TL;DR
Vibe coding, highlighted by Andrej Karpathy, means “fully giving in to the vibes” and letting AI generate code while you guide it with natural language. My practical approach involves breaking solutions into modules, demanding unit tests for everything, suggesting specific patterns, intervening when needed, and maintaining a living design document with diagrams. Select the appropriate AI model (Claude excels in programming). The key is maintaining architectural control while adopting AI’s capabilities. I recently built a solution prototype in 8 working hours that would have traditionally taken over 100 working hours. We’re no longer writing code; we’re orchestrating solutions at a higher level. Vibe coding, as Karpathy envisioned it, isn’t about becoming passive consumers of AI-generated code. It’s about ascending to a higher level of abstraction where we can focus on what truly matters: solving problems, creating value, and building systems that work.
Please be ready that I will time to time update this blog post, as the topic is very dynamic
The Dawn of Vibe Coding
Andrej Karpathy popularises the term “vibe coding” as a method where developers “fully give in to the vibes, embrace exponentials, and forget that the code even exists.”
This isn’t about abandoning programming knowledge or becoming passive observers. Instead, it’s about fundamentally reimagining the developer’s role in the age of AI. When Karpathy built prototypes like MenuGen, he demonstrated this philosophy in action: letting LLMs generate all the code while he provided goals, examples, and feedback through natural language instructions. The programmer transforms from a manual coder into a guide, tester, and feedback provider for AI-generated source code.
A Spectrum of Interpretations
Vibe coding is a relatively new term that can be interpreted differently depending on the sector and type of product. Each developer brings their own perspective to this paradigm. As the Udemy blog articulates: “Vibe coding isn’t just about creativity or aesthetics, it’s about working in flow with AI to build smarter, faster, and more intuitively. It’s when your technical instincts align with your tools, and you’re designing scalable, secure solutions at record speed, without sacrificing clarity or quality.”
The emergence of agent modes in tools like Cursor and GitHub Copilot (Agent mode) has turned vibe coding from an experimental concept into a practical reality. These aren’t just autocomplete tools anymore, they’re collaborative partners that can understand context, implement entire features, and even reason about architecture.
What makes this revolution particularly interesting is how different developers are finding their own rhythms and methodologies. Among my colleagues, I’ve observed a fascinating spectrum of approaches, each reflecting personal preferences and project requirements.
The Next Frontier: Agentic Swarms
As vibe coding matures, we’re beginning to see the emergence of agentic swarms, multiple AI agents working collaboratively on different aspects of a project simultaneously. Instead of a single AI assistant, imagine a coordinated team where:
- One agent handles frontend components while another manages backend APIs
- A testing agent continuously validates code as it’s generated
- An architecture agent ensures consistency across modules
- A security agent scans for vulnerabilities in real-time
The Fundamental Paradigm Shift
Traditional programming centers on implementation precision, every semicolon, function, and import must be manually crafted. Vibe coding shifts focus to problem articulation. Your expertise isn’t measured by memorizing syntax patterns, but by how clearly you can describe outcomes:
| Aspect | Traditional Programming | Vibe Coding |
|---|---|---|
| Code Creation | Manual writing | AI-generated from prompts |
| Developer Role | Architect/Implementer | Outcome Describer & Guide |
| Expertise Focus | Syntax/framework mastery | Problem articulation |
| Debugging | Manual analysis | AI-driven fixes via prompts |
| Quality Control | Code reviews | Outcome validation |
| Speed | Limited by typing/syntax recall | Limited by problem understanding |
From Syntax to Semantics: The New Skill Set
Vibe coding demands mastery in three critical areas:
1. Advanced Prompt Engineering
For complex modules, I use the BASIC framework:
- Background: Current project context
- Action: Specific task needed
- Specifics: Technical requirements
- Intent: Why this solution is needed
- Constraints: Limitations and patterns to follow
Example: > “Background: R package for statistical analysis. Action: Create ARD data validation module. Specifics: Check for missing values, outliers, data types. Intent: Prevent analysis errors downstream in the project XYZ. Constraints: Use tidyverse patterns, include comprehensive tests.”
