Vibe Coding: The New Way to Build
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Code10 min readJanuary 1, 2026 at 18:23

Vibe Coding: The New Way to Build

It's not just about logic anymore. It's about the flow. How LLMs allow us to code at the speed of thought - and the skills you need to thrive in this new paradigm.

Yuval Avidani

Yuval Avidani

Author

Beyond Syntax, Into Flow

There's a new term floating around Twitter and developer communities: Vibe Coding. It captures the essence of programming with an AI pair programmer like Cursor, Copilot, or Claude Code. But it's more than a catchy phrase - it represents a fundamental shift in how we interact with computers to create software.

This matters to all of us as developers because the skills that made someone a productive programmer in 2020 are being supplemented - and in some cases replaced - by different skills in 2026. Understanding this shift isn't just academic; it's about staying relevant and leveraging new tools to multiply your impact.

What Vibe Coding Actually Means

Traditionally, programming required translating your high-level intent into low-level syntax. You had an idea: "I need to validate email addresses." Then you manually implemented it: regex patterns, edge cases, error handling, tests. Every semicolon, every bracket, every import statement was your responsibility.

With Vibe Coding, you stay at the intent level:

The old way:

import re


def validate_email(email: str) -> bool:
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    if not email:
        return False
    if len(email) > 254:
        return False
    return bool(re.match(pattern, email))

The vibe coding way:

"Write a function that validates email addresses, handling common edge cases like empty strings and overly long inputs"

The AI generates the implementation. You review it, request adjustments ("also reject emails with consecutive dots"), and iterate until it's right. The mental model shifts from "writing code" to "directing code generation."

Real Examples of Vibe Coding in Action

UI Development:

  • "Make this component look like Apple News with a clean, minimal aesthetic"

  • "Add a loading skeleton that matches the content layout"

  • "Convert this to dark mode with smooth transitions"

Refactoring:

  • "Refactor this to use the repository pattern"

  • "Make this function pure - no side effects"

  • "Split this 200-line function into smaller, testable units"

Bug Fixing:

  • "Fix that weird bug with the scrollbar jumping on mobile"

  • "The form submits twice sometimes - find and fix the race condition"

  • "This works in Chrome but breaks in Safari"

Feature Development:

  • "Add infinite scroll to this list with proper virtualization"

  • "Implement optimistic updates for this mutation"

  • "Add real-time sync between tabs"

In each case, you're communicating intent, not implementation. The AI handles the mechanical translation while you focus on what you actually want to accomplish.

The Flow State Advantage

The magic of vibe coding isn't just speed - it's maintaining flow state. In traditional coding, you constantly context-switch:

1. Think about the algorithm
2. Look up syntax you forgot
3. Check documentation for an API
4. Fix a typo
5. Debug a bracket mismatch
6. Look up that import path

Each switch is a micro-interruption. Your working memory gets polluted with mechanical concerns instead of staying focused on the actual problem.

With vibe coding, you stay in the problem space:

1. Think about the algorithm
2. Describe it
3. Review the output
4. Refine if needed
5. Move to the next problem

You're thinking in concepts, not characters. The cognitive load shifts from "how do I express this in code" to "is this the right approach." That's a more valuable use of your mental energy.

Measuring the Difference

In my own work, I've tracked productivity across different coding styles:

Traditional coding (solo, no AI):

  • ~50-100 lines of production code per hour

  • Significant time on Stack Overflow, documentation

  • Context switches every 5-10 minutes

Vibe coding with AI:

  • ~200-400 lines of production code per hour

  • AI handles documentation lookup inline

  • Context switches every 20-30 minutes

The 3-4x productivity increase is real, but the quality improvement is harder to quantify. When you're not exhausted from mechanical work, you make better decisions about architecture, edge cases, and user experience.

The Risk: Losing Understanding

The obvious danger of vibe coding is losing understanding of your own codebase. If you only vibe code, do you truly know how your system works?

This is a genuine concern, not a hypothetical one. I've seen developers:

  • Accept AI-generated code without understanding it
  • Create technical debt by layering abstractions they can't modify
  • Fail to debug issues because they don't understand the generated patterns
  • Ship security vulnerabilities hidden in code they didn't review

The Understanding Spectrum

There's a spectrum of understanding, and vibe coding works differently at different points:

Deep Understanding (Required)

  • System architecture and data flow

  • Security implications and attack surfaces

  • Performance characteristics and bottlenecks

  • Business logic and edge cases

Moderate Understanding (Helpful)

  • Implementation patterns used by the AI

  • Library APIs and their trade-offs

  • Test coverage and quality

Shallow Understanding (Optional)

  • Exact syntax of every line

  • Import paths and boilerplate

  • Standard patterns you've used hundreds of times

Vibe coding should free you from the shallow understanding tasks so you can invest more in deep understanding. If you're using AI to avoid understanding your system, you're doing it wrong.

