Beyond Vibe Coding. From Coder to AI-Era Developer (ebook) Ujazd

AI is transforming software development, shifting programmers from writing code to collaborating with AI in an intent-driven workflow. Vibe coding—a prompt-first, exploratory approach where you describe what you want in natural language and let a large language model fill in the blanks—represents a radical shift in the developer's role from writing code todirecting it. However, vibe coding comes with a serious caveat: Like a high-speed exploratory vehicle, it can take you off the beaten path quickly. Beyond Vibe Coding: From Coder to AI-Era Developer explores how AI-powered coding assistants like GitHub Copilot and OpenAI Codex are reshaping the way we build software, from automating routine coding tasks to influencing architecture and design decisions. Written by Addy Osmani, this guide provides developers, tech leads, and organizations with practical strategies to integrate AI into their workflows effectively. Learn how to formulate clear goals and constraints for the AI, review AI-generated code critically, and integrate those pieces into a coherent whole. Whether you're adopting AI tools today or preparing for the future of software engineering, this book offers insights and hands-on examples to keep your skills sharp in this evolving landscape. Understand how AI-assisted development is reshaping programming Master techniques for refining, validating, and debugging AI-generated code, and understand how and why LLM generations can go wrong Explore multiagent coding systems and AI-driven software workflows Future-proof your career by adapting to AI's growing role in development Spis treści: Preface Who This Book Is For What to Expect Conventions Used in This Book OReilly Online Learning How to Contact Us I. Foundations 1. Introduction: What Is Vibe Coding? The AI Coding Spectrum: From Vibe Coding to AI-Assisted Engineering The Vibe-Coding Approach: Code by Conversation The AI-Assisted Engineering Approach: Structure with an AI Partner Different Mindsets, Different Expectations Finding Your Place on the Spectrum Beyond Lines of Code: Programming with Intent The Rise of the Prompt: From Instructions to Descriptions How It Works: The Iterative Cycle and AIs Role in Code Generation Productivity, Accessibility, and the Changing Nature of Programming A Glimpse of the Tools: The Emerging Ecosystem VSCode + Copilot: Microsofts Integrated AI Development Platform VSCode + Cline: The Open Source Autonomous Coding Agent Cursor: The AI-Driven Code Editor Windsurf: An AI-Powered IDE with Full Codebase Indexing AI Models: The Landscape for Code Generation Understanding Model Categories Choosing the Right Model for Your Task Practical Tips for Any Model Major Models Google Gemini: The Multimodal Coding Powerhouse Claude: The Reasoning Virtuoso ChatGPT: The Versatile Coding Companion Choosing the Right Model for Your Needs The Benefits and Limitations of Vibe Coding: A Nuanced View Ideal Use Cases for Vibe Coding Zero-to-one product development Feature prototyping and CRUD applications Glue code and integration Modern framework utilization Repetitive code generation When AI-assisted engineering should take precedence Recognizing the transition points Where AI Still Struggles Summary and Next Steps 2. The Art of the Prompt: Communicating Effectively with AI Prompt Engineering Fundamentals Specificity and Clarity: Writing Prompts That Deliver Iterative Refinement: The Feedback Loop with the AI Comparing Two Prompts Poor prompt Improved prompt Prompting Techniques: A Toolbox for Effective Communication Zero-Shot Prompting One-Shot and Few-Shot Prompting Chain-of-Thought Prompting Role Prompting Contextual Prompting Metaprompting Self-Consistency (Multiple Outputs and Majority Voting) ReAct (Reason + Act) Prompting Advanced Prompting: Combining Techniques and Handling Complexity Know the Models Limits Stateful Conversation Versus One-Shot Prompting Common Prompt Antipatterns and How to Avoid Them The vague prompt The overloaded prompt Missing the question Vague success criteria Ignoring AIs clarification or output Inconsistency Vague references like the above code Summary and Next Steps II. AI Coding in Practice 3. The 70% Problem: AI-Assisted Workflows That Actually Work How Developers Are Actually Using AI Common Failure Patterns Two steps back The demo-quality trap What Actually Works: Practical Workflow Patterns AI as first drafter AI as pair programmer Best practices for AI pair programming AI as validator The Golden Rules of Vibe Coding 0. Summary and Next Steps 4. Beyond the 70%: Maximizing Human Contribution Senior Engineers and Developers: Leverage Your Experience with AI Be the Architect and the Editor in Chief Use AI as a Force Multiplier for Big Initiatives Mentor and Set Standards Continue to Cultivate Domain Mastery and Foresight Hone Your Soft Skills and Leadership Midlevel Engineers: Adapt and Specialize Learn to Manage Systems Integration and Boundaries Build Your Domain Expertise Master Performance Optimization and DevOps Focus on Code Review and Quality