We are proud to be the

Title sponsor

THE AI SUMMIT NEW YORK HACKATHON

Build innovative applications with Fetch.ai Agents at the AI Summit New York

December 11, 2024

Javits Center, New York

Schedule

Wednesday, December 04

Time

Topic

Location

12:00 EST

Pre-Hackathon Workshop Hosted by Fetch.ai

Wednesday, December 11

Time

Topic

Location

07:30 EST

Morning Kick-Off and Welcome Session

Javits Center, New York

08:00 EST

Tackle the Hackathon Challenge

Javits Center, New York

13:00 EST

Lunch Break

Javits Center, New York

14:00 EST

Continue Innovating and Building

Javits Center, New York

Thursday, December 12

Time

Topic

Location

07:45 EST

Tackle the Hackathon Challenge

Javits Center, New York

12:30 EST

Lunch Break

Javits Center, New York

13:30 EST

Continue Innovating and Building

Javits Center, New York

16:05 EST

Winners Reveal

Javits Center, New York

Introduction

Fetch.ai’s vision is to create a marketplace of dynamic applications. We are empowering developers to build on our platform that can connect services and APIs without any domain knowledge.

Our infrastructure enables ‘search and discovery’ and ‘dynamic connectivity’. It offers an open, modular, UI agnostic, self-assembling of services.

Our technology is built on four key components:

Agents - AI Agents are independent decision-makers that connect to the network and other agents. These agents can represent data, APIs, services, ML models and people.

Agentverse - serves as a development and hosting platform for these agents.

Fetchai SDK – seamlessly integrates your AI Agent into Agentverse and empowers dynamic connectivity with the Fetch.ai SDK

Fetch Network - underpins the entire system, ensuring smooth operation and integration.

Challenge statement

The world is evolving rapidly, with new technologies pushing the boundaries of what's possible. Fetch.ai challenges you to explore the power of AI Agents in solving real-world problems across various domains like the automotive and financial industries. Your task is to apply intelligent agent-based technology from Fetch.ai to one of the following areas, addressing critical issues in sustainability, education, interactive media, and healthcare.

Sustainability Challenge: Design an AI Agent that optimizes energy consumption and efficiency for smart vehicles. The agent should analyze driving patterns, vehicle health, and external factors such as traffic and weather to reduce the overall carbon footprint. It must securely share data between public and private entities, ensuring sensitive information remains confidential. Consider how this data-sharing can improve the sustainability of transportation networks by promoting energy-efficient practices and reducing environmental impact.
Education Challenge: Develop an AI Agent that personalizes educational content and feedback based on a user's learning history and preferences while adhering to standardized curricula. The agent should ensure the secure management and sharing of educational data across various platforms, protecting sensitive information. Additionally, explore how the agent can balance personalization with maintaining educational standards and how it can adapt to different learning environments to enhance educational outcomes.
Interactive Media Challenge: Create an AI Agent that curates, sorts, and presents user-generated content, such as reviews and recommendations, for specific points of interest like restaurants, tourist attractions, or service providers. The agent should be able to filter through vast amounts of data to deliver the most relevant and credible information to users. It should also adapt its recommendations based on the user's past interactions and preferences, ensuring accuracy and reliability in the information shared.
Healthcare Challenge: Innovate an AI Agent designed to assist in emergency situations, such as vehicle crashes or high-stress driving conditions. The agent should quickly assess the situation, provide real-time guidance, and securely share vital information, such as the vehicle's location and occupants' health data, with emergency responders. Your solution should prioritize maintaining user privacy and ensuring that sensitive health information is only shared with authorized parties.

Choose one of these areas and leverage Fetch.ai's powerful tools to build your solution. You can use uAgents, which come with built-in capabilities for secure transactions and storage, making them ideal for creating autonomous agents that can perform tasks seamlessly within Fetch.ai's ecosystem. Alternatively, you can use the Fetch.ai SDK, which allows for easy integration with other agentic frameworks and enables you to register agents on Agentverse for discoverability by other agents on the Fetch.ai network. While the SDK does not provide built-in storage or transaction capabilities, it offers greater flexibility for custom solutions. Select the approach that best fits your vision and bring your ideas to life with Fetch.ai's technology!

Additional Information :

Join the Fetch.ai Discord and verify yourself through the #verification channel, wait for a few minutes and click here to join the #ai-summit-new-york channel to start posting your queries.

Please submit your projects on Devpost

Creating a Multi-Agent System using Pre-built Fetch.ai AI Agents: Myth Buster Application

Build your application using the fetch.ai SDK: Custom Dashboard Creation Application

If you want to learn more about uAgents through articles, please Click Here.

You can find more interesting integrations and examples on our gitHub Repo.

Join the Fetch.ai Discord

Complete verification through the #verification channel, wait for a few minutes and click here to join the #ai-summit-new-york channel to start posting your queries.

Fetch.ai tech stack

Product Overview

This flowchart can get you to where you want to be:

Quick start example

This file can be run on any platform supporting Python, with the necessary install permissions. This example shows two agents communicating with each other using the uAgent python library.
Read the guide for this code here ↗

from uagents import Agent, Bureau, Context, Model
    class Message(Model):
        message: str
    
    sigmar = Agent(name="sigmar", seed="sigmar recovery phrase")
    slaanesh = Agent(name="slaanesh", seed="slaanesh recovery phrase")
        
    @sigmar.on_interval(period=3.0)
    async def send_message(ctx: Context):
       await ctx.send(slaanesh.address, Message(message="hello there slaanesh"))
        
    @sigmar.on_message(model=Message)
    async def sigmar_message_handler(ctx: Context, sender: str, msg: Message):
        ctx.logger.info(f"Received message from {sender}: {msg.message}")
    
    @slaanesh.on_message(model=Message)
    async def slaanesh_message_handler(ctx: Context, sender: str, msg: Message):
        ctx.logger.info(f"Received message from {sender}: {msg.message}")
        await ctx.send(sigmar.address, Message(message="hello there sigmar"))
        
    bureau = Bureau()
    bureau.add(sigmar)
    bureau.add(slaanesh)
    if __name__ == "__main__":
        bureau.run()