2. AI Output Refinement
Treat AI as a skilled junior developer, verify outputs, test thoroughly, and guide architectural decisions.
3. Domain-Driven Articulation
The clearer your understanding of the problem domain, the better AI executes. Vague requests fail; specificity wins.
My Approach: Modular Construction with Safety Rails
Through extensive experimentation with GitHub Copilot’s agent mode, I’ve developed a systematic approach that balances the exponential productivity gains with necessary control mechanisms.
Agent mode solutions excel at classical web application development, where patterns are well-established and extensively documented. However, when tackling more niche problems or specialized domains, these tools often struggle and require significantly more guidance from the developer. AI models are trained on vast amounts of publicly available code, which means they’ve seen countless examples of REST APIs, React components, and CRUD operations. But for domain-specific algorithms, proprietary protocols, or cutting-edge research implementations, you’ll find yourself providing more detailed instructions and corrections to keep the AI on track.
1. Modular Decomposition
Rather than letting the AI tackle entire systems at once, I break BIG projects into discrete modules. This serves multiple purposes:
- Cognitive manageable chunks: Both for the AI and for my own understanding
- Clear boundaries: Each module has well-defined interfaces
- Incremental progress: Building confidence step by step
For each module, I provide the agent with clear context about where it fits within the larger system, while keeping the immediate scope focused.
2. Test-Driven Validation
I insist on unit tests and performance checks for every bigger piece of generated code. After each code generation cycle, I prompt:
“Please create comprehensive unit tests for this module, covering edge cases and expected behaviours. Confirm that time performance is maintained.”
Before moving forward, I ensure these tests pass. This practice has saved me countless hours. As systems grow in complexity, it’s remarkably easy for new code to break existing functionality. The testing layer acts as a safety net, preventing the accumulation of technical debt that could derail the entire vibe.
With the design document and comprehensive unit testing in place, the AI can effectively debug itself when issues arise, creating a self-correcting development cycle.
3. Living Design Documentation with Visual Diagrams
I maintain a DESIGN.md file that serves as the single source of truth for architectural decisions. Frequently, I ask the agent:
“Based on what we just implemented, please update the DESIGN.md file to reflect the current architecture, including the new module and its interactions. Include Mermaid diagrams for the UML and User Journey.”
The inclusion of Mermaid graphs for UML diagrams and User Journeys makes the architecture immediately understandable for team members. This living document becomes invaluable as projects grow, serving as both a reference and a checkpoint for ensuring the AI maintains consistency with established patterns.
4. Pattern Guidance
AI agents perform significantly better when given architectural context. Instead of vague requests, I’ve found success with specific pattern suggestions:
“Let’s consider a Factory pattern here, be critical if you not agree”
Copilot and similar tools excel when they understand not just what to build, but how to structure it according to established patterns.
5. Active Intervention
One crucial insight: it’s perfectly fine, even beneficial, to interrupt the agent mid-flow. If I see it heading in the wrong direction, I stop it immediately:
“Wait, let’s reconsider another approach …”
This isn’t a failure of vibe coding; it’s an essential part of the collaborative dance.
6. Abandon Early: The Fresh Start Strategy
From my experience, it’s better to abandon recent work at the startup phase and begin from scratch if you’re not satisfied with the development direction, rather than trying to polish something you’re not happy about. This strategy assumes you have high query limits with your AI tool, but it can save you significant frustration and deliver much better results.
The key is quick decision-making: usually within less than an hour, I can determine whether the current approach is worth continuing or if it’s time to pivot. When the vibe isn’t right, no amount of refinement will fix fundamental architectural issues. Starting fresh with lessons learned from the failed attempt often leads to cleaner, more elegant solutions.
This might seem wasteful, but in vibe coding, the cost of iteration is so low that perfectionism through restart becomes a viable strategy. It’s better to spend two hours getting it right than four hours fixing something that was wrong from the beginning.
