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I didn’t for the longest time but now I use Traefik for this. It can automatically add services (i.e. containers) to it’s routing list so the overhead is low and since I also run openwrt on my router I setup *. localhost to point to 127.0.0.1 so I don’t have to remember what ports I’m using for which service (e.g. jellyfin.localhost). You can also setup DNS entries using something like PiHole.
This is the kind of AI stuff that really annoys me. Looking at one of the mutation examples I didn’t see anything that wouldn’t normally be tested by a typical mutation tool. You took a simple, idempotent process and you got an llm to do it slower, less accurately, and using more resources.
If you wanted to marry the two in a new and possibly useful fashion I would say use an llm to analyze the results of a standard mutation test and give guidance on what issues should be acted upon first. An off-by-one calculation could mean somebody loses a million dollars or it could mean a button is grayed out. Standard mutation tools don’t give you that context.
Seems decent but depends on your usage. Memory could be a bit excessive unless you’ll actually have a lot of simultaneous users. I’d also look into the features of the integrated graphics card. If you’re doing some streaming a low cost GPU might give you better options for hardware decoding/encoding.
Don’t remember the tool, maybe someone here does, but there’s some web service out there that boasts a “no storage” approach. You provide some URI and some other value (maybe username) and it makes a password for you, but it’s always the same for a given combination. Basically it’s a purely functional generator.
Downside would be forgetting a minor detail (Did it end with a slash or not? What was the username?) or the site going down. You can achieve the same thing yourself with a hash calculator but those passwords are a bitch to type in.
tl;dr just use KeePass