Agentics Foundation TV5 Hackathon

Build the future of agentic AI - Supported by Google Cloud

The Agentics Foundation TV5 Hackathon repository provides CLI tools, MCP servers, and reference implementations for building agentic AI solutions. This includes the AI Media Discovery demo app showcasing the Agent-Ready Web (ARW) specification.

🌐 Website: agentics.org/hackathon 💬 Discord: discord.agentics.org 📦 npm: npx agentics-hackathon

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🎯 The Challenge

Every night, millions spend up to 45 minutes deciding what to watch — billions of hours lost every day. Not from lack of content, but from fragmentation across streaming platforms.

Join us to build agentic AI solutions that solve real problems using Google Cloud, Gemini, Claude, and open-source tools.

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🚀 Quick Start

# Initialize your hackathon project
npx agentics-hackathon init

# Browse and install 17+ AI tools
npx agentics-hackathon tools

# Check project status
npx agentics-hackathon status

# Start MCP server for AI assistant integration
npx agentics-hackathon mcp

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⚡ Exogenesis Omega: Distributed TV Intelligence

Powering the Media Discovery Demo - A production-grade, three-tier distributed AI system designed to serve intelligent media recommendations to 40M+ smart TVs (scalable to 400M+) with unprecedented efficiency, privacy, and performance.

🏗️ Three-Tier Architecture

Layer 1: Smart TVs (Edge Layer) - 40M+ Devices

The Omega Brain runs on each smart TV, providing blazing-fast local AI inference:

• Sub-15ms recommendation latency - SIMD-accelerated vector search on-device
• ~80MB memory footprint - Lightweight Rust implementation (7 crates)
• <0.5W power consumption - Efficient edge computing
• 12-tier memory system with 7 temporal feedback loops
• ONNX inference engine for local AI model execution
• Privacy-first design - No raw viewing data ever leaves the device

Layer 2: Constellation Servers (Backend) - 100 Servers

Federated learning backend for pattern aggregation and synchronization:

• 1.2M+ sync requests/sec - Massive concurrent connection handling
• 99.99% uptime - Production-grade reliability with Raft consensus
• gRPC-based synchronization - Efficient binary protocol
• Differential privacy (ε=0.1) - Mathematical privacy guarantees
• ~1KB delta uploads - Compressed encrypted updates every 4 hours
• 1.4TB data transfer/day - Efficient at scale

Layer 3: RuVector-Postgres (Data Layer) - Vector Database

SIMD-accelerated vector database for embeddings and pattern storage:

• 150M+ vectors - 384-dimensional embeddings
• <15ms P99 query latency - Consistently fast retrieval
• 13-41x faster than pgvector - SIMD-optimized HNSW indexing
• 75% storage reduction - Adaptive compression
• Graph Neural Networks - Advanced pattern refinement
• Raft HA cluster - High availability and fault tolerance

📊 Performance & Scale

Metric	Value	Details
Connected Devices	40M+ (→400M+)	Current deployment, scalable to 400M+
Recommendation Latency	<15ms	P99 latency, on-device inference
Sync Throughput	1.2M+ req/sec	Backend synchronization capacity
Vector Database	150M+ vectors	384-dim embeddings with SIMD acceleration
Cost per Device	$0.0006/month	Incredibly efficient at scale
Uptime SLA	99.99%	Production-grade reliability
Privacy Standard	ε=0.1	Differential privacy guarantee

🔒 Privacy & Security Architecture

Exogenesis Omega implements privacy-by-design at every layer:

1. On-Device Learning: Omega Brain learns viewing patterns locally—no raw data transmitted
2. Differential Privacy: Mathematical guarantee (ε=0.1) that individual viewing habits remain private
3. Encrypted Deltas: Only encrypted statistical gradients (~1KB) sent to servers
4. k-Anonymity: Patterns aggregated from minimum 1,000 users before being used
5. AES-256-GCM: Military-grade encryption for all network communication
6. No PII Storage: Zero personally identifiable information in the system

🚀 Scalability: 40M → 400M+ Devices

The architecture scales horizontally across all three tiers:

Edge Layer (TVs):

• Each Omega Brain is autonomous—adding devices doesn't impact latency
• Sub-15ms inference guaranteed regardless of fleet size
• No central coordination required for recommendations

