Using Ollama for Local AI Time Tracking

Large language models are powerful for categorization—turning “Slack - design feedback” into “Project: Website Redesign” or “Internal Communication.” But sending your activity logs to a cloud API raises privacy and cost concerns. Running the model locally with Ollama solves both.

What Is Ollama?

Ollama is an open-source tool that runs LLMs on your Mac (or Linux/Windows). You install it, pull a model like Llama, and run inference locally. No API keys, no internet required after download, no data leaves your machine.

Why Local AI for Time Tracking?

Privacy — Your activity log stays on your Mac. No one else sees your window titles, app names, or time blocks. Critical for freelancers, consultants, and anyone handling sensitive work.

Cost — No per-request API fees. One-time setup, unlimited categorization.

Offline — Works without internet. Useful in low-connectivity environments.

Customization — You can tweak the system prompt, add rules, and teach the model your taxonomy. It learns your workflow.

How It Works in Practice

1. Ollama runs a model (e.g., Llama 3.2) in the background.
2. Your time tracking app sends each log entry to the local model.
3. The model suggests a category based on your taxonomy and rules.
4. You correct mislabels; the model uses your feedback to improve.
5. Everything happens on your Mac—no network calls.

Requirements

You need a Mac with enough RAM and optionally a GPU. Ollama supports Apple Silicon (M1/M2/M3) and Intel Macs. Smaller models run fine on 8GB RAM; larger models benefit from 16GB+.

Tools That Use Ollama

Not every time tracker supports local AI. Look for apps that integrate with Ollama or similar local inference engines. Kronux ships with Ollama integration by default—your logs are classified locally from day one.

The Bottom Line

If you want AI-powered time tracking without sending data to the cloud, Ollama and local-first tools are the answer. Your time data stays yours, and the model runs where your data lives.