base on A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. # Awesome Artificial Intelligence A curated collection of **must-use, actively maintained resources** for building and shipping AI systems. Focus: **AI engineering** (RAG, agents, evals, guardrails, deploy) plus the best books, guides, papers, and a *carefully selected* set of tools.  --- ## 🏛 Core Resources (Evergreen) _The foundations — these will still be valuable five years from now, even if today’s tools are gone._ ### 📚 Books **Modern & Practical** - [Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/) — Scalable, maintainable ML pipelines (Chip Huyen). - [Generative Deep Learning (2nd Edition)](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/) — GANs, VAEs, diffusion models (David Foster). - [AI Engineering](https://www.oreilly.com/library/view/ai-engineering/9781098166298/) — End-to-end AI product building (Chip Huyen). - [100 Page Language Models Book](https://www.thelmbook.com/) — This book guides you through the evolution of language models, starting from machine learning fundamentals. **Foundational** - [Artificial Intelligence: A Modern Approach](https://aima.cs.berkeley.edu/) — Comprehensive AI theory (Russell & Norvig). - [Deep Learning](https://www.deeplearningbook.org/) — Neural networks & architectures (Goodfellow, Bengio, Courville). - [Reinforcement Learning: An Introduction (2nd Edition)](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf) — RL fundamentals (Sutton & Barto). --- ### 🏗 AI Engineering _Frameworks and design patterns for building robust, production-grade AI systems._ _Personal note: you don't need tons of frameworks — start with simple LLM calls and work up._ #### 📖 Guides & Playbooks - **[Building Effective Agents (Anthropic)](https://www.anthropic.com/engineering/building-effective-agents)** — ⭐ Patterns, pitfalls, and tradeoffs for designing AI agents. - [OpenAI Agents Guide](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf) — Practical guide on building agents - [Google AI Agents Paper](https://www.kaggle.com/whitepaper-agents) - Practical guide to building AI agents from Google - [Google Agents Companion Paper](https://www.kaggle.com/whitepaper-agent-companion) - Guide from Google - [OpenAI Cookbook](https://cookbook.openai.com/) — Example code, recipes, and best practices for working with OpenAI APIs. - [LLM Engineer Handbook](https://github.com/SylphAI-Inc/LLM-engineer-handbook) — A goldmine of useful links for AI engineers #### 🤖 Frameworks - [PocketFlow](https://the-pocket.github.io/PocketFlow/) — Extremely minimalist AI agent framework in just 100 lines of code. Fantastic way to learn. - [Google ADK](https://google.github.io/adk-docs/) — Google's Agent Development Kit (Python, Java). Great local development experience + A2A + MCP. - [Pydantic-AI](https://ai.pydantic.dev/) — Typed, structured LLM orchestration framework built on Pydantic models for safe, predictable outputs. - [LangGraph](https://www.langchain.com/langgraph) — Build multi-agent workflows with stateful graphs on top of LangChain. - [CrewAI](https://www.crewai.com/) — Agent orchestration with structured tasks and human-in-the-loop controls. - [AutoGen](https://microsoft.github.io/autogen/) — Microsoft’s framework for multi-agent conversation and collaboration. #### 📦 Retrieval-Augmented Generation (RAG) - [LlamaIndex](https://www.llamaindex.ai/) — Data framework for ingesting, indexing, and querying private data with LLMs. - [Haystack](https://haystack.deepset.ai/) — Open-source search/RAG framework with modular pipelines. - [Docling](https://github.com/docling-project/docling) — Great library for ingesting any kind of document for RAG ⭐ #### Evals - [OpenAI Evals](https://github.com/openai/evals) — OpenAI's framework for writing evals --- ### 📄 Landmark Papers _Research that shaped modern AI — worth reading to understand the "why" behind today’s architectures._ - [Attention Is All You Need](https://arxiv.org/abs/1706.03762) — Transformer architecture. - [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) — Model/data/compute scaling. - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) — GPT-3 capabilities. - [Constitutional AI](https://arxiv.org/abs/2212.08073) — Safer model alignment. --- ## 🎓 Courses _Learn from the best — structured content for every level._ **Beginner** - [Google Generative AI Learning Path](https://www.cloudskillsboost.google/paths/118) - [Hugging Face LLM Course](https://huggingface.co/learn/llm-course/chapter1/1) - [Fast.ai — Practical Deep Learning](https://course.fast.ai/) **Intermediate / Advanced** - [Stanford CS324: Large Language Models](https://stanford-cs324.github.io/winter2022/) - [Full Stack Deep Learning](https://fullstackdeeplearning.com/) - [MIT 6.S191: Intro to Deep Learning](https://introtodeeplearning.com/) **Focused** - [DeepLearning.AI Short Courses](https://learn.deeplearning.ai/) - [Google Deepmind| Introduction to Reinforcement Learning](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ) - [Karpathy’s LLM Zero-to-Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) - [Neural Nets - Zero-to-Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) --- ## 📰 Newsletters _Stay current with AI developments without drowning in noise._ - [The Rundown AI](https://www.therundown.ai/) - [AlphaSignal](https://alphasignal.ai/) - [Superhuman AI](https://www.superhuman.ai/) - [AI Engineer](https://newsletter.owainlewis.com) ## ⚡ Tools Tools for building and deploying AI applications. ### 💬 Models - [ChatGPT](https://openai.com/chatgpt/overview/) — Best for general coding + reasoning. - [Claude](https://www.anthropic.com/claude) — Best for long-context analysis and structured thinking. - [Gemini](https://gemini.google.com/) — Best for Google ecosystem integration. - [Perplexity](https://www.perplexity.ai/) — Best for quick research with live citations. - [Cohere](https://cohere.com/) — Best for enterprise LLMs with strong retrieval-augmented generation APIs. - [Mistral](https://mistral.ai/) — Best for lightweight, high-performance open-weight models. - [Qwen](https://qwenlm.github.io/) — Best for multilingual and Chinese-first applications. - [DeepSeek](https://deepseek.com/) — Best for efficient, cost-optimized large models with competitive reasoning. ### 👨‍💻 Code & Developer Tools - [Claude Code](https://www.anthropic.com/claude) — IDE extensions with long-context code edits. - [GitHub Copilot](https://github.com/features/copilot) — In-IDE code completion, chat, and refactors. - [Cursor](https://cursor.sh/) — LLM-powered IDE for multi-file edits and codebase-aware chat. ### 🎨 Multimedia AI Tools #### 🖼 Image - [ChatGPT-4o Image Generation](https://openai.com/chatgpt) — Integrated image creation with style control. - [Midjourney](https://www.midjourney.com/) — Artistic and photorealistic images and video. - [Adobe Firefly](https://www.adobe.com/sensei/generative-ai/firefly.html) — Integrated into Creative Cloud. - [Ideogram](https://ideogram.ai/) — Precise, legible text in generated images. - [Flux](https://blackforestlabs.ai/) — High-res, prompt-editable images. #### 🎥 Video - [Kling](https://klingai.com/) — Cinematic, realistic video generation. - [Google Veo 3](https://deepmind.google/technologies/veo/) — High-quality video with synchronized audio. - [Runway](https://runwayml.com/) — Video editing + generation. #### 🎙 Audio - [ElevenLabs](https://elevenlabs.io/) — High-quality text-to-speech. - [Suno](https://suno.ai/) — AI music from text prompts. - [Aiva](https://www.aiva.ai/) — Music composition for media. --- ", Assign "at most 3 tags" to the expected json: {"id":"9223","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"