Hi, I'm David - a 25-year-old software developer, university student, and lifelong fitness enthusiast from Austria. I built Lift as part of my master's thesis in computer science, with a singular focus: creating a training tool that respects both physiological principles and statistical rigor. This isn't a social platform or a motivational feed. It's a structured, data-driven system designed for lifters who take their training seriously and want to understand their progress beyond surface-level metrics.
My background in data science and machine learning shaped how I approached this project. I wanted to build something that could translate raw training data into meaningful insights—not through flashy animations or arbitrary gamification, but through well-designed charts, robust statistical methods, and thoughtful progression logic. Lift is opinionated by design. It doesn't try to be everything to everyone. Instead, it focuses on resistance training, hypertrophy-oriented programming, and the principles that actually drive long-term progress.
Why Lift Exists
I created Lift because existing workout apps frustrated me. Many were web-based or poorly designed, cluttered with social features I didn't need, or too flexible to provide meaningful structure. Others tracked numbers but offered no real insight into what those numbers meant or how to improve them. I wanted something different: a native iOS app that felt clean and intentional, that integrated deeply with Apple Health, and that could help intermediate to advanced lifters understand and systematically improve their training.
The core philosophy is simple: progressive overload, auto-regulation, and periodization. Consistency matters, but so does structure. Training without a plan leads to spinning your wheels. Training with a plan—but without feedback mechanisms—leads to stagnation or burnout. Lift aims to solve both problems by providing structure upfront and adapting over time based on your performance and feedback.
What Makes Lift Different
Lift is built around several key principles that distinguish it from other tracking apps:
- RIR as a first-class metric: Reps in Reserve (RIR) is integrated directly into the workout flow, not buried in settings. It's central to understanding effort and driving auto-regulated progression.
- Bodyweight-aware performance tracking: Lift reads bodyweight from Apple Health, applies Kalman filtering to smooth out noise, and incorporates bodyweight into performance calculations. If you're performing dips at a higher bodyweight, that counts as real strength progress.
- Mesocycle planning: Rather than leaving you to figure out splits and volumes on your own, Lift generates structured training plans based on your experience level, available training days, and muscle-group priorities. It then tracks your progress through the mesocycle and adjusts variables automatically.
- Auto-regulated progression: After each exercise, you provide feedback on effort, pain, and volume tolerance. Lift uses this to adjust training variables—reducing RIR, increasing load, adding reps or sets, or introducing deloads when needed.
- Smart warm-ups: The app can generate warm-up sets automatically using percentile-based analysis of your recent performance, preparing you effectively without pre-exhausting you.
- Robust strength estimates: Instead of relying on single best lifts, Lift computes 1RIR and 1RM estimates across multiple sessions and applies statistical filtering to produce stable, realistic numbers.
- Rich data visualization: Charts and statistics aren't decoration—they're tools to understand training streaks, consistency, volume distribution, bodyweight trends, and performance trajectory over time.
All of this runs natively on iOS using SwiftUI. The interface is clean, minimal, and interruption-free. There are no accounts, no ads, and no social feeds. Your data stays on your device. You can use Lift fully offline, and you own your information completely.
The Research Component
Lift is part of my master's thesis, and I'm actively collecting anonymized training data to advance scientific understanding of resistance training. If you choose to participate, you can donate your workout logs and questionnaire responses through an optional feature in the app. This data is encrypted, stored securely on a server in Zurich, and stripped of all personal identifying information. Only an anonymous device identifier is used to correlate logs.
Participation is entirely optional. You can use Lift without ever contributing data. But if you do participate, you're helping to build better models, uncover insights into training response, and improve the app for everyone. This research will form the foundation of my thesis and potentially lead to on-device machine learning features that offer more personalized recommendations over time.
Who This App Is For
Lift is designed for intermediate to advanced lifters who train consistently, value structure, and prefer data-driven decision-making over guesswork. If you're the kind of person who cares about progressive overload, understands what RIR means, and wants to see your training improve systematically rather than randomly, this app is for you. Beginners can use Lift for basic tracking, but many of its features—mesocycle planning, auto-regulation, statistical analysis—are built for users who are willing to engage seriously with their training process.
This is not a casual logging app. It's not a motivational platform. It's a tool for people who want clarity, structure, and confidence in their training decisions.
What's Next
Lift is actively evolving. I'm working on improved progression algorithms, on-device machine learning models, exercise recommendations, widgets, and deeper platform integrations. The app is currently free to use, and there are no plans to introduce subscriptions within the next one to two years. User feedback is not only welcomed—it's essential. As the system matures, breaking changes may occur, but the core philosophy will remain the same: scientifically grounded, user-owned, and built for progress.
Thank you for using Lift. Whether you're tracking your first mesocycle or refining years of training data, your commitment to structured improvement is what makes this project meaningful.