BurnBacon

Generates customized exercise routines by processing user input data (fitness level, goals, available equipment, time constraints) through an LLM-based planning engine that decomposes fitness objectives into weekly workout schedules with specific exercises, rep ranges, and rest periods. The system uses constraint-satisfaction reasoning to balance progressive overload principles with user availability and equipment limitations, producing structured workout plans that differ from generic templates by incorporating individual baseline metrics.

Monitors user-reported workout completion data (exercises performed, actual reps/sets completed vs. planned, perceived difficulty ratings) and uses feedback loops to adjust subsequent workout prescriptions. The system applies heuristic rules or lightweight ML models to detect when users are consistently underperforming (indicating plan is too hard) or overperforming (indicating insufficient progressive challenge), then modifies exercise selection, rep ranges, or intensity metrics in the next training cycle. Substitutions are drawn from a curated exercise database indexed by muscle group, equipment requirements, and difficulty tier.

Combines workout plan generation with nutritional guidance by processing user goals, dietary preferences, and caloric expenditure estimates from exercise plans to produce coordinated recommendations. The system likely uses calorie balance calculations (TDEE estimation based on activity level from workout plan + user metrics) and macronutrient targeting (protein for muscle gain, carbs for endurance, etc.) to generate meal suggestions or dietary guidelines that complement the exercise regimen. Recommendations are presented as a unified fitness strategy rather than isolated exercise and nutrition modules.

Aggregates user workout completion data, body metrics (weight, measurements, photos), and performance benchmarks (strength gains, endurance improvements) into a visual dashboard that displays progress toward fitness goals over time. The system likely calculates derived metrics (weekly average workout adherence %, strength progression rate, estimated time-to-goal based on current trajectory) and visualizes trends through charts and summary cards. This capability enables users to see whether their current plan is working and identify stagnation or rapid progress patterns.

Implements a freemium business model where core workout plan generation and basic progress tracking are available to free users, while advanced features (detailed analytics, specialized workout splits, nutrition meal planning, priority support) are restricted to paid premium subscribers. The system uses account-level feature flags or subscription status checks to control access to premium capabilities, likely with upsell prompts or feature preview screens that encourage free users to upgrade when they encounter paywalls.

Guides users through a structured questionnaire that captures baseline fitness data (current strength benchmarks, cardiovascular fitness level, mobility limitations, available equipment, weekly time commitment, specific goals) and self-assessed fitness level (beginner/intermediate/advanced). The system uses this data to establish initial constraints for workout plan generation and to calibrate exercise difficulty, rep ranges, and progression rates. Assessment results are stored as user profile data that persists across sessions and informs all subsequent plan generation and adaptation.