HydroShare is a Django-based web application for sharing, managing, and publishing hydrologic data and models. It provides a rich resource management system with support for various file types (NetCDF, GeoTIFF, timeseries, etc.), fine-grained access control (groups, communities, privileges), metadata extraction, and discovery via a Vue.js frontend. The system uses PostgreSQL, Redis, Celery, MinIO (S3-compatible storage), and MongoDB for indexing. It exposes REST APIs for programmatic access and includes background tasks for bag creation, metadata extraction, and DOI registration. While primarily an application, it incorporates data engineering patterns for scientific metadata handling and file type aggregation.
Alumnet is a full-stack, AI-powered alumni exhibition and management system designed for school history museums. It consists of three workspaces: a touch-screen kiosk frontend (client) built with Vue 3, Three.js, and Tailwind CSS; an admin dashboard (admin) using Vue 3 and Element Plus; and a backend server (server) built with Express, TypeScript, PostgreSQL (with pgvector), and Qdrant for vector search. The system provides alumni CRUD, AI-powered natural language search via RAG, 3D galaxy visualization, timeline corridor, interactive message boards, video greetings, donation management, and comprehensive admin controls including content audit, backup/restore, and data import. It features JWT authentication with role-based access, privacy data masking, rate limiting, and a layered architecture (routes → services → repositories). The codebase includes extensive testing (unit, integration, property-based with fast-check) and is deployable via Docker Compose or PM2. While primarily an application, it incorporates data engineering patterns through its RAG pipeline and knowledge base management.
This repository is a large collection of Bash scripts that automate the deployment, configuration, and update of self-hosted services on Proxmox VE, primarily through LXC containers and VMs. It is a provisioning and automation framework rather than a runtime application: each ct/*.sh script declares resource variables (CPU, RAM, disk, OS template) and an optional update_script(), while install/*.sh scripts run inside an existing container to install a specific application. Usage is typically a one-line curl command executed as root on the Proxmox host, after which the script creates a container/VM, installs dependencies, generates credentials, writes systemd units or OpenRC init scripts, and configures databases and reverse proxies. The architecture is layered: hundreds of app-specific scripts wrap a smaller set of shared Bash function libraries in the misc/ directory (build.func, install.func, vm-core.func, core.func, error_handler.func). These shared functions handle common operations such as fetching GitHub releases, setting up language runtimes, creating databases, backing up configs before updates, and standardizing error handling. Update flows generally stop the service, back up configuration/data, fetch the latest release, restore the backup, run migrations, and restart the service. This consistency is the project's main strength and makes adding a new container recipe relatively straightforward. Weaknesses are typical of a large community-driven shell-script collection: no unit tests, heavy reliance on third-party installers fetched via curl|bash, some hardcoded versions, and external dependencies on GitHub release asset names and remote core-library URLs. Many scripts assume Debian/Ubuntu or Alpine bases and run as root with no signature verification. Despite these risks, the codebase is actively maintained, consistently formatted, and the shared function libraries are practical examples of reusable Bash abstractions for Proxmox automation.
Hiddify-Manager is a comprehensive anti-censorship proxy/VPN management system designed for multi-user environments. It orchestrates a suite of proxy cores (Xray, Sing-Box, HAProxy, Nginx, WireGuard, etc.) via systemd services, a Flask-based web panel, and a CLI tool. The system supports over 20 protocols (VLESS, VMess, Trojan, Shadowsocks, Reality, Telegram proxies, DNS tunnels) and is optimized for regions with heavy internet censorship (Iran, China, Russia). It provides automated installation, configuration generation via Jinja2 templates, multi-node support, Docker deployment, and cloud-init/Terraform scripts for cloud provisioning. The codebase is actively maintained with frequent updates, but contains deprecated services and a large shell script surface that introduces maintenance complexity.
