GymBro

Inspiration

Traditional workout apps often feel like data-entry tools: you log sets, view numbers, and leave. GymBro was built to make training feel more engaging, adaptive, and accountable.

The app combines structured workout logging with gamification and social accountability. Instead of progress being hidden inside charts, users can see the effect of their consistency through avatar health and weekly progression. Completing workouts, improving performance, and reaching goals strengthen the avatar, while missed sessions create a visible consequence. This creates a clearer connection between training habits and progress.

GymBro also makes consistency social. Friends can view each other’s avatar health, weekly session progress, and shared streaks. A shared streak only grows when both people complete their planned sessions for the week, encouraging accountability without turning training into a competition.

Finally, its weekly AI Analysis analyses verified workout history, identifies meaningful patterns in performance and training balance, and provides practical suggestions for what to maintain, improve, or focus on next. The goal is to help users train with more intention by turning raw workout data into useful, personalised insight.

What it does

GymBro is a full-stack workout platform where users can:

Log workout sessions and track sets, reps, and weights
Create reusable training templates and reuse them with one tap
Organize workouts by category (push, pull, legs, core, cardio, upper, lower, full body)
Set weekly and exercise-specific goals in Goal Settings
View weekly analytics and progression trends
Add friends with a request/accept flow
Track social consistency with a shared streak view
HP-based avatar health system (0-100 HP, weekly reset to 0) that adapts to user's progress and consistency
View weekly avatar snapshots on the calendar to see historical consistency

How we built it

We built GymBro using Next.js App Router and TypeScript, with Supabase for authentication, storage, and row-level security policies. Core implementation areas included:

API routes for templates, workout sessions, goals, and social actions
Shared validation with Zod for safer request handling
Reusable React components for templates, goals modal, social panel, and calendar cards
Supabase migrations for friend requests, social visibility, goal items, and avatar snapshots
HP gamification logic that calculates weekly progress from consistency, overload, and goal completion

Challenges we ran into

Resolving merge conflicts across fast-moving files during feature expansion
Preventing hydration mismatch issues from locale-dependent date formatting
Handling schema evolution cleanly (e.g., removing duration fields)
Designing symmetric friend visibility and acceptance logic with correct RLS behavior
Keeping social and gamification data synchronized between accounts in real time

Accomplishments that we're proud of

Delivered end-to-end templated workouts with edit and one-tap session replication
Built a functional social system with friend requests, acceptance, and removal behavior
Defined clear weekly HP model tied to training habits
Added multi-goal support (sessions + exercise goals) through a user-friendly modal flow
Created weekly avatar history so users can visually track consistency over time

What we learned

Product features become much stronger when UX flow and backend data rules are designed together
Strong typing + validation reduces regressions during rapid iteration
Social features require careful edge-case thinking (pending, accepted, removed, stale requests)
Weekly reset systems are easier to understand when states are explicit and snapshot-based
Small UI details (labels, spacing, hierarchy) greatly affect usability and motivation

What's next for GymBro

Smarter template recommendations based on workout history
Richer goal tracking (deadlines, milestones, completion rates)
Notifications for friend requests, streak risk, and goal progress
Better onboarding and empty states for first-time users
Optional AI-assisted workout suggestions based on user goals and recent performance