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How I Built an Agentic AI Coach That Turns Garmin Data Into a Training Partner

Intro: I’ve been building an open-source project called Garmin AI Coach — a multi-agent endurance coach that analyzes Garmin health and activity data to generate adaptive training plans for triathletes, cyclists, and runners. What makes it…

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Intro:

I’ve been building an open-source project called Garmin AI Coach — a multi-agent endurance coach that analyzes Garmin health and activity data to generate adaptive training plans for triathletes, cyclists, and runners.



What makes it special:

It’s not just another fitness tracker dashboard. It’s an agentic AI system that actually interprets your physiological data (HRV, sleep, stress, load) and drafts a transparent, auditable plan you can inspect line-by-line. Every insight is reproducible — no black-box “trust us” recommendations.



The architecture of my AI ‘company.’ Data flows in, and two teams — the Nerds (Analysis) and the Coaches (Planning) — work together to create a perfect training week.



How to contribute:




  • 🧠 Help improve the agent reasoning or planning prompts.

  • 🧩 Add new data connectors (Thryve, Garmin Health API, or Connect IQ).

  • 🧪 Build demo datasets so new users can run the tool without real credentials.

  • 🖼️ Create front-end visualizations for the generated HTML reports.



Issues labeled good first issue are beginner-friendly. I’d love to mentor new contributors, especially anyone interested in AI workflows, sports science, or wearable integrations.



Why I’m passionate:

As a triathlete and data scientist, I wanted a coach that explains why it suggests what it does. Garmin AI Coach is my attempt to merge transparency, personalization, and open science.



Links:

🔗 GitHub — leonzzz435/garmin-ai-coach

📰 Medium story — “I Fired My Garmin Coach and Built an AI to Train for an Ironman 70.3”

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