HomeTube is a Streamlit-based web application for downloading and managing media from YouTube and 1800+ other platforms via yt-dlp. It provides a rich UI for URL analysis, quality profile selection (AV1/VP9/H.264), subtitle embedding, video cutting (keyframe/precise), SponsorBlock ad removal, and playlist synchronization with full diff-based state management. The architecture is a monolithic Streamlit app with heavy subprocess orchestration of yt-dlp and ffmpeg, backed by a typed configuration system, JSON-based workspace state files, and a comprehensive test suite with mocked Streamlit dependencies. Designed for homelab deployment, it includes Docker multi-arch builds, Nginx reverse proxy config, and daily automated rebuilds to stay current with yt-dlp updates. Core data flows: URLs are analyzed via yt-dlp (with client fallback chain default → ios → web), metadata is cached in url_info.json, download strategies are computed using format profile matching (codec priorities, audio language detection), and final files are moved to a configurable destination with optional hardlinking. Playlists are handled via a dedicated sync module that computes rename/archive/delete plans and applies them with dry-run support. The system emphasizes resilience through generic file naming, temporary workspace preservation, and intelligent caching that refuses to overwrite premium AV1/VP9 metadata with lower-quality data. The application is not a data research project; it is a production-ready media downloader and manager with no ML or analytical components. Its complexity lies in data integrity patterns (metadata caching, state machines, filesystem mutations) rather than data science.
This repository packages the BookStack wiki application into a Docker container. It provides Dockerfiles for building images on x86_64 and ARM64, using a base Alpine image with Nginx and PHP-FPM. Container initialization scripts handle database connection waiting, app key generation, Laravel migrations, and optional async queue worker. The repository includes a Jenkins pipeline and GitHub Actions workflows for automated builds and multi-registry releases. The codebase is well-structured, with clear separation of concerns between base image configuration, service management via s6-overlay, and application setup logic. It is a production-grade container build for self-hosting BookStack.
This repository is a Home Assistant add-on distribution point for Music Assistant, not a standalone application. It packages the Music Assistant server and a companion local audio output add-on as installable Home Assistant add-ons. The repository contains multiple variants—stable, beta, dev, and nightly—each with its own Dockerfile, run script, configuration schema, translations, and changelog. The local_audio add-on provides low-latency playback to local sound cards and is hardened with a custom AppArmor profile. In production, the add-ons run inside Home Assistant OS or a supervised installation. The main entry points are the add-on config.yaml files and the shell scripts that start the Music Assistant server or local audio player. The server exposes a web UI and integrates with Home Assistant; audio flows from music providers to players, with local_audio acting as an output sink. External dependencies include Home Assistant, Docker, the Music Assistant Python server, and system audio libraries. The codebase is primarily operational glue: shell scripts, Dockerfiles, YAML configuration, and security profiles. The most reusable artifacts are the AppArmor profile for sandboxing a network-exposed audio player and the git-ref-based source installer used by the development add-on. The repository is actively maintained, with frequent releases and detailed changelogs, and shows low technical drift relative to current Home Assistant add-on practices.
CloudSaver is a full-stack web application (Vue 3 + Express) for searching and saving cloud storage resources from multiple sources. It scrapes Telegram channels for shared files, parses links to popular cloud drives (115, Quark), and allows users to transfer them directly to their own cloud storage via cookie-based API. The system includes user authentication (JWT), role-based access, a responsive UI for desktop and mobile, and Docker support for easy deployment. The backend is structured with dependency injection (Inversify) and follows a controller-service-model pattern, while the frontend uses Pinia for state management and separate routing for PC and mobile views. The codebase is well-organized and demonstrates solid engineering practices, though it remains a typical CRUD application without exceptional innovation.
This repository provides a comprehensive Docker Compose configuration for building a self-hosted NAS (Network Attached Storage) system. It orchestrates over 20 services including media management (Sonarr, Radarr, Lidarr, Bazarr, Prowlarr, qBittorrent, Jellyfin, Jellyseer), a WireGuard VPN (PIA), reverse proxy (Traefik with Let's Encrypt), a homepage dashboard (Homepage), automatic updates (Watchtower), and optional services like AdGuard Home, Home Assistant, Immich, Vaultwarden, Paperless, and more. The architecture is a multi-container Docker stack with a shared network, designed to run on any Docker-capable Linux box. The primary data flow involves downloading media via qBittorrent through a VPN, managing it with *arr applications, and serving it via Jellyfin. The repository includes automation scripts for initial configuration (update-config.sh) and port forwarding updates (pia-shared/portupdate-qbittorrent.sh). It is not a software application with custom code but rather a well-documented, opinionated deployment template. The codebase is primarily configuration files (docker-compose.yml, environment files, nginx configs) with a few bash and JavaScript scripts. The quality is good for its purpose: it is organized, documented, and widely used, but it lacks reusable software components that could be extracted into a new project with minimal modification.
