Performance Testing

A builder's guide to AI-powered performance testing, from a multi-agent platform that turns 25 minutes of manual correlation into 75 seconds, written by the engineer who built it.

Break things on purpose, before production does it for you. Load tests that mirror reality and chaos experiments you can defend to management.

Your agent passed every load test and timed out in production anyway. This free practitioner's guide shows why the traditional performance playbook breaks for agentic AI, and what replaces it: latency budgets, bounded autonomy, token SLOs, and production-grade observability, from a performance engineering and observability architect with two decades in production.

Python was never built to be the fastest language, but the world it now powers demands more speed than ever. As JIT compilation pushes Python into new territory, old trade-offs are coming back into focus. What does it take to make a language faster without losing what made it popular in the first place?

A practical guide to evaluating small and local language models in the real world. Learn how to benchmark reliably, compare models across hardware and runtimes, optimize performance and avoid misleading results. With practical code and rigorous methods, you can build your own evaluation setup and make confident deployment decisions.

Python is entering a new performance era. Explore free-threaded execution, Rust extensions and modern tools for building faster, more scalable Python systems. Learn how CPython is changing, when Rust makes sense and how to turn these ideas into production-ready software.

Why is your system slow, and how do you know what to fix? This practical guide takes you from CPU and memory behavior to databases, networks and distributed systems. Learn how to profile, benchmark and diagnose real performance problems with working examples across modern languages and tools.

An advanced technical reference to Qiskit architecture, IBM Quantum hardware, pulse-level control, error mitigation, quantum algorithms, and scalable quantum-classical computing.Quantum computing is moving beyond the construction of theoretical circuits toward the practical challenge of executing workloads on noisy, hardware-constrained quantum processors. This transition requires a detailed understanding of quantum algorithms, compiler architecture, device topology, calibration, error mitigation, and the classical infrastructure that supports quantum execution. Advanced IBM Quantum Computing and Qiskit Architecture is an 80-chapter technical reference exploring the engineering principles behind modern Qiskit development and IBM Quantum systems. It connects quantum programming abstractions with hardware-aware compilation, runtime execution, performance analysis, and the challenges of building reproducible quantum-classical workflows.

This book is all about testing and closing the gap between infrastructure health and output quality, and how easily that gap can be filled for weeks by a failure that's gone unnoticed.This book looks at the less obvious side of the problem. In the next few chapters, we're going to run 41 experiments on one application. About half of them will give you a result that the system doesn't report. It's not a dig at anyone's engineering, since the systems in question are competently built and mostly behave well.

Slow SQL queries waste time, increase costs, and limit scalability. Mastering SQL Performance Optimization teaches you how to analyze execution plans, optimize queries and indexes, and solve real-world performance bottlenecks across today's leading database platforms.

Learn software testing the way it’s actually done: by doing it. The Test Analyst Field Workbook guides you step-by-step through every part of a test analyst’s job — from reading requirements and designing test cases, to logging defects, running regression cycles, and helping decide when software is ready to release. With real examples, reusable templates, and dozens of practice exercises, this book turns theory into practical skill you can use from day one.

Master the engineering principles behind Unsloth to fine-tune Large Language Models on limited hardware. Discover technical workflows for 4-bit quantization, custom Triton kernels, and Direct Preference Optimization. This guide covers the full lifecycle, including context expansion and production deployment via GGUF and vLLM. A rigorous reference for developers seeking to maximize LLM performance on consumer-grade GPUs.