CRUX

The CRUX project is a lightweight, x86-64 optimized Linux distribution designed for experienced users. It emphasizes simplicity, minimalism, and customization through a ports system inspired by BSD's ports collection. CRUX aims to provide a clean, straightforward environment for users who want to build and control their system from the ground up.

Key Features

  • Lightweight, minimalistic design
  • BSD-style ports system for package management
  • Focused on simplicity and flexibility
  • Compatible with modern x86_64 hardware

Use Cases

  • For advanced Linux users who prefer hands-on system management
  • Building custom Linux environments
  • Learning and experimentation with Linux internals

CRUX-ARM

CRUX-ARM is a port of the CRUX distribution targeted at ARM architecture devices, including both 32-bit and 64-bit ARM systems. It brings the minimalism and flexibility of CRUX to the growing ARM ecosystem, supporting devices such as the Raspberry Pi series, Orange Pi, Pine64, and more.

Key Features

  • Support for ARMv7 (32-bit) and ARMv8 (64-bit) architectures
  • Automated CI/CD pipelines for timely releases
  • Community-driven development and support
  • Suitable for embedded systems, servers, and IoT devices

Use Cases

  • ARM-based embedded Linux development
  • Home servers on ARM hardware
  • IoT prototyping and deployment
  • Lightweight ARM Linux environments

CRUX-RiscV

CRUX-RiscV is the arm of the CRUX project dedicated to the RISC-V architecture, an open standard instruction set architecture growing rapidly in popularity. CRUX-RiscV aims to provide a minimalist, flexible Linux experience tailored for RISC-V hardware such as the Orange Pi RV2.

Key Features

  • Native support for RISC-V hardware
  • Development fueled by community and hardware donations
  • Automated build and CI/CD integration
  • Suitable for edge computing, embedded systems, and development

Use Cases

  • Developing and deploying RISC-V Linux systems
  • Educational platform for RISC-V architecture
  • Testing and improving RISC-V open-source tools and kernels

FluxTuner

FluxTuner is a modern internet radio player for the terminal, desktop, and web. Built with Python, it combines a fast keyboard-oriented Textual TUI, an experimental GTK4 desktop GUI, a browser-based web/server mode, and a legacy CLI into a lightweight application designed for daily radio listening.

The project focuses on speed, usability, modularity, and practical workflows for discovering, organizing, and playing internet radio stations. FluxTuner uses a shared core for station search, playback, favorites, playlists, history, configuration, and local data storage, while exposing different interfaces depending on how and where you want to use it.

Key Features

  • Fast Textual terminal user interface for keyboard-driven workflows
  • GTK4 desktop GUI for visual station browsing
  • Browser-based web/server mode for local or container-friendly usage
  • Legacy numbered CLI for simple terminal interaction
  • Internet radio station search by name, genre/tag, and country
  • Modular playback backends with automatic detection
  • Support for mpv, ffplay, mpg123, and ogg123
  • Favorites with custom names and tags
  • Persistent playlists and dynamic tag-based playlists
  • Station history and random playback workflows
  • Live stream metadata when available
  • Data usage tracking
  • Built-in TUI themes with live preview
  • Local SQLite library database
  • XDG-style config, data, and cache locations
  • Import and export support for favorites and playlists
  • Doctor and inspection commands for troubleshooting
  • Clean modular architecture and growing documentation

Use Cases

  • Listening to internet radio from the terminal
  • Running a lightweight radio player over SSH or on low-resource systems
  • Using a desktop-friendly GTK interface for browsing and playback
  • Running a local browser-based radio interface
  • Managing favorite stations, tags, playlists, and history
  • Testing different playback backends across Linux/macOS environments
  • Experimenting with Python, Textual, GTK4, FastAPI-style web tooling, and modular media playback
  • Building and documenting a small but complete open-source desktop/server application
  • Website: Landing page
  • GitHub repository: Fluxtuner

ctxcuts

ctxcuts is a tiny CLI for defining reusable context shortcuts for AI agent workflows. It lets you create shortcuts such as :r, :f, :s, or :a and expand them into focused prompts for coding agents, chat assistants, CLIs, and custom workflows.

