awesome generative ai

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Basic Information

This repository is a curated, community-maintained collection of references across the field of Generative AI. It gathers academic papers, open-source code, models, tutorials, courses, demos, tools and directories covering text, image, audio, video and multimodal systems. The README is organized into topical sections (for example LLMs, prompt engineering, image synthesis, Stable Diffusion, agents, RAG, embeddings, datasets, ethics and education) and uses inboxes for broad overviews. Entries are presented as links and short descriptions to help readers navigate rapidly evolving research and tooling. The list is intended to centralize authoritative resources and examples, document emerging subareas, and point to hands-on notebooks, web UIs and APIs. The repository is regularly updated and encourages contributions by pull request so the index remains current and useful to a broad audience.

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Features
Extensive, categorized index of generative-AI resources spanning research papers, implementation repos, web tools, courses, datasets and community lists. Sections are organized by topic (text, image, video, audio, multimodal, agents, LLMOps, evaluation, prompt engineering, RAG and more) and references within sections are ordered with recent items first. The README highlights practical assets such as example notebooks, Web UIs, toolkit and framework listings, prompt libraries, model hubs, MCP and multi-agent resources, and deployment guides for running models locally. It includes curated tool directories, stability/Stable Diffusion guides, recommended courses, ethical and critique resources, and related awesome lists. The collection emphasizes discoverability, reverse-chronological organization, and links to hands-on examples and benchmarks to help users find both introductory and advanced materials.
Use Cases
The list helps researchers, engineers, educators, students, designers and artists discover curated entry points and advanced references across generative AI. Practitioners can find implementation repos, model checkpoints, deployment guides and local inference UIs to prototype or productionize systems. Educators and learners gain curated courses, tutorials and paper collections to structure study. Product builders and data scientists can browse tools for RAG, embeddings, LLM frameworks, LLMOps and evaluation suites to accelerate development. Artists and creators are pointed to image, video and audio synthesis tools, prompt engineering resources and galleries. The README also aggregates ethics, policy and critique material to inform responsible use. Contributors can submit PRs to add new resources and keep the list up to date.

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