FlagAI
FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model.
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License tag · curated repositories and deep analysis.
FlagAI (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model.
Import Map Package Manager
OWASP Coraza WAF is a golang modsecurity compatible web application firewall library
Copybara: A tool for transforming and moving code between repositories.
VILA is a family of state-of-the-art vision language models (VLMs) for diverse multimodal AI tasks across the edge, data center, and cloud.
MeEdu 是一款面向个人、中小机构的在线网校、知识付费、线上培训解决方案。
Compile Time Regular Expression in C++
A lightweight game engine written in modern C++
The Ballerina Programming Language
A proxy server for adding push to your API, used at the core of Fastly's Fanout service
Control what your AI can see. LeanCTX (Lean Context) is the context intelligence layer for AI agents, one local Rust binary that decides what they read, remembers what they learn, guards what they touch, and proves what they save. 60 90% fewer tokens as the receipt. 76 MCP tools, 30+ agents, local-first.
Open-source framework for the research and development of foundation models. Marin's primary use case is training language model like Llama, DeepSeek, Qwen, etc.
TurboDiffusion: 100–200× Acceleration for Video Diffusion Models
A collection of JSON schema files including full API. Visual Studio Code Create .vscode/mcp.json in your workspace: Visual Studio Add a .mcp.json file to your solution root: Any MCP Client Point your client at using HTTP transport.
A Java architecture test library, to specify and assert architecture rules in plain Java
Free and open source application for reading manga, novels, and watching animes available on Android, iOS, macOS, Linux and Windows
helm chart repository server
✔ Free and serverless uptime monitoring / status page on Cloudflare Workers, with Geo-specific checks
Maestro: Netflix’s Workflow Orchestrator
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM