Liftium Live is an evidence-based workout tracking app that analyzes your training in real-time using the Stimulus-to-Fatigue Ratio (SFR). Built for the TanStack Start Hackathon, it combines cutting-edge full-stack web technologies with sports science research to help you train smarter, not harder.

Try Liftium Live now:

• https://liftium-live.hmztkn.workers.dev/
• https://liftiumlive.hamzatekin.dev/

Built entirely with the TanStack Start Hackathon co-hosts and sponsors:

• TanStack Start - SSR app shell and routing.
• Convex - DB + optimistic updates for workouts and sessions.
• Cloudflare - Primary runtime & deployment, using cloudflare:workers env bindings.
• Netlify - Configured via the Netlify TanStack Start SDK and Vite plugin, with a dedicated deploy:netlify script so the app can also be built/deployed on Netlify.
• Autumn - Models Free/Pro-style memberships and session limits.
• CodeRabbit - Used for AI-assisted PR review during development.
• Firecrawl - Scrapes PubMed hypertrophy studies for embeddings.
• Sentry - Error monitoring and performance tracking

Most workout trackers just log numbers. They don't tell you if your sets are actually effective for muscle growth, or if you're accumulating unnecessary fatigue. Liftium Live solves this by:

• Analyzing every set using research-backed algorithms
• Providing instant feedback on training efficiency
• Citing actual studies so you can verify the science yourself
• Helping you optimize stimulus while managing fatigue

• Stimulus-to-Fatigue Ratio (SFR) calculations for every set
• Real-time feedback categorized as: Excellent, Good, Moderate, or Suboptimal
• Automatic warm-up set detection (sets that don't contribute to hypertrophy)

• RAG-powered insights using vector search over PubMed research
• Direct links to meta-analyses and studies supporting each recommendation
• Transparent attribution showing where advice comes from

• Live workout sessions with set-by-set logging
• Track: Exercise, Load (kg), Reps, and RIR (Reps in Reserve)
• Session history with workout duration and performance metrics

The Stimulus-to-Fatigue Ratio is calculated using evidence-based algorithms:

SFR = Stimulus / Fatigue;

Stimulus is calculated based on:

• Effective reps (reps within ~5 from failure)
• Load effectiveness (relative intensity)
• Proximity to failure (RIR)

Fatigue is calculated based on:

• Total reps performed
• Relative intensity (% of estimated 1RM)
• Set cost with load exponential scaling

• 🔥 Excellent (SFR ≥ 0.8): Great balance of stimulus and fatigue
• ✅ Good (SFR ≥ 0.5): Effective training
• ⚠️ Moderate (SFR ≥ 0.3): Could be optimized
• 💪 Suboptimal (SFR < 0.3): High fatigue relative to stimulus
• 🔄 Warm-up: Set below effective threshold

Each verdict comes with citations to relevant research from PubMed, sports science journals, and meta-analyses.

The muscle-building signal your training creates. Higher stimulus means more potential for growth. Sets closer to failure typically provide greater stimulus for hypertrophy.

The recovery cost of your training. All training creates fatigue, but some sets create more than others. Managing fatigue is key to sustainable progress.

How many more reps you could have done before reaching failure:

• RIR 0 = Failure
• RIR 1 = One rep left
• RIR 2 = Two reps left
• RIR 3-6 = Multiple reps left

The efficiency of your sets. Higher SFR means you're getting more muscle-building stimulus for less fatigue. Sets with moderate RIR (1-3) often have the best SFR.

This project is open source and available under the MIT License.

Built by @hamzatekin

Made with 💪 for the TanStack Start Hackathon