Forged

The problem. Commercial fitness applications—particularly hyper-specialized hypertrophy apps—are expensive, often costing up to $300 a year. Worse, they lock your personal workout data into proprietary silos, make it difficult to export your training logs, and force you into rigid, pre-defined workout structures that don’t fit custom periodization or complex set types like EMOM.

Why I built it. I wanted full ownership of my training data and a highly tailored workout tracker grounded in sports science (specifically volume landmarks and fatigue management). Instead of paying a steep annual fee for features I didn’t need and missing ones I did, I decided to build a private, invite-only tool for myself and close training partners that exactly maps to our routines.

What it does. It is an installable PWA built on React and Supabase that tracks sets, reps, and weights. Under the hood, it runs a double-progression engine that automatically adjusts targets based on performance and generates deload sessions after consecutive holds. It features superset interleaving, EMOM timers, and an analytics suite that charts estimated 1RM (Epley formula) alongside weekly set volumes compared against MEV/MAV thresholds. All data is exportable as CSV or JSON, ready for AI-assisted analysis.

The benefits. I replaced a $300/year app with a faster, custom-tailored system while gaining complete data privacy and ownership. By calculating volume landmarks and progression states programmatically, the app takes the guesswork out of fatigue management and progression. It allows me to export clean, structured logs directly into my AI knowledge base to identify training trends.

AI & Second Brain Integration. The real power of Forged lies in its connection to my Obsidian-based Second Brain. Rather than just archiving history, the exported data is ingested directly by my local AI tools via Claude Code. Using this setup, I analyze historical workout cycles, flag recovery plateaus, and plan training routines three months in advance. The system then automatically loads the newly generated workout routines back into the Supabase database, updating the app templates. Additionally, the AI engine synthesizes training metrics with sports science research to push tailored nutrition guidelines and structured fitness goals directly into my Second Brain, creating a closed-loop, self-improving personal health stack.

Features

• Automated progression engine implementing double progression rules and auto-deload triggers
• Interactive superset, circuit, and EMOM logging with automated rest timer countdowns
• Analytics dashboard tracking Epley estimated 1RM, balance, and volume vs. MEV/MAV thresholds using Recharts
• Secure, server-side invite-only sign-up gate restricting access to close friends and training partners
• Offline-capable mobile PWA configuration with full standalone display and custom iOS assets
• Raw data exporter utility providing structured CSV and JSON outputs for external AI analysis
• Closed-loop AI cycle planning integrated with Claude Code to design 3-month workout programs
• Automated Supabase template loader that programmatically syncs AI-generated routines into the database
• Structured fitness goals and nutrition synthesizer that saves custom guidelines directly to the Second Brain