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Inference Engineering by Philip Kiely

Inference Engineering

Inference Engineering is your guide to becoming an expert in inference. This book is based on the hundreds of thousands of words of documentation, blogs, and talks I've published on inference; interviews with dozens of experts from Baseten's engineering team; and countless conversations with customers and builders around the world.

Philip Kiely

Artificial Intelligence
Learning Patterns by Lydia Hallie and Addy Osmani

Learning Patterns

Patterns.dev is a free online resource on design, rendering, and performance patterns for building powerful web apps with vanilla JavaScript or modern frameworks.

Lydia Hallie and Addy Osmani

Programming
Go With The Domain by Miłosz and Robert

Go With The Domain

Building Modern Business Software in Go

Miłosz and Robert

Programming
Eloquent JavaScript by Marijn Haverbeke

Eloquent JavaScript

This is a book about JavaScript, programming, and the wonders of the digital.

Marijn Haverbeke

Programming
Eloquent JavaScript by Marijn Haverbeke

Principles of Building AI Agents

Principles of Building AI Agents is the world's leading guide to getting started with AI agents. Written by Sam Bhagwat, CEO of Mastra, it covers the core concepts, tools and patterns developers need to build agents that reason, use tools, manage memory and orchestrate workflows in production.

Sam Bhagwat

Artificial Intelligence
Eloquent JavaScript by Marijn Haverbeke

Patterns for Building AI Agents

Patterns of Building AI Agents is a practical guide to taking AI agents from prototype to production. Written by Sam Bhagwat, CEO of Mastra, it covers design patterns, context engineering, eval workflows and security emerging from teams pushing agents into production at prominent AI companies.

Sam Bhagwat

Artificial Intelligence
Architectural Metapatterns: The Pattern Language of Software Architecture

Architectural Metapatterns: The Pattern Language of Software Architecture

Architectural Metapatterns is all about patterns and their relations. It generalizes hundreds of individual patterns into several wider classes (metapatterns) each of which can be applied to a local or distributed system to change its properties in a certain way. Rinse and repeat.

Denys Poltorak

Software Architecture
Introduction to Machine Learning System

Introduction to Machine Learning System

Machine learning has evolved from a research discipline into an engineering practice. Building systems that learn from data requires more than understanding algorithms—it demands expertise spanning data pipelines, model development, optimization for deployment constraints, and operational practices. This book introduces AI engineering: the discipline of building ML systems that work in the real world. The treatment covers four areas: foundations (system characteristics, development workflows), building (deep learning mathematics, architectures, framework internals), optimization (compression, hardware acceleration, benchmarking), and deployment (serving infrastructure, operations, responsible engineering). The emphasis throughout is on engineering trade-offs and quantitative analysis.

Vijay Janapa Reddi

Artificial Intelligence
Machine Learning Systems at Scale

Machine Learning Systems at Scale

Modern machine learning operates at scales that fundamentally change engineering requirements—models too large for single GPUs, services spanning continents, deployments carrying societal responsibilities. This book addresses AI engineering at scale. The treatment follows the lifecycle of a massive-scale system: defining the distributed architecture, building the physical infrastructure fleet, ensuring operational reliability, deploying to global users, and hardening the system for safety and responsibility.

Vijay Janapa Reddi

Artificial Intelligence