[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-filipecalegario--awesome-generative-ai":3,"tool-filipecalegario--awesome-generative-ai":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",150037,2,"2026-04-10T23:33:47",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":72,"owner_website":79,"owner_url":80,"languages":81,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":86,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":92,"github_topics":93,"view_count":10,"oss_zip_url":81,"oss_zip_packed_at":81,"status":17,"created_at":112,"updated_at":113,"faqs":114,"releases":115},2408,"filipecalegario\u002Fawesome-generative-ai","awesome-generative-ai","A curated list of Generative AI tools, works, models, and references","awesome-generative-ai 是一份精心整理的生成式 AI 资源清单，旨在为快速变化的领域提供一站式导航。面对海量且分散的模型、工具、论文及应用案例，用户往往难以高效筛选有价值的内容，而这份清单通过系统化的分类，将从零基础的科普定义、伦理探讨，到进阶的代码开发、大语言模型（LLM）框架、提示词工程，再到图像合成、音视频处理及多模态应用等资源有序聚合。\n\n无论是希望快速上手的普通用户、寻求灵感的设计师，还是深耕技术的开发者与研究人员，都能在此找到契合需求的入口。其独特亮点在于不仅收录了主流的在线工具和开源项目，还涵盖了本地部署方案、智能体（Agents）、RAG 检索增强生成以及 LLMOps 等前沿工程实践，甚至包含了对技术批判性思考的深度内容。作为一份动态更新的“地图”，awesome-generative-ai 帮助用户打破信息壁垒，轻松探索生成式 AI 的无限可能，是进入该领域不可或缺的参考指南。","# Awesome Generative AI [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fawesome.re)[![Track Awesome List](https:\u002F\u002Fwww.trackawesomelist.com\u002Fbadge.svg)](https:\u002F\u002Fwww.trackawesomelist.com\u002Ffilipecalegario\u002Fawesome-generative-deep-art\u002F) \u003C!-- omit in toc -->\n\n> A curated list of Generative AI projects, tools, artworks, and models\n\n- [Generative AI Area](#generative-ai-area)\n  - [Generative AI history, timelines, maps, and definitions](#generative-ai-history-timelines-maps-and-definitions)\n  - [Ethics, Philosophical questions and Discussions about Generative AI](#ethics-philosophical-questions-and-discussions-about-generative-ai)\n  - [Critical Views about Generative AI](#critical-views-about-generative-ai)\n  - [Generative AI Processes and Artifacts](#generative-ai-processes-and-artifacts)\n  - [Generative AI Tools Directories](#generative-ai-tools-directories)\n  - [Courses and Educational Materials](#courses-and-educational-materials)\n  - [Human-AI Interaction](#human-ai-interaction)\n  - [Papers Collection](#papers-collection)\n  - [Online Tools and Applications](#online-tools-and-applications)\n- [Code and Programming](#code-and-programming)\n  - [Vibe Coding](#vibe-coding)\n  - [AI-Powered Code Generation](#ai-powered-code-generation)\n- [Text](#text)\n  - [Everything to Markdown to LLMs](#everything-to-markdown-to-llms)\n  - [Small Language Models](#small-language-models)\n  - [Large Language Models (LLMs)](#large-language-models-llms)\n    - [Model Context Protocol](#model-context-protocol)\n    - [Programming Frameworks for LLMs](#programming-frameworks-for-llms)\n    - [Prompt Engineering](#prompt-engineering)\n      - [Prompt Optimizers](#prompt-optimizers)\n      - [Prompt Engineering for Text-to-text](#prompt-engineering-for-text-to-text)\n      - [Prompt Engineering for Text-to-image](#prompt-engineering-for-text-to-image)\n    - [Mamba](#mamba)\n    - [Running LLMs Locally](#running-llms-locally)\n    - [Function Calling](#function-calling)\n    - [GPTs and Assistant API](#gpts-and-assistant-api)\n    - [Retrieval-Augmented Generation (RAG)](#retrieval-augmented-generation-rag)\n    - [Embeddings and Semantic Search](#embeddings-and-semantic-search)\n    - [Autonomous LLM Agents](#autonomous-llm-agents)\n      - [Multi-agents](#multi-agents)\n    - [LLM Evaluation](#llm-evaluation)\n    - [LLMOps](#llmops)\n    - [AI Engineering](#ai-engineering)\n    - [Attacks on LLMs](#attacks-on-llms)\n    - [LangChain](#langchain)\n    - [ChatGPT](#chatgpt)\n    - [Text-related Generative Tools](#text-related-generative-tools)\n  - [Research AI Tools](#research-ai-tools)\n    - [AI Tools for Research](#ai-tools-for-research)\n    - [AI Tools for Searching](#ai-tools-for-searching)\n- [Image](#image)\n  - [Image Synthesis](#image-synthesis)\n    - [Inbox: Stable Diffusion](#inbox-stable-diffusion)\n      - [Stable Diffusion Deployed Web Tools](#stable-diffusion-deployed-web-tools)\n      - [Web UI for Stable Diffusion via Google Colab](#web-ui-for-stable-diffusion-via-google-colab)\n      - [References Collection about Stable Diffusion](#references-collection-about-stable-diffusion)\n    - [Hypertechniques](#hypertechniques)\n      - [ControlNet](#controlnet)\n      - [Textual Inversion](#textual-inversion)\n      - [DreamBooth](#dreambooth)\n      - [Deforum](#deforum)\n    - [Creative Uses of Generative AI Image Synthesis Tools](#creative-uses-of-generative-ai-image-synthesis-tools)\n  - [Image Upscaling](#image-upscaling)\n  - [Image Restoration](#image-restoration)\n  - [Image Segmentation](#image-segmentation)\n- [Video and Animation](#video-and-animation)\n- [Audio and Music](#audio-and-music)\n- [Speech](#speech)\n  - [Text-to-speech (TTS) and avatars](#text-to-speech-tts-and-avatars)\n    - [Podcast generators](#podcast-generators)\n  - [Speech-to-text (STT) and spoken content analysis](#speech-to-text-stt-and-spoken-content-analysis)\n- [Games](#games)\n- [Multimodal](#multimodal)\n  - [Multimodal Embedding Space](#multimodal-embedding-space)\n- [Datasets](#datasets)\n- [Misc](#misc)\n  - [AI and Education](#ai-and-education)\n  - [People and works](#people-and-works)\n    - [Interesting Twitter Accounts](#interesting-twitter-accounts)\n    - [Interesting Instagram Accounts, Posts and Reels](#interesting-instagram-accounts-posts-and-reels)\n    - [Interesting Youtube Channels](#interesting-youtube-channels)\n    - [Interesting GitHub Repositories](#interesting-github-repositories)\n    - [Artists and Artworks](#artists-and-artworks)\n    - [Galleries](#galleries)\n  - [Related Awesome Lists](#related-awesome-lists)\n  - [Bio experiments](#bio-experiments)\n  - [Jobs in Generative AI](#jobs-in-generative-ai)\n  - [Improving Google Colab experience](#improving-google-colab-experience)\n  - [Auxiliary tools and concepts](#auxiliary-tools-and-concepts)\n    - [Dimension reduction techniques](#dimension-reduction-techniques)\n  - [Roadmaps, Tracks, Rails](#roadmaps-tracks-rails)\n  - [Stargazers over time](#stargazers-over-time)\n  - [Contribute](#contribute)\n  - [License](#license)\n\n## Repository Introduction\n\nWelcome to our Awesome List of Generative AI resources! This repository is a curated collection of references in the dynamic field of Generative AI, equipped with various sources such as academic papers, technical articles, online courses, tutorials, and software.\n\n### Structure\n\n1. **Sections**: Each section represents a different Generative AI-related category (e.g., LLMs, prompt engineering, image synthesis, educational resources, etc.). The Inboxes are the more general references of a category. When a new category emerges, it becomes a specific subsection.\n\n2. **References within sections**: Inside each section, references are listed in reverse chronological order, with the most recent one at the top. This order signifies the ever-evolving landscape of Generative AI, keeping you up-to-date with the latest developments.\n\nThis repository is designed to offer you the most recent advancements at your fingertips, allowing you to explore the depth of older resources at your own pace. It's regularly updated, ensuring you're always on track with the rapidly progressing world of Generative AI.\n\n### Contribute to our Repository\n\nYour contributions are welcome and greatly appreciated! If you have a valuable resource that you believe should be on this list, or if you see any outdated information, please make a Pull Request. This will help us maintain the quality and relevance of our Awesome List.\n\nFollow this roadmap, keep learning, and enjoy your journey through Generative AI!\n\n# Generative AI Area\n\n## Generative AI history, timelines, maps, and definitions\n\n* [AI Timeline](https:\u002F\u002Fnhlocal.github.io\u002FAiTimeline\u002F)\n* [Agents Marketplace](https:\u002F\u002Fmarketplace.agen.cy\u002Fagents)\n* [🔥] [2024 AI Timeline](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Freach-vb\u002F2024-ai-timeline): a Hugging Face Space by reach-vb\n* [Cartography of generative AI](https:\u002F\u002Fcartography-of-generative-ai.net\u002F): \"What set of extractions, agencies, and resources allow us to converse online with a text-generating tool or to obtain images in a matter of seconds?\"\n* [The Rise of Generative AI Large Language Models (LLMs)](https:\u002F\u002Finformationisbeautiful.net\u002Fvisualizations\u002Fthe-rise-of-generative-ai-large-language-models-llms-like-chatgpt\u002F): interactive timeline visualization made by Information Is Beautiful \n* [The AI Timeline (@TheAITimeline) \u002F X](https:\u002F\u002Fx.com\u002FTheAITimeline)\n* [Generative AI for Beginners: Part 1 — Introduction to AI | by Raja Gupta | Medium](https:\u002F\u002Fmedium.com\u002F@raja.gupta20\u002Fgenerative-ai-for-beginners-part-1-introduction-to-ai-eadb5a71f07d) \n* [Artificial Intelligence Learning Roadmap [AI Roadmap] 2024](https:\u002F\u002Fwww.mltut.com\u002Fartificial-intelligence-learning-roadmap\u002F)\n* [A Brief History of Generative AI - DATAVERSITY](https:\u002F\u002Fwww.dataversity.net\u002Fa-brief-history-of-generative-ai\u002F) \n* [A Simple Guide To The History Of Generative AI | Bernard Marr](https:\u002F\u002Fbernardmarr.com\u002Fa-simple-guide-to-the-history-of-generative-ai\u002F) \n* [Generative AI Timeline from January 2023 to July 2023](https:\u002F\u002Fgenerativeaitimeline.com\u002F) \n* [The rise of generative AI: A timeline of triumphs, hiccups and hype | CIO Dive](https:\u002F\u002Fwww.ciodive.com\u002Fnews\u002Fgenerative-ai-one-year-chatgpt-openai-timeline\u002F698110\u002F) \n* [Brief History In Time: Decoding the Evolution of Generative AI | LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fbrief-history-time-decoding-evolution-generative-ai-csmtechnologies\u002F) \n* [🔥🔥🔥] [FirstMark | 2024 MAD (ML\u002FAI\u002FData) Landscape](https:\u002F\u002Fmad.firstmark.com\u002F): Full Steam Ahead The 2024 MAD (Machine Learning, AI & Data) Landscape\n* [Timeline of AI forecasts - AI Digest](https:\u002F\u002Ftheaidigest.org\u002Ftimeline)\n* [Generative AI Iceberg](https:\u002F\u002Ficebergcharts.com\u002Fi\u002FGenerative_AI) \n* [🔥🔥🔥] [Generative AI in a nutshell](https:\u002F\u002Fblog.crisp.se\u002Fwp-content\u002Fuploads\u002F2024\u002F01\u002Fgenerative-AI-in-a-nutshell.png): a map with the most common Generative AI' concepts by Henrik Kniberg [Youtube Video explaining the map](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2IK3DFHRFfw) \n* [60+ Generative AI Terms You Must Know By Heart](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2024\u002F01\u002Fgenerative-ai-terms\u002F): by Analytics Vidhya\n* [The Four Wars of the AI Stack (Dec 2023 Recap)](https:\u002F\u002Fwww.latent.space\u002Fp\u002Fdec-2023): \"recap of top items for the AI Engineer from Dec 2023\" (\"The Data Wars, The War of the GPU Rich\u002FPoor, The Multimodality War, The RAG\u002FOps War\")\n* [GenAI Prism Infographic by Brian Solis](https:\u002F\u002Fbriansolis.com\u002F2023\u002F12\u002Fintroducing-the-genai-prism-infographic-a-framework-for-colalborating-with-generative-ai\u002F): A Framework for Collaborating with Generative AI\n* [LLM Visualization](https:\u002F\u002Fbbycroft.net\u002Fllm)\n* [[2310.04438] A Brief History of Prompt: Leveraging Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04438): the paper presents an exploration of the evolution of prompt engineering. The author, Golam Md Muktadir, extensively used ChatGPT for content generation\n* [An AI Engineer’s Guide to Machine Learning and Generative AI | by ai geek (wishesh) | Oct, 2023 | Medium](https:\u002F\u002Fmedium.com\u002F@_aigeek\u002Fan-ai-engineers-guide-to-machine-learning-and-generative-ai-b7444941ccee) \n* [Emerging Trends in Generative AI Research: A Selection of Recent Papers](https:\u002F\u002Ftxt.cohere.com\u002Ftop-nlp-papers-september-2023\u002F) \n* [The architecture of today's LLM applications - The GitHub Blog](https:\u002F\u002Fgithub.blog\u002F2023-10-30-the-architecture-of-todays-llm-applications\u002F) \n* [🔥🔥🔥] [[2310.07127] An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07127): \"a survey of 154 papers, providing a novel taxonomy and analysis of Human-GenAI Interactions from both human and Gen-AI perspectives\".\n* [The Building Blocks of Generative AI | by Jonathan Shriftman | Medium](https:\u002F\u002Fshriftman.medium.com\u002Fthe-building-blocks-of-generative-ai-a75350466a2f) \n* [🔥] [Generative AI exists because of the transformer](https:\u002F\u002Fig.ft.com\u002Fgenerative-ai\u002F): a visual story by Financial Times\n* [Early days of AI - by Elad Gil](https:\u002F\u002Fblog.eladgil.com\u002Fp\u002Fearly-days-of-ai): thoughts about AI as \"an entirely new era and discontinuity from the past\"\n* [The Next Token of Progress: 4 Unlocks on the Generative AI Horizon | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F23\u002Fthe-next-token-of-progress-4-unlocks-on-the-generative-ai-horizon\u002F) \n* [[2309.07930] Generative AI](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.07930): discusses a model-, system-, and application-level view on generative AI.\n* [The state of AI in 2023: Generative AI’s breakout year | McKinsey](https:\u002F\u002Fwww.mckinsey.com\u002Fcapabilities\u002Fquantumblack\u002Four-insights\u002Fthe-state-of-ai-in-2023-generative-ais-breakout-year#\u002F) \n* [A jargon-free explanation of how AI large language models work | Ars Technica](https:\u002F\u002Farstechnica.com\u002Fscience\u002F2023\u002F07\u002Fa-jargon-free-explanation-of-how-ai-large-language-models-work\u002F) \n* [The Generative AI Revolution: Exploring the Current Landscape | by Towards AI Editorial Team | Jun, 2023 | Towards AI](https:\u002F\u002Fpub.towardsai.net\u002Fthe-generative-ai-revolution-exploring-the-current-landscape-4b89998fcc5f)\n* [The Story of AI Winters and What it Teaches Us Today](https:\u002F\u002Fwww.turingpost.com\u002Fp\u002Faiwinters)\n* [There Would Have Been No LLMs Without This (episode#3 in the History series)](https:\u002F\u002Fwww.turingpost.com\u002Fp\u002Fllmshistory3): timeline of LLMs by Turing Post\n* [The Next Token of Progress: 4 Unlocks on the Generative AI Horizon | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F23\u002Fthe-next-token-of-progress-4-unlocks-on-the-generative-ai-horizon\u002F): critical innovations on the horizon: steering, memory, ability to use tools, and multimodality\n* [The economic potential of generative AI: The next productivity frontier](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fgenai-works_gen-ai-activity-7074980736268726272-LiiG): report by McKinsey Jun 2023\n* [A survey of Generative AI Applications | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02781): \"this survey aims to serve as a valuable resource for researchers and practitioners to navigate the rapidly expanding landscape of generative AI\"\n* [Paper Digest - ChatGPT](https:\u002F\u002Fwww.paperdigest.org\u002F2023\u002F01\u002Frecent-papers-on-chatgpt\u002F): Recent Papers on ChatGPT\n* [AI Index Report 2023 – Artificial Intelligence Index](https:\u002F\u002Faiindex.stanford.edu\u002Freport\u002F): report that measures trends in AI written by the Human-Centered Artificial Intelligence from Stanford University\n* [A Survey of Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.18223): paper that summarizes the evolution of language models, with a focus on LLMs, discussing their advances, techniques, and impact on AI development and usage\n* [The Generative AI Timeline](https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:7044233450295316480): post in Linkedin by David Foster\n* [Who Owns the Generative AI Platform? | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F01\u002F19\u002Fwho-owns-the-generative-ai-platform\u002F): this article discusses the generative AI market and presents an interesting technology stack of the area\n* [A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.04226)\n* [🔥🔥] [Toward General Design Principles for Generative AI Applications](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.05578): this paper presents a set of seven principles for the design of generative AI applications\n* [🔥] [The landscape of generative AI landscape reports | by Ramsri Goutham | Jan, 2023 | Medium](https:\u002F\u002Framsrigoutham.medium.com\u002Fthe-landscape-of-generative-ai-landscape-reports-615a417b15d): a meta report on the reports published by 9 venture capital firms\n* [Generative AI with Cohere: Part 1 - Model Prompting](https:\u002F\u002Ftxt.cohere.ai\u002Fgenerative-ai-part-1\u002F): overview of Generative AI by Cohere AI\n* [Generative AI with Cohere: Part 2 - Use Case Ideation](https:\u002F\u002Ftxt.cohere.ai\u002Fgenerative-ai-part-2\u002F): a list of Generative AI use cases by Cohere AI\n* [Large Language Models and Where to Use Them: Part 1](https:\u002F\u002Ftxt.cohere.ai\u002Fllm-use-cases\u002F): a list of LLM use cases by Cohere AI\n* [Large Language Models and Where to Use Them: Part 2](https:\u002F\u002Ftxt.cohere.ai\u002Fllm-use-cases-p2\u002F)\n* [What's the big deal with Generative AI? Is it the future or the present?](https:\u002F\u002Ftxt.cohere.ai\u002Fgenerative-ai-future-or-present\u002F): summarization of the area of Generative AI by Cohere AI\n* [Timeline of AI and language models](https:\u002F\u002Flifearchitect.ai\u002Ftimeline\u002F): LLM timeline organized by Dr Alan D. Thompson from Life Architect\n* [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09419)\n* [A Review of Generative AI from Historical Perspectives](https:\u002F\u002Fwww.techrxiv.org\u002Farticles\u002Fpreprint\u002FA_Review_of_Generative_AI_from_Historical_Perspectives\u002F22097942): paper by Dipankar Dasgupta, Deepak Venugopal and Kishor Datta Gupta\n* [Matt Shumer on Twitter: \"The definitive AI market map Twitter thread\"](https:\u002F\u002Ftwitter.com\u002Fmattshumer_\u002Fstatus\u002F1620465468229451776): \"The definitive AI market map Twitter thread\"\n* [🔥] [Base11 Research - generative-ai](https:\u002F\u002Fbase10.vc\u002Fresearch\u002Fgenerative-ai): report about Generative AI produced by the investment firm Base10\n* [Engines of Wow: AI Art Comes of Age – Steve Murch](https:\u002F\u002Fwww.stevemurch.com\u002Fengines-of-wow-ai-art-comes-of-age\u002F2022\u002F12)\n* [AI exploded on the scene at the end of 2022 \u002F Twitter](https:\u002F\u002Ftwitter.com\u002FRamaswmySridhar\u002Fstatus\u002F1613271413020037120): categories for analyzing tools of Generative AI\n* [🔥🔥🔥] [Mapping the Generative AI landscape | Antler](https:\u002F\u002Fwww.antler.co\u002Fblog\u002Fgenerative-ai) \n* [🔥🔥🔥] [AI Timeline](https:\u002F\u002Fwww.fabianmosele.com\u002Fai-timeline): A history of text-to-image ML models by Fabian Mosele\n* [AI-Generated Art](https:\u002F\u002Fwww.v7labs.com\u002Fblog\u002Fai-generated-art): From Text to Images & Beyond Examples\n* [1 week of Stable Diffusion | multimodal.art](https:\u002F\u002Fmultimodal.art\u002Fnews\u002F1-week-of-stable-diffusion)\n\n## Ethics, Philosophical questions and Discussions about Generative AI\n\n* [🔭 The Einstein AI model](https:\u002F\u002Fthomwolf.io\u002Fblog\u002Fscientific-ai.html)\n* [Machines of Loving Grace - How AI Could Transform the World for the Better by Dario Amodei](https:\u002F\u002Fdarioamodei.com\u002Fmachines-of-loving-grace)\n* [The Five Stages Of AI Grief - NOEMA](https:\u002F\u002Fwww.noemamag.com\u002Fthe-five-stages-of-ai-grief\u002F) \n* [Generative AI Ethics: 8 Biggest Concerns and Risks](https:\u002F\u002Fwww.techtarget.com\u002Fsearchenterpriseai\u002Ftip\u002FGenerative-AI-ethics-8-biggest-concerns) \n* [Automated Social Science: Language Models as Scientist and Subjects | NBER](https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw32381) \n* [It’s time to retire the term “user”](https:\u002F\u002Fwww.technologyreview.com\u002F2024\u002F04\u002F19\u002F1090872\u002Fai-users-people-terms\u002F): the proliferation of AI means we need a new word\n* [Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks | Scientific Reports](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-024-54294-4) \n* [Tracking AI](https:\u002F\u002Ftrackingai.org\u002F): Monitoring Bias in Artificial Intelligence Chatbots\n* [Will AI’s Next Wave of Super Intelligence Replace Human Ingenuity? It’s Complicated - Grit Daily News](https:\u002F\u002Fgritdaily.com\u002Fwill-ais-super-intelligence-replace-human-ingenuity\u002F) \n* [Who is Afraid of Frankenstein? And of Generative AI? | Fast Company Brasil](https:\u002F\u002Ffastcompanybrasil.com\u002Ftech\u002Finteligencia-artificial\u002Fquem-tem-medo-do-frankenstein-e-da-ia-generativa\u002F) [PT-BR]\n* [Hito Steyerl, Mean Images, NLR 140\u002F141, March–June 2023](https:\u002F\u002Fnewleftreview.org\u002Fissues\u002Fii140\u002Farticles\u002Fhito-steyerl-mean-images) \n* [The copyright conundrum of AI art - The Verge](https:\u002F\u002Fwww.theverge.com\u002F23961021\u002Fai-art-copyright-training-ownership-fair-use) \n* [Recommendations for the advancement of artificial intelligence in Brazil – ABC](https:\u002F\u002Fwww.abc.org.br\u002Fevento\u002Fdoc-ia-no-brasil\u002F) [PT-BR]\n* [We must stop AI replicating the problems of surveillance capitalism](https:\u002F\u002Fwww.ft.com\u002Fcontent\u002Fd9063c16-a4d2-4580-b8f6-a4872083d0fa) \n* [Artificial Intelligence at the Service of Collective Intelligence](https:\u002F\u002Fintlekt.io\u002F2023\u002F10\u002F29\u002Fartificial-intelligence-at-the-service-of-collective-intelligence\u002F) \n* [New Training Method Helps AI Generalize like People Do - Scientific American](https:\u002F\u002Fwww.scientificamerican.com\u002Farticle\u002Fnew-training-method-helps-ai-generalize-like-people-do\u002F) \n* [[2310.01405] Representation Engineering: A Top-Down Approach to AI Transparency](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01405): \"an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience\"\n* [Generative AI Resources for Berkeley Law Faculty & Staff - Berkeley Law](https:\u002F\u002Fwww.law.berkeley.edu\u002Flibrary\u002Flegal-research\u002Fchatgpt\u002F)\n* [Licensing is neither feasible nor effective for addressing AI risks](https:\u002F\u002Fwww.aisnakeoil.com\u002Fp\u002Flicensing-is-neither-feasible-nor) \n* [Generative AI companies must publish transparency reports](https:\u002F\u002Fwww.aisnakeoil.com\u002Fp\u002Fgenerative-ai-companies-must-publish) \n* [Does ChatGPT have a liberal bias?](https:\u002F\u002Fwww.aisnakeoil.com\u002Fp\u002Fdoes-chatgpt-have-a-liberal-bias) \n* [More human than human: measuring ChatGPT political bias | Public Choice](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11127-023-01097-2) \n* [Redefining Bias: The Human Prejudice Against AI | Medium](https:\u002F\u002Fjohnnosta.medium.com\u002Fredefining-bias-the-human-prejudice-against-ai-a1f225b0b2c2) \n* [AI Art and its Impact on Artists](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3600211.3604681): paper published in the Proceedings of the 2023 AAAI\u002FACM Conference on AI, Ethics, and Society\n* [The Age of AI has begun | Bill Gates](https:\u002F\u002Fwww.gatesnotes.com\u002FThe-Age-of-AI-Has-Begun) \n* [The AIKEA Effect](https:\u002F\u002Fpiszek.com\u002F2023\u002F08\u002F28\u002Faikea-effect\u002F): by Artur Piszek\n* [Ethics of Artificial Intelligence: Case Studies and Options for Addressing Ethical Challenges | SpringerLink](https:\u002F\u002Flink.springer.com\u002Fbook\u002F10.1007\u002F978-3-031-17040-9) \n* [Embracing change and resetting expectations | Microsoft Unlocked](https:\u002F\u002Funlocked.microsoft.com\u002Fai-anthology\u002Fterence-tao\u002F): text by Terence Tao\n* [Art and the science of generative AI | Science](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.adh4451) \n* [Where AI evolves from here](https:\u002F\u002Fwww.axios.com\u002F2023\u002F05\u002F18\u002Fai-agi-artificial-general-intelligence) \n* [The Age of AI has begun](https:\u002F\u002Fwww.gatesnotes.com\u002FThe-Age-of-AI-Has-Begun): notes by Bill Gates\n* [GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.10130): OpenAI's paper that discusses the possible implications of GPTs on the U.S. labor market \n* [Why generative AI scares artists but not content writers](https:\u002F\u002Fwww.fastcompany.com\u002F90848228\u002Fwhy-generative-ai-scares-artists-but-not-writers)\n* [Cultures in AI\u002FAI in Culture](https:\u002F\u002Fai-cultures.github.io\u002F): NeurIPS 2022 Workshop webpage\n* [AI Data Laundering - Waxy.org](https:\u002F\u002Fwaxy.org\u002F2022\u002F09\u002Fai-data-laundering-how-academic-and-nonprofit-researchers-shield-tech-companies-from-accountability\u002F): How Academic and Nonprofit Researchers Shield Tech Companies from Accountability\n* [🔥🔥🔥] [(1232) The End of Art: An Argument Against Image AIs - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tjSxFAGP9Ss&t=193s): video essay by Steven Zapata\n* [🔥🔥🔥] [The End of Art: An Argument Against Image AIs (Public) - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F128yey0VfYhM9eUdvkvCpk5zvvoIkqXfI4hEPAYeJCHU\u002Fedit): transcript of the video essay by Steven Zapata\n* [🔥🔥🔥] [Generative AI: A Creative New World | Sequoia Capital US\u002FEurope](https:\u002F\u002Fwww.sequoiacap.com\u002Farticle\u002Fgenerative-ai-a-creative-new-world\u002F): report by Sequoia Capital about the possible applications of Generative AI\n* [Synthetic Creativity - by Cavin - Deep Markets](https:\u002F\u002Fdeepmarkets.substack.com\u002Fp\u002Fsynthetic-creativity)\n* [Our Vision for the Future of Synthetic Media | by Victor Riparbelli | Medium](https:\u002F\u002Fvriparbelli.medium.com\u002Four-vision-for-the-future-of-synthetic-media-8791059e8f3a)\n* [Deep Else](https:\u002F\u002Fdejangrba.github.io\u002Fdeep-else\u002F): A Critical Framework for AI Art\n* [How Photography Became An Art Form | Aaron Hertzmann’s blog](https:\u002F\u002Faaronhertzmann.com\u002F2022\u002F08\u002F29\u002Fphotography-history.html)\n* [Can Computers Create Art? by Aaron Hertzmann](https:\u002F\u002Fwww.mdpi.com\u002F2076-0752\u002F7\u002F2\u002F18): 2018's essay published on the Arts Journal\n* [Text Is the Universal Interface - Scale](https:\u002F\u002Fscale.com\u002Fblog\u002Ftext-universal-interface) \n* [This artist is dominating AI-generated art. And he’s not happy about it. | MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002F2022\u002F09\u002F16\u002F1059598\u002Fthis-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it\u002F)\n* [The REAL fight over AI art: StableDiffusion | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgu2uo\u002Fthe_real_fight_over_ai_art\u002F)\n* [Rutkowski battling AI art overlord | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgv0dw\u002Frutkowski_battling_ai_art_overlord\u002F)\n* [Instead of mining cryptocoins with GPUs, are we now mining art? | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxg8s8e\u002Finstead_of_mining_cryptocoins_with_gpus_are_we\u002F) \n* [Using AI to create art is NOT art! | Reddit : ArtistLounge](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FArtistLounge\u002Fcomments\u002Fxczk89\u002Fusing_ai_to_create_art_is_not_art\u002F) \n* [Appreciating the Poetic Misunderstandings of A.I. Art | The New Yorker](https:\u002F\u002Fwww.newyorker.com\u002Fculture\u002Finfinite-scroll\u002Fappreciating-the-poetic-misunderstandings-of-ai-art?s=09)\n\n## Critical Views about Generative AI\n\n* [The case against AI-generated users - IDEO](https:\u002F\u002Fwww.ideo.com\u002Fjournal\u002Fthe-case-against-ai-generated-users)\n* [Why handing over total control to AI agents would be a huge mistake | MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002F2025\u002F03\u002F24\u002F1113647\u002Fwhy-handing-over-total-control-to-ai-agents-would-be-a-huge-mistake)\n* [Collection of \"The Most Thoughtful Writing about Generative AI\" by Eryk Salvaggio](https:\u002F\u002Fbsky.app\u002Fprofile\u002Feryk.bsky.social\u002Fpost\u002F3lccavgstkk2s)\n* [AI Snake Oil: Separating Hype from Reality | TechPolicy.Press](https:\u002F\u002Fwww.techpolicy.press\u002Fai-snake-oil-separating-hype-from-reality\u002F) \n* [Deconstructing the AI Myth: Fallacies and Harms of Algorithmification](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F382802495_Deconstructing_the_AI_Myth_Fallacies_and_Harms_of_Algorithmification) \n* [Challenging The Myths of Generative AI | TechPolicy.Press](https:\u002F\u002Fwww.techpolicy.press\u002Fchallenging-the-myths-of-generative-ai\u002F) \n* [I am tired of AI | On Test Automation](https:\u002F\u002Fwww.ontestautomation.com\u002Fi-am-tired-of-ai\u002F) \n* [Critique of Generative AI Can Harm Learning Study Design  by Steffi Tan, Vaikunthan Rajaratnam :: SSRN](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4898213)\n* [Generative AI Can Harm Learning by Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, Rei Mariman :: SSRN](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4895486) \n* [I Taught for Most of My Career. I Quit Because of ChatGPT | TIME](https:\u002F\u002Ftime.com\u002F7026050\u002Fchatgpt-quit-teaching-ai-essay\u002F) \n* [AI Risks that Could Lead to Catastrophe | CAIS](https:\u002F\u002Fwww.safe.ai\u002Fai-risk) \n* [The AI Risk Repository](https:\u002F\u002Fairisk.mit.edu\u002F)\n* [[2406.17864] AI Risk Categorization Decoded (AIR 2024)](https:\u002F\u002Fwww.arxiv.org\u002Fabs\u002F2406.17864): From Government Regulations to Corporate Policies\n* [\"AI for Good\" Campaigns Are the Wrong Approach - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fai-for-good) \n* [Generative AI is not the panacea we’ve been promised | Eric Siegel for Big Think+ - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=B2zCWJBnfuE) \n* [Thoughts on GenAI by James Gosling](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fthoughts-genai-james-gosling-nab0c\u002F) \n* [Automated Social Science: Language Models as Scientist and Subjects | NBER](https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw32381) \n* [When Will the GenAI Bubble Burst? - by Gary Marcus](https:\u002F\u002Fgarymarcus.substack.com\u002Fp\u002Fwhen-will-the-genai-bubble-burst) \n* [Nightshade, the tool that ‘poisons’ data, gives artists a fighting chance against AI | TechCrunch](https:\u002F\u002Ftechcrunch.com\u002F2024\u002F01\u002F26\u002Fnightshade-the-tool-that-poisons-data-gives-artists-a-fighting-chance-against-ai\u002F)\n* [How AI Fails Us | Edmond & Lily Safra Center for Ethics](https:\u002F\u002Fethics.harvard.edu\u002Fhow-ai-fails-us) \n* [Generative AI Has a Visual Plagiarism Problem - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fmidjourney-copyright): \"Experiments with Midjourney and DALL-E 3 show a copyright minefield\"\n* [[2308.03762] GPT-4 Can't Reason](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03762): \"despite the genuinely impressive improvement, there are good reasons to be highly skeptical of GPT-4's ability to reason\"\n* [Risk and Harm: Unpacking Ideologies in the AI Discourse | Proceedings of the 5th International Conference on Conversational User Interfaces](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3571884.3603751) \n* [[2305.18654] Faith and Fate: Limits of Transformers on Compositionality](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18654) \n* [[2210.02667] A Human Rights-Based Approach to Responsible AI](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02667)\n* [On the Dangers of Stochastic Parrots | Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3442188.3445922) \n* [This new data poisoning tool lets artists fight back against generative AI | MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002F2023\u002F10\u002F23\u002F1082189\u002Fdata-poisoning-artists-fight-generative-ai\u002F) \n* [The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market by Xiang Hui, Oren Reshef, Luofeng Zhou :: SSRN](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4527336) \n* [AI in Education Group Meeting Notes - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1PPHwa3KmoeRZwaoxjOS568aF2E-kUngOA2oI45G2Iaw\u002Fedit)\n* [Syllabi Policies for AI Generative Tools - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1RMVwzjc1o0Mi8Blw_-JUTcXv02b2WRH86vw7mi16W3U\u002Fedit#heading=h.1cykjn2vg2wx) \n* [Five takeaways from UK’s AI safety summit at Bletchley Park | Artificial intelligence (AI) | The Guardian](https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002F2023\u002Fnov\u002F02\u002Ffive-takeaways-uk-ai-safety-summit-bletchley-park-rishi-sunak) \n* [Frontier AI: capabilities and risks – discussion paper - GOV.UK](https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fpublications\u002Ffrontier-ai-capabilities-and-risks-discussion-paper) \n* [AI Safety Summit Policy Updates | AISS 2023](https:\u002F\u002Fwww.aisafetysummit.gov.uk\u002Fpolicy-updates\u002F#company-policies) \n* [Responsible enterprise decisions with knowledge-enriched generative AI | Deloitte Netherlands](https:\u002F\u002Fwww.deloitte.com\u002Fnl\u002Fen\u002Fservices\u002Frisk-advisory\u002Fperspectives\u002Fresponsible-enterprise-decisions-knowledge-enriched-ai.html)\n* [[2310.13149] Understanding Generative AI in Art: An Interview Study with Artists on G-AI from an HCI Perspective](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.13149) \n* [[2309.12338] Artificial Intelligence and Aesthetic Judgment](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.12338): \"as generative AI influences contemporary aesthetic judgment we outline some of the pitfalls and traps in attempting to scrutinize what AI generated media means\"\n* [AI Worship | Marginal REVOLUTION](https:\u002F\u002Fmarginalrevolution.com\u002Fmarginalrevolution\u002F2023\u002F10\u002Fai-worship.html) \n* [Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water | AP News](https:\u002F\u002Fapnews.com\u002Farticle\u002Fchatgpt-gpt4-iowa-ai-water-consumption-microsoft-f551fde98083d17a7e8d904f8be822c4) \n* [ChatGPT is fun, but not an author | Science](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.adg7879) \n* [Behind the AI boom, an army of overseas workers in ‘digital sweatshops’ | The Washington Post](https:\u002F\u002Fwww.washingtonpost.com\u002Fworld\u002F2023\u002F08\u002F28\u002Fscale-ai-remotasks-philippines-artificial-intelligence\u002F): Scale AI’s Remotasks workers in the Philippines cry foul over low pay\n* [It’s Not Intelligent If It Always Halts: A Critical Perspective on Current Approaches to AGI | Life Is Computation](https:\u002F\u002Fwww.lifeiscomputation.com\u002Fit-is-not-intelligent-if-it-always-halts\u002F) \n* [The human costs of the AI boom | TechCrunch](https:\u002F\u002Ftechcrunch.com\u002F2023\u002F08\u002F21\u002Fthe-human-costs-of-the-ai-boom\u002F) \n* [AI Scams, Spam, Hacking, Are Ruining the Internet](https:\u002F\u002Fwww.businessinsider.com\u002Fai-scam-spam-hacking-ruining-internet-chatgpt-privacy-misinformation-2023-8) \n* [The ChatGPT revolution is another tech fantasy](https:\u002F\u002Fwww.disconnect.blog\u002Fp\u002Fthe-chatgpt-revolution-is-another) \n* [Why AI Will Save the World | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F06\u002Fai-will-save-the-world\u002F) \n* [Hollywood studios proposed AI contract that would give them likeness rights ‘for the rest of eternity’ - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2023\u002F7\u002F13\u002F23794224\u002Fsag-aftra-actors-strike-ai-image-rights) \n* [The shady world of Brave selling copyrighted data for AI training](https:\u002F\u002Fstackdiary.com\u002Fbrave-selling-copyrighted-data-for-ai-training\u002F) \n* [Inside the AI Factory: the humans that make tech seem human - The Verge](https:\u002F\u002Fwww.theverge.com\u002Ffeatures\u002F23764584\u002Fai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots?s=08)\n* [Why transformative artificial intelligence is really, really hard to achieve](https:\u002F\u002Fthegradient.pub\u002Fwhy-transformative-artificial-intelligence-is-really-really-hard-to-achieve\u002F) \n* [AI and the automation of work — Benedict Evans](https:\u002F\u002Fwww.ben-evans.com\u002Fbenedictevans\u002F2023\u002F7\u002F2\u002Fworking-with-ai) \n* [Yuval Noah Harari argues that AI has hacked the operating system of human civilisation](https:\u002F\u002Fwww.economist.com\u002Fby-invitation\u002F2023\u002F04\u002F28\u002Fyuval-noah-harari-argues-that-ai-has-hacked-the-operating-system-of-human-civilisation) \n* [Generative AI Takes Stereotypes and Bias From Bad to Worse](https:\u002F\u002Fwww.bloomberg.com\u002Fgraphics\u002F2023-generative-ai-bias\u002F) \n* [Governance of superintelligence by OpenAI](https:\u002F\u002Fopenai.com\u002Fblog\u002Fgovernance-of-superintelligence) \n* [AIAAIC - AIAAIC Repository](https:\u002F\u002Fwww.aiaaic.org\u002Faiaaic-repository): \"The independent, open, public interest resource detailing incidents and controversies driven by and relating to artificial intelligence, algorithms, and automation\"\n* [Just Calm Down About GPT-4 Already - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fgpt-4-calm-down) \n* [Pause Giant AI Experiments: An Open Letter - Future of Life Institute](https:\u002F\u002Ffutureoflife.org\u002Fopen-letter\u002Fpause-giant-ai-experiments\u002F) \n* [\"OpenAI released plugins for ChatGPT\"](https:\u002F\u002Ftwitter.com\u002Fthealexbanks\u002Fstatus\u002F1639620659142881283): tweet from [@thealexbanks](https:\u002F\u002Ftwitter.com\u002Fthealexbanks) with a list of reflections about the impact of ChatGPT plugins\n* [Is a socially fair Artificial Intelligence possible? | Uma Inteligência Artificial socialmente justa é possível?](https:\u002F\u002Fwww.mabuse.art.br\u002Fpost\u002Fuma-intelig%C3%AAncia-artificial-socialmente-justa-%C3%A9-poss%C3%ADvel): post in Portuguese by H.D. Mabuse\n* [Noam Chomsky on ChatGPT: It's \"Basically High-Tech Plagiarism\" and \"a Way of Avoiding Learning\" | Open Culture](https:\u002F\u002Fwww.openculture.com\u002F2023\u002F02\u002Fnoam-chomsky-on-chatgpt.html)\n* [Despite Their Feats, Large Language Models Still Haven't Contributed to Linguistics | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fdespite-their-feats-large-language-models-still-havent-contributed-to-linguistics-657bea43a8a3) \n* [Will ChatGPT Kill the Student Essay? | The Atlantic](https:\u002F\u002Fwww.theatlantic.com\u002Ftechnology\u002Farchive\u002F2022\u002F12\u002Fchatgpt-ai-writing-college-student-essays\u002F672371\u002F)\n* [What ChatGPT and generative AI mean for science | Nature](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fd41586-023-00340-6)\n* [ChatGPT Is a Bullshit Generator Waging Class War](https:\u002F\u002Fwww.vice.com\u002Fen\u002Farticle\u002Fakex34\u002Fchatgpt-is-a-bullshit-generator-waging-class-war) \n* [Some thoughts about generative AI and the future of education – Mark Carrigan](https:\u002F\u002Fmarkcarrigan.net\u002F2023\u002F01\u002F15\u002Fsome-thoughts-about-generative-ai-and-the-future-of-education\u002F) \n* [Educator Considerations for ChatGPT - OpenAI API](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fchatgpt-education) \n* [Stable Diffusion Frivolous · Because lawsuits based on ignorance deserve a response.](http:\u002F\u002Fwww.stablediffusionfrivolous.com\u002F): a community response for the \"Stable Diffusion litigation\"\n* [Stable Diffusion litigation · Joseph Saveri Law Firm & Matthew Butterick](https:\u002F\u002Fstablediffusionlitigation.com\u002F)\n* [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations | OpenAI](https:\u002F\u002Fcdn.openai.com\u002Fpapers\u002Fforecasting-misuse.pdf) \n* [Abstracts written by ChatGPT fool scientists](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fd41586-023-00056-7) \n* [When Machines Change Art | Aaron Hertzmann’s blog](https:\u002F\u002Faaronhertzmann.com\u002F2022\u002F12\u002F17\u002Fwhen-tech-changes-art.html)\n* [The Dark Risk of Large Language Models | WIRED UK](https:\u002F\u002Fwww.wired.co.uk\u002Farticle\u002Fartificial-intelligence-language)\n* [ChatGPT, DALL-E 2 and the collapse of the creative process](https:\u002F\u002Ftheconversation.com\u002Fchatgpt-dall-e-2-and-the-collapse-of-the-creative-process-196461)\n* [What AI-Generated Art Really Means for Human Creativity | WIRED](https:\u002F\u002Fwww.wired.com\u002Fstory\u002Fpicture-limitless-creativity-ai-image-generators\u002F)\n* [Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk](https:\u002F\u002Fopenai.com\u002Fblog\u002Fforecasting-misuse\u002F) \n* [The Dark Side of AI Art: 4 Potential Issues With the Growing Trend](https:\u002F\u002Fwww.makeuseof.com\u002Fdark-side-of-ai-art-potential-issues\u002F) \n* [Armed With ChatGPT, Cybercriminals Build Malware And Plot Fake Girl Bots](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fthomasbrewster\u002F2023\u002F01\u002F06\u002Fchatgpt-cybercriminal-malware-female-chatbots\u002F?sh=6019f4315534)\n* [ChatGPT And The Mass Production Of Office Work - Farsight](https:\u002F\u002Ffarsight.cifs.dk\u002Fchatgpt-and-the-mass-production-of-office-work\u002F)\n* [The Danger Of ChatGPT Nobody Talks About | by Jacob Ferus | Dec, 2022 | Medium](https:\u002F\u002Fmedium.com\u002F@dreamferus\u002Fthe-danger-of-chatgpt-nobody-talks-about-9aff94e5dea6)\n* [Mind Control in the Metaverse. If we’ve learned anything about… | by Louis Rosenberg | Predict | Dec, 2022 | Medium](https:\u002F\u002Fmedium.com\u002Fpredict\u002Fmind-control-in-the-metaverse-48dfbd88c2ae)\n* [The Brilliance and Weirdness of ChatGPT - The New York Times](https:\u002F\u002Fwww.nytimes.com\u002F2022\u002F12\u002F05\u002Ftechnology\u002Fchatgpt-ai-twitter.html)\n* [Como o texto gerado por Inteligência Artificial está envenenando a Internet - MIT Technology Review](https:\u002F\u002Fmittechreview.com.br\u002Fcomo-o-texto-gerado-por-inteligencia-artificial-esta-envenenando-a-internet\u002F)\n* [O ChatGPT é o momento “Jurassic Park” da inteligência artificial - NeoFeed](https:\u002F\u002Fneofeed.com.br\u002Fblog\u002Fhome\u002Fo-chatgpt-e-o-momento-jurassic-park-da-inteligencia-artificial\u002F)\n* [Por favor, mais racionalidade e menos frenesi em relação ao chatGPT (Parte 1 de 2) | by Cezar Taurion | Dec, 2022 | Medium](https:\u002F\u002Fc-taurion.medium.com\u002Fpor-favor-mais-racionalidade-e-menos-frenesi-em-rela%C3%A7%C3%A3o-ao-chatgpt-parte-1-de-2-1d7637e2a854)\n* [E se estivermos usando uma IA pseudocientífica? - Diogo Cortiz](https:\u002F\u002Fdiogocortiz.com.br\u002Fcomputacao-afetiva-e-os-desafios-das-ias-pseudocientificas\u002F)\n* [As limitações da sensação tecnológica de 2023: o ChatGPT | IAgora? | Época NEGÓCIOS](https:\u002F\u002Fepocanegocios.globo.com\u002Fcolunas\u002Fiagora\u002Fcoluna\u002F2023\u002F01\u002Fas-limitacoes-da-sensacao-tecnologica-de-2023-o-chatgpt.ghtml)\n* [7 Revealing Ways AIs Fail - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fai-failures) \n\n## Generative AI Processes and Artifacts\n\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Ffilipecalegario_awesome-generative-ai_readme_bbb6f33099d0.png\" width=75% height=75%>\n\n\u003Cdetails>\n\u003Csummary>More info\u003C\u002Fsummary>\n\n**Generative AI** is a branch of artificial intelligence that focuses on creating new data based on patterns learned from existing data. Here's a step-by-step explanation of the process:\n\n1. **Starting with Data**: Every Generative AI process begins with data. This can be in various forms such as text, images, sounds, or other datasets. This data serves as the foundational material that the AI uses to recognize and understand patterns.\n\n2. **Training the AI**: With the data in hand, the next step is 'training'. During this phase, the AI processes the data multiple times to learn and internalize the patterns present. The outcome of this stage is a 'model', which acts like a digital representation of the knowledge derived from the data.\n\n3. **Fine-Tuning**: At times, there's a need for the AI to focus on specific nuances or characteristics. In such cases, an additional set of data is used to 'fine-tune' the already trained model, enhancing its capabilities in the desired direction.\n\n4. **Using the Model**: After training, the model is prepared to make inferences, which means using its acquired knowledge to process new data and come up with relevant outputs. This inference process can be executed locally on a machine or can be accessed remotely through an 'API'. The choice between local execution and API access often depends on factors like computational resources, application needs, and user preferences. Whether locally or via an API, the goal is to leverage the model's capabilities to derive meaningful results from new data inputs.