones, or it’s about normal

And without separating all we can do is guess

100%, this number is skewed by the fact that tesla will basically “recall” for any minor issue because it’s a simple software update, I imagine a lot of companies try to avoid recalls as aggressively and for as long as possible because it’s a significantly bigger burden on them

I say this as someone who drives a Tesla but is still extremely judgemental of Tesla

Honestly an interesting thought and worth keeping in mind, I would love to see a lot more examples and more timing, especially for the pythonic ones, are they more efficient or just more python like?