The Vibe Coding Toolbox: My Tested Recommendations
| Tool | Best For | My Experience | Key Strength |
|---|---|---|---|
| GitHub Copilot (Agent) | Full project orchestration | Primary choice - excellent context | Project-wide understanding |
| Claude | Complex architectural decisions | Superior for programming logic | Deep reasoning ability |
| Cursor | VS Code alternative with AI | Great for quick iterations | Seamless IDE integration |
and more …
The Model Matters: Real-World Experience
Not all AI models are created equal for vibe coding. Through extensive testing with my team, we’ve developed strong preferences:
- Claude: Me and my friends have a feeling Claude is better for programming, excellent for complex architectural decisions and understanding of requirements
- GPT: Strong for generating boilerplate and handling well-established patterns
- Specialized models: Sometimes, smaller, fine-tuned models excel at specific domains
The key is matching the model to the task at hand, not assuming one size fits all.
Real Metrics: A 10x Improvement Story
Let me share concrete numbers: I recently developed an R package prototype in 8 working hours, which would have taken me at least 100+ working hours using a traditional approach. This 10x improvement isn’t theoretical; it’s the actual result of applying the strategies I’ve outlined above.
Team Collaboration in the Vibe Coding Era
How do you handle code reviews in this paradigm? I prefer to provide code for each module to the team separately. This modular approach makes reviews manageable and focused. Team members can understand each piece without being overwhelmed by complexity. A module is, for example, a small r/python package for a specific purpose.
One developer can “vibe code” while the rest review and provide feedback later. Another team member can work on a different module if needed. This creates a natural parallelisation of effort while maintaining quality control through peer review.
When Vibe Coding Goes Wrong
The Cautionary Tale:
A recent experience with my lawyer friend perfectly illustrates the dangers of uncontrolled vibe coding. Excited by the potential of agent mode solutions, I introduced him to AI-assisted development for a side project app he was working on. Initially, he was amazed by the rapid progress and the AI’s ability to generate seemingly sophisticated code.
However, without the systematic approach, he quickly fell into what I now call the “vibe coding trap.” He became overly ambitious, asking the AI to implement increasingly complex features without proper modular breakdown, testing, or architectural planning. The AI, eager to please, generated code that looked impressive but lacked coherent structure. Within a few days, the project had spiraled into an unmaintainable mess of interconnected functions with no clear boundaries or testing strategy.
This experience taught me that vibe coding without discipline is worse than traditional development. The speed of AI code generation can mask fundamental problems until they become catastrophic. We had to abandon the entire project and start over using the structured approach I outline in this post.
The lesson is clear: vibe coding amplifies both good practices and bad practices. If you bring discipline, modular thinking, and systematic testing to the process, AI can accelerate your development exponentially. If you approach it as a magic solution that requires no oversight or structure, it will create technical debt faster than you ever thought possible.
This has broader implications for the industry. As more developers adopt AI-assisted development, we’re likely to see a divergence: those who master disciplined vibe coding will become incredibly productive, while those who treat AI as a replacement for good engineering practices will create increasingly problematic codebases. The emergence of “vibe coding cleaners”, specialists who can untangle and refactor AI-generated code disasters, may become a necessary profession in the near future.
Maintaining Your Edge
I’ve already identified a critical problem: losing touch with code by using too much vibe coding. When you’re not writing code directly, you feel less connected to it. The syntax becomes foreign, the patterns less intuitive.
To combat this, I deliberately continue programming myself on picked-up projects and specific critical components. It’s essential to maintain that direct connection to code, even as we leverage AI for productivity. Think of it like a pianist who still practices scales despite having access to digital composition tools.
The Hallucination Problem
AI can generate plausible-looking but fundamentally broken code. My rule: Always test before integration. I’ve seen AI confidently implement non-existent APIs or use deprecated patterns that look correct.
Security Vulnerabilities
Generated code may include vulnerable dependencies or insecure patterns. I never deploy AI-generated code without:
- Automated security scanning
- Manual review of authentication/authorization logic
- Verification of data handling practices
Technical Debt Acceleration
Without proper structure, AI can generate technical debt faster than any human programmer. The speed advantage becomes a liability when the codebase becomes unmaintainable.
