Meta AI technology page Top Builders

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

Meta

Meta, founded in 2004, is a global technology leader that revolutionizes how people connect and interact in the digital world. Originally known as Facebook, Meta is renowned for its pioneering advancements in social media, with platforms like Facebook, Instagram, and WhatsApp, which collectively reach billions of users worldwide. In addition to its social media prowess, Meta is a global technology company at the forefront of AI innovation, focusing on enhancing human connectivity and creating immersive digital experiences. Among its leading products related to AI technology are the LLaMA (Large Language Model Meta AI) series and Meta AI.

General
Company	Meta Platforms, Inc.
Founded	January 4, 2004
Headquarters	Menlo Park, California, U.S.
Repository	https://github.com/facebook

Key Products and Research

Meta has developed a range of AI products designed to enhance various aspects of technology and user experience. Here’s a brief overview of these AI products:

LLaMA (Large Language Model Meta AI)

LLaMA is a series of large language models designed for natural language processing tasks. These models, including the latest LLaMA 3.1, are known for their advanced capabilities in text generation, understanding, and multilingual processing. They are available as open-source models, promoting innovation and research in AI Meta | Social Metaverse Company,Facebook.

Meta AI

Meta AI is an intelligent assistant integrated across Meta’s platforms, such as Facebook, Instagram, WhatsApp, and Messenger. Powered by LLaMA models, it helps users with tasks like content creation, information retrieval, and personalized interactions Meta | Social Metaverse Company.

PyTorch

PyTorch is an open-source machine learning library developed by Meta and widely used in both research and industry. It provides tools for building and training deep learning models and has become a standard framework in the AI community Facebook.

Meta AI Research (FAIR)

Meta’s AI research division, formerly known as FAIR (Facebook AI Research), focuses on advancing the field of AI through open research and collaboration. This division works on various AI challenges, including computer vision, natural language processing, and generative AI Facebook.

Meta AI in the Metaverse

Meta is also incorporating AI into its metaverse initiatives, using AI to create immersive experiences in virtual and augmented reality. This includes developing AI-driven avatars, enhancing virtual environments, and improving interaction within the metaverse Meta | Social Metaverse Company.

AI for Ads

Meta leverages AI to optimize ad targeting, delivery, and measurement across its platforms. AI algorithms analyze vast amounts of data to improve the effectiveness of advertising campaigns, making them more relevant to users and efficient for advertisers Meta | Social Metaverse Company.

LLaMA Impact Grants

The LLaMA Impact Grants program, launched by Meta, aims to support and encourage the innovative use of its LLaMA (Large Language Model Meta AI) models to address critical challenges in various sectors, including education, environmental sustainability, and public good. This initiative offers financial grants and resources to researchers, nonprofits, and other organizations that seek to leverage LLaMA models for impactful projects. The program highlights Meta’s commitment to responsible AI development and its belief in the potential of AI to drive positive social change.

For more details, visit the LLaMA Impact Grants page.

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Meta AI technology page Hackathon projects

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

AWEBO AI

Our AI-powered Product Manager simplifies task execution by automating end-to-end workflows. Users input tasks through natural conversation, and the agent takes over, managing execution and ensuring alignment with objectives. At critical decision points, human involvement is seamlessly integrated to guarantee accuracy and adaptability. Ideal for hackathons, this tool eliminates repetitive management tasks, accelerates project completion, and enhances productivity. Without reliance on specific channels, it focuses on delivering value through streamlined operations and clear progress tracking. Perfect for teams needing quick, efficient, and reliable task management solutions in fast-paced, innovation-driven environments

Grantserve

Grantserve is transforming environmental grant funding with an intelligent AI system that learns from thousands of successful and unsuccessful grant proposals. Powered by WatsonX Assistant, our platform generates preliminary grant drafts and guides organizations through the entire application process via an intuitive chatbot interface. This dramatically simplifies the traditionally complex grant writing process, helping environmental nonprofits, farmers, and land trusts—who manage 80% of conservation projects but receive only 3.25% of federal grants—secure vital funding. By reducing the typical $60,000+ annual cost of grant writing to an accessible $150-200 per user subscription, we're democratizing access to the $8-10 billion in annual federal and state grants while building a foundation for streamlined carbon credit validation.

