Top Builders

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

Llama 4

Llama 4 is Meta AI’s newest open-weight model series.
It introduces Mixture-of-Experts (MoE) routing for efficient inference, accepts both text and images natively, and stretches context windows to record-breaking lengths—10 M tokens in the Scout variant. Meta positions Llama 4 as a research-friendly, production-ready alternative to proprietary frontier models, while keeping the code and weights downloadable from its GitHub repos and the official llama.com portal.

General
Release date5 Apr 2025
DeveloperMeta AI
TypeOpen-weight multimodal LLM
LicenseLlama 4 Community License
GitHubmeta-llama/llama-models

Core Features

  • Mixture-of-Experts architecture – Each query activates a subset of specialised “experts,” yielding higher throughput per FLOP while scaling to trillions of total parameters (TechCrunch).
  • Native multimodality – Models ingest both text and images without external adapters (The Verge).
  • Extended context windows – Scout handles up to 10 M tokens; Maverick supports 1 M tokens (llm-stats).
  • Multilingual training – Optimised across 200+ languages for global deployments (Data Scientist Guide).
  • Fine-tunable & agent-ready – Models ship with recipes for supervised fine-tuning, LoRA, and RAG inside the Llama Cookbook.

Model Variants

VariantActive ParamsExpertsTotal ParamsContext WindowBest for
Scout17 B16109 B10 M tokensLong-context RAG, document analysis (stats)
Maverick17 B128400 B1 M tokensCoding & reasoning tasks, general chat (Oracle Docs)
Behemoth288 B*16~2 TTBAHigh-end STEM, under training (not yet released)

Tools & Resources


Ecosystem & Integrations

  • Meta AI assistant now runs Llama 4 across WhatsApp, Messenger, Instagram, and web chat (The Verge).
  • OCI Generative AI offers managed Scout & Maverick endpoints for enterprise workloads (Oracle Docs).
  • Community hosting – Providers such as DeepInfra, Groq, and Together price Llama 4 as low as $0.08 / 1 M input tokens (llm-stats).
  • Research & open-source – Thousands of fine-tuned checkpoints already live on Hugging Face; Meta’s annual LlamaCon (29 Apr 2025) spotlights academic collaborations (TechCrunch).

Llama 4 pushes open-weight LLMs into frontier-model territory—combining trillion-scale capacity with permissive licensing. Start experimenting by cloning the GitHub repo, reading the cookbook, or provisioning a managed endpoint on Oracle OCI.

Meta Llama 4 AI technology Hackathon projects

Discover innovative solutions crafted with Meta Llama 4 AI technology, developed by our community members during our engaging hackathons.

PHRAXIS - Voice to Production Quantum Opt System

PHRAXIS - Voice to Production Quantum Opt System

Every software team knows the pattern: a 10-minute meeting where everyone agrees on a feature, then nothing ships for weeks. PHRAXIS removes that gap entirely. A developer speaks a feature request out loud, and PHRAXIS delivers a production-ready, quantum-optimized GitHub pull request in under 4 minutes. PIPELINE: 1. SPEAK - Developers describe features naturally through browser voice input, with optional text fallback. 2. TRANSCRIBE - IBM Watson Speech-to-Text converts audio into structured text with timestamps and confidence scoring. 3. EXTRACT - IBM Natural Language Understanding identifies developer intent, target modules, parameters, and constraints. Structured output is stored in IBM Cloudant. 4. QUANTUM OPTIMIZE - IBM Quantum QAOA calculates the optimal subset of files to modify before generation begins. Classical evaluation across N candidate files requires 2^N combinations. At enterprise scale (N=50), that exceeds 1 quadrillion possibilities. IBM Quantum evaluates the solution space simultaneously and returns the conflict-free optimal change set, directly guiding IBM Bob’s planning workflow. 5. GENERATE - IBM Bob reads the full repository in Architect mode, receives the quantum-selected files, plans implementation in Plan mode, and writes production-ready code in Code mode that matches existing repository patterns instead of generating generic boilerplate. IBM Granite via watsonx.ai pre-analyzes the codebase, while watsonx Orchestrate coordinates the pipeline. 6. SHIP - Bob Shell executes /review on generated code, and the GitHub API opens a pull request containing the transcript, optimization result, and Bob session IDs. IBM Bob is the execution engine behind PHRAXIS. IBM Quantum determines exactly which files Bob modifies, producing mathematically conflict-free pull requests at enterprise scale.

Agentic Security & Observability Platform

Agentic Security & Observability Platform

Governance.AI is an agentic security and observability platform designed for autonomous AI systems and multi-agent workflows. As AI agents become more integrated into real-world products, most systems still operate with very little visibility, monitoring, or governance. We wanted to build infrastructure that helps developers understand and control how AI systems behave internally. The platform introduces a centralized governance layer between users and AI agents. Instead of acting like a simple chatbot wrapper, Governance.AI continuously monitors workflows, traces execution paths, analyzes risky behavior, and enforces governance policies before actions are executed. Core capabilities include: Risk Detection & Prompt Analysis Policy Enforcement & Access Control Agent Monitoring & Observability Audit Trails & Explainability Red-Team Testing & Unsafe Prompt Detection Real-Time Governance Workflows The platform is built using a modular FastAPI service architecture connected through a scalable API gateway. We integrated LangSmith for tracing and observability, Auth0 for authentication, Neon PostgreSQL for infrastructure data, and a real enterprise-style dashboard with live workflows, analytics, traces, and governance events. Governance.AI can be integrated using: Python SDK REST APIs Gateway-based integrations Developers can test governance services directly from the dashboard, inspect traces, monitor workflows, and integrate Governance.AI into their own AI systems using SDKs or APIs. One important aspect of this project is that we intentionally avoided building a purely mocked prototype. Our focus was building a realistic developer infrastructure platform that could evolve into a production-grade governance layer for future AI ecosystems. We believe governance, trust, and observability will become foundational infrastructure for autonomous AI systems, similar to how monitoring and security became essential for cloud computing.

SRE-Healer: Autonomous Remediation Agent

SRE-Healer: Autonomous Remediation Agent

In today's fast-paced software development environments, engineers and Site Reliability Engineers (SREs) face a massive productivity bottleneck: alert fatigue and repetitive infrastructure debugging. When a system crashes or experiences performance degradation, developers are forced to stop building new features and instead spend hours digging through logs to find the root cause. Enter SRE-Healer, a next-generation autonomous remediation agent designed to turn ideas into impact faster by eliminating manual debugging. SRE-Healer operates on a Closed-Loop Autonomous Remediation paradigm: it continuously observes system metrics, reasons through the root causes using AI, executes safety-first fixes, and verifies the system's recovery—all without human intervention. By handling repetitive infrastructure issues in the background, SRE-Healer buys back valuable time for developers. Building a complex, multi-layered AI agent architecture usually takes weeks of rigorous coding. However, we brought SRE-Healer to life in record time by partnering with the IBM Bob IDE. Bob served as our core development partner throughout the hackathon. We utilized Bob's Plan and Code modes to architect the system logic, generate the FastAPI event gateway, and write the core diagnostic scripts. By establishing an AGENTS.md knowledge base via the /init command, Bob understood our exact project intent and safely generated boilerplate code. Furthermore, we integrated IBM Bob Shell as the core "reasoning engine" of SRE-Healer to analyze error logs and orchestrate remediation actions securely. SRE-Healer proves that with IBM Bob as a development partner, builders can quickly construct sophisticated autonomous systems that drastically reduce Mean Time to Remediation (MTTR) and empower teams to deliver software with greater efficiency and confidence.