Software Engineering

Stop getting stuck in manual regression cycles and learn to engineer zero-touch automation pipelines. In 30 days, go from Manual QA to SDET by building a production-grade test framework from scratch with Playwright, TypeScript, and GitHub Actions—complete with a live portfolio report for hiring managers.

AI-assisted code often looks finished while resting on a premise nobody checked. A practical, evidence-based method for developers, tech leads and architects to find system-level defects before they ship.

The model is one box. This book is about every other box: retries, contracts, evals, budgets, guardrails and agents for backend engineers shipping LLM features to production.

Retries, timeouts, webhooks and month-end reconciliation: the parts tutorials skip. A practical book for backend engineers who build or own payment systems, from cards to real-time rails like Pix, UPI and QRIS.

Move from observing AI agents to operating them through runtime evidence, evaluation, economics, reliability, and governed action.This practitioner-focused book develops an architecture for production AI systems where traces, evaluations, safety signals, cost, business outcomes, and operational authority become part of one evidence model.

A short, hands-on guide to reverse engineering with AI agents and REA 6.4.0: drive Ghidra from Claude Code, Codex or Cursor, check every claim against evidence, and work through native, Electron, Android, .NET and web targets you are allowed to analyse. Every output shown is a real lab run.

Curious about agentic AI but not sure where to start? This beginner-friendly guide takes you from the basics to building your own AI agents, even if you have no coding experience. Learn how agents work, choose the right tools and turn your ideas into practical, reliable solutions for real-world problems.

Kill an AI agent mid-refund and the retry bills every model call again and refunds twice. This book crashes one agent on purpose and recovers it on DBOS, Temporal, Restate and Prefect, with the real output of every run.

Use AI-assisted engineering from core concepts to secure production workflows. Visual explanations and a continuing FleetOps case study connect context design, coding, testing, delivery, operations, and bounded agents with practical verification and human accountability.

Your LLM pipeline passed every check, and a fallback model still served 50 of its 71 successful calls. This case study follows one real multi-stage pipeline through its failures and shows how to make each stage traceable, attributable and honestly measured.

Decision models answer a typed question with a calibrated probability in one forward pass: no token stream, no parsing, no values outside your schema. This guide explains TypeSafe's Jev and the open "JEV-like" family, then shows how to choose, run, serve, fine-tune and evaluate local variants on your own hardware.

Polars 2.0 changed defaults that don't raise errors: lazy queries now stream, and joins no longer keep row order. This short guide shows exactly what breaks when you upgrade from 1.x, with every example run under both 1.44.2 and 2.0.0 and the real output printed side by side.

The battle-tested prompt framework and architectural system rules to turn any AI coding assistant into a principal full-stack engineer.

Você viu um modelo de linguagem fazer meia hora de trabalho sozinho. Este livro trata da pergunta que veio logo depois: até que ponto você pode confiar nele?

Skip the trial and error. 53 agent skills picked for Claude Code and Codex, with setup for both, each with who it fits, install commands, and a first run.