Coding

• Codex CLI - based local terminal agent; lightweight and fast.
• Gemini CLI - source terminal agent; long-context repo exploration.
• Cursor CLI - native agent with sandboxed permissions.
• Aider - integrated pair programming with surgical edits and undo.
• OpenCode - agnostic terminal harness with a strong TUI.
• Cline - source agentic IDE extension with strong multi-provider support.
• Continue - source IDE + CLI assistant with source-controlled rules.
• Goose - driven local agent.
• Factory Droid - leading multi-model harness with BYOK local execution.
• Amp
• Mistral Vibe
• Qwen Code
• Pi - driven.
• Nanocoder - first agent for Ollama and LM Studio.
• Kilo CLI - mode agent with a unified gateway to 500+ models.
• Terminal-Bench - bench](https://www.swebench.com/)._

Evals

• OpenAI Evals

Frameworks

• PocketFlow
• Google ADK
• Pydantic-AI
• LangGraph - agent workflows with stateful graphs on top of LangChain.
• CrewAI - in-the-loop controls.
• AutoGen - agent conversation and collaboration.
• LlamaIndex
• Haystack - source search/RAG framework with modular pipelines.
• Docling

Guides & Playbooks

• Building Effective Agents (Anthropic)
• OpenAI Agents Guide
• Google AI Agents Paper
• Google Agents Companion Paper
• OpenAI Cookbook
• LLM Engineer Handbook

IDEs

• GitHub Copilot - IDE code completion, chat, and refactors.

📄 Landmark Papers

• Google Deepmind| Introduction to Reinforcement Learning

Commerical Tools

• MIT Artificial Intelligence Videos - MIT AI Course
• Grokking Deep Learning in Motion - Beginner's course to learn deep learning and neural networks without frameworks.
• Intro to Artificial Intelligence - Learn the Fundamentals of AI. Course run by Peter Norvig
• Machine Learning - Basic machine learning algorithms for supervised and unsupervised learning
• Deep Learning - An Introductory course to Deep Learning using TensorFlow.
• Stanford Statistical Learning - Introductory course on machine learning focusing on linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines.
• Deep RL Bootcamp Lectures - Deep Reinforcement Bootcamp Lectures - August 2017
• Machine Learning Crash Course By Google - world case studies, and hands-on practice exercises.
• Deep Learning Crash Course
• Introduction to Artificial Intelligence (AI) - A high-level introduction to AI from IBM on Coursera
• Introduction to Generative AI - A beginner-level introduction to Generative AI from Google on Coursera
• Knowledge Based Artificial Intelligence - Georgia Tech's course on Artificial Intelligence focussing on Symbolic AI.
• EdX Artificial Intelligence - The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems

• CS50’s Intro to Artificial Intelligence - This course explores the concepts and algorithms at the foundation of modern artificial intelligence
• EdX Artificial Intelligence - The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems
• Artificial Intelligence For Robotics - This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics
• Python Class By Google
• Artificial Intelligence: A Modern Approach - Stuart Russell & Peter Norvig
• list of recommended reading
• Paradigms Of Artificial Intelligence Programming: Case Studies in Common Lisp - Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems
• Reinforcement Learning: An Introduction - This introductory textbook on reinforcement learning is targeted toward engineers and scientists in artificial intelligence, operations research, neural networks, and control systems, and we hope it will also be of interest to psychologists and neuroscientists.
• The Cambridge Handbook Of Artificial Intelligence - Written for non-specialists, it covers the discipline's foundations, major theories, and principal research areas, plus related topics such as artificial life
• The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind - In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work
• Artificial Intelligence: A New Synthesis - Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI
• On Intelligence - Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. Also audio version available from audible.com
• How To Create A Mind - Kurzweil discusses how the brain works, how the mind emerges, brain-computer interfaces, and the implications of vastly increasing the powers of our intelligence to address the world’s problems
• Deep Learning and the Game of Go - Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex human-flavored reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game.
• Deep Learning for Search - Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.
• Deep Learning with PyTorch - PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.
• Deep Reinforcement Learning in Action - Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects.
• Grokking Deep Reinforcement Learning - Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.
• Fusion in Action - Fusion in Action teaches you to build a full-featured data analytics pipeline, including document and data search and distributed data clustering.
• Real-World Natural Language Processing - Early access book on how to create practical NLP applications using Python.
• Grokking Machine Learning - Early access book that introduces the most valuable machine learning techniques.
• Succeeding with AI - An introduction to managing successful AI projects and applying AI to real-life situations.
• Elements of AI (Part 1) - Reaktor/University of Helsinki - An Introduction to AI is a free online course for everyone interested in learning what AI is, what is possible (and not possible) with AI, and how it affects our lives – with no complicated math or programming required.
• Transfer Learning for Natural Language Processing - A book that gets you up to speed with the relevant ML concepts and then dives into transfer learning for NLP.
• Amazon Machine Learning Developer Guide - A book for ML developers which introduces the ML concepts & strategies with lots of practical usages.
• Machine Learning for Humans - A series of simple, plain-English explanations accompanied by math, code, and real-world examples.
• Machine Learning Observability Course - Self-guided course covers the intuition, math, and best practices for effective machine learning observability.

Newsletters

• Superhuman AI
• The Rundown AI
• AlphaSignal
• AI Engineer

Books

• Designing Machine Learning Systems
• Generative Deep Learning (2nd Edition)
• AI Engineering - to-end AI product building (Chip Huyen).
• The 100-Page Language Models Book - grounded path from n-grams to transformers (Andriy Burkov).
• Artificial Intelligence: A Modern Approach
• Deep Learning
• Reinforcement Learning: An Introduction (2nd Edition)
• Build a Large Language Model from Scratch
• Hands-On Large Language Models
• LLM Engineer's Handbook - tuning, quantization, serving (Labonne, Iusztin).
• Deep Learning: Foundations and Concepts - grounded modern DL (Bishop & Bishop).
• Understanding Deep Learning
• Speech and Language Processing (3rd Edition)

Courses

• Google Generative AI Learning Path
• Hugging Face LLM Course
• Fast.ai — Practical Deep Learning
• Stanford CS324: Large Language Models
• Full Stack Deep Learning
• MIT 6.S191: Intro to Deep Learning
• DeepLearning.AI Short Courses

Landmark Papers

• Attention Is All You Need
• Scaling Laws for Neural Language Models
• Language Models are Few-Shot Learners - 3 capabilities.
• Constitutional AI

🎙 Audio

• ElevenLabs - quality text-to-speech and voice cloning.
• Suno

📊 Compare

• OpenRouter
• LMArena - preference Elo rankings for text, image, and video.
• Artificial Analysis

🖼 Image

• Midjourney
• Ideogram
• Flux - res, prompt-editable, open-weight images.
• Adobe Firefly - safe.
• GPT Image - perfect text rendering.

💬 Language

• Gemini
• ChatGPT
• Cohere - augmented generation APIs.
• Mistral - performance open-weight models.
• Qwen - first applications.
• DeepSeek - efficient reasoning with open weights.
• Grok - time information via X and very long context.
• Llama - weight family for self-hosting and fine-tuning.
• Kimi - context instruction following.
• GLM - tier Chinese model with open weights.

🎥 Video

• Runway
• Kling

📄 Landmark Papers

• AI Engineer