The goal is simple: stop repeating long prompts, avoid oversized always-on context, and load only the task contract you need when you need it. It is designed to be small, local-first, versionable, portable, and vendor-neutral.

Think of it as an .editorconfig for agent context: a lightweight way to keep AI task instructions explicit, reusable, and close to the project they belong to.

Key Features

  • Reusable context shortcuts for agent workflows
  • Local .ctxcuts/ configuration per project
  • Shortcut expansion into focused prompts
  • Built-in commands to list, inspect, validate, and expand contexts
  • doctor checks for missing or suspicious context files
  • stats command for lightweight context/token-ish estimates
  • Template variables such as target, focus, output, and custom vars
  • Works with tools such as Codex, Claude, Aider, or any CLI that accepts piped input
  • Vendor-neutral and dependency-light design
  • CI coverage with Ruff, pytest, mypy, and package builds

Use Cases

  • Reducing repeated prompt boilerplate
  • Keeping coding-agent instructions consistent across projects
  • Creating reusable review, fix, test, docs, security, audit, and performance prompts
  • Sharing versioned AI workflow contracts with a repository
  • Avoiding oversized global agent context files
  • Building lightweight, portable workflows around existing AI tools
  • GitHub repository: ctxcuts

EuroLedger XRPL

EuroLedger XRPL is an open, testnet-first proof of concept for euro-denominated payment intents, XRPL payment detection, merchant webhooks, and WooCommerce checkout integration.

The project explores how the XRP Ledger can be used as an open technical settlement layer while keeping the merchant and customer experience centered around euro-denominated payments, references, reconciliation, and order workflows.

EuroLedger XRPL is not a cryptocurrency investment product, a custody service, a stablecoin issuer, or production financial infrastructure. It is a technical experiment focused on architecture, integration, observability, and payment flow validation.

Key Features

  • FastAPI backend for merchant-scoped payment intents
  • Idempotent payment intent creation and lifecycle management
  • XRPL testnet payment detection using references and memos
  • Payment expiration and cancellation flows
  • Merchant API key authentication
  • Signed merchant webhook delivery with retry tracking
  • Prometheus metrics, Grafana dashboards, and Alertmanager examples
  • n8n alerting workflows for Telegram notifications
  • WooCommerce payment gateway integration
  • Support for classic WooCommerce checkout and Checkout Blocks

Use Cases

  • Exploring XRPL-based payment intent flows
  • Testing euro-denominated checkout experiences on XRPL testnet
  • Building merchant webhook and reconciliation workflows
  • Experimenting with WooCommerce payment gateway architecture
  • Studying observability, alerting, and worker-based payment detection
  • Prototyping open payment infrastructure concepts
  • GitHub repository: euroledger-xrpl

ai.vjml.es

ai.vjml.es is a personal, self-hosted AI image generation gallery powered by solar energy. The project explores how local hardware, renewable energy, automation, and generative AI can be combined to create a practical and independent image generation platform.

It is used to generate, collect, compare, and refine AI-created images, with a focus on wallpapers, concept art, portraits, and themed visual collections. Beyond the visual results, the project is also an experiment in running AI workloads on privately managed infrastructure instead of relying entirely on external cloud services.

Key Features

  • Self-hosted AI image generation environment
  • Solar-powered infrastructure
  • Gallery for generated images and visual experiments
  • Local AI workflows running on privately managed hardware
  • Support for different formats, themes, and visual styles
  • Prompt-driven creative automation
  • Focus on reproducibility, efficiency, and infrastructure independence

Use Cases

  • Generating wallpapers and visual assets
  • Testing local AI image generation models
  • Building themed image collections
  • Experimenting with prompts, styles, and automation
  • Exploring sustainable self-hosted AI infrastructure
  • Running creative AI workloads with renewable energy