Blog
AI insights, evidence-based training science, and the engineering behind Arvo.
How Arvo's Senior Reviewer AI catches bad workouts before they ship
An 8-guardrail deterministic review plus a gpt-5 sub-agent on every generated workout. Volume caps, hallucinated exercise detection, equipment mismatch — 30/30 fixture pass.
Why strength training apps ignore Wear OS (and how we fixed it)
Native Kotlin + Jetpack Compose companion for Pixel Watch and Galaxy Watch. The JSON protocol, Health Connect integration, and what it took to hit parity with Apple Watch.
How long does each muscle actually need to recover? (and why your app gets it wrong)
Chest and shoulders in ~48h, biceps and triceps in ~36h, back and quads in ~72h. The evidence, the variance, and how Arvo's Recovery Map encodes per-muscle windows.
Why AI-generated cardio beats C25K for lifters (with data)
Fixed templates ignore strength load. HR zones 1-5, polarized training, concurrent interference — how AI prescribes cardio that compounds with lifting instead of colliding with it.
Hevy, Strong, and Fitbod don't have gym crews. Here's why it matters.
Strong is tracker-only. Fitbod is individual. Hevy shares single workouts. Arvo's Gym Crew is invite-only, max 8, with a live activity feed and 5 emoji reactions.
60+ chat commands: what you can actually ask your AI coach
The full catalog of Arvo's support-chat tools grouped into 8 JTBD categories — workout editing, session control, history import, equipment management, and more.
How to import your Hevy or Strong workout history into Arvo (in 60 seconds)
Step-by-step migration from Hevy or Strong. Export CSV, drop it in Arvo chat, the AI parses your entire history — weights, reps, RPE — and feeds your AI coach.
4 ways a gym crew of 8 can change how you train (stories from Arvo users)
Illustrative scenarios of how a small crew transforms adherence: the dormant revival, the PR cascade, cross-timezone accountability, and the injury comeback.
Deload Weeks: What 10,000 Workouts Taught Us About Recovery
Most lifters skip deloads or do them wrong. Data from 10,000+ workouts reveals optimal timing, intensity reduction, and what happens to strength when you deload.
From GPT-4o to Structured Outputs: A Migration Playbook
Schema design, validation retry patterns, and quality tradeoffs for migrating from free-form LLM responses to structured outputs.
Push Pull Legs vs Upper Lower: Which Split Is Better? (Data Analysis)
We compared progression data from thousands of users running PPL vs Upper Lower splits. What the numbers say about strength gains, volume, and adherence.
How Personal Trainers Can Use AI to Scale to 50+ Clients
AI isn't replacing personal trainers — it's letting them serve more clients without sacrificing quality. A practical guide.
Why Your Gym App Probably Isn't Using AI (And Why That's About to Change)
Most fitness apps claim to be 'AI-powered' but just serve static templates. Here's what real AI programming looks like.
The Science of Training Volume: How Many Sets Do You Actually Need?
MEV, MAV, MRV explained with real data. We analyzed thousands of workout logs to find the volume sweet spot for each muscle group.
I Tracked My Macros With AI for 90 Days — Here's What Happened
A real 90-day experiment combining AI workout programming with macro tracking. Body composition data, strength gains, and what surprised me.
Building an MCP Server for Fitness Data: Lessons from Arvo
How we built a Model Context Protocol server to let AI assistants query workout history, training insights, and exercise data.
Can AI Replace a Personal Trainer? An Honest Assessment
AI workout apps are getting smarter, but can they truly replace a human coach? We break down what AI does better, where coaches still win, and who benefits most from each.
Why You're Not Making Progress: 7 Mistakes We See in User Data
Analyzing anonymized workout data from thousands of Arvo users reveals 7 patterns that stall muscle growth.
We Cut AI Costs by 54% Moving to GPT-5.4-mini
A practical guide to migrating from GPT-4o to GPT-5.4-mini/nano while maintaining quality. Cost routing, prompt restructuring, and the tradeoffs we made.
How I Built a Real-Time Periodization Engine Using Multi-Agent AI
Orchestrating 30+ specialized AI agents to generate adaptive training programs. Cost routing, prefix caching, and the mistakes I made along the way.