# LLM Fundamentals — Resources

## Official & Research

- [Anthropic Research](https://www.anthropic.com/research) — Claude safety, scaling, and capabilities.
- [Attention Is All You Need](https://arxiv.org/abs/1706.03762) — Original transformer paper (Vaswani et al.).

## Videos

- [3Blue1Brown — Neural Networks](https://www.youtube.com/watch?v=aircAruvnKk) — Visual intuition for neural networks.
- [Andrej Karpathy — State of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A) — How GPT models work, training, and inference.
- [Andrej Karpathy — Intro to Large Language Models](https://www.youtube.com/watch?v=zjkBMFhNj_g) — Conceptual overview of LLMs.
- [AI Explained — How do LLMs work?](https://www.youtube.com/results?search_query=AI+Explained+LLM) — Accessible explainers.

## Articles

- [Lilian Weng — LLM Survey](https://lilianweng.github.io/posts/2023-01-10-the-gen-ai-layer/) — Overview of the LLM stack.
- [Chip Huyen — LLM Observability](https://huyenchip.com/) — Production considerations.
- [Simon Willison — LLMs](https://simonwillison.net/series/llms/) — Practical LLM usage.

## Books

- **Build a Large Language Model (From Scratch)** by Sebastian Raschka — Implement an LLM step by step.
- **AI Engineering** by Chip Huyen — Production ML and LLM systems.

## Tools

- [OpenAI Tokenizer](https://platform.openai.com/tokenizer) — See how text is tokenized.
- [tiktoken](https://github.com/openai/tiktoken) — OpenAI's tokenizer library for Python.
- [Claude API](https://docs.anthropic.com/en/api/getting-started) — Experiment with context and temperature.