100%
31.10.2025
seconds, for example:
# nc -p 16000 -w 30 examplehost.tld 22
If firewalling is in place and you need to originate your connection from a specific IP address to open a port, then you can enter:
# nc -s 1.2.3 ... 12
99%
31.10.2025
starting 12.34, so, for example, 12.34.56.78 will be allowed to connect to ALL services and not just SSH. As well as these flexible options, you can also declare old school subnets directly:
sshd: 1.2.3 ... 12
99%
31.10.2025
.cluster.openlava
#-----------------------------------------------------------------------
# T H I S I S A O N E P E R C L U S T E R F I L E
#
# This is a sample cluster definition file. There is a cluster
# definition file for each cluster. This file's name should be
# lsf ... 12
99%
31.10.2025
password 8 ZDF339a.20a3E
05 log file /var/log/quagga/zebra.log
06 service password-encryption
07 !
08 interface eth0
09 multicast
10 ipv6 nd suppress-ra
11 !
12 interface eth1
13 ip address 10 ... 12
62%
04.12.2024
Rubén Llorente ... " {
11 endpoint = "https://192.168.3.15:8006/"
12 username = "root@pam"
13 password = "proxmox"
14 insecure = true
15 tmp_dir = "/var/tmp"
16
17 ssh {
18 agent = true
19 }
20
18%
04.12.2024
applications from creating child processes
No
d4f940ab-401b-4efc-aadc-ad5f3c50688a
Blocks credential theft from the local security authority subsystem (lsass.exe)
Yes
9e6c4e1f
18%
31.10.2025
.nmap.org (64.13.134.52):
Not shown: 994 filtered ports
PORT STATE SERVICE VERSION
22/tcp open ssh OpenSSH 4.3 (protocol 2.0)
25/tcp closed smtp
17%
28.07.2025
[i] + b[i];
}
When the number of cycles is known at compile time, a loop can be fully unrolled:
c[0] = a[0] + b[0];
c[1] = a[1] + b[1];
c[2] = a[2] + b[2];
c[3] = a[3] + b[3];
However, it remains
17%
07.10.2025
experimenting with the keyboard-integrated computer format so popular back in the age of Commodore's Amiga and C64 machines, launching the Pi 400 [3] and its recent upgrade, the Pi 500 [4], soon after the launch
17%
03.12.2024
to check the version installed:
$ python3
>>> import tensorflow as tf
>>> print(tf.__version__)
It doesn’t make too much difference, but if you are curious, I used TensorFlow 2.9.2 and Keras 2.9
