What changed#
Over the past two weeks I rebuilt the home lab with a specific constraint: every service has to do something useful in daily life, not just demonstrate that it runs.
The infrastructure is the same — k3s cluster across heavyarms and the NUC fleet, ArgoCD for GitOps, Cloudflare Tunnel for access. What changed is what runs on top of it. I added dedicated ML inference instances running Ollama and stood up an n8n workflow engine. The goal was to see how fast I could go from “idea” to “running automation” once the platform was in place.
Answer: pretty fast.
First project: AI personal trainer#
I wanted to build something with stakes. My partner and I are working on fitness goals — weight, gym consistency, progressive overload — and staying accountable when life gets busy is hard. A tool that watches the data and sends context-aware messages felt like a good test case.
Three workflows, each targeting a different failure mode.
Weight progress motivation#
Problem: The scale moves slowly. It’s easy to lose context on whether you’re trending the right direction.
How it works:
- Google Sheets holds the weekly weigh-ins
- n8n polls on a schedule, calculates deltas
- Ollama generates a personalized message based on the trend (up, down, or flat)
- Message lands in Telegram
The AI piece isn’t just “you lost 0.3 kg, good job.” It reads the trend across multiple weeks and adjusts tone accordingly — more energetic when momentum is building, steadier when things plateau. The message changes based on context, not just the latest number.
Next iteration: Pull directly from the Google Fit API instead of manual sheet entries.
Gym consistency tracking#
Problem: Knowing you should go 4 days a week and actually going 4 days a week are different things.
How it works:
- Workout log lives in Google Sheets (one row per session)
- n8n counts sessions per week against the 4-day target
- Hit the goal → celebratory message
- Miss the goal → supportive nudge, not guilt
The tone calibration here was important. Shame-based reminders don’t work long-term. The LLM prompt is tuned to stay in coach mode, not scorekeeper mode.
Adaptive workout programming#
Problem: Static workout plans stop working the moment life deviates from the plan.
How it works:
- Weekly plan is generated based on previous week’s performance
- Two paths:
- Success path: Increase reps, weight, or exercise complexity
- Adjustment path: Build on incomplete workouts instead of advancing past them
- Ollama analyzes the log and generates next week’s plan accordingly
This is the most LLM-heavy workflow. The model receives the full training history for the week and outputs a structured plan. Running locally on heavyarms means no token costs and no data leaving the network — workout history stays private.
The stack in practice#
Google Sheets → n8n trigger → conditional logic → Ollama (local) → Telegramn8n handles the orchestration. Each workflow is a visual graph: triggers, conditions, HTTP calls to the Ollama endpoint, and output formatting before the Telegram send. The visual representation turns out to be useful not just for building but for debugging — you can step through a run and see exactly where data transforms.
The Ollama endpoint is internal only. n8n calls it over the cluster network. No egress, no API keys, no usage limits.
Why this matters beyond fitness#
The fitness workflows are the first real test of a pattern I want to apply more broadly:
- Edge AI (Ollama) — inference runs locally, no external dependencies
- Container orchestration (k3s) — workloads are managed, restartable, scalable
- Workflow automation (n8n) — complex conditional logic without writing glue code
- Real-world data integration — Google Sheets today, more APIs next
The infrastructure cost to add a new AI-powered workflow is now close to zero. The pattern is established. Plugging in a new data source and a new prompt takes an afternoon.
What’s next#
Expanding the same framework to other areas:
- Habit tracking — general-purpose streaks and accountability
- Photography — automated culling or tagging workflows
- Learning goals — spaced repetition nudges, reading summaries
The platform is in place. The next projects are mostly about writing the right prompts and connecting the right data sources.
Behind the scenes#
The n8n workflows look exactly like you’d expect — nodes connected by edges, data flowing left to right. Triggers on the left, conditional branches in the middle, output nodes on the right. The Ollama call is a standard HTTP node hitting the local inference endpoint.
Fair warning: the naming conventions in the current workflows are rough. Built fast to validate the idea. Cleanup is on the list.
The interesting part isn’t the individual nodes — it’s that the entire decision loop (collect data, reason about it, send a message) is visual, auditable, and restartable. When something goes wrong, you can see it.