Assurance Learn Systems Thinking Be Adaptableand Never Stop Learning Get Good at Cross-Functional Communication Learn System Design and Architecture Use AI! Venture into UI and UX Design Junior Developers: Thrive Alongside AI Learn the FundamentalsDont Skip the Why Practice Problem Solving and Debugging Without the AI Safety Net Focus on Testing and Verification Build an Eye for Maintainability Develop Your Prompting and Tooling Skills (Wisely) Seek Feedback and Mentorship Communicate and Collaborate Shift Your Mindset: From Consuming to Creating Summary and Next Steps 5. Understanding Generated Code: Review, Refine, Own From Intent to Implementation: Understanding the AIs Interpretation The Majority Problem: Most Common Doesnt Mean Most Appropriate Code Readability and Structure: Patterns and Potential Issues Debugging Strategies: Finding and Fixing Errors Refactoring for Maintainability: Making AI Code Your Code The Importance of Testing: Unit, Integration, and End to End Summary and Next Steps 6. AI-Driven Prototyping: Tools and Techniques Rapid Prototyping with AI Assistants AI Prototyping Tools From Concept to Prototype: Iterative Refinement Evolving a Prototype Toward Production Addressing Challenges in AI Prototyping Summary and Next Steps 7. Building Web Applications with AI Setting Up the Project: Scaffolding with AI Frontend Development Patterns with AI Implementing components from descriptions Styling and layout Integrating APIs and state management Handling complexity with AI guidance Framework-specific tips Backend/API Development Patterns with AI Implementing API endpoints Database integration Business logic and validation Using frameworks or boilerplates Orchestrating multistep operations API documentation and testing Database Design and Integration Using an ORM Database Queries Checking AI-Generated Queries Full Stack Integration: Marrying Frontend and Backend Aligning Frontend and Backend Contracts Real-Time Collaboration with AI State management and sync WebSockets and advanced integrations Example: full stack flow with AI Optimizing AI-human collaboration in full stack development Testing and Validation for AI-Generated Web Applications Examples of Successful AI-Built Web Projects Summary and Next Steps III. Trust and Autonomy 8. Security, Maintainability, and Reliability Common Security Vulnerabilities in AI-Generated Code Improper Authentication and Authorization Package Management Issues Security Audits Leverage Automated Security Scanners Use a Separate AI as a Reviewer Perform a Human Code Review with a Security Checklist Penetration Testing and Fuzzing Add Security-Focused Unit Tests Provide Updates to Compensate for Training Cutoffs Optimize Your Logging Practices Use Updated Models or Tools with a Security Focus Pay Attention to Warnings in Context Slow Down Building Effective Testing Frameworks for AI-Generated Systems Performance Optimization Ensuring Maintainability in AI-Accelerated Codebases While Prompting Working with Code Output Follow-Up Code Review Strategies Best Practices for Reliable Deployment Before and During Deployment Ongoing Best Practices Summary and Next Steps 9. The Ethical Implications of Vibe Coding Intellectual Property Considerations What to Do If You Get Suspicious Output Gray Areas Transparency and Attribution Bias and Fairness Golden Rules for Responsible AI Use Summary and Next Steps 10. Autonomous Background Coding Agents From Copilots to Autonomous Agents: What Are Background Coding Agents? How Do Autonomous Coding Agents Work? How Do Background Agents Compare to In-IDE AI Assistants? Combining Multiple AI Models to Maximize Strengths Differentiate Models by Task Type Use an Orchestration System Human-AI Hybrid Teams Major Players in Autonomous Coding Agents Challenges and Limitations Best Practices for Using AI Coding Agents Effectively Strategically Select the Tasks Autonomous Agents Are Going to Implement Leverage Agent-Specific Planning and Oversight Features Manage Concurrent Agent Operations Evolve Your Team Practices to Integrate Agents Build Feedback Loops with Autonomous Systems Summary and Next Steps 11. Beyond Code Generation: The Future of AI-Augmented Development AI in Testing, Debugging, and Maintenance Automated Test Generation Intelligent Debugging Predictive Maintenance and Refactoring AI-Driven Design and User Experience Personalization Generative Design Tools AI for UX Research Personalized User Experiences The Evolution of Project Management with AI How Autonomous Agents Could Change Software Engineering The Future of Programming Languages: Natural-Language-Driven Development? How Vibe Coding Is Reshaping the Industry Summary and Next Steps Index O autorze: Addy Osmani to główny lider zespołu inżynieryjnego w Google, odpowiedzialny za doświadczenie programistów przeglądarki Chrome. Ma 25 lat doświadczenia w branży IT, jest autorem wielu książek o najlepszych praktykach inżynierii oprogramowania. Intensywnie testował narzędzia AI takie jak Cursor, Copilot, Claude Code, a jego publikacje o programowaniu wspomaganym AI wpłynęły na tysiące programistów.