AI Researcher the Hard Way
A complete, brutally practical guide to becoming an AI researcher — built the hard way, from first principles to frontier systems. Eight concise pages cover every layer of the field, from theory and tools to workflow and discipline.
Topics Overview
The map of the field. A high-level tour of artificial intelligence: machine learning, deep learning, NLP, computer vision, reinforcement learning, robotics, alignment, and beyond.
Foundations
The core vocabulary of AI. Twenty-four terms every researcher must know — models, loss functions, gradients, activations, embeddings, and evaluation metrics.
Intermediate Concepts
The building blocks of modern deep learning systems: attention, transformers, tokenizers, distributed training, and optimization techniques.
Advanced Concepts
The methods powering frontier models — RLHF, Mixture-of-Experts, retrieval-augmented generation, scaling laws, and interpretability.
Frontier & Experimental Concepts
The speculative edge of AI: agentic systems, self-refinement loops, neural architecture search, world models, embodied AI, and the long road toward AGI.
Research Tools & Ecosystem
The practical stack: PyTorch, JAX, Hugging Face, NeMo, DeepSpeed, Docker, Kubernetes, MLflow, and the infrastructure that makes research real.
Career Path & Skills Map
The human roadmap. Twenty-four skills that chart your growth from beginner to independent researcher — from reading papers to leading projects.
Workflow & Daily Practice
The discipline behind discovery. The daily habits, checklists, and reflection rituals that make research productive and sustainable.
The hard way isn’t about difficulty — it’s about depth. Master these eight pages and you’ll speak the full language of AI research, from mathematics to methodology, from systems to ethics.