\n\n5. **Generating New Data**: With the model set up, the AI can now produce or 'generate' new data. By giving the AI certain 'input parameters' or guidelines, it returns with 'generated output', which is the newly created content.\n\n6. **Applications**: The output generated by the AI can be incorporated into a range of applications, be it websites, mobile apps, or other digital platforms. The 'interface' refers to the user-facing portion of these applications, enabling users to interact with and benefit from the AI's capabilities.\n\nIn essence, Generative AI is about feeding an AI system vast amounts of data, training it to grasp underlying patterns, and then utilizing that trained knowledge to produce novel data. The potential applications and benefits of this technology are vast and continue to grow as the field evolves.\n\n\u003C\u002Fdetails>\n\n## Generative AI Tools Directories\n\n* [AI Presentation Makers](https:\u002F\u002Fwww.aipresentationmakers.com\u002F): In-depth reviews of dozens of AI presentation makers\n* [A.I. Productivity Tools](https:\u002F\u002Fwww.aiproductivitytoolkit.com\u002F) \n* [ToolList.ai](https:\u002F\u002Ftoollist.ai\u002F): AI Tools Aggregator\n* [Toolify](https:\u002F\u002Fwww.toolify.ai\u002F): AI Tools Directory & AI Tools List\n* [LLM Explorer](https:\u002F\u002Fllm.extractum.io\u002F): A Curated LLM List. Explore LLM List of the Open-Source LLM Models\n* [OrbicAI](https:\u002F\u002Forbic.ai\u002F): \"The Larget AI Directory, GPT Stores, AWS PartyRocks Apps and Lots of Free AI Tools\"\n* [Altern](https:\u002F\u002Faltern.ai\u002F): \"Gateway to AI Discoveries\"\n* [ainave](https:\u002F\u002Fwww.ainave.com): \"navigate the world of AI with ease\", curated AI Tools and AI News\n* [AI Search](https:\u002F\u002Fai-search.io): Find AI Tools & Apps | Search The Most Complete AI Tools Directory | AI Search\n* [AiSuperSmart Ai Tool Directory](https:\u002F\u002Faisupersmart.com\u002Fai-tools-directory\u002F): Find Ai Tools According to your Use Cases!\n* [HD Robots](https:\u002F\u002Fhdrobots.com\u002F): AI tools directory with chatbot assistant\n* [AIForme](https:\u002F\u002Fwww.aiforme.wiki\u002F): AI tools discovery platform with comparison feature\n* [Technologies in LabLab](https:\u002F\u002Flablab.ai\u002Ftech): list of AI tools suggested by [lablab.ai](https:\u002F\u002Flablab.ai) for their hackathons\n* [Vondy - Next Generation AI Apps](https:\u002F\u002Fwww.vondy.com\u002F): collection of AI tools organized by tasks \n* [AI Tool Master List](https:\u002F\u002Fdoc.clickup.com\u002F25598832\u002Fd\u002Fh\u002Frd6vg-14247\u002F0b79ca1dc0f7429\u002Frd6vg-12207): directory maintained by ClickUp\n* [AI Valley](https:\u002F\u002Faivalley.ai\u002F): \"The Newest AI Tools And Prompts\"\n* [AI Finder](https:\u002F\u002Fai-finder.net\u002F): repository with more than 1500 AI tools \n* [BestWebbs](https:\u002F\u002Fbestwebbs.com\u002F): \"one-stop destination for all AI Tools\"\n* [Future Tools - Find The Exact AI Tool For Your Needs](https:\u002F\u002Fwww.futuretools.io\u002F): list of AI tools\n* [Futurepedia - The Largest AI Tools Directory | Home](https:\u002F\u002Fwww.futurepedia.io\u002F): directory of AI tools\n* [There's An AI For That](https:\u002F\u002Ftheresanaiforthat.com\u002F): AI database\n* [AI Depot - Discover New AI Tools](https:\u002F\u002Faidepot.co\u002F): collection of AI tools organized by tags and presented in a card format\n* [Generative AI Database](https:\u002F\u002Faaronsim.notion.site\u002FGenerative-AI-Database-Types-Models-Sector-URL-API-more-b5196c870594498fb1e0d979428add2d): a database in Notion with types, models, sectors, URLs, and APIs\n* [Altern](https:\u002F\u002Faltern.ai) - The place to discover new AI tools and products.\n* [The Generative AI Landscape](https:\u002F\u002Fai-collection.org\u002F): \"a collection of awesome generative AI applications\"\n* [The ultimate list of AI tools for creators | Descript](https:\u002F\u002Fwww.descript.com\u002Fblog\u002Farticle\u002Fthe-ultimate-list-of-ai-tools-for-creators): collection organized by Descript\n* [Maxim AI](https:\u002F\u002Fwww.getmaxim.ai): a generative AI evaluation and observability platform\n* [AI Tool List](https:\u002F\u002Fwww.aitoollist.org): An awesome directory of AI tools\n\n## Courses and Educational Materials\n\n* [Gemini by Example](https:\u002F\u002Fgeminibyexample.com): Learn the Gemini SDK through (annotated) code examples.\n* [Niraj-Lunavat\u002FArtificial-Intelligence](https:\u002F\u002Fgithub.com\u002FNiraj-Lunavat\u002FArtificial-Intelligence?tab=readme-ov-file#researchers): Awesome AI Learning with +100 AI Cheat-Sheets, Free online Books, Top Courses, Best Videos and Lectures, Papers, Tutorials, +99 Researchers, Premium Websites, +121 Datasets, Conferences, Frameworks, Tools\n* [Generative AI Explained by NVIDIA](https:\u002F\u002Flearn.nvidia.com\u002Fcourses\u002Fcourse-detail?course_id=course-v1:DLI+S-FX-07+V1): A no-coding course by NVIDIA that presents Generative AI concepts and applications, as well as the challenges and opportunities in the field \n* [Paulescu\u002Fhands-on-rl: Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻‍🦸🏽](https:\u002F\u002Fgithub.com\u002FPaulescu\u002Fhands-on-rl) \n* [DataCamp's Become a Generative AI Developer series](https:\u002F\u002Fwww.datacamp.com\u002Fai-code-alongs): 9 code-alongs on building chatbots using LangChain and the OpenAI and Pinecone APIs, and working with the Hugging Face ecosystem. Free, for a limited time only.\n* [rasbt\u002FLLMs-from-scratch](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch): Implementing a ChatGPT-like LLM from scratch, step by step\n* [Introduction to Generative AI | SqillPlan](https:\u002F\u002Fsqillplan.com\u002Fcourse?hash=-4862018582618510846): introduction to Generative AI, including models such as GANs, Variational Autoencoders, Autoregressive Models, and their applications, evaluation, ethics, and challenges\n* [udlbook\u002Fudlbook](https:\u002F\u002Fgithub.com\u002Fudlbook\u002Fudlbook): Understanding Deep Learning by Professor Simon J.D. Prince\n* [Book: Understanding Deep Learning](https:\u002F\u002Fudlbook.github.io\u002Fudlbook\u002F): website with the book draft and Google Colabs of the book by Simon J.D. Prince\n* [List of Generative AI Learning resources from AWS and Google](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Faagarwal29_generativeai-learn-aws-activity-7081761811129118720-i128\u002F): list organized as a LinkedIn post by Ankit Agarwal\n* [How AI chatbots like ChatGPT or Bard work – visual explainer | The Guardian](https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002Fng-interactive\u002F2023\u002Fnov\u002F01\u002Fhow-ai-chatbots-like-chatgpt-or-bard-work-visual-explainer) \n* [🔥🔥] [Generative AI for Beginners](https:\u002F\u002Fmicrosoft.github.io\u002Fgenerative-ai-for-beginners\u002F#\u002F): introductory 12 lesson course by Microsoft\n* [Introduction to Generative AI](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fyoussef-hosni-b2960b135_if-you-want-to-start-studying-generative-activity-7125908710702350336-vhsm\u002F): series of Medium articles by Youssef Hosni\n* [Animated AI](https:\u002F\u002Fanimatedai.github.io\u002F): animations and instructional videos about neural networks\n* [Deep Learning AI - Learn the fundamentals of generative AI for real-world applications](https:\u002F\u002Fwww.deeplearning.ai\u002Fcourses\u002Fgenerative-ai-with-llms\u002F): created in partnership with AWS, this course presents the fundamentals of how generative AI works and how to deploy it in real-world applications.\n* [Google Cloud Skills Boost - Introduction to Generative AI](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fcourse_templates\u002F536): an introductory level microlearning course covering Google Tools aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods.\n* [Google Cloud Skills Boost: Generative AI learning path](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fjourneys\u002F118): curated content on Generative AI \"from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud\"\n* [AI for Industrial Design](https:\u002F\u002Findustrialdesign.ai\u002F): \"students at the National University of Singapore explore AI’s capability for design in a semester course and share what they learned. Directed by Donn Koh at the Division of Industrial Design, NUS.\"\n* [Let Us Show You How GPT Works — Using Jane Austen - The New York Times](https:\u002F\u002Fwww.nytimes.com\u002Finteractive\u002F2023\u002F04\u002F26\u002Fupshot\u002Fgpt-from-scratch.html) \n* [🔥🔥🔥] [ChatGPT Prompt Engineering for Developers - DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fchatgpt-prompt-engineering-for-developers\u002F): short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) that provide best practices for prompt engineering\n* [🔥🔥🔥] [DAIR.AI](https:\u002F\u002Fgithub.com\u002Fdair-ai): Democratizing Artificial Intelligence Research, Education, and Technologies\n* [Welcome to the 🤗 Deep Reinforcement Learning Course](https:\u002F\u002Fhuggingface.co\u002Fdeep-rl-course\u002Funit0\u002Fintroduction?fw=pt): a Hugging Face Course on Deep Reinforcement Learning\n* [Crash course in AI art generation by PromptHero](https:\u002F\u002Fprompthero.com\u002Facademy): paid ($99) course focused on prompt engineering\n* [Visual intuition for diffusion models and AI art. #stablediffusionart #aiart #aiartwork #aiartcommunity](https:\u002F\u002Fwww.tiktok.com\u002F@ham_made_art\u002Fvideo\u002F7154863972729113899) \n* [The Illustrated Stable Diffusion by Jay Alammar](https:\u002F\u002Fjalammar.github.io\u002Fillustrated-stable-diffusion\u002F): \"gentle introduction [on] how Stable Diffusion works\"\n* [🔥][johnowhitaker\u002Ftglcourse](https:\u002F\u002Fgithub.com\u002Fjohnowhitaker\u002Ftglcourse): The Generative Landscape - a course on generative modelling (currently unfinished)\n* [Words are Images | BustBright - Machine Learning Art](https:\u002F\u002Fwww.bustbright.com\u002Fproduct\u002Fwords-are-images-7-week-online-class-starting-october-24th-2022-\u002F331): 7-week Online class starting October 24th, 2022 by [Derrick Schultz](https:\u002F\u002Ftwitter.com\u002Fdvsch\u002F)\n* [Grokking Stable Diffusion.ipynb - Colaboratory - Part 1](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1?usp=sharing): notebook by [@johnowhitaker](https:\u002F\u002Ftwitter.com\u002Fjohnowhitaker) exploring Stable Diffusion details\n* [Grokking Stable Diffusion: Textual Inversion.ipynb - Colaboratory - Part 2](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1RTHDzE-otzmZOuy8w1WEOxmn9pNcEz3u?usp=sharing): sequel to Grokking Stable Diffusion by [@johnowhitaker](https:\u002F\u002Ftwitter.com\u002Fjohnowhitaker) that focus on Text Inversion\n* [GitHub - johnowhitaker\u002Faiaiart](https:\u002F\u002Fgithub.com\u002Fjohnowhitaker\u002Faiaiart): Course content and resources for the AIAIART course\n* [Implementation\u002Ftutorial of stable diffusion with side-by-side notes by labml.ai | Twitter](https:\u002F\u002Ftwitter.com\u002Flabmlai\u002Fstatus\u002F1571080112459878401)\n* [Practical Deep Learning for Coders 2023 - Part II](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_7rMfsA24Ls&list=PLfYUBJiXbdtRUvTUYpLdfHHp9a58nWVXP): continuation of the course focusing on the implementation of Stable Diffusion from scratch.\n* [Practical Deep Learning for Coders 2022 - Part I](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU): \"free course designed for people with some coding experience who want to learn how to apply deep learning and machine learning to practical problems\" by Jeremy Howard\n\n## Human-AI Interaction\n\n* [UX for AI: How to Power Human Experiences with AI - Design Tool Tuesday - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=50Of7_lubN4) \n* [Behind-the-Design: Meet Copilot by Microsoft Design](https:\u002F\u002Fmedium.com\u002Fmicrosoft-design\u002Fbehind-the-design-meet-copilot-2c68182a0e70) \n* [🔥🔥🔥] [[2310.07127] An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07127): \"a survey of 154 papers, providing a novel taxonomy and analysis of Human-GenAI Interactions from both human and Gen-AI perspectives\".\n* [Guidelines for Human-AI Interaction - Microsoft Research](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpublication\u002Fguidelines-for-human-ai-interaction\u002F): a set of \"18 generally applicable design guidelines for human-AI\" interaction\n\n## Papers Collection\n\n* [Paper Digest - ChatGPT](https:\u002F\u002Fwww.paperdigest.org\u002F2023\u002F01\u002Frecent-papers-on-chatgpt\u002F): Recent Papers on ChatGPT\n* [dair-ai\u002FML-Papers-Explained](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FML-Papers-Explained): Explanation to key concepts in ML\n* [AI Reading List - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1bEQM1W-1fzSVWNbS4ne5PopB2b7j8zD4Jc3nm4rbK-U\u002Fedit): reading list organized by [Jack Soslow (@JackSoslow)](https:\u002F\u002Ftwitter.com\u002FJackSoslow) \n* [Aman's AI Journal • Papers List](https:\u002F\u002Faman.ai\u002Fpapers\u002F): set of seminal AI\u002FML papers curated by Aman Chadha\n* [Casual GAN Papers Reading Club](https:\u002F\u002Fcasualgan.notion.site\u002Fcasualgan\u002FCasual-GAN-Papers-Reading-Club-327c158518e44d5296a5def74486c7e8): Community knowledge base for Casual GAN Papers\n* [Casual GAN Papers](https:\u002F\u002Fwww.casualganpapers.com\u002F): Easy to read summaries of popular AI papers\n* [The Illustrated VQGAN](https:\u002F\u002Fljvmiranda921.github.io\u002Fnotebook\u002F2021\u002F08\u002F08\u002Fclip-vqgan\u002F): illustrated explanation on how VQGAN works\n* [CLIP: Connecting Text and Images](https:\u002F\u002Fopenai.com\u002Fblog\u002Fclip\u002F): OpenAI's explanation on how CLIP works\n* [VQGAN+CLIP — How does it work?. The synthetic imagery (“GAN Art”) scene… | by Alexa Steinbrück | Medium](https:\u002F\u002Falexasteinbruck.medium.com\u002Fvqgan-clip-how-does-it-work-210a5dca5e52)\n* [The Methods Corpus | Papers With Code](https:\u002F\u002Fpaperswithcode.com\u002Fmethods)\n* https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9043519: A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks\n* [Utilizando redes adversárias generativas (GANs) como agente de apoio à inspiração para artistas](https:\u002F\u002Fwww.cin.ufpe.br\u002F~tg\u002F2020-1\u002FTG_CC\u002Ftg_cco2.pdf): Trabalho de Graduação de Cláudio Carvalho no Centro de Informática - UFPE\n* [GAN Lab](https:\u002F\u002Fpoloclub.github.io\u002Fganlab\u002F): Play with Generative Adversarial Networks in Your Browser!\n* [[PDF] Music2Video: Automatic Generation of Music Video with fusion of audio and text | Semantic Scholar](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMusic2Video%3A-Automatic-Generation-of-Music-Video-of-Jang-Shin\u002F38e37c3a7dc22bb3356552e93e6685b99ca04264)\n* [[PDF] Active Divergence with Generative Deep Learning - A Survey and Taxonomy | Semantic Scholar](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FActive-Divergence-with-Generative-Deep-Learning-A-Broad-Berns\u002F091c4ea2efaba23cd9024d8a063609c9a313b5cb)\n* [[PDF] Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities | Semantic Scholar](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomating-Generative-Deep-Learning-for-Artistic-Berns-Broad\u002Ff3479740d4ec7f91b6d7a01167e9c875a72d386e)\n\n## Online Tools and Applications\n\n* [Lunroo](https:\u002F\u002Flunroo.com): 45+ Free AI Tools for Social Media Marketing. Save your time on routine tasks using AI.\n* [COUNT](https:\u002F\u002Fgetcount.com): AI-powered accounting for small businesses\n* [Competitor Research](https:\u002F\u002Fwww.competitoresearch.com): AI tool to help companies track their competitors\n* [StartKit.AI](https:\u002F\u002Fstartkit.ai): Boilerplate for quickly building AI products\n* [No-Code Scraper](https:\u002F\u002Fwww.nocodescraper.com\u002F): Data Scraping without Code - Seamlessly extract data from any website with just a few simple inputs.\n* [BacklinkGPT](https:\u002F\u002Fwww.backlinkgpt.com\u002F): AI-powered link-building platform that helps you generate personalized outreach messages for faster link building.\n* [VocalReplica](https:\u002F\u002Fvocalreplica.com\u002F): AI-Powered Vocal and Instrumental Isolation for Your Favorite Tracks\n* [LangMagic](https:\u002F\u002Feasytolearn.io): Learn languages from native content.\n* [Persuva](https:\u002F\u002Fpersuva.ai): Persuva is the AI-driven platform to create persuasive, high-converting ad copy at scale.\n* [Dittto.ai](https:\u002F\u002Fdittto.ai): Fix your hero copy with an AI trained on top SaaS websites.\n* [SEOByAI](https:\u002F\u002Fseoby.ai): Rank Faster on Google with FREE AI SEO Tools\n* [SinglebaseCloud](https:\u002F\u002Fsinglebase.cloud): AI-powered backend platform with Vector DB, DocumentDB, Auth, and more to speed up app development.\n* [TrollyAI](https:\u002F\u002Ftrolly.ai\u002F): Create professional SEO articles, 2x faster\n* [WebscrapeAI](https:\u002F\u002Fwebscrapeai.com\u002F): Scrape any website without code using AI\n* [Architecture Helper](https:\u002F\u002Farchitecturehelper.com): Analyze any building architecture, and generate your own custom styles, in seconds.\n* [AI-Flow](https:\u002F\u002Fai-flow.net\u002F): Connect multiple AI models easily\n* [Code to Flow](https:\u002F\u002Fcodetoflow.com): Visualize, Analyze, and Understand Your Code flow. Turn Code into Interactive Flowcharts with AI. Simplify Complex Logic Instantly.\n* [Recast Studio](https:\u002F\u002Frecast.studio): AI-powered podcast marketing assistant.\n* [Clipwing](https:\u002F\u002Fclipwing.pro\u002F): A tool for cutting long videos into dozens of short clips.\n* [Tailor](https:\u002F\u002Fwww.usetailor.com): Get a daily podcast and newsletter, created for you by an AI\n* [ZZZ Code AI](https:\u002F\u002Fzzzcode.ai\u002F): AI-powered free website to get any programming question answered or code generated.\n* [Scribble Diffusion](https:\u002F\u002Fscribblediffusion.com\u002F): turn your sketch into a refined image using AI\n* [Paint by Text](https:\u002F\u002Fpaintbytext.chat\u002F): Edit your photos using written instructions, with the help of an AI.\n* [Scenario AI](https:\u002F\u002Fwww.scenario.gg\u002F): AI-generated game assets\n* [AnimalAI](https:\u002F\u002Fanimalai.co\u002F): custom AI-generated animal portraits (profits are directed to various wildlife conservation organizations)\n* [starryai](https:\u002F\u002Fwww.starryai.com\u002F): AI Art Generator App - AI Art Maker\n* [ProsePainter](https:\u002F\u002Fwww.prosepainter.com\u002F): an interactive tool to \"paint with words.\" It incorporates guidable text-to-image generation into a traditional digital painting interface\n* [ProsePainter: Image + Sketching Interface + CLIP! - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mK4F32xNrdw&t=429s)\n* [Cocreator AI](https:\u002F\u002Fcocreator.ai\u002F): creative computer agent (in wait list)\n* [Runway ML](http:\u002F\u002Frunwayml.com\u002F): AI video creation suite\n* [Hotpot.ai - Hotpot.ai](https:\u002F\u002Fhotpot.ai\u002F): set of AI Tools to post-process images\n* [Toonify yourself by Justin Pinkney](https:\u002F\u002Fwww.justinpinkney.com\u002Ftoonify-yourself\u002F): turn a human face into a cartoon\n* [deepart.io](https:\u002F\u002Fdeepart.io\u002F): a online tool for applying style transfer\n* [Artbreeder](https:\u002F\u002Fwww.artbreeder.com\u002F): web-based tool to generate images by breeding existing images\n* [Ostagram.ru](https:\u002F\u002Fwww.ostagram.me\u002F): image style transfer plataform\n* [cleanup.pictures](https:\u002F\u002Fcleanup.pictures\u002F): remove objects, people, text and defects from any picture for free\n* [remove.bg](https:\u002F\u002Fwww.remove.bg\u002F): remove background from images\n* [Quick, Draw!](https:\u002F\u002Fquickdraw.withgoogle.com\u002F): can a neural network learn to recognize doodling? A game to help NL by adding users drawing\n* [Nekton.ai](https:\u002F\u002Fnekton.ai\u002F): automate your workflows with AI\n* [Documind.chat](https:\u002F\u002Fdocumind.chat): Chat with PDF using AI. Documind is a powerful chat with pdf tool that lets you ask questions from your pdf documents.\n* [Snowpixel](https:\u002F\u002Fsnowpixel.app): Generate Images\u002FVideos\u002FAnimations\u002FAudio\u002FMusic\u002F3D Objects with Text and\u002For Image. Upload your own data to create custom models.\n* [Chatpdf.so](https:\u002F\u002Fchatpdf.so): Talk to PDF using GPT4 AI. Chatpdf.so is a chatpdf tool that lets you do question answering on your pdf documents.\n* [Yona.ai](https:\u002F\u002Fyona.ai): Create deeply personalized AI chatbots from your own conversations, your stories, your data. You can harness the power of your chat history to build an AI companion for a nostalgic trip down memory lane, whimsical fantasies, or any other unique purpose.\n* [Voicesphere](https:\u002F\u002Fwww.voicesphere.co\u002F): Chat with your documents to get intelligent, context specific answers.\n* [Tune AI](https:\u002F\u002Fchat.tune.app\u002F): AI chat app powered by open source models\n* [GPT Mobile](https:\u002F\u002Fgithub.com\u002FTaewan-P\u002Fgpt_mobile) GPT Mobile is an Android app that can chat with multiple LLMs at once! Currently supports ChatGPT, Anthropic Claude, and Google Gemini.\n* [PageGen](https:\u002F\u002Fpagegen.ai) - An AI Page Generator with Claude AI, React and Shadcn UI. Generate web pages from text, screenshot and templates with one click.\n* [PerchanceStory](https:\u002F\u002Fperchancestory.com\u002F): PerchanceStory is an AI-based interactive story generator, which generates ever-changing story endings with endless possibilities based on simple user-provided input. \n\n# Code and Programming\n\n## Vibe Coding\n\n* [filipecalegario\u002Fawesome-vibe-coding](https:\u002F\u002Fgithub.com\u002Ffilipecalegario\u002Fawesome-vibe-coding): A curated list of vibe coding references, colaborating with AI to write code.\n* [Andrej Karpathy on X](https:\u002F\u002Fx.com\u002Fkarpathy\u002Fstatus\u002F1886192184808149383): \"There's a new kind of coding I call \"vibe coding\", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.\"\n* [Windsurf Editor by Codeium](https:\u002F\u002Fcodeium.com\u002Fwindsurf): agentic IDE, \"where the work of developers and AI truly flow together, allowing for a coding experience that feels like literal magic\"\n* [Bolt.new](https:\u002F\u002Fbolt.new\u002F): Prompt, run, edit, and deploy full-stack web and mobile apps.\n* [Lovable](https:\u002F\u002Flovable.dev\u002F): \"Idea to app in seconds. Lovable is your superhuman full stack engineer.\"\n* [v0 by Vercel](https:\u002F\u002Fv0.dev\u002Fchat): assistant to build NextJS frontend\n* [Cursor](https:\u002F\u002Fwww.cursor.com\u002F): The AI Code Editor, \"the best way to code with AI\"\n* [Replit](https:\u002F\u002Freplit.com\u002F): \"Simply describe your idea above and let the Agent build it for you\"\n\n## AI-Powered Code Generation\n\n* [batchai](https:\u002F\u002Fgithub.com\u002Fqiangyt\u002Fbatchai): A supplement to Copilot and Cursor - utilizes AI for batch processing of project codes\n* [Archie](https:\u002F\u002Farchie.8base.com\u002F): AI-Driven Product Architect that Designs and Plans Software Applications\n* [DhiWise](https:\u002F\u002Fdhiwise.com): DhiWise is an app development platform that automates coding tasks, letting developers focus on core functionalities.\n* [New study on coding behavior raises questions about impact of AI on software development – GeekWire](https:\u002F\u002Fwww.geekwire.com\u002F2024\u002Fnew-study-on-coding-behavior-raises-questions-about-impact-of-ai-on-software-development\u002F) \n* [CostGPT: Software Development Cost Calculator](https:\u002F\u002Fcostgpt.ai\u002F): \"find the cost, time and the best tech stack for any kind of software, tools that you want to build using the power of AI\"\n* [codefuse-ai\u002FAwesome-Code-LLM](https:\u002F\u002Fgithub.com\u002Fcodefuse-ai\u002FAwesome-Code-LLM): a curated list of language modeling researches for code and related datasets.\n* [tldraw\u002Fdraw-a-ui](https:\u002F\u002Fgithub.com\u002Ftldraw\u002Fdraw-a-ui): draw a mockup and generate HTML for it\n* [deepseek-ai\u002FDeepSeek-Coder](https:\u002F\u002Fgithub.com\u002Fdeepseek-ai\u002FDeepSeek-Coder): a tool that experiments the motto \"let the code write itself\"\n* [Cody](https:\u002F\u002Fabout.sourcegraph.com\u002Fcody): AI coding assistant\n* [Kombai](https:\u002F\u002Fkombai.com\u002F): generate UI code per component from Figma\n* [geekan\u002FMetaGPT](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT): the multi-agent framework that, give one line requirement, return PRD, design, tasks, repo\n* [ZZZ Code AI](https:\u002F\u002Fzzzcode.ai\u002F): AI-powered free website to get any programming question answered or code generated.\n* [Rapidpages](https:\u002F\u002Fwww.rapidpages.io\u002F): create React & Tailwind landing pages using AI\n* [Teaching Programming in the Age of ChatGPT – O’Reilly](https:\u002F\u002Fwww.oreilly.com\u002Fradar\u002Fteaching-programming-in-the-age-of-chatgpt\u002F) \n* [GPT Web App Generator](https:\u002F\u002Fmagic-app-generator.wasp-lang.dev\u002F): generates a webapp from a title, description, and other simple parameters\n* [wolfia-app\u002Fgpt-code-search](https:\u002F\u002Fgithub.com\u002Fwolfia-app\u002Fgpt-code-search\u002F): search a codebase with natural language using AI\n* [Dedicated File for Inbox for GenAI + Dev](https:\u002F\u002Fgithub.com\u002Ffilipecalegario\u002Fawesome-generative-ai\u002Fblob\u002Fmain\u002Finbox-gen-ai-dev.md): a list for further analysis and organization of GenAI + dev references \n* [e2b-dev\u002Fe2b](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fe2b): \"Open-source platform for building AI-powered virtual software developers\"\n* [Metabob](https:\u002F\u002Fmetabob.com\u002F): Generative AI to improve and automate code reviews\n* [gventuri\u002Fpandas-ai](https:\u002F\u002Fgithub.com\u002Fgventuri\u002Fpandas-ai): Pandas AI is a Python library that integrates LLMs capabilities into Pandas, making dataframes conversational\n* [A Systematic Evaluation of Large Language Models of Code](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.13169): arxiv paper\n* [pgosar\u002FChatGDB](https:\u002F\u002Fgithub.com\u002Fpgosar\u002FChatGDB): \"Harness the power of ChatGPT inside the GDB debugger\"\n* [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06590) \n* [openai\u002Fopenai-cookbook](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-cookbook): Examples and guides for using the OpenAI API\n* [Reduce costs when prompting using GPT](https:\u002F\u002Fwww.codium.ai\u002Fblog\u002Freduce-your-costs-by-30-when-using-gpt-3-for-python-code\u002F)\n* [Co-Developer GPT engine](https:\u002F\u002Fgithub.com\u002Fstoerr\u002FCoDeveloperGPTengine) - local r\u002Fw file access and execute actions from an OpenAI GPT\n* [Potpie](https:\u002F\u002Fpotpie.ai) - Open Source AI Agents for your codebase in minutes. Use pre-built agents for Q&A, Testing, Debugging and System Design or create your own purpose-built agents.\n\n# Text\n\n## Everything to Markdown to LLMs\n\n* [bytedance\u002FDolphin](https:\u002F\u002Fgithub.com\u002Fbytedance\u002FDolphin): The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.\n* [NuExtract 2.0 by NuMind](https:\u002F\u002Fnumind.ai\u002Fblog\u002Foutclassing-frontier-llms----nuextract-2-0-takes-the-lead-in-information-extraction): \"Outclassing Frontier LLMs in Information Extraction\"\n* [unclecode\u002Fcrawl4ai: 🚀🤖 Crawl4AI](https:\u002F\u002Fgithub.com\u002Funclecode\u002Fcrawl4ai): Open-source LLM Friendly Web Crawler & Scraper\n* [LLMSTXT.NEW](https:\u002F\u002Fwww.llmstxt.new\u002F): Generate consolidated text files from websites for LLM training and inference – Powered by Firecrawl\n* [Mistral OCR \u002F Mistral AI](https:\u002F\u002Fmistral.ai\u002Fnews\u002Fmistral-ocr): A document understanding API\n* [opendatalab\u002FMinerU](https:\u002F\u002Fgithub.com\u002Fopendatalab\u002FMinerU): A high-quality tool for convert PDF to Markdown and JSON\n* [microsoft\u002Fmarkitdown](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown): Python tool for converting files and office documents to Markdown.\n* [docling-project\u002Fdocling](https:\u002F\u002Fgithub.com\u002Fdocling-project\u002Fdocling): get your documents ready for gen AI\n* [Firecrawl](https:\u002F\u002Fwww.firecrawl.dev\u002F): Turn websites into LLM-ready data\n* [CatchTheTornado\u002Ftext-extract-api](https:\u002F\u002Fgithub.com\u002FCatchTheTornado\u002Ftext-extract-api): document (PDF, Word, PPTX ...) extraction and parse API using OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown\n* [R Jina](https:\u002F\u002Fr.jina.ai\u002F): convert websites into Markdown by placing the URL in the search bar\n* [Gitingest](https:\u002F\u002Fgitingest.com\u002F): turn any Git repository into a simple text digest of its codebase.\n* [uithub](https:\u002F\u002Fuithub.com\u002F): convert GitHub repositories into Markdown by placing the URL in the search bar\n\n## Small Language Models\n\n* [[2409.15790] Small Language Models: Survey, Measurements, and Insights](https:\u002F\u002Farxiv.org\u002Fabs\u002F2409.15790) \n* [[2402.17764] The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.17764) \n* [mbzuai-oryx\u002FMobiLlama](https:\u002F\u002Fgithub.com\u002Fmbzuai-oryx\u002FMobiLlama): Small Language Model tailored for edge devices\n\n## Large Language Models (LLMs)\n\n* [lunary-ai\u002Fabso](https:\u002F\u002Fgithub.com\u002Flunary-ai\u002Fabso): TypeScript SDK to easily call 100+ LLMs using OpenAI's format\n* [oumi-ai\u002Foumi](https:\u002F\u002Fgithub.com\u002Foumi-ai\u002Foumi): open universal machine intelligence, open-source platform that streamlines the entire lifecycle of foundation models - from data preparation and training to evaluation and deployment\n* [🔥] [Transformer Explainer](https:\u002F\u002Fpoloclub.github.io\u002Ftransformer-explainer\u002F): LLM Transformer Model Visually Explained [YouTube Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ECR4oAwocjs) \n* [comet-ml\u002Fopik](https:\u002F\u002Fgithub.com\u002Fcomet-ml\u002Fopik): Evaluate, test, and ship LLM applications with a suite of observability tools to calibrate language model outputs across your dev and production lifecycle.\n* [mendableai\u002Ffirecrawl](https:\u002F\u002Fgithub.com\u002Fmendableai\u002Ffirecrawl): Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.\n* [QuivrHQ\u002FMegaParse](https:\u002F\u002Fgithub.com\u002Fquivrhq\u002Fmegaparse): File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.\n* [LiteLLM](https:\u002F\u002Fwww.litellm.ai\u002F): a proxy server to manage auth, loadbalancing, and spend tracking across 100+ LLMs, all in the OpenAI format\n* [youssefHosni\u002FHands-On-LangChain-for-LLM-Applications-Development](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FHands-On-LangChain-for-LLM-Applications-Development): Practical LangChain tutorials for LLM applications development\n* [unclecode\u002Fcrawl4ai: Crawl4AI](https:\u002F\u002Fgithub.com\u002Funclecode\u002Fcrawl4ai): Open-source LLM Friendly Web Crawler & Scrapper\n* [microsoft\u002FLMOps](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLMOps): General technology for enabling AI capabilities w\u002F LLMs and MLLMs\n* [F*** You, Show Me The Prompt](https:\u002F\u002Fhamel.dev\u002Fblog\u002Fposts\u002Fprompt\u002F): quickly understand inscrutable LLM frameworks by intercepting API calls\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric): fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.\n* [Langfuse](https:\u002F\u002Flangfuse.com\u002F): Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. Integrates with LlamaIndex, Langchain, OpenAI SDK, LiteLLM, and more. [#opensource](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse)\n* [naklecha\u002Fllama3-from-scratch](https:\u002F\u002Fgithub.com\u002Fnaklecha\u002Fllama3-from-scratch): llama3 implementation one matrix multiplication at a time\n* [[2405.03825] Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence](https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.03825) \n* [Open challenges in LLM research](https:\u002F\u002Fhuyenchip.com\u002F2023\u002F08\u002F16\u002Fllm-research-open-challenges.html) \n* [stanfordnlp\u002Fdspy](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy): DSPy: The framework for programming — not prompting — foundation models\n* [Groq](https:\u002F\u002Fgroq.com\u002F): service focused on fast inference speed, providing API access to Llama 2 70B-4K and Mixtral 8x7B-32K \n* [🔥🔥🔥] [LLMLingua](https:\u002F\u002Fllmlingua.com\u002F): Designing a Language for LLMs via **Prompt Compression**\n* [Floom](https:\u002F\u002Fgithub.com\u002FFloomAI\u002FFloom) AI gateway and marketplace for developers, enables streamlined integration of AI features into products\n* [rasbt\u002FLLMs-from-scratch](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch): Implementing a ChatGPT-like LLM from scratch, step by step\n* [GoogleCloudPlatform\u002Fgenerative-ai](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenerative-ai): Sample code and notebooks for Generative AI on Google Cloud\n* [LLM Visualization](https:\u002F\u002Fbbycroft.net\u002Fllm)\n* [Automatic Hallucination detection with SelfCheckGPT NLI](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fdhuynh95\u002Fautomatic-hallucination-detection) \n* [StreamingLLM gives language models unlimited context](https:\u002F\u002Fbdtechtalks.com\u002F2023\u002F11\u002F27\u002Fstreamingllm\u002F): giving language models unlimited context\n* [iusztinpaul\u002Fhands-on-llms](https:\u002F\u002Fgithub.com\u002Fiusztinpaul\u002Fhands-on-llms): learn about LLMs, LLMOps, and vector DBs for free by designing, training, and deploying a real-time financial advisor LLM system ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 𝘷𝘪𝘥𝘦𝘰 & 𝘳𝘦𝘢𝘥𝘪𝘯𝘨 𝘮𝘢𝘵𝘦𝘳𝘪𝘢𝘭𝘴\n* [Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)](https:\u002F\u002Fmagazine.sebastianraschka.com\u002Fp\u002Fpractical-tips-for-finetuning-llms?) \n* [Poe](https:\u002F\u002Fpoe.com\u002F): a platform that lets people ask questions, get instant answers, and have back-and-forth conversations with a wide variety of AI-powered bots\n* [[2311.01555] Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.01555) \n* [🔥🔥] [State of LLM Apps 2023 · Streamlit](https:\u002F\u002Fstate-of-llm.streamlit.app\u002F) \n* [The architecture of today's LLM applications - The GitHub Blog](https:\u002F\u002Fgithub.blog\u002F2023-10-30-the-architecture-of-todays-llm-applications\u002F) \n* [Demystifying LLMs: How they can do things they weren't trained to do - The GitHub Blog](https:\u002F\u002Fgithub.blog\u002F2023-10-27-demystifying-llms-how-they-can-do-things-they-werent-trained-to-do\u002F) \n* [How AI chatbots like ChatGPT or Bard work – visual explainer | The Guardian](https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002Fng-interactive\u002F2023\u002Fnov\u002F01\u002Fhow-ai-chatbots-like-chatgpt-or-bard-work-visual-explainer)\n* [cpacker\u002FMemGPT](https:\u002F\u002Fgithub.com\u002Fcpacker\u002FMemGPT): teaching LLMs memory management for unbounded context [[demo page]](https:\u002F\u002Fmemgpt.ai\u002F) [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08560) \n* [[2307.10169] Challenges and Applications of Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10169): a systematic set of open problems and application successes of LLM area\n* [Related resources from around the web | OpenAI Cookbook](https:\u002F\u002Fcookbook.openai.com\u002Farticles\u002Frelated_resources): tools and papers for improving outputs from GPT\n* [🔥🔥🔥] [Patterns for Building LLM-based Systems & Products](https:\u002F\u002Feugeneyan.com\u002Fwriting\u002Fllm-patterns\u002F): \"practical patterns for integrating large language models (LLMs) into systems & products\" by Eugene Yan\n* [Hannibal046\u002FAwesome-LLM: Awesome-LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM): a curated list of Large Language Model\n* [[2309.06794] Cognitive Mirage: A Review of Hallucinations in Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.06794)\n* [Generative AI for Strategy & Innovation](https:\u002F\u002Fwww.hbritalia.it\u002FuserUpload\u002Febook_Generative_AI_inglese.pdf): an experiment about management theories with ChatGPT by Harvard Business Review Italia\n* [The TextFX project](https:\u002F\u002Ftextfx.withgoogle.com\u002F): \"AI-powered tools for rappers, writers and wordsmiths\" (partnership between Lupe Fiasco and Google)\n* [A jargon-free explanation of how AI large language models work | Ars Technica](https:\u002F\u002Farstechnica.com\u002Fscience\u002F2023\u002F07\u002Fa-jargon-free-explanation-of-how-ai-large-language-models-work\u002F) \n* [🔥🔥🔥] [What We Know About LLMs (Primer)](https:\u002F\u002Fwillthompson.name\u002Fwhat-we-know-about-llms-primer) \n* [A simple guide to fine-tuning Llama 2 | Brev docs](https:\u002F\u002Fbrev.dev\u002Fblog\u002Ffine-tuning-llama-2)\n* [microsoft\u002Fsemantic-kernel](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-kernel): integrate cutting-edge LLM technology quickly and easily into your apps\n* [CoPrompt](https:\u002F\u002Fwww.coprompt.io\u002Flogin): platform for teams to use ChatGPT together\n* [🔥🔥🔥] [Emerging Architectures for LLM Applications | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F20\u002Femerging-architectures-for-llm-applications\u002F): \"a reference architecture for the emerging LLM app stack\"\n* [Advanced Guide to ChatGPT](https:\u002F\u002Faaditsh.notion.site\u002Faaditsh\u002FAdvanced-Guide-to-ChatGPT-b8d5901b8bba44f580bb0c0835644567): guide by Neatprompts.com \n* [Falcon LLM - Home](https:\u002F\u002Ffalconllm.tii.ae\u002F): a foundational large language model (LLM) with 40 billion parameters trained on one trillion tokens shared by Technology Innovation Institute from Abu Dhabi\n* [🔥🔥🔥] [The Hugging Face Open LLM Leaderboard](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FHuggingFaceH4\u002Fopen_llm_leaderboard): \"the 🤗 Open LLM Leaderboard aims to track, rank and evaluate LLMs and chatbots as they are released\"\n* [google\u002FBIG-bench](https:\u002F\u002Fgithub.com\u002Fgoogle\u002FBIG-bench): \"a collaborative benchmark intended to probe large language models and extrapolate their future capabilities\"\n* [togethercomputer\u002FOpenChatKit](https:\u002F\u002Fgithub.com\u002Ftogethercomputer\u002FOpenChatKit): provides an open-source base to create both specialized and general purpose chatbots for various applications\n* [Paper Digest - ChatGPT](https:\u002F\u002Fwww.paperdigest.org\u002F2023\u002F01\u002Frecent-papers-on-chatgpt\u002F): Recent Papers on ChatGPT\n* [Let Us Show You How GPT Works — Using Jane Austen - The New York Times](https:\u002F\u002Fwww.nytimes.com\u002Finteractive\u002F2023\u002F04\u002F26\u002Fupshot\u002Fgpt-from-scratch.html)\n* [Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.14732): \"a novel framework called Search-in-the-Chain (SearChain) to improve the accuracy, credibility and traceability of LLM-generated content for multi-hop question answering\"\n* [🔥🔥🔥] [Mooler0410\u002FLLMsPracticalGuide](https:\u002F\u002Fgithub.com\u002FMooler0410\u002FLLMsPracticalGuide): list of practical guide resources of LLMs based on the paper [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13712)\n* [hpcaitech\u002FColossalAI](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI): Making large AI models cheaper, faster and more accessible\n* [microsoft\u002FLoRA](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLoRA): Code for loralib, an implementation of \"LoRA: Low-Rank Adaptation of Large Language Models\"s\n* [kyrolabs\u002Fawesome-langchain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain): 😎 Awesome list of tools and project with the awesome LangChain framework\n* [Stability AI Launches the First of its StableLM Suite of Language Models — Stability AI](https:\u002F\u002Fstability.ai\u002Fblog\u002Fstability-ai-launches-the-first-of-its-stablelm-suite-of-language-models) \n* [Free Dolly | The Databricks Blog](https:\u002F\u002Fwww.databricks.com\u002Fblog\u002F2023\u002F04\u002F12\u002Fdolly-first-open-commercially-viable-instruction-tuned-llm): open source, instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use\n* [Summary of ChatGPT\u002FGPT-4 Research and Perspective Towards the Future of Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01852): paper with \"a comprehensive survey of ChatGPT and GPT-4 and their prospective applications across diverse domains\"\n* [lm-sys\u002FFastChat](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat): The release repo for \"Vicuna: An Open Chatbot Impressing GPT-4\" [[demo](https:\u002F\u002Fchat.lmsys.org\u002F)]\n* [🔥🔥🔥] [oobabooga\u002Ftext-generation-webui](https:\u002F\u002Fgithub.com\u002Foobabooga\u002Ftext-generation-webui): a gradio web UI for running Large Language Models like GPT-J 6B, OPT, GALACTICA, LLaMA, and Pygmalion\n* [Why LLaMa Is A Big Deal | Hackaday](https:\u002F\u002Fhackaday.com\u002F2023\u002F03\u002F22\u002Fwhy-llama-is-a-big-deal\u002F): post that discusses the impact of LLaMa and Alpaca in popularizing LLMs and even using them in small hardware devices\n* [logspace-ai\u002Flangflow](https:\u002F\u002Fgithub.com\u002Flogspace-ai\u002Flangflow): a UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows\n* [More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12173): paper on LLM Security\n* [Cohere AI](https:\u002F\u002Fdocs.cohere.ai\u002F): a way to integrate state-of-the-art language models to applications\n* [Langchain for paper summarization](https:\u002F\u002Flancemartin.notion.site\u002Flancemartin\u002FLangchain-for-paper-summarization-d4ad122ea9a64c0eb1f981e743d6c419): using langchain to build a app for paper summarization\n* [Red-Teaming Large Language Models | Hugging Faces](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fred-teaming): strategies for testing LLMs against jailbreaks and attacks\n* [hwchase17\u002Flangchain](https:\u002F\u002Fgithub.com\u002Fhwchase17\u002Flangchain\u002F): \"building applications with LLMs through composability\"\n* [Top Large Language Models (LLMs) in 2023 | MarkTechPost](https:\u002F\u002Fwww.marktechpost.com\u002F2023\u002F02\u002F22\u002Ftop-large-language-models-llms-in-2023-from-openai-google-ai-deepmind-anthropic-baidu-huawei-meta-ai-ai21-labs-lg-ai-research-and-nvidia\u002F): list with large language models from diverse companies\n* [Godly](https:\u002F\u002Fgodly.ai): Instant context for GPT3\n* [GPTZero](https:\u002F\u002Fgptzero.me\u002F): \"Detect AI Plagiarism. Accurately\"\n* [GPT-3 Apps](https:\u002F\u002Fgpt-apps.com\u002F): GPT-3 Powered Micro Products (ex: cat namer, poet pocket, summarize)\n* [Inside language models (from GPT-3 to PaLM) – Dr Alan D. Thompson – Life Architect](https:\u002F\u002Flifearchitect.ai\u002Fmodels\u002F)\n* [Google AI Blog: Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance](https:\u002F\u002Fai.googleblog.com\u002F2022\u002F04\u002Fpathways-language-model-palm-scaling-to.html) \n* [DeepMind says its new language model can beat others 25 times its size | MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002F2021\u002F12\u002F08\u002F1041557\u002Fdeepmind-language-model-beat-others-25-times-size-gpt-3-megatron\u002F) \n* [Integrated AI: How to talk to AI for free using nine platforms (Megatron, GPT-3, GPT-J, Wudao, J1..) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yWM_8QwLyuY&list=LL&index=1&t=17s) by Dr Alan D. Thompson. The following references came from this video description\n* [Haystack](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack): framework for building applications with LLMs and Transformers (e.g. agents, semantic search, question-answering)\n* [SolidUI](https:\u002F\u002Fgithub.com\u002FCloudOrc\u002FSolidUI): AI-generated visualization prototyping and editing platform, support 2D, 3D models, combined with LLM(Large Language Model) for quick editing.\n\n### Model Context Protocol\n\n* [Introducing the Model Context Protocol \\ Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fmodel-context-protocol)\n  * an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools.\n  * developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.\n* [Model Context Protocol](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol): Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools.