AI Agents Hackathon 2.0

Welcome to Opening Stream! Join us for sessions and the Hackathon Guide.

Hackathon Kick-Off

The founders at NewNative and lablab.ai will introduce participants to our mission and the opportunities you have as a member of the lablab.ai community.

NewNative Introduction

Let's go through the Technologies for the Hackathon and how you can learn it.

Technology and Challenge

We will guide you through the website, and Discord community - to make sure you will succeed at the Hackathon

Hackathon Guide

Albert Astabatsyan, a no-code veteran and Bubble.io expert, will be joining us for a demo of some of the AI agents created with Fine-Tuner!

No-Code AI Agents with Fine-Tuner

Divyansh Garg, an AI and RL researcher at the world's biggest AI labs and creator of MultiON, is joining the Kick-Off to introduce you to AI agents and inspire you in building your own!

Guide through AI personal agent - MultiOn

After the Kick-Off stream, focus on finding your team for the Hackathon and start brainstorming your idea!

Team Formation time!

Submission Deadline

Egregore: LangChain + Claude for Accelerated Competitive Analysis


Knowledge Harvesting

ltwilson

andresberrios

Egregore: 
An implementation of LangChain and Claude for Accelerated Competitive Analysis
Our Knowledge Harvesting team aimed to create an AI solution for a crucial stage of the product development process: Analyzing competition.
Understanding the competition is vital to decipher a market and assess market attractiveness. Accurate knowledge of the competition helps product and marketing managers communicate product functions, features, and benefits effectively.
We explored techniques to structure information for LangChain and opted to use prompts, testing them in GPT-4 and Claude 2. 
Additionally, we experimented with semantic triples to drive agent behaviors. 
Triples and knowledge graphs are powerful and interpretable ways to structure and curate data.
In a real-world application, we focused on five ultrasound devices, enabling rich comparisons, including regulatory requirements. 
We delved into FDA processes for medical devices. 
Though we didn't fully structure this complex data, we captured the necessary elements to make it AI-digestible.
With a 55-page Integrated Product Management Process as reference, we converted its step-by-step guidance into prompts and agents.
While we encountered some uncompleted tests to assess AI configurations, 
we successfully developed workflows, data structures, and prompts to leverage LangChain's potential. 
Although more work lies ahead, we established a strong foundation for further exploration.
Our team's experience in using AI for a real-world scenario was good! 
In just 3 days, we covered significant ground, demonstrating the tremendous potential of AI to augment human intelligence. 
Our project's ambition affirmed the possibilities of utilizing AI to enhance product management and innovation.


Egregore: An implementation of LangChain and Claude for Accelerated Competitive Analysis

"Knowledge Harvesting team on AI Agents Hackathon 2.0 Hackathon"

Team Idea

Submission