How to Vibe Code Responsibly

1. Review every generated block before accepting
- Understand what it does, even if you didn't write it
- Look for security issues, edge cases, performance problems
- If you can't explain it, don't ship it

2. Write the hard parts yourself sometimes
- Complex algorithms deserve manual implementation
- Security-critical code needs human verification
- Core business logic should be intimately understood

3. Read the generated code, don't just run it
- Build a mental model of patterns the AI uses
- Notice when it makes suboptimal choices
- Learn from its implementations

4. Test rigorously
- Generated code needs tests just like human code
- Write tests yourself to verify understanding
- Use AI to generate additional edge case tests

5. Debug manually sometimes
- Don't always ask AI "why doesn't this work"
- Step through code to build intuition
- Understand error messages before asking for help

The New Skills: Prompt Engineering Meets Architecture

Vibe coding requires different skills than traditional programming:

1. Problem Decomposition

The better you break down problems, the better AI can help. Instead of:

  • "Build me a todo app"

Do this:

  • "Create the data model for a todo app with lists, items, and due dates"

  • "Build the API layer with CRUD operations for todos"

  • "Create the React components for the todo list"

  • "Add drag-and-drop reordering"

  • "Implement due date notifications"

Small, well-scoped prompts get better results than vague, ambitious ones.

2. Quality Judgment

You need to recognize good code when you see it, even if you didn't write it:

  • Is this idiomatic for the language?

  • Are there hidden performance issues?

  • Does this follow our project's patterns?

  • Are edge cases handled?

This skill becomes more important, not less, when AI is generating code.

3. Codebase Knowledge

You need deep knowledge of your system:

  • Where does this code fit in the architecture?

  • What other systems does it interact with?

  • What patterns does this codebase use?

AI doesn't know your codebase as well as you do (though it's getting better with tools like @codebase in Cursor).

4. Iterative Refinement

Rarely is the first generated response perfect. You need to:

  • Identify what's wrong or suboptimal

  • Articulate specific improvements

  • Recognize when to accept "good enough" versus when to push for better

This is a feedback loop skill that improves with practice.

5. Context Management

AI has limited context windows. You need to:

  • Provide relevant context without overwhelming

  • Structure projects so AI can understand them

  • Know when to start a fresh conversation

The Workflow: How I Vibe Code

Here's my actual workflow using Cursor with Claude:

Starting a Feature

1. Context gathering: Read relevant existing code, understand the patterns
2. Decomposition: Break the feature into logical chunks
3. First prompt: Describe the first chunk with relevant context
4. Review and iterate: Check output, request modifications
5. Integration: Fit the generated code into the codebase
6. Testing: Write tests (sometimes AI-generated, always human-reviewed)

Example Session

Me: "I need to add rate limiting to our API endpoints. We're using Express
    with TypeScript. The rate limit should be per-user based on their auth
    token, stored in Redis. Look at our existing middleware pattern in
    src/middleware/auth.ts"

AI: [Generates rate limiting middleware]


Me: "Good, but we need different limits for different endpoints. Let's make
    the limit configurable per-route."


AI: [Updates with configurable limits]


Me: "Add a custom error response that includes retry-after header and follows
    our existing error format from src/errors/ApiError.ts"


AI: [Updates error handling]


Me: "Now write tests for this middleware"


AI: [Generates test file]


Me: [Reviews tests, runs them, fixes edge cases]

The conversation is collaborative. I'm directing, reviewing, and refining. The AI is implementing, suggesting, and handling mechanical work.

The Future: Even More Vibe

This is just the beginning. Current limitations that will dissolve:

Today's Limitations:

  • Context windows cap how much code AI can see

  • AI doesn't persist knowledge between sessions

  • Real-time collaboration is awkward

  • Voice input is unreliable

Near-Future Capabilities:

  • Full codebase understanding and indexing

  • Persistent project memory

  • Real-time pair programming with voice

  • Proactive suggestions and refactoring

The trend is clear: the gap between thought and code will continue shrinking. Programming becomes less about syntax and more about intent, design, and judgment.

Embracing the Shift

Some developers resist vibe coding:

  • "It's cheating"

  • "You won't really learn"

  • "AI code is bad"

These objections miss the point. Every generation of programmers has had tools that previous generations would consider "cheating":

  • Assembly programmers thought high-level languages were cheating

  • Manual memory management folks thought garbage collection was cheating

  • VI purists thought IDEs with autocomplete were cheating

Tools evolve. The developers who thrive are those who adopt effective tools while maintaining the core skills that matter: understanding systems, solving problems, making good decisions.

Vibe coding isn't about being lazy. It's about leverage. It's about spending mental energy on valuable work instead of mechanical work. It's about staying in flow.

My Take: Find Your Balance

In my opinion, vibe coding is the most significant productivity enhancement in programming since the IDE with autocomplete. The developers who embrace it thoughtfully will outpace those who resist it and those who adopt it uncritically.

The key is balance:

  • Use AI for mechanical work, not for avoiding understanding

  • Review everything, understand everything you ship

  • Stay sharp on fundamentals through deliberate practice

  • Build deep knowledge of your systems, not just surface patterns

The goal isn't to stop thinking about code. It's to think about the right parts of code - the architecture, the edge cases, the user experience - while letting AI handle the translation from thought to syntax.

That's vibe coding. Not coding without thinking, but coding at the speed of thought.

Try it. You'll feel the flow.