\n* [Introduction - Model Context Protocol](https:\u002F\u002Fmodelcontextprotocol.io\u002Fintroduction)\n  * Think of MCP like a USB-C port for AI applications.\n  * MCP helps you build agents and complex workflows on top of LLMs.\n* Examples\n  * [Example Servers - Model Context Protocol](https:\u002F\u002Fmodelcontextprotocol.io\u002Fexamples)\n  * [abhiz123\u002Ftodoist-mcp-server](https:\u002F\u002Fgithub.com\u002Fabhiz123\u002Ftodoist-mcp-server\u002Ftree\u002Fmain): MCP server for Todoist integration enabling natural language task management with Claude\n* List of Servers\n  * [modelcontextprotocol\u002Fservers: Model Context Protocol Servers](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol\u002Fservers)\n  * [Awesome MCP Servers](https:\u002F\u002Fmcpservers.org\u002F)\n  * [punkpeye\u002Fawesome-mcp-servers](https:\u002F\u002Fgithub.com\u002Fpunkpeye\u002Fawesome-mcp-servers): A collection of MCP servers.\n  * [Composio MCP Server](https:\u002F\u002Fmcp.composio.dev\u002F): Connect Cursor, Windsurf, and Claude to 100+ fully managed MCP Servers with built-in auth\n    * These servers are built by the community and are hosted by Composio\n* [Example Clients - Model Context Protocol](https:\u002F\u002Fmodelcontextprotocol.io\u002Fclients)\n* [Building MCP with LLMs - Model Context Protocol](https:\u002F\u002Fmodelcontextprotocol.io\u002Ftutorials\u002Fbuilding-mcp-with-llms)\n* [Add Supabase to Cursor via MCP](https:\u002F\u002Fx.com\u002Fdshukertjr\u002Fstatus\u002F1896531501514109056)\n* [Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kQmXtrmQ5Zg): AI Engineer Summit workshop\n* [loopwork-ai\u002Femcee](https:\u002F\u002Fgithub.com\u002Floopwork-ai\u002Femcee): a tool that provides a Model Context Protocol (MCP) server for any web application with an OpenAPI specification.\n* [MCP Run](https:\u002F\u002Fdocs.mcp.run\u002F): a registry of AI tools that can be developed by anyone and used inside any AI application\n* [modelcontextprotocol\u002Finspector](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol\u002Finspector): Visual testing tool for MCP servers\n\n### Programming Frameworks for LLMs\n\n* [DSPy: Not Your Average Prompt Engineering](https:\u002F\u002Fjina.ai\u002Fnews\u002Fdspy-not-your-average-prompt-engineering\u002F): a post about the DSPy, a framework developed by the Stanford NLP group aimed at algorithmically optimizing language model prompts\n* [🔥🔥🔥] [stanfordnlp\u002Fdspy](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy): DSPy: The framework for programming — not prompting — foundation models\n\n### Prompt Engineering\n\n* [Narrow AI](https:\u002F\u002Fwww.getnarrow.ai\u002F): Automated Prompt Engineering and Optimization Platform\n* [Anthropic's Prompt Engineering Interactive Tutorial](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fcourses\u002Ftree\u002Fmaster\u002Fprompt_engineering_interactive_tutorial) \n* [ncwilson78\u002FSystem-Prompt-Library](https:\u002F\u002Fgithub.com\u002Fncwilson78\u002FSystem-Prompt-Library): A library of shared system prompts for creating customized educational GPT agents.\n* [Promptstacks](https:\u002F\u002Fwww.promptstacks.com\u002F): a prompt engineering community\n* [Prompt engineering - OpenAI API](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Fprompt-engineering): OpenAI's document with strategies and tactics for getting better results from large language models\n* [[2310.04438] A Brief History of Prompt: Leveraging Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04438): the paper presents an exploration of the evolution of prompt engineering. The author, Golam Md Muktadir, extensively used ChatGPT for content generation\n* [[2311.05661] Prompt Engineering a Prompt Engineer](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05661): this paper deals with the problem of \"constructing a meta-prompt that more effectively guides LLMs to perform automatic prompt engineering\"\n* [[2311.04155] Black-Box Prompt Optimization: Aligning Large Language Models without Model Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.04155) \n* [🔥🔥] [Prompt Engineering Roadmap - roadmap.sh](https:\u002F\u002Froadmap.sh\u002Fprompt-engineering) \n* [🔥🔥🔥] [Learn Prompting](https:\u002F\u002Flearnprompting.org\u002F): series of lessons of prompt engineering\n* [🔥🔥🔥] [Prompt Engineering | Lil'Log](https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2023-03-15-prompt-engineering\u002F): prompt engineering learning notes by Lilian Weng\n* [🔥🔥🔥] [ChatGPT Prompt Engineering for Developers - DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fchatgpt-prompt-engineering-for-developers\u002F): short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) that provide best practices for prompt engineering\n* [🔥🔥🔥] [Prompt Engineering Guide](https:\u002F\u002Fwww.promptingguide.ai\u002F): a project by DAIR.AI that intends to educate researchers and practitioners about prompt engineering\n* [the Book](https:\u002F\u002Ffedhoneypot.notion.site\u002F25fdbdb69e9e44c6877d79e18336fe05?v=1d2bf4143680451986fd2836a04afbf4): collection of prompts and hints of prompt engineering\n* [dair-ai\u002FPrompt-Engineering-Guide](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FPrompt-Engineering-Guide): Guide and resources for prompt engineering\n\n#### Prompt Optimizers\n\n* [zou-group\u002Ftextgrad](https:\u002F\u002Fgithub.com\u002Fzou-group\u002Ftextgrad): Automatic \"Differentiation\" via Text, using large language models to backpropagate textual gradients.\n* [🔥🔥🔥] [stanfordnlp\u002Fdspy](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy): DSPy: The framework for programming — not prompting — foundation models\n* [vaibkumr\u002Fprompt-optimizer](https:\u002F\u002Fgithub.com\u002Fvaibkumr\u002Fprompt-optimizer): Minimize LLM token complexity to save API costs and model computations.\n* [PromptPerfect](https:\u002F\u002Fpromptperfect.jina.ai\u002F): \"Optimize Your Prompts to Perfection\"\n* [🔥🔥🔥] [LLMLingua](https:\u002F\u002Fllmlingua.com\u002F): Designing a Language for LLMs via **Prompt Compression**\n\n#### Prompt Engineering for Text-to-text\n\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric): fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.\n* [ChatGPT for designers](https:\u002F\u002Ftibidavid.gumroad.com\u002Fl\u002FChatGPT-Cheat-Sheet-V2?ref=filipecalegario-awesome-generative-ai): ChatGPT Cheat Sheet V2 to craft better prompts\n* [🔥] [[2307.11760] Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11760) \n* [🔥] [[2305.13252] \"According to ...\" Prompting Language Models Improves Quoting from Pre-Training Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13252) \n* [🔥] [[2307.05300] Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.05300)\n* [timqian\u002Fopenprompt.co](https:\u002F\u002Fgithub.com\u002Ftimqian\u002Fopenprompt.co): Create. Use. Share. ChatGPT prompts\n* [60 ChatGPT Prompts for Data Science (Tried, Tested, and Rated)](https:\u002F\u002Fmedium.datadriveninvestor.com\u002F60-chatgpt-prompts-for-data-science-tried-tested-and-rated-4994c7e6adb2): post by Travis Tang from DataDrivenInvestor\n* [f\u002Fawesome-chatgpt-prompts](https:\u002F\u002Fgithub.com\u002Ff\u002Fawesome-chatgpt-prompts): this repo includes ChatGPT prompt curation to use ChatGPT better\n* [brexhq\u002Fprompt-engineering](https:\u002F\u002Fgithub.com\u002Fbrexhq\u002Fprompt-engineering): \"Tips and tricks for working with Large Language Models like OpenAI's GPT-4\"\n* [How to write an effective GPT-3 prompt | Zapier](https:\u002F\u002Fzapier.com\u002Fblog\u002Fgpt-3-prompt\u002F): a list of 6 GPT-3 tips for getting the desired output\n* [The Art of ChatGPT Prompting: A Guide to Crafting Clear and Effective Prompts](https:\u002F\u002Ffka.gumroad.com\u002Fl\u002Fart-of-chatgpt-prompting): e-book by Fatih Kadir Akın ([@fkadev](http:\u002F\u002Ftwitter.com\u002Ffkadev))\n\n#### Prompt Engineering for Text-to-image\n\n* [USP AI Prompt Book](https:\u002F\u002Fapp.usp.ai\u002Fstatic\u002FStable%20Diffusion%202.1%20Prompt%20Book%20by%20USP.ai.pdf): Stable Diffusion v2.1 Prompt Book\n* [daspartho\u002Fprompt-extend](https:\u002F\u002Fgithub.com\u002Fdaspartho\u002Fprompt-extend): extending stable diffusion prompts with suitable style cues using text generation \n* [Prompt Box](https:\u002F\u002Fwww.promptbox.ai\u002F): \"organize and save your AI prompts\"\n* [Midjourney artist reference - Google Sheets](https:\u002F\u002Fdocs.google.com\u002Fspreadsheets\u002Fd\u002F1e2MZ1K6WMTUuxlPAQ_2A0rz-H55NBykb66TY7DuerVg\u002Fedit#gid=2088669480) \n* [Stable Diffusion Prompt Book — Stability.Ai](https:\u002F\u002Fstability.ai\u002Fsdv2-prompt-book): prompt book for Stable Diffusion v2.0 and v2.1 released by Stability.AI\n* [The Ultimate Stable Diffusion Prompt Guide by PromptHero](https:\u002F\u002Fprompthero.com\u002Fstable-diffusion-prompt-guide) \n* [CLIP Interrogator - a Hugging Face Space by pharma](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fpharma\u002FCLIP-Interrogator): image-to-text tool to figure out what a good prompt might be to create new images like an existing one\n* [🔥🔥🔥] [Prompt book for data lovers II - Google Slides](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1V8d6TIlKqB1j5xPFH7cCmgKOV_fMs4Cb4dwgjD5GIsg\u002Fedit#slide=id.g1834b964b0f_3_4): An open source exploration on text-to-image and data visualization\n* [some9000\u002FStylePile](https:\u002F\u002Fgithub.com\u002Fsome9000\u002FStylePile): A helper script for AUTOMATIC1111\u002Fstable-diffusion-webui. Basically a mix and match to quickly get different results without wasting a lot of time writing prompts.\n* [Artists To Study | All images generated with Google Colab TPUs + CompVis\u002Fstable-diffusion-v1-4 + Huggingface Diffusers](https:\u002F\u002Fartiststostudy.pages.dev\u002F): a systematic study of artists' styles made by [@camenduru](https:\u002F\u002Ftwitter.com\u002Fcamenduru)\n* [CLIP retrieval for laion5B](https:\u002F\u002From1504.github.io\u002Fclip-retrieval\u002F?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false): CLIP retrieval using Laion5B. \"It works by converting the text query to a CLIP embedding , then using that embedding to query a knn index of clip image embedddings\".\n* [rom1504\u002Fclip-retrieval](https:\u002F\u002Fgithub.com\u002From1504\u002Fclip-retrieval): Easily compute CLIP embeddings and build a CLIP retrieval system with them\n* [PromptDesign | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FPromptDesign\u002F): Reddit community for \"the art of communicating with natural language models\"\n* [Prompt Engineering and Zero-Shot\u002FFew-Shot Learning [Guide] - inovex GmbH](https:\u002F\u002Fwww.inovex.de\u002Fde\u002Fblog\u002Fprompt-engineering-guide\u002F): prompt engineering for text generation\n* [clip-interrogator.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fpharmapsychotic\u002Fclip-interrogator\u002Fblob\u002Fmain\u002Fclip_interrogator.ipynb#scrollTo=rbDEMDGJrJEo): a tool for image-to-prompt\n* [Useful Prompt Engineering tools and resources | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxcrm4d\u002Fuseful_prompt_engineering_tools_and_resources\u002F) \n* [PromptHero](https:\u002F\u002Fprompthero.com\u002F): Search the best prompts for Stable Diffusion, DALL-E and Midjourney\n* [promptoMANIA](https:\u002F\u002Fpromptomania.com\u002F): AI art community with prompt generator\n* [Lexica](https:\u002F\u002Flexica.art\u002F): search over 10M+ Stable Diffusion images and prompts\n* [list of artists for SD v1.4 A-C \u002F D-I \u002F J-N \u002F O-Z](https:\u002F\u002Frentry.org\u002Fartists_sd-v1-4) \n* [succinctly\u002Ftext2image-prompt-generator · Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fsuccinctly\u002Ftext2image-prompt-generator): a GPT-2 model fine-tuned on the succinctly\u002Fmidjourney-prompts dataset, which contains 250k text prompts that users issued to the Midjourney text-to-image service over a month period\n* [The Prompter | vicc | Substack](https:\u002F\u002Ftheprompter.substack.com\u002F): a newsletter about news, tips and thoughts around prompt engineering\n* [(19) Nikhil Agrawal 📌 on Twitter](https:\u002F\u002Ftwitter.com\u002FHeyNikhila\u002Fstatus\u002F1570005481896255490): 11 AI Images Prompt websites to level up the image quality\n* [Phraser](https:\u002F\u002Fphraser.tech\u002F): a tool that support prompt creation\n* [PromptBase | Prompt Marketplace](https:\u002F\u002Fpromptbase.com\u002F): PromptBase is a marketplace for DALL·E, Midjourney & GPT-3 prompts, where people can sell prompts and make money from their prompt crafting skills.\n* [Professional AI whisperers have launched a marketplace for DALL-E prompts - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2022\u002F9\u002F2\u002F23326868\u002Fdalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview)\n* [Visual Prompt Builder](https:\u002F\u002Ftools.saxifrage.xyz\u002Fprompt): simple deck of illustrated card to combine modifiers for prompt building\n* [Prompt Engineering Template - Google Sheets](https:\u002F\u002Fdocs.google.com\u002Fspreadsheets\u002Fd\u002F1-snKDn38-KypoYCk9XLPg799bHcNFSBAVu2HVvFEAkA\u002Fedit#gid=0): spreadsheet with lists of modifiers for prompt building and a lot of interesting links for reference\n* [Prompt Engineering: From Words to Art - Saxifrage Blog](https:\u002F\u002Fwww.saxifrage.xyz\u002Fpost\u002Fprompt-engineering)\n* [DALL·Ery GALL·Ery Resources](https:\u002F\u002Fdallery.gallery\u002Fprompt-resources-tools-ai-art\u002F): DALL·E 2 and AI art prompt resources & tools to inspire beautiful images\n* [[2204.13988] A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13988) \n* [List of Aesthetics | Aesthetics Wiki | Fandom](https:\u002F\u002Faesthetics.fandom.com\u002Fwiki\u002FList_of_Aesthetics) \n* [Artist Directory (Volcano Comparison) | AI Art Creation Wiki | Fandom](https:\u002F\u002Faiartcreation.fandom.com\u002Fwiki\u002FArtist_Directory_(Volcano_Comparison)) \n* [The DALL·E 2 Prompt Book – DALL·Ery GALL·Ery](https:\u002F\u002Fdallery.gallery\u002Fthe-dalle-2-prompt-book\u002F)\n* [DALL·Ery GALL·Ery](https:\u002F\u002Fdallery.gallery\u002F): A guide to OpenAI's DALL·E – prompts, projects, examples, and tips\n* [(2) MASSIVE 💥 DALL-E 2 ANIME ⚡︎ KEYWORDS + MODIFIERS LIST ★ : haaaaven](https:\u002F\u002Fwww.reddit.com\u002Fuser\u002Fhaaaaven\u002Fcomments\u002Fw05f56\u002Fmassive_dalle_2_anime_keywords_modifiers_list\u002F): image prompt modifier collection by haaaaven\n* [DrawBench](https:\u002F\u002Fdocs.google.com\u002Fspreadsheets\u002Fd\u002F1y7nAbmR4FREi6npB1u-Bo3GFdwdOPYJc617rBOxIRHY\u002Fedit#gid=0): a list of prompts the Google Imagen is organizing as a benchmark\n* [CLIP Prompt Engineering for Generative Art - matthewmcateer.me](https:\u002F\u002Fmatthewmcateer.me\u002Fblog\u002Fclip-prompt-engineering\u002F): list of styles tested with Quick CLIP Guided Diffusion\n* [Adobe should make a boring app for prompt engineers (Interconnected)](https:\u002F\u002Finterconnected.org\u002Fhome\u002F2022\u002F06\u002F02\u002Fdalle)\n* [[2206.00169] Discovering the Hidden Vocabulary of DALLE-2](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00169)\n* [When SD just doesn't understand the prompt no matter how hard I try | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgwcab\u002Fwhen_sd_just_doesnt_understand_the_prompt_no\u002F) \n* [It's very interesting how some prompts have very defined output but other specific ones are not | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgplii\u002Fits_very_interesting_how_some_prompts_have_very\u002F)\n\n### Mamba\n\n* [[2312.00752] Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.00752): alternative to Transformer architecture.\n* [Mamba: A shallow dive into a new architecture for LLMs | by Geronimo (@geronimo7) | Dec, 2023 | Medium](https:\u002F\u002Fmedium.com\u002F@geronimo7\u002Fmamba-a-shallow-dive-into-a-new-architecture-for-llms-54c70ade5957)\n* [Mamba-Chat](https:\u002F\u002Fgithub.com\u002Fhavenhq\u002Fmamba-chat): A chat LLM based on the state-space model architecture\n\n### Running LLMs Locally\n\n* [llama.cpp guide](https:\u002F\u002Fsteelph0enix.github.io\u002Fposts\u002Fllama-cpp-guide\u002F): Running LLMs locally, on any hardware, from scratch\n* [PowerInfer](https:\u002F\u002Fgithub.com\u002FSJTU-IPADS\u002FPowerInfer): a high-speed inference engine for deploying LLMs locally \n* [🔥🔥] [Ollama](https:\u002F\u002Follama.ai\u002F): run Llama 2, Code Llama, and other models locally\n* [GPT4All](https:\u002F\u002Fgpt4all.io\u002Findex.html): A free-to-use, locally running, privacy-aware chatbot. No GPU or internet is required.\n* [LM Studio](https:\u002F\u002Flmstudio.ai\u002F): Discover, download, and run local LLMs\n* [ggerganov\u002Fllama.cpp](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp): Port of Facebook's LLaMA model in C\u002FC++\n\n### Function Calling\n\n* [Nexusflow\u002FNexusRaven-V2-13B · Hugging Face](https:\u002F\u002Fhuggingface.co\u002FNexusflow\u002FNexusRaven-V2-13B): \"surpassing GPT-4 for Zero-shot Function Calling\"\n\n### GPTs and Assistant API\n\n* [Featured GPTs](https:\u002F\u002Fwww.featuredgpts.com\u002F):  curated custom GPTs list for daily tasks\n* [AllGPTs](https:\u002F\u002Fallgpts.co\u002F): a directory to find GPTs\n\n### Retrieval-Augmented Generation (RAG)\n\n* [Benchmarking Hallucination Detection Methods in RAG | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fbenchmarking-hallucination-detection-methods-in-rag-6a03c555f063\u002F)\n* [bRAGAI\u002FbRAG-langchain](https:\u002F\u002Fgithub.com\u002FbRAGAI\u002FbRAG-langchain): Everything you need to know to build your own RAG application\n* [ragapp\u002Fragapp](https:\u002F\u002Fgithub.com\u002Fragapp\u002Fragapp): an alternative to use Agentic RAG in enterprises\n* [LlamaParse](https:\u002F\u002Fwww.llamaindex.ai\u002Fblog\u002Flaunching-the-first-genai-native-document-parsing-platform): GenAI-native document parsing platform by LlamaIndex\n* [Retrieval-Augmented Generation for Large Language Models: A Survey](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10997) \n* [weaviate\u002FVerba](https:\u002F\u002Fgithub.com\u002Fweaviate\u002FVerba): Retrieval Augmented Generation (RAG) chatbot powered by Weaviate\n* [imartinez\u002FprivateGPT](https:\u002F\u002Fgithub.com\u002Fimartinez\u002FprivateGPT): \"Interact with your documents using the power of GPT, 100% privately, no data leaks\"\n* [pinecone-io\u002Fcanopy](https:\u002F\u002Fgithub.com\u002Fpinecone-io\u002Fcanopy): Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone\n* [Forget RAG, the Future is RAG-Fusion](https:\u002F\u002Ftowardsdatascience.com\u002Fforget-rag-the-future-is-rag-fusion-1147298d8ad1): post by Adrian H. Raudaschl in Towards Data Science\n* [Rerankers and Two-Stage Retrieval | Pinecone](https:\u002F\u002Fwww.pinecone.io\u002Flearn\u002Fseries\u002Frag\u002Frerankers\u002F)\n* [Retrieval Augmented Generation | Pinecone](https:\u002F\u002Fwww.pinecone.io\u002Flearn\u002Fseries\u002Frag\u002F)\n* [dssjon\u002Fbiblos: biblos.app](https:\u002F\u002Fgithub.com\u002Fdssjon\u002Fbiblos): example of RAG architecture using semantic search and summarization for retrieving Bible passages\n\n### Embeddings and Semantic Search\n\n* [🪆 Introduction to Matryoshka Embedding Models](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fmatryoshka) \n* [Getting creative with embeddings by Amelia Wattenberger](https:\u002F\u002Fwattenberger.com\u002Fthoughts\u002Fyay-embeddings-math) \n* [The Hidden Life of Embeddings: Linus Lee - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YvobVu1l7GI) \n* [neuml\u002Ftxtai](https:\u002F\u002Fgithub.com\u002Fneuml\u002Ftxtai): semantic search and workflows powered by language models\n* [facebookresearch\u002Ffaiss](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss): A library for efficient similarity search and clustering of dense vectors \n* [Optimize Your Chatbot’s Conversational Intelligence Using GPT-3 | by Amogh Agastya | Better Programming](https:\u002F\u002Fbetterprogramming.pub\u002Fhow-to-give-your-chatbot-the-power-of-neural-search-with-openai-ebcff5194170): tutorial presenting semantic search concepts\n* [🔥] [whitead\u002Fpaper-qa](https:\u002F\u002Fgithub.com\u002Fwhitead\u002Fpaper-qa): \"LLM Chain for answering questions from documents with citations\", [demo](https:\u002F\u002Ftwitter.com\u002Fandrewwhite01\u002Fstatus\u002F1629346569756483584?s=20)\n* [What is Semantic Search?](https:\u002F\u002Ftxt.cohere.ai\u002Fwhat-is-semantic-search\u002F)\n* [Learning Center | Pinecone](https:\u002F\u002Fwww.pinecone.io\u002Flearn\u002F): Pinecone's guides to vector embeddings\n* [BLIP+CLIP | CLIP Interrogator | Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Fcode\u002Fleonidkulyk\u002Flb-0-45836-blip-clip-clip-interrogator): a Kaggle notebook for image description and captioning (imate-to-text)\n* [jerryjliu\u002Fgpt_index: GPT Index (LlamaIndex)](https:\u002F\u002Fgithub.com\u002Fjerryjliu\u002Fgpt_index): a project to make it easier to use large external knowledge bases with LLMs\n* [Llama Hub](https:\u002F\u002Fllamahub.ai\u002F): a repository of data loaders for LlamaIndex (GPT Index) and LangChain\n* [Chroma](https:\u002F\u002Fwww.trychroma.com\u002F): an open-source AI-native database that makes it easy to use embeddings\n\n### Autonomous LLM Agents\n\n* [🔥] [Building effective agents by Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Fresearch\u002Fbuilding-effective-agents): this article introduces basic concepts related to agents and didactically presents agent architectures.\n* [Complete Guide to LLM Agents (2025)](https:\u002F\u002Fbotpress.com\u002Fblog\u002Fllm-agents): summarization of terms related to LLM agents\n* [pydantic\u002Fpydantic-ai](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai): Agent Framework \u002F shim to use Pydantic with LLMs\n* [NirDiamant\u002FGenAI_Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents): tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.\n* [Hexabot](https:\u002F\u002Fgithub.com\u002Fhexastack\u002Fhexabot) Open-Source AI Chatbot \u002F Agent builder with support for LLMs as well as social media channels integration.\n* [NirDiamant\u002FGenAI_Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents): tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems. \n* [TailorTask](https:\u002F\u002Fwww.tailortask.ai) - Automate any boring task, without code and without having to learn a new tool\n* [[2406.04784] SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.04784) \n* [[2406.04692] Mixture-of-Agents Enhances Large Language Model Capabilities](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.04692) \n* [MervinPraison\u002FPraisonAI](https:\u002F\u002Fgithub.com\u002FMervinPraison\u002FPraisonAI): PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration. \n* [Practices for Governing Agentic AI Systems](https:\u002F\u002Fopenai.com\u002Fresearch\u002Fpractices-for-governing-agentic-ai-systems): paper by OpenAI that offers a set of practices for keeping agents’ operations safe and accountable.\n* [[2312.05230] Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05230) \n* [[2309.02427] Cognitive Architectures for Language Agents](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.02427): \"we draw on the rich history of cognitive science and symbolic artificial intelligence to propose Cognitive Architectures for Language Agents (CoALA)\" \n* [[2309.07864] The Rise and Potential of Large Language Model Based Agents: A Survey](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.07864)\n* [[2310.01444] Adapting LLM Agents Through Communication](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01444)\n* [[2309.17288] AutoAgents: A Framework for Automatic Agent Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.17288)\n* [Exploring Multi-Persona Prompting for Better Outputs](https:\u002F\u002Fwww.prompthub.us\u002Fblog\u002Fexploring-multi-persona-prompting-for-better-outputs): \"method of prompt engineering that instructs the LLM to summon multiple personas and have them work together to solve a task\"\n* [Conceptual Framework for Autonomous Cognitive Entities](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.06775): a paper that \"introduces the Autonomous Cognitive Entity (ACE) model, a novel framework for a cognitive architecture, enabling machines and software agents to operate more independently\"\n* [Mindstorms in Natural Language-Based Societies of Mind](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17066): a paper that evaluates the natural language-based societies of mind (NLSOMs), leveraging mindstorms in them to solve some practical AI tasks\n* [AutoGen | Microsoft](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F): multi-agent conversation framework as a high-level abstraction by Microsoft [[github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen)]\n* [OpenBMB\u002FChatDev](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev): create customized software using natural language idea (through llm-powered multi-agent collaboration)\n* [a16z-infra\u002Fai-town](https:\u002F\u002Fgithub.com\u002Fa16z-infra\u002FAI-town): A MIT-licensed, deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize.\n* [AI Town](https:\u002F\u002Fwww.convex.dev\u002Fai-town): a virtual town where AI characters live, chat and socialize.\n* [joonspk-research\u002Fgenerative_agents - Generative Agents](https:\u002F\u002Fgithub.com\u002Fjoonspk-research\u002Fgenerative_agents): code for interactive simulacra of human behavior [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03442) \n* [AgentBench: Evaluating LLMs as Agents](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2308.03688): Hugging Face paper page on a benchmark to evaluate LLMs agents\n* [geekan\u002FMetaGPT](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT): the multi-agent framework that, give one line requirement, return PRD, design, tasks, repo \n* [GPT Researcher](https:\u002F\u002Fapp.tavily.com\u002F): AI agents for insights and research\n* [Multi-agent Simulation by Jim Fan on Twitter](https:\u002F\u002Ftwitter.com\u002FDrJimFan\u002Fstatus\u002F1682086586593443841): \"The next frontier of emergent intelligence will be multi-agent simulation: a crowd of AI characters carry out their daily lives through complex social interactions\"\n* [Introducing AACP | SuperAGI](https:\u002F\u002Fsuperagi.com\u002Fintroducing-aacp-agent-to-agent-communication-protocol\u002F): agent to agent communication protocol\n* [BrainstormGPT](https:\u002F\u002Fbrainstormgpt.ai\u002F#\u002F): AI multi-agent problem solving\n* [ChatArena](https:\u002F\u002Fwww.chatarena.org\u002F): building multi-agent environments for LLMs\n* [🔥🔥🔥] [LLM Powered Autonomous Agents | Lil'Log](https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2023-06-23-agent\u002F): the LLM agents learning notes by Lilian Weng\n* [Vercel for AI agents](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fe2b): \"help developers to build, deploy, and monitor AI agents, focusing on specialized AI agents that build software for you - your personal software developers\"\n* [101dotxyz\u002FGPTeam](https:\u002F\u002Fgithub.com\u002F101dotxyz\u002FGPTeam): \"GPTeam uses GPT-4 to create multiple agents who collaborate to achieve predefined goals\"\n* [Fine-Tuner.ai](https:\u002F\u002Ffine-tuner.ai\u002F): no code approach to build AI agents\n* [AI Agent Basics: Let’s Think Step By Step - by Jon Stokes](https:\u002F\u002Fwww.jonstokes.com\u002Fp\u002Fai-agent-basics-lets-think-step-by) \n* [🔥🔥] [Transformers Agent](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Ftransformers_agents): provides a natural language API on top of Hugging Face's transformers library\n* [AgentGPT](https:\u002F\u002Fagentgpt.reworkd.ai\u002F): \"assemble, configure, and deploy autonomous AI Agents in your browser\"\n* [yoheinakajima\u002Fbabyagi](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi): an AI-powered task management system that uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks\n* [Torantulino\u002FAuto-GPT](https:\u002F\u002Fgithub.com\u002FTorantulino\u002FAuto-GPT): \"an experimental open-source attempt to make GPT-4 fully autonomous\"\n* [Generative Agents: Interactive Simulacra of Human Behavior](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03442): a paper that presents computational software agents that simulate believable human behavior\n* [microsoft\u002FJARVIS](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FJARVIS): JARVIS, a system to connect LLMs with ML community\n* [HuggingGPT](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17580): Solving AI Tasks with ChatGPT and its Friends in HuggingFace\n\n#### Multi-agents\n\n* [[2411.00114] Project Sid: Many-agent simulations toward AI civilization](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.00114) \n* [joonspk-research\u002Fgenerative_agents](https:\u002F\u002Fgithub.com\u002Fjoonspk-research\u002Fgenerative_agents): implementation of the paper Generative Agents: Interactive Simulacra of Human Behavior\n* [Generative Agent Simulations of 1,000 People | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.10109) [[GitHub: joonspk-research\u002Fgenagents]](https:\u002F\u002Fgithub.com\u002Fjoonspk-research\u002Fgenagents) \n* [microsoft\u002FTinyTroupe](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FTinyTroupe): LLM-powered multiagent persona simulation for imagination enhancement and business insights\n* [Multi-Agent Research Outline](https:\u002F\u002Fthinkwee.top\u002Fmultiagent_ebook\u002Findex.html): an interactive eBook that compiles an extensive collection of research papers on large language model (LLM)-based multi-agent systems\n* [openai\u002Fswarm](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm): Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.\n* [[2307.05300] Unleashing Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.05300)\n* [[2308.07201] ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.07201)\n* [OpenBMB\u002FChatDev](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev): create customized software using natural language idea (through llm-powered multi-agent collaboration)\n* [[2308.10848] AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.10848)\n* [BrainSoup](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup): multi-agent & multi-LLM client with RAG, multi-modality, automation, code interpreter, and sandboxed file system\n\n### LLM Evaluation\n\n* [Cleanlab Trustworthy Language Model: Score the trustworthiness of any LLM response](https:\u002F\u002Fhelp.cleanlab.ai\u002Ftlm\u002F)\n* [PAIR-code\u002Fllm-comparator](https:\u002F\u002Fgithub.com\u002FPAIR-code\u002Fllm-comparator): LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed by the PAIR team.\n* [confident-ai\u002Fdeepeval](https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepeval): The LLM Evaluation Framework\n* [LLM Benchmarks: MMLU, HellaSwag, BBH, and Beyond - Confident AI](https:\u002F\u002Fwww.confident-ai.com\u002Fblog\u002Fllm-benchmarks-mmlu-hellaswag-and-beyond) \n* [LLM Leaderboards](https:\u002F\u002Fllm.extractum.io\u002Fstatic\u002Fllm-leaderboards\u002F) \n* [Reward Bench Leaderboard - a Hugging Face Space by allenai](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fallenai\u002Freward-bench) \n* [LiveBench](https:\u002F\u002Flivebench.ai\u002F): A Challenging, Contamination-Free LLM Benchmark\n* [Evaluating Large Language Models](https:\u002F\u002Fwww.lakera.ai\u002Fblog\u002Flarge-language-model-evaluation): Methods, Best Practices & Tools | Lakera – Protecting AI teams that disrupt the world\n* [ianarawjo\u002FChainForge](https:\u002F\u002Fgithub.com\u002Fianarawjo\u002FChainForge?tab=readme-ov-file): An open-source visual programming environment for battle-testing prompts to LLMs.\n* [Prometheus-2 Cookbook - LlamaIndex](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Flatest\u002Fexamples\u002Fcookbooks\u002Fprometheus2_cookbook\u002F): \"An Open Source Language Model Specialized in Evaluating Other Language Models.\"\n* [[2305.13711] LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13711) \n* [LLM Evaluation](https:\u002F\u002Fllm-eval.github.io\u002F): research on evaluation of LLMs conducted by Microsoft Research and other collaborated institutes. (Updated at: 2023\u002F10) \n* [LLM Evaluation: Everything You Need To Run, Benchmark Evals](https:\u002F\u002Farize.com\u002Fblog-course\u002Fllm-evaluation-the-definitive-guide\u002F)\n* [The Ultimate Guide to LLM Product Evaluation](https:\u002F\u002Fblog.context.ai\u002Fthe-ultimate-guide-to-llm-product-evaluation\u002F)\n* [How to Evaluate, Compare, and Optimize LLM Systems](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fhow-evaluate-compare-optimize-llm-systems-b%C3%BClent-uyaniker-jw4qc)\n* [LLM Evaluation | Clarifai Guide](https:\u002F\u002Fdocs.clarifai.com\u002Fportal-guide\u002Fevaluate\u002Fllms\u002F)\n* [How to Evaluate LLM Applications: The Complete Guide - Confident AI](https:\u002F\u002Fwww.confident-ai.com\u002Fblog\u002Fhow-to-evaluate-llm-applications)\n* [AI Evaluation Metrics | Microsoft Learn](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fai\u002Fplaybook\u002Ftechnology-guidance\u002Fgenerative-ai\u002Fworking-with-llms\u002Feval-metrics)\n* [How to Evaluate Large Language Model Outputs: Current Best Practices | FinetuneDB](https:\u002F\u002Ffinetunedb.com\u002Fblog\u002Fhow-to-evaluate-large-language-model-outputs\u002F)\n* [The Ultimate Guide to LLM Evaluation | Deci](https:\u002F\u002Fdeci.ai\u002Fblog\u002Fllm-evaluation-ultimate-guide\u002F)\n* [Large Language Model Evaluation in 2024: 5 Methods](https:\u002F\u002Fresearch.aimultiple.com\u002Flarge-language-model-evaluation\u002F)\n* [Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2403.16950v2)\n* [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05685) \n* [LLM Evaluation Metrics: Everything You Need for LLM Evaluation - Confident AI](https:\u002F\u002Fwww.confident-ai.com\u002Fblog\u002Fllm-evaluation-metrics-everything-you-need-for-llm-evaluation)\n* [Criteria Evaluation | 🦜️🔗 LangChain](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fguides\u002Fproductionization\u002Fevaluation\u002Fstring\u002Fcriteria_eval_chain\u002F)\n* [Evaluation of LLMs - Part 1](https:\u002F\u002Fblog.premai.io\u002Fevaluation-of-llms-part-1\u002F) \n* [Evaluation of LLMs - Part 2](https:\u002F\u002Fblog.premai.io\u002Fevaluation-of-llms-part-2\u002F)\n* [The Crucial Role of Model Evaluation in LLM and AI Integrations](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fcrucial-role-model-evaluation-llm-ai-integrations-vijay-chaudhary)\n* [MLGroupJLU\u002FLLM-eval-survey: The official GitHub page for the survey paper \"A Survey on Evaluation of Large Language Models\".](https:\u002F\u002Fgithub.com\u002FMLGroupJLU\u002FLLM-eval-survey) \n* [A Survey on Evaluation of Large Language Models | ACM Transactions on Intelligent Systems and Technology](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3641289)\n* [[2307.03109] A Survey on Evaluation of Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03109) \n* [qcri\u002FLLMeBench](https:\u002F\u002Fgithub.com\u002Fqcri\u002FLLMeBench\u002F): Benchmarking Large Language Models\n* [TruLens for LLMs](https:\u002F\u002Fwww.trulens.org\u002F): Evaluate and Track LLM Applications\n* [LLM Testing Guide](https:\u002F\u002Fgo.kolena.com\u002Fllm-testing-guide): Comprehensive Strategies for Testing and Behavior Analysis by Kolena\n* [Chatbot Arena](https:\u002F\u002Fchat.lmsys.org\u002F?arena): benchmarking LLMs through pairwise confrontation and evaluation\n* [[2311.12022] GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12022) \n* [OpenAI Cookbook: Evaluating RAG systems | by Ravi Theja | Nov, 2023 | LlamaIndex Blog](https:\u002F\u002Fblog.llamaindex.ai\u002Fopenai-cookbook-evaluating-rag-systems-fe393c61fb93) \n* [Amazon will offer human benchmarking teams to test AI models - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2023\u002F11\u002F29\u002F23981129\u002Famazon-aws-ai-model-evaluation-bias-toxicity) \n* [[2311.05020] First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05020): \"that meaningful evaluation informed by actual use is still an open problem\"\n* [[2311.12983] GAIA: a benchmark for General AI Assistants](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12983)\n* [Sharing LangSmith Benchmarks](https:\u002F\u002Fblog.langchain.dev\u002Fpublic-langsmith-benchmarks\u002F) \n* [[2311.09247] Comparing Humans, GPT-4, and GPT-4V On Abstraction and Reasoning Tasks](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.09247) \n* [vectara\u002Fhallucination-leaderboard](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fhallucination-leaderboard): \"leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents\"\n* [[2305.16938] Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16938) \n* [LLM Comparison\u002FTest](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F17fhp9k\u002Fhuge_llm_comparisontest_39_models_tested_7b70b\u002F): 39 models tested (7B-70B + ChatGPT\u002FGPT-4)\n* [LLM Evaluation at Scale – Airtrain](https:\u002F\u002Fwww.airtrain.ai\u002F): no-code batch compute platform for LLM evaluation and tuning workloads\n* [How to evaluate a summarization task | OpenAI Cookbook](https:\u002F\u002Fcookbook.openai.com\u002Fexamples\u002Fevaluation\u002Fhow_to_eval_abstractive_summarization) \n* [openai\u002Fevals](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fevals): Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.\n* [Red teaming and model evaluations | Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Fuk-government-internal-ai-safety-policy-response\u002Fred-teaming-and-model-evaluations)\n* [Challenges in evaluating AI systems | Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Findex\u002Fevaluating-ai-systems)\n* [Evaluating LLMs is a minefield](https:\u002F\u002Fwww.cs.princeton.edu\u002F~arvindn\u002Ftalks\u002Fevaluating_llms_minefield\u002F): talk by Princeton professor Arvind Narayanan\n* [Indico LLM Leaderboard](https:\u002F\u002Findicodata.ai\u002Fllm): Indico Data runs a monthly benchmarking exercise across providers (LLama, Azure OpenAI, Google, AWS Bedrock, and Indico trained discriminative standard language models RoBERTa and DeBERTa), datasets (e.g. cord and CUAD), and capabilities (text classification, key information extraction, and generative summarization).\n* [LLM Rankings](https:\u002F\u002Fopenrouter.ai\u002Frankings): a leaderboard that compares LLMs for all prompts.\n* [LLM Use Case Leaderboard](https:\u002F\u002Fllmleaderboard.goml.io): a leaderboard that features LLM use cases.\n* [LMExamQA](https:\u002F\u002Flmexam.com): a leaderboard that benchmarks foundation models with Language-Model-as-an-Examiner.\n* [The Pile](https:\u002F\u002Fpile.eleuther.ai): a leaderboard of The Pile benchmark.\n\n### LLMOps\n\n* [Lunary](https:\u002F\u002Flunary.ai): Open-source platform for LLM chatbots and agents: observability, prompt management, testing & more\n* [Eden AI](https:\u002F\u002Fwww.edenai.co\u002F?referral=partner-producthunt8&ref=producthunt): provides a unique API connected to the AI engines \n* [Dify](https:\u002F\u002Fdify.ai\u002F): LLMOps platform for creating and operating AI-native apps based on GPT-4\n* [LLM App](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fllm-app): LLM App is a Python library that helps you build real-time AI-powered data pipelines with few lines of code.\n\n### AI Engineering\n\n* [An AI Engineer’s Guide to Machine Learning and Generative AI | by ai geek (wishesh) | Oct, 2023 | Medium](https:\u002F\u002Fmedium.com\u002F@_aigeek\u002Fan-ai-engineers-guide-to-machine-learning-and-generative-ai-b7444941ccee)\n* [Keywords AI](https:\u002F\u002Fwww.keywordsai.co\u002F): The enterprise-grade software to build, monitor, and improve your AI application. Keywords AI is a full-stack LLM engineering platform for developers and PMs.\n* [Marvin](https:\u002F\u002Fwww.askmarvin.ai\u002F): AI engineering framework for building natural language interfaces\n* [Instructor](https:\u002F\u002Fjxnl.github.io\u002Finstructor\u002F): library for structured LLM extraction in Python\n* [One AI](https:\u002F\u002Foneai.com\u002F): an NLP-as-a-service platform\n* [LangSmith](https:\u002F\u002Fwww.langchain.com\u002Flangsmith): a developer platform for deploying LLM apps\n\n### Attacks on LLMs\n\n* [Constitutional Classifiers](https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.18837): Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming\n* [briland\u002FLLM-security-and-privacy](https:\u002F\u002Fgithub.com\u002Fbriland\u002FLLM-security-and-privacy): LLM security and privacy\n* [ZombAIs](https:\u002F\u002Fembracethered.com\u002Fblog\u002Fposts\u002F2024\u002Fclaude-computer-use-c2-the-zombais-are-coming\u002F): From Prompt Injection to C2 with Claude Computer Use\n* [[2310.04451] AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04451)\n* [MITRE ATLAS™](https:\u002F\u002Fatlas.mitre.org\u002F): knowledge base of adversary tactics and techniques based on real-world attack observations and realistic demonstrations from AI red teams and security groups, modeled after the MITRE ATT&CK® framework.\n* [OWASP Top 10 for Large Language Model Applications](https:\u002F\u002Fllmtop10.com\u002F): the Open Worldwide Application Security Project's list related to LLMs [[Youtube video]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=engR9tYSsug)  \n* [Scalable Extraction of Training Data from (Production) Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.17035): extracting training data from ChatGPT [[webpage]](https:\u002F\u002Fnot-just-memorization.github.io\u002Fextracting-training-data-from-chatgpt.html)\n* [The Emerging Attacks on Large Language Models (LLMs)](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Femerging-attacks-large-language-models-llms-soumak-roy\u002F): \"key attack vectors that threat actors can exploit to compromise or manipulate LLMs\".\n* [Adversarial Attacks on LLMs | Lil'Log](https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2023-10-25-adv-attack-llm\u002F)\n* [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12173)\n* [Attacking Large Language Models](https:\u002F\u002Fsystemweakness.com\u002Fattacking-large-language-models-37229085d4ff): an overview of the current attack techniques on LLMs by Marcello Carboni \n* [corca-ai\u002Fawesome-llm-security](https:\u002F\u002Fgithub.com\u002Fcorca-ai\u002Fawesome-llm-security): A curation of awesome tools, documents and projects about LLM Security.\n* [Adversarial Prompting](https:\u002F\u002Fwww.promptingguide.ai\u002Frisks\u002Fadversarial): a list of adversarial prompts attacks by Prompt Engineering Guide\n\n### LangChain\n\n* [LangChain Cheatsheet](https:\u002F\u002Fpub.towardsai.net\u002Flangchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde): All Secrets on a Single Page | by Ivan Reznikov | Nov, 2023 | Towards AI\n* [LangChain Template: Research Assistant](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002Ftree\u002Fmaster\u002Ftemplates\u002Fresearch-assistant)\n* [Embedchain](https:\u002F\u002Fgithub.com\u002Fembedchain\u002Fembedchain): Framework to create ChatGPT like bots over your dataset\n* [FlowiseAI](https:\u002F\u002Fflowiseai.com\u002F): \"Open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript\u002FJavascript\"\n* [Langchain for paper summarization](https:\u002F\u002Flancemartin.notion.site\u002Flancemartin\u002FLangchain-for-paper-summarization-d4ad122ea9a64c0eb1f981e743d6c419)\n* [LangChain Docs](https:\u002F\u002Flangchain.readthedocs.io\u002Fen\u002Flatest\u002F#): Python library that helps building applications with LLMs through composability\n* [Getting started with LangChain | by Avra | Feb, 2023 | Medium](https:\u002F\u002Fmedium.com\u002F@avra42\u002Fgetting-started-with-langchain-a-powerful-tool-for-working-with-large-language-models-286419ba0842): A powerful tool for working with Large Language Models\n\n### ChatGPT\n\n* [Advanced Guide to ChatGPT](https:\u002F\u002Faaditsh.notion.site\u002Faaditsh\u002FAdvanced-Guide-to-ChatGPT-b8d5901b8bba44f580bb0c0835644567): guide by Neatprompts.com\n* [🔥] [104 Growth Hacking Swipe (ChatGPT)](https:\u002F\u002Fdoc.clickup.com\u002F25598832\u002Fp\u002Fh\u002Frd6vg-11110\u002F502bfba03b21bad): set of ChatGPT prompts for design, products and marketing\n* [acheong08's list \u002F Awesome ChatGPT](https:\u002F\u002Fgithub.com\u002Fstars\u002Facheong08\u002Flists\u002Fawesome-chatgpt): list of wrappers for accessing ChatGPT in platform such as Discord, Telegram, and languages such as Python, JS.