For a while I have been looking for a good self-hosted solution for recording my gym sessions. I have a good solution for tracking my cardio sessions (cycling and running mainly) in Endurain, but never for strength training. I have tried a few:

• wger - a good solution, but the UI is a bit dated and it is also geared towards managing a gym rather than a single user.
• Wingfit - also good, but I just did not click with it. Shame, because the developer is from Brittany, which is my adopted home.
• A multitude of different open source Android apps. These all had UX issues and no syncing, so kind of sucked for me.

As a result, I stayed on Strong for much longer than I would have liked.

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.

My initial plan is to do react to an alert from Prometheus and give myself some advise about what to do. The plan is pretty simple and adds to my existing Alerts through Telegram:

Alertmanager
  ├─► Telegram (existing)
  └─► n8n webhook
        └─ Python Code (build prompt)
              └─ HTTP Request → Ollama
                    └─ Telegram (post analysis)

1. Alertmanager fires a webhook to n8n alongside the existing Telegram notification
2. n8n builds a prompt from the alert payload and sends it to Ollama
3. Ollama analyses the alert and suggests a fix
4. n8n posts the analysis back to Telegram

So let’s start by getting it installed

Installing n8n on Kubernetes

There is no official Helm chart, but the community-maintained chart is excellent and well maintained. To get started we need:

• Redis which we will install with Helm
• PostgreSQL for which we will use Cloudnative-PG
• Some storage (I will use Longhorn as usual)
• n8n itself

Dependencies first

Before we can do anything else, let’s get Redis and PostgreSQL setup. The simplest is Redis using the Cloud Pirates Helm Chart:

helm --namespace n8n install n8n-redis \
  oci://registry-1.docker.io/cloudpirates/redis \
  -f redis-values.yaml

The contents of redis-values.yaml is very simple:

architecture: standalone
master:
  persistence:
    enabled: true
auth:
  enabled: true

That will create a secret (n8n-redis) to we use later.

Recently, I decided to jump on the latest AI bandwagon. While cloud-based large language models are powerful and convenient, I wanted complete control over my data, plus the satisfaction of running everything on my own infrastructure. Enter OpenClaw — a personal AI assistant that runs locally and integrates with messaging platforms you already use.