LibreNMS VS Installation

cd /opt sudo git clone https://github.com/librenms/librenms.git sudo chown -R www-data:www-data /opt/librenms

I recommend [librenms/librenms: Community-based GPL-licensed network monitoring system](https://github.com/librenms/librenms), which is Open source and free.

Bug report is over here

I playing with LibreNMS in my lab, just for the heck of it.

git clone https://github.com/librenms/librenms.git

LibreNMS is awesome! It's a fork of Observium, but has a more "friendly" development philosophy—there's some history there.

Look at the issue list...that is why.

https://github.com/netbox-community/netbox/issues?q=is%3Aiss...

Note how they want to be "NetBox functions as the source of truth for your network infrastructure."

Your individual situation dictates what is important, but had netbox targeted being a central repository vs insisting on not allow other systems to be truthful for certain items it could be a different story.

We have learned that trying to centralize complexity and control doesn't work, heck we knew that almost immediately after the Clinger Cohen Act passed and even ITIL and TOGAF fully call this out now and I expect this to be targeted by consultants over the next few years.

You need a central constant way to find state, to remove any questions or doubt regarding where to find the authoritative information, but generally if you aspire to scale and grow or adapt to new changes you really need to avoid having some centralized, god-box, and prescriptive system like this.

"The site" is actually just their blog. The github repo at https://github.com/netbox-community/netbox is more like the real homepage and has a good presentation.

Sweet! If it ends up anything like Netbox it will be so sweet. By looking at the screenshots I am very excited to see this implemented!

NetBox Release v3.6.6 is now live (as of November 29, 2023)!

Take a look at netbox. https://netbox.dev/

Pre-trained systems dominate AI today (as deep as the P in GPT). My team and I have been researching and building alternatives given how the hallucination, blackbox and other problems don't seem solvable within* the current paradigm.

One of our first applications is a network automation solution for Netbox-- locally trained AI Agents unique to each Netbox account to predict roles of newly added devices given the current local list of devices, like a context-aware autocomplete.

Agents are lightweight by design, this particular Netbox Agent is 40-neuron, and when hooked up demo.netbox.dev and consistently gets 80%+ accuracy predicting device roles even when trained on ~60 devices only.

Try it out: https://aolabs-netbox.streamlit.app/

You can use dummy data from demo.netbox.dev. More on Netbox: https://github.com/netbox-community/netbox

We'd keen for feedback, if this is useful, how we could extend it if it is, or if it sparks other application ideas.

* Somebody has to be the pre-trainer, leaving an irreducible gap of misunderstanding between AI and its application which we are trying to diminish by adding a layer of local training.