\n* [🔥🔥🔥] [Awesome ChatGPT Prompts](https:\u002F\u002Fprompts.chat\u002F): repo that includes curated ChatGPT prompts to obtain better results from ChatGPT\n* [(\"Publicly announced ChatGPT variants and competitors: a thread\" \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Fgoodside\u002Fstatus\u002F1606611869661384706): a Twitter thread by [@goodside](https:\u002F\u002Ftwitter.com\u002Fgoodside) with alternatives to ChatGPT \n\n### Text-related Generative Tools\n\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric): fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere\n* [Jack AI](https:\u002F\u002Fwww.usejackai.com): AI Marketing Copywriter tool\n* [aiPDF](https:\u002F\u002Faipdf.ai): The most advanced AI document assistant\n* [AICamp](https:\u002F\u002Faicamp.so\u002F): ChatGPT for Teams\n* [Yomu](https:\u002F\u002Fwww.yomu.ai): AI writing assistant for students and academics\n* [Google Sheets Formula Generator](https:\u002F\u002Fbettersheets.co\u002Fgoogle-sheets-formula-generator?ref=filipecalegario-awesome-generative-ai): Forget about frustrating formulas in Google Sheets.\n* [Elephas](https:\u002F\u002Felephas.app\u002F?ref=filipecalegario-awesome-generative-ai): Personal AI writing assistant for the Mac.\n* [Lemmy](https:\u002F\u002Flemmy.co\u002F?ref=filipecalegario-awesome-generative-ai): Autonomous AI Assistant for Work.\n* [Fable Fiesta](https:\u002F\u002Ffablefiesta.com): Creative AI writing assistant\n* [Plus AI for Google Slides](https:\u002F\u002Fwww.plusdocs.com\u002Fplus-ai-for-google-slides): Create AI-powered presentations in Google Slides\n* [ChatBotKit](https:\u002F\u002Fchatbotkit.com\u002F): toolkit to build AI chat bots\n* [Boring Report](https:\u002F\u002Fwww.boringreport.org\u002F): \"an app that uses AI to remove sensationalism from the news and makes it boring to read\"\n* [ChatPDF - Chat with any PDF!](https:\u002F\u002Fwww.chatpdf.com\u002F): upload a PDF file and make questions about it #semanticsearch \n* [Character.AI](https:\u002F\u002Fbeta.character.ai\u002F): platform for creating and talking to advanced AI Characters\n* [SlidesAI](https:\u002F\u002Fwww.slidesai.io\u002F): \"create presentation slides with AI in minutes\"\n* [Rationale](https:\u002F\u002Frationale.jina.ai\u002F): decision-making tool powered by the latest GPT and in-context learning\n* [DetangleAI](https:\u002F\u002Fdetangle.ai): AI-generated summaries of provided legal docs\n* [GPT-2 Output Detector](https:\u002F\u002Fhuggingface.co\u002Fopenai-detector): tool that estimate is a given text is real or generated by GPT\n* [HyperWrite](https:\u002F\u002Fhyperwriteai.com\u002F): a personal writing assistant with suggestions and sentence completions\n* [DeepStory](https:\u002F\u002Fwww.deepstory.ai\u002F#!\u002F): A tale of co-creation between man & machine\n* [InferKit](https:\u002F\u002Fapp.inferkit.com\u002Fdemo)\n* [CopyHat](https:\u002F\u002Fcopyhat.com\u002F)\n* [Lucid Lyrics - AI Assisted Art](https:\u002F\u002Fwww.lucidlyricsart.com\u002F): AI-Assisted Lyrical Interpretations by Walter Arnold\n* [Authors A.I.](https:\u002F\u002Fauthors.ai\u002F): AI-powered text analysis\n* [Rytr](https:\u002F\u002Frytr.me\u002F): Rytr is an AI writing assistant that helps creating content\n* [Charisma](https:\u002F\u002Fcharisma.ai\u002F): Charisma is a platform for creating interactive stories with believable virtual characters\n* [Riku.AI | The vault for your A.I. creations](https:\u002F\u002Friku.ai\u002F) \n* [First look - Riku.ai - inference platform Mar\u002F2022 - J1, GPT-3, Fairseq-13B, GPT-NeoX-20B, Cohere-XL - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=t6FESjmPeJ8) \n* [Taskade](https:\u002F\u002Ftaskade.com\u002F): Taskade is an AI outliner and mind map generator for teams with built-in AI chat\n* [AI Story Generator (Advance Options)](https:\u002F\u002Faistorygenerator.chat\u002F) Create Unique and Engaging Stories Instantly with Customized Tone, Genre, and Narration.\n* [AI Story Generator](https:\u002F\u002Fwww.aistorygenerator.org): Free and fast online AI-powered story generator that writes short stories for you\n* [AI Story Generate](https:\u002F\u002Faistorygenerate.com): Generate stories using LLM with custom emotion, genre, and word count.\n* [Composum AI](https:\u002F\u002Fgithub.com\u002Fist-dresden\u002Fcomposum-AI) Plugin for CMS Adobe Experience Manager (AEM) or Composum Pages helping the editor to create \u002F edit \u002F translate texts\n* [TextCraft](https:\u002F\u002Fgithub.com\u002Fsuncloudsmoon\u002FTextCraft) Add-in for Microsoft Word that seamlessly integrates essential AI tools, including text generation, proofreading, and more, directly into the user interface.\n\n## Research AI Tools\n\n### AI Tools for Research\n\n* [Undermind - AI-Powered Scientific Research Assistant](https:\u002F\u002Fundermind.ai\u002Fhome\u002F): an AI assistant that reads academic papers.\n* [Scite](https:\u002F\u002Fscite.ai\u002F): AI Assistant or search the literature to transform the way you discover, evaluate, and understand research on any topic.\n* [SciSummary](https:\u002F\u002Fscisummary.com\u002F): AI to summarize scientific articles and research papers in seconds\n* [SciSpace](https:\u002F\u002Ftypeset.io\u002F): AI Chat for scientific PDFs\n* [Scholarcy](https:\u002F\u002Fwww.scholarcy.com\u002F): Summarize, analyze and organize your research\n* [Research Rabbit](https:\u002F\u002Fresearchrabbitapp.com\u002Fhome) \n* [Nested Knowledge](https:\u002F\u002Fnested-knowledge.com\u002F): Powerful evidence synthesis tools for medical researchers. Accelerate, collaborate, automate and share.\n* [Litmaps](https:\u002F\u002Fwww.litmaps.com\u002F): Literature Review Assistant\n* [Keenious](https:\u002F\u002Fkeenious.com\u002F): Find research relevant to any text\n* [Inciteful](https:\u002F\u002Finciteful.xyz\u002F): Using Citations to Explore Academic Literature\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric): fabric is an open-source framework for augmenting humans using AI. It provides a modular framework for solving specific problems using a crowdsourced set of AI prompts that can be used anywhere.\n* [AI Research Tools | x post](https:\u002F\u002Fx.com\u002Fairesearchtools\u002Fstatus\u002F1704031145476648992): Some AI tools that can be used for research\u002Fteaching\n* [Unlocking productivity and personalizing learning with AI | Microsoft EDU](https:\u002F\u002Feducationblog.microsoft.com\u002Fen-us\u002F2024\u002F01\u002Funlocking-productivity-and-personalizing-learning-with-ai) \n* [Sourcely](https:\u002F\u002Fwww.sourcely.net\u002F): Academic Citation Finding Tool with AI\n* [GummySearch](https:\u002F\u002Fgummysearch.com\u002F?ref=filipecalegario-awesome-generative-ai): AI-based customer research via Reddit. Discover problems to solve, sentiment on current solutions, and people who want to buy your product.\n* [[2310.17143] Supercharging academic writing with generative AI: framework, techniques, and caveats](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17143) \n* [Elicit](https:\u002F\u002Felicit.org\u002F): automate research workflow for literature review\n* [Paper Brain](https:\u002F\u002Fwww.paperbrain.study\u002F): summarizer for paper parts. The user needs to copy and paste into their interface.\n* [Explainpaper](https:\u002F\u002Fwww.explainpaper.com\u002F): \"Upload a paper, highlight confusing text, get an explanation\"\n* [Paper Player](https:\u002F\u002Fpaperplayerapp.com\u002F): A new way for busy scientists and technologists to consume open science\n* [TalkToPapers - namuan\u002Fdr-doc-search: Converse with book - Built with GPT-3](https:\u002F\u002Fgithub.com\u002Fnamuan\u002Fdr-doc-search): a github util where AI will do the paper reading for you instead\n* [hwaseem04\u002FResearch-digest](https:\u002F\u002Fgithub.com\u002Fhwaseem04\u002FResearch-digest): Research paper summariser application for our hackathon\n\n### AI Tools for Searching\n\n* [whitead\u002Fpaper-qa](https:\u002F\u002Fgithub.com\u002Fwhitead\u002Fpaper-qa): \"LLM Chain for answering questions from documents with citations\"\n* [Metaphor](https:\u002F\u002Fmetaphor.systems\u002F): search engine that \"understands language — in the form of prompts — so you can say what you're looking for in all the expressive and creative ways\"\n* [MemFree](https:\u002F\u002Fgithub.com\u002Fmemfreeme\u002Fmemfree) - Open Source Hybrid AI Search Engine, Instantly Get Accurate Answers from the Internet, Bookmarks, Notes, and Docs. Support One-Click Deployment.\n\n# Image\n\n## Image Synthesis\n\n* [TokenVerse](https:\u002F\u002Ftoken-verse.github.io\u002F): Versatile Multi-concept Personalization in Token Modulation Space\n* [The FLUX.1 family of models – Replicate](https:\u002F\u002Freplicate.com\u002Fcollections\u002Fflux) \n* [ToTheBeginning\u002FPuLID: Official code for PuLID: Pure and Lightning ID Customization via Contrastive Alignment](https:\u002F\u002Fgithub.com\u002FToTheBeginning\u002FPuLID)\n* [Edit Your Image](https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fysharma\u002Fedit-your-image-662be093bf97b697957c3c3f): Find all the trending and useful Gradio demos that you can use to edit your images\n* [OutfitAnyone - a Hugging Face Space by HumanAIGC](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FHumanAIGC\u002FOutfitAnyone): Ultra-high quality virtual try-on for Any Clothing and Any Person\n* [StockPhotoAI.net](https:\u002F\u002Fwww.stockphotoai.net\u002F?ref=filipecalegario-awesome-generative-ai): Great stock photos, made for you.\n* [Transforming 2D Images into 3D with the AdaMPI AI Model](https:\u002F\u002Fnotes.aimodels.fyi\u002Ftransforming-2d-images-into-3d-with-the-adampi-ai-model\u002F): guide on how to use the AdaMPI AI model for creating 3D photos from 2D images\n* [deep-floyd\u002FIF](https:\u002F\u002Fgithub.com\u002Fdeep-floyd\u002FIF): open-source text-to-image model with a high degree of photorealism and language understanding by Stability.AI\n* [Word-As-Image for Semantic Typography](https:\u002F\u002Fwordasimage.github.io\u002FWord-As-Image-Page\u002F): semantically transforming fonts into illustrations\n* [Scribble Diffusion](https:\u002F\u002Fscribblediffusion.com\u002F): turn your sketch into a refined image using AI\n* [Muse: Text-To-Image Generation via Masked Generative Transformers](https:\u002F\u002Fmuse-model.github.io\u002F)\n* [openai\u002Fpoint-e](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fpoint-e): OpenAI's point cloud diffusion for 3D model synthesis\n* [[arxiv\u002F2211.11319] VectorFusion](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.11319): Text-to-SVG by Abstracting Pixel-Based Diffusion Models\n* [Parrot Zone](https:\u002F\u002Fproximacentaurib.notion.site\u002Fproximacentaurib\u002Fparrot-zone-74a5c04d4feb4f12b52a41fc8750b205): a database of image synthesis references\n* [Image Synth Link List](https:\u002F\u002Fproximacentaurib.notion.site\u002F39805c50735849cfa54b5d688587e12e?v=b9ea748623e342fdae02d07c86c668bf): a collection of links organized by the collective parrot zone\n* [🔥🔥🔥] [Ai generative art tools](https:\u002F\u002Fpharmapsychotic.com\u002Ftools.html): a massive list of shared Google Colab notebooks and tools organized by [@pharampsychotic](https:\u002F\u002Ftwitter.com\u002Fpharmapsychotic)\n* [Introduction — PyTTI-Tools](https:\u002F\u002Fpytti-tools.github.io\u002Fpytti-book\u002Fintro.html)\n* [pyttitools-PYTTI.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fpytti-tools\u002Fpytti-notebook\u002Fblob\u002Fmain\u002Fpyttitools-PYTTI.ipynb) \n* [pixray\u002Fpixray](https:\u002F\u002Fgithub.com\u002Fpixray\u002Fpixray): Pixray is an image generation system\n* [pixray\u002Fpixray_notebooks](https:\u002F\u002Fgithub.com\u002Fpixray\u002Fpixray_notebooks): pixray demo notebooks\n* [dribnet\u002Fpixray-text2image – Run with an API on Replicate](https:\u002F\u002Freplicate.com\u002Fdribnet\u002Fpixray-text2image) \n* [sberbank-ai\u002Fru-dalle](https:\u002F\u002Fgithub.com\u002Fsberbank-ai\u002Fru-dalle): Generate images from texts. In Russian.\n* [Pyttipanna](https:\u002F\u002Fpyttipanna.xyz\u002F): visual interface for Pytti by [@_staus](https:\u002F\u002Ftwitter.com\u002F_staus). Pytti is created by [@sportsracer48](https:\u002F\u002Ftwitter.com\u002Fsportsracer48)\n* [Imagen](https:\u002F\u002Fimagen.research.google\u002F): Google's Text-to-Image Diffusion Models\n* [Make-A-Scene](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Fgreater-creative-control-for-ai-image-generation\u002F): Meta's creative control for AI image generation\n* [Stable Diffusion](https:\u002F\u002Fstability.ai\u002Fblog\u002Fstable-diffusion-announcement): Stability.Ai's text-to-image model that is a breakthrough in speed and quality meaning that it can run on consumer GPUs\n* [CLIPasso](https:\u002F\u002Fclipasso.github.io\u002Fclipasso\u002F): Semantically-Aware Object Sketching\n* [DreamFusion \u002F Twitter](https:\u002F\u002Ftwitter.com\u002F_akhaliq\u002Fstatus\u002F1575541930905243652?t=m17X6zyC0c8-VvIWjICc1w&s=33): Text-to-3D using 2D Diffusion paper\n* [apple\u002Fml-no-token-left-behind](https:\u002F\u002Fgithub.com\u002Fapple\u002Fml-no-token-left-behind): PyTorch Implementation of No Token Left Behind: Explainability-Aided Image Classification and Generation\n* [disco-diffusion\u002FLocal_Disco_Diffusion_v4_1.ipynb at main · Midgraph\u002Fdisco-diffusion](https:\u002F\u002Fgithub.com\u002FMidgraph\u002Fdisco-diffusion\u002Fblob\u002Fmain\u002FLocal_Disco_Diffusion_v4_1.ipynb)\n* [Audio to keyframe string](https:\u002F\u002Faudio-keyframe-generator.glitch.me\u002F): this tool is used to generate strings for the keyframes of AI animation notebooks, such as [this VQGAN+CLIP Animations notebook](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fchigozienri\u002FVQGAN-CLIP-animations\u002Fblob\u002Fmain\u002FVQGAN-CLIP-animations.ipynb), using the volume of audio tracks.\n* [🔥] [S2ML Image Generator](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fjustin-bennington\u002FS2ML-Generators\u002Fblob\u002Fmain\u002FS2ML_Image_Generator.ipynb): evolution of the first VQGAN+CLIP Google Colab notebook by Katherine Crownson maintained by Justin Bennington\n* [🔥] [Create Variations on Images With Looking Glass 1.1 (ru-DALLE) - YouTube | Artificial Images](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=37_Zjreghw4)\n* [🔥] [Looking Glass 1.1 (ru-DALLE)](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F11vdS9dpcZz2Q2efkOjcwyax4oob6N40G): Making ruDALL-E fine tuning quick and painless. Copyright (C) 2021 Bearsharktopus Studios\n* [NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion (ML Research Paper Explained) - YouTube | Yannic Kilcher](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=InhMx1h0N40&t=603s) \n* [🔥] [yuval-alaluf\u002Fhyperstyle](https:\u002F\u002Fgithub.com\u002Fyuval-alaluf\u002Fhyperstyle): Official Implementation for \"HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing\" https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.15666\n* [🔥] [Vadim Epstein’s Aphantasia library](https:\u002F\u002Fgithub.com\u002Feps696\u002Faphantasia): CLIP + FFT\u002FDWT\u002FRGB = text to image\u002Fvideo\n* [mikaelalafriz\u002Flucid-sonic-dreams](https:\u002F\u002Fgithub.com\u002Fmikaelalafriz\u002Flucid-sonic-dreams): syncs GAN-generated visuals to music\n* [Greg Surma - Portfolio](https:\u002F\u002Fgsurma.github.io\u002F) \n* [crowsonkb (Katherine Crowson)](https:\u002F\u002Fgithub.com\u002Fcrowsonkb): who wrote [the tutorial of VQGAN+CLIP](https:\u002F\u002Fsourceful.us\u002Fdoc\u002F935\u002Fintroduction-to-vqganclip)\n* [DALL·E](https:\u002F\u002Fopenai.com\u002Fblog\u002Fdall-e\u002F): Creating Images from Text\n* [DALL-E mini](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fflax-community\u002Fdalle-mini): DALL·E mini is an AI model that generates images from any prompt you give!\n* [DALL-E mini GitHub](https:\u002F\u002Fgithub.com\u002Fborisdayma\u002Fdalle-mini)\n* [DALL-E mini Project Report](https:\u002F\u002Fwandb.ai\u002Fdalle-mini\u002Fdalle-mini\u002Freports\u002FDALL-E-mini--Vmlldzo4NjIxODA)\n* [CLIPIT PixelDraw - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdribnet\u002Fclipit\u002Fblob\u002Fmaster\u002Fdemos\u002FPixelDrawer.ipynb) \n* [CLIP Guided Diffusion HQ 512x512.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1V66mUeJbXrTuQITvJunvnWVn96FEbSI3#scrollTo=X5gODNAMEUCR) \n* [Smooth Transitioning Between Position \u002F Rotation \u002F Zoom and Text Inputs by Keyframing Parameters: A Proof of Concept [15,000 Frames] : deepdream](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fdeepdream\u002Fcomments\u002Fpagqjx\u002Fsmooth_transitioning_between_position_rotation\u002F) \n* [neural-dream Alternatives and Similar Photos & Graphics Apps | AlternativeTo](https:\u002F\u002Falternativeto.net\u002Fsoftware\u002Fneural-dream\u002F) \n* [CoG 21](https:\u002F\u002Fwww.ea.com\u002Fseed\u002Fnews\u002Fcog2021-adversarial-rl-content-generation): Adversarial Reinforcement Learning for Procedural Content Generation\n* [GitHub Repositories of Hugging Face](https:\u002F\u002Fgithub.com\u002Fhuggingface)\n\n### Inbox: Stable Diffusion\n\n* [Complete guide to samplers in Stable Diffusion - Félix Sanz](https:\u002F\u002Fwww.felixsanz.dev\u002Farticles\u002Fcomplete-guide-to-samplers-in-stable-diffusion)\n* [Stable Diffusion Models](https:\u002F\u002Frentry.org\u002Fsdmodels): list of custom Stable Diffusion models\n* [Stable Diffusion KLMC2 Animation.ipynb forked](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdmarx\u002Fnotebooks\u002Fblob\u002Fmain\u002FStable_Diffusion_KLMC2_Animation.ipynb): fork by [@DigThatData](https:\u002F\u002Ftwitter.com\u002FDigThatData)\n* [Stable Diffusion KLMC2 Animation.ipynb](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1m8ovBpO2QilE2o4O-p2PONSwqGn4_x2G): notebook by [@RiversHaveWings](https:\u002F\u002Ftwitter.com\u002FRiversHaveWings) to generate animation based on scripted prompts using a technique called KLMC2 discretization of underdamped Langevin dynamics\n* [DETEXTIFY](https:\u002F\u002Fgithub.com\u002Fiuliaturc\u002Fdetextify): A Python library to remove unwanted pseudo-text from images generated by your favorite generative AI models (Stable Diffusion, Midjourney, DALL·E)\n* [InvokeAI](https:\u002F\u002Finvoke-ai.github.io\u002FInvokeAI\u002F): Stable Diffusion Toolkit and application that runs Windows, Mac and Linux machines, and on GPU cards with as little as 4 GB or RAM\n* [Stability.ai REST API Documentation](https:\u002F\u002Fapi.stability.ai\u002Fdocs): service provided by Stability.ai. DreamStudio authentication required to access this REST API\n* [🔥🔥🔥] [SD GUIDE FOR ARTISTS AND NON-ARTISTS - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1R2UZi5G-DXiz2HcCrfAFLYJoer_JPDEoZmV7wy1tEz0\u002Fedit#): a Google Docs with in-depth tips, tricks, tutorials and more related to Stable Diffusion\n* [NEWS][Canva Adds a Free and Unlimited AI Text-to-Image Generator | PetaPixel](https:\u002F\u002Fpetapixel.com\u002F2022\u002F11\u002F10\u002Fcanva-adds-a-free-and-unlimited-ai-text-to-image-generator\u002F)\n* [prompthero\u002Fmidjourney-v4-diffusion · Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fprompthero\u002Fmidjourney-v4-diffusion): Stable Diffusion fine tuned on Midjourney v4 images, by [PromptHero](https:\u002F\u002Fprompthero.com\u002F)\n* [CHARL-E](https:\u002F\u002Fwww.charl-e.com\u002F): Run Stable Diffusion on your M1 Mac\n* [The Illustrated Stable Diffusion](https:\u002F\u002Fjalammar.github.io\u002Fillustrated-stable-diffusion\u002F): explained by Jay Alammar (Visualizing machine learning one concept at a time)\n* [Img To Music](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffffiloni\u002Fimg-to-music) a Hugging Face Space by fffiloni\n* [Atlas KREA Stable Diffusion](https:\u002F\u002Fatlas.nomic.ai\u002Fmap\u002F809ef16a-5b2d-4291-b772-a913f4c8ee61\u002F9ed7d171-650b-4526-85bf-3592ee51ea31): An explorable map of KREA AI's Stable Diffusion Search Engine\n* [TheLastBen\u002Ffast-stable-diffusion](https:\u002F\u002Fgithub.com\u002FTheLastBen\u002Ffast-stable-diffusion): fast-stable-diffusion, +25-50% speed increase + memory efficient + DreamBooth\n* [NovelAI Improvements on Stable Diffusion | by NovelAI | Oct, 2022 | Medium](https:\u002F\u002Fblog.novelai.net\u002Fnovelai-improvements-on-stable-diffusion-e10d38db82ac)\n* [ashawkey\u002Fstable-dreamfusion](https:\u002F\u002Fgithub.com\u002Fashawkey\u002Fstable-dreamfusion): A pytorch implementation of text-to-3D dreamfusion, powered by stable diffusion.\n* [🔥🔥🔥] [JoePenna\u002FDreambooth-Stable-Diffusion](https:\u002F\u002Fgithub.com\u002FJoePenna\u002FDreambooth-Stable-Diffusion): Implementation of Dreambooth (https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12242) with Stable Diffusion (tweaks focused on training faces)\n* [🔥🔥🔥] [DreamBooth](https:\u002F\u002Fdreambooth.github.io\u002F): fine tuning text-to-image diffusion models for subject-driven generation\n* [🔥] [Arki's Stable Diffusion Guides](https:\u002F\u002Fstablediffusionguides.carrd.co\u002F#one)\n* [examples\u002Fstable-diffusion-finetuning at main · LambdaLabsML\u002Fexamples](https:\u002F\u002Fgithub.com\u002FLambdaLabsML\u002Fexamples\u002Ftree\u002Fmain\u002Fstable-diffusion-finetuning): Fine Tuning Stable Diffusion\n* [lkwq007\u002Fstablediffusion-infinity](https:\u002F\u002Fgithub.com\u002Flkwq007\u002Fstablediffusion-infinity): Outpainting with Stable Diffusion on an infinite canvas \n* [🔥🔥🔥] [ML News Stable Diffusion Takes Over! (Open Source AI Art) by Yannic Kilcher - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xbxe-x6wvRw): video with examples, updates, and discussion about the impact of Stable Diffusion\n* [Diffusion Models in Vision: A Survey | DeepAI](https:\u002F\u002Fdeepai.org\u002Fpublication\u002Fdiffusion-models-in-vision-a-survey): paper about the diffusion techniques which also discuss the relation with other generative deep learning models\n* [ThereforeGames\u002Ftxt2mask](https:\u002F\u002Fgithub.com\u002FThereforeGames\u002Ftxt2mask): Automatically create masks for Stable Diffusion inpainting using natural language\n* [basujindal\u002Fstable-diffusion](https:\u002F\u002Fgithub.com\u002Fbasujindal\u002Fstable-diffusion): Optimized Stable Diffusion modified to run on lower GPU VRAM\n* [Stable WarpFusion v0.5 (restricted to patreons)](https:\u002F\u002Fwww.patreon.com\u002Fsxela): conditioning video frames with Stable Diffusion by [@devdef](https:\u002F\u002Ftwitter.com\u002Fdevdef)\n* [nateraw\u002Fstable-diffusion-videos](https:\u002F\u002Fgithub.com\u002Fnateraw\u002Fstable-diffusion-videos): Create videos with Stable Diffusion by exploring the latent space and morphing between text prompts\n\n#### Stable Diffusion Deployed Web Tools\n\n* [DecorAI](https:\u002F\u002Fdecorai.io): Generate Interior and Exterior Ideas in Seconds\n* [dreamlike.art](https:\u002F\u002Fdreamlike.art\u002F): image generator based on Stable Diffusion with fine-tuned models such as Dreamlike Photoreal 2.0. Users receive 1 credit per hour up to 50 credits\n* [AITWO.CO](https:\u002F\u002Faitwo.co\u002F): a AI-powered design platform with multiple features\n* [aiimagegenerator.org](https:\u002F\u002Fwww.aiimagegenerator.org\u002F): free AI art generator that supports Stable Diffusion txt2img and img2img generation, drawing and inpainting\n* [InteriorAIDesigns](https:\u002F\u002Finterioraidesigns.com\u002F): a platform which allows the easy redesign of rooms.\n* [Playground AI](https:\u002F\u002Fplaygroundai.com\u002F): frontend for Stable Diffusion with 1000 image generations per day\n* [Astria](https:\u002F\u002Fwww.astria.ai\u002F): tailor-made AI image generation\n* [drawanyone](https:\u002F\u002Fdrawanyone.com\u002F): generate drawings based on five input images\n* [DiffusionBee](https:\u002F\u002Fdiffusionbee.com\u002F): stable diffusion GUI App\n* [getimg.ai](https:\u002F\u002Fgetimg.ai\u002F): Generate photo-realistic images from text using Stable Diffusion\n* [Enstil: Fast, open, AI-generated images](https:\u002F\u002Fenstil.ai\u002F?source=12)\n* [Dezgo - Text-to-Image AI generator](https:\u002F\u002Fdezgo.com\u002F)\n* [PhotoAIStudio](https:\u002F\u002Fphotoaistudio.com\u002F): a AI-powered photoshot platform with multiple styles\n* [Baseten](https:\u002F\u002Fapp.baseten.co\u002Fapps\u002FVqK2vYP\u002Foperator_views\u002Fpqvba2q): Stable Diffusion Demo\n* [DreamStudio](https:\u002F\u002Fbeta.dreamstudio.ai\u002F): Frontend for Stable Diffusion API by Stability.ai\n* [Pollinations - pollinations\u002Fstable-diffusion-private](https:\u002F\u002Fpollinations.ai\u002Fcreate\u002Fstablediffusion)\n* [tencentarc\u002Fgfpgan – Run with an API on Replicate](https:\u002F\u002Freplicate.com\u002Ftencentarc\u002Fgfpgan) \n* [andreasjansson\u002Fstable-diffusion-wip – Run with an API on Replicate](https:\u002F\u002Freplicate.com\u002Fandreasjansson\u002Fstable-diffusion-wip) \n* [stability-ai\u002Fstable-diffusion – Run with an API on Replicate](https:\u002F\u002Freplicate.com\u002Fstability-ai\u002Fstable-diffusion)\n* [Osmosis.Studio](http:\u002F\u002Fosmosis.studio\u002F) : web-based content-aware collaborative design tool for generating AI ads that sell real products\n* [Artistic.wtf](https:\u002F\u002Fwww.artistic.wtf\u002F): stable diffusion GUI App\n* [Prodia](https:\u002F\u002Fapp.prodia.com\u002F#\u002Fart-ai): Stable diffusion-based art generator that does not require signup\n* [ComicsMaker.ai](https:\u002F\u002Fwww.comicsmaker.ai): Stable diffusion-based comic book generator with support for text2img, img2img, inpainting and controlnet\n* [POTO.AI](https:\u002F\u002Fpoto.ai\u002F): Finetune Stable Difussion model as AI Photographer to generate headshots, portrait and couple wedding photos\n\n#### Web UI for Stable Diffusion via Google Colab\n\n* [camenduru\u002Fstable-diffusion-webui-colab](https:\u002F\u002Fgithub.com\u002Fcamenduru\u002Fstable-diffusion-webui-colab): collection of stable diffusion webui colab for different checkpoints\n* [StableDiffusion_WebUI_Simplified.ipynb](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffilipecalegario\u002Fawesome-generative-deep-art\u002Fblob\u002Fmain\u002FStableDiffusion_WebUI_Simplified.ipynb): versão em português do notebook para rodar a Web UI do Stable Diffusion no Google Colab de graça\n* [GitHub - AUTOMATIC1111\u002Fstable-diffusion-webui: Stable Diffusion web UI](https:\u002F\u002Fgithub.com\u002FAUTOMATIC1111\u002Fstable-diffusion-webui): expanded Stable Diffusion web UI\n* [GitHub - sd-webui\u002Fstable-diffusion-webui](https:\u002F\u002Fgithub.com\u002Fhlky\u002Fstable-diffusion-webui): Stable Diffusion web UI\n* [Stable_Diffusion_WebUi_Simplified.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fpinilpypinilpy\u002Fsd-webui-colab-simplified\u002Fblob\u002Fmain\u002FStable_Diffusion_WebUi_Simplified.ipynb#scrollTo=gk1TyBA0Arxt) \n\n#### References Collection about Stable Diffusion\n\n* [GitHub - awesome-stable-diffusion\u002Fawesome-stable-diffusion](https:\u002F\u002Fgithub.com\u002Fawesome-stable-diffusion\u002Fawesome-stable-diffusion): Curated list of resources for the Stable Diffusion AI Model\n* [Stable Diffusion General Updates Posted by u\u002FImeniSottoITreni | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxcclmf\u002Fcan_we_please_make_a_general_update_on_all_the\u002F): a general update on all the \"most important\" news\u002Frepos available\n* [List of Stable Diffusion systems | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fwqaizj\u002Flist_of_stable_diffusion_systems\u002F)\n* [Stable Diffusion Akashic Records | Maks-s\u002Fsd-akashic](https:\u002F\u002Fgithub.com\u002FMaks-s\u002Fsd-akashic): A compendium of information regarding Stable Diffusion (SD)\n* [1 week of Stable Diffusion | multimodal.art](https:\u002F\u002Fmultimodal.art\u002Fnews\u002F1-week-of-stable-diffusion)\n* [Voldy Guide](https:\u002F\u002Frentry.co\u002Fvoldy): detailed beginners guide for Stable Diffusion\n* [Dreamer's Guide to Getting Started w\u002F Stable Diffusion! | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxcq819\u002Fdreamers_guide_to_getting_started_w_stable\u002F)\n* [A collection of sites using Stable Diffusion (and other handy links) | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fwzj8kk\u002Fa_collection_of_sites_using_stable_diffusion_and\u002F) \n\n### Hypertechniques\n\n* [Prompt+](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.09522): extended textual conditioning in text-to-image generation [[unofficial repo]](https:\u002F\u002Fgithub.com\u002Fcloneofsimo\u002Fpromptplusplus) [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.09522) [[page]](https:\u002F\u002Fprompt-plus.github.io\u002F) \n\n#### ControlNet\n\n* [A Beginner's Guide to Line Detection and Image Transformation with ControlNet](https:\u002F\u002Fnotes.aimodels.fyi\u002Fa-dive-into-line-detection-image-transformation-and-much-more-with\u002F) \n* [Scribble Diffusion](https:\u002F\u002Fscribblediffusion.com\u002F): turn your sketch into a refined image using AI (based on ControlNet)\n\n#### Textual Inversion\n\n* [rinongal\u002Ftextual_inversion](https:\u002F\u002Fgithub.com\u002Frinongal\u002Ftextual_inversion): repo contains the official code, data and sample inversions of Textual Inversion paper\n* [2208.01618 An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.01618): paper that describes the Textual Inversion technique\n* [sd-concepts-library (Stable Diffusion concepts library)](https:\u002F\u002Fhuggingface.co\u002Fsd-concepts-library): Stable Diffusion Textual Inversion Concepts Library - browse through objects and styles taught by the community to Stable Diffusion and use them in your prompts!\n\n#### DreamBooth\n\n* [AI Profile Pictures](https:\u002F\u002Fwww.aiprofilepictures.com\u002F): paid service for generating profile pictures using AI\n* [Training Stable Diffusion with Dreambooth using Diffusers](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fdreambooth): experiments to analyze the effect of different settings in Dreambooth\n* [fast-DreamBooth.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FTheLastBen\u002Ffast-stable-diffusion\u002Fblob\u002Fmain\u002Ffast-DreamBooth.ipynb): train custom concepts from input images with this simplified DreamBooth colab\n* [(1166) Como Criar Artes Incríveis com o seu Próprio Rosto Usando o Dreambooth! DE FORMA FÁCIL E DE GRAÇA! - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3e4jwgqy-0A): tutorial in Portuguese on how to train DreamBooth with your own face\n\n#### Deforum\n\n* [🔥🔥🔥] [Parseq](https:\u002F\u002Fsd-parseq.web.app): parameter sequencer for Stable Diffusion [[Youtube Tutorials]](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLXbx1PHKHwIHsYFfb5lq2wS8g1FKz6aP8)\n* [deforum-art\u002Fsd-webui-deforum](https:\u002F\u002Fgithub.com\u002Fdeforum-art\u002Fsd-webui-deforum): Deforum extension for AUTOMATIC1111's Stable Diffusion webui [[wiki docs]](https:\u002F\u002Fgithub.com\u002Fdeforum-art\u002Fsd-webui-deforum\u002Fwiki)\n* [Deforum Stable Diffusion Animation - v5 Math Functions - Demo and Test - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=6snk7gw898g)\n* [Deforum Stable Diffusion](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdeforum\u002Fstable-diffusion\u002Fblob\u002Fmain\u002FDeforum_Stable_Diffusion.ipynb#scrollTo=63UOJvU3xdPS): generating videos from scripted prompts\n* [(5) Deforum notebook v0.5 for Stable Diffusion animations is out! Now with math automation, perspective flips, prompt weights, video masking and waifus! : StableDiffusion](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxuytx5\u002Fdeforum_notebook_v05_for_stable_diffusion\u002F)\n\n### Creative Uses of Generative AI Image Synthesis Tools\n\n* [De-painting historical photographs | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgbug2\u002Fdepainting_historical_photographs\u002F) \n* [img2img animation with hands | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fx92itm\u002Fproof_of_concept_using_img2img_ebsynth_to_animate\u002F)\n* [VID 2 VID user script | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgo87s\u002Fwip_vid_2_vid_user_script\u002F)\n* [Seamless textures AI generator for Blender by Antonio Freyre | Twitter](https:\u002F\u002Ftwitter.com\u002Fmerlino_games\u002Fstatus\u002F1571205845819559936)\n* [\"Shattered\" by Ronny Khalil | Twitter](https:\u002F\u002Ftwitter.com\u002Fronnykhalil\u002Fstatus\u002F1569956085905203200): using warp fusion to generate a shattered glass effect\n* [Acid Dance by aiplague | Twitter](https:\u002F\u002Ftwitter.com\u002Faiplague\u002Fstatus\u002F1564989456318451714) \n* [Fused video by [@remi_molettee](https:\u002F\u002Ftwitter.com\u002Fremi_molettee)](https:\u002F\u002Ftwitter.com\u002Fremi_molettee\u002Fstatus\u002F1568245586494738432)\n* [Animation with Dall-e + AE | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMediaSynthesis\u002Fcomments\u002Fxgeehe\u002Fanimation_with_dalle_ae_patent_drawing_of_an\u002F): Patent drawing of an electronic device that ...\n* [You Describe & AI Photoshops Faces For You [StyleCLIP] - YouTube](https:\u002F\u002Fyoutu.be\u002Fd1OET63Ulwc)\n* [Experimental Films + Machine Learning Week 7 Part 1 (Aphantasia with OpenAI CLIP) - YouTube](https:\u002F\u002Fyoutu.be\u002F-FrIui8Mp-8)\n* [GitHub - Sanster\u002Flama-cleaner](https:\u002F\u002Fgithub.com\u002FSanster\u002Flama-cleaner): Image inpainting tool powered by SOTA AI Model\n* [AgaMiko\u002Fpixel_character_generator](https:\u002F\u002Fgithub.com\u002FAgaMiko\u002Fpixel_character_generator): Generating retro pixel game characters with Generative Adversarial Networks. Dataset \"TinyHero\" included.\n* [Wilco Sierra](https:\u002F\u002Ftrywilco.com\u002Fsierra): A platform that generates engineering challenges for software engineers using GPT.\n\n## Image Upscaling\n\n* [Leonardo AI Upscaler](https:\u002F\u002Fleonadoai.com\u002Fupscaler\u002F): free image upscaler\n* [Remini - AI Photo Enhancer](https:\u002F\u002Fremini.ai\u002F): photo and video enhancer\n* [AI Image Upscaler - Enlarge & Enhance Your Photos for Free - Upscale.media](https:\u002F\u002Fwww.upscale.media\u002F): simple free alternative for image upscaling\n* [Topaz Labs: AI Image Quality Software]([https:\u002F\u002Fwww.topazlabs.com\u002F](https:\u002F\u002Fflight.beehiiv.net\u002Fv2\u002Fclicks\u002FeyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1cmwiOiJodHRwczovL3RvcGF6bGFicy5jb20vcmVmLzIwODIvP3V0bV9zb3VyY2U9bmVqY3N1c2VjLmJlZWhpaXYuY29tJnV0bV9tZWRpdW09cmVmZXJyYWwmdXRtX2NhbXBhaWduPXdoeS15b3Utc2hvdWxkLXVwc2NhbGUteW91ci1pbWFnZXMiLCJwb3N0X2lkIjoiZWI2OWY3OTItMTNmZC00ZmViLWFjZTYtYWQ5M2YyM2Y2ZDRmIiwicHVibGljYXRpb25faWQiOiI2NDU2OWQyOC1jYzhjLTQ1N2YtOGZlNy03Y2JiYjdiOWExZWEiLCJ2aXNpdF90b2tlbiI6ImE3YjE1NzNmLTljNzMtNDFlNy1hNzUyLWQ3ODQ2NWQ3ZWQ4OCIsImlhdCI6MTY4ODM5Nzg2NS44NzksImlzcyI6Im9yY2hpZCJ9.oISexuNHzvMdv2CGWolS6doN8qRFGTjuICBq8z908Yc)): \"professional grade workflow, with many features\" (this is an affiliate link by nejcsusec.beehiiv.com).\n* [AI Image Upscaler - Upscale Photo, Cartoons in Batch Free](https:\u002F\u002Fwww.imgupscaler.com\u002F): \"free, browser-based, with five credits per day\" reference by nejcsusec.beehiiv.com\n* [Why you should upscale your images](https:\u002F\u002Fnejcsusec.beehiiv.com\u002Fp\u002Fupscale-images): comparing different tools\n* [Model Database - Upscale Wiki](https:\u002F\u002Fupscale.wiki\u002Fwiki\u002FModel_Database): list of models for upscaling images\n* [Gigapixel AI](https:\u002F\u002Fwww.topazlabs.com\u002Fgigapixel-ai): paid AI image upscaler delivering enhanced detail and resolution\n* [Image Super-Resolution](https:\u002F\u002Fidealo.github.io\u002Fimage-super-resolution\u002F) \n* [Upscale to huge sizes and add detail with SD Upscale : StableDiffusion](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxkjjf9\u002Fupscale_to_huge_sizes_and_add_detail_with_sd\u002F): tutorial on Reddit\n\n## Image Restoration\n\n* [sczhou\u002Fcodeformer](https:\u002F\u002Freplicate.com\u002Fsczhou\u002Fcodeformer): face restoration algorithm for old photos and AI-generated faces\n* [TencentARC\u002FGFPGAN](https:\u002F\u002Fgithub.com\u002FTencentARC\u002FGFPGAN): GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration\n\n## Image Segmentation\n\n* [Segment Anything | Meta AI](https:\u002F\u002Fsegment-anything.com\u002F): \"a new AI model from Meta AI that can \"cut out\" any object, in any image, with a single click\"\n\n# Video and Animation\n\n* [FramePack](https:\u002F\u002Fwww.framepack.video\u002F): next-frame prediction neural network structure that generates videos progressively\n* [Keyla.AI](https:\u002F\u002Fkeyla.ai\u002F): Create video ads in minutes\n* [Melies](https:\u002F\u002Fmelies.co\u002F): All-in-one AI filmmaking software\n* [Pyramid Flow](https:\u002F\u002Fpyramid-flow.github.io\u002F) \n* [Infinity AI](https:\u002F\u002Finfinity.ai\u002F): a video foundation model that allows you to craft characters and animate them\n* [Sora](https:\u002F\u002Fopenai.com\u002Fsora): OpenAI's text-to-video model [[technical report]](https:\u002F\u002Fopenai.com\u002Fresearch\u002Fvideo-generation-models-as-world-simulators)\n* [SDV (Stable Diffusion Image To Video)](https:\u002F\u002Ftwitter.com\u002Fstevemills\u002Fstatus\u002F1727898404787986873?s=46&t=CQsRDjHr9sNtph3xC84hXQ): generates 3 seconds of video in about 30 seconds using an A100 GPU on Colab+.\n* [[Emu Video | Meta](https:\u002F\u002Femu-video.metademolab.com\u002F) ](https:\u002F\u002Femu-video.metademolab.com\u002Fdemo#\u002Fdemo): state-of-the-art text-to-video generation\n* [AILab-CVC\u002FVideoCrafter](https:\u002F\u002Fgithub.com\u002Failab-cvc\u002Fvideocrafter): Open Diffusion Models for High-Quality Video Generation\n* [Ssemble](https:\u002F\u002Fwww.ssemble.com\u002F): collaborative video editor with a collection of AI plugins\n* [Transforming 2D Images into 3D with the AdaMPI AI Model](https:\u002F\u002Fnotes.aimodels.fyi\u002Ftransforming-2d-images-into-3d-with-the-adampi-ai-model\u002F): guide on how to use the AdaMPI AI model for creating 3D photos from 2D images\n* [Nathan Lands on Twitter: \"AI video has started to produce mindblowing results and could eventually disrupt Hollywood \u002F Twitter](https:\u002F\u002Ftwitter.com\u002FNathanLands\u002Fstatus\u002F1659195191591596033): Twitter thread with examples of Generative AI tools for video\n* [Stable Animation SDK](https:\u002F\u002Fstability.ai\u002Fblog\u002Fstable-animation-sdk): a text-to-animation tool for developers by Stability AI [[dev platform]](https:\u002F\u002Fplatform.stability.ai\u002Fdocs\u002Ffeatures\u002Fanimation)\n* [Twelve Labs](https:\u002F\u002Ftwelvelabs.io\u002F): multimodal, contextual understanding for video search\n* [Align your Latents](https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Ftoronto-ai\u002FVideoLDM\u002F): high-resolution video synthesis with latent diffusion models [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.08818)\n* [Gen-2 by Runway](https:\u002F\u002Fresearch.runwayml.com\u002Fgen2): \"a multi-modal AI system that can generate novel videos with text, images, or video clips\" [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03011) \n* [CiaraRowles\u002FTemporalNet · Hugging Face](https:\u002F\u002Fhuggingface.co\u002FCiaraRowles\u002FTemporalNet): a ControlNet model designed to enhance the temporal consistency of generated outputs [[tweet]](https:\u002F\u002Ftwitter.com\u002Fciararowles1\u002Fstatus\u002F1639321818581303310)\n* [Video-P2P UI - a Hugging Face Space by video-p2p-library](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fvideo-p2p-library\u002FVideo-P2P-Demo): video editing with cross-attention control [[tweet]](https:\u002F\u002Ftwitter.com\u002F_akhaliq\u002Fstatus\u002F1637838648463749120)\n* [Text2Video-Zero - a Hugging Face Space by PAIR](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FPAIR\u002FText2Video-Zero): zero-shot text-to-video synthesis diffusion framework [[tweet]](https:\u002F\u002Ftwitter.com\u002F_akhaliq\u002Fstatus\u002F1639062868850266112) [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13439)\n* [ModelScope - a Hugging Face Space by damo-vilab](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fdamo-vilab\u002Fmodelscope-text-to-video-synthesis): text-to-video synthesis [[page]](https:\u002F\u002Fwww.modelscope.cn\u002Fmodels\u002Fdamo\u002Ftext-to-video-synthesis\u002Fsummary)\n* [neural frames](https:\u002F\u002Fwww.neuralframes.com\u002Ffirstframe): tools for animation creation inspired on deforum\n* [🔥] [dmarx\u002Fvideo-killed-the-radio-star](https:\u002F\u002Fgithub.com\u002Fdmarx\u002Fvideo-killed-the-radio-star): Notebook and tools for end-to-end automation of music video production with generative AI\n* [🔥🔥🔥] [Phenaki – Google Research](https:\u002F\u002Fphenaki.research.google\u002F): realistic video generation from open-domain textual descriptions\n* [THUDM\u002FCogVideo](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FCogVideo): text-to-video generation\n* [baowenbo\u002FDAIN](https:\u002F\u002Fgithub.com\u002Fbaowenbo\u002FDAIN): Depth-Aware Video Frame Interpolation (CVPR 2019)\n* [Dain-App 1.0 [Nvidia Only] by GRisk](https:\u002F\u002Fgrisk.itch.io\u002Fdain-app): Depth-Aware Video Frame Interpolation (CVPR 2019)\n* [Content Studio AI](https:\u002F\u002Fcontentstudioai.com\u002F): Faceless Video Generator\n\n# Audio and Music\n\n* [StemGen: A music generation model that listens](https:\u002F\u002Fjulian-parker.github.io\u002Fstemgen\u002F) \n* [Mustango](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fdeclare-lab\u002Fmustango): \"Toward Controllable Text-to-Music Generation\"\n* [Lyria by Google DeepMind](https:\u002F\u002Fdeepmind.google\u002Fdiscover\u002Fblog\u002Ftransforming-the-future-of-music-creation\u002F): \"transforming the future of music creation\" \n* [Suno AI](https:\u002F\u002Fwww.suno.ai\u002F): \"make any song you can imagine\"\n* [Riffusion](https:\u002F\u002Fwww.riffusion.com\u002F): this AI system generates singing voice for literally any text as input\n* [Stable Audio - Generative AI for music & sound fx](https:\u002F\u002Fwww.stableaudio.com\u002F) \n* [An early look our AI Music experiment - YouTube Blog](https:\u002F\u002Fblog.youtube\u002Finside-youtube\u002Fai-and-music-experiment\u002F) \n* [What's Generative Music? - Generative Music AI - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9QNG56fc_l8&list=PL-wATfeyAMNqAPjwGT3ikEz3gMo23pl-D&index=3) \n* [Ultimate Vocal Remover](https:\u002F\u002Fultimatevocalremover.com\u002F): vocal removal using AI\n* [Introducing Voicebox](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Fvoicebox-generative-ai-model-speech): The first generative AI model for speech to generalize across tasks with state-of-the-art performance\n* [MusicGen](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffacebook\u002FMusicGen): Meta's tool for generating music\n* [facebookresearch\u002Faudiocraft](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Faudiocraft): a library for audio processing and generation with deep learning.