As part of my continuing experiments with AI, I regularly use models from Hugging Face. Ollama makes it really easy to run models locally from their own repo, but sometimes they do not have exactly the model one wants.

Why Use Hugging Face

While Ollama has its own curated repository of models you can use, Hugging Face has kind of become the community default. The choice is much larger and opens up possibilities that are not available if you just stick with Ollama’s own repository. For mainstream models, there maybe quantization that are not available in Ollama’s own repo. There are plenty of fine-tunes of more mainstream models that are only on Hugging Face.

Once again I have a new tool I have been playing with, once again AI related. One of the tools I have been using is n8n which is a workflow automation platform. It enables us to integrate multiple applications and services through a visual interface. While it is very much an enterprise solution, it is FLOSS and we can deploy it at home, albeit with some caveats and/or workarounds.

One of the really powerful parts of N8n is that we can integrate with various AI platforms, including all the usual suspects: Claude, ChatGPT, etc. Of course I want to keep things local, which N8n caters for with Ollama.

Some more Prometheus content now, this time I will share how I do my alerting. I want to to receive alerts from my homelab, but I do not care about them too much. If something goes wrong at 2am, I really could not careless. In fact, there is no reason why I would want to be disturbed by them under any circumstances.

To that end, the whole point of my homelab is that I can use the same techniques I would use in production. To that end, I do want to set up alerts, I just want to keep them as non-intrusive as possible. A good solution for my alerts then is Telegram, as I can have them sent to a channel that I mute. Now I can see them, but I am never actually disturbed by them.

I run Kubernetes in my homelab and use the (now classic) Prometheus/Grafana stack for monitoring everything. In my experience, the thing that Prometheus does not do well is long-term storage. Prometheus’ own database is fine for a few days storage, but performance and reliability quickly degrade. There are quite a few solutions to this problem, but I choose to use Thanos, partly because Kube Prometheus Stack has very good support for it.