Maintaining Control in the Exponential Flow
The essence of successful vibe coding isn’t about control; it’s about exercising a different kind of control. We’re no longer micromanaging syntax and implementation details. Instead, we’re:
- Orchestrating the overall direction
- Validating the quality and correctness
- Refining the approach based on outcomes
- Maintaining architectural coherence
- Preserving our coding skills through deliberate practice
This requires us to be more aware, not less. We need to understand what’s happening at a higher level, even if we’re not writing every line of code.
Future-Proofing Your Vibe Coding Skills
The field is evolving rapidly toward:
Outcome-Driven Development: Describing what you want, not how to build it. The focus shifts from implementation details to business value.
Architecture-First Thinking: As AI handles implementation, human value concentrates on system design, integration patterns, and strategic technical decisions.
In my opinion, the most valuable developers will excel at:
- Problem decomposition and clear articulation
- System architecture and integration strategy
- Quality validation and testing frameworks
- Human-AI collaboration workflows
- Maintaining technical judgment in an AI-augmented world
As we adopt vibe coding, we’re not just adopting new tools; we’re participating in the evolution of software development itself. The exponential improvements in AI capabilities suggest that this is just the beginning.
The developers who thrive in this new paradigm won’t be those who resist the change or those who unquestioningly accept all AI output. They’ll be the ones who find the sweet spot: leveraging AI’s exponential capabilities while maintaining the judgment, creativity, architectural vision, and hands-on skills that make great software possible.
Conclusion
Vibe coding isn’t replacing developers, it’s elevating us. By offloading syntax to AI, we focus on what truly matters: creative problem-solving, architecture, and building systems that create real value.
We’re no longer writing code; we’re orchestrating solutions at a higher level of abstraction. The future belongs to those who can clearly describe outcomes and guide AI toward elegant implementations while maintaining the technical judgment to validate and refine the results.
My approach, with its emphasis on modularity, testing, visual documentation, and active guidance, may seem to add friction to the pure “vibe.” But I’ve found that they actually enable me to “ride the exponential wave more confidently”. They’re the surfboard that lets me navigate the powerful currents of AI-assisted development.
The 10x productivity gains are real, but they require discipline, systematic thinking, and continuous learning. Those who master this balance will thrive; those who treat AI as magic will struggle with increasingly complex technical debt.
“The best code is no code at all. The next best? Code that writes itself while you focus on what it should accomplish.”
As we continue exploring this frontier, remember: the future of programming isn’t humans versus machines, it’s humans with machines, creating at unprecedented scales while staying grounded in engineering fundamentals.
What’s your approach to vibe coding? How do you balance control with the exponential possibilities of AI assistance? How do you maintain your coding skills while leveraging AI? Please share your experiences, and let’s continue this conversation about the future of development.
References
- Karpathy, A., Personal blog and Twitter discussions on vibe coding methodology. Available at: https://karpathy.ai
- Vaswani, A., et al. (2017). “Attention Is All You Need.” Advances in Neural Information Processing Systems, 30.
- GitHub. (2024). “GitHub Copilot Documentation: Agent Mode and Advanced Features.” GitHub Docs. Available at: https://docs.github.com/copilot
- Anthropic. (2024). “Claude 4.0 Documentation for Developers.” Available at: https://docs.anthropic.com
- OpenAI. (2024). “GPT-4.1 Technical Documentation and Best Practices.” Available at: https://platform.openai.com/docs
- Cursor. (2024). “Cursor IDE: AI-First Code Editor Documentation.” Available at: https://cursor.sh/docs
- Udemy Blog. (2025). “What is vibe coding.” Available at: https://blog.udemy.com/what-is-vibe-coding/
- Various contributors. (2024). “Vibe Coding.” Wikipedia. Available at: https://en.wikipedia.org/wiki/Vibe_coding
- ASHISH, Vaswani. Attention is all you need. Advances in neural information processing systems, 2017, 30: I. https://research.google/pubs/attention-is-all-you-need/