Master reading list

Papers — read the originals, in order

1. Attention is All You Need (Vaswani et al., 2017) — the transformer paper.
2. Retrieval-Augmented Generation (Lewis et al., 2020) — original RAG paper.
3. ReAct (Yao et al., 2022) — the reasoning + acting agent pattern.
4. GPT-4 Technical Report — what foundation-model capability looks like.
5. QLoRA (Dettmers et al., 2023) — efficient fine-tuning.
6. Mixtral (Jiang et al., 2024) — mixture-of-experts at production quality.

Blogs — subscribe to all of these

• Latent Space — podcast + newsletter; senior AI engineers as guests
• Eugene Yan — production AI patterns, evals
• Hamel Husain — opinionated, production-focused
• Lilian Weng — comprehensive technical deep-dives
• Anthropic Research — frontier model behaviour

Courses + lectures

• fast.ai practical deep learning
• Karpathy — Let's build GPT
• Hamel + Dan — Fine-tuning LLMs
• DeepLearning.AI short courses — quick wins on specific topics

Communities worth being in

• r/LocalLLaMA — open source weights + tooling
• Latent Space Discord
• The Hugging Face forums for fine-tuning specifics

How to use this list

One paper a week. One short course a month. Skim two blog posts a week from the list above. Build something every weekend. The field will move faster than you can keep up — accept it. Anchor on principles (cost, eval, reliability, tradeoffs); update specifics as the surface changes.