npx pnpm i -g pnpm@latest

.

.^.

Just restart jenkins:

service jenkins restart

Specifically for the wco  stack, gem 'iron_warbler' had to be included in that particular instance's (micros_*) Gemfile. Which is a bit disappointing since I wanted libraries to be decoupled - but hey, I'm not going to fix this right now. But please be aware: it may seem that there is a separation between libraries and some libraries (namely models and content) can be installed without the rest of the stack - but that's not the case. The whole stack must be installed.

That happens because the${CONTAINER_VOLUME} path must belong to a user other than root, or prosody fails to start.

This happened to me recently... I may have unplugged an external 5Tb harddrive without ejecting it from mac os x 13, and it could not be mounted again. I plugged the harddrive into a windows machine, run "repair" there (whatever that means) and the data was perfectly readable. It's just that the disk would not mount on mac.

Stackoverflow to the rescue! The solution is:

sudo pkill -f fsck

Or actually

ps aux | grep fsck 

It appears that you can just re-run the setup in order to do that:

bundle exec rails ffcrm:setup

.

Change

host: localhost

to 

host: 127.0.0.1

in config/database.yml, to make the connection over TCP/IP rather than local socket.

You may also need to specify port 3306 explicitly.

APN Name	Google Fi
APN	h2g2
Proxy	
Port	
Username	
Password	
Server	
MMSC	http://m.fi.goog/mms/wapenc
MMS Proxy	
MMS Port	
MCC	310
MNC	260
Authentication Type	
APN Type	
APN Protocol	IPv4/IPv6
APN Roaming Protocol	IPv4/IPv6
Bearer	
MVNO Type	
MVNO Type

.

keytool -genkey -v -keystore ~/.android/debug.keystore \
  -alias debug_keystore -keyalg RSA -keysize 2048 -validity 10000

.

Paperclip::Errors::NotIdentifiedByImageMagickError

 ## /etc/ImageMagick-6/policy.xml
 <policy domain="coder" rights="read | write" pattern="PDF" />

.

If you see an error similar to this one:

RuntimeError: CUDA out of memory. Tried to allocate 11.88 MiB (GPU 4; 15.75 GiB total capacity; 10.50 GiB already allocated; 1.88 MiB free; 3.03 GiB cached)

then, try reducing the batch size.

    train_loader = DataLoader(
        train_dataset,
        shuffle=False,
        pin_memory=False,
        batch_size=2, # was: 16
        num_workers=12,
    )

You may consider clicking 'enable proxy compatibility' in the settings, to resolve this issue: