Chapter 1: Further Reading

An annotated bibliography of resources for deeper exploration of the concepts introduced in Chapter 1. Resources are organized by category and include a brief description of what each offers and why it is relevant.

Foundational References

1. Andrej Karpathy's Original "Vibe Coding" Post (February 2025)

Source: X (formerly Twitter), @kaborphy Format: Social media post and thread The original post that coined the term "vibe coding." Essential primary source material for understanding the origin and original intent behind the concept. Karpathy describes his personal experience of using AI to generate entire programs through conversation, including his observation that the approach "mostly works" for personal projects. The surrounding thread includes responses from other notable developers and researchers, capturing the immediate community reaction.

2. "Software 2.0" by Andrej Karpathy (2017)

Source: Medium / Karpathy's blog Format: Blog post A prescient essay written years before vibe coding, in which Karpathy argues that neural networks represent a new paradigm of software development — "Software 2.0" — where programs are not written by hand but learned from data. This earlier essay provides essential context for understanding the intellectual trajectory that led to the vibe coding concept. While Software 2.0 focused on machine learning models themselves, vibe coding extends the idea to general-purpose software creation.

3. "The Rise of the AI Engineer" by Swyx (2023)

Source: Latent Space blog Format: Blog post and talk An influential essay arguing that a new role — the "AI Engineer" — was emerging alongside traditional software engineers and machine learning engineers. The AI Engineer uses AI models as building blocks rather than building AI models from scratch. This is closely related to vibe coding, where the practitioner directs AI code generation without necessarily understanding the underlying AI models. Provides useful context for understanding the professional landscape around vibe coding.

AI and Software Development

4. "Generative AI for Software Development: A Systematic Review" (2024)

Source: Various academic publications (IEEE, ACM) Format: Academic survey paper A comprehensive review of research on AI-assisted software development, covering code generation, bug detection, test generation, and documentation. Provides empirical evidence for the capabilities and limitations of AI coding tools, grounding the more anecdotal claims about vibe coding productivity in systematic research. Particularly relevant for readers who want to understand the science behind the tools.

5. "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot" (2024)

Source: GitHub / Microsoft Research Format: Research report The study referenced in Section 1.4, finding that developers using AI tools completed tasks up to 55% faster. This report provides detailed methodology, nuanced findings (productivity gains vary by task type, developer experience, and language), and discussion of implications. Essential reading for anyone who wants to cite specific evidence about AI-assisted development productivity.

6. "Large Language Models for Code: Capabilities, Limitations, and Opportunities" (2024)

Source: arXiv / academic preprint Format: Survey paper A thorough technical survey of how large language models handle code-related tasks. Covers code generation, code understanding, bug fixing, code translation, and more. While more technical than most readers of Chapter 1 may need, it provides the empirical basis for understanding what AI can and cannot do with code — directly relevant to Section 1.7's assessment of what you can build with vibe coding.

Practical Guides and Tutorials

7. "Prompt Engineering for Developers" — Various Online Courses (2024-2025)

Source: DeepLearning.AI, Coursera, and other platforms Format: Online course Several excellent online courses have emerged teaching the art of writing effective prompts for AI coding tools. These courses provide practical, hands-on training in the skill that Section 1.3 identifies as the primary skill for vibe coding: "crafting effective descriptions and prompts." Look for courses that specifically focus on coding-related prompts rather than general-purpose prompt engineering, as the techniques differ.

8. Documentation for Major AI Coding Tools

Source: Official documentation sites Format: Documentation, tutorials, and guides - Claude (anthropic.com): Documentation for Claude's coding capabilities - GitHub Copilot (github.com/features/copilot): Setup guides and best practices - Cursor (cursor.com): Editor documentation and AI features guide - Replit (replit.com): Platform documentation for AI-assisted development

These official resources are the most up-to-date guides for the tools discussed in Chapter 1 and explored in depth in Chapters 3 and 4. Tool capabilities evolve rapidly, so check these sources regularly.

Broader Context and Perspectives

9. "The Pragmatic Programmer" by David Thomas and Andrew Hunt (20th Anniversary Edition, 2019)

Source: Addison-Wesley Professional Format: Book A classic software development book that remains deeply relevant in the age of vibe coding. Its emphasis on thinking about what you are building (not just how), understanding your tools, and maintaining a pragmatic approach to software quality translates directly to vibe coding practice. Many of the book's "tips" — like "Don't Live with Broken Windows" and "Use Tracer Bullets" — apply just as well when the code is AI-generated. Provides the professional mindset that Chapter 1 argues is essential for effective vibe coding.

10. "Don't Make Me Think" by Steve Krug (Revisited Edition, 2014)

Source: New Riders Format: Book The classic book on web usability and user experience design. Relevant to vibe coding because one of the most common applications of vibe coding is building web interfaces. Understanding basic UX principles helps you describe what you want more effectively and evaluate the AI's output more critically. When you tell the AI "make it user-friendly," knowing what that actually means produces better results.

11. "AI 2041: Ten Visions for Our Future" by Kai-Fu Lee and Chen Qiufan (2021)

Source: Currency / Random House Format: Book A blend of science fiction stories and non-fiction analysis exploring how AI will transform various aspects of life over the coming decades. While not specifically about vibe coding, it provides valuable context for the broader AI transformation that vibe coding is part of. Helps readers think about the long-term implications discussed in Section 1.4 (Why Vibe Coding Matters Now) and the trajectory toward Level 5 autonomous development.

Community and Ongoing Discussion

12. Hacker News Discussions on Vibe Coding (2025-ongoing)

Source: news.ycombinator.com Format: Discussion forum The Hacker News community has hosted extensive, high-quality discussions about vibe coding since the term was coined. Searching for "vibe coding" on the site surfaces debates between skeptics and enthusiasts, practical experience reports, and nuanced analysis from working developers. These discussions capture the living, evolving nature of the vibe coding phenomenon better than any static publication.

13. r/vibecoding and r/ChatGPTCoding on Reddit

Source: reddit.com Format: Community forums Active communities where practitioners share their vibe coding experiences, ask for help, showcase projects, and discuss best practices. Particularly useful for seeing the range of projects being built with vibe coding (relevant to Section 1.7) and the real challenges people encounter (relevant to Section 1.8's misconceptions and realities).

14. "Latent Space" Podcast

Source: latent.space Format: Podcast A podcast focused on AI engineering that frequently covers AI-assisted development topics, including vibe coding. Episodes feature interviews with tool builders, researchers, and practitioners. Provides ongoing, current perspectives on the rapidly evolving landscape described in Chapter 1.

15. Simon Willison's Blog (simonwillison.net)

Source: simonwillison.net Format: Blog Simon Willison, a respected software developer and creator of Datasette, maintains one of the most thoughtful and prolific blogs about practical AI-assisted development. His posts include detailed accounts of using AI to build real software, analysis of AI coding tool capabilities, and honest assessments of limitations. His work exemplifies the kind of informed, skeptical-but-enthusiastic approach to vibe coding that this textbook advocates.

How to Use These Resources

Start with entries 1 and 2 if you want to understand the intellectual origins of vibe coding directly from Karpathy's writing.
Read entries 9 and 10 if you want to build a stronger foundation in software development and user experience principles that will make you a better vibe coder.
Explore entries 12-15 for ongoing community discussion and up-to-date perspectives, since the vibe coding landscape evolves rapidly.
Consult entries 4-6 for evidence-based understanding of what AI can and cannot do with code.
Use entries 7-8 when you are ready to start practicing (beginning with Chapter 4 of this textbook).

Continue to Chapter 2: How AI Coding Assistants Work