On day one of joining a startup, a CTO handed me this article as essential reading. It lays out core practices and common pitfalls every founder and early team member should know before scaling.

This post lays out a curated list of books spanning history, science, philosophy, economics and literature to help broad-minded learners build a solid foundation across disciplines. Each recommendation comes with a brief note on its importance for generalists seeking context and depth in multiple fields.

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.