Your Bench Doesn't Progress Like Your Squat: Per-Movement Load Progression
Bench, squat, and deadlift progress at different rates for structural reasons. A one-size-fits-all progression rule under- or over-loads every lift.
RPE Calibration: Why Your 9 Is Someone Else's 7 (and Why It Matters)
Self-reported RPE drifts with fatigue, stress, and experience. Calibration against objective markers (velocity, RIR, recovery) catches the drift.
Per-Muscle Recovery Half-Lives: Why Legs ≠ Shoulders ≠ Chest
Your chest isn't recovered in the same window as your quads. Here's how per-muscle recovery half-lives change training frequency and session design.
Detraining: How Fast You Actually Lose Strength (and What the Research Shows)
Strength is more durable than aerobic fitness. Here's what the detraining literature shows about what you lose, when, and how to ramp back safely.
Maximum Adaptive Volume (MAV): Why Per-Muscle Training Ceilings Beat Generic Volume Rules
MEV and MRV are population ranges. MAV is the per-muscle weekly ceiling your body can currently tolerate — learned from your history, not a table.
Adaptive Training Intelligence: The Load Signal Your Wearable Isn't Showing You
How per-muscle volume tolerance, recovery half-lives, and Bayesian load models translate raw wearable data into actionable training prescriptions.
What Is ACWR and Why Does It Matter for Training?
The acute-to-chronic workload ratio is the single best predictor of training-related injury. Here's what it measures, where the 0.8-1.3 sweet spot comes from, how Omnio calculates yours, and the mistakes that get people hurt.
Adaptive Training With Wearable Readiness Data
Omnio doesn't just track your workouts — it derives your training schedule, gates every session through biometric readiness, adjusts for nutrition, and learns from outcomes. Here's how we're building a genuinely adaptive training system.
Why We Built a Bayesian Brain for Your Training Plan
Every fitness app says it 'learns.' We wanted to prove it. Here's why we chose Bayesian parameter estimation over neural nets, how six independent sub-models personalize your training, and why the system can never be worse than a textbook.