Claude
The reasoning core. It interprets the data, applies the coaching logic, and produces the day's recommendation in plain language.
Reasoning engineA steeptech project
KOMputer connects to the platforms you already use and builds a real picture of your fitness. It knows your load, your fatigue, and your weak spots, and it tells you exactly what you should ride today, and why.
The idea
Serious cyclists live across a scatter of apps. Power files and fitness curves in Intervals.icu. Outdoor rides, segments, and Relative Effort in Strava. Sleep, HRV, and readiness on the wrist. Each tells part of the story, but none of them tell the whole one, and none of them coach.
KOMputer closes that gap. Not generic plans, not guesswork. Tell it about a hard week or a sore knee and it adjusts, weighing your season goals, current fitness and fatigue, and today's biometrics into a clear call on the next ride.
Not another dashboard. A coach that reads the data so you don't have to.
The process
KOMputer started with a question — what happens if you give a capable AI direct access to your training data? It took about a month of hands-on experimentation with Strava and Intervals.icu MCP servers to find the answer.
The foundation was a pair of local MCP servers wired directly into the Claude desktop client. One for Strava, one for Intervals.icu — exposing rides, streams, fitness metrics, wellness, and segments as tools the model could actually call.
With live data on tap, the next weeks were about discovery — probing what Claude could infer from a power curve, how it read HR against Relative Effort, evaluating my training history, where it got recommendations wrong, and which questions produced genuinely useful coaching versus generic advice.
Every insight that held up got recorded: physiological benchmarks, the "diesel rider" profile, how readiness scores should be weighted, which metrics to trust and which to ignore. This became a living coaching brief and ultimately helped me spot the key differences between a generic assistant and one that knows this rider.
From that base, the feature set grew organically: weather-gated ride selection, readiness-driven intensity gating, post-ride analysis feeding the next recommendation. Each capability earned its place by proving useful in real training, not because a roadmap called for it.
Under the hood
The reasoning core. It interprets the data, applies the coaching logic, and produces the day's recommendation in plain language.
Reasoning engineLocal Model Context Protocol servers for Strava and Intervals.icu that expose rides, streams, fitness metrics, and wellness as callable tools.
Data layerPrimary source for fitness metrics — CTL/ATL/TSB, power profile, and zone distribution on power-metered indoor rides.
Fitness metricsThe reliable source for outdoor rides — sport types, segments, and Relative Effort where no power meter exists.
Outdoor ridesApple Health and Sleep++ as the authoritative readiness source: HRV, resting heart rate, and a daily readiness score that gates intensity decisions.
ReadinessA living coaching brief encoding the athlete profile, benchmarks, and hard-won analysis rules so guidance stays consistent session to session.
PersonalizationKOMputer is a small, focused app built on a handful of well-known, reputable services rather than one giant proprietary platform.
Core features
Weather-gated logic decides indoor vs. outdoor, factoring temperature, wind, and rain against real thresholds — then names the ride and the reason behind it.
Reads HRV, resting heart rate, and readiness to gate how hard the day should be — calibrated to how this athlete actually recovers, not a generic template.
Pulls streams after every ride, calls out what went well and what to sharpen, and feeds that read straight into the next recommendation.
Knows the current block — sharpening, base, taper — and steers toward its priorities, like weekly VO2 work and disciplined Zone 2.
Recommends specific structured sessions matched to the day's goal and recent load, sourced to fit the training phase.
Knows which numbers to trust — power indoors, HR and effort outdoors — and refuses to fabricate metrics when a source comes up empty.
Questions or feedback? hello@steeptech.dev