Aegis Node: Predictive Compute Distribution

Anticipating system bottlenecks before they crash your workflow.

Aegis Node is a proactive, localized resource management engine and simulated peer-to-peer compute grid. Built for DevGathering 2K26, it shifts the paradigm from reactive monitoring to predictive load balancing using edge-optimized AI.

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🚀 The Core Innovation: Embedded Edge Neural Net

Modern development workflows (Local LLMs, multi-container Docker builds, heavy IDEs) have outpaced standard consumer hardware. Aegis Node solves this by:

1. Predicting the Crash: A background daemon feeds real-time hardware telemetry (CPU/RAM/Thermals) into an embedded, feedforward Neural Network (MLP) written in pure TypeScript.
2. Ultra-Lightweight Edge AI: The AI inference runs in < 1 millisecond directly inside the Node.js runtime. It has 0% CPU/RAM overhead and 0 dependencies, completely replacing heavy 3.8B parameter local LLMs (like Phi-3) and eliminating the need for background Ollama servers.
3. Autonomous Offloading: When a 90%+ probability of memory exhaustion is predicted, the system automatically triggers a grid auction, routing the pending heavy process to an available "idle node" on a localized P2P community network.

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🛠 Repository Structure

This repository contains the core services of the Aegis Node ecosystem:

• /aegis-cli: The "Watcher." A background Node.js/TypeScript daemon that monitors system health and runs the local MLP Neural Network.
• /aegis-api: The "Router." An Express.js grid controller that coordinates the P2P auction, manages node registration, and handles offload routing.
• /aegis-dashboard: The "Command Center." A Next.js (App Router) visual dashboard that renders real-time telemetry, network status, and auction streams.
• /aegis-website: The "Portal." A premium cyber-brutalist landing page for the project featuring an interactive offload simulator.

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⚡ Tech Stack

• Frontend & Website: Next.js (App Router), TailwindCSS, Recharts, Framer Motion.
• Backend: Node.js, Express.js, Socket.io (Real-time Full-Duplex).
• CLI Daemon: TypeScript, systeminformation, socket.io-client.
• AI/ML: Custom Embedded Feedforward Neural Network (3-Layer MLP with ReLU and Sigmoid activations).
• Database: Supabase (PostgreSQL).

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🔧 Installation & Setup

Prerequisites

• Node.js (v18+)
• pnpm (npm install -g pnpm)

Running the Ecosystem (Host Machine - Laptop A)

A single script is provided to spin up all backend and dashboard services concurrently:

1. Launch the Orchestrator:

   ./start-all.sh

   This starts the Express API (port 8080) and the Next.js Dashboard (port 3000) in the background, piping logs to the logs/ directory.
2. Start the Local CLI Watcher:

   cd aegis-cli && pnpm install && pnpm start

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🌐 Connecting Peer Nodes (Laptop B - Kamal)

Peers on your local network (e.g., Windows/macOS laptops) can join the grid in seconds:

1. Configure Network:
   • Connect both laptops to the same Wi-Fi/Mobile Hotspot.
   • Find Host Laptop A's local IP address (e.g., 10.11.19.162).
2. Configure .env on Laptop B:
   • Open aegis-cli/.env on Laptop B and point it to Laptop A's IP:

     API_URL=http://<LAPTOP_A_IP>:8080
     WS_URL=http://<LAPTOP_A_IP>:8080
     POLL_INTERVAL=2000

3. Run Client:
   • On Windows, use standard npm to avoid symlink bugs:

     npm install
     npm start

   • The peer node will instantly connect to Laptop A's grid controller and become visible on the dashboard!

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👥 The Team

Built by Team Aegis (Dev Sharma & Kamal Sharma) for DevGathering 2K26.