Generative Agents AI technology page Top Builders

Explore the top contributors showcasing the highest number of Generative Agents AI technology page app submissions within our community.

Generative Agents

Generative Agents are computer programs designed to replicate human actions and responses within interactive software. To create believable individual and group behavior, they utilize memory, reflection, and planning in combination. These agents have the ability to recall past experiences, make inferences about themselves and others, and devise strategies based on their surroundings. They have a wide range of applications, including creating immersive environments, rehearsing interpersonal communication, and prototyping. In a simulated world resembling The Sims, automated agents can interact, build relationships, and collaborate on group tasks while users watch and intervene as necessary.

General
Relese dateApril 7, 2023
TypeAutonomous Agent Simulation

Start building with Generative Agents

We have collected the best Generative Agents libraries and resources to help you get started to build with Generative Agents today. To see what others are building with Generative Agents, check out the community built Generative Agents Use Cases and Applications.

Generative Agents Tutorials

    👉 Discover more Generative Agents Tutorials on lablab.ai


    Generative Agents resources

    Kickstart your development with Generative Agents.


    Generative Agents AI technology page Hackathon projects

    Discover innovative solutions crafted with Generative Agents AI technology page, developed by our community members during our engaging hackathons.

    Super Human AI

    Super Human AI

    The job application process can be daunting and time-consuming, especially in today's dynamic job market. With advancements in AI and the proliferation of job platforms, there is a growing need for a solution that streamlines and automates the job application process. Inspired by this challenge, we introduce Super Human AI, an advanced AI agent designed to revolutionize job application procedures. Super Human AI is an ultimate hyper-personalized AI agent that help to increase and automate the process of reaching companies. Problem statement: Job seekers, experienced or freshers, encounter significant obstacles in reaching out to companies and securing employment opportunities. Despite possessing relevant education and skills, many struggle to connect with a sufficient number of potential employers. The fragmentation of job listings across various platforms exacerbates this issue, highlighting the need for a platform-agnostic solution to automate and optimize the job application process. Solution: Super Human AI is an AI Agent to simplify and optimize the job application process. Here are the core components: 1. Selenium Browser Automation Integration: Selenium is utilized to automate web browsers, enabling Super Human AI to navigate job platforms, search for relevant positions, and fill out application forms seamlessly. 2. Advanced RAG-based Job Application Filler: The AI agent incorporates a sophisticated algorithm based on Red, Amber, and Green (RAG) indicators to prioritize job listings and customize application responses. This ensures that job seekers focus their efforts on opportunities that align closely with their qualifications and preferences. 3. Email Automation Integration: Super Human AI integrates email automation capabilities to facilitate communication with employers. Automated email responses are sent to confirm application submissions, follow up on application status, and schedule interviews, streamlining the entire job application process.

    Stock AI

    Stock AI

    With just a name of the stock, Buy or sell option and risk strategy, The platform provides you graph analysis(short, medium and long term), News analysis , sentiment and fundamentals analysis and lastly using all the information processed, an advisory strategy for the future. The platform fetches the real time stock data and analyses short, medium and long term graph data, and performs a visual analysis on it using Gemini Pro Vision model. We have GPT 3.5 32k model to analyze the news fetched from news API in the context of the stock. The graph analysis, news analysis, Fundamentals, user's strategy are all provided to a fine tuned LLM to provide recommendations on strategies for the user. With the evolution we plan to use an RAG with time series, where each time stamp with 5 days, 1 month and a year data is vectorized with the current value equal to zero and the rest time stamps value normalized. this helps the model to predict future time stamp by taking considerations into previous similar chart movements of k examples. This helps us to track the chart patterns without explicitly defining it. In summary we used RAG to semantically search k similar Graph patterns of a similar time frame and averaging the k graphs to predict the future chart movements. Feautures : 1. The models could be easily swapped. From GPT to Gemini in 5 mins 2. With fine tuning each agent the performance of the overall platform would rise exponentially. 3. Vectors created for the time series could be more advanced by including the Volume dimension as well as the news sentiment dimension