Reading List

Over my career I've read a lot of books — these are the foundational ones: the titles that shaped how I think about computers, code, and the systems underneath them.

• Computer Systems: A Programmer's Perspective

  How programs actually run on hardware — bits and numeric representation, machine code, the memory hierarchy, linking, virtual memory, and concurrency. The book that turned "magic" into mechanics for me.

• The Elements of Computing Systems: Building a Modern Computer from First Principles

  Builds a working computer from NAND gates up: logic, ALU, CPU, assembler, VM, compiler, OS. The hands-on companion to CS:APP — you don't just read about the stack, you build it.

• Code: The Hidden Language of Computer Hardware and Software

  Petzold walks from Morse code and flashlight signals, through relays and logic gates, all the way to a working CPU — every step motivated before it appears. The gentlest on-ramp to how computers actually work.

• Code Complete

  A thousand pages of concrete advice on the craft of writing code: naming, routines, control flow, defensive programming, debugging, refactoring. Dense but readable — the kind of book you keep reaching for years later.

• Designing Data-Intensive Applications

  Kleppmann maps the landscape of modern data systems: storage engines, replication, partitioning, transactions, consensus, batch and stream processing. Explains the tradeoffs behind every database and message queue decision you'll ever make.

• Why Machines Learn: The Elegant Math Behind Modern AI

  Ananthaswamy traces the mathematical ideas — from the perceptron through gradient descent, kernels, and backpropagation — that quietly underpin modern machine learning. A readable bridge between the hand-wavy pop-sci story and a real textbook.

• Deep Learning with Python, Third Edition

  Chollet's hands-on tour of modern deep learning in Keras — from dense nets and convnets to transformers and generative models. Practical, opinionated, and written by the person who built the framework.

• High Performance Browser Networking

  Grigorik unpacks the network stack the browser actually uses: TCP, TLS, HTTP/1.1, HTTP/2, WebSocket, WebRTC, and the mobile radio layer underneath it all. If you ship anything over the wire, it explains why your page is slow.

• Fluent Python: Clear, Concise, and Effective Programming

  Ramalho goes past the syntax and into Python's data model: how dunder methods, descriptors, iterators, coroutines, and the async machinery actually fit together. The book that turns a Python user into a Python programmer.

• JavaScript: The Definitive Guide

  Flanagan's encyclopedic reference to the language: types, objects, closures, classes, modules, async, and the full standard library. Thick, precise, and the one book to own if you want the whole language in one place.

• Pro Git

  Chacon and Straub's free, official Git book: from daily workflow through branching, merging, and rebasing, all the way down to the object model and plumbing commands. The clearest explanation of what Git is actually doing when you type `git`.

• Seven Concurrency Models in Seven Weeks

  Butcher walks through threads and locks, functional programming, the Clojure identity/state split, actors, CSP, data parallelism on the GPU, and the lambda architecture — one paradigm per week. The book that makes "pick the right concurrency model" a real choice instead of a slogan.