AthleteData – AI coach for endurance athletes that messages you first
Im a triathlete and the data for my training lives in 6 apps: Garmin, Strava, WHOOP, Intervals.icu, Wahoo, Withings, Apple Health, sometimes Hevy. Every morning Id eyeball a few of them and make a call on whether to do the planned session. For the past month I have been building a thing that does this for me, and got it to the point where I use it myself every day. It OAuths into whatever platforms you connect, reconciles the activities (tbh harder than it sounds — same ride shows up in Strava, Garmin, and Wahoo with different timestamps and rounding), computes daily load and readiness, and…
What it does
In the maker’s words, at launch
Im a triathlete and the data for my training lives in 6 apps: Garmin, Strava, WHOOP, Intervals.icu, Wahoo, Withings, Apple Health, sometimes Hevy. Every morning Id eyeball a few of them and make a call on whether to do the planned session. For the past month I have been building a thing that does this for me, and got it to the point where I use it myself every day. It OAuths into whatever platforms you connect, reconciles the activities (tbh harder than it sounds — same ride shows up in Strava, Garmin, and Wahoo with different timestamps and rounding), computes daily load and readiness, and proactively messages you over Telegram or Whatsapp when something matters. Stack is straightforward: Typescript all the way, Postgres, an agent loop running on Claude (via Bedrock) with tool access to all your data + my computed metrics: zones, CTL/ATL/TSB, power/pace curves, anomaly detection on HRV and RHR, etc Two things that were harder than expected: 1. Garmins API only exposes the last 90 days. So for anyone with Garmin as their primary device, you have to backfill from Strava and stitch the two together. Strava has full history but misses some fields (e.g. HR-based TSS only — no power). Wahoo and intervals.icu fill different gaps. The dedup pipeline is ugly and I'd welcome feedback from anyone who has solved this better. 2. Deciding when to message vs. stay silent is entirely a product problem. Too chatty -> muted. Too quiet -> feels dead. One honest caveat though: no RCT data, and Id be skeptical of anyone who claims they have it for AI coaching at this stage. I am at ~50 paying users, I personally reach out to every user to build the next iterations of the product based on feedback. Already got testimonials from Ironman world championship finishers and other pro athletes. Theres also a $9/mo MCP tier for people who would rather pipe their data into their own Claude/ChatGPT. Happy to go deep on any topic! e.g. the tool-calling architecture, or the cost-per-user question (running an agent on every athlete daily is not free, and the margins here are worth discussing).
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