I’m building a smart fitness companion that adapts every workout to how much discomfort a user is feeling that day. The core of the product is an algorithm that generates personalized plans, asks the user to rate their pain on a simple in-app scale, and then adjusts intensity, exercise selection, and rest periods automatically. Over time it should learn from completed sessions and update routines while presenting clear progress trends so the user can see where they’re improving without aggravating problem areas. Here’s what matters most: • Personalized fitness plans generated by AI. • Live pain-level monitoring through self-reported scales (no wearables in the first release). • Visual progress tracking that highlights correlations between pain scores and performance. I’m flexible on the final tech stack—React Native, Flutter, native iOS/Android, or a responsive web front end are all acceptable as long as the user experience stays fluid. A lightweight Python or Node.js backend running TensorFlow, PyTorch, or a comparable ML library would suit the recommendation engine, but I’m open to your proposal. Deliverables 1. Fully functional MVP with user onboarding, daily pain check-ins, dynamic workout generator, and progress dashboard. 2. Clean, well-documented code repo plus brief setup guide. 3. Post-launch support window to fix critical bugs and fine-tune the recommendation model. Acceptance criteria • The app must modify a workout immediately after a new pain score is entered and log that adjustment. • Historical pain vs. performance graphs render in under two seconds on average devices. • All key screens pass basic accessibility checks (voice-over, font scaling). If this blend of fitness science and machine learning excites you, tell me how you’d architect it and share any similar projects you’ve shipped.