The project’s momentum is powered by an international community of hobbyists, startup engineers, and academic researchers who all share a common goal: . 🚀 Core Features That Set wxfaki Apart | Feature | Why It Matters | |---------|----------------| | Zero‑Copy Messaging | Eliminates unnecessary memory duplication, slashing latency to sub‑millisecond levels. | | Cross‑Platform Runtime | Runs natively on Linux, Windows, macOS, and even on micro‑controllers (ARM Cortex‑M). | | WebSocket + QUIC Hybrid Transport | Provides fallback compatibility while leveraging the speed of QUIC when available. | | Modular Kernel Hooks | Plug‑in architecture lets you attach custom processing modules (e.g., ML inference, encryption). | | Built‑in Security Sandbox | Each module runs in an isolated namespace, preventing a rogue plugin from compromising the whole system. | | Declarative Configuration (YAML/JSON) | No need to write boilerplate code; just describe your data flows and let wxfaki spin them up. | | Telemetry & Auto‑Scaling | Real‑time metrics feed into auto‑scaling policies, ensuring your edge network stays responsive under load. | 🛠️ Getting Started – Your First wxfaki Project Below is a minimal “Hello, World!” example that sets up a publisher on a Raspberry Pi and a subscriber on a laptop. Both nodes will exchange JSON messages over a secure WebSocket tunnel. Prerequisite: Node ≥ 18 (or Python 3.11+) and git installed. 1️⃣ Clone the Repository git clone https://github.com/wxfaki/wxfaki.git cd wxfaki 2️⃣ Install the Runtime # Using npm (JavaScript) or pip (Python) – choose one npm i -g wxfaki-cli # JavaScript # or pip install wxfaki # Python 3️⃣ Define Your Data Flow (YAML) Create a file called pipeline.yaml :
wxfaki run --config pipeline.yaml --node temperature-subscriber You should see temperature readings appear in the console whenever the sensor reports values above 25 °C. 🎉 | Use Case | How wxfaki Helps | |----------|------------------| | Smart‑City Traffic Management | Aggregate video‑analytics streams from thousands of cameras, apply edge‑ML models for congestion detection, and push alerts to traffic lights in real time. | | Industrial Predictive Maintenance | Pipe sensor data from PLCs directly to an on‑prem AI inference engine, trigger maintenance tickets before a failure occurs. | | Decentralized Gaming | Synchronize game state across peers without a central server, using wxfaki’s low‑latency QUIC transport. | | Real‑Time Financial Feeds | Connect market data providers to algorithmic trading bots with sub‑millisecond latency, while sandboxing each strategy for compliance. | 📈 Performance Benchmarks (v2.2) | Scenario | Avg. Latency | Throughput | CPU % (single core) | |----------|--------------|------------|----------------------| | Local LAN (100 Mbps) | 0.73 ms | 1.2 M msgs/s | 12 % | | WAN over QUIC (5 ms RTT) | 1.41 ms | 800 k msgs/s | 18 % | | Edge‑to‑Cloud (TLS + QUIC) | 2.03 ms | 600 k msgs/s | 22 % | wxfaki
🚀
In short, is a lightweight, open‑source framework designed to streamline real‑time data orchestration for edge‑computing environments. It was originally coined by a small group of developers who wanted a “ W eb‑ X ‑friendly F ramework for A daptive K ernel I ntegration**” — hence the acronym wxfaki . The project’s momentum is powered by an international
pipeline: - name: temperature-publisher type: publisher transport: websocket endpoint: wss://edge.example.com:443 payload: type: json schema: temperature: float timestamp: iso8601 - name: temperature-subscriber type: subscriber transport: websocket endpoint: wss://edge.example.com:443 filter: temperature > 25.0 action: log_to_console On the Raspberry Pi (publisher): | | WebSocket + QUIC Hybrid Transport |
wxfaki run --config pipeline.yaml --node temperature-publisher On the (subscriber):