Fighting with computers

I manage a few Linux servers at different places. I use SSH for that purpose, but I noticed that keeping port 22 open was like a light sign to the Internet hacking community. I do not feel comfortable knowing that my servers are subject to constant probing from other already hacked computers. I moved the servers to non-standard ports to keep most of the noise away, but it seems that is not enough nowadays, as I can see a bunch of hosts trying what I guess are common root passwords over and over again or some known SSH brute-forcing attacks. I keep the password login option open as I never know where I may need to connect from. So, there is a non-zero chance that my password could be guessed. So, I am now giving it a go to the fail2ban tool. As the name suggests, it will ban attackers' addresses after a programmable number of login fails. I hope this will remove some noise from /var/log/auth.log

I like to give it a go to the Advent of Code programming contest problems. They are usually funny and entertaining, sometimes too entertaining. But this year, I was busy with other matters, and I could not see much of it, so it dawned on me that perhaps I could use that as an excuse to give it a go to some of the newest LLMs that some people were raving about.   Coping and pasting the text of an AoC problem did not require much attention, so armed with LMStudio software and a couple of Qwen2.5 LLMs, I started testing. The 8B model dashes (at around 24 tokens/sec on my PC equipped with an Nvidia RTX 4060 Ti with 16 GB of VRAM). Unfortunately, the output is usually not good enough. However, when using the 32B LLM, the results, though slow at around 2.5 tokens/sec, are usually correct and produce Python source code that runs perfectly and delivers the proper solution, at least for the first question of each problem, in just a few minutes. Even with that slow generation rate, th...