LLM fundamentals
Tokens, context windows, safety, and how models are trained.
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Video45 minAndrej Karpathy - Intro to Large Language Models
Foundational intuition for pretraining, RLHF, and how tokens become completions.
Open resource- Article55 min
Stanford CS324 - Generative models (course hub)
Course hub with readings on scaling laws, data, and evaluation basics.
Open resource - Docs25 min
OpenAI - GPT models documentation
Official model cards, context limits, and capability notes.
Open resource - Docs20 min
Anthropic - Claude product documentation
Safety, context windows, and API usage patterns.
Open resource - Article35 min
Jay Alammar - A visual intro to transformers
Classic illustrated walkthrough of attention and encoder-decoder stacks.
Open resource - Article30 min
Hugging Face - Tokenizers course
How text becomes token IDs and why it matters for cost and quality.
Open resource - Docs15 min
Google - Gemini API quickstart
First request, auth, and model selection on Google AI Studio.
Open resource 
Video20 min3Blue1Brown - But what is a neural network?
Gentle prerequisite if you need intuition on gradients and layers.
Open resource- Article40 min
Lilian Weng - LLM-powered autonomous agents
Survey connecting LLM fundamentals to planning and tool use.
Open resource - Article25 min
NIST AI - Risk management framework overview
High-level governance vocabulary for safety and compliance discussions.
Open resource - Article22 min
Weights & Biases - Evaluating LLMs
Why offline metrics differ from user-perceived quality.
Open resource - Article15 min
Simon Willison - Context windows and tokens
Practical notes on tokenizer quirks and counting tokens in apps.
Open resource