Vibe Engineering, Best practices, mistakes, and tradeoffs, Version 4, MEAP, Lelek T., Skowronski A., 2026

Vibe Engineering, Best practices, mistakes, and tradeoffs, Version 4, MEAP, Lelek T., Skowronski A., 2026.

   In Vibe Engineering, we explore the tension between speed and understanding, convenience and control. You’ll learn to identify the illusions of progress that arise from over-reliance on AI tools and how to design processes that preserve human judgment while amplifying productivity. We focus on practical techniques—frameworks for reasoning about cost versus accuracy, scientific methods for validating AI-generated code, and strategies for maintaining high engineering standards in an AI-augmented environment.
To get the most out of this book, you should have a basic familiarity with Java (and optionally Python) and feel comfortable using a modern IDE such as IntelliJ IDEA or Visual Studio Code. You’ll also need access to ChatGPT (a free account works fine) and, if you’d like to follow along with the examples, Cursor IDE or Visual Studio Code with the Cline extension.




Illusion of speed or “vibe over engineering”.
Mistakes and errors ripple through AI projects like background static: omnipresent, easy to ignore, and full of lessons if you listen. In this climate of rapidly evolving tools, "vibe coding" has emerged as a discipline: a lightning-fast, iterative mode of app creation powered by LLMs but often stripped of professional rigor, tests, and security hygiene -essentially trusting AI outputs "by feel" without deep verification. That approach speeds up prototyping, yet shipping it to production is a recipe for disaster.

The cases below are not theoretical; they are documented failures that forcefully expose the trade-offs, blind spots, and consequences of mistaking velocity for safety. We examine them not to criticize, but to extract the patterns and principles that separate reckless speed from disciplined AI engineering. We chose these four cases deliberately: each represents a different failure vector - external attack surface, irreversible data loss, supply-chain compromise, and unconstrained autonomy - so the patterns we extract apply broadly, not just to one style of mistake.

Contents.
1 Building on quicksand: the challenges of vibe engineering.
2 Building a legacy modernization framework powered by Vibe Engineering.
3 Context fabric: optimizing context for AI agents.
4 LLM-Driven Data Science.
5 Continuous AI development with AI-Native SDLC.
6 A scientific approach for validating LLM-based solutions.
7 Vibe performance engineering: when assumptions mislead.
8 Evaluation is King: Ultra-tight engineering loop for AI-powered codebases.
9 FinOps for LLMs: Cost-cutting, confidentiality, and the right chips.
10 Code Organization for AI: Tame your codebase with Monorepo & Friends.



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Теги: учебник по информатике :: информатика :: компьютеры :: Lelek :: Skowronski