# How to Vibe Code Your Own Fitbod (and Stop Paying for It) > Less Planning. More Progress. - Site: https://fitbod.me - Category: Health & Fitness - Platforms: iOS app, Android app, Web app - Verdict: **Serious undertaking** (45/100 vibecodeable) - Estimated effort: 4-6 weeks of part-time development ## Verdict Build a personal workout logger clone using Expo, but keep paying for Fitbod if you want their proprietary muscle-recovery algorithm. Cloning the UI and logging flow of a fitness app is straightforward with React Native, but replicating Fitbod's core value—the smart workout generation algorithm and muscle recovery fatigue map—requires complex constraint programming and extensive domain logic. Furthermore, managing offline-first SQLite state and native health synchronization involves frustrating edge cases that will consume weeks of debugging. ### What you can't replicate - Proprietary fatigue and progressive overload tuning derived from 120 million user workouts - Ecosystem trust and hundreds of thousands of organic app store reviews ## What it does An AI-powered strength training application that dynamically generates customized workout routines based on recovery, available equipment, and fitness goals. ### Core features - Dynamic workout generation algorithm based on muscle recovery - Progressive overload adjustment engine (weights/sets/reps) - Exercise library with instructional videos and categories - Equipment profile filtering (gym vs bodyweight vs limited gear) - Interactive workout logger with live rest timers - Apple Health and Google Health Connect bi-directional sync - Offline-first local persistence and background syncing ## The business ### Pricing - Monthly Subscription: $15.99/mo — Full access to dynamic workout generation and muscle recovery tracking. - Yearly Subscription: $95.99/yr — Billed annually, equivalent to $8.00/mo. ### Funding $5.7M raised. - Seed - Series A Investors: TechNexus Venture Collaborative, Jason Calacanis Founded 2015. Team size: 11-60. ## The hard parts - Designing a deterministic or heuristic constraint-satisfaction algorithm that schedules muscle splits without causing overuse injuries - Building robust offline-first synchronization for users lifting in basement gyms with zero cellular connectivity - Integrating bi-directional native health platform frameworks (HealthKit and Health Connect) cleanly - Managing and streaming hundreds of high-definition exercise demonstration assets efficiently inside mobile views ## How to vibe code Fitbod ### Prerequisites - Node.js (free): Required runtime for Expo and TypeScript tooling. - Expo Account (free): Required for building native iOS and Android client binaries. - GitHub (free): Version control and repository hosting. ### Recommended AI tools - Claude Code: Handles multi-file TypeScript scaffolding, state management logic, and database schema generation directly from the terminal. - Cursor: Ideal for iterative UI tuning of workout logging screens and component styling. ### Stack - Frontend: React Native with Expo (TypeScript) - Backend: Local SQLite database via Expo SQLite - Database: Local SQLite for offline-first workout persistence - Auth: Local single-user profile state (no cloud auth required for personal use) - Payments: None (personal clone) - Other: Expo Router for file-based navigation, Zustand for client state management ### Hosting - Expo (Over-the-air updates and local simulator builds): $0/mo ### Build guide 1. **Initialize Expo TypeScript Project with Expo Router** — Scaffold a clean React Native mobile app structure using Expo with file-based routing and install required state management and local storage dependencies. ``` Initialize a new Expo project using TypeScript with Expo Router file-based navigation. Set up a professional directory layout including /app (screens), /components (reusable UI elements), /constants (theme colors, exercise metadata), /db (SQLite management), and /store (Zustand state). Install lucide-react-native for icons and zustand for state management. Ensure strict TypeScript configurations are enabled and configure a clean dark mode color palette inspired by professional fitness apps with slate backgrounds and high-contrast accent colors. ``` 2. **Build Local SQLite Schema for Exercises and Workout Logs** — Create a robust local database schema using Expo SQLite to store exercises, muscle groups, equipment profiles, workout sessions, and historical sets. ``` Create a robust local database layer using expo-sqlite for an offline-first fitness tracker. Write migration scripts that initialize tables for: exercises (id, name, target_muscle, secondary_muscles, required_equipment, category), workouts (id, date, duration, notes), workout_exercises (id, workout_id, exercise_id, order_index), and exercise_sets (id, workout_exercise_id, set_number, weight_lbs, reps, completed). Seed the exercises table with at least 30 common strength training exercises covering chest, back, legs, shoulders, and arms with accurate muscle mapping and equipment requirements (barbell, dumbbell, bodyweight, machine). ``` 3. **Develop Equipment Profile and Muscle Recovery State Engine** — Implement user settings for available equipment and a fatigue tracking calculation utility based on recent training history. ``` Implement a TypeScript service layer that calculates muscle recovery percentages based on historical workout logs stored in SQLite. For each major muscle group (chest, lats, quads, hamstrings, shoulders, biceps, triceps, core), compute a fatigue score that decays linearly over 72 hours since the last logged session targeting that muscle. Create an equipment filter module allowing users to toggle available equipment (barbells, dumbbells, bodyweight, pull-up bar, resistance bands) which filters the active exercise pool accordingly. ``` 4. **Implement Heuristic Workout Generation Algorithm** — Build the core recommendation engine that selects exercises and suggests weight/rep targets based on available equipment and muscle recovery status. ``` Build a heuristic workout generation engine in TypeScript that selects 4 to 6 exercises for a requested workout split (e.g., Upper Body, Lower Body, Full Body). The algorithm must filter exercises by the user's available equipment profile and prioritize muscle groups with the lowest accumulated fatigue scores. For each selected exercise, query the user's historical performance logs in SQLite to automatically apply progressive overload by suggesting an incremental weight or rep increase over their last successful session for that movement. ``` 5. **Create Interactive Workout Logging Interface** — Design and build the active workout session screen with set logging, weight/rep adjustments, live rest timers, and exercise swapping. ``` Build an active workout tracking screen using React Native and Expo Router. The screen must display a list of generated exercises for the current session, each with a table of sets showing previous performance (weight and reps), editable inputs for the current set's actual weight and reps, and a checkbox/button to mark sets as complete. Include a persistent bottom bar with a live workout elapsed timer, a functional rest timer modal with preset countdown options (30s, 60s, 90s, 2m), and a button to swap any exercise with an alternative movement matching the same target muscle group. ``` 6. **Add Progress Dashboard and History Log** — Build summary views showing completed workouts, volume progression charts, and muscle recovery heatmaps. ``` Create a progress and history tab in the app. Build a workout history feed listing past completed sessions with expandable details showing total volume, duration, and exercise breakdown. Implement a visual muscle recovery screen or widget that displays a body map or list representing current fatigue levels across muscle groups based on recent training frequency. Ensure all queries perform efficiently against the local SQLite database. ``` ### Cost vs paying **Starting costs (one-time):** - Claude Pro / Cursor Pro (AI Coding tools): $20.00 - Total: ~$20.00 one-time **Ongoing costs (monthly):** - Hosting / Database: $0.00/mo (Local SQLite) - Total: $0.00/mo - Paying for the SaaS instead: $15.99/mo - Build time: 25-35 hours - AI tool credits: $20.00 - Break-even: 1 month vs monthly subscription ## Sources - [Fitbod Official Website & FAQ](https://fitbod.me) - [Sensor Tower - Fitbod App Store Intelligence](https://sensortower.com) - [Tracxn & Wellfound - Fitbod Company and Funding Profiles](https://tracxn.com)