Meta

Top Builders

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

Harshith Tata

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.

AuraOS

Aura is a sovereign, neuro-symbolic AI operating system designed to make advanced artificial intelligence more transparent, efficient, collaborative, and accountable. Instead of sending every instruction directly to a language model, Aura can locally interpret natural-language intent, organize it into structured semantic and routing representations, identify relevant code or knowledge, and prepare a bounded task before model use is required. Aura combines deterministic routing, a 4,096-primitive lexical system, polysynthetic six-slot intent structures, visual code topology, guarded human-agent workflows, and a Learning Arena where verified experiences can be tested before becoming reusable knowledge. Its Observatory allows users to see how Aura understood an instruction, while the Human Agent Arena supports node-specific dialogue, approval gates, verification, and preserved attempt artifacts—including failed work that may help future refactoring. Aura is built around human authority. Models can propose, explain, investigate, and assist, but they cannot silently expand scope, bypass evidence requirements, or merge changes without approval. The result is an AI architecture focused not only on capability, but on sovereignty, inspectability, learning, and responsible action.

AI_lifeOS

**AI LifeOS** is an AI-powered all-in-one student platform designed to simplify learning, career planning, and skill development. Many students struggle with using multiple websites and tools for studying, tracking progress, preparing for interviews, building resumes, and choosing the right career path. AI LifeOS solves this problem by bringing everything together in one intelligent platform. The platform uses artificial intelligence to create personalized learning roadmaps based on each student's goals, interests, and current skill level. It provides an AI study assistant that answers questions, explains concepts, recommends learning resources, and helps students stay on track. Students can also explore different career paths, compare opportunities, understand the skills required for each role, and receive a step-by-step plan to achieve their career goals. In addition, AI LifeOS supports project recommendations, resume building, interview preparation, skill assessment, and progress tracking. The platform continuously analyzes student performance and provides personalized suggestions to improve learning outcomes and job readiness. Our goal is to create a smart learning ecosystem where students can learn, practice, build real-world skills, and prepare for successful careers—all from a single platform. By combining education, AI assistance, and career development, AI LifeOS empowers students to make informed decisions, stay motivated, and achieve their full potential.

SOAP Copilot: AI Clinical Scribe on AMD

Physicians spend roughly two hours a day on clinical documentation, time pulled directly away from patient care, and a well-documented driver of burnout that costs the US healthcare system an estimated $8.3B a year in lost productivity. SOAP Copilot is a multi-agent AI system that turns a raw doctor-patient conversation into complete, structured clinical documentation in under 30 seconds. Three specialized agents, all built on Llama 3.3 70B Instruct, handle the pipeline in sequence: a SOAP Generator produces a structured Subjective/Objective/Assessment/Plan note, an ICD Coder extracts diagnosis codes with confidence scores, and a Summary Writer rewrites the note as a plain-language patient summary. The entire stack runs on open-source models, so no PHI ever needs to leave the user's own infrastructure. The project was originally prototyped and LoRA fine-tuned on a single AMD MI300X (192GB HBM3) via AMD Developer Cloud, running vLLM on ROCm 7.2. That memory headroom is what makes serving a 70B-parameter model in BF16 precision practical on one chip, something no consumer GPU and few cloud GPUs can do. For this submission, production inference is served through Fireworks AI, which itself runs on AMD Instinct hardware, so the project demonstrates the AMD AI stack end to end: self-managed fine-tuning through to hosted inference. Roadmap items include a distilled specialist model for lower-cost deployment, retrieval-augmented generation over clinical guidelines using the Qdrant vector database already wired into the architecture, and an on-premise, HIPAA-compliant deployment path for health systems that cannot send patient data to any external API.

SmartBandit Router

BanditRoute treats each AI model as an "arm" in a multi-armed bandit problem. Using Thompson Sampling conditioned on task type (code, math, QA, creative), it learns over time which model to send each query to balancing correctness against token cost and latency via a custom reward function, rather than optimizing for accuracy alone. No routing rule was ever hand-written; behaviors like sending math questions to the strongest model and easy QA to a cheap one emerged entirely from feedback. The project is validated two ways: a full simulation (2000 queries, 4 mock model profiles) that shows clean convergence and ~93% cost savings, and a live integration that runs the same bandit logic against real free-tier models via the OpenRouter API proving the approach works beyond simulation, not just in theory.

Thymus

Thymus is a lightweight hybrid token-efficient router designed to maximize accuracy while minimizing token costs in multi‑task LLM pipelines. It dynamically routes user queries across local and remote models on LLM providers. The general architecture of the router is as follows. 1. Classify: a fast regex/keyword pass handles the obvious cases for free; anything ambiguous falls back to the local model for classification. 2. Infer locally: the local model (Qwen2.5-3B-Instruct, GGUF, CPU) answers the query and self-reports a routing preference and confidence. 3. Verify: a task-aware verifier sanity-checks the answer (JSON structure, code execution, entity coverage, self-consistency, depending on task type). 4. Decide: LOCAL (free, done), SHEPHERD (local answer used as context, one cheap cloud pass to double check), or RESET (send straight to Fireworks).

career launch ai

CareerLaunch AI is a high-performance workspace architect designed to solve technical fragmentation for engineering students. By analyzing individual skill levels and goals, it constructs a complete, structured development environment. Key features: AI Synthesis: Leverages Llama 3 via Fireworks AI to generate personalized 90-day learning roadmaps and productivity workflows. Project Forge: Automatically scaffolds directory layouts and project structures for immediate coding. Hardware Synergy: Maps development requirements to AMD-accelerated hardware profiles, providing specific recommendations for CPU/GPU optimizations. Unified Dashboard: A clean, telemetry-rich UI that integrates all study and project needs into one interactive environment. Built for the AMD Track, it optimizes the development lifecycle from roadmap to deployment.