A curated, opinionated list of best-in-class books for modern software development, popular programming languages, and AI/ML.
- Software Engineering Essentials
- Architecture, Data, and Distributed Systems
- Computer Science Foundations (Worth Knowing Forever)
- Version Control
- Security
- Programming Languages
- AI / Machine Learning
- Contributing
- The Pragmatic Programmer (20th Anniversary Edition) - Andrew Hunt and David Thomas - Practical habits, thinking tools, and engineering instincts that age incredibly well.
- Code Complete (2nd Edition) - Steve McConnell - The “how to write good code” encyclopedia: construction, readability, and craft.
- Clean Code - Robert C. Martin - Principles and examples for writing code that stays readable under pressure.
- Clean Architecture - Robert C. Martin - Core architectural ideas for keeping systems maintainable as they grow.
- Refactoring (2nd Edition) - Martin Fowler - The classic playbook for improving design without changing behavior.
- Working Effectively with Legacy Code - Michael Feathers - How to add tests and make changes safely in scary, untested codebases.
- Design Patterns: Elements of Reusable Object-Oriented Software - Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides - The OG patterns catalog (still useful when you treat it as vocabulary, not dogma).
- The Mythical Man-Month - Frederick P. Brooks Jr. - Timeless lessons on why software schedules explode and what to do about it.
- Designing Data-Intensive Applications - Martin Kleppmann - The go-to guide for storage, streams, consistency, and real-world system tradeoffs.
- Domain-Driven Design - Eric Evans - Modeling complex domains and aligning code with business reality.
- Release It! - Michael T. Nygard - Stability patterns and failure-mode thinking for production systems.
- Site Reliability Engineering - Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Murphy (eds.) - Reliability principles, SLOs, toil reduction, and operating at scale.
- Kubernetes: Up & Running - Kelsey Hightower, Brendan Burns, Joe Beda - A practical mental model for Kubernetes and container orchestration.
- PostgreSQL Books - A curated list of books and guides for PostgreSQL.
- Structure and Interpretation of Computer Programs (SICP) - Harold Abelson, Gerald Jay Sussman, Julie Sussman - Abstraction, interpreters, and “how to think like a programmer” at a deep level.
- Introduction to Algorithms (CLRS) - Cormen, Leiserson, Rivest, Stein - The standard reference for algorithms and data structures.
- The Art of Computer Programming - Donald E. Knuth - The deep end of algorithms, analysis, and programming elegance.
- Computer Systems: A Programmer’s Perspective - Randal E. Bryant and David R. O’Hallaron - What’s actually happening under your code: memory, caching, linking, concurrency.
- Operating Systems: Three Easy Pieces - Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau - OS fundamentals explained clearly (and free online).
- Pro Git - Scott Chacon and Ben Straub - The most readable Git book, from basics to power-user workflows.
- Serious Cryptography - Jean-Philippe Aumasson - Cryptography concepts explained for engineers who need to use it correctly.
- The Web Application Hacker’s Handbook - Dafydd Stuttard and Marcus Pinto - Practical web security testing and attack/defense thinking.
- Fluent Python - Luciano Ramalho - Idiomatic Python: data model, iterators, concurrency, typing, and best practices.
- Effective Python - Brett Slatkin - Sharp, practical items for writing clean, correct, modern Python.
- Python Cookbook (3rd Edition) - David Beazley and Brian K. Jones - Recipe-driven solutions to real Python problems (from basics to advanced).
- Python Books - A comprehensive list of Python books, tutorials, and learning materials.
- Eloquent JavaScript - Marijn Haverbeke - Best “learn JS by understanding it” book (and free online).
- You Don’t Know JS Yet - Kyle Simpson - Deep dive into JS mechanics: scope, closures, objects, async, and more.
- Programming TypeScript - Boris Cherny - How to scale JS codebases with TypeScript’s type system and patterns.
- JavaScript Books - A collection of books and learning resources for JavaScript.
- Effective Java (3rd Edition) - Joshua Bloch - The definitive guide to writing robust, idiomatic Java.
- Java Concurrency in Practice - Brian Goetz et al. - The classic on writing safe and scalable concurrent Java code.
- Boring Go! - Caleb Doxsey - A hands-on, approachable guide focusing on real-world Go practices and simplicity.
- The Go Programming Language - Alan A. A. Donovan and Brian W. Kernighan - Clear, thorough coverage of Go from core language to concurrency.
- GoBooks - A large, curated collection of Go books, tutorials, and learning resources.
- The Rust Programming Language (“the book”) - Steve Klabnik and Carol Nichols (with contributors) - The best first principles introduction to Rust (free online).
- Programming Rust - Jim Blandy, Jason Orendorff, Leonora F. S. Tindall - Practical, detailed Rust for real systems work.
- The C Programming Language - Brian W. Kernighan and Dennis M. Ritchie - The classic C text (short, sharp, foundational).
- Effective Modern C++ - Scott Meyers - Best practices for C++11/14 and “how to stop hurting yourself.”
- C++ Primer - Stanley B. Lippman, Josée Lajoie, Barbara E. Moo - A thorough, structured path through modern C++.
- C# in Depth - Jon Skeet - The book for understanding the “why” behind C# features (not just syntax).
- Kotlin in Action - Dmitry Jemerov and Svetlana Isakova - Practical Kotlin with the patterns you’ll actually use on real projects.
- Artificial Intelligence: A Modern Approach (AIMA) - Stuart Russell and Peter Norvig - The classic survey of AI concepts, agents, search, logic, probability, and learning.
- Pattern Recognition and Machine Learning (PRML) - Christopher M. Bishop - Core probabilistic ML foundations with a Bayesian viewpoint.
- The Elements of Statistical Learning (ESL) - Trevor Hastie, Robert Tibshirani, Jerome Friedman - Deep statistical learning theory; a reference you’ll keep coming back to.
- An Introduction to Statistical Learning (ISLR) - James, Witten, Hastie, Tibshirani, Taylor - Friendlier, more applied companion to ESL (great first stats-ML book).
- Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville - The deep learning reference text (also free online).
- Hands-On Machine Learning - Aurélien Géron - Practical ML with end-to-end projects using modern Python tooling.
- Deep Learning with Python - François Chollet - Intuitive deep learning practice from the creator of Keras (and free to read online).
- Designing Machine Learning Systems - Chip Huyen - End-to-end ML system design: data, training, deployment, monitoring, iteration.
- Natural Language Processing with Transformers - Lewis Tunstall, Leandro von Werra, Thomas Wolf - Modern NLP and transformer workflows with hands-on examples.
- Reinforcement Learning: An Introduction - Richard S. Sutton and Andrew G. Barto - The RL classic, from bandits to policy gradients.
- Generative Deep Learning - David Foster - Practical generative modeling: VAEs, GANs, diffusion, and transformers.
- AI Books - A broad, curated list of books and resources covering AI, ML, and deep learning.
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