Backend Layer (Constellation Servers):

• Each server handles ~12,000 sync requests/second
• Linear scaling: 100 servers → 1.2M req/sec, 1000 servers → 12M req/sec
• Auto-scaling with Kubernetes for demand spikes
• Raft consensus ensures consistency across server fleet

Data Layer (Vector Database):

• Horizontal sharding for vector storage
• Read replicas for query load distribution
• SIMD optimization maintains <15ms latency at scale
• Adaptive compression reduces storage costs 75%

Cost Efficiency:

• $0.0006/device/month at 40M scale
• Projected $0.0004/device/month at 400M+ scale (economies of scale)
• Edge-first design minimizes cloud costs

🎯 Real-Time Dashboard

The Media Discovery app showcases live system metrics:

• Live sync request rates (updates every 2 seconds)
• Real-time query latency monitoring
• Active device count tracking
• System health indicators

Try it: cd apps/media-discovery && npm run dev

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🏆 Hackathon Tracks

Track	Description
Entertainment Discovery	Solve the 45-minute decision problem - help users find what to watch
Multi-Agent Systems	Build collaborative AI agents with Google ADK and Vertex AI
Agentic Workflows	Create autonomous workflows with Claude, Gemini, and orchestration
Open Innovation	Bring your own idea - any agentic AI solution that makes an impact

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✨ Features

🛠 CLI Tool (npx agentics-hackathon)

• init - Interactive project setup with track selection and tool installation
• tools - Browse and install 17+ AI development tools across 6 categories
• status - View project configuration and installed tools
• info - Hackathon information and resources
• mcp - Start MCP server (stdio or SSE transport)
• discord - Join the community
• help - Detailed guides and examples

🤖 MCP Server

Full Model Context Protocol implementation with:

• Tools: get_hackathon_info, get_tracks, get_available_tools, get_project_status, check_tool_installed, get_resources
• Resources: Project configuration, track information
• Prompts: hackathon_starter, choose_track

📱 Demo Applications

App	Description
Media Discovery	AI-powered movie/TV discovery with Exogenesis Omega backend, real-time metrics dashboard, and ARW implementation. Features Next.js 15, shadcn/ui components, Framer Motion animations, and live system monitoring.
ARW Chrome Extension	Browser extension for inspecting ARW compliance

📐 ARW (Agent-Ready Web) Components

This repository includes reference implementations of the ARW specification:

• Specification: ARW v0.1 Draft
• Schemas: JSON schemas for validation (packages/schemas/)
• Validators: Python and Node.js validation tools (packages/validators/)
• Badges: Compliance level badges (packages/badges/)

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📦 Repository Structure

hackathon-tv5/
├── src/                             # Hackathon CLI source
│   ├── cli.ts                      # Main CLI entry point
│   ├── commands/                   # CLI commands (init, tools, status, etc.)
│   ├── mcp/                        # MCP server implementation
│   │   ├── server.ts              # MCP tools, resources, prompts
│   │   ├── stdio.ts               # STDIO transport
│   │   └── sse.ts                 # SSE transport
│   ├── constants.ts               # Tracks, tools, configuration
│   └── utils/                     # Helpers and utilities
│
├── apps/                           # Demo Applications
│   ├── media-discovery/           # AI Media Discovery (Next.js + ARW)
│   │   ├── public/
│   │   │   ├── .well-known/arw-manifest.json  # ARW manifest
│   │   │   └── llms.txt                       # ARW discovery file
│   │   └── src/                   # React components & API routes
│   └── arw-chrome-extension/      # ARW Inspector Chrome Extension
│       ├── manifest.json          # Chrome Manifest V3
│       └── src/                   # Popup, content script, service worker
│
├── packages/                       # Shared Packages
│   ├── @arw/schemas/              # TypeScript ARW schemas with Zod
│   ├── schemas/                   # JSON schemas for ARW validation
│   ├── validators/                # Python & Node.js validators
│   ├── validator/                 # ARW validator CLI tool
│   ├── badges/                    # ARW compliance badges (SVG)
│   ├── cli/                       # Rust ARW CLI (advanced)
│   ├── crawler-sdk/               # TypeScript SDK for ARW crawler service
│   ├── crawler-service/           # High-performance crawler API service
│   ├── nextjs-plugin/             # Next.js plugin for ARW integration
│   └── benchmark/                 # ARW benchmark evaluation
│
├── spec/                           # ARW Specification
│   └── ARW-0.1-draft.md           # Editor's draft specification
│
├── docs/                           # Documentation
├── ai_docs/                        # AI-focused documentation
├── scripts/                        # Build and utility scripts
│
├── .claude/                        # Claude Code configuration
│   ├── commands/                  # Slash commands
│   └── agents/                    # Sub-agent definitions
│
├── CLAUDE.md                       # Claude Code guidance
└── README.md                       # This file