iHelp

iHelp is a fast, intuitive, and easy-to-use platform designed to help businesses quickly create and deploy AI-powered customer support assistants. By leveraging IBM Watson's cutting-edge conversational AI, iHelp seamlessly transforms your website into a smart, automated assistant that can efficiently handle customer inquiries, provide support, and automate routine tasks. With iHelp, businesses can generate a fully functional assistant in just a few simple steps, eliminating the need for complex coding or technical expertise. Whether you're looking to automate FAQs, assist customers with order tracking, or provide detailed product information, iHelp empowers your assistant to engage with customers 24/7, ensuring that your business is always accessible. This not only reduces the reliance on human agents but also significantly improves customer service efficiency and response times. Designed for businesses of all sizes, iHelp streamlines support operations, enhances customer satisfaction, and enables a seamless user experience—all while saving time and costs. From small businesses looking to automate basic support to larger enterprises needing a robust, scalable solution, iHelp is the simplest and most effective way to launch a Watson-powered assistant and enhance your customer experience in just minutes.

Synth Dev

## Problem 1. AI coding assistants (Copilot, Cursor, Aider.chat) accelerate software development. 2. People typically code not by reading documentation but by asking Llama, ChatGPT, Claude, or other LLMs. 3. LLMs struggle to understand documentation as it requires reasoning. 4. New projects or updated documentation often get overshadowed by legacy code. ## Solution - To help LLMs comprehend new documentation, we need to generate a large number of usage examples. ## How we do it 1. Download the documentation from the URL and clean it by removing menus, headers, footers, tables of contents, and other boilerplate. 2. Analyze the documentation to extract main ideas, tools, use cases, and target audiences. 3. Brainstorm relevant use cases. 4. Refine each use case. 5. Conduct a human review of the code. 6. Organize the validated use cases into a dataset or RAG system. ## Tools we used https://github.com/kirilligum/synth-dev - **Restack**: To run, debug, log, and restart all steps of the pipeline. - **TogetherAI**: For LLM API and example usage. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py - **Llama**: We used Llama 3.2 3b, breaking the pipeline into smaller steps to leverage a more cost-effective model. Scientific research shows that creating more data with smaller models is more efficient than using larger models. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py - **LlamaIndex**: For LLM calls, prototyping, initial web crawling, and RAG. See: https://github.com/kirilligum/synth-dev/blob/main/streamlit_fastapi_togetherai_llama/src/functions/function.py

Heurist Multiverse Decision-Making Platform

Big decisions, whether business, social or personal, often lack clarity regarding their implications. "Heurist AI: Multiverse Decision-Making Platform" is a revolutionary AI-based tool that simulates "What if?" for critical decisions, providing predictive insights and interactive narratives. Our solution allows impact analysis in multiple contexts, adjusts variables in real time and offers iterative feedback. With the potential to transform businesses, educate individuals and empower governments, Heurist AI connects decision making with advanced data science and conversational AI technology. Target audience includes companies, professionals and educators looking for evidence-based decisions.

Rag Custom Pipiline Generator

Creating a custom RAG (Retrieve and Generate) pipeline without any coding is now accessible to users and beginners through intuitive no-code platforms. Start by selecting a user-friendly tool like Microsoft Power Automate, Zapier, or specialized AI platforms such as LangChain’s no-code solutions. These platforms offer drag-and-drop interfaces where you can easily configure data sources for retrieval, such as databases, APIs, or cloud storage. Next, integrate AI models for the generation phase by selecting from pre-built templates or connecting to services like OpenAI. Customize the workflow by setting parameters, defining triggers, and establishing how retrieved information is used to generate responses. Additionally, leverage built-in analytics and testing features to refine your pipeline. No-code RAG pipelines empower users to efficiently build robust, tailored solutions for tasks like customer support, content creation, and data analysis without needing programming expertise. This approach accelerates deployment, reduces errors, and allows for easy adjustments, making advanced AI capabilities accessible to everyone.