Meta

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

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 Technologies Hackathon projects

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

Archie

AI agents are becoming incredibly capable at reasoning and coding. Yet a hard limit remains. The moment a task requires payment, the agent stops. This is because the internet is built for humans. Payments require credit cards and manual accounts. This makes software dependent on human intervention. Archie represents a shift toward machine native commerce. We believe that autonomous software should be able to spend money per action as easily as it spends compute per token. Archie is an agent that manages its own wallet. It uses Circle Nanopayments to buy premium data in real time. Our system uses the Arc Network to settle sub cent transactions. We use the x402 protocol to bridge the gap between intelligence and value. Every action is recorded on the blockchain as a proof of reputation. This creates a verifiable history of reliability for every agent. This is not just a tool. It is an economic participant. Archie demonstrates that when transaction costs disappear, agents can finally pay their own way. We are building the infrastructure for a world where machines transact directly with one another without human oversight.

InferPay: Pay-Per-Inference for the Agent Economy

InferPay is payment infrastructure for the agent economy on Arc Testnet. It allows AI agents to access LLMs and pay in USDC for each request using x402 and Circle Nanopayments, without API keys, user accounts, or subscription billing. The product is built around a simple idea: if agents are becoming economic actors, they need a native way to buy inference and services on the internet. InferPay provides that flow by combining pay-per-request access, machine-readable payment negotiation, and micropayment settlement into one system. The current version already includes a working x402 proxy, buyer agent flow, playground, Circle Nanopayments integration, and dynamic pricing, routing, and usage visibility. Rather than forcing a premature marketplace, this project focuses first on the core payment primitive that agent-to-agent commerce depends on: an agent can request inference, pay in USDC, and receive a result in one machine-native flow. The goal of this submission is to show that machine-to-machine AI payments are not just a concept, but a working infrastructure layer for agentic applications.

JaaS — Jurisprudence-as-a-Service

JaaS (Jurisprudence-as-a-Service) is an autonomous legal intelligence engine that transforms how jurisprudential knowledge is consumed, computed, and monetized—entirely machine-to-machine. THE PROBLEM: Traditional legal research is slow, expensive, and locked behind rigid SaaS subscriptions that cannot serve the emerging agentic economy. When AI agents need specialized legal knowledge on demand, they face two barriers: (1) no programmatic access to curated jurisprudence, and (2) gas costs on Ethereum L1 ($2.00+) that make micro-transactions economically impossible. THE SOLUTION: JaaS deploys a multi-agent orchestration architecture where Gemini 3 Pro acts as the reasoning engine, routing complex queries through specialized extraction models (Featherless Qwen2.5-3B) via the x402 HTTP Payment Protocol. Every query is settled in USDC on the Arc blockchain for fractions of a cent, enabling a true pay-per-compute model with zero subscriptions and zero counterparty risk. TECHNICAL ARCHITECTURE: - Orchestrator Agent (Gemini 3 Pro): Parses legal queries, establishes reasoning paths, and synthesizes final jurisprudential reports. - Extractor Agent (Featherless Qwen2.5-3B): Performs low-level doctrine extraction and citation mapping via isolated API calls. - Payment Layer (Circle DCW + x402 + Arc): Every agent computation triggers an HTTP 402 nanopayment, settled on-chain via Circle Developer-Controlled Wallets on the Arc Testnet. UNIT ECONOMICS (Validated): - Revenue per query: $0.01 USDC - AI inference cost: $0.0020 USDC - Arc network gas: $0.00002 USDC - Gross margin: 79.8% - On Ethereum L1, the same operation yields -5,000% margin. STRESS TEST: We executed 50+ sequential on-chain legal queries with a 100% success rate, zero failures, and sub-second USDC settlement on every transaction—proving Arc's viability for high-frequency agentic workloads.

Atlas Arc: Decentralized AI Agentic Economy

`Atlas Arc is an Economic OS for AI Agents. In traditional blockchains, high gas fees make micro-transactions for AI inference impossible. Atlas Arc solves this by integrating Circle’s USDC on the Arc L1 network, enabling settlement at a scale of $0.0001 per action. Our system features a hierarchical multi-agent architecture where "Brain" agents use a Random Forest Regression model to predict market demand and price volatility. "Executor" agents handle high-frequency settlements via Circle Developer Controlled Wallets, while "Guardian" agents maintain security using Z-Score statistical analysis to detect anomalies. The project demonstrates a 99.9% reduction in gas overhead compared to legacy L1s, proving that a machine-to-machine economy is not just possible, but economically viable. By combining Google Gemini for analytical reasoning and Circle’s nano payment infrastructure, Atlas Arc creates a transparent, scalable, and autonomous marketplace for the next generation of AI services.`

Atlas Arc: Decentralized AI Agentic Economy

NeuroPay — Pay-Per-Inference AI on Arc

NeuroPay is a live, deployed Pay-Per-Inference AI API competing in the Usage-Based Compute Billing track. It is hosted at https://neuropay-arc.onrender.com and powered end-to-end by Circle's infrastructure on Arc. THE PROBLEM: AI inference requires fair usage-based pricing — but traditional blockchain gas fees destroy the economics. Ethereum gas costs $2.50 per transaction, which is 1,667 times more than a $0.0015 inference charge. Even Polygon at $0.05 gas is 33 times the payment value. Sub-cent AI billing is economically impossible on traditional chains. THE SOLUTION: Circle Nanopayments on Arc eliminates gas overhead entirely. NeuroPay charges exactly $0.001 per 100 tokens in USDC, settled on Arc in under one second with zero gas cost — giving providers 100% margin retention. WHAT WE BUILT: - Real AI inference using Groq's Llama 3.1 model — actual tokens counted, actual billing - Circle Nanopayments integration for gas-free USDC settlement on Arc - Agent-to-agent payment simulation showing autonomous AI economics - Live transaction feed with Arc tx hashes verifiable on the block explorer - Cumulative analytics dashboard tracking USDC volume vs traditional gas costs - Full REST API with /infer, /deposit, /stats, /margin-analysis endpoints - Professional web dashboard showing real-time billing metrics KEY PROOF POINTS: - 55+ on-chain transactions demonstrated at sub-cent pricing - $0.00 gas cost across all transactions via Nanopayments - Traditional gas equivalent saved: $5.50+ across the demo - Margin multiplier: 66x more efficient than Polygon, 1,667x vs Ethereum NeuroPay proves that usage-based AI billing at machine scale is only viable on Arc with Circle Nanopayments — and shows exactly what the agentic economy infrastructure layer looks like.