> If i'm reading this correctly, can run Immich from a Raspberry Pi 4? That's pretty cool.
Yes! It runs completely fine on a Pi, except for the machine learning service. The good news is that you can run that service on a separate computer with a GPU and point Immich to it. Immich will use it when it's available and I think you can configure it to run all the queued ML jobs at a certain time when you expect the computer running the ML service to be on.
Yeah that's more or less my goal for the setup in the post... I didn't have much time to investigate what else I could move off of the SD card but I suspect there's a few other things I could do. NixOS makes it super easy too.
I have taken a (short) look at KOReader and saw that there's an instructions page on its wiki on how to install it on the rM2; it still uses rm2fb but it suggests using timower's version, which works on newer versions of the OS. What I should've made more clear in the post was that there are options, they're just less convenient to use because Toltec doesn't work.
Bluesky lets you select the language your post is written in before posting it and it is attached as metadata to the skeet. I guess the backend for this only searches posts in English, but it's possible the dataset is not 100% accurate due to some users forgetting to switch language before posting.
Turns out I managed to use almost all of these during a refactor of a project at work, even metaclasses... (Metaclass usage is justified in my case: we have a sort of language evaluator and using a metaclass lets us define function arguments with their types and validators in a very coincise and obvious way similar to Pydantic.)
I think this list should also include descriptors[0]: it's another metaprogramming feature that allows running code when accessing or setting class attributes similar to @property but more powerful. (edit: nvm, I saw that they are covered in the proxy properties section!)
I think the type system is quite good actually, even if you end up having to sidestep it when doing this kind of meta-programming. The errors I do get are generally the library's fault (old versions of SQLAlchemy make it impossible to assign types anywhere...) and there's a few gotchas (like mutable collections being invariant, so if you take a list as an argument you may have to type it as `Sequence[]` or you'll get type errors) but it's functional and makes the language usable for me.
I stopped using Ruby because upstream would not commit on type checking (yes I know you have a few choices if you want typing, but they're a bit too much overhead for what I usually use Ruby for, which is writing scripts), and I'm glad Python is committing here.
Some points about the introduction, but otherwise this seems like an interesting collection of (slightly deranged?) patterns in C.
> The truth is that any reasonably complicated software system created by humans will have bugs, regardless of what technology was used to create it.
"Drivers wearing seatbelts still die in car accidents and in some cases seatbelts prevent drivers from getting out of the wreckage so we're better off without them." This is cope.
> Using a stricter language helps with reducing some classes of bugs, at the cost of reduced flexibility in expressing a solution and increased effort creating the software.
...and a much smaller effort debugging the software. A logic error is much easier to reason about than memory corruption or race condition on shared memory. The time you spend designing your system and handling the errors upfront pays dividends later when you get the inevitable errors.
I'm not saying that all software should be rewritten in memory-safe languages, but I'd rather those who choose to use the only language where this kind of errors regularly happens be honest about it.
