openGym

wger is a free, open-source, self-hosted fitness and workout manager developed by the wger project. It supports custom workout routines with automatic weight-progression rules, diet and calorie logging using a food database sourced from Open Food Facts, body-weight and custom-measurement tracking, progress photos, and a built-in exercise wiki. The application can be deployed with Docker and provides Android, iOS, F-Droid, and Flathub clients, multilingual support, multi-user accounts with basic gym-management features, and a REST API for integrations and automations. Application code is licensed under AGPL-3.0-or-later; exercise and ingredient data and documentation have separate licenses.

Workout Guide is an open exercise illustration library and framework-neutral npm package by Bryl Lim. It provides structured metadata for 302 exercises, three consistent frames per exercise, transparent normalized assets, and typed APIs such as `getExercise`, `searchExercises`, and `getAssetUrl`; its searchable static gallery includes exercise detail pages and integration guidance. The repository is an npm-workspace monorepo containing the package API, canonical manifest, asset files, gallery site, documentation, and deterministic catalog import and validation scripts. Code and documentation are licensed under MIT, while the visual assets are licensed under CC BY-SA 4.0, including artwork derived from Everkinetic.

NutriTrace is a self-hosted personal nutrition tracker developed by TraceApps. It runs as a single Docker container and provides a browser Progressive Web App and a native Android app for food diaries, meal and recipe logging, calorie and nutrition goals, adaptive TDEE tracking, intermittent fasting, manual activities, and wellness data. It can use Open Food Facts and USDA data, import recipes from Mealie, and integrate with Fitbit via Google Health, Withings, Garmin, and Health Connect. Its Trace AI features can read diary and wellness data, log food or activities conversationally, and propose food entries from photos for user confirmation. It also exposes selected diary, goal, and food data to external AI agents through an opt-in, read-only Model Context Protocol endpoint. The project supports multi-user access and OIDC single sign-on, provides migration tools for several nutrition trackers, and distributes its server as a Docker image. The source is licensed under AGPL-3.0; the project states that it has no telemetry or cloud synchronization unless a user opts into a third-party integration.

Shell Gym is an interactive Linux command-line trainer implemented as a background daemon with a built-in web UI. It presents short assignments, or “reps,” such as entering a directory, creating a file, killing a process, or freeing a port; the assignment completes automatically when the corresponding system state changes. Users type real commands in an ordinary terminal while the UI observes the host and provides guidance, without modifying the shell or using prompt hooks, wrappers, special functions, a check button, or copy-and-paste. Its outside-the-shell observation uses procfs to discover interactive shells and inspect working directories, processes, files, and ports; the Linux kernel proc connector to observe exec() events; and direct state checks for files, processes, and listening ports. It runs on a Linux host as root, with an online playground also available, and the project advises using a disposable VM or other disposable Linux system because exercises perform real actions on the live host.