Table of contents
- What is vibe coding?
- What is traditional programming?
- Methodology and mindset
- Tooling and workflow
- Comparison of key aspects
- Example: Vibe coding in action
- Example: Traditional programming in action
- Pros and cons of vibe coding
- Trends and community adoption
- How Graphite can help vibe coders
- Conclusion
What is vibe coding?
Vibe coding is an AI-powered, conversational approach to development where the programmer describes what they want in natural language, and the AI generates executable code to match that intent. The term was coined in early 2025 by AI researcher Andrej Karpathy, who characterized it as coding by intuition or "feel" rather than strict step-by-step logic. In practice, vibe coding lets developers focus on high-level ideas (the "vibe" of the software) while delegating low-level implementation details to AI. This makes it possible to build prototypes quickly by simply explaining your intentions to an intelligent assistant.
What is traditional programming?
Traditional programming is the conventional approach where developers manually write software code line by line in a programming language. This method requires a deep understanding of algorithms, syntax, and software design, but it offers the programmer complete control over the code's logic and structure. While traditional coding can be time-consuming — since every feature is hand-crafted and debugged by an engineer — it yields software built to exact specifications and standards set by the development team.
Methodology and mindset
Vibe coding favors an experimental, "code first, refine later" philosophy. Developers jump into building a prototype with AI assistance, prioritizing quick results over upfront design. This mindset is intuitive and creative – developers often skip detailed planning, trusting the process and using AI suggestions to iterate rapidly.
Traditional programming encourages more structure and upfront planning. Developers typically design the architecture and consider requirements before coding, adhering to an engineering mindset that values logic, correctness, and optimization from the outset. This methodical approach sacrifices some spontaneity for predictability and full control over the implementation.
Tooling and workflow
Vibe coding uses conversational AI coding assistants that interpret natural language prompts and generate code suggestions. The workflow typically involves describing a desired feature or bug fix to the AI, running the generated code, then refining with further prompts – an iterative cycle of prompt, generation, and feedback. Vibe coding platforms often integrate these AI assistants directly into the development environment. This approach can accelerate development by sharing the work with AI, while traditional tools keep the developer in the driver's seat. As a result, vibe coding can produce a working solution faster (but often less polished), whereas traditional coding delivers a well-structured result at the cost of more time.
Traditional programming uses a more hands-on toolchain. Developers write code in text editors or IDEs and use compilers or interpreters to run it. The typical workflow involves writing some code, running tests or the program, debugging errors manually, and iterating. Tooling is oriented around manual control — version control systems, debuggers, build tools, and testing frameworks support the developer in writing and maintaining code. No AI is involved; programmers rely on their own knowledge, documentation, and libraries to implement features. Developers act as the architects, coders, and debuggers all in one–giving maximum control but also full responsibility for every detail.
Comparison of key aspects
To summarize, here is a side-by-side comparison of vibe coding and traditional programming:
| Aspect | Vibe coding | Traditional programming |
|---|---|---|
| Planning & design | Minimal upfront design; quick, iterative prototyping. | Thorough upfront design; architecture defined before coding. |
| Debugging | AI-assisted (describe issues to AI). Harder if you didn't write the code. | Manual debugging (code reading, breakpoints, tests); easier since dev knows the code. |
| Documentation | Can be AI-generated (docstrings/comments), but often inconsistent; human clarification needed. | Written by developers (comments, docs); usually clearer for maintenance. |
| Performance | "Make it work" first, optimize later. AI code may be non-optimal; tuning needed post-generation. | Optimized during development; efficiency considered from the start. |
| Use cases | Best for prototypes, small-scale or creative projects where speed matters (e.g. hackathons, demos). | Best for large-scale, critical systems needing high reliability and security (e.g. enterprise apps). |
Example: Vibe coding in action
A startup founder with no coding experience uses vibe coding to build a quick mobile app prototype. They describe the desired features to an AI assistant, which generates the code. The founder can then request adjustments or new features (for example, adding an email confirmation step), and the AI updates the app. By the end of the day, they have a working prototype without writing any code. This demonstrates vibe coding's strength in rapid prototyping an idea for quick validation.