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🔧 Available Tools (17+)

The CLI provides access to tools across 6 categories:

AI Assistants

• Claude Code CLI - Anthropic's AI-powered coding assistant
• Gemini CLI - Google's Gemini model interface

Orchestration & Agent Frameworks

• Claude Flow - #1 agent orchestration platform with 101 MCP tools
• Agentic Flow - Production AI orchestration with 66 agents
• Flow Nexus - Competitive agentic platform on MCP
• Google ADK - Build multi-agent systems with Google's Agent Development Kit

Cloud Platform

• Google Cloud CLI - gcloud SDK for Vertex AI, Cloud Functions
• Vertex AI SDK - Google Cloud's unified ML platform

Databases & Memory

• RuVector - Vector database and embeddings toolkit
• AgentDB - Database for agentic AI state management

Synthesis & Advanced Tools

• Agentic Synth - Synthesis tools for agentic development
• Strange Loops - Consciousness exploration SDK
• SPARC 2.0 - Autonomous vector coding agent

Python Frameworks

• LionPride - Python agentic AI framework
• Agentic Framework - AI agents with natural language
• OpenAI Agents SDK - Multi-agent workflows from OpenAI

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🌐 ARW (Agent-Ready Web)

This repository demonstrates the ARW specification through the Media Discovery app.

What is ARW?

ARW provides infrastructure for efficient agent-web interaction:

• 85% token reduction - Machine views vs HTML scraping
• 10x faster discovery - Structured manifests vs crawling
• OAuth-enforced actions - Safe agent transactions
• AI- headers* - Full observability of agent traffic

ARW in Media Discovery

The media-discovery app implements ARW with:

// /.well-known/arw-manifest.json
{
  "version": "0.1",
  "profile": "ARW-1",
  "site": {
    "name": "AI Media Discovery",
    "description": "Discover movies and TV shows through natural language"
  },
  "actions": [
    {
      "id": "semantic_search",
      "endpoint": "/api/search",
      "method": "POST"
    }
  ]
}

See the ARW Specification for full details.

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💻 Development

Prerequisites

• Node.js 18+
• npm or pnpm

Build & Run

# Install dependencies
npm install

# Build the CLI
npm run build

# Run locally
npm start

# Development mode (watch)
npm run dev

# Run linter
npm run lint

MCP Server

# STDIO transport (for Claude Desktop, etc.)
npm run mcp:stdio

# SSE transport (for web integrations)
npm run mcp:sse

Media Discovery App

cd apps/media-discovery
npm install
npm run dev

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🔌 MCP Integration

Add to your Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "agentics-hackathon": {
      "command": "npx",
      "args": ["agentics-hackathon", "mcp"]
    }
  }
}

Or use SSE transport:

npx agentics-hackathon mcp sse --port 3000

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🤝 Contributing

We welcome contributions! Areas of focus:

1. CLI Improvements - New commands, better UX
2. Tool Integrations - Add more AI tools
3. Demo Apps - Build showcases for hackathon tracks
4. ARW Implementation - Expand specification coverage
5. Documentation - Guides and tutorials

Development Workflow

See CLAUDE.md for development guidelines including:

• SPARC methodology for systematic development
• Concurrent execution patterns
• File organization rules

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📜 License

This project is licensed under the Apache License 2.0.

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🔗 Links

• 🌐 Hackathon Website: agentics.org/hackathon
• 💬 Discord: discord.agentics.org
• 📦 GitHub: github.com/agenticsorg/hackathon-tv5
• 📖 ARW Spec: ARW v0.1 Draft

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🚀 Agentics Foundation TV5 Hackathon

Building the Future of Agentic AI - Supported by Google Cloud

Website | Discord | GitHub