Book
Applied Computer Science
Production systems, built from first principles — A narrative companion to the Applied CS curriculum: the engineering concepts behind production software, data, ML, and infrastructure systems. Part I teaches the cross-cutting ideas — concurrency, type systems, memory, error handling, testing, performance — once, comparatively across six languages; the rest are field guides and domain deep-dives. Every chapter is cross-linked to a real, runnable implementation in the companion projects. The AI-engineering material lives in the separate AI Engineering book.
What's inside
The book is organized into 6 parts that map roughly to career progression.
Who it's for
- →Engineers who want the concepts behind production systems, not just syntax
- →Polyglot developers working across Python, TypeScript, Go, Java, C++, and Rust
- →Backend and data engineers strengthening their systems fundamentals
- →Self-taught developers filling in computer-science foundations
- →CS students who want to see how the theory shows up in real systems
Cross-Language Foundations
The cross-cutting concepts — concurrency, type systems, memory, error handling, testing, performance — taught once, comparatively across all six languages.
Language Field Guides
Python, TypeScript, Go, Java, C++, and Rust — each slimmed to its distinctive idioms and ecosystem.
- ch 8Python: Advanced Language Features
- ch 9Python: Design Patterns & Architecture
- ch 10Python: Web Development
- ch 11Python: Microservices
- ch 12Python: Observability
- ch 13TypeScript: Fundamentals
- ch 14TypeScript: The Node Ecosystem
- ch 15TypeScript: Frontend with React
- ch 16Go: Fundamentals
- ch 17Go: Packages & Modules
- ch 18Go: Web Services & gRPC
- ch 19Java: Modern Java
- ch 20Java: Streams & Functional Programming
- ch 21Java: Spring Boot & Web Services
- ch 22C++: Fundamentals
- ch 23C++: Modern C++
- ch 24C++: Build Systems
- ch 25Rust: Fundamentals
- ch 26Rust: Ownership & Borrowing
- ch 27Rust: Unsafe Rust
Data Engineering
Orchestration, processing, warehousing, streaming, quality, and the infrastructure behind production data systems.
Machine Learning Engineering
From ML foundations and deep learning to distributed training, GPUs, and production ML systems.
Cross-Cutting Concerns
What production demands across every system: CI/CD, observability, security, and cost.
- ch 42What Production Demands
- ch 43CI/CD and Deployment Automation
- ch 44Observability
- ch 45Security
- ch 46Cost Optimization
Infrastructure
Containerization, orchestration, and benchmarking the systems that run it all.