ScaleTail is a curated collection of Docker Compose configurations for self-hosting 80+ services with a consistent Tailscale sidecar pattern. Each service directory contains a `compose.yaml` that defines a `tailscale` container (using `tailscale/tailscale:latest`) and an application container using `network_mode: service:tailscale`, enabling secure HTTPS access via Tailscale Serve or MagicDNS. The repository provides environment-driven configuration (`.env` files), health checks, and per-service READMEs. It is not a standalone application or library but a deployment template repository. The dominant character is 'application' as it facilitates deploying applications, though it is purely configuration and documentation. The pattern is highly reusable for any Docker-based self-hosted service wanting Tailscale integration.
iKy (I Know You) is a modular OSINT (Open Source Intelligence) aggregation platform built with FastAPI, Celery, Redis, and an Angular frontend. The system ingests a username or email and concurrently executes 26+ parallel tasks across social media, data breaches, search engines, and dark web sources, returning a structured JSON contract (module, param, validation, raw, graphic, profile, timeline, tasks) consumed by a visual graph/timeline UI. Key architectural patterns include a dynamic module registry that maps route names to Celery task paths, a decorator-based task wrapper (@iky_task) that centralizes dev-mode golden-file bypass, error classification, and performance logging, and a pluggable search provider orchestration layer. The codebase demonstrates a strong separation of concerns between API routing, task execution, and data enrichment, but suffers from fragile scraping logic (LinkedIn RSC parsing, TikTok SSR regex), heavy reliance on browser session cookies, and several deprecated external APIs (Twitter v1.1, FullContact). The project is actively maintained with an extensive test suite (~4K lines) and Docker Compose deployment, though many tests are heavily mocked and may not detect real API drift.
This repository provides a fully automated, one-command deployment of a self-hosted Supabase stack. The setup script (`setup.sh`) orchestrates the entire process: it installs dependencies, clones the repository, generates cryptographic secrets and JWT tokens, configures environment variables, and layers optional docker-compose overrides for Authelia 2FA, Nginx or Caddy reverse proxy, and SSL via Let's Encrypt. The architecture is a Docker Compose multi-service deployment including Supabase Studio, Kong API gateway, GoTrue auth, PostgREST, Realtime, Storage, Edge Functions, and a custom Postgres image. The code is production-grade with robust error handling, CI/CD pipelines (GitHub Actions) that test every configuration matrix (caddy/nginx, basic auth/authelia/redis), and comprehensive integration tests covering CRUD operations, storage, realtime subscriptions, and edge functions. The repository also includes advanced utilities for key management (asymmetric JWTs, opaque API keys) and a sophisticated Postgres 15-to-17 upgrade script (`upgrade-pg17.sh`) that handles binary extraction, pg_upgrade, and post-migration patches. While primarily a deployment tool, the scripts demonstrate high craftsmanship and are safely reusable as templates for similar self-hosting scenarios.
This repository provides a complete solution for integrating AmneziaWG (a WireGuard fork with traffic obfuscation) with MikroTik RouterOS. It includes multi-architecture Dockerfiles (amd64, arm, arm64) that build minimal Alpine-based containers running the AmneziaWG client, along with OpenRC init scripts to manage the VPN tunnel and configure NAT/MSS clamping via nftables or iptables. The repository also contains pre-built container images and a detailed step-by-step guide (in Russian) covering two deployment options: running the container directly inside RouterOS (for devices that support containers) or using a separate Linux machine as a VPN gateway. The actual code is limited to Dockerfiles and shell scripts; the bulk of the repository is documentation and build artifacts. It is not a standalone application but a deployment pattern and configuration toolkit for network engineers.
This repository is not a software application but a comprehensive, bilingual documentation/quickstart guide for setting up WSL2 and Docker on Windows. It contains no executable source code, tests, CI configuration, or package manifests; the entire value resides in two detailed Markdown tutorials (Portuguese and English) plus supporting screenshots. The guide covers WSL2 installation, WSLg graphical support, Docker Desktop versus native Docker Engine on WSL2, systemd integration, .wslconfig and wsl.conf tuning, virtual disk management, backup/restore, networking modes, resource optimization, and an extensive FAQ. Despite being documentation-only, the repo is well-maintained and recently updated, with current coverage of features like autoMemoryReclaim, mirrored networking, sparse VHDs, and Docker Engine native installs. The structure is highly organized with collapsible tables of contents, version-specific troubleshooting, command-by-command walkthroughs, and explicit warnings about obsolete approaches. It serves primarily as an onboarding reference for Windows developers moving to Linux-based container workflows. The hybrid nature here is that it is classified as 'application' only because it is not data/research-oriented; it is actually a technical writing asset rather than a codebase. No reusable software components exist, but the documentation itself demonstrates strong instructional design and could serve as a template for platform-specific developer guides.
