We are proud to be the

Title sponsor

WHACK 2024

Warwick's largest hackathon fosters creativity and collaboration to build innovative solutions.

October 26, 2024

Oculus Lecture Theatre, University of Warwick

Schedule

Tuesday, October 22

Time

Topic

Location

12:00 BST

Intro to Fetch.ai Tech Stack: Learn how to best utilise Fetch.ai to Build Solutions

Saturday, October 26

Time

Topic

Location

09:00 BST

Arrivals

Oculus Lecture Theatre

10:00 BST

Opening Ceremony

Oculus Lecture Theatre

11:00 BST

Hacking Begins!

Junction Hall

12:00 BST

Lunch

Junction Hall

14:20 BST

Learn about Fetch.ai : Interactive Session

JX2.03

17:00 BST

Speed Networking event

JX2.03

Sunday, October 27

Time

Topic

Location

09:00 BST

Breakfast

Junction Hall

11:00 BST

Submissions Close

Junction Hall

12:00 BST

Lunch

Junction Hall

13:00 BST

Presentations

Oculus Lecture Theatre

15:30 BST

Closing Ceremony

Oculus Lecture Theatre

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:

uAgents - uAgents are autonomous AI agents built to connect seamlessly with networks and other agents. They can represent and interact with data, APIs, services, machine learning models, and individuals, enabling intelligent and dynamic decision-making in decentralized environments.

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.

ASI-1 Mini - A Web3-native large language model (LLM) optimized for agent-based workflows.

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. Your task is to apply intelligent agent-based technology from Fetch.ai to one of the following areas, addressing critical issues in automobile, sustainability, education and interactive media.

SafeDrive 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.
Sustainability in the Auto Industry Challenge: 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.

Choose one of the areas above to utilize your AI Agents creatively. Consider how your AI Agent can create a huge impact in any one of the problem areas. The agent should be able to securely handle sensitive, assist in decision-making processes, and adapt to the evolving needs of a growing business. Additionally, think about how your AI Agent can contribute to the success of startups and scale-ups by providing innovative tools and insights that drive growth and efficiency.

Note: It's a must to include uAgents in your applications.

Additional Information : You can post all your queries in the #innovation-labs channel on Discord.

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

Submission Link: Devpost

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

Pre-built Fetch.ai Agents list

Feel free to integrate the Pre-built Fetch.ai Agents into your applications.

Fetch.ai tech stack

Product Overview

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()