Example: Traditional programming in action
A large bank's engineering team is building a secure online banking platform. They take a traditional approach: carefully designing the system architecture upfront, writing the code manually with thorough reviews for security, and debugging issues by hand. This slower methodical process yields a robust, secure, and maintainable system. In high-stakes domains like banking, the predictability and control of traditional development outweigh the speed of vibe coding.
Pros and cons of vibe coding
When comparing vibe coding to traditional programming, each approach has distinct advantages and drawbacks:
Pros of vibe coding:
- Extremely fast development and iteration – AI can generate code in seconds, enabling rapid prototyping and quick iterations.
- Low barrier to entry – even non-programmers can contribute using natural language. Routine code is automated, allowing developers to focus on creative problem-solving.
Cons of vibe coding:
- Higher risk of bugs or insecure code, since AI might introduce errors that a human coder would catch.
- Inconsistent code quality without human oversight – AI-generated code can be messy or inefficient, leading to technical debt. Such codebases can be harder to debug or extend, especially if you weren't the one who wrote the code.
(Conversely, traditional programming excels in code quality, maintainability, and precision, but requires more time and expertise.)
Trends and community adoption
Vibe coding is quickly finding a following in certain tech circles, particularly where speed and creativity are valued:
Game development: Many game developers (especially indies) use vibe coding tools to prototype gameplay and mechanics rapidly. By describing game concepts to an AI, they can generate playable code without hand-coding everything.
Creative coding: Digital artists and creative coders experiment with vibe coding for generative art and interactive visuals. The emphasis on fluid, expressive code aligns with artistic exploration.
Indie startups: Small startups and solo developers leverage vibe coding to build MVPs quickly without large engineering teams.
How Graphite can help vibe coders
Whether you're vibe coding or manually writing code, Graphite—especially its AI-powered reviewer, Diamond—offers significant advantages. It serves as a unified review platform that adapts to the distinct needs of both workflows.
Catching AI-generated risks in vibe coding
Vibe-coded projects often evolve rapidly with minimal human oversight, increasing the likelihood of bugs, security lapses, or poor-quality patterns. Graphite's Diamond scans pull requests in real time, flags logic errors, style inconsistencies, performance issues, and possible vulnerabilities, while understanding the broader codebase context.
Reinforcing rigor in traditional workflows
In traditional programming, reviews are methodical but time-consuming. Graphite enhances these workflows by automating the first pass. Diamond suggests improvements—such as naming conventions, documentation gaps, and standard antipatterns—before a human reviewer engages, speeding up quality control without replacing human insight.
Optimizing stacked pull request workflows
Graphite excels with its "stacked PRs" feature: developers break large changes into incremental, focused pull requests. Diamond reviews each layer, keeping the codebase clean and manageable—ideal for both rapid vibe-coding iteration and methodical traditional refactors.
Customizable rules fit both worlds
Graphite allows teams to define coding standards and integrate enterprise-grade security policies. This flexibility ensures AI-generated vibe code meets internal guidelines, and traditional contributions stay compliant—making it effective across diverse project styles.
Conclusion
Vibe coding and traditional programming represent two ends of the software development spectrum. Vibe coding offers speed, accessibility, and creative flexibility—ideal for prototyping, exploration, and fast feedback cycles. Traditional programming, on the other hand, prioritizes precision, control, and long-term maintainability—essential for building complex, mission-critical systems.
Rather than viewing them as mutually exclusive, developers can think of them as complementary. Vibe coding excels in the early stages of development or in creative contexts, while traditional methods shine when performance, reliability, and security matter most. As AI tooling continues to evolve, the future of development may lie in blending both approaches—using vibe coding to move fast, and traditional practices to build things that last.