\n* [AudioGPT | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12995): Understanding and Generating Speech, Music, Sound, and Talking Head [[code]](https:\u002F\u002Fgithub.com\u002FAIGC-Audio\u002FAudioGPT) [[demo]](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FAIGC-Audio\u002FAudioGPT) \n* [AudioLDM](https:\u002F\u002Faudioldm.github.io\u002F): Text-to-Audio Generation with Latent Diffusion Models - Speech Research\n* [lucidrains\u002Fmusiclm-pytorch](https:\u002F\u002Fgithub.com\u002Flucidrains\u002Fmusiclm-pytorch): Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch\n* [🔥🔥🔥] [archinetai\u002Faudio-ai-timeline](https:\u002F\u002Fgithub.com\u002Farchinetai\u002Faudio-ai-timeline): A timeline of the latest AI models for audio generation, starting in 2023\n* [MusicLM](https:\u002F\u002Fgoogle-research.github.io\u002Fseanet\u002Fmusiclm\u002Fexamples\u002F): generating music from text\n* [Harmonai's Dance Diffusion](https:\u002F\u002Fwandb.ai\u002Fwandb_gen\u002Faudio\u002Freports\u002FHarmonai-s-Dance-Diffusion-Open-Source-AI-Audio-Generation-Tool-For-Music-Producers--VmlldzoyNjkwOTM1): Open-Source AI Audio Generation Tool For Music Producers – Weights & Biases\n* [Dance Diffusion](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fharmonai\u002Fdance-diffusion): the Hugging Face Space by harmonai\n* [MubertAI\u002FMubert-Text-to-Music](https:\u002F\u002Fgithub.com\u002FMubertAI\u002FMubert-Text-to-Music): a simple notebook demonstrating prompt-based music generation via Mubert API\n* [DDSP-VST](https:\u002F\u002Fmagenta.tensorflow.org\u002Fddsp-vst-blog): Neural Audio Synthesis for All\n* [LOVO AI](https:\u002F\u002Fwww.lovo.ai\u002F): AI Voiceover & Text to Speech Platform with human-like voices\n* [AIVA](https:\u002F\u002Fwww.aiva.ai\u002F): The AI composing emotional soundtrack music\n* [Jukebox](https:\u002F\u002Fopenai.com\u002Fblog\u002Fjukebox\u002F): \"a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles\"\n* [Magenta](https:\u002F\u002Fmagenta.tensorflow.org\u002F): Music and Art Generation with Machine Intelligence\n* [magenta\u002Fmagenta](https:\u002F\u002Fgithub.com\u002Fmagenta\u002Fmagenta): Magenta's official GitHub repository\n* [AI Image to sound [Melobytes.com]](https:\u002F\u002Fmelobytes.com\u002Fen\u002Fapp\u002Fai_image2sound)\n* [archinetai\u002Faudio-diffusion-pytorch](https:\u002F\u002Fgithub.com\u002Farchinetai\u002Faudio-diffusion-pytorch): Audio generation using diffusion models, in PyTorch\n* [MuseGen](https:\u002F\u002Fmusegen.org): An AI music studio for lyric writing and song generation, built for creators\n\n# Speech\n\n## Text-to-speech (TTS) and avatars\n\n* [COVAL](https:\u002F\u002Fapp.coval.dev\u002Fthe-ultimate-voice-ai-stack): architecture of voice AI, from speech recognition to emotional intelligence, and learn how to build, scale, and evaluate them\n* [Parler-TTS](https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fparler-tts\u002Fparler-tts-fully-open-source-high-quality-tts-66164ad285ba03e8ffde214c): fully open-source high-quality TTS\n* [p0n1\u002Fepub_to_audiobook](https:\u002F\u002Fgithub.com\u002Fp0n1\u002Fepub_to_audiobook): EPUB to audiobook converter, optimized for Audiobookshelf\n* [The \"Voice Cloning AIs\" they never tell you about (and how they work)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vhArHsfsLAQ): Youtube video by @bycloud summarizing the available technologies for voice cloning\n* [Voice-Swap](https:\u002F\u002Fwww.voice-swap.ai\u002F?ref=producthunt): transform vocals to match the style of a list of singers\n* [Shaunwei\u002FRealChar](https:\u002F\u002Fgithub.com\u002FShaunwei\u002FRealChar): AI Character\u002FCompanion in Realtime\n* [UneeQ Digital Humans](https:\u002F\u002Fwww.digitalhumans.com\u002F): 3D character lib synced\n* [AI Voice Generator](https:\u002F\u002Fwww.aivoicegenerator.org): free online AI-powered text-to-speech generator that creates voice overs with natural, realistic voices\n* [KangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation](https:\u002F\u002Fgithub.com\u002FKangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation): A curated list of resources of audio-driven talking face generation\n* [Play.ht](https:\u002F\u002Fplay.ht\u002F): \"AI voice generator and realistic text to speech online\"\n* [Murf AI | AI Voice Generator](https:\u002F\u002Fmurf.ai\u002F): versatile text to tpeech software\n* [VALL-E](https:\u002F\u002Fvalle-demo.github.io\u002F): synthesize high-quality personalized speech with only a 3-second samples\n* [🔥] [Eleven Labs Beta](https:\u002F\u002Fblog.elevenlabs.io\u002Fthe_first_ai_that_can_laugh\u002F): a TTS service that adds emotion to the generated voice\n* [neonbjb\u002Ftortoise-tts](https:\u002F\u002Fgithub.com\u002Fneonbjb\u002Ftortoise-tts#voice-customization-guide): \"A multi-voice TTS system trained with an emphasis on quality\"\n* [Studio D-ID](https:\u002F\u002Fstudio.d-id.com\u002F): create video with still images synced with text-to-speech tool [#avatar]\n* [Synthesia](https:\u002F\u002Fwww.synthesia.io\u002F): AI Video Generation Platform [#avatar]\n* [Speech Studio - Microsoft Azure](https:\u002F\u002Fspeech.microsoft.com\u002Fportal): Microsoft's cloud cognitive services\n\n### Podcast generators\n\n* [Google NotebookLM](https:\u002F\u002Fnotebooklm.google.com\u002F): generate podcast episodes based on your uploaded references\n* [Illuminate](https:\u002F\u002Filluminate.google.com\u002Fhome?pli=1): transform your content into engaging AI‑generated audio discussions also by Google\n\n## Speech-to-text (STT) and spoken content analysis\n\n* [Introducing Universal-1](https:\u002F\u002Fwww.assemblyai.com\u002Fblog\u002Fannouncing-universal-1-speech-recognition-model\u002F): multilingual speech-to-text \n* [ggerganov\u002Fwhisper.cpp](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fwhisper.cpp): Port of OpenAI's Whisper model in C\u002FC++. It can be executed locally.\n* [Good Tape](https:\u002F\u002Fgoodtape.io\u002F): paid service for transcription\n* [shashikg\u002FWhisperS2T](https:\u002F\u002Fgithub.com\u002Fshashikg\u002FWhisperS2T): An Optimized Speech-to-Text Pipeline for the Whisper Model\n* [Vaibhavs10\u002Finsanely-fast-whisper](https:\u002F\u002Fgithub.com\u002FVaibhavs10\u002Finsanely-fast-whisper): accelerates transcription with the combination of OpenAI's Whisper Large v2, HF Transformers, Optimum, and flash attention\n* [facebookresearch\u002Fseamless_communication](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fseamless_communication): Foundational Models for State-of-the-Art Speech and Text Translation\n* [LeMUR](https:\u002F\u002Fwww.assemblyai.com\u002Fblog\u002Flemur\u002F): a single API, enabling developers to reason over their spoken data with a few lines of code\n\n# Games\n\n* [The Generative AI Revolution in Games | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2022\u002F11\u002F17\u002Fthe-generative-ai-revolution-in-games\u002F): this article presents a list of use cases of generative AI in games\n* [AI for Game Development](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fml-for-games-1): Creating a Farming Game in 5 Days. Part 1\n\n# Multimodal\n\n* [[2406.09403] Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.09403) \n* [BradyFU\u002FAwesome-Multimodal-Large-Language-Models](https:\u002F\u002Fgithub.com\u002FBradyFU\u002FAwesome-Multimodal-Large-Language-Models): Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.\n* [NExT-Chat: An LMM for Chat, Detection and Segmentation](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2311.04498) \n* [roboflow\u002Fawesome-openai-vision-api-experiments](https:\u002F\u002Fgithub.com\u002Froboflow\u002Fawesome-openai-vision-api-experiments): Examples showing how to use the OpenAI vision API to run inference on images, video files and webcam streams\n\n## Multimodal Embedding Space\n\n* [Microsoft KOSMOS-2](https:\u002F\u002Ftwitter.com\u002Fmervenoyann\u002Fstatus\u002F1720126908384366649): new capabilities of perceiving object descriptions (e.g., bounding boxes) and grounding text to the visual world [[HF demo]](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fydshieh\u002FKosmos-2) [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.14824) \n* [Segment Anything | Meta AI](https:\u002F\u002Fsegment-anything.com\u002F): \"a new AI model from Meta AI that can \"cut out\" any object, in any image, with a single click\"\n* [facebookresearch\u002FImageBind](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FImageBind): ImageBind One Embedding Space to Bind Them All\n\n# Datasets\n\n* [Ego-Exo4D](https:\u002F\u002Fai.meta.com\u002Fblog\u002Fego-exo4d-video-learning-perception\u002F): a foundational dataset by Meta for research on video learning and multimodal perception [Dataset Download](https:\u002F\u002Fego-exo4d-data.org\u002F) \n* [Carolina](https:\u002F\u002Fsites.usp.br\u002Fcorpuscarolina\u002Fcorpus\u002F): General Corpus of Contemporary Brazilian Portuguese with provenance and typology information - Corpus Geral do Português Brasileiro Contemporâneo\n* [RedPajama-Data-v2 by Together AI](https:\u002F\u002Ftogether.ai\u002Fblog\u002Fredpajama-data-v2): an open dataset with 30 trillion tokens for training Large Language Models \n* [Have I Been Trained?](https:\u002F\u002Fhaveibeentrained.com\u002F): tool for searching 5.8 billion images used to train popular AI art models\n* [laion-aesthetic-6pls](https:\u002F\u002Flaion-aesthetic.datasette.io\u002Flaion-aesthetic-6pls\u002Fimages): exploring 12 million of the 2.3 billion images used to train Stable Diffusion's image generator\n* [CLIP retrieval for laion5B](https:\u002F\u002From1504.github.io\u002Fclip-retrieval\u002F?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false): CLIP retrieval using Laion5B. \"It works by converting the text query to a CLIP embedding , then using that embedding to query a knn index of clip image embedddings\".\n* [rom1504\u002Fclip-retrieval](https:\u002F\u002Fgithub.com\u002From1504\u002Fclip-retrieval): Easily compute CLIP embeddings and build a CLIP retrieval system with them\n* [LAION](https:\u002F\u002Flaion.ai\u002F): Large-scale Artificial Intelligence Open Network\n* [gabolsgabs\u002FDALI](https:\u002F\u002Fgithub.com\u002Fgabolsgabs\u002FDALI): a large Dataset of synchronised Audio, LyrIcs and vocal notes\n\n# Misc\n\n## AI and Education\n\n* [Teaching AI to Teach Us: A New Era of Personalized Education](https:\u002F\u002Ftwitter.com\u002FIntuitMachine\u002Fstatus\u002F1783079852578377788) \n\n## People and works\n\n### Interesting Twitter Accounts\n\n* [Hassan El Mghari (@nutlope) \u002F X](https:\u002F\u002Ftwitter.com\u002Fnutlope): the creator of [roomgpt](https:\u002F\u002Froomgpt.io)\n\n### Interesting Instagram Accounts, Posts and Reels\n\n* [science on Instagram: “Human evolution generated by AI Stable Diffusion”](https:\u002F\u002Fwww.instagram.com\u002Freel\u002FCjnYBJbqABH\u002F?igshid=YmMyMTA2M2Y%3D)\n* [Deep Music Visualizer](https:\u002F\u002Fwww.instagram.com\u002Fdeep_music_visualizer\u002F)\n* [Lucid Sonic Dreams (@lucidsonicdreams)](https:\u002F\u002Fwww.instagram.com\u002Flucidsonicdreams\u002F) \n\n### Interesting Youtube Channels\n\n* [Artificial Images](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCaZuPdmZ380SFUMKHVsv_AA): Demos and explanations to make art using machine learning\n* [Glenn Marshall Neural Art](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fglenniszen)\n* [How to Generate Art - Intro to Deep Learning #8](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Oex0eWoU7AQ) \n\n### Interesting GitHub Repositories\n\n* [dvschultz](https:\u002F\u002Fgithub.com\u002Fdvschultz): Derrick Schultz's GitHub\n* [dvschultz\u002Fml-art-colabs](https:\u002F\u002Fgithub.com\u002Fdvschultz\u002Fml-art-colabs): collection of Google Colab Notebooks for ML Arts\n* [🔥] [Structured State Space for Sequence Modeling (S4)](https:\u002F\u002Fgithub.com\u002Fstate-spaces\u002Fs4): Stole generation from the gods\n\n### Artists and Artworks\n\n* [Ai Generated Music Video - Deltron 3030 - Virus - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=WJaxFbdjm8c)\n* [Artificial Realities: Coral \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Frefikanadol\u002Fstatus\u002F1613927561939099650): artwork by [@refikanadol](https:\u002F\u002Ftwitter.com\u002Frefikanadol) commissioned by World Economic Forum\n* [🔥] [Creep - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=c6LlG4g_9lk) by [Glenn Marshall Neural Art](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCes-tiSj7VO6nNOsUB76lZw): how did they translated the images using VQGAN+CLIP? How did they seamlessly wander on the latent space?\n* [35 Artists Using AI With Under 1000 Followers That You Need To Follow Today \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Finfiniteyay\u002Fstatus\u002F1583465675166609408?s=43&t=XvooFiMyC-YPv0i98HmjVQ) \n* [Computer Vision Art Gallery : CVPR 2021](https:\u002F\u002Fcomputervisionart.com\u002F): artworks dealing with computer vision technologies\n* [Confluence](https:\u002F\u002Fdeviparikh.github.io\u002Fconfluence\u002F): a generative art project by Devi Parikh on BrainDrops.\n* [Learning to See – Memo Akten | Mehmet Selim Akten | The Mega Super Awesome Visuals Company](http:\u002F\u002Fwww.memo.tv\u002Fworks\u002Flearning-to-see\u002F)\n* [Alien Dreams: An Emerging Art Scene - ML@B Blog](https:\u002F\u002Fml.berkeley.edu\u002Fblog\u002Fposts\u002Fclip-art\u002F)\n* [Neural Zoo | Sofia Crespo](https:\u002F\u002Fneuralzoo.com\u002F)\n* [KRЯRL DЯAWINGS: Runway ML -- 3rd \"Model\" (based on long poses)](http:\u002F\u002Fkrrrl.blogspot.com\u002F2020\u002F08\u002Frunway-ml-3rd-model-based-on-long-poses.html)\n* [Frea Buckler ~ Artist](https:\u002F\u002Fwww.freabuckler.com\u002F): obras usadas para criar essa rede [(19) derrick has started yet another project on Twitter: \"Just sent @buntworthy a demo StyleGAN model I trained \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Fdvsch\u002Fstatus\u002F1255885874560225284)\n* [(Non-)Human](https:\u002F\u002Fwww.ygzhang.com\u002Fnon-human.html) \n* [Authentic Digital Art - Unknown Departure | SuperRare](https:\u002F\u002Fsuperrare.com\u002Fartwork-v2\u002Funknown-departure-16212) \n* [A Selection of Machine Learning Art Inspiration](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HNwXrHiHW7Q)\n* [Top 25 AI Artists of 2021 (Photos, Profiles & History of AI Art)- AIArtists.org](https:\u002F\u002Faiartists.org\u002F): AIArtists.org showcases leading artists using Artificial Intelligence, tools to make AI Art, and a timeline of AI Art History.\n* [Helena Sarin – Artist Profile (Photos, Videos, Exhibitions) — AIArtists.org](https:\u002F\u002Faiartists.org\u002Fhelena-sarin)\n* [Images Generated By AI Machines (@images_ai) \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Fimages_ai?s=08)\n* https:\u002F\u002Fwww.instagram.com\u002Frefikanadol\u002F\n* [The Steampunk Circus Human Machine Collaboration - Video, Sound and Stories with AI \u002F YouTube](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCa1xYBYCzBoJ08U9lgbYAFw)\n\n### Galleries\n\n* [AICAN](https:\u002F\u002Faican.io\u002F)\n* [Ganvas Studio - Neural Network Paintings](https:\u002F\u002Fganvas.studio\u002F)\n* [Syn Feather Sweater \u002F STRELITZIA – HATRA E STORE](https:\u002F\u002Fhatroid.com\u002Fcollections\u002Fsynthetic-feather\u002Fproducts\u002Fsyn-feather-sweater-strelitzia)\n\n## Related Awesome Lists\n\n* [mahseema\u002Fawesome-ai-tools](https:\u002F\u002Fgithub.com\u002Fmahseema\u002Fawesome-ai-tools): A curated list of Artificial Intelligence Top Tools\n* [Hannibal046\u002FAwesome-LLM: Awesome-LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM): a curated list of Large Language Model\n* [AlexChalakov\u002Fawesome-generative-ai-companies](https:\u002F\u002Fgithub.com\u002FAlexChalakov\u002Fawesome-generative-ai-companies): a curated list of Gеnerative AI companies, sorted by focus area and total fundraised amount\n* [kyrolabs\u002Fawesome-langchain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain): 😎 Awesome list of tools and project with the awesome LangChain framework\n* [KangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation](https:\u002F\u002Fgithub.com\u002FKangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation): A curated list of resources of audio-driven talking face generation\n* [🔥] [amrzv\u002Fawesome-colab-notebooks](https:\u002F\u002Fgithub.com\u002Famrzv\u002Fawesome-colab-notebooks): Collection of google colaboratory notebooks for fast and easy experiments\n* [🔥🔥🔥] [steven2358\u002Fawesome-generative-ai](https:\u002F\u002Fgithub.com\u002Fsteven2358\u002Fawesome-generative-ai): A curated list of modern Generative Artificial Intelligence projects and services\n* [🔥🔥🔥] [jonathandinu\u002Fawesome-ai-art](https:\u002F\u002Fgithub.com\u002Fjonathandinu\u002Fawesome-ai-art): \"A list of AI Art courses, tools, libraries, people, and places\"\n* [margaretmz\u002Fawesome-ai-art-design](https:\u002F\u002Fgithub.com\u002Fmargaretmz\u002Fawesome-ai-art-design): An awesome list: AI for art and design.\n* [toxtli\u002Fawesome-machine-learning-jupyter-notebooks-for-colab](https:\u002F\u002Fgithub.com\u002Ftoxtli\u002Fawesome-machine-learning-jupyter-notebooks-for-colab): A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory\n* [chaosreactor\u002Fawesome-generative-ai](https:\u002F\u002Fgithub.com\u002Fchaosreactor\u002Fawesome-generative-ai): An awesome list of low- and no-code generative AI resources\n* [🔥] [altryne\u002Fawesome-ai-art-image-synthesis](https:\u002F\u002Fgithub.com\u002Faltryne\u002Fawesome-ai-art-image-synthesis): A list of awesome tools, ideas, prompt engineering tools, colabs, models, and helpers for the prompt designer playing with aiArt and image synthesis. Covers Dalle2, MidJourney, StableDiffusion, and open source tools.\n* [justinpinkney\u002Fawesome-pretrained-stylegan2](https:\u002F\u002Fgithub.com\u002Fjustinpinkney\u002Fawesome-pretrained-stylegan2): A collection of pre-trained StyleGAN 2 models to download\n\n## Bio experiments\n\n* [fMRI-to-image](https:\u002F\u002Ftwitter.com\u002Fdanberridge\u002Fstatus\u002F1631489991435243520): tweet by [danberridge](https:\u002F\u002Ftwitter.com\u002Fdanberridge) \"The 'presented images' were shown to a group of humans. The 'reconstructed images' were the result of an fMRI output to Stable Diffusion. In other words, Stable Diffusion literally read people's minds.\"\n\n## Jobs in Generative AI\n\n* [Jobs and talents in AI\u002FML, Data Science and Big Data | ai-jobs.net](https:\u002F\u002Fai-jobs.net\u002F)\n* [Latest Jobs and News in AI at trending startups and big companies | AIJobster](https:\u002F\u002Faijobster.work\u002F)\n\n## Improving Google Colab experience\n\n* [7 ways to load external data into Google Colab | by B. Chen | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002F7-ways-to-load-external-data-into-google-colab-7ba73e7d5fc7) \n* [10 tricks for a better Google Colab experience | by Cyprien NIELLY | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002F10-tips-for-a-better-google-colab-experience-33f8fe721b82)\n* [Quickly share ML WebApps from Google Colab using ngrok for Free | by AbdulMajedRaja RS | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fquickly-share-ml-webapps-from-google-colab-using-ngrok-for-free-ae899ca2661a)\n* [Jupyter Widgets for Interactivity in Google Colab](https:\u002F\u002Fcolab.research.google.com\u002Fnotebooks\u002Fforms.ipynb#scrollTo=62YnDE7i9dqP): notebook with examples of using Jupyter Widgets in Colab, allowing interactive inputs\n* [Jupyter Widgets official documentation](https:\u002F\u002Fipywidgets.readthedocs.io\u002Fen\u002Flatest\u002Fexamples\u002FWidget%20Basics.html)\n\n## Auxiliary tools and concepts\n\n* [Rosie](https:\u002F\u002Fheyrosie.com\u002F): AI Phone Answering Service\n* [MuckBrass](https:\u002F\u002Fwww.muckbrass.com): Find & Validate Startup Ideas using AI\n* [ResumeDive](https:\u002F\u002Fresumedive.com): A resume boosting service using AI\n* [Owlbot](https:\u002F\u002Fwww.owlbot.ai\u002F): AI Support Agent\n* [fynk](https:\u002F\u002Ffynk.com\u002F): AI powered contract management software   \n* [Taskbase](https:\u002F\u002Fwww.taskbase.co.uk): Virtual assistants packaged with AI powered software.\n* [AI Wedding Toast](https:\u002F\u002Faiweddingtoast.com): Generate a personalized wedding speech with AI\n* [Interviews Chat](https:\u002F\u002Fwww.interviews.chat\u002F): Your Personal Interview Prep & Copilot\n* [Inline Help](https:\u002F\u002Finlinehelp.com): Answer customer questions before they ask\n* [LinkActions](https:\u002F\u002Flinkactions.com): AI Internal Links Assistant \n* [Marblism](https:\u002F\u002Fmarblism.com): Generate a SaaS boilerplate from a prompt\n* [SiteSpeakAI](https:\u002F\u002Fsitespeak.ai): Automate your customer support with AI\n* [Room Reinvented](https:\u002F\u002Froomreinvented.com): Transform your room effortlessly with Room Reinvented! Upload a photo and let AI create over 30 stunning interior styles. Elevate your space today.\n* [FairyTailAI](https:\u002F\u002Ffairytailai.com\u002F): Personalized bedtime story generator\n* [PromptPal](https:\u002F\u002Fpromptpal.net): Search for prompts and bots, then use them with your favourite AI. All in one place.\n* [Never Jobless LinkedIn Message Generator](https:\u002F\u002Fneverjobless.com\u002F?ref=filipecalegario-awesome-generative-ai): Maximize Your Interview Chances with AI-Powered LinkedIn Messaging.\n* [Aispect](https:\u002F\u002Faispect.io\u002F?ref=filipecalegario-awesome-generative-ai): New way to experience events.\n* [SiteGPT](https:\u002F\u002Fsitegpt.ai\u002F?ref=filipecalegario-awesome-generative-ai): Make AI your expert customer support agent.\n* [PressPulse AI](https:\u002F\u002Fwww.presspulse.ai\u002F?ref=filipecalegario-awesome-generative-ai): Get personalized media coverage leads every morning.\n* [GPTHelp.ai](https:\u002F\u002Fgpthelp.ai\u002F?ref=filipecalegario-awesome-generative-ai): ChatGPT for your website \u002F AI customer support chatbot.\n* [chaiNNer-org\u002FchaiNNer](https:\u002F\u002Fgithub.com\u002FchaiNNer-org\u002FchaiNNer): A node-based image processing and AI upscaling GUI that makes it easy to chain together complex processing tasks \n* [BIRME](https:\u002F\u002Fwww.birme.net\u002F): Bulk Image Resizing Made Easy 2.0 (Online & Free)\n* [The Art of PNG Glitch](https:\u002F\u002Fucnv.github.io\u002Fpnglitch\u002F)\n* [HashLips\u002Fhashlips_art_engine](https:\u002F\u002Fgithub.com\u002FHashLips\u002Fhashlips_art_engine): tool used to create multiple different instances of artworks based on provided layers\n* [Taplio](https:\u002F\u002Ftaplio.com\u002F?ref=filipecalegario-awesome-generative-ai): The all-in-one, AI-powered LinkedIn tool.\n* [Galichat.com](https:\u002F\u002Fwww.galichat.com\u002F?ref=filipecalegario-awesome-generative-ai): AI Support Assistant that helps you grow your business.\n* [Aidbase](https:\u002F\u002Fwww.aidbase.ai) - AI-Powered Support for your SaaS startup.\n* [Socialsonic](https:\u002F\u002Fsocialsonic.com) - AI LinkedIn Coach: Personalized content, trends & scheduling.\n\n### Dimension reduction techniques\n\n* [Why you should use Topological Data Analysis over t-SNE or UMAP?](https:\u002F\u002Fdatarefiner.com\u002Ffeed\u002Fwhy-tda) \n* [YingfanWang\u002FPaCMAP: PaCMAP: Large-scale Dimension Reduction Technique Preserving Both Global and Local Structure](https:\u002F\u002Fgithub.com\u002FYingfanWang\u002FPaCMAP)\n* [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.03426)\n* [Visualizing Data using t-SNE](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv9\u002Fvandermaaten08a.html) \n\n## Roadmaps, Tracks, Rails\n\n* [(1166) A Hackers' Guide to Language Models - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jkrNMKz9pWU&t=21s) \n* [🔥🔥] [Generative AI for Beginners](https:\u002F\u002Fmicrosoft.github.io\u002Fgenerative-ai-for-beginners\u002F#\u002F): introductory 12 lesson course by Microsoft\n* [Introduction to Generative AI](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fyoussef-hosni-b2960b135_if-you-want-to-start-studying-generative-activity-7125908710702350336-vhsm\u002F): series of Medium articles by Youssef Hosni\n* [Prompt Engineering Roadmap - roadmap.sh](https:\u002F\u002Froadmap.sh\u002Fprompt-engineering)\n* [Prompt Engineering Guide | Learn Prompting: Your Guide to Communicating with AI](https:\u002F\u002Flearnprompting.org\u002Fdocs\u002Fintro)\n* [Short Courses | Learn Generative AI from DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002F) \n\n## Stargazers over time\n\n[![Stargazers over time](https:\u002F\u002Fstarchart.cc\u002Ffilipecalegario\u002Fawesome-generative-ai.svg)](https:\u002F\u002Fstarchart.cc\u002Ffilipecalegario\u002Fawesome-generative-ai)\n![](https:\u002F\u002Fvbr.wocr.tk\u002Fbadge?page_id=filipecalegario.awesome-generative-ai&color=55acb7&style=for-the-badge&logo=Github)\n\n## Contribute\n\nContributions welcome! Read the [contribution guidelines](contributing.md) first.\n\n## License\n\n[![CC0](https:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F88x31\u002Fsvg\u002Fcc-zero.svg)](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0)\n\nTo the extent possible under law, Filipe Calegario has waived all copyright and\nrelated or neighboring rights to this work.\n\n[![\"Buy Me A Coffee\"](https:\u002F\u002Fwww.buymeacoffee.com\u002Fassets\u002Fimg\u002Fcustom_images\u002Forange_img.png)](https:\u002F\u002Fwww.buymeacoffee.com\u002Ffilipecalegario)\n","# 令人惊叹的生成式AI [![Awesome](https:\u002F\u002Fawesome.re\u002Fbadge.svg)](https:\u002F\u002Fawesome.re)[![追踪Awesome列表](https:\u002F\u002Fwww.trackawesomelist.com\u002Fbadge.svg)](https:\u002F\u002Fwww.trackawesomelist.com\u002Ffilipecalegario\u002Fawesome-generative-deep-art\u002F) \u003C!-- omit in toc -->\n\n> 一份精心整理的生成式AI项目、工具、艺术作品和模型清单\n\n- [生成式AI领域](#generative-ai-area)\n  - [生成式AI的历史、时间线、地图与定义](#generative-ai-history-timelines-maps-and-definitions)\n  - [关于生成式AI的伦理、哲学问题及讨论](#ethics-philosophical-questions-and-discussions-about-generative-ai)\n  - [对生成式AI的批判性观点](#critical-views-about-generative-ai)\n  - [生成式AI的过程与产物](#generative-ai-processes-and-artifacts)\n  - [生成式AI工具目录](#generative-ai-tools-directories)\n  - [课程与教育资料](#courses-and-educational-materials)\n  - [人机交互](#human-ai-interaction)\n  - [论文集](#papers-collection)\n  - [在线工具与应用](#online-tools-and-applications)\n- [代码与编程](#code-and-programming)\n  - [氛围编码](#vibe-coding)\n  - [AI驱动的代码生成](#ai-powered-code-generation)\n- [文本](#text)\n  - [从一切到Markdown再到LLMs](#everything-to-markdown-to-llms)\n  - [小型语言模型](#small-language-models)\n  - [大型语言模型（LLMs）](#large-language-models-llms)\n    - [模型上下文协议](#model-context-protocol)\n    - [LLMs的编程框架](#programming-frameworks-for-llms)\n    - [提示工程](#prompt-engineering)\n      - [提示优化器](#prompt-optimizers)\n      - [文本到文本的提示工程](#prompt-engineering-for-text-to-text)\n      - [文本到图像的提示工程](#prompt-engineering-for-text-to-image)\n    - [Mamba](#mamba)\n    - [在本地运行LLMs](#running-llms-locally)\n    - [函数调用](#function-calling)\n    - [GPTs与助手API](#gpts-and-assistant-api)\n    - [检索增强生成（RAG）](#retrieval-augmented-generation-rag)\n    - [嵌入与语义搜索](#embeddings-and-semantic-search)\n    - [自主LLM智能体](#autonomous-llm-agents)\n      - [多智能体](#multi-agents)\n    - [LLM评估](#llm-evaluation)\n    - [LLMOps](#llmops)\n    - [AI工程](#ai-engineering)\n    - [针对LLMs的攻击](#attacks-on-llms)\n    - [LangChain](#langchain)\n    - [ChatGPT](#chatgpt)\n    - [与文本相关的生成工具](#text-related-generative-tools)\n  - [科研AI工具](#research-ai-tools)\n    - [用于研究的AI工具](#ai-tools-for-research)\n    - [用于搜索的AI工具](#ai-tools-for-searching)\n- [图像](#image)\n  - [图像合成](#image-synthesis)\n    - [收件箱：Stable Diffusion](#inbox-stable-diffusion)\n      - [Stable Diffusion部署的Web工具](#stable-diffusion-deployed-web-tools)\n      - [通过Google Colab访问Stable Diffusion的Web界面](#web-ui-for-stable-diffusion-via-google-colab)\n      - [关于Stable Diffusion的参考文献合集](#references-collection-about-stable-diffusion)\n    - [超技术](#hypertechniques)\n      - [ControlNet](#controlnet)\n      - [文本反转](#textual-inversion)\n      - [DreamBooth](#dreambooth)\n      - [Deforum](#deforum)\n    - [生成式AI图像合成工具的创意应用](#creative-uses-of-generative-ai-image-synthesis-tools)\n  - [图像超分辨率](#image-upscaling)\n  - [图像修复](#image-restoration)\n  - [图像分割](#image-segmentation)\n- [视频与动画](#video-and-animation)\n- [音频与音乐](#audio-and-music)\n- [语音](#speech)\n  - [文本转语音（TTS）及虚拟形象](#text-to-speech-tts-and-avatars)\n    - [播客生成器](#podcast-generators)\n  - [语音转文字（STT）及口语内容分析](#speech-to-text-stt-and-spoken-content-analysis)\n- [游戏](#games)\n- [多模态](#multimodal)\n  - [多模态嵌入空间](#multimodal-embedding-space)\n- [数据集](#datasets)\n- [杂项](#misc)\n  - [AI与教育](#ai-and-education)\n  - [人物与作品](#people-and-works)\n    - [有趣的Twitter账号](#interesting-twitter-accounts)\n    - [有趣的Instagram账号、帖子和短视频](#interesting-instagram-accounts-posts-and-reels)\n    - [有趣的YouTube频道](#interesting-youtube-channels)\n    - [有趣的GitHub仓库](#interesting-github-repositories)\n    - [艺术家与艺术作品](#artists-and-artworks)\n    - [画廊](#galleries)\n  - [相关Awesome列表](#related-awesome-lists)\n  - [生物实验](#bio-experiments)\n  - [生成式AI领域的职位](#jobs-in-generative-ai)\n  - [改善Google Colab体验](#improving-google-colab-experience)\n  - [辅助工具与概念](#auxiliary-tools-and-concepts)\n    - [降维技术](#dimension-reduction-techniques)\n  - [路线图、轨迹、轨道](#roadmaps-tracks-rails)\n  - [星标用户随时间的变化](#stargazers-over-time)\n  - [贡献](#contribute)\n  - [许可证](#license)\n\n## 仓库简介\n\n欢迎来到我们的生成式AI资源Awesome列表！本仓库是一个精心编纂的生成式AI领域参考资料集合，涵盖了学术论文、技术文章、在线课程、教程以及软件等多种资源。\n\n### 结构\n\n1. **章节**：每个章节代表一个不同的生成式AI相关类别（例如，LLMs、提示工程、图像合成、教育资源等）。收件箱部分则包含了该类别的更通用参考。当出现新的类别时，它会成为一个特定的子章节。\n\n2. **章节内的参考文献**：在每个章节内，参考文献按时间倒序排列，最新的放在最前面。这种排序方式反映了生成式AI领域日新月异的发展态势，确保您能够及时了解最新进展。\n\n本仓库旨在为您提供触手可及的最新研究成果，并允许您按照自己的节奏深入探索较早的资源。我们会定期更新此仓库，以保证您始终紧跟快速发展的生成式AI世界。\n\n### 参与贡献\n\n我们非常欢迎您的贡献！如果您认为某个有价值的资源应该被列入本列表，或者发现任何过时的信息，请提交Pull Request。这将有助于我们保持Awesome列表的质量和相关性。\n\n遵循这份路线图，不断学习，尽情享受您在生成式AI领域的旅程吧！\n\n# 生成式AI领域\n\n## 生成式AI的历史、时间线、地图与定义\n\n* [AI 时间线](https:\u002F\u002Fnhlocal.github.io\u002FAiTimeline\u002F)\n* [代理市场](https:\u002F\u002Fmarketplace.agen.cy\u002Fagents)\n* [🔥] [2024 年 AI 时间线](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Freach-vb\u002F2024-ai-timeline)：由 reach-vb 在 Hugging Face 上创建的空间\n* [生成式 AI 的地图集](https:\u002F\u002Fcartography-of-generative-ai.net\u002F)：“哪些提取、机构和资源使我们能够在线与文本生成工具对话，或在几秒钟内获得图像？”\n* [生成式 AI 大型语言模型（LLMs）的崛起](https:\u002F\u002Finformationisbeautiful.net\u002Fvisualizations\u002Fthe-rise-of-generative-ai-large-language-models-llms-like-chatgpt\u002F)：由 Information Is Beautiful 制作的交互式时间线可视化\n* [AI 时间线 (@TheAITimeline) \u002F X](https:\u002F\u002Fx.com\u002FTheAITimeline)\n* [面向初学者的生成式 AI：第一部分——AI 简介 | 作者：Raja Gupta | Medium](https:\u002F\u002Fmedium.com\u002F@raja.gupta20\u002Fgenerative-ai-for-beginners-part-1-introduction-to-ai-eadb5a71f07d)\n* [人工智能学习路线图 [AI 路线图] 2024](https:\u002F\u002Fwww.mltut.com\u002Fartificial-intelligence-learning-roadmap\u002F)\n* [生成式 AI 简史 - DATAVERSITY](https:\u002F\u002Fwww.dataversity.net\u002Fa-brief-history-of-generative-ai\u002F)\n* [生成式 AI 历史简明指南 | Bernard Marr](https:\u002F\u002Fbernardmarr.com\u002Fa-simple-guide-to-the-history-of-generative-ai\u002F)\n* [2023 年 1 月至 7 月的生成式 AI 时间线](https:\u002F\u002Fgenerativeaitimeline.com\u002F)\n* [生成式 AI 的兴起：辉煌、挫折与炒作的时间线 | CIO Dive](https:\u002F\u002Fwww.ciodive.com\u002Fnews\u002Fgenerative-ai-one-year-chatgpt-openai-timeline\u002F698110\u002F)\n* [时间中的简史：解读生成式 AI 的演变 | LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fbrief-history-time-decoding-evolution-generative-ai-csmtechnologies\u002F)\n* [🔥🔥🔥] [FirstMark | 2024 MAD (ML\u002FAI\u002F数据) 景观](https:\u002F\u002Fmad.firstmark.com\u002F)：全速前进——2024 年 MAD（机器学习、AI 和数据）景观\n* [AI 预测时间线 - AI Digest](https:\u002F\u002Ftheaidigest.org\u002Ftimeline)\n* [生成式 AI 冰山图](https:\u002F\u002Ficebergcharts.com\u002Fi\u002FGenerative_AI)\n* [🔥🔥🔥] [生成式 AI 概览](https:\u002F\u002Fblog.crisp.se\u002Fwp-content\u002Fuploads\u002F2024\u002F01\u002Fgenerative-AI-in-a-nutshell.png)：由 Henrik Kniberg 绘制的包含最常见生成式 AI 概念的地图 [解释该地图的 YouTube 视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2IK3DFHRFfw)\n* [你必须熟记的 60 多个生成式 AI 术语](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2024\u002F01\u002Fgenerative-ai-terms\u002F)：由 Analytics Vidhya 提供\n* [AI 栈的四场战争（2023 年 12 月回顾）](https:\u002F\u002Fwww.latent.space\u002Fp\u002Fdec-2023)：“2023 年 12 月对 AI 工程师来说最重要的内容回顾”（“数据之战、GPU 富贫之战、多模态之战、RAG\u002F运维之战”）\n* [Brian Solis 的 GenAI 光谱信息图](https:\u002F\u002Fbriansolis.com\u002F2023\u002F12\u002Fintroducing-the-genai-prism-infographic-a-framework-for-colalborating-with-generative-ai\u002F)：与生成式 AI 合作的框架\n* [LLM 可视化](https:\u002F\u002Fbbycroft.net\u002Fllm)\n* [[2310.04438] 提示词简史：利用语言模型](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04438)：该论文探讨了提示工程的演变。作者 Golam Md Muktadir 大量使用 ChatGPT 进行内容生成\n* [一位 AI 极客（wishesh）撰写的关于机器学习和生成式 AI 的指南 | 2023 年 10 月 | Medium](https:\u002F\u002Fmedium.com\u002F@_aigeek\u002Fan-ai-engineers-guide-to-machine-learning-and-generative-ai-b7444941ccee)\n* [生成式 AI 研究的新兴趋势：近期论文精选](https:\u002F\u002Ftxt.cohere.com\u002Ftop-nlp-papers-september-2023\u002F)\n* [当今 LLM 应用的架构 — GitHub 博客](https:\u002F\u002Fgithub.blog\u002F2023-10-30-the-architecture-of-todays-llm-applications\u002F)\n* [🔥🔥🔥] [[2310.07127] 以人为中心的人机-生成式 AI 交互综述与分类](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07127)：“对 154 篇论文的综述，从人类和生成式 AI 两个角度提供了新颖的分类和分析。”\n* [生成式 AI 的构建模块 | 作者：Jonathan Shriftman | Medium](https:\u002F\u002Fshriftman.medium.com\u002Fthe-building-blocks-of-generative-ai-a75350466a2f)\n* [🔥] [生成式 AI 的存在源于 Transformer](https:\u002F\u002Fig.ft.com\u002Fgenerative-ai\u002F)：金融时报的一篇视觉故事\n* [AI 的早期阶段 — 由 Elad Gil 撰写](https:\u002F\u002Fblog.eladgil.com\u002Fp\u002Fearly-days-of-ai)：关于 AI 是“一个全新的时代，与过去截然不同”的思考\n* [进步的下一个标志：生成式 AI 前景中的四个突破 | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F23\u002Fthe-next-token-of-progress-4-unlocks-on-the-generative-ai-horizon\u002F)\n* [[2309.07930] 生成式 AI](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.07930)：从模型、系统和应用层面探讨生成式 AI。\n* [2023 年 AI 现状：生成式 AI 的爆发之年 | 麦肯锡](https:\u002F\u002Fwww.mckinsey.com\u002Fcapabilities\u002Fquantumblack\u002Four-insights\u002Fthe-state-of-ai-in-2023-generative-ais-breakout-year#\u002F)\n* [无术语解释 AI 大型语言模型的工作原理 | Ars Technica](https:\u002F\u002Farstechnica.com\u002Fscience\u002F2023\u002F07\u002Fa-jargon-free-explanation-of-how-ai-large-language-models-work\u002F)\n* [生成式 AI 革命：探索当前格局 | 由 Towards AI 编辑团队撰写 | 2023 年 6 月 | Towards AI](https:\u002F\u002Fpub.towardsai.net\u002Fthe-generative-ai-revolution-exploring-the-current-landscape-4b89998fcc5f)\n* [AI 冬季的故事及其对今天的启示](https:\u002F\u002Fwww.turingpost.com\u002Fp\u002Faiwinters)\n* [如果没有这个，就不会有 LLM（历史系列第 3 集）](https:\u002F\u002Fwww.turingpost.com\u002Fp\u002Fllmshistory3)：由 Turing Post 提供的 LLM 时间线\n* [进步的下一个标志：生成式 AI 前景中的四个突破 | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F23\u002Fthe-next-token-of-progress-4-unlocks-on-the-generative-ai-horizon\u002F)：前景中的关键创新：引导、记忆、使用工具的能力以及多模态性\n* [生成式 AI 的经济潜力：下一道生产力前沿](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fgenai-works_gen-ai-activity-7074980736268726272-LiiG)：麦肯锡 2023 年 6 月发布的报告\n* [生成式 AI 应用综述 | arXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.02781)：“本综述旨在为研究人员和从业者提供有价值的参考，帮助他们应对快速扩展的生成式 AI 领域。”\n* [Paper Digest - ChatGPT](https:\u002F\u002Fwww.paperdigest.org\u002F2023\u002F01\u002Frecent-papers-on-chatgpt\u002F)：关于 ChatGPT 的近期论文\n* [2023 年 AI 指数报告 – 人工智能指数](https:\u002F\u002Faiindex.stanford.edu\u002Freport\u002F)：由斯坦福大学以人为本的人工智能研究中心编写的衡量 AI 趋势的报告\n* [大型语言模型综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.18223)：总结语言模型演进的论文，重点关注 LLM，讨论其进展、技术及对 AI 发展和应用的影响\n* [生成式 AI 时间线](https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:7044233450295316480)：David Foster 在 LinkedIn 上发布的帖子\n* [谁拥有生成式 AI 平台？| Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F01\u002F19\u002Fwho-owns-the-generative-ai-platform\u002F)：本文探讨了生成式 AI 市场，并展示了该领域的有趣技术栈\n* [AI 生成内容（AIGC）综合调查：从 GAN 到 ChatGPT 的生成式 AI 历史 | arXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.04226)\n* [🔥🔥] [迈向生成式 AI 应用的通用设计原则](https:\u002F\u002Farxiv.org\u002Fabs\u002F2301.05578)：该论文提出了七条生成式 AI 应用设计原则\n* [🔥] [生成式 AI 景观报告概览 | 作者：Ramsri Goutham | 2023 年 1 月 | Medium](https:\u002F\u002Framsrigoutham.medium.com\u002Fthe-landscape-of-generative-ai-landscape-reports-615a417b15d)：一份关于 9 家风险投资公司发布的报告的元分析\n* [Cohere 的生成式 AI：第一部分——模型提示](https:\u002F\u002Ftxt.cohere.ai\u002Fgenerative-ai-part-1\u002F)：Cohere AI 对生成式 AI 的概述\n* [Cohere 的生成式 AI：第二部分——用例构思](https:\u002F\u002Ftxt.cohere.ai\u002Fgenerative-ai-part-2\u002F)：Cohere AI 提供的生成式 AI 用例列表\n* [大型语言模型及其应用场景：第一部分](https:\u002F\u002Ftxt.cohere.ai\u002Fllm-use-cases\u002F)：Cohere AI 提供的 LLM 用例列表\n* [大型语言模型及其应用场景：第二部分](https:\u002F\u002Ftxt.cohere.ai\u002Fllm-use-cases-p2\u002F)\n* [生成式 AI 有什么大不了的？它是未来还是现在？](https:\u002F\u002Ftxt.cohere.ai\u002Fgenerative-ai-future-or-present\u002F)：Cohere AI 对生成式 AI 领域的总结\n* [AI 和语言模型时间线](https:\u002F\u002Flifearchitect.ai\u002Ftimeline\u002F)：由 Life Architect 的 Alan D. Thompson 整理的 LLM 时间线\n* [预训练基础模型综合调查：从 BERT 到 ChatGPT 的历史 | arXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.09419)\n* [从历史视角看生成式 AI 的综述](https:\u002F\u002Fwww.techrxiv.org\u002Farticles\u002Fpreprint\u002FA_Review_of_Generative_AI_from_Historical_Perspectives\u002F22097942)：由 Dipankar Dasgupta、Deepak Venugopal 和 Kishor Datta Gupta 撰写的论文\n* [Matt Shumer 在 Twitter 上：“终极 AI 市场地图推文串”](https:\u002F\u002Ftwitter.com\u002Fmattshumer_\u002Fstatus\u002F1620465468229451776)：“终极 AI 市场地图推文串”\n* [🔥] [Base11 Research - 生成式 AI](https:\u002F\u002Fbase10.vc\u002Fresearch\u002Fgenerative-ai)：由投资公司 Base10 出品的生成式 AI 报告\n* [惊叹引擎：AI 艺术走向成熟——Steve Murch](https:\u002F\u002Fwww.stevemurch.com\u002Fengines-of-wow-ai-art-comes-of-age\u002F2022\u002F12)\n* [AI 于 2022 年底突然爆红 \u002F Twitter](https:\u002F\u002Ftwitter.com\u002FRamaswmySridhar\u002Fstatus\u002F1613271413020037120)：用于分析生成式 AI 工具的类别\n* [🔥🔥🔥] [绘制生成式 AI 景观 | Antler](https:\u002F\u002Fwww.antler.co\u002Fblog\u002Fgenerative-ai)\n* [🔥🔥🔥] [AI 时间线](https:\u002F\u002Fwww.fabianmosele.com\u002Fai-timeline)：Fabian Mosele 制作的文本到图像 ML 模型历史\n* [AI 生成的艺术](https:\u002F\u002Fwww.v7labs.com\u002Fblog\u002Fai-generated-art)：从文本到图像及其他更多案例\n* [Stable Diffusion 一周 | multimodal.art](https:\u002F\u002Fmultimodal.art\u002Fnews\u002F1-week-of-stable-diffusion)\n\n## 伦理、哲学问题及生成式人工智能讨论\n\n* [🔭 爱因斯坦人工智能模型](https:\u002F\u002Fthomwolf.io\u002Fblog\u002Fscientific-ai.html)\n* [充满慈爱的机器——达里奥·阿莫迪如何通过AI让世界变得更美好](https:\u002F\u002Fdarioamodei.com\u002Fmachines-of-loving-grace)\n* [AI悲伤的五个阶段——NOEMA](https:\u002F\u002Fwww.noemamag.com\u002Fthe-five-stages-of-ai-grief\u002F)\n* [生成式AI伦理：8大担忧与风险](https:\u002F\u002Fwww.techtarget.com\u002Fsearchenterpriseai\u002Ftip\u002FGenerative-AI-ethics-8-biggest-concerns)\n* [自动化社会科学：语言模型作为科学家与研究对象 | NBER](https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw32381)\n* [是时候淘汰“用户”这个词了](https:\u002F\u002Fwww.technologyreview.com\u002F2024\u002F04\u002F19\u002F1090872\u002Fai-users-people-terms\u002F)：AI的普及意味着我们需要一个新的词汇\n* [理解人格特质、经历和态度如何塑造对AI生成艺术作品的负面偏见 | Scientific Reports](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-024-54294-4)\n* [追踪AI](https:\u002F\u002Ftrackingai.org\u002F)：监测人工智能聊天机器人的偏见\n* [AI下一波超智能会取代人类的创造力吗？情况很复杂——Grit Daily News](https:\u002F\u002Fgritdaily.com\u002Fwill-ais-super-intelligence-replace-human-ingenuity\u002F)\n* [谁害怕弗兰肯斯坦？又谁害怕生成式AI？| Fast Company巴西](https:\u002F\u002Ffastcompanybrasil.com\u002Ftech\u002Finteligencia-artificial\u002Fquem-tem-medo-do-frankenstein-e-da-ia-generativa\u002F) [PT-BR]\n* [希托·施泰耶尔，《低俗图像》，NLR 140\u002F141，2023年3–6月](https:\u002F\u002Fnewleftreview.org\u002Fissues\u002Fii140\u002Farticles\u002Fhito-steyerl-mean-images)\n* [AI艺术的版权难题——The Verge](https:\u002F\u002Fwww.theverge.com\u002F23961021\u002Fai-art-copyright-training-ownership-fair-use)\n* [关于推进巴西人工智能发展的建议——ABC](https:\u002F\u002Fwww.abc.org.br\u002Fevento\u002Fdoc-ia-no-brasil\u002F) [PT-BR]\n* [我们必须阻止AI复制监视资本主义的问题](https:\u002F\u002Fwww.ft.com\u002Fcontent\u002Fd9063c16-a4d2-4580-b8f6-a4872083d0fa)\n* [人工智能服务于集体智慧](https:\u002F\u002Fintlekt.io\u002F2023\u002F10\u002F29\u002Fartificial-intelligence-at-the-service-of-collective-intelligence\u002F)\n* [新训练方法帮助AI像人类一样进行泛化——Scientific American](https:\u002F\u002Fwww.scientificamerican.com\u002Farticle\u002Fnew-training-method-helps-ai-generalize-like-people-do\u002F)\n* [[2310.01405] 表征工程：一种自上而下的AI透明度方法](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01405)：“一种借鉴认知神经科学见解来提升AI系统透明度的方法”\n* [伯克利法学院师生的生成式AI资源——伯克利法学院](https:\u002F\u002Fwww.law.berkeley.edu\u002Flibrary\u002Flegal-research\u002Fchatgpt\u002F)\n* [许可制度既不可行也不足以应对AI风险](https:\u002F\u002Fwww.aisnakeoil.com\u002Fp\u002Flicensing-is-neither-feasible-nor)\n* [生成式AI公司必须发布透明度报告](https:\u002F\u002Fwww.aisnakeoil.com\u002Fp\u002Fgenerative-ai-companies-must-publish)\n* [ChatGPT是否存在自由主义偏见？](https:\u002F\u002Fwww.aisnakeoil.com\u002Fp\u002Fdoes-chatgpt-have-a-liberal-bias)\n* [比人类更人性化：衡量ChatGPT的政治偏见 | Public Choice](https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11127-023-01097-2)\n* [重新定义偏见：人类对AI的偏见 | Medium](https:\u002F\u002Fjohnnosta.medium.com\u002Fredefining-bias-the-human-prejudice-against-ai-a1f225b0b2c2)\n* [AI艺术及其对艺术家的影响](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3600211.3604681)：发表于2023年AAAI\u002FACM人工智能、伦理与社会会议论文集中的论文\n* [AI时代已经开启 | 比尔·盖茨](https:\u002F\u002Fwww.gatesnotes.com\u002FThe-Age-of-AI-Has-Begun)\n* [AIKEA效应](https:\u002F\u002Fpiszek.com\u002F2023\u002F08\u002F28\u002Faikea-effect\u002F)：作者为Artur Piszek\n* [人工智能伦理：案例研究及应对伦理挑战的方案 | SpringerLink](https:\u002F\u002Flink.springer.com\u002Fbook\u002F10.1007\u002F978-3-031-17040-9)\n* [拥抱变化并重置期望 | Microsoft Unlocked](https:\u002F\u002Funlocked.microsoft.com\u002Fai-anthology\u002Fterence-tao\u002F)：文章作者为陶哲轩\n* [艺术与生成式AI的科学 | Science](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.adh4451)\n* [AI将从这里走向何方](https:\u002F\u002Fwww.axios.com\u002F2023\u002F05\u002F18\u002Fai-agi-artificial-general-intelligence)\n* [AI时代已经开启](https:\u002F\u002Fwww.gatesnotes.com\u002FThe-Age-of-AI-Has-Begun)：比尔·盖茨的笔记\n* [GPT就是GPT：大型语言模型对劳动力市场影响潜力的早期观察](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.10130)：OpenAI发表的一篇探讨GPT对美国劳动力市场可能影响的论文\n* [为什么生成式AI会让艺术家感到恐惧，却不会让内容写作者感到害怕](https:\u002F\u002Fwww.fastcompany.com\u002F90848228\u002Fwhy-generative-ai-scares-artists-but-not-writers)\n* [AI中的文化\u002F文化中的AI](https:\u002F\u002Fai-cultures.github.io\u002F)：NeurIPS 2022研讨会网页\n* [AI数据清洗——Waxy.org](https:\u002F\u002Fwaxy.org\u002F2022\u002F09\u002Fai-data-laundering-how-academic-and-nonprofit-researchers-shield-tech-companies-from-accountability\u002F)：学术界和非营利研究人员如何使科技公司免于承担责任\n* [🔥🔥🔥] [(1232) 艺术的终结：反对图像AI的论点——YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tjSxFAGP9Ss&t=193s)：史蒂文·萨帕塔的视频评论\n* [🔥🔥🔥] [艺术的终结：反对图像AI的论点（公开版）——Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F128yey0VfYhM9eUdvkvCpk5zvvoIkqXfI4hEPAYeJCHU\u002Fedit)：史蒂文·萨帕塔视频评论的文字稿\n* [🔥🔥🔥] [生成式AI：一个富有创造力的新世界——红杉资本美国\u002F欧洲](https:\u002F\u002Fwww.sequoiacap.com\u002Farticle\u002Fgenerative-ai-a-creative-new-world\u002F)：红杉资本关于生成式AI潜在应用的报告\n* [合成创意——作者：Cavin——Deep Markets](https:\u002F\u002Fdeepmarkets.substack.com\u002Fp\u002Fsynthetic-creativity)\n* [我们对合成媒体未来的愿景——作者：维克多·里帕贝利——Medium](https:\u002F\u002Fvriparbelli.medium.com\u002Four-vision-for-the-future-of-synthetic-media-8791059e8f3a)\n* [深异](https:\u002F\u002Fdejangrba.github.io\u002Fdeep-else\u002F)：一种针对AI艺术的批判性框架\n* [摄影如何成为一种艺术形式——亚伦·赫兹曼的博客](https:\u002F\u002Faaronhertzmann.com\u002F2022\u002F08\u002F29\u002Fphotography-history.html)\n* [计算机能创作艺术吗？——亚伦·赫兹曼](https:\u002F\u002Fwww.mdpi.com\u002F2076-0752\u002F7\u002F2\u002F18)：2018年发表在《艺术杂志》上的文章\n* [文本是通用接口——Scale](https:\u002F\u002Fscale.com\u002Fblog\u002Ftext-universal-interface)\n* [这位艺术家正在主导AI生成的艺术领域。而他对此并不高兴。| MIT技术评论](https:\u002F\u002Fwww.technologyreview.com\u002F2022\u002F09\u002F16\u002F1059598\u002Fthis-artist-is-dominating-ai-generated-art-and-hes-not-happy-about-it\u002F)\n* [关于AI艺术的真正争斗：StableDiffusion | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgu2uo\u002Fthe_real_fight_over_ai_art\u002F)\n* [鲁特科夫斯基对抗AI艺术霸主 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgv0dw\u002Frutkowski_battling_ai_art_overlord\u002F)\n* [我们现在是不是用GPU挖的是艺术，而不是加密货币？| Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxg8s8e\u002Finstead_of_mining_cryptocoins_with_gpus_are_we\u002F)\n* [用AI创作艺术并不等于艺术！| Reddit：ArtistLounge](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FArtistLounge\u002Fcomments\u002Fxczk89\u002Fusing_ai_to_create_art_is_not_art\u002F)\n* [欣赏AI艺术的诗意误解——纽约客](https:\u002F\u002Fwww.newyorker.com\u002Fculture\u002Finfinite-scroll\u002Fappreciating-the-poetic-misunderstandings-of-ai-art?s=09)\n\n## 关于生成式人工智能的批判性观点\n\n* [反对由AI生成的用户的理由 - IDEO](https:\u002F\u002Fwww.ideo.com\u002Fjournal\u002Fthe-case-against-ai-generated-users)\n* [为何将完全控制权交给AI智能体将是一个巨大错误 | MIT技术评论](https:\u002F\u002Fwww.technologyreview.com\u002F2025\u002F03\u002F24\u002F1113647\u002Fwhy-handing-over-total-control-to-ai-agents-would-be-a-huge-mistake)\n* [Eryk Salvaggio整理的“关于生成式AI最深刻的思考”合集](https:\u002F\u002Fbsky.app\u002Fprofile\u002Feryk.bsky.social\u002Fpost\u002F3lccavgstkk2s)\n* [AI伪科学：区分炒作与现实 | TechPolicy.Press](https:\u002F\u002Fwww.techpolicy.press\u002Fai-snake-oil-separating-hype-from-reality\u002F)\n* [解构AI神话：算法化的谬误与危害](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F382802495_Deconstructing_the_AI_Myth_Fallacies_and_Harms_of_Algorithmification)\n* [挑战生成式AI的神话 | TechPolicy.Press](https:\u002F\u002Fwww.techpolicy.press\u002Fchallenging-the-myths-of-generative-ai\u002F)\n* [我厌倦了AI | 关于测试自动化](https:\u002F\u002Fwww.ontestautomation.com\u002Fi-am-tired-of-ai\u002F)\n* [Steffi Tan、Vaikunthan Rajaratnam撰写的《生成式AI批判可能损害学习研究设计》论文 | SSRN](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4898213)\n* [Hamsa Bastani、Osbert Bastani、Alp Sungu、Haosen Ge、Özge Kabakcı、Rei Mariman撰写的《生成式AI可能损害学习》论文 | SSRN](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4895486)\n* [我执教大半生，却因ChatGPT而辞职 | 时代周刊](https:\u002F\u002Ftime.com\u002F7026050\u002Fchatgpt-quit-teaching-ai-essay\u002F)\n* [可能导致灾难的AI风险 | CAIS](https:\u002F\u002Fwww.safe.ai\u002Fai-risk)\n* [AI风险库](https:\u002F\u002Fairisk.mit.edu\u002F)\n* [[2406.17864] AI风险分类解读（AIR 2024）](https:\u002F\u002Fwww.arxiv.org\u002Fabs\u002F2406.17864)：从政府监管到企业政策\n* [\"AI向善\"运动是错误的做法 - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fai-for-good)\n* [生成式AI并非我们所承诺的万能药 | Eric Siegel在Big Think+上的演讲 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=B2zCWJBnfuE)\n* [James Gosling对GenAI的思考](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fthoughts-genai-james-gosling-nab0c\u002F)\n* [自动化社会科学：语言模型作为科学家与研究对象 | NBER](https:\u002F\u002Fwww.nber.org\u002Fpapers\u002Fw32381)\n* [生成式AI泡沫何时会破裂？ - Gary Marcus](https:\u002F\u002Fgarymarcus.substack.com\u002Fp\u002Fwhen-will-the-genai-bubble-burst)\n* [Nightshade：这款“毒化”数据的工具让艺术家有机会对抗AI | TechCrunch](https:\u002F\u002Ftechcrunch.com\u002F2024\u002F01\u002F26\u002Fnightshade-the-tool-that-poisons-data-gives-artists-a-fighting-chance-against-ai\u002F)\n* [AI如何让我们失望 | 哈佛大学埃德蒙与莉莉·萨弗拉伦理中心](https:\u002F\u002Fethics.harvard.edu\u002Fhow-ai-fails-us)\n* [生成式AI存在视觉剽窃问题 - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fmidjourney-copyright)：“使用Midjourney和DALL-E 3进行的实验显示出版权雷区”\n* [[2308.03762] GPT-4无法进行推理](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.03762)：“尽管确实有了令人印象深刻的改进，但我们仍有充分理由对GPT-4的推理能力持高度怀疑态度”\n* [风险与危害：剖析AI话语中的意识形态 | 第五届国际对话式用户界面会议论文集](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3571884.3603751)\n* [[2305.18654] 信仰与命运：Transformer模型在组合性方面的局限性](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.18654)\n* [[2210.02667] 基于人权的负责任AI方法](https:\u002F\u002Farxiv.org\u002Fabs\u002F2210.02667)\n* [论随机鹦鹉的危害 | 2021年ACM公平、问责与透明度会议论文集](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3442188.3445922)\n* [这款新的数据投毒工具使艺术家能够反击生成式AI | MIT技术评论](https:\u002F\u002Fwww.technologyreview.com\u002F2023\u002F10\u002F23\u002F1082189\u002Fdata-poisoning-artists-fight-generative-ai\u002F)\n* [生成式人工智能对就业的短期影响：来自在线劳动力市场的证据 - Xiang Hui、Oren Reshef、Luofeng Zhou | SSRN](https:\u002F\u002Fpapers.ssrn.com\u002Fsol3\u002Fpapers.cfm?abstract_id=4527336)\n* [教育领域AI小组会议记录 - Google文档](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1PPHwa3KmoeRZwaoxjOS568aF2E-kUngOA2oI45G2Iaw\u002Fedit)\n* [AI生成工具的教学大纲政策 - Google文档](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1RMVwzjc1o0Mi8Blw_-JUTcXv02b2WRH86vw7mi16W3U\u002Fedit#heading=h.1cykjn2vg2wx)\n* [英国布莱切利公园AI安全峰会五大要点 | 人工智能 | 卫报](https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002F2023\u002Fnov\u002F02\u002Ffive-takeaways-uk-ai-safety-summit-bletchley-park-rishi-sunak)\n* [前沿AI：能力与风险——讨论文件 - GOV.UK](https:\u002F\u002Fwww.gov.uk\u002Fgovernment\u002Fpublications\u002Ffrontier-ai-capabilities-and-risks-discussion-paper)\n* [AI安全峰会政策更新 | AISS 2023](https:\u002F\u002Fwww.aisafetysummit.gov.uk\u002Fpolicy-updates\u002F#company-policies)\n* [借助知识增强型生成式AI做出负责任的企业决策 | 德勤荷兰](https:\u002F\u002Fwww.deloitte.com\u002Fnl\u002Fen\u002Fservices\u002Frisk-advisory\u002Fperspectives\u002Fresponsible-enterprise-decisions-knowledge-enriched-ai.html)\n* [[2310.13149] 从HCI视角理解艺术中的生成式AI：对艺术家关于G-AI的访谈研究](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.13149)\n* [[2309.12338] 人工智能与审美判断](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.12338)：“随着生成式AI影响当代审美判断，我们概述了在审视AI生成内容意义时可能遇到的一些陷阱与误区”\n* [AI崇拜 | Marginal REVOLUTION](https:\u002F\u002Fmarginalrevolution.com\u002Fmarginalrevolution\u002F2023\u002F10\u002Fai-worship.html)\n* [ChatGPT背后的人工智能技术是在爱荷华州打造的——耗费大量水资源 | AP新闻](https:\u002F\u002Fapnews.com\u002Farticle\u002Fchatgpt-gpt4-iowa-ai-water-consumption-microsoft-f551fde98083d17a7e8d904f8be822c4)\n* [ChatGPT很有趣，但它并不是作者 | Science](https:\u002F\u002Fwww.science.org\u002Fdoi\u002F10.1126\u002Fscience.adg7879)\n* [AI热潮背后，是一支在“数字血汗工厂”工作的海外劳工大军 | 华盛顿邮报](https:\u002F\u002Fwww.washingtonpost.com\u002Fworld\u002F2023\u002F08\u002F28\u002Fscale-ai-remotasks-philippines-artificial-intelligence\u002F)：Scale AI在菲律宾的Remotasks员工抱怨工资过低\n* [如果总是停机就不算智能：对当前AGI方法的批判性视角 | Life Is Computation](https:\u002F\u002Fwww.lifeiscomputation.com\u002Fit-is-not-intelligent-if-it-always-halts\u002F)\n* [AI热潮的人力代价 | TechCrunch](https:\u002F\u002Ftechcrunch.com\u002F2023\u002F08\u002F21\u002Fthe-human-costs-of-the-ai-boom\u002F)\n* [AI诈骗、垃圾邮件、黑客攻击正在毁掉互联网](https:\u002F\u002Fwww.businessinsider.com\u002Fai-scam-spam-hacking-ruining-internet-chatgpt-privacy-misinformation-2023-8)\n* [ChatGPT革命不过是又一个科技幻想](https:\u002F\u002Fwww.disconnect.blog\u002Fp\u002Fthe-chatgpt-revolution-is-another)\n* [为什么AI将拯救世界 | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F06\u002Fai-will-save-the-world\u002F)\n* [好莱坞制片厂曾提议一项AI合同，赋予其演员形象权“直至永远” - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2023\u002F7\u002F13\u002F23794224\u002Fsag-aftra-actors-strike-ai-image-rights)\n* [Brave公司暗中出售受版权保护的数据用于AI训练的黑幕](https:\u002F\u002Fstackdiary.com\u002Fbrave-selling-copyrighted-data-for-ai-training\u002F)\n* [AI工厂内幕：让科技看似人性化的幕后工作者 - The Verge](https:\u002F\u002Fwww.theverge.com\u002Ffeatures\u002F23764584\u002Fai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots?s=08)\n* [为什么变革性人工智能真的非常难以实现](https:\u002F\u002Fthegradient.pub\u002Fwhy-transformative-artificial-intelligence-is-really-really-hard-to-achieve\u002F)\n* [AI与工作自动化 — Benedict Evans](https:\u002F\u002Fwww.ben-evans.com\u002Fbenedictevans\u002F2023\u002F7\u002F2\u002Fworking-with-ai)\n* [Yuval Noah Harari认为AI已经攻破了人类文明的操作系统](https:\u002F\u002Fwww.economist.com\u002Fby-invitation\u002F2023\u002F04\u002F28\u002Fyuval-noah-harari-argues-that-ai-has-hacked-the-operating-system-of-human-civilisation)\n* [生成式AI使刻板印象与偏见愈发严重](https:\u002F\u002Fwww.bloomberg.com\u002Fgraphics\u002F2023-generative-ai-bias\u002F)\n* [OpenAI关于超智能的治理](https:\u002F\u002Fopenai.com\u002Fblog\u002Fgovernance-of-superintelligence)\n* [AIAAIC - AIAAIC资源库](https:\u002F\u002Fwww.aiaaic.org\u002Faiaaic-repository)：“一个独立、开放、公益性的资源，详细记录由人工智能、算法和自动化驱动或相关的事件与争议”\n* [关于GPT-4，大家还是冷静点吧 - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fgpt-4-calm-down)\n* [暂停巨型AI实验：一封公开信 - 生命未来研究所](https:\u002F\u002Ffutureoflife.org\u002Fopen-letter\u002Fpause-giant-ai-experiments\u002F)\n* [“OpenAI发布了ChatGPT插件”](https:\u002F\u002Ftwitter.com\u002Fthealexbanks\u002Fstatus\u002F1639620659142881283)：[@thealexbanks]发布的推文，附带对ChatGPT插件影响的反思列表\n* [是否存在社会公平的人工智能？ | Uma Inteligência Artificial socialmente justa é possível？](https:\u002F\u002Fwww.mabuse.art.br\u002Fpost\u002Fuma-intelig%C3%AAncia-artificial-socialmente-justa-%C3%A9-poss%C3%ADvel)：H.D. Mabuse用葡萄牙语发表的文章\n* [Noam Chomsky谈ChatGPT：它“基本上就是高科技剽窃”，也是“一种逃避学习的方式” | Open Culture](https:\u002F\u002Fwww.openculture.com\u002F2023\u002F02\u002Fnoam-chomsky-on-chatgpt.html)\n* [尽管取得了诸多成就，大型语言模型仍未对语言学作出贡献 | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fdespite-their-feats-large-language-models-still-havent-contributed-to-linguistics-657bea43a8a3)\n* [ChatGPT会扼杀学生作文吗？ | 大西洋月刊](https:\u002F\u002Fwww.theatlantic.com\u002Ftechnology\u002Farchive\u002F2022\u002F12\u002Fchatgpt-ai-writing-college-student-essays\u002F672371\u002F)\n* [ChatGPT和生成式AI对科学意味着什么 | 自然](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fd41586-023-00340-6)\n* [ChatGPT是个制造废话的家伙，正在发动阶级战争](https:\u002F\u002Fwww.vice.com\u002Fen\u002Farticle\u002Fakex34\u002Fchatgpt-is-a-bullshit-generator-waging-class-war)\n* [关于生成式AI与教育未来的几点思考 – Mark Carrigan](https:\u002F\u002Fmarkcarrigan.net\u002F2023\u002F01\u002F15\u002Fsome-thoughts-about-generative-ai-and-the-future-of-education\u002F)\n* [教育者对ChatGPT的考量 - OpenAI API](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fchatgpt-education)\n* [Stable Diffusion frivolous · 因为基于无知的诉讼理应得到回应。](http:\u002F\u002Fwww.stablediffusionfrivolous.com\u002F)：针对“Stable Diffusion诉讼”的社区回应\n* [Stable Diffusion诉讼 · Joseph Saveri律师事务所 & Matthew Butterick](https:\u002F\u002Fstablediffusionlitigation.com\u002F)\n* [生成式语言模型与自动化影响力行动：新兴威胁及潜在应对措施 | OpenAI](https:\u002F\u002Fcdn.openai.com\u002Fpapers\u002Fforecasting-misuse.pdf)\n* [ChatGPT撰写的摘要竟然骗过了科学家](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fd41586-023-00056-7)\n* [当机器改变艺术 | Aaron Hertzmann的博客](https:\u002F\u002Faaronhertzmann.com\u002F2022\u002F12\u002F17\u002Fwhen-tech-changes-art.html)\n* [大型语言模型的黑暗风险 | WIRED UK](https:\u002F\u002Fwww.wired.co.uk\u002Farticle\u002Fartificial-intelligence-language)\n* [ChatGPT、DALL-E 2与创作过程的崩溃](https:\u002F\u002Ftheconversation.com\u002Fchatgpt-dall-e-2-and-the-collapse-of-the-creative-process-196461)\n* [AI生成的艺术对人类创造力究竟意味着什么 | WIRED](https:\u002F\u002Fwww.wired.com\u002Fstory\u002Fpicture-limitless-creativity-ai-image-generators\u002F)\n* [预测语言模型可能被滥用于虚假信息传播——以及如何降低风险](https:\u002F\u002Fopenai.com\u002Fblog\u002Fforecasting-misuse\u002F)\n* [AI艺术的阴暗面：这一日益流行的趋势可能带来的4个问题](https:\u002F\u002Fwww.makeuseof.com\u002Fdark-side-of-ai-art-potential-issues\u002F)\n* [网络罪犯利用ChatGPT开发恶意软件并策划虚假女性聊天机器人](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fthomasbrewster\u002F2023\u002F01\u002F06\u002Fchatgpt-cybercriminal-malware-female-chatbots\u002F?sh=6019f4315534)\n* [ChatGPT与办公室工作的批量生产 - Farsight](https:\u002F\u002Ffarsight.cifs.dk\u002Fchatgpt-and-the-mass-production-of-office-work\u002F)\n* [无人谈及的ChatGPT危险 | Jacob Ferus著 | 2022年12月 | Medium](https:\u002F\u002Fmedium.com\u002F@dreamferus\u002Fthe-danger-of-chatgpt-nobody-talks-about-9aff94e5dea6)\n* [元宇宙中的精神控制。如果我们从中有所领悟…… | Louis Rosenberg著 | Predict | 2022年12月 | Medium](https:\u002F\u002Fmedium.com\u002Fpredict\u002Fmind-control-in-the-metaverse-48dfbd88c2ae)\n* [ChatGPT的辉煌与怪异之处 - 纽约时报](https:\u002F\u002Fwww.nytimes.com\u002F2022\u002F12\u002F05\u002Ftechnology\u002Fchatgpt-ai-twitter.html)\n* [人工智能生成的文字如何毒害互联网 - MIT技术评论](https:\u002F\u002Fmittechreview.com.br\u002Fcomo-o-texto-gerado-por-inteligencia-artificial-esta-envenenando-a-internet\u002F)\n* [ChatGPT是人工智能界的“侏罗纪公园”时刻 | NeoFeed](https:\u002F\u002Fneofeed.com.br\u002Fblog\u002Fhome\u002Fo-chatgpt-e-o-momento-jurassic-park-da-inteligencia-artificial\u002F)\n* [请对ChatGPT保持更多理性、减少狂热（第1部分，共2部分） | Cezar Taurion著 | 2022年12月 | Medium](https:\u002F\u002Fc-taurion.medium.com\u002Fpor-favor-mais-racionalidade-e-menos-frenesi-em-rela%C3%A7%C3%A3o-ao-chatgpt-parte-1-de-2-1d7637e2a854)\n* [如果我们正在使用伪科学的AI呢？ - Diogo Cortiz](https:\u002F\u002Fdiogocortiz.com.br\u002Fcomputacao-afetiva-e-os-desafios-das-ias-pseudocientificas\u002F)\n* [2023年技术热潮的局限性：ChatGPT | IAgora？ | Época NEGÓCIOS](https:\u002F\u002Fepocanegocios.globo.com\u002Fcolunas\u002Fiagora\u002Fcoluna\u002F2023\u002F01\u002Fas-limitacoes-da-sensacao-tecnologica-de-2023-o-chatgpt.ghtml)\n* [AI失败的7种显著表现 - IEEE Spectrum](https:\u002F\u002Fspectrum.ieee.org\u002Fai-failures)\n\n## 生成式人工智能流程与成果\n\n\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Ffilipecalegario_awesome-generative-ai_readme_bbb6f33099d0.png\" width=75% height=75%>\n\n\u003Cdetails>\n\u003Csummary>更多信息\u003C\u002Fsummary>\n\n**生成式人工智能**是人工智能的一个分支，专注于根据从现有数据中学习到的模式来创建新数据。以下是该过程的逐步解释：\n\n1. **以数据为起点**：每个生成式人工智能过程都始于数据。这些数据可以是文本、图像、声音或其他形式的数据集。这些数据作为基础材料，供人工智能识别和理解其中的模式。\n\n2. **训练人工智能**：有了数据之后，下一步就是“训练”。在此阶段，人工智能会多次处理数据，以学习并内化其中的模式。这一阶段的成果是一个“模型”，它就像是从数据中提取的知识的数字化表示。\n\n3. **微调**：有时，人工智能需要关注某些特定的细微之处或特征。在这种情况下，会使用另一组数据对已经训练好的模型进行“微调”，从而在期望的方向上增强其能力。\n\n4. **使用模型**：训练完成后，模型就可以进行推理了，也就是说，利用其已掌握的知识来处理新数据，并生成相关输出。这个推理过程可以在本地机器上执行，也可以通过“API”远程访问。选择本地执行还是通过API访问，通常取决于计算资源、应用需求以及用户偏好等因素。无论是在本地还是通过API，目标都是利用模型的能力，从新的数据输入中得出有意义的结果。\n\n5. **生成新数据**：当模型准备就绪后，人工智能现在可以生成新数据。通过向人工智能提供某些“输入参数”或指导方针，它会返回“生成的输出”，即新创建的内容。\n\n6. **应用场景**：人工智能生成的输出可以被整合到各种应用中，无论是网站、移动应用，还是其他数字平台。“界面”指的是这些应用中面向用户的部分，使用户能够与人工智能的功能互动并从中受益。\n\n总之，生成式人工智能的核心在于向人工智能系统输入大量数据，训练它掌握潜在的模式，然后利用这些训练好的知识来生成全新的数据。这项技术的应用潜力和益处非常广泛，并且随着该领域的不断发展而持续增长。\n\n\u003C\u002Fdetails>\n\n## 生成式人工智能工具目录\n\n* [AI演示制作工具](https:\u002F\u002Fwww.aipresentationmakers.com\u002F)：深度评测数十款AI演示制作工具\n* [A.I. 生产力工具](https:\u002F\u002Fwww.aiproductivitytoolkit.com\u002F)\n* [ToolList.ai](https:\u002F\u002Ftoollist.ai\u002F)：AI工具聚合平台\n* [Toolify](https:\u002F\u002Fwww.toolify.ai\u002F)：AI工具目录及工具列表\n* [LLM Explorer](https:\u002F\u002Fllm.extractum.io\u002F)：精选的LLM列表。探索开源LLM模型列表\n* [OrbicAI](https:\u002F\u002Forbic.ai\u002F)：“最大的AI目录，GPT商店、AWS PartyRocks应用以及大量免费AI工具”\n* [Altern](https:\u002F\u002Faltern.ai\u002F)：“通往AI发现的大门”\n* [ainave](https:\u002F\u002Fwww.ainave.com)：“轻松驾驭AI世界”，精选AI工具与AI新闻\n* [AI Search](https:\u002F\u002Fai-search.io)：查找AI工具与应用 | 搜索最全面的AI工具目录 | AI搜索\n* [AiSuperSmart AI工具目录](https:\u002F\u002Faisupersmart.com\u002Fai-tools-directory\u002F)：根据您的使用场景查找AI工具！\n* [HD Robots](https:\u002F\u002Fhdrobots.com\u002F)：带有聊天机器人助手的AI工具目录\n* [AIForme](https:\u002F\u002Fwww.aiforme.wiki\u002F)：具有比较功能的AI工具发现平台\n* [Technologies in LabLab](https:\u002F\u002Flablab.ai\u002Ftech)：由[lablab.ai](https:\u002F\u002Flablab.ai)为其黑客马拉松推荐的AI工具列表\n* [Vondy - 下一代AI应用](https:\u002F\u002Fwww.vondy.com\u002F)：按任务分类的AI工具集合\n* [AI工具主列表](https:\u002F\u002Fdoc.clickup.com\u002F25598832\u002Fd\u002Fh\u002Frd6vg-14247\u002F0b79ca1dc0f7429\u002Frd6vg-12207)：由ClickUp维护的目录\n* [AI Valley](https:\u002F\u002Faivalley.ai\u002F)：“最新的AI工具与提示”\n* [AI Finder](https:\u002F\u002Fai-finder.net\u002F)：收录超过1500种AI工具的资源库\n* [BestWebbs](https:\u002F\u002Fbestwebbs.com\u002F)：“所有AI工具的一站式目的地”\n* [Future Tools - 找到满足您需求的精确AI工具](https:\u002F\u002Fwww.futuretools.io\u002F)：AI工具列表\n* [Futurepedia - 最大的AI工具目录 | 首页](https:\u002F\u002Fwww.futurepedia.io\u002F)：AI工具目录\n* [There's An AI For That](https:\u002F\u002Ftheresanaiforthat.com\u002F)：AI数据库\n* [AI Depot - 发现新的AI工具](https:\u002F\u002Faidepot.co\u002F)：按标签分类并以卡片形式呈现的AI工具集合\n* [生成式AI数据库](https:\u002F\u002Faaronsim.notion.site\u002FGenerative-AI-Database-Types-Models-Sector-URL-API-more-b5196c870594498fb1e0d979428add2d)：在Notion中建立的数据库，包含类型、模型、行业、网址和API等信息\n* [Altern](https:\u002F\u002Faltern.ai)——发现全新AI工具与产品的场所。\n* [生成式AI全景图](https:\u002F\u002Fai-collection.org\u002F)：“一系列出色的生成式AI应用”\n* [创作者终极AI工具清单 | Descript](https:\u002F\u002Fwww.descript.com\u002Fblog\u002Farticle\u002Fthe-ultimate-list-of-ai-tools-for-creators)：由Descript整理的工具集合\n* [Maxim AI](https:\u002F\u002Fwww.getmaxim.ai)：一个用于评估和可观测性的生成式AI平台\n* [AI工具列表](https:\u002F\u002Fwww.aitoollist.org)：一份超棒的AI工具目录\n\n## 课程与教育资源\n\n* [Gemini 示例](https:\u002F\u002Fgeminibyexample.com)：通过（带注释）代码示例学习 Gemini SDK。\n* [Niraj-Lunavat\u002F人工智能](https:\u002F\u002Fgithub.com\u002FNiraj-Lunavat\u002FArtificial-Intelligence?tab=readme-ov-file#researchers)：超赞的人工智能学习资源，包含100多张AI速查表、免费在线书籍、顶级课程、最佳视频与讲座、论文、教程、99位顶尖研究者、优质网站、121个数据集、会议、框架和工具。\n* [NVIDIA生成式AI详解](https:\u002F\u002Flearn.nvidia.com\u002Fcourses\u002Fcourse-detail?course_id=course-v1:DLI+S-FX-07+V1)：NVIDIA推出的零代码课程，介绍生成式AI的概念与应用，以及该领域的挑战与机遇。\n* [Paulescu\u002Fhands-on-rl：从零开始的强化学习实战课程 🦸🏻‍🦸🏽](https:\u002F\u002Fgithub.com\u002FPaulescu\u002Fhands-on-rl)\n* [DataCamp生成式AI开发者系列](https:\u002F\u002Fwww.datacamp.com\u002Fai-code-alongs)：9节代码实操课程，教你使用LangChain及OpenAI、Pinecone API构建聊天机器人，并探索Hugging Face生态。限时免费。\n* [rasbt\u002FLLMs-from-scratch](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch)：逐步从零实现类似ChatGPT的大型语言模型。\n* [生成式AI入门 | SqillPlan](https:\u002F\u002Fsqillplan.com\u002Fcourse?hash=-4862018582618510846)：生成式AI入门课程，涵盖GAN、变分自编码器、自回归模型等架构及其应用、评估、伦理与挑战。\n* [udlbook\u002Fudlbook](https:\u002F\u002Fgithub.com\u002Fudlbook\u002Fudlbook)：西蒙·J·D·普林斯教授编写的《深度学习理解》。\n* [书：深度学习理解](https:\u002F\u002Fudlbook.github.io\u002Fudlbook\u002F)：西蒙·J·D·普林斯所著书籍的草稿及配套Google Colab笔记本的在线平台。\n* [AWS与Google生成式AI学习资源列表](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Faagarwal29_generativeai-learn-aws-activity-7081761811129118720-i128\u002F)：由Ankit Agarwal整理的LinkedIn帖子形式列表。\n* [像ChatGPT或Bard这样的AI聊天机器人如何工作——图文解析 | 卫报](https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002Fng-interactive\u002F2023\u002Fnov\u002F01\u002Fhow-ai-chatbots-like-chatgpt-or-bard-work-visual-explainer)\n* [🔥🔥] [面向初学者的生成式AI](https:\u002F\u002Fmicrosoft.github.io\u002Fgenerative-ai-for-beginners\u002F#\u002F)：微软推出的12节入门课程。\n* [生成式AI入门](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fyoussef-hosni-b2960b135_if-you-want-to-start-studying-generative-activity-7125908710702350336-vhsm\u002F)：Youssef Hosni在Medium上发表的一系列文章。\n* [Animated AI](https:\u002F\u002Fanimatedai.github.io\u002F)：关于神经网络的动画与教学视频。\n* [深度学习AI——学习生成式AI基础知识，应用于实际场景](https:\u002F\u002Fwww.deeplearning.ai\u002Fcourses\u002Fgenerative-ai-with-llms\u002F)：与AWS合作开发的课程，讲解生成式AI的工作原理及如何将其部署到实际应用中。\n* [Google Cloud Skills Boost——生成式AI入门](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fcourse_templates\u002F536)：初级微学习课程，介绍Google工具，旨在解释什么是生成式AI、其用途以及它与传统机器学习方法的区别。\n* [Google Cloud Skills Boost：生成式AI学习路径](https:\u002F\u002Fwww.cloudskillsboost.google\u002Fjourneys\u002F118)：精选生成式AI内容，“从大型语言模型的基础知识到如何在Google Cloud上创建并部署生成式AI解决方案”。\n* [工业设计中的AI](https:\u002F\u002Findustrialdesign.ai\u002F)：“新加坡国立大学的学生在一门学期课程中探索AI在设计领域的潜力，并分享他们的学习成果。该课程由新加坡国立大学工业设计系的Donn Koh指导。”\n* [让我们用简·奥斯汀的作品向你展示GPT是如何工作的——纽约时报](https:\u002F\u002Fwww.nytimes.com\u002Finteractive\u002F2023\u002F04\u002F26\u002Fupshot\u002Fgpt-from-scratch.html)\n* [🔥🔥🔥] [ChatGPT提示工程：面向开发者的课程 — DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fchatgpt-prompt-engineering-for-developers\u002F)：由Isa Fulford（OpenAI）和Andrew Ng（DeepLearning.AI）主讲的短期课程，提供提示工程的最佳实践。\n* [🔥🔥🔥] [DAIR.AI](https:\u002F\u002Fgithub.com\u002Fdair-ai)：推动人工智能研究、教育和技术的普及。\n* [欢迎来到🤗深度强化学习课程](https:\u002F\u002Fhuggingface.co\u002Fdeep-rl-course\u002Funit0\u002Fintroduction?fw=pt)：Hugging Face推出的深度强化学习课程。\n* [PromptHero的AI艺术生成速成课](https:\u002F\u002Fprompthero.com\u002Facademy)：收费（99美元）课程，专注于提示工程。\n* [扩散模型与AI艺术的直观理解。#stablediffusionart #aiart #aiartwork #aiartcommunity](https:\u002F\u002Fwww.tiktok.com\u002F@ham_made_art\u002Fvideo\u002F7154863972729113899)\n* [Jay Alammar的图解Stable Diffusion](https:\u002F\u002Fjalammar.github.io\u002Fillustrated-stable-diffusion\u002F)：“温和地介绍Stable Diffusion的工作原理”。\n* [🔥][johnowhitaker\u002Ftglcourse](https:\u002F\u002Fgithub.com\u002Fjohnowhitaker\u002Ftglcourse)：生成式景观——一门关于生成式建模的课程（目前尚未完成）。\n* [文字即图像 | BustBright——机器学习艺术](https:\u002F\u002Fwww.bustbright.com\u002Fproduct\u002Fwords-are-images-7-week-online-class-starting-october-24th-2022-\u002F331)：由[Derrick Schultz](https:\u002F\u002Ftwitter.com\u002Fdvsch\u002F)主导的7周在线课程，于2022年10月24日开课。\n* [深入理解Stable Diffusion.ipynb——Colaboratory——第1部分](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1?usp=sharing)：由[@johnowhitaker](https:\u002F\u002Ftwitter.com\u002Fjohnowhitaker)编写的笔记本，深入探讨Stable Diffusion的细节。\n* [深入理解Stable Diffusion：文本反演.ipynb——Colaboratory——第2部分](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1RTHDzE-otzmZOuy8w1WEOxmn9pNcEz3u?usp=sharing)：@johnowhitaker继“深入理解Stable Diffusion”之后的续作，聚焦于文本反演。\n* [GitHub - johnowhitaker\u002Faiaiart](https:\u002F\u002Fgithub.com\u002Fjohnowhitaker\u002Faiaiart)：AIAIART课程的内容与资源。\n* [labml.ai在Twitter上发布的Stable Diffusion实现\u002F教程，附有并排注释](https:\u002F\u002Ftwitter.com\u002Flabmlai\u002Fstatus\u002F1571080112459878401)\n* [2023年实用深度学习编程课程——第二部分](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_7rMfsA24Ls&list=PLfYUBJiXbdtRUvTUYpLdfHHp9a58nWVXP)：继续讲解如何从头实现Stable Diffusion。\n* [2022年实用深度学习编程课程——第一部分](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU)：“专为有一定编程经验、希望学习如何将深度学习和机器学习应用于实际问题的人设计的免费课程”，由Jeremy Howard主讲。\n\n## 人机交互\n\n* [AI的用户体验：如何用AI赋能人类体验——设计工具星期二 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=50Of7_lubN4) \n* [设计背后：认识微软设计团队打造的Copilot](https:\u002F\u002Fmedium.com\u002Fmicrosoft-design\u002Fbehind-the-design-meet-copilot-2c68182a0e70) \n* [🔥🔥🔥] [[2310.07127] 以人机交互为中心的人工智能生成式交互综述与分类](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.07127)：“对154篇论文进行了综述，从人类和生成式AI两个角度提供了关于人机生成式AI交互的全新分类与分析”。\n* [人机交互指南 - 微软研究院](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpublication\u002Fguidelines-for-human-ai-interaction\u002F)：一套“适用于人机交互的18条通用设计指南”。\n\n## 论文合集\n\n* [Paper Digest - ChatGPT](https:\u002F\u002Fwww.paperdigest.org\u002F2023\u002F01\u002Frecent-papers-on-chatgpt\u002F)：关于ChatGPT的最新论文\n* [dair-ai\u002FML-Papers-Explained](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FML-Papers-Explained)：对机器学习关键概念的解释\n* [AI阅读清单 - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1bEQM1W-1fzSVWNbS4ne5PopB2b7j8zD4Jc3nm4rbK-U\u002Fedit)：由[Jack Soslow (@JackSoslow)](https:\u002F\u002Ftwitter.com\u002FJackSoslow)整理的阅读清单\n* [Aman's AI Journal • 论文列表](https:\u002F\u002Faman.ai\u002Fpapers\u002F)：由Aman Chadha精选的一系列人工智能与机器学习领域的经典论文\n* [Casual GAN论文读书会](https:\u002F\u002Fcasualgan.notion.site\u002Fcasualgan\u002FCasual-GAN-Papers-Reading-Club-327c158518e44d5296a5def74486c7e8)：Casual GAN论文的社区知识库\n* [Casual GAN Papers](https:\u002F\u002Fwww.casualganpapers.com\u002F)：通俗易懂的热门AI论文摘要\n* [图解VQGAN](https:\u002F\u002Fljvmiranda921.github.io\u002Fnotebook\u002F2021\u002F08\u002F08\u002Fclip-vqgan\u002F)：关于VQGAN工作原理的图文详解\n* [CLIP：连接文本与图像](https:\u002F\u002Fopenai.com\u002Fblog\u002Fclip\u002F)：OpenAI对CLIP工作原理的说明\n* [VQGAN+CLIP——它是如何工作的？合成图像（“GAN艺术”）领域…… | 作者：Alexa Steinbrück | Medium](https:\u002F\u002Falexasteinbruck.medium.com\u002Fvqgan-clip-how-does-it-work-210a5dca5e52)\n* [The Methods Corpus | Papers With Code](https:\u002F\u002Fpaperswithcode.com\u002Fmethods)\n* https:\u002F\u002Fieeexplore.ieee.org\u002Fabstract\u002Fdocument\u002F9043519：关于使用生成对抗网络进行图像合成的最新综述\n* [利用生成对抗网络（GANs）作为艺术家创作灵感的支持工具](https:\u002F\u002Fwww.cin.ufpe.br\u002F~tg\u002F2020-1\u002FTG_CC\u002Ftg_cco2.pdf)：Cláudio Carvalho在伯南布哥联邦大学信息中心完成的本科毕业论文\n* [GAN Lab](https:\u002F\u002Fpoloclub.github.io\u002Fganlab\u002F)：在浏览器中玩转生成对抗网络！\n* [[PDF] Music2Video：融合音频与文本自动生成音乐视频 | Semantic Scholar](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FMusic2Video%3A-Automatic-Generation-of-Music-Video-of-Jang-Shin\u002F38e37c3a7dc22bb3356552e93e6685b99ca04264)\n* [[PDF] 基于生成式深度学习的主动发散——综述与分类 | Semantic Scholar](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FActive-Divergence-with-Generative-Deep-Learning-A-Broad-Berns\u002F091c4ea2efaba23cd9024d8a063609c9a313b5cb)\n* [[PDF] 为艺术目的自动化生成式深度学习：挑战与机遇 | Semantic Scholar](https:\u002F\u002Fwww.semanticscholar.org\u002Fpaper\u002FAutomating-Generative-Deep-Learning-for-Artistic-Berns-Broad\u002Ff3479740d4ec7f91b6d7a01167e9c875a72d386e)\n\n## 在线工具与应用\n\n* [Lunroo](https:\u002F\u002Flunroo.com)：45+ 款社交媒体营销免费 AI 工具。利用 AI 节省日常任务时间。\n* [COUNT](https:\u002F\u002Fgetcount.com)：面向中小企业的 AI 驱动会计工具\n* [Competitor Research](https:\u002F\u002Fwww.competitoresearch.com)：帮助企业追踪竞争对手的 AI 工具\n* [StartKit.AI](https:\u002F\u002Fstartkit.ai)：用于快速构建 AI 产品的模板库\n* [No-Code Scraper](https:\u002F\u002Fwww.nocodescraper.com\u002F)：无需代码的数据抓取工具——只需简单输入，即可无缝从任何网站提取数据。\n* [BacklinkGPT](https:\u002F\u002Fwww.backlinkgpt.com\u002F)：AI 驱动的外链建设平台，帮助生成个性化外链邀约信息，加速外链获取。\n* [VocalReplica](https:\u002F\u002Fvocalreplica.com\u002F)：为喜爱的音乐曲目提供 AI 驱动的人声与乐器分离功能\n* [LangMagic](https:\u002F\u002Feasytolearn.io)：通过母语内容学习语言。\n* [Persuva](https:\u002F\u002Fpersuva.ai)：Persuva 是一个由 AI 驱动的平台，可大规模创建具有说服力且转化率高的广告文案。\n* [Dittto.ai](https:\u002F\u002Fdittto.ai)：借助基于顶级 SaaS 网站训练的 AI，优化您的宣传文案。\n* [SEOByAI](https:\u002F\u002Fseoby.ai)：使用免费 AI SEO 工具，在 Google 上更快获得排名\n* [SinglebaseCloud](https:\u002F\u002Fsinglebase.cloud)：搭载向量数据库、文档数据库、身份验证等功能的 AI 驱动后端平台，助力加速应用开发。\n* [TrollyAI](https:\u002F\u002Ftrolly.ai\u002F)：以两倍速度创作专业级 SEO 文章\n* [WebscrapeAI](https:\u002F\u002Fwebscrapeai.com\u002F)：利用 AI 无需代码即可抓取任意网站数据\n* [Architecture Helper](https:\u002F\u002Farchitecturehelper.com)：几秒钟内分析任何建筑结构，并生成您自定义的风格方案。\n* [AI-Flow](https:\u002F\u002Fai-flow.net\u002F)：轻松连接多个 AI 模型\n* [Code to Flow](https:\u002F\u002Fcodetoflow.com)：可视化、分析并理解代码流程。借助 AI 将代码转化为交互式流程图，即时简化复杂逻辑。\n* [Recast Studio](https:\u002F\u002Frecast.studio)：AI 驱动的播客营销助手。\n* [Clipwing](https:\u002F\u002Fclipwing.pro\u002F)：一款将长视频切割成数十段短视频的工具。\n* [Tailor](https:\u002F\u002Fwww.usetailor.com)：每天为您量身定制由 AI 创作的播客和新闻简报\n* [ZZZ Code AI](https:\u002F\u002Fzzzcode.ai\u002F)：免费的 AI 驱动网站，可解答任何编程问题或生成代码。\n* [Scribble Diffusion](https:\u002F\u002Fscribblediffusion.com\u002F)：利用 AI 将草图转化为精致图像\n* [Paint by Text](https:\u002F\u002Fpaintbytext.chat\u002F)：借助 AI，根据文字指令编辑您的照片。\n* [Scenario AI](https:\u002F\u002Fwww.scenario.gg\u002F)：AI 生成的游戏资源\n* [AnimalAI](https:\u002F\u002Fanimalai.co\u002F)：定制 AI 生成的动物肖像（收益将捐赠给各类野生动物保护组织）\n* [starryai](https:\u002F\u002Fwww.starryai.com\u002F)：AI 艺术生成应用——AI 艺术创作者\n* [ProsePainter](https:\u002F\u002Fwww.prosepainter.com\u002F)：一款“用文字作画”的互动工具。它将可控的文字转图像技术融入传统的数字绘画界面。\n* [ProsePainter：图像 + 绘图界面 + CLIP！ - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mK4F32xNrdw&t=429s)\n* [Cocreator AI](https:\u002F\u002Fcocreator.ai\u002F)：创意计算机代理（目前在候补名单中）\n* [Runway ML](http:\u002F\u002Frunwayml.com\u002F)：AI 视频制作套件\n* [Hotpot.ai - Hotpot.ai](https:\u002F\u002Fhotpot.ai\u002F)：一系列用于图像后处理的 AI 工具\n* [Justin Pinkney 的 Toonify Yourself](https:\u002F\u002Fwww.justinpinkney.com\u002Ftoonify-yourself\u002F)：将人脸转化为卡通形象\n* [deepart.io](https:\u002F\u002Fdeepart.io\u002F)：一款在线风格迁移工具\n* [Artbreeder](https:\u002F\u002Fwww.artbreeder.com\u002F)：基于现有图像进行杂交生成新图像的网页工具\n* [Ostagram.ru](https:\u002F\u002Fwww.ostagram.me\u002F)：图像风格迁移平台\n* [cleanup.pictures](https:\u002F\u002Fcleanup.pictures\u002F)：免费移除图片中的物体、人物、文字及瑕疵\n* [remove.bg](https:\u002F\u002Fwww.remove.bg\u002F)：自动去除图片背景\n* [Quick, Draw!](https:\u002F\u002Fquickdraw.withgoogle.com\u002F)：神经网络能否学会识别涂鸦？一款通过用户绘图来帮助训练神经网络的游戏\n* [Nekton.ai](https:\u002F\u002Fnekton.ai\u002F)：用 AI 自动化您的工作流\n* [Documind.chat](https:\u002F\u002Fdocumind.chat)：使用 AI 与 PDF 对话。Documind 是一款强大的 PDF 对话工具，允许您就 PDF 文档提出问题。\n* [Snowpixel](https:\u002F\u002Fsnowpixel.app)：通过文本和\u002F或图像生成图片\u002F视频\u002F动画\u002F音频\u002F音乐\u002F3D 对象。上传您自己的数据以创建自定义模型。\n* [Chatpdf.so](https:\u002F\u002Fchatpdf.so)：使用 GPT4 AI 与 PDF 对话。Chatpdf.so 是一款 PDF 对话工具，可让您对 PDF 文档进行问答。\n* [Yona.ai](https:\u002F\u002Fyona.ai)：根据您自己的对话、故事和数据，创建高度个性化的 AI 聊天机器人。您可以利用聊天记录的力量，打造一位陪伴您重温回忆、畅想奇思妙想或其他独特用途的 AI 伙伴。\n* [Voicesphere](https:\u002F\u002Fwww.voicesphere.co\u002F)：与您的文档对话，获取智能且上下文相关的答案。\n* [Tune AI](https:\u002F\u002Fchat.tune.app\u002F)：基于开源模型的 AI 聊天应用\n* [GPT Mobile](https:\u002F\u002Fgithub.com\u002FTaewan-P\u002Fgpt_mobile)：一款可在同一时间与多个大型语言模型对话的 Android 应用！目前支持 ChatGPT、Anthropic Claude 和 Google Gemini。\n* [PageGen](https:\u002F\u002Fpagegen.ai)：一款结合 Claude AI、React 和 Shadcn UI 的 AI 页面生成器。只需点击一下，即可根据文本、截图和模板生成网页。\n* [PerchanceStory](https:\u002F\u002Fperchancestory.com\u002F)：PerchanceStory 是一款基于 AI 的互动故事生成器，可根据用户提供的简单输入，生成不断变化的故事结局，拥有无限可能。\n\n# 代码与编程\n\n## 氛围编码\n\n* [filipecalegario\u002Fawesome-vibe-coding](https:\u002F\u002Fgithub.com\u002Ffilipecalegario\u002Fawesome-vibe-coding)：精选的氛围编码参考资料列表，与 AI 合作编写代码。\n* [Andrej Karpathy 在 X 上](https:\u002F\u002Fx.com\u002Fkarpathy\u002Fstatus\u002F1886192184808149383)：“有一种新型编码方式，我称之为‘氛围编码’，在这种方式下，你完全沉浸在代码的氛围中，拥抱指数级增长，甚至忘记代码的存在。”\n* [Codeium 的 Windsurf 编辑器](https:\u002F\u002Fcodeium.com\u002Fwindsurf)：一种代理式 IDE，“开发者与 AI 的协作真正融为一体，带来宛如魔法般的编码体验”\n* [Bolt.new](https:\u002F\u002Fbolt.new\u002F)：提示、运行、编辑和部署全栈 Web 和移动应用。\n* [Lovable](https:\u002F\u002Flovable.dev\u002F)：“几秒钟内将想法变为应用。Lovable 就是您的超人级全栈工程师。”\n* [v0 by Vercel](https:\u002F\u002Fv0.dev\u002Fchat)：用于构建 NextJS 前端的助手\n* [Cursor](https:\u002F\u002Fwww.cursor.com\u002F)：AI 代码编辑器，“与 AI 一起编码的最佳方式”\n* [Replit](https:\u002F\u002Freplit.com\u002F)：“只需在上方描述您的想法，让代理为您构建出来”\n\n## 人工智能驱动的代码生成\n\n* [batchai](https:\u002F\u002Fgithub.com\u002Fqiangyt\u002Fbatchai)：Copilot 和 Cursor 的补充工具——利用 AI 对项目代码进行批量处理\n* [Archie](https:\u002F\u002Farchie.8base.com\u002F)：AI 驱动的产品架构师，用于设计和规划软件应用\n* [DhiWise](https:\u002F\u002Fdhiwise.com)：DhiWise 是一个自动化编码任务的应用开发平台，让开发者专注于核心功能。\n* [关于编码行为的新研究引发对 AI 对软件开发影响的质疑——GeekWire](https:\u002F\u002Fwww.geekwire.com\u002F2024\u002Fnew-study-on-coding-behavior-raises-questions-about-impact-of-ai-on-software-development\u002F)\n* [CostGPT：软件开发成本计算器](https:\u002F\u002Fcostgpt.ai\u002F)：“利用 AI 的力量，为任何类型的软件、您想构建的工具找到成本、时间和最佳技术栈”\n* [codefuse-ai\u002FAwesome-Code-LLM](https:\u002F\u002Fgithub.com\u002Fcodefuse-ai\u002FAwesome-Code-LLM)：一份精心整理的代码相关语言建模研究及数据集列表。\n* [tldraw\u002Fdraw-a-ui](https:\u002F\u002Fgithub.com\u002Ftldraw\u002Fdraw-a-ui)：绘制原型并为其生成 HTML 代码\n* [deepseek-ai\u002FDeepSeek-Coder](https:\u002F\u002Fgithub.com\u002Fdeepseek-ai\u002FDeepSeek-Coder)：一款践行“让代码自我编写”理念的工具\n* [Cody](https:\u002F\u002Fabout.sourcegraph.com\u002Fcody)：AI 编程助手\n* [Kombai](https:\u002F\u002Fkombai.com\u002F)：根据 Figma 中的组件生成 UI 代码\n* [geekan\u002FMetaGPT](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT)：多智能体框架，只需提供一行需求，即可返回 PRD、设计、任务清单和代码仓库\n* [ZZZ Code AI](https:\u002F\u002Fzzzcode.ai\u002F)：一款由 AI 提供支持的免费网站，可解答任何编程问题或生成代码。\n* [Rapidpages](https:\u002F\u002Fwww.rapidpages.io\u002F)：使用 AI 创建 React 和 Tailwind 登陆页面\n* [ChatGPT 时代下的编程教学——O’Reilly](https:\u002F\u002Fwww.oreilly.com\u002Fradar\u002Fteaching-programming-in-the-age-of-chatgpt\u002F)\n* [GPT Web 应用生成器](https:\u002F\u002Fmagic-app-generator.wasp-lang.dev\u002F)：根据标题、描述及其他简单参数生成 Web 应用\n* [wolfia-app\u002Fgpt-code-search](https:\u002F\u002Fgithub.com\u002Fwolfia-app\u002Fgpt-code-search\u002F)：使用 AI 通过自然语言搜索代码库\n* [GenAI + Dev 收件箱专用文件](https:\u002F\u002Fgithub.com\u002Ffilipecalegario\u002Fawesome-generative-ai\u002Fblob\u002Fmain\u002Finbox-gen-ai-dev.md)：一份用于进一步分析和整理 GenAI + 开发相关参考资料的列表\n* [e2b-dev\u002Fe2b](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fe2b)：“用于构建 AI 驱动的虚拟软件开发人员的开源平台”\n* [Metabob](https:\u002F\u002Fmetabob.com\u002F)：生成式 AI 用于改进和自动化代码评审\n* [gventuri\u002Fpandas-ai](https:\u002F\u002Fgithub.com\u002Fgventuri\u002Fpandas-ai)：Pandas AI 是一个 Python 库，将 LLM 的能力集成到 Pandas 中，使 DataFrame 具备对话交互能力\n* [代码大型语言模型的系统性评估](https:\u002F\u002Farxiv.org\u002Fabs\u002F2202.13169)：一篇 arXiv 论文\n* [pgosar\u002FChatGDB](https:\u002F\u002Fgithub.com\u002Fpgosar\u002FChatGDB)：“在 GDB 调试器中发挥 ChatGPT 的强大功能”\n* [AI 对开发者生产力的影响：来自 GitHub Copilot 的证据 | arXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.06590)\n* [openai\u002Fopenai-cookbook](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-cookbook)：使用 OpenAI API 的示例和指南\n* [使用 GPT 时降低提示成本](https:\u002F\u002Fwww.codium.ai\u002Fblog\u002Freduce-your-costs-by-30-when-using-gpt-3-for-python-code\u002F)\n* [Co-Developer GPT 引擎](https:\u002F\u002Fgithub.com\u002Fstoerr\u002FCoDeveloperGPTengine)：本地读写文件并执行 OpenAI GPT 指令\n* [Potpie](https:\u002F\u002Fpotpie.ai)：几分钟内为您的代码库部署开源 AI 代理。可使用预构建的问答、测试、调试和系统设计代理，也可创建自定义代理。\n\n# 文本\n\n## 从一切到 Markdown 再到 LLM\n\n* [bytedance\u002FDolphin](https:\u002F\u002Fgithub.com\u002Fbytedance\u002FDolphin)：官方仓库，介绍“海豚：基于异构锚点提示的文档图像解析”，ACL 2025 年会议论文\n* [NuMind 的 NuExtract 2.0](https:\u002F\u002Fnumind.ai\u002Fblog\u002Foutclassing-frontier-llms----nuextract-2-0-takes-the-lead-in-information-extraction)：“在信息提取方面超越前沿 LLM”\n* [unclecode\u002Fcrawl4ai：🚀🤖 Crawl4AI](https:\u002F\u002Fgithub.com\u002Funclecode\u002Fcrawl4ai)：开源且适合 LLM 的网络爬虫和数据抓取工具\n* [LLMSTXT.NEW](https:\u002F\u002Fwww.llmstxt.new\u002F)：利用 Firecrawl 将网站生成整合文本文件，用于 LLM 训练和推理\n* [Mistral OCR \u002F Mistral AI](https:\u002F\u002Fmistral.ai\u002Fnews\u002Fmistral-ocr)：文档理解 API\n* [opendatalab\u002FMinerU](https:\u002F\u002Fgithub.com\u002Fopendatalab\u002FMinerU)：一款高质量的工具，可将 PDF 转换为 Markdown 和 JSON 格式\n* [microsoft\u002Fmarkitdown](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fmarkitdown)：Python 工具，用于将文件和办公文档转换为 Markdown 格式\n* [docling-project\u002Fdocling](https:\u002F\u002Fgithub.com\u002Fdocling-project\u002Fdocling)：为生成式 AI 准备文档\n* [Firecrawl](https:\u002F\u002Fwww.firecrawl.dev\u002F)：将网站转化为 LLM 可用的数据\n* [CatchTheTornado\u002Ftext-extract-api](https:\u002F\u002Fgithub.com\u002FCatchTheTornado\u002Ftext-extract-api)：使用 OCR 和 Ollama 支持的模型提取和解析文档（PDF、Word、PPTX 等）。可匿名化文档、去除 PII，并将任何文档或图片转换为结构化 JSON 或 Markdown 格式\n* [R Jina](https:\u002F\u002Fr.jina.ai\u002F)：只需在搜索栏中输入网址，即可将网站转换为 Markdown\n* [Gitingest](https:\u002F\u002Fgitingest.com\u002F)：将任何 Git 代码库转化为其代码基础的简洁文本摘要\n* [uithub](https:\u002F\u002Fuithub.com\u002F)：只需在搜索栏中输入 GitHub 仓库的 URL，即可将其转换为 Markdown\n\n## 小型语言模型\n\n* [[2409.15790] 小型语言模型：综述、测量与见解](https:\u002F\u002Farxiv.org\u002Fabs\u002F2409.15790)\n* [[2402.17764] 1 位 LLM 时代：所有大型语言模型都压缩至 1.58 位](https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.17764)\n* [mbzuai-oryx\u002FMobiLlama](https:\u002F\u002Fgithub.com\u002Fmbzuai-oryx\u002FMobiLlama)：专为边缘设备优化的小型语言模型\n\n## 大型语言模型 (LLMs)\n\n* [lunary-ai\u002Fabso](https:\u002F\u002Fgithub.com\u002Flunary-ai\u002Fabso): 一个 TypeScript SDK，可使用 OpenAI 格式轻松调用 100 多种大语言模型。\n* [oumi-ai\u002Foumi](https:\u002F\u002Fgithub.com\u002Foumi-ai\u002Foumi): 开放的通用机器智能平台，开源且简化了基础模型的全生命周期流程——从数据准备、训练到评估与部署。\n* [🔥] [Transformer Explainer](https:\u002F\u002Fpoloclub.github.io\u002Ftransformer-explainer\u002F): 可视化解释大语言模型 Transformer 架构 [YouTube 视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ECR4oAwocjs)\n* [comet-ml\u002Fopik](https:\u002F\u002Fgithub.com\u002Fcomet-ml\u002Fopik): 提供一套可观测性工具，用于评估、测试和部署大语言模型应用，帮助在开发和生产周期中校准语言模型输出。\n* [mendableai\u002Ffirecrawl](https:\u002F\u002Fgithub.com\u002Fmendableai\u002Ffirecrawl): 将整个网站转换为适合大语言模型的 Markdown 或结构化数据。只需一个 API 即可完成抓取、爬取和提取。\n* [QuivrHQ\u002FMegaParse](https:\u002F\u002Fgithub.com\u002Fquivrhq\u002Fmegaparse): 针对大语言模型输入优化的文件解析器，无任何信息丢失。以适合大语言模型的格式解析 PDF、Docx 和 PPTx 文件。\n* [LiteLLM](https:\u002F\u002Fwww.litellm.ai\u002F): 一个代理服务器，用于管理 100 多种大语言模型的身份验证、负载均衡和费用跟踪，全部采用 OpenAI 格式。\n* [youssefHosni\u002FHands-On-LangChain-for-LLM-Applications-Development](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FHands-On-LangChain-for-LLM-Applications-Development): 实用的大语言模型应用开发 LangChain 教程。\n* [unclecode\u002Fcrawl4ai: Crawl4AI](https:\u002F\u002Fgithub.com\u002Funclecode\u002Fcrawl4ai): 开源的、对大语言模型友好的网页爬虫和抓取工具。\n* [microsoft\u002FLMOps](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLMOps): 用于通过大语言模型和多模态大语言模型实现 AI 能力的通用技术。\n* [F*** You, Show Me The Prompt](https:\u002F\u002Fhamel.dev\u002Fblog\u002Fposts\u002Fprompt\u002F)：通过拦截 API 调用快速理解难以捉摸的大语言模型框架。\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric)：Fabric 是一个开源框架，旨在利用 AI 增强人类能力。它提供了一个模块化的框架，可通过众包的 AI 提示集解决特定问题，并可在任何地方使用。\n* [Langfuse](https:\u002F\u002Flangfuse.com\u002F)：开源的大语言模型工程平台：可观测性、指标、评估、提示管理、游乐场、数据集等。与 LlamaIndex、Langchain、OpenAI SDK、LiteLLM 等集成。[#opensource](https:\u002F\u002Fgithub.com\u002Flangfuse\u002Flangfuse)\n* [naklecha\u002Fllama3-from-scratch](https:\u002F\u002Fgithub.com\u002Fnaklecha\u002Fllama3-from-scratch)：逐次进行矩阵乘法实现 Llama3。\n* [[2405.03825] 组织语言模型社会：增强集体智能的结构与机制](https:\u002F\u002Farxiv.org\u002Fabs\u002F2405.03825)\n* [大语言模型研究中的开放性挑战](https:\u002F\u002Fhuyenchip.com\u002F2023\u002F08\u002F16\u002Fllm-research-open-challenges.html)\n* [stanfordnlp\u002Fdspy](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy)：DSPy 是一个用于编程而非提示的基础模型框架。\n* [Groq](https:\u002F\u002Fgroq.com\u002F)：专注于快速推理速度的服务，提供对 Llama 2 70B-4K 和 Mixtral 8x7B-32K 的 API 访问。\n* [🔥🔥🔥] [LLMLingua](https:\u002F\u002Fllmlingua.com\u002F)：通过**提示压缩**设计大语言模型专用语言。\n* [Floom](https:\u002F\u002Fgithub.com\u002FFloomAI\u002FFloom)：面向开发者的 AI 网关与市场，支持将 AI 功能无缝集成到产品中。\n* [rasbt\u002FLLMs-from-scratch](https:\u002F\u002Fgithub.com\u002Frasbt\u002FLLMs-from-scratch)：逐步从零开始实现类似 ChatGPT 的大语言模型。\n* [GoogleCloudPlatform\u002Fgenerative-ai](https:\u002F\u002Fgithub.com\u002FGoogleCloudPlatform\u002Fgenerative-ai)：谷歌云上生成式 AI 的示例代码和笔记本。\n* [LLM 可视化](https:\u002F\u002Fbbycroft.net\u002Fllm)\n* [使用 SelfCheckGPT NLI 自动检测幻觉](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fdhuynh95\u002Fautomatic-hallucination-detection)\n* [StreamingLLM 为语言模型提供无限上下文](https:\u002F\u002Fbdtechtalks.com\u002F2023\u002F11\u002F27\u002Fstreamingllm\u002F)：让语言模型拥有无限上下文。\n* [iusztinpaul\u002Fhands-on-llms](https:\u002F\u002Fgithub.com\u002Fiusztinpaul\u002Fhands-on-llms)：通过设计、训练和部署一个实时金融顾问大语言模型系统，免费学习大语言模型、LLMOps 和向量数据库知识 ~ 包含源代码 + 视频及阅读材料。\n* [使用 LoRA（低秩适应）微调大语言模型的实用技巧](https:\u002F\u002Fmagazine.sebastianraschka.com\u002Fp\u002Fpractical-tips-for-finetuning-llms?)\n* [Poe](https:\u002F\u002Fpoe.com\u002F)：一个允许用户提问、获得即时回答并与各种 AI 驱动的机器人进行互动的平台。\n* [[2311.01555] 指令蒸馏使大型语言模型成为高效的零样本排序器](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.01555)\n* [🔥🔥] [2023 年大语言模型应用现状 · Streamlit](https:\u002F\u002Fstate-of-llm.streamlit.app\u002F)\n* [当今大语言模型应用的架构 - GitHub 博客](https:\u002F\u002Fgithub.blog\u002F2023-10-30-the-architecture-of-todays-llm-applications\u002F)\n* [揭秘大语言模型：它们如何做到未被训练过的事情 - GitHub 博客](https:\u002F\u002Fgithub.blog\u002F2023-10-27-demystifying-llms-how-they-can-do-things-they-werent-trained-to-do\u002F)\n* [像 ChatGPT 或 Bard 这样的 AI 聊天机器人是如何工作的——可视化讲解 | 英国卫报](https:\u002F\u002Fwww.theguardian.com\u002Ftechnology\u002Fng-interactive\u002F2023\u002Fnov\u002F01\u002Fhow-ai-chatbots-like-chatgpt-or-bard-work-visual-explainer)\n* [cpacker\u002FMemGPT](https:\u002F\u002Fgithub.com\u002Fcpacker\u002FMemGPT)：教导大语言模型进行内存管理以实现无界上下文 [[演示页面]](https:\u002F\u002Fmemgpt.ai\u002F) [[arXiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.08560)\n* [[2307.10169] 大型语言模型的挑战与应用](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.10169)：系统性地总结了大语言模型领域的开放性问题及应用成果。\n* [来自网络的相关资源 | OpenAI 烹饪书](https:\u002F\u002Fcookbook.openai.com\u002Farticles\u002Frelated_resources)：用于改进 GPT 输出的工具和论文。\n* [🔥🔥🔥] [构建基于大语言模型的系统与产品的模式](https:\u002F\u002Feugeneyan.com\u002Fwriting\u002Fllm-patterns\u002F)：由 Eugene Yan 编写的“将大型语言模型 (LLMs) 集成到系统与产品中的实用模式”。\n* [Hannibal046\u002FAwesome-LLM: Awesome-LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM)：精选的大型语言模型列表。\n* [[2309.06794] 认知幻象：大型语言模型中的幻觉综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.06794)\n* [战略与创新中的生成式 AI](https:\u002F\u002Fwww.hbritalia.it\u002FuserUpload\u002Febook_Generative_AI_inglese.pdf)：哈佛商业评论意大利版关于使用 ChatGPT 探讨管理理论的实验。\n* [TextFX 项目](https:\u002F\u002Ftextfx.withgoogle.com\u002F)：“为说唱歌手、作家和文字工作者提供的 AI 驱动工具”（Lupe Fiasco 与 Google 的合作项目）。\n* [无术语解释 AI 大型语言模型的工作原理 | Ars Technica](https:\u002F\u002Farstechnica.com\u002Fscience\u002F2023\u002F07\u002Fa-jargon-free-explanation-of-how-ai-large-language-models-work\u002F)\n* [🔥🔥🔥] [我们对大语言模型的了解（入门指南）](https:\u002F\u002Fwillthompson.name\u002Fwhat-we-know-about-llms-primer)\n* [Llama 2 微调简易指南 | Brev 文档](https:\u002F\u002Fbrev.dev\u002Fblog\u002Ffine-tuning-llama-2)\n* [microsoft\u002Fsemantic-kernel](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-kernel)：帮助您快速简便地将前沿的大语言模型技术集成到应用程序中。\n* [CoPrompt](https:\u002F\u002Fwww.coprompt.io\u002Flogin)：团队协作使用 ChatGPT 的平台。\n* [🔥🔥🔥] [大语言模型应用的新兴架构 | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2023\u002F06\u002F20\u002Femerging-architectures-for-llm-applications\u002F)：“新兴大语言模型应用栈的参考架构”。\n* [ChatGPT 高级指南](https:\u002F\u002Faaditsh.notion.site\u002Faaditsh\u002FAdvanced-Guide-to-ChatGPT-b8d5901b8bba44f580bb0c0835644567)：由 Neatprompts.com 编写的指南。\n* [Falcon LLM - 主页](https:\u002F\u002Ffalconllm.tii.ae\u002F)：阿布扎比技术创新研究所发布的一款具有 400 亿参数、基于 1 万亿标记训练的基础大型语言模型。\n* [🔥🔥🔥] [Hugging Face 开放式大语言模型排行榜](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FHuggingFaceH4\u002Fopen_llm_leaderboard)：“🤗 开放式大语言模型排行榜旨在跟踪、排名和评估新发布的大语言模型和聊天机器人。”\n* [google\u002FBIG-bench](https:\u002F\u002Fgithub.com\u002Fgoogle\u002FBIG-bench)：“一种旨在探测大型语言模型并推断其未来能力的合作基准测试。”\n* [togethercomputer\u002FOpenChatKit](https:\u002F\u002Fgithub.com\u002Ftogethercomputer\u002FOpenChatKit)：提供开源基础，可用于创建针对不同应用场景的专用或通用聊天机器人。\n* [Paper Digest - ChatGPT](https:\u002F\u002Fwww.paperdigest.org\u002F2023\u002F01\u002Frecent-papers-on-chatgpt\u002F)：近期关于 ChatGPT 的论文。\n* [让我们用简·奥斯汀来展示 GPT 是如何工作的——纽约时报](https:\u002F\u002Fwww.nytimes.com\u002Finteractive\u002F2023\u002F04\u002F26\u002Fupshot\u002Fgpt-from-scratch.html)\n* [链中搜索：迈向准确、可信且可追溯的大语言模型，用于知识密集型任务 | arXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.14732)：“一种名为‘链中搜索’（SearChain）的新框架，旨在提高大语言模型生成内容在多跳问答中的准确性、可信度和可追溯性。”\n* [🔥🔥🔥] [Mooler0410\u002FLLMsPracticalGuide](https:\u002F\u002Fgithub.com\u002FMooler0410\u002FLLMsPracticalGuide)：基于论文《实践中的大语言模型力量：对 ChatGPT 及其之后的调查》（https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.13712）整理的大语言模型实用指南资源列表。\n* [hpcaitech\u002FColossalAI](https:\u002F\u002Fgithub.com\u002Fhpcaitech\u002FColossalAI)：致力于使大型 AI 模型更便宜、更快、更易访问。\n* [microsoft\u002FLoRA](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FLoRA)：loralib 的代码，实现了“LoRA：大型语言模型的低秩适应”。\n* [kyrolabs\u002Fawesome-langchain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain)：😎 关于优秀 LangChain 框架的工具和项目的精彩列表。\n* [Stability AI 发布首个 StableLM 系列语言模型 — Stability AI](https:\u002F\u002Fstability.ai\u002Fblog\u002Fstability-ai-launches-the-first-of-its-stablelm-suite-of-language-models)\n* [免费 Dolly | Databricks 博客](https:\u002F\u002Fwww.databricks.com\u002Fblog\u002F2023\u002F04\u002F12\u002Fdolly-first-open-commercially-viable-instruction-tuned-llm)：一款开源、遵循指令的大语言模型，基于人类生成的指令数据集进行微调，该数据集获授权用于研究和商业用途。\n* [ChatGPT\u002FGPT-4 研究总结及对大型语言模型未来的展望](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.01852)：一篇“全面回顾 ChatGPT 和 GPT-4 及其在不同领域中的潜在应用”的论文。\n* [lm-sys\u002FFastChat](https:\u002F\u002Fgithub.com\u002Flm-sys\u002FFastChat)：“Vicuna：一款媲美 GPT-4 的开源聊天机器人”的发布仓库 [[演示](https:\u002F\u002Fchat.lmsys.org\u002F)]\n* [🔥🔥🔥] [oobabooga\u002Ftext-generation-webui](https:\u002F\u002Fgithub.com\u002Foobabooga\u002Ftext-generation-webui)：一个 Gradio Web UI，用于运行 GPT-J 6B、OPT、GALACTICA、LLaMA 和 Pygmalion 等大型语言模型。\n* [为什么 LLaMa 如此重要 | Hackaday](https:\u002F\u002Fhackaday.com\u002F2023\u002F03\u002F22\u002Fwhy-llama-is-a-big-deal\u002F)：一篇文章讨论了 LLaMa 和 Alpaca 在普及大语言模型以及将其应用于小型硬件设备方面的影响。\n* [logspace-ai\u002Flangflow](https:\u002F\u002Fgithub.com\u002Flogspace-ai\u002Flangflow)：LangChain 的 UI，采用 react-flow 设计，提供了一种轻松试验和原型化流程的方式。\n* [超出你所要求的范围：针对应用集成型大语言模型的新型提示注入威胁综合分析](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12173)：一篇关于大语言模型安全性的论文。\n* [Cohere AI](https:\u002F\u002Fdocs.cohere.ai\u002F)：一种将最先进的语言模型集成到应用程序中的方式。\n* [Langchain 用于论文摘要](https:\u002F\u002Flancemartin.notion.site\u002Flancemartin\u002FLangchain-for-paper-summarization-d4ad122ea9a64c0eb1f981e743d6c419)：使用 Langchain 构建论文摘要应用。\n* [大型语言模型的红队测试 | Hugging Faces](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fred-teaming)：针对大语言模型抵御越狱和攻击的策略。\n* [hwchase17\u002Flangchain](https:\u002F\u002Fgithub.com\u002Fhwchase17\u002Flangchain\u002F)：“通过组合性构建基于大语言模型的应用程序”。\n* [2023 年顶级大型语言模型 (LLMs) | MarkTechPost](https:\u002F\u002Fwww.marktechpost.com\u002F2023\u002F02\u002F22\u002Ftop-large-language-models-llms-in-2023-from-openai-google-ai-deepmind-anthropic-baidu-huawei-meta-ai-ai21-labs-lg-ai-research-and-nvidia\u002F)：一份包含来自不同公司的大型语言模型列表。\n* [Godly](https:\u002F\u002Fgodly.ai)：为 GPT3 提供即时上下文。\n* [GPTZero](https:\u002F\u002Fgptzero.me\u002F)：“准确检测 AI 剽窃行为”。\n* [GPT-3 应用](https:\u002F\u002Fgpt-apps.com\u002F)：基于 GPT-3 的微型产品（如猫咪命名器、口袋诗人、摘要生成器等）。\n* [语言模型内部（从 GPT-3 到 PaLM）——艾伦·D·汤普森博士——生命架构师](https:\u002F\u002Flifearchitect.ai\u002Fmodels\u002F)\n* [谷歌 AI 博客：Pathways 语言模型 (PaLM)：扩展至 5400 亿参数以实现突破性性能](https:\u002F\u002Fai.googleblog.com\u002F2022\u002F04\u002Fpathways-language-model-palm-scaling-to.html)\n* [DeepMind 表示其新款语言模型能击败自身规模 25 倍的对手 | MIT 技术评论](https:\u002F\u002Fwww.technologyreview.com\u002F2021\u002F12\u002F08\u002F1041557\u002Fdeepmind-language-model-beat-others-25-times-size-gpt-3-megatron\u002F)\n* [集成 AI：如何通过九个平台（Megatron、GPT-3、GPT-J、Wudao、J1..）免费与 AI 对话——YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yWM_8QwLyuY&list=LL&index=1&t=17s)：由艾伦·D·汤普森博士分享。以下参考资料均源自该视频描述。\n* [Haystack](https:\u002F\u002Fgithub.com\u002Fdeepset-ai\u002Fhaystack)：用于构建基于大语言模型和 Transformer 的应用的框架（例如智能体、语义搜索、问答系统）。\n* [SolidUI](https:\u002F\u002Fgithub.com\u002FCloudOrc\u002FSolidUI)：AI 生成的可视化原型设计与编辑平台，支持 2D、3D 模型，并结合大语言模型进行快速编辑。\n\n### 模型上下文协议\n\n* [介绍模型上下文协议 \\ Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Fnews\u002Fmodel-context-protocol)\n  * 一种开放标准，使开发者能够在他们的数据源与人工智能驱动的工具之间建立安全的双向连接。\n  * 开发者可以选择通过 MCP 服务器公开其数据，或构建连接到这些服务器的人工智能应用（MCP 客户端）。\n* [模型上下文协议](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol): 模型上下文协议 (MCP) 是一种开放协议，可实现大型语言模型应用与外部数据源和工具之间的无缝集成。\n* [简介 - 模型上下文协议](https:\u002F\u002Fmodelcontextprotocol.io\u002Fintroduction)\n  * 可以把 MCP 看作是人工智能应用的 USB-C 接口。\n  * MCP 帮助你在大型语言模型的基础上构建智能体和复杂的工作流。\n* 示例\n  * [示例服务器 - 模型上下文协议](https:\u002F\u002Fmodelcontextprotocol.io\u002Fexamples)\n  * [abhiz123\u002Ftodoist-mcp-server](https:\u002F\u002Fgithub.com\u002Fabhiz123\u002Ftodoist-mcp-server\u002Ftree\u002Fmain): 用于 Todoist 集成的 MCP 服务器，支持使用 Claude 进行自然语言任务管理。\n* 服务器列表\n  * [modelcontextprotocol\u002Fservers: 模型上下文协议服务器](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol\u002Fservers)\n  * [Awesome MCP Servers](https:\u002F\u002Fmcpservers.org\u002F)\n  * [punkpeye\u002Fawesome-mcp-servers](https:\u002F\u002Fgithub.com\u002Fpunkpeye\u002Fawesome-mcp-servers): 一个 MCP 服务器的集合。\n  * [Composio MCP 服务器](https:\u002F\u002Fmcp.composio.dev\u002F): 将 Cursor、Windsurf 和 Claude 连接到 100 多个完全托管的 MCP 服务器，内置身份验证功能。\n    * 这些服务器由社区构建，并由 Composio 托管。\n* [示例客户端 - 模型上下文协议](https:\u002F\u002Fmodelcontextprotocol.io\u002Fclients)\n* [使用大型语言模型构建 MCP - 模型上下文协议](https:\u002F\u002Fmodelcontextprotocol.io\u002Ftutorials\u002Fbuilding-mcp-with-llms)\n* [通过 MCP 将 Supabase 添加到 Cursor](https:\u002F\u002Fx.com\u002Fdshukertjr\u002Fstatus\u002F1896531501514109056)\n* [使用模型上下文协议构建智能体 - Anthropic 的 Mahesh Murag 主持的完整研讨会 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kQmXtrmQ5Zg): AI 工程师峰会研讨会。\n* [loopwork-ai\u002Femcee](https:\u002F\u002Fgithub.com\u002Floopwork-ai\u002Femcee): 一款为任何具有 OpenAPI 规范的 Web 应用程序提供模型上下文协议 (MCP) 服务器的工具。\n* [MCP Run](https:\u002F\u002Fdocs.mcp.run\u002F): 一个任何人都可以开发并在任何人工智能应用程序中使用的 AI 工具注册表。\n* [modelcontextprotocol\u002Finspector](https:\u002F\u002Fgithub.com\u002Fmodelcontextprotocol\u002Finspector): MCP 服务器的可视化测试工具。\n\n### 面向大型语言模型的编程框架\n\n* [DSPy：不只是普通的提示工程](https:\u002F\u002Fjina.ai\u002Fnews\u002Fdspy-not-your-average-prompt-engineering\u002F)：一篇关于 DSPy 的文章，该框架由斯坦福 NLP 小组开发，旨在以算法方式优化语言模型的提示。