This repository provides Docker Compose and Kubernetes configuration files for deploying a variety of self-hosted applications and system services. It serves as a template repository for deploying services like AdGuard Home, Bitwarden, MinIO, Traefik, and others, as well as system tools like Fail2ban and acme.sh for certificate management. The architecture is modular, with each application having its own directory containing deployment configurations (docker-compose.yml, Helm charts, Kubernetes manifests). There is no single unified application; instead, it offers reusable deployment patterns for individual services. Key entry points are the docker-compose.yml or Helm chart files within each application's directory. The repository relies on external dependencies like Docker, Kubernetes, and Helm for deployment, and the applications themselves depend on their respective upstream container images.
Container Tool Kit (CTK) is a web application that provides a visual editor for Docker Compose and Kubernetes manifest files. Users can create and manage projects by dragging and dropping services, volumes, and networks onto a canvas, editing node configurations through detailed forms, and viewing the resulting YAML/JSON in a synchronized code editor. The backend is built with FastAPI and PostgreSQL, handling project persistence, user authentication (JWT-based), manifest generation (including calling kompose for Kubernetes), and project import from remote YAML URLs. The frontend is a React/TypeScript single-page application using jsPlumb for the interactive canvas, Formik for form management, and Monaco Editor for code editing. The application supports both anonymous and authenticated usage, with features like autosave, visibility toggles, and pagination for project lists.
Glance is a self-hostable web application designed as a customizable dashboard (startpage) that aggregates information from various sources into widgets. Built in Go, it features a modular widget architecture where each widget type (e.g., RSS, weather, clock) implements a common interface, allowing users to create personalized pages with multiple columns and widgets. The application serves HTML pages with embedded static assets (CSS, JS, images) and uses Go templates for server-side rendering, enhanced by a comprehensive set of template functions. Client-side JavaScript adds interactivity for dynamic updates, masonry layouts, and popovers. The system relies on a YAML-driven configuration (`glance.yml`) that supports variable substitution, file includes, and defines pages, columns, and widget settings. Data fetching is handled by centralized HTTP utilities with caching and user-agent rotation to avoid blocking external services. A theming system generates CSS variables from HSL-based configuration, enabling light/dark modes and custom themes. Optional authentication secures the dashboard, and the application includes diagnostic and CLI tools for maintenance. While the project demonstrates strong engineering in areas like request deduplication (singleflight), flexible configuration, and reusable template functions, it faces challenges due to its extensive widget proliferation (30+ types), coupling between frontend and backend logic, and reliance on an outdated Go version (1.26.6) which is no longer supported, indicating moderate technical drift. Despite these issues, Glance remains a popular and functional solution for personal dashboards.
This repository is a monorepo of self-contained demo applications showing how to build and deploy multi-agent AI systems using Docker Compose, Docker Model Runner, and the MCP Gateway. It contains 12+ independent examples spanning Google ADK, A2A, Agno, CrewAI, LangGraph, Langchaingo (Go), Spring AI, Akka, and a Next.js agent UI. Each subproject is a standalone composition with its own compose.yaml, Dockerfile, and README, designed to run with a single `docker compose up --build` after creating a `.mcp.env` file. The demos are reference architectures rather than a single product, emphasizing local LLM inference, MCP tool integration, and switching between local and cloud models via compose override files.
This is a large Go and TypeScript project for managing and monitoring network devices, specifically MikroTik routers. It has a web interface, role-based access control, and supports features like backups, settings management, and notifications.
The repository is a suite of tools for UniFi network management, including a dashboard, Wi-Fi Stalker, Threat Watch, and Network Pulse. It's built using FastAPI, Python 3.9+, and utilizes a multi-stage Docker build. The application has a React-based frontend and uses a modular design with separate tools for different features.