Strength training, cardio, and body metrics land in one workout log on your own server, backed by an optional AI coach that explains its advice from your real numbers. Quick entry such as 100x8@9 logs weight, reps, and effort in one line, with a rest timer, plate-loading calculator, and warm-up ramp beside the set table. A weight picker offers only loads your equipment can build, and a readiness check-in adjusts the suggested load and says why. Templates for 5/3/1 BBB, GZCLP, nSuns, PPL, and Upper/Lower run as written and edit like any custom program. Progress pages chart estimated 1RM, weekly volume per muscle group against MEV and MRV bands, a muscle heat map, bodyweight goals, and a training calendar. Garmin FIT, TCX, and GPX imports need no cloud account and add heart-rate charts and aerobic decoupling to each run or ride. The coach writes a weekly debrief, chats in a streaming conversation, and drafts a full program from a one-sentence goal, using your own Anthropic or OpenRouter key. History imports from Strong or Hevy CSV files with a dry-run preview and exports to CSV or TCX, and an MCP connector lets ChatGPT or other agents analyse it. The app installs as an offline-capable PWA, speaks English, French, Russian, and German, and keeps every user's data separate. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Lyftr delivers a self-hosted workout and nutrition tracker that runs on a small VPS — no subscriptions, no vendor lock-in, no "export is a Pro feature" paywalls. The Go backend with Gin framework provides JWT authentication and a REST API while storing everything in a single SQLite database file you can back up with one copy command. The exercise library ships with 800+ movements auto-seeded on first run, letting you build reusable workout programs with target sets, reps, and weights from day one. Active workout mode provides a guided set-by-set flow through your session while Gym Mode offers a full-screen card layout showing one exercise at a time with an integrated rest timer for focused training. The React frontend built with TypeScript and Tailwind delivers a mobile-first interface featuring a dashboard with weekly session KPIs, daily calorie and protein tracking, 12-week consistency heatmap, volume trend charts, and muscle balance visualization with sparklines. Nutrition tracking connects to Open Food Facts for food search with camera-based barcode scanning, logging calories and macros across customizable meal sections. Bodyweight logging with trend graphs supports both lbs and kg units across all data. An Android APK connects to your self-hosted instance for native mobile access. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.

Training, meals, and body changes usually live in three different apps, and LiftCut puts them in one bilingual tracker where an AI agent helps keep the log instead of just drawing charts. Tell it what you ate, what you lifted, or what the scale said in plain language, in Chinese or English, and it prepares the entry from the exact numbers you gave, never inventing nutrients or weights, and saves nothing until you confirm it. Workout plans are editable rather than rigid programs, text import brings existing routines in, logging tracks completion and exercise performance, and personal records and PDF exports mark how far you have come. Food logs roll into nutrition summaries, and dated body measurements draw trend charts beside your training, so cause and effect sit on the same screen. AI training and nutrition proposals read your saved goals, restrictions, measurements, and recent actual loads first, then pass through extraction, strict validation, and an editable preview, and a proposal is rejected if the records it relied on changed in the meantime. Begin in the public no-sign-in demo or local-first guest mode, then create an account and migrate your history when ready, with offline queues holding entries until a connection returns. Providers span DeepSeek, any OpenAI-compatible endpoint, or a local vLLM server, so the intelligence can stay on hardware you control. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT licensed.