Autonomous Agents Hackathon

SuperAGI is hosting Twitter space before the Hackathon, having an open discussion on AI Agents with AI experts from SuperAGI, Weaviate, Llamaindex, and lablab.ai

Join the discussion on Twitter on @_superAGI page!

Twitter Space Talk!

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

Introduction to SuperAGI: Exploring AI Agents

AI Agents Explained: A Talk by Logan Kilpatrick (from OpenAI)

Vector Databases & AI Agents: A Talk by Etienne Dilocker, Weaviate

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

Weaviate Workshop: 45-Minute Session with Mentors on Discord

AWS Workshop with Goutam Kurumella

Open Q&A with mentors

Live Q&A: Meet the Auto-GPT Team

Join us for an insightful Q&A session with Vasek Mlejnsky of E2B and the expert mentors from lablab.ai!

Book Your Slot with Industry Experts! Collaborate with Mentors from Arize, Lablab.ai, LlamaIndex, SuperAGI, and Weaviate to Finalize Your Concepts and Ideas!

Mentorship Sessions

Arize Workshop on Discord: Troubleshooting Retrieval Augmented Generation

Q&A Session with GPT Engineer Anton Osika

Submission Deadline

Finnalists Pitching and Winners Announcament Stream

Fake bot agents that mimics a given user profile and interacts with a e-commerce website

Botify

Jin Suk Park

drcdev-gh

Early e-commerce shops suffer from low level of user activity to collect user behavioral data to train their search/recommendation models, gain data driven insights of customers. By deploying chatgpt agents, prompt-engineered with a profile that describes a imaginary user, early start-ups can collect user logs from multiple fake agents exploring their website. In our demo, the "agents" is where users can create a fake user giving rough information of an imaginary user with age, location, interests and description. Upon clicking the new task, it dispatches the fake user to interact with a shopping mall (in our demo we used amazon), and the agent with search, click recommended items, ponder around reviews just like the ordinary user would do when coming to a conclusion to buy a product. The "logs" page is a list of behavior logs that is crucial in training search/recommendation models, as it can become a train dataset to find relationship of query-to-product (search) and product-to-product (recommendation). Additionally, data scientists can analyze the logs to gain insights from user behaviors. Our project Botify is dedicated to solve the chicken-and-egg problem in e-commerce, where they first need user data to sophisticate their algorithm for better user experience. While the demo is yet preliminary proof of concept, we hope to see much more development when properly used with advanced prompt engineering techniques

Early e-commerce shops suffer from low level of user activity to collect user behavioral data to train their search/recommendation models, gain insights of customers. Botify let's early builders to deploy fake user agent to solve this problem.

"Botify team on Autonomous Agents Hackathon Hackathon"

Team Idea

Submission