\n* [🔥🔥🔥] [stanfordnlp\u002Fdspy](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy): DSPy：用于编程——而非提示——基础模型的框架。\n\n### 提示工程\n\n* [Narrow AI](https:\u002F\u002Fwww.getnarrow.ai\u002F)：自动化提示工程与优化平台。\n* [Anthropic 的提示工程交互式教程](https:\u002F\u002Fgithub.com\u002Fanthropics\u002Fcourses\u002Ftree\u002Fmaster\u002Fprompt_engineering_interactive_tutorial)。\n* [ncwilson78\u002FSystem-Prompt-Library](https:\u002F\u002Fgithub.com\u002Fncwilson78\u002FSystem-Prompt-Library)：一个共享系统提示库，用于创建定制化的教育类 GPT 智能体。\n* [Promptstacks](https:\u002F\u002Fwww.promptstacks.com\u002F)：一个提示工程社区。\n* [提示工程 - OpenAI API](https:\u002F\u002Fplatform.openai.com\u002Fdocs\u002Fguides\u002Fprompt-engineering)：OpenAI 提供的文档，包含从大型语言模型中获得更好结果的策略和技巧。\n* [[2310.04438] 提示简史：利用语言模型](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04438)：该论文探讨了提示工程的发展历程。作者 Golam Md Muktadir 广泛使用 ChatGPT 来生成内容。\n* [[2311.05661] 为提示工程师进行提示工程](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05661)：本文探讨了“构建一种元提示，更有效地引导大型语言模型自动进行提示工程”的问题。\n* [[2311.04155] 黑盒提示优化：无需训练模型即可对齐大型语言模型](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.04155)。\n* [🔥🔥] [提示工程路线图 - roadmap.sh](https:\u002F\u002Froadmap.sh\u002Fprompt-engineering)。\n* [🔥🔥🔥] [学习提示工程](https:\u002F\u002Flearnprompting.org\u002F)：一系列提示工程课程。\n* [🔥🔥🔥] [提示工程 | Lil'Log](https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2023-03-15-prompt-engineering\u002F)：Lilian Weng 的提示工程学习笔记。\n* [🔥🔥🔥] [面向开发者的 ChatGPT 提示工程 - DeepLearning.AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002Fchatgpt-prompt-engineering-for-developers\u002F)：由 Isa Fulford（OpenAI）和 Andrew Ng（DeepLearning.AI）教授的短期课程，提供提示工程的最佳实践。\n* [🔥🔥🔥] [提示工程指南](https:\u002F\u002Fwww.promptingguide.ai\u002F)：DAIR.AI 发起的一个项目，旨在教育研究人员和从业者有关提示工程的知识。\n* [这本书](https:\u002F\u002Ffedhoneypot.notion.site\u002F25fdbdb69e9e44c6877d79e18336fe05?v=1d2bf4143680451986fd2836a04afbf4)：提示和提示工程技巧的合集。\n* [dair-ai\u002FPrompt-Engineering-Guide](https:\u002F\u002Fgithub.com\u002Fdair-ai\u002FPrompt-Engineering-Guide)：提示工程指南及资源。\n\n#### 提示优化器\n\n* [zou-group\u002Ftextgrad](https:\u002F\u002Fgithub.com\u002Fzou-group\u002Ftextgrad)：通过文本实现自动“微分”，利用大型语言模型反向传播文本梯度。\n* [🔥🔥🔥] [stanfordnlp\u002Fdspy](https:\u002F\u002Fgithub.com\u002Fstanfordnlp\u002Fdspy)：DSPy：用于编程——而非提示——基础模型的框架。\n* [vaibkumr\u002Fprompt-optimizer](https:\u002F\u002Fgithub.com\u002Fvaibkumr\u002Fprompt-optimizer)：最小化 LLM 的标记复杂度，以节省 API 费用和模型计算成本。\n* [PromptPerfect](https:\u002F\u002Fpromptperfect.jina.ai\u002F)：“将您的提示优化至完美”。\n* [🔥🔥🔥] [LLMLingua](https:\u002F\u002Fllmlingua.com\u002F)：通过 **提示压缩** 为大型语言模型设计语言。\n\n#### 文本到文本的提示工程\n\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric)：Fabric 是一个利用 AI 增强人类能力的开源框架。它提供了一个模块化的框架，用于通过可随处使用的众包 AI 提示词集来解决特定问题。\n* [ChatGPT for designers](https:\u002F\u002Ftibidavid.gumroad.com\u002Fl\u002FChatGPT-Cheat-Sheet-V2?ref=filipecalegario-awesome-generative-ai)：ChatGPT 备忘单 V2，帮助你编写更优质的提示词。\n* [🔥] [[2307.11760] 大型语言模型能够理解情绪刺激，并可通过情绪刺激加以增强](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.11760)\n* [🔥] [[2305.13252] “根据……”式的提示能提升语言模型从预训练数据中引用内容的能力](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13252)\n* [🔥] [[2307.05300] 释放大型语言模型中的认知协同效应：通过多角色自我协作的任务解决代理](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.05300)\n* [timqian\u002Fopenprompt.co](https:\u002F\u002Fgithub.com\u002Ftimqian\u002Fopenprompt.co)：创建、使用、分享 ChatGPT 提示词。\n* [60 个数据科学领域的 ChatGPT 提示词（亲测有效并评分）](https:\u002F\u002Fmedium.datadriveninvestor.com\u002F60-chatgpt-prompts-for-data-science-tried-tested-and-rated-4994c7e6adb2)：DataDrivenInvestor 的 Travis Tang 所撰文章。\n* [f\u002Fawesome-chatgpt-prompts](https:\u002F\u002Fgithub.com\u002Ff\u002Fawesome-chatgpt-prompts)：该仓库收录了精选的 ChatGPT 提示词，帮助用户更好地使用 ChatGPT。\n* [brexhq\u002Fprompt-engineering](https:\u002F\u002Fgithub.com\u002Fbrexhq\u002Fprompt-engineering)：“与 OpenAI 的 GPT-4 等大型语言模型合作的技巧与窍门”。\n* [如何编写有效的 GPT-3 提示词 | Zapier](https:\u002F\u002Fzapier.com\u002Fblog\u002Fgpt-3-prompt\u002F)：一份包含 6 条 GPT-3 使用技巧的清单，帮助用户获得期望的输出。\n* [ChatGPT 提示词的艺术：撰写清晰高效提示词指南](https:\u002F\u002Ffka.gumroad.com\u002Fl\u002Fart-of-chatgpt-prompting)：Fatih Kadir Akın（[@fkadev](http:\u002F\u002Ftwitter.com\u002Ffkadev)）编写的电子书。\n\n#### 文本到图像的提示工程\n\n* [USP AI 提示词手册](https:\u002F\u002Fapp.usp.ai\u002Fstatic\u002FStable%20Diffusion%202.1%20Prompt%20Book%20by%20USP.ai.pdf)：Stable Diffusion v2.1 提示词手册\n* [daspartho\u002Fprompt-extend](https:\u002F\u002Fgithub.com\u002Fdaspartho\u002Fprompt-extend)：使用文本生成技术，通过合适的风格提示扩展 Stable Diffusion 的提示词\n* [Prompt Box](https:\u002F\u002Fwww.promptbox.ai\u002F)：“整理并保存你的 AI 提示词”\n* [Midjourney 艺术家参考 - Google 表格](https:\u002F\u002Fdocs.google.com\u002Fspreadsheets\u002Fd\u002F1e2MZ1K6WMTUuxlPAQ_2A0rz-H55NBykb66TY7DuerVg\u002Fedit#gid=2088669480)\n* [Stable Diffusion 提示词手册 — Stability.Ai](https:\u002F\u002Fstability.ai\u002Fsdv2-prompt-book)：由 Stability.AI 发布的 Stable Diffusion v2.0 和 v2.1 提示词手册\n* [PromptHero 的终极 Stable Diffusion 提示词指南](https:\u002F\u002Fprompthero.com\u002Fstable-diffusion-prompt-guide)\n* [CLIP Interrogator - pharma 的 Hugging Face Space](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fpharma\u002FCLIP-Interrogator)：一款图像转文本工具，用于找出能够生成类似现有图像的新图像的好提示词\n* [🔥🔥🔥] [数据爱好者 II 提示词手册 - Google Slides](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1V8d6TIlKqB1j5xPFH7cCmgKOV_fMs4Cb4dwgjD5GIsg\u002Fedit#slide=id.g1834b964b0f_3_4)：一项关于文本到图像和数据可视化的开源探索\n* [some9000\u002FStylePile](https:\u002F\u002Fgithub.com\u002Fsome9000\u002FStylePile)：AUTOMATIC1111\u002Fstable-diffusion-webui 的辅助脚本。基本上是一种混搭方式，可以快速获得不同的结果，而无需花费大量时间编写提示词。\n* [值得研究的艺术家 | 所有图像均由 Google Colab TPU + CompVis\u002Fstable-diffusion-v1-4 + Huggingface Diffusers 生成](https:\u002F\u002Fartiststostudy.pages.dev\u002F)：由 [@camenduru](https:\u002F\u002Ftwitter.com\u002Fcamenduru) 进行的艺术家风格系统性研究\n* [基于 Laion5B 的 CLIP 检索](https:\u002F\u002From1504.github.io\u002Fclip-retrieval\u002F?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false)：“它通过将文本查询转换为 CLIP 嵌入，然后使用该嵌入查询 CLIP 图像嵌入的 k-近邻索引。”\n* [rom1504\u002Fclip-retrieval](https:\u002F\u002Fgithub.com\u002From1504\u002Fclip-retrieval)：轻松计算 CLIP 嵌入，并用它们构建 CLIP 检索系统\n* [PromptDesign | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FPromptDesign\u002F)：一个关于“与自然语言模型沟通的艺术”的 Reddit 社区\n* [提示工程与零样本\u002F少样本学习【指南】- inovex GmbH](https:\u002F\u002Fwww.inovex.de\u002Fde\u002Fblog\u002Fprompt-engineering-guide\u002F)：用于文本生成的提示工程\n* [clip-interrogator.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fpharmapsychotic\u002Fclip-interrogator\u002Fblob\u002Fmain\u002Fclip_interrogator.ipynb#scrollTo=rbDEMDGJrJEo)：一款图像转提示词的工具\n* [有用的提示工程工具和资源 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxcrm4d\u002Fuseful_prompt_engineering_tools_and_resources\u002F)\n* [PromptHero](https:\u002F\u002Fprompthero.com\u002F)：搜索适用于 Stable Diffusion、DALL-E 和 Midjourney 的最佳提示词\n* [promptoMANIA](https:\u002F\u002Fpromptomania.com\u002F)：拥有提示词生成器的 AI 艺术社区\n* [Lexica](https:\u002F\u002Flexica.art\u002F)：搜索超过 1000 万张 Stable Diffusion 图像及提示词\n* [SD v1.4 A-C \u002F D-I \u002F J-N \u002F O-Z 艺术家列表](https:\u002F\u002Frentry.org\u002Fartists_sd-v1-4)\n* [succinctly\u002Ftext2image-prompt-generator · Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fsuccinctly\u002Ftext2image-prompt-generator)：基于 succinctly\u002Fmidjourney-prompts 数据集微调的 GPT-2 模型，该数据集包含用户在一个月内向 Midjourney 文本到图像服务发出的 25 万个文本提示词\n* [The Prompter | vicc | Substack](https:\u002F\u002Ftheprompter.substack.com\u002F)：一份关于提示工程相关新闻、技巧和思考的通讯\n* [(19) Nikhil Agrawal 📌 在 Twitter 上](https:\u002F\u002Ftwitter.com\u002FHeyNikhila\u002Fstatus\u002F1570005481896255490)：11 个 AI 图像提示词网站，可提升图像质量\n* [Phraser](https:\u002F\u002Fphraser.tech\u002F)：一款支持提示词创建的工具\n* [PromptBase | 提示词市场](https:\u002F\u002Fpromptbase.com\u002F)：PromptBase 是 DALL·E、Midjourney 和 GPT-3 提示词的市场平台，人们可以在这里出售提示词，凭借自己的提示词创作技能赚钱。\n* [专业的 AI 提示词专家推出了 DALL·E 提示词市场 - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2022\u002F9\u002F2\u002F23326868\u002Fdalle-midjourney-ai-promptbase-prompt-market-sales-artist-interview)\n* [视觉提示词构建器](https:\u002F\u002Ftools.saxifrage.xyz\u002Fprompt)：一套简单的插图卡片，用于组合修饰符以构建提示词\n* [提示工程模板 - Google 表格](https:\u002F\u002Fdocs.google.com\u002Fspreadsheets\u002Fd\u002F1-snKDn38-KypoYCk9XLPg799bHcNFSBAVu2HVvFEAkA\u002Fedit#gid=0)：包含提示词构建修饰符列表以及大量有趣参考资料的电子表格\n* [提示工程：从文字到艺术 - Saxifrage 博客](https:\u002F\u002Fwww.saxifrage.xyz\u002Fpost\u002Fprompt-engineering)\n* [DALL·Ery GALL·Ery 资源](https:\u002F\u002Fdallery.gallery\u002Fprompt-resources-tools-ai-art\u002F)：DALL·E 2 和 AI 艺术的提示词资源与工具，旨在激发创作美丽图像的灵感\n* [[2204.13988] 文本到图像生成的提示词修饰符分类](https:\u002F\u002Farxiv.org\u002Fabs\u002F2204.13988)\n* [美学列表 | 美学维基 | Fandom](https:\u002F\u002Faesthetics.fandom.com\u002Fwiki\u002FList_of_Aesthetics)\n* [艺术家目录（火山比较）| AI 艺术创作维基 | Fandom](https:\u002F\u002Faiartcreation.fandom.com\u002Fwiki\u002FArtist_Directory_(Volcano_Comparison))\n* [DALL·E 2 提示词手册 – DALL·Ery GALL·Ery](https:\u002F\u002Fdallery.gallery\u002Fthe-dalle-2-prompt-book\u002F)\n* [DALL·Ery GALL·Ery](https:\u002F\u002Fdallery.gallery\u002F)：OpenAI 的 DALL·E 指南——提示词、项目、示例和技巧\n* [(2) 大量 💥 DALL·E 2 动漫 ⚡︎ 关键词 + 修饰符 列表 ★ ：haaaaven](https:\u002F\u002Fwww.reddit.com\u002Fuser\u002Fhaaaaven\u002Fcomments\u002Fw05f56\u002Fmassive_dalle_2_anime_keywords_modifiers_list\u002F)：由 haaaaven 收集的图像提示词修饰符集合\n* [DrawBench](https:\u002F\u002Fdocs.google.com\u002Fspreadsheets\u002Fd\u002F1y7nAbmR4FREi6npB1u-Bo3GFdwdOPYJc617rBOxIRHY\u002Fedit#gid=0)：Google Imagen 作为基准测试而整理的一系列提示词列表\n* [面向生成艺术的 CLIP 提示工程 - matthewmcateer.me](https:\u002F\u002Fmatthewmcateer.me\u002Fblog\u002Fclip-prompt-engineering\u002F)：使用 Quick CLIP Guided Diffusion 测试的各种风格列表\n* [Adobe 应该为提示工程师打造一款无聊的应用程序（Interconnected）](https:\u002F\u002Finterconnected.org\u002Fhome\u002F2022\u002F06\u002F02\u002Fdalle)\n* [[2206.00169] 探索 DALLE-2 的隐藏词汇](https:\u002F\u002Farxiv.org\u002Fabs\u002F2206.00169)\n* [当 SD 无论如何都无法理解我的提示词时 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgwcab\u002Fwhen_sd_just_doesnt_understand_the_prompt_no\u002F)\n* [有些提示词的输出非常明确，而另一些特定的提示词却并非如此，这真的很有趣 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgplii\u002Fits_very_interesting_how_some_prompts_have_very\u002F)\n\n### Mamba\n\n* [[2312.00752] Mamba：基于选择性状态空间的线性时间序列建模](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.00752)：一种替代Transformer架构的方法。\n* [Mamba：浅析LLM的新架构 | 作者：Geronimo (@geronimo7) | 2023年12月 | Medium](https:\u002F\u002Fmedium.com\u002F@geronimo7\u002Fmamba-a-shallow-dive-into-a-new-architecture-for-llms-54c70ade5957)\n* [Mamba-Chat](https:\u002F\u002Fgithub.com\u002Fhavenhq\u002Fmamba-chat)：基于状态空间模型架构的聊天LLM。\n\n### 在本地运行LLM\n\n* [llama.cpp指南](https:\u002F\u002Fsteelph0enix.github.io\u002Fposts\u002Fllama-cpp-guide\u002F)：从零开始，在任何硬件上本地运行LLM。\n* [PowerInfer](https:\u002F\u002Fgithub.com\u002FSJTU-IPADS\u002FPowerInfer)：用于在本地部署LLM的高速推理引擎。\n* [🔥🔥] [Ollama](https:\u002F\u002Follama.ai\u002F)：在本地运行Llama 2、Code Llama等模型。\n* [GPT4All](https:\u002F\u002Fgpt4all.io\u002Findex.html)：一款免费使用、本地运行且注重隐私的聊天机器人，无需GPU或互联网连接。\n* [LM Studio](https:\u002F\u002Flmstudio.ai\u002F)：发现、下载并运行本地LLM。\n* [ggerganov\u002Fllama.cpp](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fllama.cpp)：Facebook的LLaMA模型的C\u002FC++移植版本。\n\n### 函数调用\n\n* [Nexusflow\u002FNexusRaven-V2-13B · Hugging Face](https:\u002F\u002Fhuggingface.co\u002FNexusflow\u002FNexusRaven-V2-13B)：“在零样本函数调用任务中超越GPT-4”。\n\n### GPTs与Assistant API\n\n* [精选GPTs](https:\u002F\u002Fwww.featuredgpts.com\u002F)：为日常任务精心挑选的自定义GPT列表。\n* [AllGPTs](https:\u002F\u002Fallgpts.co\u002F)：一个用于查找GPT的目录。\n\n### 检索增强生成（RAG）\n\n* [RAG中的幻觉检测方法基准测试 | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fbenchmarking-hallucination-detection-methods-in-rag-6a03c555f063\u002F)\n* [bRAGAI\u002FbRAG-langchain](https:\u002F\u002Fgithub.com\u002FbRAGAI\u002FbRAG-langchain)：构建自己的RAG应用所需的一切知识。\n* [ragapp\u002Fragapp](https:\u002F\u002Fgithub.com\u002Fragapp\u002Fragapp)：企业中使用代理式RAG的另一种方案。\n* [LlamaParse](https:\u002F\u002Fwww.llamaindex.ai\u002Fblog\u002Flaunching-the-first-genai-native-document-parsing-platform)：由LlamaIndex推出的原生GenAI文档解析平台。\n* [面向大型语言模型的检索增强生成：综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.10997)\n* [weaviate\u002FVerba](https:\u002F\u002Fgithub.com\u002Fweaviate\u002FVerba)：由Weaviate驱动的检索增强生成（RAG）聊天机器人。\n* [imartinez\u002FprivateGPT](https:\u002F\u002Fgithub.com\u002Fimartinez\u002FprivateGPT)：“以GPT的强大能力与您的文档互动，100%私密，无数据泄露”。\n* [pinecone-io\u002Fcanopy](https:\u002F\u002Fgithub.com\u002Fpinecone-io\u002Fcanopy)：由Pinecone支持的检索增强生成（RAG）框架和上下文引擎。\n* [忘掉RAG吧，未来是RAG融合](https:\u002F\u002Ftowardsdatascience.com\u002Fforget-rag-the-future-is-rag-fusion-1147298d8ad1)：Adrian H. Raudaschl在Towards Data Science上发表的文章。\n* [重排序器与两阶段检索 | Pinecone](https:\u002F\u002Fwww.pinecone.io\u002Flearn\u002Fseries\u002Frag\u002Frerankers\u002F)\n* [检索增强生成 | Pinecone](https:\u002F\u002Fwww.pinecone.io\u002Flearn\u002Fseries\u002Frag\u002F)\n* [dssjon\u002Fbiblos: biblos.app](https:\u002F\u002Fgithub.com\u002Fdssjon\u002Fbiblos)：利用语义搜索和摘要技术检索圣经经文的RAG架构示例。\n\n### 嵌入与语义搜索\n\n* [🪆 马特里奥什卡嵌入模型简介](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fmatryoshka)\n* [Amelia Wattenberger关于嵌入的创意应用](https:\u002F\u002Fwattenberger.com\u002Fthoughts\u002Fyay-embeddings-math)\n* [嵌入的隐秘生活：Linus Lee - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YvobVu1l7GI)\n* [neuml\u002Ftxtai](https:\u002F\u002Fgithub.com\u002Fneuml\u002Ftxtai)：由语言模型驱动的语义搜索与工作流。\n* [facebookresearch\u002Ffaiss](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Ffaiss)：用于高效相似度搜索和稠密向量聚类的库。\n* [使用GPT-3优化聊天机器人的对话智能 | 作者：Amogh Agastya | Better Programming](https:\u002F\u002Fbetterprogramming.pub\u002Fhow-to-give-your-chatbot-the-power-of-neural-search-with-openai-ebcff5194170)：介绍语义搜索概念的教程。\n* [🔥] [whitead\u002Fpaper-qa](https:\u002F\u002Fgithub.com\u002Fwhitead\u002Fpaper-qa)：“用于回答带有引用的文档问题的LLM链”，[演示](https:\u002F\u002Ftwitter.com\u002Fandrewwhite01\u002Fstatus\u002F1629346569756483584?s=20)。\n* [什么是语义搜索？](https:\u002F\u002Ftxt.cohere.ai\u002Fwhat-is-semantic-search\u002F)\n* [学习中心 | Pinecone](https:\u002F\u002Fwww.pinecone.io\u002Flearn\u002F)：Pinecone提供的向量嵌入指南。\n* [BLIP+CLIP | CLIP询问器 | Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Fcode\u002Fleonidkulyk\u002Flb-0-45836-blip-clip-clip-interrogator)：一个用于图像描述和标题生成的Kaggle笔记本（图像到文本）。\n* [jerryjliu\u002Fgpt_index: GPT Index (LlamaIndex)](https:\u002F\u002Fgithub.com\u002Fjerryjliu\u002Fgpt_index)：一个旨在简化LLM使用大型外部知识库的项目。\n* [Llama Hub](https:\u002F\u002Fllamahub.ai\u002F)：LlamaIndex（GPT Index）和LangChain的数据加载器仓库。\n* [Chroma](https:\u002F\u002Fwww.trychroma.com\u002F)：一个开源的原生AI数据库，使嵌入的使用更加便捷。\n\n### 自主LLM智能体\n\n* [🔥] [Anthropic：构建高效智能体](https:\u002F\u002Fwww.anthropic.com\u002Fresearch\u002Fbuilding-effective-agents)：本文介绍了与智能体相关的基本概念，并以教学方式展示了智能体架构。\n* [LLM智能体完全指南（2025）](https:\u002F\u002Fbotpress.com\u002Fblog\u002Fllm-agents)：对LLM智能体相关术语的总结。\n* [pydantic\u002Fpydantic-ai](https:\u002F\u002Fgithub.com\u002Fpydantic\u002Fpydantic-ai)：一个用于将Pydantic与LLM结合使用的智能体框架\u002F桥接工具。\n* [NirDiamant\u002FGenAI_Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents)：涵盖从基础到高级的各种生成式AI智能体技术的教程与实现，是构建智能、交互式AI系统的全面指南。\n* [Hexabot](https:\u002F\u002Fgithub.com\u002Fhexastack\u002Fhexabot)：一款开源AI聊天机器人\u002F智能体构建工具，支持LLM并可集成社交媒体渠道。\n* [NirDiamant\u002FGenAI_Agents](https:\u002F\u002Fgithub.com\u002FNirDiamant\u002FGenAI_Agents)：涵盖从基础到高级的各种生成式AI智能体技术的教程与实现，是构建智能、交互式AI系统的全面指南。\n* [TailorTask](https:\u002F\u002Fwww.tailortask.ai)：无需代码、无需学习新工具，即可自动完成任何繁琐任务。\n* [[2406.04784] SelfGoal：你的语言模型智能体已经知道如何实现高层次目标](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.04784)\n* [[2406.04692] 智能体混合增强大型语言模型能力](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.04692)\n* [MervinPraison\u002FPraisonAI](https:\u002F\u002Fgithub.com\u002FMervinPraison\u002FPraisonAI)：PraisonAI应用将AutoGen和CrewAI等框架整合为低代码解决方案，用于构建和管理多智能体LLM系统，专注于简洁性、可定制性和高效的人机协作。\n* [治理智能体AI系统的实践](https:\u002F\u002Fopenai.com\u002Fresearch\u002Fpractices-for-governing-agentic-ai-systems)：OpenAI发布的一篇论文，提供了一系列确保智能体操作安全且可问责的实践方法。\n* [[2312.05230] 语言模型、智能体模型与世界模型：机器推理与规划的LAW框架](https:\u002F\u002Farxiv.org\u002Fabs\u002F2312.05230)\n* [[2309.02427] 语言智能体的认知架构](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.02427)：“我们借鉴认知科学和符号人工智能的丰富历史，提出语言智能体的认知架构（CoALA）”。\n* [[2309.07864] 基于大型语言模型的智能体的兴起与潜力：综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.07864)\n* [[2310.01444] 通过沟通调整LLM智能体](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.01444)\n* [[2309.17288] AutoAgents：自动智能体生成框架](https:\u002F\u002Farxiv.org\u002Fabs\u002F2309.17288)\n* [探索多角色提示以获得更好输出](https:\u002F\u002Fwww.prompthub.us\u002Fblog\u002Fexploring-multi-persona-prompting-for-better-outputs)：“一种提示工程方法，指示LLM调用多个角色，并让它们协同工作以解决任务”。\n* [自主认知实体的概念框架](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.06775)：一篇论文，提出了“自主认知实体（ACE）模型”，这是一种新颖的认知架构框架，使机器和软件智能体能够更独立地运行。\n* [基于自然语言的心灵社会中的思维风暴](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.17066)：一篇评估基于自然语言的心灵社会（NLSOMs）的论文，利用其中的思维风暴来解决一些实际的AI任务。\n* [AutoGen | 微软](https:\u002F\u002Fmicrosoft.github.io\u002Fautogen\u002F)：微软提供的多智能体对话框架，作为高层抽象[[github](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fautogen)]\n* [OpenBMB\u002FChatDev](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev)：使用自然语言构思（通过LLM驱动的多智能体协作）创建定制化软件。\n* [a16z-infra\u002Fai-town](https:\u002F\u002Fgithub.com\u002Fa16z-infra\u002FAI-town)：一款MIT许可的可部署入门套件，用于构建和定制你自己的AI小镇——一个AI角色生活、聊天和社交的虚拟城镇。\n* [AI Town](https:\u002F\u002Fwww.convex.dev\u002Fai-town)：一个AI角色生活、聊天和社交的虚拟城镇。\n* [joonspk-research\u002Fgenerative_agents - 生成式智能体](https:\u002F\u002Fgithub.com\u002Fjoonspk-research\u002Fgenerative_agents)：用于模拟人类行为的交互式仿真程序代码[[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03442)\n* [AgentBench：评估LLM作为智能体](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2308.03688)：Hugging Face关于评估LLM智能体基准测试的页面。\n* [geekan\u002FMetaGPT](https:\u002F\u002Fgithub.com\u002Fgeekan\u002FMetaGPT)：一个多智能体框架，只需输入一行需求，即可生成PRD、设计、任务列表和代码仓库。\n* [GPT Researcher](https:\u002F\u002Fapp.tavily.com\u002F)：用于洞察与研究的AI智能体。\n* [Jim Fan在Twitter上发布的多智能体仿真](https:\u002F\u002Ftwitter.com\u002FDrJimFan\u002Fstatus\u002F1682086586593443841)：“涌现智能的下一个前沿将是多智能体仿真：一群AI角色通过复杂的社会互动过着日常生活。”\n* [介绍AACP | SuperAGI](https:\u002F\u002Fsuperagi.com\u002Fintroducing-aacp-agent-to-agent-communication-protocol\u002F)：一种智能体间通信协议。\n* [BrainstormGPT](https:\u002F\u002Fbrainstormgpt.ai\u002F#\u002F)：AI多智能体问题解决平台。\n* [ChatArena](https:\u002F\u002Fwww.chatarena.org\u002F)：为LLM构建多智能体环境。\n* [🔥🔥🔥] [LLM驱动的自主智能体 | Lil'Log](https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2023-06-23-agent\u002F)：Lilian Weng关于LLM智能体的学习笔记。\n* [Vercel for AI agents](https:\u002F\u002Fgithub.com\u002Fe2b-dev\u002Fe2b)：“帮助开发者构建、部署和监控AI智能体，重点关注为你构建软件的专用AI智能体——你的私人软件开发人员。”\n* [101dotxyz\u002FGPTeam](https:\u002F\u002Fgithub.com\u002F101dotxyz\u002FGPTeam)：“GPTeam利用GPT-4创建多个智能体，协同实现预设目标。”\n* [Fine-Tuner.ai](https:\u002F\u002Ffine-tuner.ai\u002F)：无需代码即可构建AI智能体的方法。\n* [AI智能体基础：让我们逐步思考——Jon Stokes著](https:\u002F\u002Fwww.jonstokes.com\u002Fp\u002Fai-agent-basics-lets-think-step-by)\n* [🔥🔥] [Transformers Agent](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Ftransformers_agents)：在Hugging Face的Transformers库之上提供自然语言API。\n* [AgentGPT](https:\u002F\u002Fagentgpt.reworkd.ai\u002F)：“在浏览器中组装、配置并部署自主AI智能体”。\n* [yoheinakajima\u002Fbabyagi](https:\u002F\u002Fgithub.com\u002Fyoheinakajima\u002Fbabyagi)：一个由AI驱动的任务管理系统，利用OpenAI和Pinecone API创建、优先排序并执行任务。\n* [Torantulino\u002FAuto-GPT](https:\u002F\u002Fgithub.com\u002FTorantulino\u002FAuto-GPT)：“一项实验性的开源尝试，旨在使GPT-4完全自主运行”。\n* [生成式智能体：人类行为的交互式仿真](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.03442)：一篇论文，介绍能够模拟可信人类行为的计算软件智能体。\n* [microsoft\u002FJARVIS](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FJARVIS)：JARVIS是一个将LLM与机器学习社区连接起来的系统。\n* [HuggingGPT](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.17580)：利用ChatGPT及其在Hugging Face中的伙伴解决AI任务。\n\n#### 多智能体\n\n* [[2411.00114] 项目Sid：迈向AI文明的多智能体模拟](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.00114) \n* [joonspk-research\u002Fgenerative_agents](https:\u002F\u002Fgithub.com\u002Fjoonspk-research\u002Fgenerative_agents)：实现论文《生成式智能体：人类行为的交互式仿真》\n* [1,000人的生成式智能体模拟 | arXiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2411.10109) [[GitHub：joonspk-research\u002Fgenagents]](https:\u002F\u002Fgithub.com\u002Fjoonspk-research\u002Fgenagents) \n* [microsoft\u002FTinyTroupe](https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FTinyTroupe)：由大语言模型驱动的多智能体角色扮演模拟，用于提升想象力与商业洞察力\n* [多智能体研究概览](https:\u002F\u002Fthinkwee.top\u002Fmultiagent_ebook\u002Findex.html)：一本交互式电子书，汇集了大量基于大语言模型（LLM）的多智能体系统研究论文\n* [openai\u002Fswarm](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fswarm)：教育性框架，探索符合人体工学、轻量级的多智能体编排。由OpenAI解决方案团队维护。\n* [[2307.05300] 在大型语言模型中释放认知协同效应：通过多角色自我协作的任务解决智能体](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.05300)\n* [[2308.07201] ChatEval：通过多智能体辩论打造更优秀的基于LLM的评估工具](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.07201)\n* [OpenBMB\u002FChatDev](https:\u002F\u002Fgithub.com\u002FOpenBMB\u002FChatDev)：利用自然语言构思，通过大语言模型驱动的多智能体协作来创建定制化软件\n* [[2308.10848] AgentVerse：促进多智能体协作并探索涌现行为](https:\u002F\u002Farxiv.org\u002Fabs\u002F2308.10848)\n* [BrainSoup](https:\u002F\u002Fwww.nurgo-software.com\u002Fproducts\u002Fbrainsoup)：具备RAG、多模态、自动化、代码解释器和沙箱文件系统功能的多智能体&多LLM客户端\n\n\n\n### LLM评估\n\n* [Cleanlab 可信语言模型：为任何 LLM 响应打分其可信度](https:\u002F\u002Fhelp.cleanlab.ai\u002Ftlm\u002F)\n* [PAIR-code\u002Fllm-comparator](https:\u002F\u002Fgithub.com\u002FPAIR-code\u002Fllm-comparator)：LLM 比较器是由 PAIR 团队开发的一款交互式数据可视化工具，用于并排评估和分析 LLM 的响应。\n* [confident-ai\u002Fdeepeval](https:\u002F\u002Fgithub.com\u002Fconfident-ai\u002Fdeepeval)：LLM 评估框架\n* [LLM 基准测试：MMLU、HellaSwag、BBH 及其之外——Confident AI](https:\u002F\u002Fwww.confident-ai.com\u002Fblog\u002Fllm-benchmarks-mmlu-hellaswag-and-beyond)\n* [LLM 排行榜](https:\u002F\u002Fllm.extractum.io\u002Fstatic\u002Fllm-leaderboards\u002F)\n* [Reward Bench 排行榜——由 allenai 提供的 Hugging Face 空间](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fallenai\u002Freward-bench)\n* [LiveBench](https:\u002F\u002Flivebench.ai\u002F)：一个具有挑战性且无污染的 LLM 基准测试\n* [评估大型语言模型](https:\u002F\u002Fwww.lakera.ai\u002Fblog\u002Flarge-language-model-evaluation)：方法、最佳实践与工具 | Lakera——保护那些颠覆世界的 AI 团队\n* [ianarawjo\u002FChainForge](https:\u002F\u002Fgithub.com\u002Fianarawjo\u002FChainForge?tab=readme-ov-file)：一个开源的可视化编程环境，用于对 LLM 的提示进行实战测试。\n* [Prometheus-2 食谱——LlamaIndex](https:\u002F\u002Fdocs.llamaindex.ai\u002Fen\u002Flatest\u002Fexamples\u002Fcookbooks\u002Fprometheus2_cookbook\u002F)：“一个专门用于评估其他语言模型的开源语言模型。”\n* [[2305.13711] LLM-Eval：面向大型语言模型的开放域对话的统一多维自动评估](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.13711)\n* [LLM 评估](https:\u002F\u002Fllm-eval.github.io\u002F)：由微软研究院及其他合作机构开展的 LLM 评估研究。（更新于：2023年10月）\n* [LLM 评估：运行和基准测试评估所需的一切](https:\u002F\u002Farize.com\u002Fblog-course\u002Fllm-evaluation-the-definitive-guide\u002F)\n* [LLM 产品评估终极指南](https:\u002F\u002Fblog.context.ai\u002Fthe-ultimate-guide-to-llm-product-evaluation\u002F)\n* [如何评估、比较和优化 LLM 系统](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fhow-evaluate-compare-optimize-llm-systems-b%C3%BClent-uyaniker-jw4qc)\n* [LLM 评估 | Clarifai 指南](https:\u002F\u002Fdocs.clarifai.com\u002Fportal-guide\u002Fevaluate\u002Fllms\u002F)\n* [如何评估 LLM 应用程序：完整指南——Confident AI](https:\u002F\u002Fwww.confident-ai.com\u002Fblog\u002Fhow-to-evaluate-llm-applications)\n* [AI 评估指标 | Microsoft Learn](https:\u002F\u002Flearn.microsoft.com\u002Fen-us\u002Fai\u002Fplaybook\u002Ftechnology-guidance\u002Fgenerative-ai\u002Fworking-with-llms\u002Feval-metrics)\n* [如何评估大型语言模型的输出：当前最佳实践——FinetuneDB](https:\u002F\u002Ffinetunedb.com\u002Fblog\u002Fhow-to-evaluate-large-language-model-outputs\u002F)\n* [LLM 评估终极指南——Deci](https:\u002F\u002Fdeci.ai\u002Fblog\u002Fllm-evaluation-ultimate-guide\u002F)\n* [2024 年大型语言模型评估：5 种方法](https:\u002F\u002Fresearch.aimultiple.com\u002Flarge-language-model-evaluation\u002F)\n* [与人类判断对齐：成对偏好在大型语言模型评估者中的作用](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2403.16950v2)\n* [使用 MT-Bench 和 Chatbot Arena 对 LLM 进行“法官式”评估](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.05685)\n* [LLM 评估指标：LLM 评估所需的一切——Confident AI](https:\u002F\u002Fwww.confident-ai.com\u002Fblog\u002Fllm-evaluation-metrics-everything-you-need-for-llm-evaluation)\n* [标准评估 | 🦜️🔗 LangChain](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fguides\u002Fproductionization\u002Fevaluation\u002Fstring\u002Fcriteria_eval_chain\u002F)\n* [LLM 评估——第 1 部分](https:\u002F\u002Fblog.premai.io\u002Fevaluation-of-llms-part-1\u002F)\n* [LLM 评估——第 2 部分](https:\u002F\u002Fblog.premai.io\u002Fevaluation-of-llms-part-2\u002F)\n* [模型评估在 LLM 和 AI 集成中的关键作用](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Fcrucial-role-model-evaluation-llm-ai-integrations-vijay-chaudhary)\n* [MLGroupJLU\u002FLLM-eval-survey：关于“大型语言模型评估综述”论文的官方 GitHub 页面](https:\u002F\u002Fgithub.com\u002FMLGroupJLU\u002FLLM-eval-survey)\n* [大型语言模型评估综述 | ACM 智能系统与技术汇刊](https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3641289)\n* [[2307.03109] 大型语言模型评估综述](https:\u002F\u002Farxiv.org\u002Fabs\u002F2307.03109)\n* [qcri\u002FLLMeBench](https:\u002F\u002Fgithub.com\u002Fqcri\u002FLLMeBench\u002F)：大型语言模型基准测试\n* [TruLens for LLMs](https:\u002F\u002Fwww.trulens.org\u002F)：评估和跟踪 LLM 应用程序\n* [LLM 测试指南](https:\u002F\u002Fgo.kolena.com\u002Fllm-testing-guide)：Kolena 提供的全面测试和行为分析策略\n* [Chatbot Arena](https:\u002F\u002Fchat.lmsys.org\u002F?arena)：通过成对对抗和评估来基准测试 LLM\n* [[2311.12022] GPQA：一项研究生级别的防谷歌问答基准测试](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12022)\n* [OpenAI 食谱：评估 RAG 系统 | 作者：Ravi Theja | 2023 年 11 月 | LlamaIndex 博客](https:\u002F\u002Fblog.llamaindex.ai\u002Fopenai-cookbook-evaluating-rag-systems-fe393c61fb93)\n* [亚马逊将提供人工基准测试团队来测试 AI 模型——The Verge](https:\u002F\u002Fwww.theverge.com\u002F2023\u002F11\u002F29\u002F23981129\u002Famazon-aws-ai-model-evaluation-bias-toxicity)\n* [[2311.05020] 先有悲剧，后有解析：大型语言模型新时代的历史重演](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.05020)：“基于实际使用情况的有效评估仍然是一个未解的问题”\n* [[2311.12983] GAIA：通用 AI 助手的基准测试](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.12983)\n* [分享 LangSmith 基准测试](https:\u002F\u002Fblog.langchain.dev\u002Fpublic-langsmith-benchmarks\u002F)\n* [[2311.09247] 比较人类、GPT-4 和 GPT-4V 在抽象与推理任务上的表现](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.09247)\n* [vectara\u002Fhallucination-leaderboard](https:\u002F\u002Fgithub.com\u002Fvectara\u002Fhallucination-leaderboard)：“比较 LLM 在总结短文档时产生幻觉性能的排行榜”\n* [[2305.16938] 少样本微调 vs. 上下文学习：公平的比较与评估](https:\u002F\u002Farxiv.org\u002Fabs\u002F2305.16938)\n* [LLM 比较\u002F测试](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F17fhp9k\u002Fhuge_llm_comparisontest_39_models_tested_7b70b\u002F)：共测试了 39 个模型（7B–70B + ChatGPT\u002FGPT-4）\n* [大规模 LLM 评估——Airtrain](https:\u002F\u002Fwww.airtrain.ai\u002F)：一款用于 LLM 评估和调优工作的无代码批量计算平台\n* [如何评估摘要任务 | OpenAI 食谱](https:\u002F\u002Fcookbook.openai.com\u002Fexamples\u002Fevaluation\u002Fhow_to_eval_abstractive_summarization)\n* [openai\u002Fevals](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fevals)：Evals 是一个用于评估 LLM 和 LLM 系统的框架，同时也是基准测试的开源注册表。\n* [红队演练与模型评估 | Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Fuk-government-internal-ai-safety-policy-response\u002Fred-teaming-and-model-evaluations)\n* [评估 AI 系统的挑战 | Anthropic](https:\u002F\u002Fwww.anthropic.com\u002Findex\u002Fevaluating-ai-systems)\n* [评估 LLM 就像走在雷区上](https:\u002F\u002Fwww.cs.princeton.edu\u002F~arvindn\u002Ftalks\u002Fevaluating_llms_minefield\u002F)：普林斯顿大学教授 Arvind Narayanan 的演讲\n* [Indico LLM 排行榜](https:\u002F\u002Findicodata.ai\u002Fllm)：Indico Data 每月都会对不同提供商（LLama、Azure OpenAI、Google、AWS Bedrock，以及 Indico 训练的判别型标准语言模型 RoBERTa 和 DeBERTa）、数据集（如 cord 和 CUAD）和能力（文本分类、关键信息提取和生成式摘要）进行基准测试。\n* [LLM 排名](https:\u002F\u002Fopenrouter.ai\u002Frankings)：一个针对所有提示比较 LLM 的排行榜\n* [LLM 使用场景排行榜](https:\u002F\u002Fllmleaderboard.goml.io)：一个展示 LLM 使用场景的排行榜\n* [LMExamQA](https:\u002F\u002Flmexam.com)：一个以“语言模型即考官”为基础对基础模型进行基准测试的排行榜\n* [The Pile](https:\u002F\u002Fpile.eleuther.ai)：The Pile 基准测试的排行榜。\n\n### LLMOps\n\n* [Lunary](https:\u002F\u002Flunary.ai)：开源的LLM聊天机器人和智能体平台，提供可观测性、提示词管理、测试等功能。\n* [Eden AI](https:\u002F\u002Fwww.edenai.co\u002F?referral=partner-producthunt8&ref=producthunt)：提供连接到AI引擎的独特API。\n* [Dify](https:\u002F\u002Fdify.ai\u002F)：基于GPT-4构建和运营原生AI应用的LLMOps平台。\n* [LLM App](https:\u002F\u002Fgithub.com\u002Fpathwaycom\u002Fllm-app)：LLM App是一个Python库，只需几行代码即可帮助你构建实时的AI驱动数据管道。\n\n### AI工程\n\n* [一位AI工程师的机器学习与生成式AI指南 | 作者：ai geek (wishesh) | 2023年10月 | Medium](https:\u002F\u002Fmedium.com\u002F@_aigeek\u002Fan-ai-engineers-guide-to-machine-learning-and-generative-ai-b7444941ccee)\n* [Keywords AI](https:\u002F\u002Fwww.keywordsai.co\u002F)：用于构建、监控和优化AI应用的企业级软件。Keywords AI是面向开发者和产品经理的全栈LLM工程平台。\n* [Marvin](https:\u002F\u002Fwww.askmarvin.ai\u002F)：用于构建自然语言界面的AI工程框架。\n* [Instructor](https:\u002F\u002Fjxnl.github.io\u002Finstructor\u002F)：一个用于在Python中进行结构化LLM提取的库。\n* [One AI](https:\u002F\u002Foneai.com\u002F)：一个NLP即服务平台。\n* [LangSmith](https:\u002F\u002Fwww.langchain.com\u002Flangsmith)：用于部署LLM应用的开发者平台。\n\n### 对LLM的攻击\n\n* [宪法分类器](https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.18837)：防御跨越数千小时红队演练的通用越狱攻击。\n* [briland\u002FLLM-security-and-privacy](https:\u002F\u002Fgithub.com\u002Fbriland\u002FLLM-security-and-privacy)：关于LLM安全与隐私的资源。\n* [ZombAIs](https:\u002F\u002Fembracethered.com\u002Fblog\u002Fposts\u002F2024\u002Fclaude-computer-use-c2-the-zombais-are-coming\u002F)：从提示注入到C2控制——利用Claude Computer Use实现。\n* [[2310.04451] AutoDAN：在对齐的大语言模型上生成隐蔽的越狱提示](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.04451)\n* [MITRE ATLAS™](https:\u002F\u002Fatlas.mitre.org\u002F)：基于真实世界攻击观察及AI红队和安全团队的模拟演示所构建的对手战术与技术知识库，其模式参考了MITRE ATT&CK®框架。\n* [OWASP大型语言模型应用十大风险](https:\u002F\u002Fllmtop10.com\u002F)：开放全球应用安全项目针对LLM的相关列表[[YouTube视频]](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=engR9tYSsug)  \n* [从（生产环境）语言模型中可扩展地提取训练数据](https:\u002F\u002Farxiv.org\u002Fabs\u002F2311.17035)：从ChatGPT中提取训练数据[[网页]](https:\u002F\u002Fnot-just-memorization.github.io\u002Fextracting-training-data-from-chatgpt.html)\n* [大型语言模型（LLMs）的新兴攻击](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Femerging-attacks-large-language-models-llms-soumak-roy\u002F)：“威胁行为者可以利用的关键攻击向量，以破坏或操纵LLMs”。\n* [LLMs的对抗性攻击 | Lil'Log](https:\u002F\u002Flilianweng.github.io\u002Fposts\u002F2023-10-25-adv-attack-llm\u002F)\n* [并非你所期望的：通过间接提示注入攻陷现实世界的LLM集成应用](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.12173)\n* [攻击大型语言模型](https:\u002F\u002Fsystemweakness.com\u002Fattacking-large-language-models-37229085d4ff)：Marcello Carboni对当前LLM攻击技术的概述。\n* [corca-ai\u002Fawesome-llm-security](https:\u002F\u002Fgithub.com\u002Fcorca-ai\u002Fawesome-llm-security)：关于LLM安全的优秀工具、文档和项目的精选合集。\n* [对抗性提示](https:\u002F\u002Fwww.promptingguide.ai\u002Frisks\u002Fadversarial)：由Prompt Engineering Guide整理的对抗性提示攻击列表。\n\n### LangChain\n\n* [LangChain速查表](https:\u002F\u002Fpub.towardsai.net\u002Flangchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde)：一页尽览所有秘籍 | 作者：Ivan Reznikov | 2023年11月 | Towards AI\n* [LangChain模板：研究助理](https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain\u002Ftree\u002Fmaster\u002Ftemplates\u002Fresearch-assistant)\n* [Embedchain](https:\u002F\u002Fgithub.com\u002Fembedchain\u002Fembedchain)：可在你的数据集上创建类似ChatGPT的聊天机器人框架。\n* [FlowiseAI](https:\u002F\u002Fflowiseai.com\u002F)：“开源的UI可视化工具，使用LangchainJS构建自定义LLM流程，基于Node Typescript\u002FJavascript开发”。\n* [LangChain用于论文摘要](https:\u002F\u002Flancemartin.notion.site\u002Flancemartin\u002FLangchain-for-paper-summarization-d4ad122ea9a64c0eb1f981e743d6c419)\n* [LangChain文档](https:\u002F\u002Flangchain.readthedocs.io\u002Fen\u002Flatest\u002F#)：一个通过组合性帮助构建LLM应用的Python库。\n* [LangChain入门指南 | 作者：Avra | 2023年2月 | Medium](https:\u002F\u002Fmedium.com\u002F@avra42\u002Fgetting-started-with-langchain-a-powerful-tool-for-working-with-large-language-models-286419ba0842)：一款强大的大型语言模型工作工具。\n\n### ChatGPT\n\n* [ChatGPT高级指南](https:\u002F\u002Faaditsh.notion.site\u002Faaditsh\u002FAdvanced-Guide-to-ChatGPT-b8d5901b8bba44f580bb0c0835644567)：由Neatprompts.com提供的指南。\n* [🔥] [104个增长黑客妙招（ChatGPT）](https:\u002F\u002Fdoc.clickup.com\u002F25598832\u002Fp\u002Fh\u002Frd6vg-11110\u002F502bfba03b21bad)：一套用于设计、产品和营销的ChatGPT提示词集合。\n* [acheong08的列表 \u002F Awesome ChatGPT](https:\u002F\u002Fgithub.com\u002Fstars\u002Facheong08\u002Flists\u002Fawesome-chatgpt)：包含用于在Discord、Telegram等平台以及Python、JS等语言中访问ChatGPT的封装库列表。\n* [🔥🔥🔥] [Awesome ChatGPT提示](https:\u002F\u002Fprompts.chat\u002F)：一个收录了精选ChatGPT提示的仓库，旨在帮助用户从ChatGPT获得更好的结果。\n* [(\"公开宣布的ChatGPT变体及竞争对手：一条推文\" \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Fgoodside\u002Fstatus\u002F1606611869661384706)：由[@goodside](https:\u002F\u002Ftwitter.com\u002Fgoodside)发布的关于ChatGPT替代方案的推文串列。\n\n### 文本相关生成工具\n\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric): fabric 是一个开源框架，旨在利用 AI 增强人类能力。它提供了一个模块化的框架，用于通过众包的 AI 提示集来解决特定问题，这些提示可以在任何地方使用。\n* [Jack AI](https:\u002F\u002Fwww.usejackai.com): AI 营销文案撰写工具\n* [aiPDF](https:\u002F\u002Faipdf.ai): 最先进的 AI 文档助手\n* [AICamp](https:\u002F\u002Faicamp.so\u002F): 面向团队的 ChatGPT\n* [Yomu](https:\u002F\u002Fwww.yomu.ai): 面向学生和学者的 AI 写作助手\n* [Google Sheets 公式生成器](https:\u002F\u002Fbettersheets.co\u002Fgoogle-sheets-formula-generator?ref=filipecalegario-awesome-generative-ai): 再也不用为 Google Sheets 中复杂的公式头疼了。\n* [Elephas](https:\u002F\u002Felephas.app\u002F?ref=filipecalegario-awesome-generative-ai): 适用于 Mac 的个人 AI 写作助手。\n* [Lemmy](https:\u002F\u002Flemmy.co\u002F?ref=filipecalegario-awesome-generative-ai): 自主工作的 AI 助理。\n* [Fable Fiesta](https:\u002F\u002Ffablefiesta.com): 创意 AI 写作助手\n* [Plus AI for Google Slides](https:\u002F\u002Fwww.plusdocs.com\u002Fplus-ai-for-google-slides): 在 Google Slides 中创建由 AI 驱动的演示文稿\n* [ChatBotKit](https:\u002F\u002Fchatbotkit.com\u002F): 构建 AI 聊天机器人的工具包\n* [Boring Report](https:\u002F\u002Fwww.boringreport.org\u002F): “一款利用 AI 消除新闻中的耸人听闻、让阅读变得无趣的应用”\n* [ChatPDF - 与任意 PDF 对话！](https:\u002F\u002Fwww.chatpdf.com\u002F): 上传 PDF 文件并对其提问 #语义搜索 \n* [Character.AI](https:\u002F\u002Fbeta.character.ai\u002F): 用于创建和对话先进 AI 角色的平台\n* [SlidesAI](https:\u002F\u002Fwww.slidesai.io\u002F): “几分钟内用 AI 创建演示文稿幻灯片”\n* [Rationale](https:\u002F\u002Frationale.jina.ai\u002F): 基于最新 GPT 和上下文学习的决策工具\n* [DetangleAI](https:\u002F\u002Fdetangle.ai): 根据提供的法律文件生成的 AI 摘要\n* [GPT-2 输出检测器](https:\u002F\u002Fhuggingface.co\u002Fopenai-detector): 用于判断给定文本是真实内容还是由 GPT 生成的工具\n* [HyperWrite](https:\u002F\u002Fhyperwriteai.com\u002F): 一款带有建议和句子补全功能的个人写作助手\n* [DeepStory](https:\u002F\u002Fwww.deepstory.ai\u002F#!\u002F): 人机共创的故事\n* [InferKit](https:\u002F\u002Fapp.inferkit.com\u002Fdemo)\n* [CopyHat](https:\u002F\u002Fcopyhat.com\u002F)\n* [Lucid Lyrics - AI 辅助艺术](https:\u002F\u002Fwww.lucidlyricsart.com\u002F): Walter Arnold 的 AI 辅助歌词解读\n* [Authors A.I.](https:\u002F\u002Fauthors.ai\u002F): 基于 AI 的文本分析\n* [Rytr](https:\u002F\u002Frytr.me\u002F): Rytr 是一款帮助创作内容的 AI 写作助手\n* [Charisma](https:\u002F\u002Fcharisma.ai\u002F): Charisma 是一个用于创建具有可信虚拟角色的互动故事的平台\n* [Riku.AI | 您的 AI 创作宝库](https:\u002F\u002Friku.ai\u002F) \n* [初探 - Riku.ai - 推理平台 2022 年 3 月 - J1, GPT-3, Fairseq-13B, GPT-NeoX-20B, Cohere-XL - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=t6FESjmPeJ8) \n* [Taskade](https:\u002F\u002Ftaskade.com\u002F): Taskade 是一款面向团队的 AI 大纲和思维导图生成器，内置 AI 聊天功能\n* [AI 故事生成器（高级选项）](https:\u002F\u002Faistorygenerator.chat\u002F): 使用自定义语气、类型和叙述方式，即时创建独特而引人入胜的故事。\n* [AI 故事生成器](https:\u002F\u002Fwww.aistorygenerator.org): 免费且快速的在线 AI 驱动故事生成器，可为您撰写短篇小说\n* [AI Story Generate](https:\u002F\u002Faistorygenerate.com): 使用 LLM 生成故事，可自定义情感、类型和字数。\n* [Composum AI](https:\u002F\u002Fgithub.com\u002Fist-dresden\u002Fcomposum-AI)：Adobe Experience Manager (AEM) 或 Composum Pages CMS 的插件，帮助编辑创建\u002F编辑\u002F翻译文本\n* [TextCraft](https:\u002F\u002Fgithub.com\u002Fsuncloudsmoon\u002FTextCraft)：Microsoft Word 的插件，可将文本生成、校对等核心 AI 工具无缝集成到用户界面中。\n\n## 研究类 AI 工具\n\n### 研究用 AI 工具\n\n* [Undermind - AI 驱动的科研助理](https:\u002F\u002Fundermind.ai\u002Fhome\u002F): 一款能够阅读学术论文的 AI 助手。\n* [Scite](https:\u002F\u002Fscite.ai\u002F): AI 助手或文献搜索引擎，改变您发现、评估和理解任何主题研究的方式。\n* [SciSummary](https:\u002F\u002Fscisummary.com\u002F): AI 可在几秒钟内总结科学文章和研究论文\n* [SciSpace](https:\u002F\u002Ftypeset.io\u002F): 科学 PDF 的 AI 聊天机器人\n* [Scholarcy](https:\u002F\u002Fwww.scholarcy.com\u002F): 总结、分析和整理您的研究\n* [Research Rabbit](https:\u002F\u002Fresearchrabbitapp.com\u002Fhome) \n* [Nested Knowledge](https:\u002F\u002Fnested-knowledge.com\u002F): 强大的证据综合工具，专为医学研究人员设计。加速、协作、自动化并共享研究成果。\n* [Litmaps](https:\u002F\u002Fwww.litmaps.com\u002F): 文献综述助手\n* [Keenious](https:\u002F\u002Fkeenious.com\u002F): 查找与任何文本相关的研究\n* [Inciteful](https:\u002F\u002Finciteful.xyz\u002F): 利用引用探索学术文献\n* [danielmiessler\u002Ffabric](https:\u002F\u002Fgithub.com\u002Fdanielmiessler\u002Ffabric): fabric 是一个开源框架，旨在利用 AI 增强人类能力。它提供了一个模块化的框架，用于通过众包的 AI 提示集来解决特定问题，这些提示可以在任何地方使用。\n* [AI 研究工具 | x 发布](https:\u002F\u002Fx.com\u002Fairesearchtools\u002Fstatus\u002F1704031145476648992): 一些可用于研究\u002F教学的 AI 工具\n* [借助 AI 解锁生产力并实现个性化学习 | Microsoft EDU](https:\u002F\u002Feducationblog.microsoft.com\u002Fen-us\u002F2024\u002F01\u002Funlocking-productivity-and-personalizing-learning-with-ai) \n* [Sourcely](https:\u002F\u002Fwww.sourcely.net\u002F): 带有 AI 的学术引用查找工具\n* [GummySearch](https:\u002F\u002Fgummysearch.com\u002F?ref=filipecalegario-awesome-generative-ai): 基于 AI 的客户调研工具，通过 Reddit 进行。发现待解决的问题、对现有解决方案的情绪，以及希望购买您产品的人群。\n* [[2310.17143] 用生成式 AI 加速学术写作：框架、技巧与注意事项](https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.17143) \n* [Elicit](https:\u002F\u002Felicit.org\u002F): 自动化文献综述的研究工作流程\n* [Paper Brain](https:\u002F\u002Fwww.paperbrain.study\u002F): 论文片段摘要工具。用户需要将内容复制并粘贴到其界面中。\n* [Explainpaper](https:\u002F\u002Fwww.explainpaper.com\u002F): “上传论文，高亮显示困惑的文本，获取解释”\n* [Paper Player](https:\u002F\u002Fpaperplayerapp.com\u002F): 忙碌的科学家和技术人员消费开放科学的新方式\n* [TalkToPapers - namuan\u002Fdr-doc-search: 与书籍对话 - 基于 GPT-3 打造](https:\u002F\u002Fgithub.com\u002Fnamuan\u002Fdr-doc-search): 一个 GitHub 工具，可以让 AI 代替您阅读论文。\n* [hwaseem04\u002FResearch-digest](https:\u002F\u002Fgithub.com\u002Fhwaseem04\u002FResearch-digest): 我们黑客马拉松的论文摘要应用\n\n### 用于搜索的AI工具\n\n* [whitead\u002Fpaper-qa](https:\u002F\u002Fgithub.com\u002Fwhitead\u002Fpaper-qa)：用于根据带有引用的文档回答问题的“大模型链”\n* [Metaphor](https:\u002F\u002Fmetaphor.systems\u002F)：一款能够“理解语言——以提示的形式——因此你可以用各种富有表现力和创造性的方式表达你想要寻找的内容”的搜索引擎\n* [MemFree](https:\u002F\u002Fgithub.com\u002Fmemfreeme\u002Fmemfree) - 开源混合型AI搜索引擎，可从互联网、书签、笔记和文档中即时获取准确答案。支持一键部署。\n\n# 图片\n\n## 图像合成\n\n* [TokenVerse](https:\u002F\u002Ftoken-verse.github.io\u002F): Token调制空间中的多功能多概念个性化\n* [FLUX.1模型家族 – Replicate](https:\u002F\u002Freplicate.com\u002Fcollections\u002Fflux) \n* [ToTheBeginning\u002FPuLID: PuLID官方代码：通过对比对齐实现纯净且快速的身份定制](https:\u002F\u002Fgithub.com\u002FToTheBeginning\u002FPuLID)\n* [编辑你的图像](https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fysharma\u002Fedit-your-image-662be093bf97b697957c3c3f): 找到所有热门且实用的Gradio演示，可用于编辑你的图像\n* [OutfitAnyone - HumanAIGC的Hugging Face Space](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FHumanAIGC\u002FOutfitAnyone): 超高品质的任意服装与任意人物虚拟试穿\n* [StockPhotoAI.net](https:\u002F\u002Fwww.stockphotoai.net\u002F?ref=filipecalegario-awesome-generative-ai): 专为你打造的优质图库\n* [使用AdaMPI AI模型将2D图像转换为3D](https:\u002F\u002Fnotes.aimodels.fyi\u002Ftransforming-2d-images-into-3d-with-the-adampi-ai-model\u002F): 关于如何使用AdaMPI AI模型从2D图像生成3D照片的指南\n* [deep-floyd\u002FIF](https:\u002F\u002Fgithub.com\u002Fdeep-floyd\u002FIF): Stability.AI推出的高逼真度和语言理解能力的开源文生图模型\n* [语义排版中的文字即图像](https:\u002F\u002Fwordasimage.github.io\u002FWord-As-Image-Page\u002F): 将字体语义化地转化为插图\n* [Scribble Diffusion](https:\u002F\u002Fscribblediffusion.com\u002F): 使用AI将你的草图转化为精致图像\n* [Muse: 基于掩码生成式Transformer的文生图](https:\u002F\u002Fmuse-model.github.io\u002F)\n* [openai\u002Fpoint-e](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fpoint-e): OpenAI的点云扩散模型，用于3D模型合成\n* [[arxiv\u002F2211.11319] VectorFusion](https:\u002F\u002Farxiv.org\u002Fabs\u002F2211.11319): 通过抽象像素基扩散模型实现文生SVG\n* [鹦鹉区](https:\u002F\u002Fproximacentaurib.notion.site\u002Fproximacentaurib\u002Fparrot-zone-74a5c04d4feb4f12b52a41fc8750b205): 图像合成参考数据库\n* [图像合成链接列表](https:\u002F\u002Fproximacentaurib.notion.site\u002F39805c50735849cfa54b5d688587e12e?v=b9ea748623e342fdae02d07c86c668bf): 由鹦鹉区集体整理的链接集合\n* [🔥🔥🔥] [生成式艺术工具](https:\u002F\u002Fpharmapsychotic.com\u002Ftools.html): 由[@pharampsychotic](https:\u002F\u002Ftwitter.com\u002Fpharmapsychotic)整理的海量共享Google Colab笔记本及工具列表\n* [简介 — PyTTI-Tools](https:\u002F\u002Fpytti-tools.github.io\u002Fpytti-book\u002Fintro.html)\n* [pyttitools-PYTTI.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fpytti-tools\u002Fpytti-notebook\u002Fblob\u002Fmain\u002Fpyttitools-PYTTI.ipynb) \n* [pixray\u002Fpixray](https:\u002F\u002Fgithub.com\u002Fpixray\u002Fpixray): Pixray是一个图像生成系统\n* [pixray\u002Fpixray_notebooks](https:\u002F\u002Fgithub.com\u002Fpixray\u002Fpixray_notebooks): Pixray演示笔记本\n* [dribnet\u002Fpixray-text2image – 在Replicate上通过API运行](https:\u002F\u002Freplicate.com\u002Fdribnet\u002Fpixray-text2image) \n* [sberbank-ai\u002Fru-dalle](https:\u002F\u002Fgithub.com\u002Fsberbank-ai\u002Fru-dalle): 用俄语根据文本生成图像。\n* [Pyttipanna](https:\u002F\u002Fpyttipanna.xyz\u002F): 由[@_staus](https:\u002F\u002Ftwitter.com\u002F_staus)开发的Pytti可视化界面。Pytti由[@sportsracer48](https:\u002F\u002Ftwitter.com\u002Fsportsracer48)创建。\n* [Imagen](https:\u002F\u002Fimagen.research.google\u002F): Google的文生图扩散模型\n* [Make-A-Scene](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Fgreater-creative-control-for-ai-image-generation\u002F): Meta为AI图像生成提供的创意控制功能\n* [Stable Diffusion](https:\u002F\u002Fstability.ai\u002Fblog\u002Fstable-diffusion-announcement): Stability.Ai推出的文生图模型，在速度和质量上实现了突破，能够在消费级GPU上运行\n* [CLIPasso](https:\u002F\u002Fclipasso.github.io\u002Fclipasso\u002F): 语义感知的对象草图绘制\n* [DreamFusion \u002F Twitter](https:\u002F\u002Ftwitter.com\u002F_akhaliq\u002Fstatus\u002F1575541930905243652?t=m17X6zyC0c8-VvIWjICc1w&s=33): 利用2D扩散论文实现文生3D\n* [apple\u002Fml-no-token-left-behind](https:\u002F\u002Fgithub.com\u002Fapple\u002Fml-no-token-left-behind): “不留任何标记”方法的PyTorch实现：基于可解释性的图像分类与生成\n* [disco-diffusion\u002FLocal_Disco_Diffusion_v4_1.ipynb at main · Midgraph\u002Fdisco-diffusion](https:\u002F\u002Fgithub.com\u002FMidgraph\u002Fdisco-diffusion\u002Fblob\u002Fmain\u002FLocal_Disco_Diffusion_v4_1.ipynb)\n* [音频转关键帧字符串](https:\u002F\u002Faudio-keyframe-generator.glitch.me\u002F): 此工具用于根据音频音量生成AI动画笔记本中的关键帧字符串，例如[这个VQGAN+CLIP动画笔记本](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fchigozienri\u002FVQGAN-CLIP-animations\u002Fblob\u002Fmain\u002FVQGAN-CLIP-animations.ipynb)。\n* [🔥] [S2ML图像生成器](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fjustin-bennington\u002FS2ML-Generators\u002Fblob\u002Fmain\u002FS2ML_Image_Generator.ipynb): 由Justin Bennington维护的Katherine Crownson首个VQGAN+CLIP Google Colab笔记本的演进版本\n* [🔥] [使用Looking Glass 1.1 (ru-DALLE)为图像创建变体 - YouTube | Artificial Images](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=37_Zjreghw4)\n* [🔥] [Looking Glass 1.1 (ru-DALLE)](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F11vdS9dpcZz2Q2efkOjcwyax4oob6N40G): 让ruDALL-E微调变得快速而轻松。版权（C）2021 Bearsharktopus Studios\n* [NÜWA: 面向神经视觉世界创造的视觉合成预训练（ML研究论文解读） - YouTube | Yannic Kilcher](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=InhMx1h0N40&t=603s) \n* [🔥] [yuval-alaluf\u002Fhyperstyle](https:\u002F\u002Fgithub.com\u002Fyuval-alaluf\u002Fhyperstyle): “HyperStyle: 基于超网络的StyleGAN反演用于真实图像编辑”的官方实现 https:\u002F\u002Farxiv.org\u002Fabs\u002F2111.15666\n* [🔥] [Vadim Epstein的Aphantasia库](https:\u002F\u002Fgithub.com\u002Feps696\u002Faphantasia): CLIP + FFT\u002FDWT\u002FRGB = 文本转图像\u002F视频\n* [mikaelalafriz\u002Flucid-sonic-dreams](https:\u002F\u002Fgithub.com\u002Fmikaelalafriz\u002Flucid-sonic-dreams): 将GAN生成的视觉效果与音乐同步\n* [Greg Surma - 作品集](https:\u002F\u002Fgsurma.github.io\u002F) \n* [crowsonkb (Katherine Crowson)](https:\u002F\u002Fgithub.com\u002Fcrowsonkb): 她撰写了[VQGAN+CLIP教程](https:\u002F\u002Fsourceful.us\u002Fdoc\u002F935\u002Fintroduction-to-vqganclip)\n* [DALL·E](https:\u002F\u002Fopenai.com\u002Fblog\u002Fdall-e\u002F): 根据文本创建图像\n* [DALL-E mini](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fflax-community\u002Fdalle-mini): DALL·E mini是一款能够根据你提供的任何提示生成图像的AI模型！\n* [DALL-E mini GitHub](https:\u002F\u002Fgithub.com\u002Fborisdayma\u002Fdalle-mini)\n* [DALL-E mini项目报告](https:\u002F\u002Fwandb.ai\u002Fdalle-mini\u002Fdalle-mini\u002Freports\u002FDALL-E-mini--Vmlldzo4NjIxODA)\n* [CLIPIT PixelDraw - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdribnet\u002Fclipit\u002Fblob\u002Fmaster\u002Fdemos\u002FPixelDrawer.ipynb) \n* [CLIP引导扩散HQ 512x512.