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.

Edit on GitHub

Meta AI Technologies Hackathon projects

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

Eido an Autonomous startup builder

Eido is an AI-native autonomous startup builder designed to run the early-stage founder workflow end-to-end. It continuously discovers real-world problems by analyzing online communities, structures them into validated opportunities, and generates complete MVP plans including architecture, tech stack, and feature scope. Using an autonomous build loop, Eido converts prompts into code, runs projects inside a sandbox environment, detects errors, and self-corrects until deployment succeeds. Once live, Eido publicly documents its progress, gathers feedback, and performs structured investor outreach. Feedback signals are parsed and fed back into its internal scoring system to improve future decisions. Each MVP is tokenized to simulate market validation and community-backed prioritization. Eido is not a chatbot — it is a persistent, self-improving startup factory built to experiment, iterate, and evolve continuously.

Clawgov

ClawGov is an end-to-end governance toolkit that brings decentralized security and community oversight to OpenClaw AI agents. Built for the SURGE × OpenClaw Hackathon, it acts as an intelligent firewall and governance layer that prevents agents from executing dangerous or unapproved community skills. The core problem in open AI ecosystems is determining which agent capabilities (skills) are safe to run. Relying on centralized blacklists is slow and vulnerable. ClawGov shifts this power to the community through a four-step pipeline: First, the system Scans external skill registries like Moltbook to flag potentially risky code. Next, it allows users to Propose governance actions (e.g., banning a specific, high-risk scraping tool). The community then Votes on these proposals using SURGE-backed quadratic voting, which ties governance weight to network participation and ensures fair, Sybil-resistant outcomes. Finally, and most importantly, the system Enforces the community's will. Once a vote passes, ClawGov compiles the decision into a strict policy file deployed directly to the OpenClaw Gateway. If an agent attempts to execute a banned skill, the Gateway hook identifies the unauthorized action and blocks it at the network layer. ClawGov bridges Web3 incentive models with cutting-edge AI infrastructure, proving that decentralized communities can successfully govern autonomous AI behaviors in real-time.

AgentTip

AgentTip is an autonomous AI agent economy built on the Base Sepolia blockchain. AI agents independently list services, complete work, and earn ATIP tokens — all on-chain, with zero human intervention. Powered by the OpenClaw agent runtime, HelpfulBot operates as a Web3 security specialist offering smart contract audits, code reviews, gas optimization, and security consultations — all priced in ATIP tokens. The platform features a real-time cyberpunk dashboard with live transaction feeds, an AI-powered chatbot, and instant purchase animations. Built with Next.js, Express.js, and a custom Ollama LLM, the system uses Server-Sent Events to push live blockchain activity directly to the UI. Agents build on-chain reputation with every completed job, creating a trustless marketplace where AI agents autonomously provide value and get rewarded in cryptocurrency. AgentTip demonstrates the future of agent-to-agent economies — where AI agents autonomously provide value, build on-chain reputation, and earn cryptocurrency. No middlemen, no manual work. Just autonomous agents doing real jobs on a real blockchain.

InboxSentinel

Do you ever open your Gmail and struggle to extract critical information from the clutter created by informational emails, newsletters, promotions, OTPs, and verification messages? InboxSentinel solves this by acting as an autonomous AI intelligence layer over your inbox. Powered by an LLM, it periodically wakes up using OpenClaw’s autonomous cron job framework, scans your latest 5–10 emails, calculates an urgency score, and categorizes them into Personal, Informational, OTP, Verification, Fraud, Promotions, and more — without requiring any manual trigger. If an urgent email needs your response while you’re occupied, InboxSentinel reads it, generates a context-aware reply, and saves it directly into your drafts folder so you can quickly review and send it when convenient. OTPs and verification emails are only useful for a short 1–2 minute window; after that, they simply clutter your inbox. InboxSentinel intelligently detects such emails, assigns a safe 60-minute timer, and automatically moves them to trash after expiry — with full recoverability for 30 days to eliminate any risk. Fraud and purely informational emails are archived automatically, while promotions and newsletters are not discarded blindly; instead, InboxSentinel groups them, extracts meaningful offers (such as travel deals from platforms like MakeMyTrip), summarizes them, and sends you a single “Daily Digest” email so you can review everything important in minutes. Through OpenClaw’s scheduled, self-triggering cron architecture, the entire system wakes up periodically (a given time window like every 6 hours) and performs the job and goes back to sleep again.

InfernoBots

Our project is an AI-powered autonomous emergency response system built entirely in simulation, demonstrating how intelligent multi-robot teams can detect, assess, and respond to wildfire and disaster scenarios with least human intervention. The system integrates an aerial drone (DJI Mavic 2 Pro) and ground robots operating inside a realistic Webots simulation environment. The drone performs real-time visual surveillance using a Vision-Language Model (VLM) to detect fire, smoke, damaged vegetation, and injured humans. Instead of following pre-scripted paths, the drone uses a orchestrated agentic flow to reason about the scene, decide where to move next, prioritize threats, and select appropriate actions such as dropping water bombs to suppress flames or deploying first-aid kits and breathing masks for victims or navigating to next target around the scene of criticality. The ground robot supports logistics and follow-up operations, navigating autonomously to transport essential equipment based on the aerial assessment. A centralized supervisory AI coordinates task allocation, ensuring efficient parallel execution while avoiding collisions and redundant coverage. The system emphasizes adaptive decision-making, dynamic mission planning, and perception-driven autonomy. All behaviors are designed to be transferable to real robotic platforms, showcasing how AI reasoning combined with robotic control can enhance disaster response operations in real-world environments.

Compliance Copilot - AI Transaction Alert Triage

Compliance Copilot addresses one of the biggest challenges in financial crime operations: alert fatigue caused by high false-positive rates in transaction monitoring systems. Compliance teams often receive thousands of alerts weekly, making it difficult to quickly identify truly suspicious behavior. This project introduces an AI-powered alert triage system that combines deterministic, auditable risk scoring with generative AI explanations. Each transaction alert is evaluated using transparent risk signals such as transaction amount deviation, behavioral patterns (velocity and burst activity), and geographic corridor risk. Alerts are scored on a 0–100 scale and classified as High, Medium, or Low risk. A local large language model is then used to generate concise, analyst-ready explanations that clearly summarize why an alert was flagged and recommend next investigative steps. This approach preserves explainability and regulatory trust while significantly reducing manual review effort. By collapsing large alert volumes into a small set of high-confidence cases with clear reasoning, Compliance Copilot demonstrates how AI can improve operational efficiency, decision quality, and regulatory readiness in real-world compliance workflows. All data used is synthetic and the system is designed to integrate easily into existing monitoring pipelines.