owainlewis/awesome-artificial-intelligence

Source: https://github.com/owainlewis/awesome-artificial-intelligence

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.

📚 Learn

Deep, durable knowledge — still valuable five years from now.

Books

Modern & Practical

• Designing Machine Learning Systems — Scalable, maintainable ML pipelines (Chip Huyen).
• AI Engineering — End-to-end AI product building (Chip Huyen).
• Build a Large Language Model from Scratch — Transformers in raw PyTorch, layer by layer (Sebastian Raschka).
• Hands-On Large Language Models — Visual + practical guide to LLM applications (Jay Alammar, Maarten Grootendorst).
• LLM Engineer’s Handbook — Production LLMOps: fine-tuning, quantization, serving (Labonne, Iusztin).
• The 100-Page Language Models Book — Concise, math-grounded path from n-grams to transformers (Andriy Burkov).
• Generative Deep Learning (2nd Edition) — GANs, VAEs, diffusion models (David Foster).

Foundational

• Artificial Intelligence: A Modern Approach — The canonical AI theory text (Russell, Norvig).
• Deep Learning — Mathematical foundations of neural networks (Goodfellow, Bengio, Courville).
• Deep Learning: Foundations and Concepts — Bishop’s 2024 update; probability-grounded modern DL (Bishop & Bishop).
• Understanding Deep Learning — Math + intuition + Python notebooks (Simon Prince).
• Speech and Language Processing (3rd Edition) — The NLP reference, kept current through the deep learning era (Jurafsky, Martin).
• Reinforcement Learning: An Introduction (2nd Edition) — RL foundations (Sutton, Barto).

Courses

Beginner

• Google Generative AI Learning Path
• Hugging Face LLM Course
• Fast.ai — Practical Deep Learning

Intermediate / Advanced

• Stanford CS324: Large Language Models
• Full Stack Deep Learning
• MIT 6.S191: Intro to Deep Learning

Focused

• DeepLearning.AI Short Courses
• Google DeepMind — Introduction to Reinforcement Learning
• Karpathy — Neural Networks: Zero to Hero

Landmark Papers

Research that shaped modern AI — worth reading to understand the “why” behind today’s architectures.

• Attention Is All You Need — Transformer architecture.
• Scaling Laws for Neural Language Models — Model/data/compute scaling.
• Language Models are Few-Shot Learners — GPT-3 capabilities.
• Constitutional AI — Safer model alignment.

🛠 Build

The toolchain for building with AI. Personal note: you don’t need tons of frameworks — start with simple LLM calls and work up.

Guides & Playbooks

• Building Effective Agents (Anthropic) — ⭐ Patterns, pitfalls, and tradeoffs for designing AI agents.
• OpenAI Agents Guide — Practical guide on building agents.
• Google AI Agents Paper — Practical guide to building AI agents from Google.
• Google Agents Companion Paper — Companion guide from Google.
• OpenAI Cookbook — Example code, recipes, and best practices for working with OpenAI APIs.
• LLM Engineer Handbook — A goldmine of useful links for AI engineers.

Frameworks

• PocketFlow — Extremely minimalist AI agent framework in just 100 lines of code. Fantastic way to learn.
• Google ADK — Google’s Agent Development Kit (Python, Java). Great local development experience + A2A + MCP.
• Pydantic-AI — Typed, structured LLM orchestration framework built on Pydantic models for safe, predictable outputs.
• LangGraph — Build multi-agent workflows with stateful graphs on top of LangChain.
• CrewAI — Agent orchestration with structured tasks and human-in-the-loop controls.
• AutoGen — Microsoft’s framework for multi-agent conversation and collaboration.
• LlamaIndex — Data framework for ingesting, indexing, and querying private data with LLMs.
• Haystack — Open-source search/RAG framework with modular pipelines.
• Docling — Great library for ingesting any kind of document for RAG ⭐

Evals

• OpenAI Evals — OpenAI’s framework for writing evals.

IDEs

• Cursor — LLM-powered IDE for multi-file edits and codebase-aware chat.
• GitHub Copilot — In-IDE code completion, chat, and refactors.

🤖 Agents

Harnesses that turn LLMs into autonomous workers. The model is swappable; the harness is the product.

Coding

For live capability comparison, see Terminal-Bench and SWE-bench.

• Claude Code — Anthropic’s CLI agent; multi-file codebase refactoring with long context.
• Codex CLI — OpenAI’s Rust-based local terminal agent; lightweight and fast.
• Gemini CLI — Google’s official open-source terminal agent; long-context repo exploration.
• Cursor CLI — Cursor’s terminal-native agent with sandboxed permissions.
• Aider — Git-integrated pair programming with surgical edits and undo.
• OpenCode — Provider-agnostic terminal harness with a strong TUI.
• OpenHands — Open-source autonomous SWE platform; browser + shell + editor loop.
• Cline — Open-source agentic IDE extension with strong multi-provider support.
• Continue — Open-source IDE + CLI assistant with source-controlled rules.
• Goose — Block’s extensible, MCP-driven local agent.
• Factory Droid — Benchmark-leading multi-model harness with BYOK local execution.
• Amp — Sourcegraph’s commercial agentic coding tool with strong product UX.
• Mistral Vibe — Mistral’s agentic coding CLI, powered by Devstral.
• Qwen Code — Alibaba’s terminal coding agent, optimized for Qwen models.
• Pi — Highly customizable terminal harness; minimal base prompt, extension-driven.
• Nanocoder — Private, local-first agent for Ollama and LM Studio.
• Kilo CLI — Multi-mode agent with a unified gateway to 500+ models.

🧠 Models

State-of-the-art models by modality.

💬 Language

The major frontier labs.

• ChatGPT — Best for general reasoning, tool use, and the broadest ecosystem.
• Claude — Best for long-context analysis, coding, and structured thinking.
• Gemini — Best for multimodal tasks and Google ecosystem integration.
• Grok — Best for real-time information via X and very long context.
• Llama — Best open-weight family for self-hosting and fine-tuning.
• Mistral — Best for lightweight, high-performance open-weight models.
• DeepSeek — Best for cost-efficient reasoning with open weights.
• Qwen — Best for multilingual and Chinese-first applications.
• Kimi — Best for long-context instruction following.
• GLM — Frontier-tier Chinese model with open weights.
• Cohere — Best for enterprise LLMs with strong retrieval-augmented generation APIs.

🖼 Image

• GPT Image — OpenAI’s integrated image generation with near-perfect text rendering.
• Midjourney — Artistic and photorealistic images.
• Adobe Firefly — Integrated into Creative Cloud; commercial-safe.
• Ideogram — Precise, legible text in generated images.
• Flux — High-res, prompt-editable, open-weight images.

🎥 Video

• Google Veo — High-quality video with synchronized audio.
• Runway — Video editing + generation with granular creative control.
• Kling — Cinematic, realistic video generation.

🎙 Audio

• ElevenLabs — High-quality text-to-speech and voice cloning.
• Suno — AI music from text prompts.

📊 Compare

Live benchmarks, pricing, and the latest model versions.

• OpenRouter — Unified API + live pricing across ~300 models.
• LMArena — Human-preference Elo rankings for text, image, and video.
• Artificial Analysis — Speed, price, and quality benchmarks across providers.

📡 Follow

Stay current without drowning in noise.

Newsletters

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