This post highlights the first book that pulls together language modeling, inference optimization, reinforcement learning, system scaling, agentic AI, retrieval-augmented generation, memory, environments, and benchmarks in one volume. It then points you to paperswithcode.co’s “most cited” list and recommends reading the top ten papers, coding them, and writing about your findings.

• A single book reportedly covers language modeling, inference optimization, RL, system scaling, agentic AI, RAG, memory, and benchmarks together—rare breadth even after five years of rapid AI progress.
• Recommended self-study path: go to paperswithcode.co's "most cited" list and work through the top ten papers.
• Suggested pace is one to two papers per week, each time reading, breaking down the math, building a toy implementation, and writing up findings.