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1V66mUeJbXrTuQITvJunvnWVn96FEbSI3#scrollTo=X5gODNAMEUCR) \n* [通过关键帧参数平滑过渡位置\u002F旋转\u002F缩放与文本输入：一个包含15,000帧的概念验证：deepdream](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Fdeepdream\u002Fcomments\u002Fpagqjx\u002Fsmooth_transitioning_between_position_rotation\u002F) \n* [neural-dream的替代品及类似的照片和图形应用 | AlternativeTo](https:\u002F\u002Falternativeto.net\u002Fsoftware\u002Fneural-dream\u002F) \n* [CoG 21](https:\u002F\u002Fwww.ea.com\u002Fseed\u002Fnews\u002Fcog2021-adversarial-rl-content-generation): 针对程序化内容生成的对抗性强化学习\n* [Hugging Face的GitHub仓库](https:\u002F\u002Fgithub.com\u002Fhuggingface)\n\n### 收件箱：Stable Diffusion\n\n* [Stable Diffusion 中采样器的完整指南 - Félix Sanz](https:\u002F\u002Fwww.felixsanz.dev\u002Farticles\u002Fcomplete-guide-to-samplers-in-stable-diffusion)\n* [Stable Diffusion 模型](https:\u002F\u002Frentry.org\u002Fsdmodels)：自定义 Stable Diffusion 模型列表\n* [Stable Diffusion KLMC2 动画.ipynb 分支](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdmarx\u002Fnotebooks\u002Fblob\u002Fmain\u002FStable_Diffusion_KLMC2_Animation.ipynb)：由 [@DigThatData](https:\u002F\u002Ftwitter.com\u002FDigThatData) 分支\n* [Stable Diffusion KLMC2 动画.ipynb](https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1m8ovBpO2QilE2o4O-p2PONSwqGn4_x2G)：由 [@RiversHaveWings](https:\u002F\u002Ftwitter.com\u002FRiversHaveWings) 编写的笔记本，使用称为 KLMC2 的欠阻尼朗之万动力学离散化技术，基于脚本化的提示生成动画\n* [DETEXTIFY](https:\u002F\u002Fgithub.com\u002Fiuliaturc\u002Fdetextify)：一个 Python 库，用于从您最喜欢的生成式 AI 模型（Stable Diffusion、Midjourney、DALL·E）生成的图像中移除不需要的伪文本\n* [InvokeAI](https:\u002F\u002Finvoke-ai.github.io\u002FInvokeAI\u002F)：可在 Windows、Mac 和 Linux 机器上运行的 Stable Diffusion 工具包及应用程序，并且仅需 4 GB 或更少的显存即可在 GPU 上运行\n* [Stability.ai REST API 文档](https:\u002F\u002Fapi.stability.ai\u002Fdocs)：由 Stability.ai 提供的服务。访问此 REST API 需要 DreamStudio 身份验证\n* [🔥🔥🔥] [面向艺术家与非艺术家的 Stable Diffusion 使用指南 - Google Docs](https:\u002F\u002Fdocs.google.com\u002Fdocument\u002Fd\u002F1R2UZi5G-DXiz2HcCrfAFLYJoer_JPDEoZmV7wy1tEz0\u002Fedit#)：一份包含深入技巧、窍门、教程等内容的 Google Docs，专门针对 Stable Diffusion\n* [新闻][Canva 添加免费且无限制的 AI 文本转图像生成器 | PetaPixel](https:\u002F\u002Fpetapixel.com\u002F2022\u002F11\u002F10\u002Fcanva-adds-a-free-and-unlimited-ai-text-to-image-generator\u002F)\n* [prompthero\u002Fmidjourney-v4-diffusion · Hugging Face](https:\u002F\u002Fhuggingface.co\u002Fprompthero\u002Fmidjourney-v4-diffusion)：由 [PromptHero](https:\u002F\u002Fprompthero.com\u002F) 在 Midjourney v4 图像上微调的 Stable Diffusion\n* [CHARL-E](https:\u002F\u002Fwww.charl-e.com\u002F)：在您的 M1 Mac 上运行 Stable Diffusion\n* [图解 Stable Diffusion](https:\u002F\u002Fjalammar.github.io\u002Fillustrated-stable-diffusion\u002F)：由 Jay Alammar 解释（每次讲解一个机器学习概念）\n* [Img To Music](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffffiloni\u002Fimg-to-music)：由 fffiloni 创建的 Hugging Face 空间\n* [Atlas KREA Stable Diffusion](https:\u002F\u002Fatlas.nomic.ai\u002Fmap\u002F809ef16a-5b2d-4291-b772-a913f4c8ee61\u002F9ed7d171-650b-4526-85bf-3592ee51ea31)：KREA AI 的 Stable Diffusion 搜索引擎可探索地图\n* [TheLastBen\u002Ffast-stable-diffusion](https:\u002F\u002Fgithub.com\u002FTheLastBen\u002Ffast-stable-diffusion)：快速 Stable Diffusion，速度提升 25–50%，内存效率更高，并支持 DreamBooth\n* [NovelAI 对 Stable Diffusion 的改进 | by NovelAI | 2022 年 10 月 | Medium](https:\u002F\u002Fblog.novelai.net\u002Fnovelai-improvements-on-stable-diffusion-e10d38db82ac)\n* [ashawkey\u002Fstable-dreamfusion](https:\u002F\u002Fgithub.com\u002Fashawkey\u002Fstable-dreamfusion)：基于 Stable Diffusion 的文本到 3D 梦境融合的 PyTorch 实现\n* [🔥🔥🔥] [JoePenna\u002FDreambooth-Stable-Diffusion](https:\u002F\u002Fgithub.com\u002FJoePenna\u002FDreambooth-Stable-Diffusion)：实现 Dreambooth（https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.12242）与 Stable Diffusion 结合（重点优化人脸训练）\n* [🔥🔥🔥] [DreamBooth](https:\u002F\u002Fdreambooth.github.io\u002F)：对文本到图像扩散模型进行微调，以实现主题驱动的生成\n* [🔥] [Arki 的 Stable Diffusion 指南](https:\u002F\u002Fstablediffusionguides.carrd.co\u002F#one)\n* [examples\u002Fstable-diffusion-finetuning at main · LambdaLabsML\u002Fexamples](https:\u002F\u002Fgithub.com\u002FLambdaLabsML\u002Fexamples\u002Ftree\u002Fmain\u002Fstable-diffusion-finetuning)：Stable Diffusion 微调\n* [lkwq007\u002Fstablediffusion-infinity](https:\u002F\u002Fgithub.com\u002Flkwq007\u002Fstablediffusion-infinity)：在无限画布上使用 Stable Diffusion 进行扩展绘画\n* [🔥🔥🔥] [ML News Stable Diffusion 占据主导地位！（开源 AI 艺术）由 Yannic Kilcher - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xbxe-x6wvRw)：一段包含示例、最新动态以及关于 Stable Diffusion 影响讨论的视频\n* [视觉领域的扩散模型综述 | DeepAI](https:\u002F\u002Fdeepai.org\u002Fpublication\u002Fdiffusion-models-in-vision-a-survey)：一篇关于扩散技术的论文，同时也探讨了其与其他生成式深度学习模型的关系\n* [ThereforeGames\u002Ftxt2mask](https:\u002F\u002Fgithub.com\u002FThereforeGames\u002Ftxt2mask)：利用自然语言自动为 Stable Diffusion 的修复功能创建掩码\n* [basujindal\u002Fstable-diffusion](https:\u002F\u002Fgithub.com\u002Fbasujindal\u002Fstable-diffusion)：优化版 Stable Diffusion，修改后可在较低显存的 GPU 上运行\n* [Stable WarpFusion v0.5（仅限赞助者）](https:\u002F\u002Fwww.patreon.com\u002Fsxela)：由 [@devdef](https:\u002F\u002Ftwitter.com\u002Fdevdef) 使用 Stable Diffusion 对视频帧进行条件化处理\n* [nateraw\u002Fstable-diffusion-videos](https:\u002F\u002Fgithub.com\u002Fnateraw\u002Fstable-diffusion-videos)：通过探索潜在空间并在不同文本提示之间进行变形，使用 Stable Diffusion 制作视频\n\n#### 部署了 Stable Diffusion 的 Web 工具\n\n* [DecorAI](https:\u002F\u002Fdecorai.io)：几秒钟内生成室内和室外设计方案\n* [dreamlike.art](https:\u002F\u002Fdreamlike.art\u002F)：基于Stable Diffusion的图像生成器，配备如Dreamlike Photoreal 2.0等微调模型。用户每小时可获得1个积分，上限为50个积分。\n* [AITWO.CO](https:\u002F\u002Faitwo.co\u002F)：一款功能丰富的AI驱动设计平台。\n* [aiimagegenerator.org](https:\u002F\u002Fwww.aiimagegenerator.org\u002F)：免费的AI艺术生成工具，支持Stable Diffusion的txt2img和img2img生成、绘图及修复上色功能。\n* [InteriorAIDesigns](https:\u002F\u002Finterioraidesigns.com\u002F)：一个可轻松重新设计房间的平台。\n* [Playground AI](https:\u002F\u002Fplaygroundai.com\u002F)：Stable Diffusion的前端界面，每日可生成1000张图片。\n* [Astria](https:\u002F\u002Fwww.astria.ai\u002F)：定制化的AI图像生成服务。\n* [drawanyone](https:\u002F\u002Fdrawanyone.com\u002F)：基于五张输入图片生成绘画作品。\n* [DiffusionBee](https:\u002F\u002Fdiffusionbee.com\u002F)：Stable Diffusion的图形化用户界面应用。\n* [getimg.ai](https:\u002F\u002Fgetimg.ai\u002F)：使用Stable Diffusion根据文本生成照片级逼真图像。\n* [Enstil：快速、开源的AI生成图像](https:\u002F\u002Fenstil.ai\u002F?source=12)\n* [Dezgo - 文本到图像的AI生成器](https:\u002F\u002Fdezgo.com\u002F)\n* [PhotoAIStudio](https:\u002F\u002Fphotoaistudio.com\u002F)：一款支持多种风格的AI驱动摄影平台。\n* [Baseten](https:\u002F\u002Fapp.baseten.co\u002Fapps\u002FVqK2vYP\u002Foperator_views\u002Fpqvba2q)：Stable Diffusion演示。\n* [DreamStudio](https:\u002F\u002Fbeta.dreamstudio.ai\u002F)：由Stability.ai提供的Stable Diffusion API前端。\n* [Pollinations - pollinations\u002Fstable-diffusion-private](https:\u002F\u002Fpollinations.ai\u002Fcreate\u002Fstablediffusion)\n* [tencentarc\u002Fgfpgan – 在Replicate上通过API运行](https:\u002F\u002Freplicate.com\u002Ftencentarc\u002Fgfpgan)\n* [andreasjansson\u002Fstable-diffusion-wip – 在Replicate上通过API运行](https:\u002F\u002Freplicate.com\u002Fandreasjansson\u002Fstable-diffusion-wip)\n* [stability-ai\u002Fstable-diffusion – 在Replicate上通过API运行](https:\u002F\u002Freplicate.com\u002Fstability-ai\u002Fstable-diffusion)\n* [Osmosis.Studio](http:\u002F\u002Fosmosis.studio\u002F)：基于Web的内容感知协作设计工具，用于生成能够销售真实产品的AI广告。\n* [Artistic.wtf](https:\u002F\u002Fwww.artistic.wtf\u002F)：Stable Diffusion的图形化用户界面应用。\n* [Prodia](https:\u002F\u002Fapp.prodia.com\u002F#\u002Fart-ai)：基于Stable Diffusion的艺术生成器，无需注册即可使用。\n* [ComicsMaker.ai](https:\u002F\u002Fwww.comicsmaker.ai)：基于Stable Diffusion的漫画书生成器，支持text2img、img2img、修复上色及ControlNet等功能。\n* [POTO.AI](https:\u002F\u002Fpoto.ai\u002F)：将Stable Diffusion模型微调为AI摄影师，用于生成头像、人像及情侣婚纱照。\n\n#### 通过Google Colab运行Stable Diffusion的Web UI\n\n* [camenduru\u002Fstable-diffusion-webui-colab](https:\u002F\u002Fgithub.com\u002Fcamenduru\u002Fstable-diffusion-webui-colab)：包含多个针对不同检查点的Stable Diffusion WebUI Colab集合。\n* [StableDiffusion_WebUI_Simplified.ipynb](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffilipecalegario\u002Fawesome-generative-deep-art\u002Fblob\u002Fmain\u002FStableDiffusion_WebUI_Simplified.ipynb)：葡萄牙语版本的Notebook，可在Google Colab上免费运行Stable Diffusion的Web UI。\n* [GitHub - AUTOMATIC1111\u002Fstable-diffusion-webui: Stable Diffusion web UI](https:\u002F\u002Fgithub.com\u002FAUTOMATIC1111\u002Fstable-diffusion-webui)：扩展版的Stable Diffusion Web UI。\n* [GitHub - sd-webui\u002Fstable-diffusion-webui](https:\u002F\u002Fgithub.com\u002Fhlky\u002Fstable-diffusion-webui)：Stable Diffusion的Web UI。\n* [Stable_Diffusion_WebUi_Simplified.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fpinilpypinilpy\u002Fsd-webui-colab-simplified\u002Fblob\u002Fmain\u002FStable_Diffusion_WebUi_Simplified.ipynb#scrollTo=gk1TyBA0Arxt)\n\n#### 关于Stable Diffusion的参考资料汇总\n\n* [GitHub - awesome-stable-diffusion\u002Fawesome-stable-diffusion](https:\u002F\u002Fgithub.com\u002Fawesome-stable-diffusion\u002Fawesome-stable-diffusion)：Stable Diffusion AI模型相关资源的精选列表。\n* [Reddit上u\u002FImeniSottoITreni发布的Stable Diffusion综合更新](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxcclmf\u002Fcan_we_please_make_a_general_update_on_all_the\u002F)：关于所有“最重要”新闻和代码库的综合更新。\n* [Reddit上的Stable Diffusion系统列表](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fwqaizj\u002Flist_of_stable_diffusion_systems\u002F)\n* [Stable Diffusion阿卡西记录 | Maks-s\u002Fsd-akashic](https:\u002F\u002Fgithub.com\u002FMaks-s\u002Fsd-akashic)：关于Stable Diffusion（SD）的信息汇编。\n* [multimodal.art的Stable Diffusion一周回顾](https:\u002F\u002Fmultimodal.art\u002Fnews\u002F1-week-of-stable-diffusion)\n* [Voldy指南](https:\u002F\u002Frentry.co\u002Fvoldy)：详细的Stable Diffusion入门指南。\n* [Reddit上的Stable Diffusion新手指南！](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxcq819\u002Fdreamers_guide_to_getting_started_w_stable\u002F)\n* [Reddit上使用Stable Diffusion的网站集合及其他实用链接](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fwzj8kk\u002Fa_collection_of_sites_using_stable_diffusion_and\u002F)\n\n### 超技术\n\n* [Prompt+](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.09522): 文本到图像生成中的扩展文本条件控制 [[非官方仓库]](https:\u002F\u002Fgithub.com\u002Fcloneofsimo\u002Fpromptplusplus) [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.09522) [[页面]](https:\u002F\u002Fprompt-plus.github.io\u002F) \n\n#### ControlNet\n\n* [ControlNet入门指南：线条检测与图像变换](https:\u002F\u002Fnotes.aimodels.fyi\u002Fa-dive-into-line-detection-image-transformation-and-much-more-with\u002F) \n* [Scribble Diffusion](https:\u002F\u002Fscribblediffusion.com\u002F): 使用AI将你的草图转化为精致的图像（基于ControlNet）\n\n#### 文本反转\n\n* [rinongal\u002Ftextual_inversion](https:\u002F\u002Fgithub.com\u002Frinongal\u002Ftextual_inversion): 该仓库包含文本反转论文的官方代码、数据及示例反转模型\n* [2208.01618 一张图胜过千言万语：利用文本反转个性化文本到图像生成](https:\u002F\u002Farxiv.org\u002Fabs\u002F2208.01618): 描述文本反转技术的论文\n* [sd-concepts-library (Stable Diffusion概念库)](https:\u002F\u002Fhuggingface.co\u002Fsd-concepts-library): Stable Diffusion文本反转概念库——浏览社区教授给Stable Diffusion的对象和风格，并在你的提示词中使用它们！\n\n#### DreamBooth\n\n* [AI个人头像](https:\u002F\u002Fwww.aiprofilepictures.com\u002F): 使用AI生成个人头像的付费服务\n* [使用Diffusers通过Dreambooth训练Stable Diffusion](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fdreambooth): 分析Dreambooth中不同设置效果的实验\n* [fast-DreamBooth.ipynb - Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002FTheLastBen\u002Ffast-stable-diffusion\u002Fblob\u002Fmain\u002Ffast-DreamBooth.ipynb): 使用这个简化的DreamBooth Colab，从输入图像中训练自定义概念\n* [(1166) 如何用Dreambooth轻松免费地用自己的脸创作出惊艳的艺术作品！ - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3e4jwgqy-0A): 一段葡萄牙语教程，教你如何用自己的脸训练Dreambooth\n\n#### Deforum\n\n* [🔥🔥🔥] [Parseq](https:\u002F\u002Fsd-parseq.web.app): Stable Diffusion的参数序列器 [[YouTube教程]](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLXbx1PHKHwIHsYFfb5lq2wS8g1FKz6aP8)\n* [deforum-art\u002Fsd-webui-deforum](https:\u002F\u002Fgithub.com\u002Fdeforum-art\u002Fsd-webui-deforum): AUTOMATIC1111的Stable Diffusion webui的Deforum扩展 [[wiki文档]](https:\u002F\u002Fgithub.com\u002Fdeforum-art\u002Fsd-webui-deforum\u002Fwiki)\n* [Deforum Stable Diffusion动画 - v5数学函数 - 演示与测试 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=6snk7gw898g)\n* [Deforum Stable Diffusion](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fdeforum\u002Fstable-diffusion\u002Fblob\u002Fmain\u002FDeforum_Stable_Diffusion.ipynb#scrollTo=63UOJvU3xdPS): 从脚本化提示词生成视频\n* [(5) Stable Diffusion动画的Deforum笔记本v0.5已发布！现在支持数学自动化、透视翻转、提示权重、视频遮罩和美少女角色！ : StableDiffusion](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxuytx5\u002Fdeforum_notebook_v05_for_stable_diffusion\u002F)\n\n### 生成式AI图像合成工具的创意应用\n\n* [修复历史照片 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgbug2\u002Fdepainting_historical_photographs\u002F) \n* [使用img2img进行手部动画 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fx92itm\u002Fproof_of_concept_using_img2img_ebsynth_to_animate\u002F)\n* [VID 2 VID用户脚本 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxgo87s\u002Fwip_vid_2_vid_user_script\u002F)\n* [Antonio Freyre为Blender开发的无缝纹理AI生成器 | Twitter](https:\u002F\u002Ftwitter.com\u002Fmerlino_games\u002Fstatus\u002F1571205845819559936)\n* [\"破碎\" by Ronny Khalil | Twitter](https:\u002F\u002Ftwitter.com\u002Fronnykhalil\u002Fstatus\u002F1569956085905203200): 使用warp融合生成破碎玻璃效果\n* [Acid Dance by aiplague | Twitter](https:\u002F\u002Ftwitter.com\u002Faiplague\u002Fstatus\u002F1564989456318451714) \n* [由[@remi_molettee](https:\u002F\u002Ftwitter.com\u002Fremi_molettee)制作的融合视频](https:\u002F\u002Ftwitter.com\u002Fremi_molettee\u002Fstatus\u002F1568245586494738432)\n* [Dall-e + AE动画 | Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FMediaSynthesis\u002Fcomments\u002Fxgeehe\u002Fanimation_with_dalle_ae_patent_drawing_of_an\u002F): 一张电子设备的专利图纸……\n* [你描述，AI帮你修图换脸【StyleCLIP】 - YouTube](https:\u002F\u002Fyoutu.be\u002Fd1OET63Ulwc)\n* [实验电影+机器学习 第7周 第1部分（使用OpenAI CLIP处理失忆症） - YouTube](https:\u002F\u002Fyoutu.be\u002F-FrIui8Mp-8)\n* [GitHub - Sanster\u002Flama-cleaner](https:\u002F\u002Fgithub.com\u002FSanster\u002Flama-cleaner): 基于最先进AI模型的图像修复工具\n* [AgaMiko\u002Fpixel_character_generator](https:\u002F\u002Fgithub.com\u002FAgaMiko\u002Fpixel_character_generator): 使用生成对抗网络生成复古像素游戏角色。包含“TinyHero”数据集。\n* [Wilco Sierra](https:\u002F\u002Ftrywilco.com\u002Fsierra): 一个利用GPT为软件工程师生成工程挑战的平台。\n\n## 图像超分辨率\n\n* [Leonardo AI Upscaler](https:\u002F\u002Fleonadoai.com\u002Fupscaler\u002F): 免费图像超分辨率工具\n* [Remini - AI照片增强器](https:\u002F\u002Fremini.ai\u002F): 照片和视频增强工具\n* [AI图像超分辨率 - 免费放大并增强你的照片 - Upscale.media](https:\u002F\u002Fwww.upscale.media\u002F): 简单的免费图像超分辨率替代方案\n* [Topaz Labs: AI图像质量软件]([https:\u002F\u002Fwww.topazlabs.com\u002F](https:\u002F\u002Fflight.beehiiv.net\u002Fv2\u002Fclicks\u002FeyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1cmwiOiJodHRwczovL3RvcGF6bGFicy5jb20vcmVmLzIwODIvP3V0bV9zb3VyY2U9bmVqY3N1c2VjLmJlZWhpaXYuY29tJnV0bV9tZWRpdW09cmVmZXJyYWxlbWFsbGVyJnV0bV9jYW1wbmFnZT13aGl5LXlvdS1zaG93dWxkLXVwc2NhbGUtdW91ci1pbWFnZXMiLCJwb3N0X2lkIjoiZWI2OWY3OTItMTNmZC00ZmViLWFjZTYtYWQ5M2YyM2Y2ZDRmIiwicHVibGljYXRpb25faWQiOiI2NDU2OWQyOC1jYzhjLTQ1N2YtOGZlNy03Y2JiYjdiOWExZWEiLCJ2aXNpdF90b2tlbiI6ImE3YjE1NzNmLTljNzMtNDFlNy1hNzUyLWQ3ODQ2NWQ3ZWQ4OCIsImlhdCI6MTY4ODM5Nzg2NS44NzksImlzcyI6Im9yY2hpZCJ9.oISexuNHzvMdv2CGWolS6doN8qRFGTjuICBq8z908Yc)): “专业级工作流程，功能丰富”（此为nejcsusec.beehiiv.com的联盟链接）。\n* [AI图像超分辨率 - 批量免费放大照片、卡通图片](https:\u002F\u002Fwww.imgupscaler.com\u002F): “免费、基于浏览器，每天五次机会”，出自nejcsusec.beehiiv.com的引用\n* [为什么你应该对图像进行超分辨率处理](https:\u002F\u002Fnejcsusec.beehiiv.com\u002Fp\u002Fupscale-images): 对比不同的工具\n* [模型数据库 - Upscale Wiki](https:\u002F\u002Fupscale.wiki\u002Fwiki\u002FModel_Database): 图像超分辨率模型列表\n* [Gigapixel AI](https:\u002F\u002Fwww.topazlabs.com\u002Fgigapixel-ai): 收费的AI图像超分辨率工具，可提供更精细的细节和更高的分辨率\n* [Image Super-Resolution](https:\u002F\u002Fidealo.github.io\u002Fimage-super-resolution\u002F) \n* [使用SD Upscale将图像放大至巨大尺寸并添加细节 : StableDiffusion](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FStableDiffusion\u002Fcomments\u002Fxkjjf9\u002Fupscale_to_huge_sizes_and_add_detail_with_sd\u002F): Reddit上的教程\n\n## 图像修复\n\n* [sczhou\u002Fcodeformer](https:\u002F\u002Freplicate.com\u002Fsczhou\u002Fcodeformer)：用于老照片和AI生成人脸的面部修复算法\n* [TencentARC\u002FGFPGAN](https:\u002F\u002Fgithub.com\u002FTencentARC\u002FGFPGAN)：GFPGAN旨在开发适用于现实场景的人脸修复实用算法\n\n## 图像分割\n\n* [Segment Anything | Meta AI](https:\u002F\u002Fsegment-anything.com\u002F)：“Meta AI推出的一款全新AI模型，只需单击一下，即可从任何图像中‘抠出’任意物体”\n\n# 视频与动画\n\n* [FramePack](https:\u002F\u002Fwww.framepack.video\u002F)：一种逐帧预测的神经网络结构，可逐步生成视频\n* [Keyla.AI](https:\u002F\u002Fkeyla.ai\u002F)：几分钟内即可创建视频广告\n* [Melies](https:\u002F\u002Fmelies.co\u002F)：一体化AI电影制作软件\n* [Pyramid Flow](https:\u002F\u002Fpyramid-flow.github.io\u002F)\n* [Infinity AI](https:\u002F\u002Finfinity.ai\u002F)：一款视频基础模型，允许用户创作角色并为其制作动画\n* [Sora](https:\u002F\u002Fopenai.com\u002Fsora)：OpenAI的文字转视频模型 [[技术报告]](https:\u002F\u002Fopenai.com\u002Fresearch\u002Fvideo-generation-models-as-world-simulators)\n* [SDV (Stable Diffusion 图像转视频)](https:\u002F\u002Ftwitter.com\u002Fstevemills\u002Fstatus\u002F1727898404787986873?s=46&t=CQsRDjHr9sNtph3xC84hXQ)：在Colab+上使用A100 GPU大约30秒即可生成3秒视频\n* [[Emu Video | Meta](https:\u002F\u002Femu-video.metademolab.com\u002F) ](https:\u002F\u002Femu-video.metademolab.com\u002Fdemo#\u002Fdemo)：最先进的文字转视频生成技术\n* [AILab-CVC\u002FVideoCrafter](https:\u002F\u002Fgithub.com\u002Failab-cvc\u002Fvideocrafter)：用于高质量视频生成的开源扩散模型\n* [Ssemble](https:\u002F\u002Fwww.ssemble.com\u002F)：一款具备多种AI插件的协作式视频编辑器\n* [使用AdaMPI AI模型将2D图像转换为3D](https:\u002F\u002Fnotes.aimodels.fyi\u002Ftransforming-2d-images-into-3d-with-the-adampi-ai-model\u002F)：关于如何利用AdaMPI AI模型将2D图像转化为3D照片的指南\n* [Nathan Lands在Twitter上的帖子：“AI视频已经开始产生令人震惊的效果，并最终可能颠覆好莱坞” \u002F Twitter](https:\u002F\u002Ftwitter.com\u002FNathanLands\u002Fstatus\u002F1659195191591596033)：包含视频生成AI工具示例的Twitter线程\n* [Stable Animation SDK](https:\u002F\u002Fstability.ai\u002Fblog\u002Fstable-animation-sdk)：Stability AI为开发者提供的文字转动画工具 [[开发平台]](https:\u002F\u002Fplatform.stability.ai\u002Fdocs\u002Ffeatures\u002Fanimation)\n* [Twelve Labs](https:\u002F\u002Ftwelvelabs.io\u002F)：用于视频搜索的多模态上下文理解系统\n* [Align your Latents](https:\u002F\u002Fresearch.nvidia.com\u002Flabs\u002Ftoronto-ai\u002FVideoLDM\u002F)：基于潜在扩散模型的高分辨率视频合成 [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.08818)\n* [Runway推出的Gen-2](https:\u002F\u002Fresearch.runwayml.com\u002Fgen2)：“一个能够通过文本、图像或视频片段生成全新视频的多模态AI系统” [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2302.03011)\n* [CiaraRowles\u002FTemporalNet · Hugging Face](https:\u002F\u002Fhuggingface.co\u002FCiaraRowles\u002FTemporalNet)：一种ControlNet模型，旨在提升生成内容的时间一致性 [[推文]](https:\u002F\u002Ftwitter.com\u002Fciararowles1\u002Fstatus\u002F1639321818581303310)\n* [Video-P2P UI - 由video-p2p-library创建的Hugging Face Space](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fvideo-p2p-library\u002FVideo-P2P-Demo)：通过交叉注意力控制进行视频编辑 [[推文]](https:\u002F\u002Ftwitter.com\u002F_akhaliq\u002Fstatus\u002F1637838648463749120)\n* [Text2Video-Zero - 由PAIR创建的Hugging Face Space](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FPAIR\u002FText2Video-Zero)：零样本文字转视频合成扩散框架 [[推文]](https:\u002F\u002Ftwitter.com\u002F_akhaliq\u002Fstatus\u002F1639062868850266112) [[arxiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2303.13439)\n* [ModelScope - 由damo-vilab创建的Hugging Face Space](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fdamo-vilab\u002Fmodelscope-text-to-video-synthesis)：文字转视频合成 [[页面]](https:\u002F\u002Fwww.modelscope.cn\u002Fmodels\u002Fdamo\u002Ftext-to-video-synthesis\u002Fsummary)\n* [neural frames](https:\u002F\u002Fwww.neuralframes.com\u002Ffirstframe)：受deforum启发的动画制作工具\n* [🔥] [dmarx\u002Fvideo-killed-the-radio-star](https:\u002F\u002Fgithub.com\u002Fdmarx\u002Fvideo-killed-the-radio-star)：用于借助生成式AI实现音乐视频全流程自动化的笔记本及工具\n* [🔥🔥🔥] [Phenaki – Google Research](https:\u002F\u002Fphenaki.research.google\u002F)：根据开放域文本描述生成逼真视频\n* [THUDM\u002FCogVideo](https:\u002F\u002Fgithub.com\u002FTHUDM\u002FCogVideo)：文字转视频生成\n* [baowenbo\u002FDAIN](https:\u002F\u002Fgithub.com\u002Fbaowenbo\u002FDAIN)：深度感知视频帧插值（CVPR 2019）\n* [GRisk发布的Dain-App 1.0 [仅限Nvidia] ](https:\u002F\u002Fgrisk.itch.io\u002Fdain-app)：深度感知视频帧插值（CVPR 2019）\n* [Content Studio AI](https:\u002F\u002Fcontentstudioai.com\u002F)：无脸视频生成器\n\n# 音频与音乐\n\n* [StemGen：一款会“听”的音乐生成模型](https:\u002F\u002Fjulian-parker.github.io\u002Fstemgen\u002F) \n* [Mustango](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fdeclare-lab\u002Fmustango)：“迈向可控的文本转音乐生成”\n* [Lyria by Google DeepMind](https:\u002F\u002Fdeepmind.google\u002Fdiscover\u002Fblog\u002Ftransforming-the-future-of-music-creation\u002F)：“变革音乐创作的未来” \n* [Suno AI](https:\u002F\u002Fwww.suno.ai\u002F)：“创作你所能想象的任何歌曲”\n* [Riffusion](https:\u002F\u002Fwww.riffusion.com\u002F)：该AI系统可为任意输入文本生成歌声\n* [Stable Audio - 面向音乐与音效的生成式AI](https:\u002F\u002Fwww.stableaudio.com\u002F) \n* [我们AI音乐实验的早期探索 - YouTube博客](https:\u002F\u002Fblog.youtube\u002Finside-youtube\u002Fai-and-music-experiment\u002F) \n* [什么是生成式音乐？ - 生成式音乐AI - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9QNG56fc_l8&list=PL-wATfeyAMNqAPjwGT3ikEz3gMo23pl-D&index=3) \n* [Ultimate Vocal Remover](https:\u002F\u002Fultimatevocalremover.com\u002F)：利用AI进行人声分离\n* [隆重推出Voicebox](https:\u002F\u002Fai.facebook.com\u002Fblog\u002Fvoicebox-generative-ai-model-speech)：首个能够跨任务泛化并达到最先进水平的语音生成式AI模型\n* [MusicGen](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Ffacebook\u002FMusicGen)：Meta推出的音乐生成工具\n* [facebookresearch\u002Faudiocraft](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Faudiocraft)：用于深度学习音频处理与生成的库。\n* [AudioGPT | arxiv](https:\u002F\u002Farxiv.org\u002Fabs\u002F2304.12995)：理解与生成语音、音乐、声音及说话头像 [[代码]](https:\u002F\u002Fgithub.com\u002FAIGC-Audio\u002FAudioGPT) [[演示]](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FAIGC-Audio\u002FAudioGPT) \n* [AudioLDM](https:\u002F\u002Faudioldm.github.io\u002F)：基于潜在扩散模型的文本到音频生成——语音研究\n* [lucidrains\u002Fmusiclm-pytorch](https:\u002F\u002Fgithub.com\u002Flucidrains\u002Fmusiclm-pytorch)：使用PyTorch实现Google最新SOTA音乐生成模型MusicLM，该模型基于注意力网络\n* [🔥🔥🔥] [archinetai\u002Faudio-ai-timeline](https:\u002F\u002Fgithub.com\u002Farchinetai\u002Faudio-ai-timeline)：自2023年起最新的音频生成AI模型时间线\n* [MusicLM](https:\u002F\u002Fgoogle-research.github.io\u002Fseanet\u002Fmusiclm\u002Fexamples\u002F)：根据文本生成音乐\n* [Harmonai的Dance Diffusion](https:\u002F\u002Fwandb.ai\u002Fwandb_gen\u002Faudio\u002Freports\u002FHarmonai-s-Dance-Diffusion-Open-Source-AI-Audio-Generation-Tool-For-Music-Producers--VmlldzoyNjkwOTM1)：面向音乐制作人的开源AI音频生成工具——Weights & Biases\n* [Dance Diffusion](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fharmonai\u002Fdance-diffusion)：由harmonai提供的Hugging Face空间\n* [MubertAI\u002FMubert-Text-to-Music](https:\u002F\u002Fgithub.com\u002FMubertAI\u002FMubert-Text-to-Music)：一个简单的Notebook，演示如何通过Mubert API基于提示词生成音乐\n* [DDSP-VST](https:\u002F\u002Fmagenta.tensorflow.org\u002Fddsp-vst-blog)：面向所有人的神经音频合成\n* [LOVO AI](https:\u002F\u002Fwww.lovo.ai\u002F)：具备类人声音的AI配音与文本转语音平台\n* [AIVA](https:\u002F\u002Fwww.aiva.ai\u002F)：能够创作情感配乐的AI作曲家\n* [Jukebox](https:\u002F\u002Fopenai.com\u002Fblog\u002Fjukebox\u002F)：“一种神经网络，能以原始音频形式生成包括基础歌唱在内的多种流派和艺术家风格的音乐”\n* [Magenta](https:\u002F\u002Fmagenta.tensorflow.org\u002F)：用机器智能创作音乐与艺术\n* [magenta\u002Fmagenta](https:\u002F\u002Fgithub.com\u002Fmagenta\u002Fmagenta)：Magenta官方GitHub仓库\n* [AI图像转声音[Melobytes.com]](https:\u002F\u002Fmelobytes.com\u002Fen\u002Fapp\u002Fai_image2sound)\n* [archinetai\u002Faudio-diffusion-pytorch](https:\u002F\u002Fgithub.com\u002Farchinetai\u002Faudio-diffusion-pytorch)：使用扩散模型在PyTorch中进行音频生成\n* [MuseGen](https:\u002F\u002Fmusegen.org)：专为创作者打造的AI音乐工作室，支持歌词创作和歌曲生成\n\n# 语音\n\n## 文本转语音（TTS）与虚拟形象\n\n* [COVAL](https:\u002F\u002Fapp.coval.dev\u002Fthe-ultimate-voice-ai-stack)：语音AI架构，涵盖语音识别到情感智能，并教你如何构建、扩展和评估这些系统\n* [Parler-TTS](https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Fparler-tts\u002Fparler-tts-fully-open-source-high-quality-tts-66164ad285ba03e8ffde214c)：完全开源的高质量TTS\n* [p0n1\u002Fepub_to_audiobook](https:\u002F\u002Fgithub.com\u002Fp0n1\u002Fepub_to_audiobook)：EPUB转有声书转换器，专为Audiobookshelf优化\n* [他们从未告诉你的“语音克隆AI”及其工作原理](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vhArHsfsLAQ)：@bycloud发布的YouTube视频，总结了现有的语音克隆技术\n* [Voice-Swap](https:\u002F\u002Fwww.voice-swap.ai\u002F?ref=producthunt)：将人声转换为一系列歌手的风格\n* [Shaunwei\u002FRealChar](https:\u002F\u002Fgithub.com\u002FShaunwei\u002FRealChar)：实时AI角色\u002F伴侣\n* [UneeQ Digital Humans](https:\u002F\u002Fwww.digitalhumans.com\u002F)：同步的3D角色库\n* [AI Voice Generator](https:\u002F\u002Fwww.aivoicegenerator.org)：免费在线AI驱动的文本转语音生成器，可创建自然逼真的旁白\n* [KangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation](https:\u002F\u002Fgithub.com\u002FKangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation)：精心整理的音频驱动人脸生成资源列表\n* [Play.ht](https:\u002F\u002Fplay.ht\u002F)：“AI语音生成器及逼真的在线文本转语音服务”\n* [Murf AI | AI语音生成器](https:\u002F\u002Fmurf.ai\u002F)：多功能文本转语音软件\n* [VALL-E](https:\u002F\u002Fvalle-demo.github.io\u002F)：仅需3秒样本即可合成高质量个性化语音\n* [🔥] [Eleven Labs Beta](https:\u002F\u002Fblog.elevenlabs.io\u002Fthe_first_ai_that_can_laugh\u002F)：一项可在生成语音中加入情感的TTS服务\n* [neonbjb\u002Ftortoise-tts](https:\u002F\u002Fgithub.com\u002Fneonbjb\u002Ftortoise-tts#voice-customization-guide)：“一款注重质量的多语音TTS系统”\n* [Studio D-ID](https:\u002F\u002Fstudio.d-id.com\u002F)：使用文本转语音工具将静态图片同步生成视频 [#avatar]\n* [Synthesia](https:\u002F\u002Fwww.synthesia.io\u002F)：AI视频生成平台 [#avatar]\n* [Speech Studio - Microsoft Azure](https:\u002F\u002Fspeech.microsoft.com\u002Fportal)：微软的云认知服务\n\n### 播客生成器\n\n* [Google NotebookLM](https:\u002F\u002Fnotebooklm.google.com\u002F)：根据你上传的参考资料生成播客节目\n* [Illuminate](https:\u002F\u002Filluminate.google.com\u002Fhome?pli=1)：同样来自谷歌，可将你的内容转化为引人入胜的AI生成音频讨论\n\n## 语音转文本（STT）与语音内容分析\n\n* [介绍 Universal-1](https:\u002F\u002Fwww.assemblyai.com\u002Fblog\u002Fannouncing-universal-1-speech-recognition-model\u002F)：多语言语音转文本\n* [ggerganov\u002Fwhisper.cpp](https:\u002F\u002Fgithub.com\u002Fggerganov\u002Fwhisper.cpp)：OpenAI Whisper 模型的 C\u002FC++ 移植版本，可在本地运行。\n* [Good Tape](https:\u002F\u002Fgoodtape.io\u002F)：付费转录服务\n* [shashikg\u002FWhisperS2T](https:\u002F\u002Fgithub.com\u002Fshashikg\u002FWhisperS2T)：针对 Whisper 模型优化的语音转文本流水线\n* [Vaibhavs10\u002Finsanely-fast-whisper](https:\u002F\u002Fgithub.com\u002FVaibhavs10\u002Finsanely-fast-whisper)：结合 OpenAI 的 Whisper Large v2、HF Transformers、Optimum 和闪注意力机制，加速转录过程\n* [facebookresearch\u002Fseamless_communication](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fseamless_communication)：用于先进语音和文本翻译的基础模型\n* [LeMUR](https:\u002F\u002Fwww.assemblyai.com\u002Fblog\u002Flemur\u002F)：一个单一 API，使开发者只需几行代码即可对语音数据进行推理。\n\n# 游戏\n\n* [游戏中的生成式 AI 革命 | Andreessen Horowitz](https:\u002F\u002Fa16z.com\u002F2022\u002F11\u002F17\u002Fthe-generative-ai-revolution-in-games\u002F)：本文列出了生成式 AI 在游戏中的多种应用场景\n* [游戏开发中的 AI](https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fml-for-games-1)：5 天内制作一款农场游戏。第一部分\n\n# 多模态\n\n* [[2406.09403] Visual Sketchpad：作为多模态语言模型的视觉思维链的草图绘制](https:\u002F\u002Farxiv.org\u002Fabs\u002F2406.09403)\n* [BradyFU\u002FAwesome-Multimodal-Large-Language-Models](https:\u002F\u002Fgithub.com\u002FBradyFU\u002FAwesome-Multimodal-Large-Language-Models)：关于多模态大型语言模型的最新论文和数据集及其评估\n* [NExT-Chat：用于聊天、检测和分割的 LMM](https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2311.04498)\n* [roboflow\u002Fawesome-openai-vision-api-experiments](https:\u002F\u002Fgithub.com\u002Froboflow\u002Fawesome-openai-vision-api-experiments)：展示如何使用 OpenAI 视觉 API 对图像、视频文件和网络摄像头流进行推理的示例\n\n## 多模态嵌入空间\n\n* [微软 KOSMOS-2](https:\u002F\u002Ftwitter.com\u002Fmervenoyann\u002Fstatus\u002F1720126908384366649)：新增感知物体描述（如边界框）以及将文本与视觉世界关联的能力 [[HF 演示]](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002Fydshieh\u002FKosmos-2) [[arXiv]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2306.14824)\n* [Segment Anything | Meta AI](https:\u002F\u002Fsegment-anything.com\u002F)：“Meta AI 推出的一款新型 AI 模型，只需单击一下即可从任何图像中‘抠出’任意物体”\n* [facebookresearch\u002FImageBind](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FImageBind)：ImageBind——一个统一的嵌入空间，连接一切\n\n# 数据集\n\n* [Ego-Exo4D](https:\u002F\u002Fai.meta.com\u002Fblog\u002Fego-exo4d-video-learning-perception\u002F)：Meta 提供的基础数据集，用于视频学习和多模态感知研究 [数据集下载](https:\u002F\u002Fego-exo4d-data.org\u002F)\n* [Carolina](https:\u002F\u002Fsites.usp.br\u002Fcorpuscarolina\u002Fcorpus\u002F)：包含来源和类型信息的当代巴西葡萄牙语通用语料库——Corpus Geral do Português Brasileiro Contemporâneo\n* [Together AI 的 RedPajama-Data-v2](https:\u002F\u002Ftogether.ai\u002Fblog\u002Fredpajama-data-v2)：一个开放数据集，包含 30 万亿个 token，可用于训练大型语言模型\n* [Have I Been Trained?](https:\u002F\u002Fhaveibeentrained.com\u002F)：用于搜索被用于训练热门 AI 艺术模型的 58 亿张图片的工具\n* [laion-aesthetic-6pls](https:\u002F\u002Flaion-aesthetic.datasette.io\u002Flaion-aesthetic-6pls\u002Fimages)：探索用于训练 Stable Diffusion 图像生成器的 23 亿张图片中的 1200 万张\n* [CLIP 检索 Laion5B](https:\u002F\u002From1504.github.io\u002Fclip-retrieval\u002F?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false)：“通过将文本查询转换为 CLIP 嵌入，然后利用该嵌入查询 CLIP 图像嵌入的 k-近邻索引来实现检索。”\n* [rom1504\u002Fclip-retrieval](https:\u002F\u002Fgithub.com\u002From1504\u002Fclip-retrieval)：轻松计算 CLIP 嵌入，并基于这些嵌入构建 CLIP 检索系统\n* [LAION](https:\u002F\u002Flaion.ai\u002F)：大规模人工智能开放网络\n* [gabolsgabs\u002FDALI](https:\u002F\u002Fgithub.com\u002Fgabolsgabs\u002FDALI)：一个大型的音频、歌词和人声音符同步数据集\n\n# 杂项\n\n## AI 与教育\n\n* [教 AI 教我们：个性化教育的新时代](https:\u002F\u002Ftwitter.com\u002FIntuitMachine\u002Fstatus\u002F1783079852578377788)\n\n## 人物与作品\n\n### 有趣的 Twitter 账号\n\n* [Hassan El Mghari (@nutlope) \u002F X](https:\u002F\u002Ftwitter.com\u002Fnutlope)：[roomgpt](https:\u002F\u002Froomgpt.io) 的创建者\n\n### 有趣的 Instagram 账号、帖子和 Reels\n\n* [Instagram 上的科学：“由 AI Stable Diffusion 生成的人类进化”](https:\u002F\u002Fwww.instagram.com\u002Freel\u002FCjnYBJbqABH\u002F?igshid=YmMyMTA2M2Y%3D)\n* [Deep Music Visualizer](https:\u002F\u002Fwww.instagram.com\u002Fdeep_music_visualizer\u002F)\n* [Lucid Sonic Dreams (@lucidsonicdreams)](https:\u002F\u002Fwww.instagram.com\u002Flucidsonicdreams\u002F)\n\n### 有趣的 YouTube 频道\n\n* [Artificial Images](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCaZuPdmZ380SFUMKHVsv_AA)：使用机器学习创作艺术的演示和讲解\n* [Glenn Marshall Neural Art](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Fglenniszen)\n* [如何生成艺术——深度学习入门 #8](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Oex0eWoU7AQ)\n\n### 有趣的 GitHub 仓库\n\n* [dvschultz](https:\u002F\u002Fgithub.com\u002Fdvschultz)：Derrick Schultz 的 GitHub\n* [dvschultz\u002Fml-art-colabs](https:\u002F\u002Fgithub.com\u002Fdvschultz\u002Fml-art-colabs)：ML 艺术相关的 Google Colab 笔记本合集\n* [🔥] [用于序列建模的结构化状态空间（S4）](https:\u002F\u002Fgithub.com\u002Fstate-spaces\u002Fs4)：从神明那里窃取的创造力\n\n### 艺术家与艺术作品\n\n* [AI生成音乐视频——Deltron 3030——Virus——YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=WJaxFbdjm8c)\n* [人工现实：珊瑚 \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Frefikanadol\u002Fstatus\u002F1613927561939099650)：由[@refikanadol](https:\u002F\u002Ftwitter.com\u002Frefikanadol)创作的艺术作品，受世界经济论坛委托\n* [🔥] [Creep——YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=c6LlG4g_9lk)，由[Glenn Marshall Neural Art](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCes-tiSj7VO6nNOsUB76lZw)制作：他们是如何使用VQGAN+CLIP来转换图像的？又是如何在潜在空间中无缝游走的？\n* [35位使用AI且粉丝不足1000的艺术家，你今天就需要关注 \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Finfiniteyay\u002Fstatus\u002F1583465675166609408?s=43&t=XvooFiMyC-YPv0i98HmjVQ) \n* [计算机视觉艺术画廊：CVPR 2021](https:\u002F\u002Fcomputervisionart.com\u002F)：以计算机视觉技术为主题的艺术作品\n* [Confluence](https:\u002F\u002Fdeviparikh.github.io\u002Fconfluence\u002F)：Devi Parikh在BrainDrops上的生成艺术项目。\n* [学会观看——Memo Akten | Mehmet Selim Akten | 超级酷炫视觉公司](http:\u002F\u002Fwww.memo.tv\u002Fworks\u002Flearning-to-see\u002F)\n* [异星之梦：新兴的艺术场景——ML@B博客](https:\u002F\u002Fml.berkeley.edu\u002Fblog\u002Fposts\u002Fclip-art\u002F)\n* [神经动物园 | Sofia Crespo](https:\u002F\u002Fneuralzoo.com\u002F)\n* [KRЯRL DЯAWINGS：Runway ML——第3个“模型”（基于长姿势）](http:\u002F\u002Fkrrrl.blogspot.com\u002F2020\u002F08\u002Frunway-ml-3rd-model-based-on-long-poses.html)\n* [Frea Buckler ~ 艺术家](https:\u002F\u002Fwww.freabuckler.com\u002F)：用于创建该网络的作品 [(19) derrick又在Twitter上启动了一个新项目：“刚刚给@buntworthy发了一个我训练好的StyleGAN模型演示 \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Fdvsch\u002Fstatus\u002F1255885874560225284)\n* [(非)人类](https:\u002F\u002Fwww.ygzhang.com\u002Fnon-human.html) \n* [真实的数字艺术——未知的启程 | SuperRare](https:\u002F\u002Fsuperrare.com\u002Fartwork-v2\u002Funknown-departure-16212) \n* [机器学习艺术灵感精选](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=HNwXrHiHW7Q)\n* [2021年顶级25位AI艺术家（照片、简介及AI艺术史）— AIArtists.org](https:\u002F\u002Faiartists.org\u002F)：AIArtists.org展示了使用人工智能进行创作的顶尖艺术家、相关工具以及AI艺术的发展历程。\n* [Helena Sarin——艺术家简介（照片、视频、展览）— AIArtists.org](https:\u002F\u002Faiartists.org\u002Fhelena-sarin)\n* [由AI机器生成的图像 (@images_ai) \u002F Twitter](https:\u002F\u002Ftwitter.com\u002Fimages_ai?s=08)\n* https:\u002F\u002Fwww.instagram.com\u002Frefikanadol\u002F\n* [蒸汽朋克马戏团：人机协作——视频、声音与AI故事 \u002F YouTube](https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCa1xYBYCzBoJ08U9lgbYAFw)\n\n### 画廊\n\n* [AICAN](https:\u002F\u002Faican.io\u002F)\n* [Ganvas Studio——神经网络绘画](https:\u002F\u002Fganvas.studio\u002F)\n* [合成羽毛毛衣 \u002F STRELITZIA – HATRA E STORE](https:\u002F\u002Fhatroid.com\u002Fcollections\u002Fsynthetic-feather\u002Fproducts\u002Fsyn-feather-sweater-strelitzia)\n\n## 相关精彩列表\n\n* [mahseema\u002Fawesome-ai-tools](https:\u002F\u002Fgithub.com\u002Fmahseema\u002Fawesome-ai-tools)：精选的人工智能顶级工具列表\n* [Hannibal046\u002FAwesome-LLM：Awesome-LLM](https:\u002F\u002Fgithub.com\u002FHannibal046\u002FAwesome-LLM)：大型语言模型精选列表\n* [AlexChalakov\u002Fawesome-generative-ai-companies](https:\u002F\u002Fgithub.com\u002FAlexChalakov\u002Fawesome-generative-ai-companies)：按重点领域和累计融资额排序的生成式AI公司精选列表\n* [kyrolabs\u002Fawesome-langchain](https:\u002F\u002Fgithub.com\u002Fkyrolabs\u002Fawesome-langchain)：😎 使用LangChain框架的工具和项目精彩列表\n* [KangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation](https:\u002F\u002Fgithub.com\u002FKangweiiLiu\u002FAwesome_Audio-driven_Talking-Face-Generation)：音频驱动的说话人脸生成资源精选列表\n* [🔥] [amrzv\u002Fawesome-colab-notebooks](https:\u002F\u002Fgithub.com\u002Famrzv\u002Fawesome-colab-notebooks)：用于快速便捷实验的Google Colaboratory笔记本合集\n* [🔥🔥🔥] [steven2358\u002Fawesome-generative-ai](https:\u002F\u002Fgithub.com\u002Fsteven2358\u002Fawesome-generative-ai)：现代生成式人工智能项目和服务精选列表\n* [🔥🔥🔥] [jonathandinu\u002Fawesome-ai-art](https:\u002F\u002Fgithub.com\u002Fjonathandinu\u002Fawesome-ai-art)：“AI艺术课程、工具、库、人物和地点列表”\n* [margaretmz\u002Fawesome-ai-art-design](https:\u002F\u002Fgithub.com\u002Fmargaretmz\u002Fawesome-ai-art-design)：关于AI在艺术与设计领域应用的精彩列表。\n* [toxtli\u002Fawesome-machine-learning-jupyter-notebooks-for-colab](https:\u002F\u002Fgithub.com\u002Ftoxtli\u002Fawesome-machine-learning-jupyter-notebooks-for-colab)：可在Google Colaboratory中直接运行的机器学习和深度学习教程精选列表\n* [chaosreactor\u002Fawesome-generative-ai](https:\u002F\u002Fgithub.com\u002Fchaosreactor\u002Fawesome-generative-ai)：低代码或无代码生成式AI资源精选列表\n* [🔥] [altryne\u002Fawesome-ai-art-image-synthesis](https:\u002F\u002Fgithub.com\u002Faltryne\u002Fawesome-ai-art-image-synthesis)：为AI艺术和图像合成领域的提示工程师准备的优秀工具、创意、提示工程工具、Colab、模型和辅助资源列表。涵盖Dalle2、MidJourney、StableDiffusion及开源工具。\n* [justinpinkney\u002Fawesome-pretrained-stylegan2](https:\u002F\u002Fgithub.com\u002Fjustinpinkney\u002Fawesome-pretrained-stylegan2)：预训练StyleGAN 2模型合集\n\n## 生物实验\n\n* [fMRI转图像](https:\u002F\u002Ftwitter.com\u002Fdanberridge\u002Fstatus\u002F1631489991435243520)：由[danberridge](https:\u002F\u002Ftwitter.com\u002Fdanberridge)发布的推文：“‘呈现的图像’展示给一组人，而‘重建的图像’则是fMRI输出到Stable Diffusion的结果。换句话说，Stable Diffusion简直就是在读取人们的思想。”\n\n## 生成式AI相关职位\n\n* [AI\u002FML、数据科学和大数据领域的工作与人才 | ai-jobs.net](https:\u002F\u002Fai-jobs.net\u002F)\n* [热门初创公司和大型企业最新AI职位与资讯 | AIJobster](https:\u002F\u002Faijobster.work\u002F)\n\n## 提升Google Colab体验\n\n* [7种将外部数据加载到Google Colab的方法 | B. Chen著 | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002F7-ways-to-load-external-data-into-google-colab-7ba73e7d5fc7) \n* [10个让Google Colab体验更佳的小技巧 | Cyprien NIELLY著 | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002F10-tips-for-a-better-google-colab-experience-33f8fe721b82)\n* [使用ngrok免费快速分享Google Colab中的ML WebApp | AbdulMajedRaja RS著 | Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fquickly-share-ml-webapps-from-google-colab-using-ngrok-for-free-ae899ca2661a)\n* [Google Colab中的交互式Jupyter Widgets](https:\u002F\u002Fcolab.research.google.com\u002Fnotebooks\u002Fforms.ipynb#scrollTo=62YnDE7i9dqP)：包含在Colab中使用Jupyter Widgets示例的笔记本，支持交互式输入\n* [Jupyter Widgets官方文档](https:\u002F\u002Fipywidgets.readthedocs.io\u002Fen\u002Flatest\u002Fexamples\u002FWidget%20Basics.html)\n\n## 辅助工具和概念\n\n* [Rosie](https:\u002F\u002Fheyrosie.com\u002F)：AI电话接听服务\n* [MuckBrass](https:\u002F\u002Fwww.muckbrass.com)：利用AI寻找并验证创业想法\n* [ResumeDive](https:\u002F\u002Fresumedive.com\u002F)：基于AI的简历优化服务\n* [Owlbot](https:\u002F\u002Fwww.owlbot.ai\u002F)：AI客服助手\n* [fynk](https:\u002F\u002Ffynk.com\u002F)：AI驱动的合同管理软件\n* [Taskbase](https:\u002F\u002Fwww.taskbase.co.uk)：结合AI软件的虚拟助理服务\n* [AI Wedding Toast](https:\u002F\u002Faiweddingtoast.com\u002F)：用AI生成个性化婚礼致辞\n* [Interviews Chat](https:\u002F\u002Fwww.interviews.chat\u002F)：你的个人面试准备与协作伙伴\n* [Inline Help](https:\u002F\u002Finlinehelp.com)：在客户提问之前就解答他们的问题\n* [LinkActions](https:\u002F\u002Flinkactions.com)：AI内部链接助手\n* [Marblism](https:\u002F\u002Fmarblism.com)：根据提示生成SaaS样板代码\n* [SiteSpeakAI](https:\u002F\u002Fsitespeak.ai)：用AI自动化客户支持\n* [Room Reinvented](https:\u002F\u002Froomreinvented.com)：轻松改造你的房间！上传一张照片，让AI为你打造30多种惊艳的室内风格。立即提升你的空间品质。\n* [FairyTailAI](https:\u002F\u002Ffairytailai.com\u002F)：个性化睡前故事生成器\n* [PromptPal](https:\u002F\u002Fpromptpal.net)：搜索提示词和机器人，并在你喜爱的AI平台上直接使用，所有功能一站式提供。\n* [Never Jobless LinkedIn Message Generator](https:\u002F\u002Fneverjobless.com\u002F?ref=filipecalegario-awesome-generative-ai)：通过AI驱动的LinkedIn消息，最大化你的面试机会。\n* [Aispect](https:\u002F\u002Faispect.io\u002F?ref=filipecalegario-awesome-generative-ai)：体验活动的新方式。\n* [SiteGPT](https:\u002F\u002Fsitegpt.ai\u002F?ref=filipecalegario-awesome-generative-ai)：让AI成为你的专业客户支持代理。\n* [PressPulse AI](https:\u002F\u002Fwww.presspulse.ai\u002F?ref=filipecalegario-awesome-generative-ai)：每天早上获取个性化的媒体报道线索。\n* [GPTHelp.ai](https:\u002F\u002Fgpthelp.ai\u002F?ref=filipecalegario-awesome-generative-ai)：为你的网站配备ChatGPT \u002F AI客户支持聊天机器人。\n* [chaiNNer-org\u002FchaiNNer](https:\u002F\u002Fgithub.com\u002FchaiNNer-org\u002FchaiNNer)：一个基于节点的图像处理和AI超分辨率GUI，可轻松将复杂的处理任务串联起来。\n* [BIRME](https:\u002F\u002Fwww.birme.net\u002F)：批量图片调整大小2.0（在线免费）\n* [The Art of PNG Glitch](https:\u002F\u002Fucnv.github.io\u002Fpnglitch\u002F)\n* [HashLips\u002Fhashlips_art_engine](https:\u002F\u002Fgithub.com\u002FHashLips\u002Fhashlips_art_engine)：用于根据提供的图层创建多种不同版本艺术作品的工具。\n* [Taplio](https:\u002F\u002Ftaplio.com\u002F?ref=filipecalegario-awesome-generative-ai)：一体化、AI驱动的LinkedIn工具。\n* [Galichat.com](https:\u002F\u002Fwww.galichat.com\u002F?ref=filipecalegario-awesome-generative-ai)：帮助你发展业务的AI支持助手。\n* [Aidbase](https:\u002F\u002Fwww.aidbase.ai)：为你的SaaS初创公司提供AI驱动的支持。\n* [Socialsonic](https:\u002F\u002Fsocialsonic.com)：AI LinkedIn教练：个性化内容、趋势分析及日程安排。\n\n### 降维技术\n\n* [为什么你应该使用拓扑数据分析而不是t-SNE或UMAP？](https:\u002F\u002Fdatarefiner.com\u002Ffeed\u002Fwhy-tda)\n* [YingfanWang\u002FPaCMAP：PaCMAP——同时保留全局与局部结构的大规模降维技术](https:\u002F\u002Fgithub.com\u002FYingfanWang\u002FPaCMAP)\n* [UMAP：用于降维的均匀流形近似与投影](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.03426)\n* [使用t-SNE进行数据可视化](https:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv9\u002Fvandermaaten08a.html)\n\n## 路线图、学习路径、指南\n\n* [(1166) 针对黑客的语言模型指南 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jkrNMKz9pWU&t=21s)\n* [🔥🔥] [面向初学者的生成式AI](https:\u002F\u002Fmicrosoft.github.io\u002Fgenerative-ai-for-beginners\u002F#\u002F)：微软推出的入门级12课课程\n* [生成式AI入门](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fyoussef-hosni-b2960b135_if-you-want-to-start-studying-generative-activity-7125908710702350336-vhsm\u002F)：由Youssef Hosni撰写的Medium系列文章\n* [提示工程路线图 - roadmap.sh](https:\u002F\u002Froadmap.sh\u002Fprompt-engineering)\n* [提示工程指南 | 学习提示：与AI沟通的指南](https:\u002F\u002Flearnprompting.org\u002Fdocs\u002Fintro)\n* [短期课程 | 从DeepLearning.AI学习生成式AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fshort-courses\u002F)\n\n## 星标随时间变化\n\n[![星标随时间变化](https:\u002F\u002Fstarchart.cc\u002Ffilipecalegario\u002Fawesome-generative-ai.svg)](https:\u002F\u002Fstarchart.cc\u002Ffilipecalegario\u002Fawesome-generative-ai)\n![](https:\u002F\u002Fvbr.wocr.tk\u002Fbadge?page_id=filipecalegario.awesome-generative-ai&color=55acb7&style=for-the-badge&logo=Github)\n\n## 参与贡献\n\n欢迎贡献！请先阅读[贡献指南](contributing.md)。\n\n## 许可证\n\n[![CC0](https:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F88x31\u002Fsvg\u002Fcc-zero.svg)](https:\u002F\u002Fcreativecommons.org\u002Fpublicdomain\u002Fzero\u002F1.0)\n\n在法律允许的最大范围内，Filipe Calegario已放弃本作品的所有版权及相关权利。\n\n[![\"给我买杯咖啡\"](https:\u002F\u002Fwww.buymeacoffee.com\u002Fassets\u002Fimg\u002Fcustom_images\u002Forange_img.png)](https:\u002F\u002Fwww.buymeacoffee.com\u002Ffilipecalegario)","# Awesome Generative AI 快速上手指南\n\n`awesome-generative-ai` 并非一个需要安装运行的软件工具或代码库，而是一个**精选资源列表（Awesome List）**。它汇集了生成式 AI 领域的项目、工具、艺术作品、模型、论文及学习路径。\n\n因此，本指南将指导你如何**访问、浏览和利用**这份资源清单，以加速你的学习与开发进程。\n\n## 环境准备\n\n由于这是一个基于 GitHub 的文档资源库，你只需要具备以下基础环境即可开始探索：\n\n*   **操作系统**：Windows, macOS, 或 Linux 均可。\n*   **网络环境**：\n    *   需要能够访问 **GitHub** (github.com)。\n    *   部分链接指向 Hugging Face、Google Colab 或 YouTube，建议配置好相应的网络加速工具以确保流畅访问。\n    *   **国内加速方案**：如果直接访问 GitHub 较慢，可使用国内镜像站（如 `https:\u002F\u002Fghproxy.net\u002F` 或 `https:\u002F\u002Ffastly.jsdelivr.net\u002F`）加速加载页面，或在 Gitee 上搜索是否有同步镜像。\n*   **浏览器**：推荐使用 Chrome, Edge 或 Firefox 以获得最佳的阅读体验。\n*   **前置知识**：对人工智能、大语言模型（LLM）或深度学习有基本兴趣或了解。\n\n## 安装步骤（获取资源）\n\n你无需执行复杂的安装命令，只需通过以下方式“获取”该资源列表：\n\n### 方式一：在线浏览（推荐）\n直接在浏览器中打开项目主页，利用目录结构快速查找所需内容。\n\n```bash\n# 在浏览器地址栏输入以下网址\nhttps:\u002F\u002Fgithub.com\u002Ffilipecalegario\u002Fawesome-generative-ai\n```\n\n### 方式二：克隆到本地（便于离线查阅或贡献）\n如果你希望将列表保存到本地进行检索，或计划提交 PR 贡献新资源，可以使用 Git 克隆。\n\n```bash\n# 1. 确保已安装 Git\ngit --version\n\n# 2. 克隆仓库到本地\ngit clone https:\u002F\u002Fgithub.com\u002Ffilipecalegario\u002Fawesome-generative-ai.git\n\n# 3. 进入目录\ncd awesome-generative-ai\n\n# 4. (可选) 使用 VS Code 或其他编辑器打开 README.md 查看\ncode README.md\n```\n\n> **提示**：若克隆速度慢，可使用国内镜像源命令：\n> `git clone https:\u002F\u002Fgitee.com\u002Fmirrors\u002Fawesome-generative-ai.git` (注：需确认 Gitee 上是否存在实时同步的镜像，若无则直接使用官方源配合加速代理)。\n\n## 基本使用\n\n该项目的核心用法是**按图索骥**。README 文件已经按照技术领域进行了严密的分类。以下是高效使用指南：\n\n### 1. 利用目录导航\n打开 `README.md` 文件，顶部提供了详细的目录（Table of Contents）。根据你的需求点击跳转：\n\n*   **初学者入门**：查看 `Generative AI Area` -> `Courses and Educational Materials` 或 `Generative AI history...` 了解历史与定义。\n*   **开发者实战**：\n    *   找代码框架：前往 `Code and Programming` 或 `Large Language Models (LLMs)` -> `Programming Frameworks for LLMs`。\n    *   找绘图工具：前往 `Image` -> `Image Synthesis` (包含 Stable Diffusion, ControlNet 等)。\n    *   找语音处理：前往 `Audio and Music` 或 `Speech`。\n*   **前沿研究**：查看 `Papers Collection` 或 `Research AI Tools` 获取最新论文和科研工具。\n\n### 2. 追踪最新动态\n该项目内的资源链接通常按**时间倒序**排列（最新的在最上面）。\n*   **操作**：进入具体子章节（例如 `Prompt Engineering`），直接点击列表顶部的链接，即可获取该领域最新的工具或教程。\n\n### 3. 示例：寻找本地运行 LLM 的方案\n假设你想在本地电脑运行一个大语言模型：\n1.  在目录中找到 `Large Language Models (LLMs)`。\n2.  点击子项 `Running LLMs Locally`。\n3.  阅读列出的项目（如 Ollama, LM Studio, llama.cpp 等），点击对应链接跳转到原项目仓库进行具体的下载和安装。\n\n### 4. 参与贡献\n如果你发现了优质的新工具或资源：\n1.  在 GitHub 上 Fork 该仓库。\n2.  编辑 `README.md`，按照现有格式添加新链接（注意保持倒序）。\n3.  提交 Pull Request (PR)。\n\n---\n**总结**：`awesome-generative-ai` 是你探索生成式 AI 世界的地图。不需要“运行”它，而是通过它提供的链接，快速定位并启动具体的 AI 项目或学习课程。","某初创公司的技术负责人正计划为电商项目引入生成式 AI 功能，需要从海量的开源模型、框架和工具中筛选出最适合的技术栈。\n\n### 没有 awesome-generative-ai 时\n- **信息检索效率极低**：面对 GitHub 上数以万计的分散项目，团队需花费数天时间手动搜索文本生成、图像合成及 RAG 架构相关的资源，难以辨别优劣。\n- **技术选型视野狭窄**：容易遗漏如 Mamba 架构、小语言模型（SLM）或最新的 ControlNet 变体等前沿方案，导致产品技术路线保守且缺乏竞争力。\n- **学习成本高昂**：缺乏系统的伦理讨论、课程资料及论文合集，团队成员在理解模型原理和规避法律风险时需各自摸索，重复造轮子。\n- **工具链整合困难**：难以快速找到能协同工作的“多智能体”框架或本地部署方案，导致原型开发周期被无限拉长。\n\n### 使用 awesome-generative-ai 后\n- **一站式精准导航**：直接通过分类目录（如\"LLM 编程框架”、“图像修复”）定位到经过社区验证的高质量项目，将调研时间从数天压缩至几小时。\n- **紧跟前沿趋势**：迅速发现并评估列表中包含的最新技术（如自主 LLM 代理、提示词优化工具），确保产品架构具备行业领先性。\n- **知识体系完备**：利用内置的课程、论文及伦理指南，团队能快速统一认知，规范开发流程，有效规避潜在的合规风险。\n- **全栈资源覆盖**：从数据处理、模型微调到最终的应用部署（LLMOps），所有环节均有对应的成熟工具推荐，大幅加速了从概念验证到落地的过程。\n\nawesome-generative-ai 充当了生成式 AI 领域的“权威导航图”，帮助开发者在混乱的技术爆炸中快速构建高效、前沿且合规的解决方案。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Ffilipecalegario_awesome-generative-ai_23a4f24b.png","filipecalegario","Filipe Calegario","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Ffilipecalegario_9ea4ea70.jpg","assistant professor @ Centro de Informática, UFPE, Brazil.","Universidade Federal de Pernambuco","Recife, Brazil","fcac@cin.ufpe.br","filipecalegario.net","https:\u002F\u002Fgithub.com\u002Ffilipecalegario",null,3407,716,"2026-03-30T18:23:03","CC0-1.0",1,"","未说明",{"notes":90,"python":88,"dependencies":91},"该仓库（awesome-generative-ai）是一个生成式 AI 资源、工具、论文和模型的精选列表（Awesome List），本身不是一个可执行的软件工具或框架，因此没有特定的运行环境、依赖库或硬件需求。用户可以根据列表中链接的具体项目去查看各自的环境要求。",[],[14,13,35,15],[94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111],"awesome-list","awesome","dall-e","dalle2","midjourney","prompt-engineering","ai-art","txt2img","text-to-image","generative-ai","chatgpt","embeddings","gpt-4","semantic-search","stable-diffusion","llm","llm-agent","openai","2026-03-27T02:49:30.150509","2026-04-11T18:52:28.276969",[],[]]