if i remember correctly, i just replaced gitea with forgejo for image: in my docker-compose, and it just worked
it was a couple of versions back, so i don't know if that still works
if i remember correctly, i just replaced gitea with forgejo for image: in my docker-compose, and it just worked
it was a couple of versions back, so i don't know if that still works
I'm using leng in an dedicated LXC container in Proxmox
https://github.com/cottand/leng
I'm using defaults + some local dns lookups. Works fine for my use, and lighter than pihole. No web ui
Which apps are you testing?
I set up minio s3 for testing myself, but found that most of my docker services doesn't really support it. So I went back to good old folders
I use nforwardauth . It is simple, but only supports username/password
yes, regular markdown notes has been a good decision 😅
In the beginning, the query results were stored in the markdown files, which could be useful if reading them in another app. But now I just get the query code. I think there were reasons
I'm glad to hear things have cooled down. Does it take much effort to understand and use the templating stuff? I just remember templates got pushed to a different view, and I needed some header tags to get it working
So you like spaces or not? I never got that far with silverbullet. And I haven't used Trillium. I loved evernote when it came out. But it made me aware of the value of maintaining my own data.
Now I try to have data in a directory structure and not in databases
Firefox because I like the UI and I think chrome has gotten too dominant.
Brave if I need to chromecast something
I am not thinking of the most recent versions.
The query system was updated, around version 0.6 if i remember correctly. I don't think the updates were bad, but some things broke and I am too old for "bleeding edge". The template system was also updated at some point
I don't have a great solution. I use syncthing to keep notes local on all devices and MarkText on desktop and Zettel Notes on android.
what i really liked about silverbullet was that it had offline support. but there were made some changes there as well along the way, and for me it became less stable after it became optional. But I haven't actively used it for some time. I still got an instance running tho
How do you like the newer versions? I liked it in the beginning, but then there were breaking changes and new concepts and it started to feel a bit too complicated. So I am taking a break until things cool down
I don't use multiple users or ldap, but miniflux supports many users. And based on this pull request it seems to have the necessary interface for ldap?
https://github.com/miniflux/v2/pull/570
I enjoy and recommend miniflux for rss reading. I have used it for a long time now together with flux news android app. I also use save integration with wallabag sometimes.
I use proxmox and proxmox backup server (in a vm). I reinstall them both, and re-add lxc and vm and their drives from backup. has already worked once.
important files are additionaly synced to laptop and phone using syncthing.
proxmox backups (which are encrypted) are rcloned to backblaze for offsite backup
dovecot provides a proper shared imap server. But not all email clients allow moving emails between accounts (gmail and local email server), but Thunderbird does.
I can access the emails from any client
Yes it is correct. TLDR; threads run one code at the time, but can access same data. processes is like running python many times, and can run code simultaneously, but sharing data is cumbersome.
If you use multiple threads, they all run on the same python instance, and they can share memory (i.e. objects/variables can be shared). Because of GIL (explained by other comment), the threads cannot run at the same time. This is OK if you are IO bound, but not CPU bound
If you use multiprocessing, it is like running python (from terminal) multiple times. There is no shared memory, and you have a large overhead since you have to start up python many times. But if you have large calculations you can do in parallell that takes long time, it will be much faster than threads as it can use all cpu cores.
If these processes need to share data, it is more complicated. You need to use special functions to share data, like queues and pipes. If you need to share many MB of data, this takes a lot of time in my experience (10s of milliseconds).
If you need to do large calculations, using numpy functions or numba may be faster than multiple processes, due to good optimizations. But if you need to crunch a lot of data, multiprocessing is usually the way to go