Remember when writing code meant typing every line, every bracket, every semicolon? That world is fading fast. In 2026, we are living in what many call the era of vibe coding, where describing what you want in plain English can get you most of the way to working code.
I have been building with these tools every day, and the shift is real. This is not about replacing developers. It is about changing how we work.
What Is Vibe Coding
Vibe coding means you describe your intent in natural language, and the AI writes the implementation. You focus on the what and why, while the AI handles the how.
Instead of writing:
def calculate_fibonacci(n):
if n <= 1:
return n
return calculate_fibonacci(n-1) + calculate_fibonacci(n-2)
You tell Cursor or Claude: Create a function that calculates the nth Fibonacci number using recursion.
The AI writes it, you review it, you ship it.
The Main Players in 2026
GitHub Copilot
Copilot lives inside your editor. You type a comment, it suggests the next few lines. You accept, modify, or reject.
It works in VS Code, JetBrains, Neovim. It is the most widely adopted tool because it fits into existing workflows without forcing you to change editors.
Example workflow:
// Function to fetch user data from API and cache it
Copilot suggests:
async function fetchUserData(userId) {
const cacheKey = `user-${userId}`;
const cached = await cache.get(cacheKey);
if (cached) {
return JSON.parse(cached);
}
const response = await fetch(`/api/users/${userId}`);
const userData = await response.json();
await cache.set(cacheKey, JSON.stringify(userData), 3600);
return userData;
}
You review it. Maybe you change the cache TTL. Maybe you add error handling. But the structure is there.
Cursor
Cursor is an AI-first editor built on VS Code. It understands your entire codebase, not just the file you are editing.
You can ask it: Find everywhere we are making API calls without proper error handling.
It scans your repo, shows you every instance, and can fix them all at once.
Cursor has two modes:
- Chat mode for asking questions
- Composer mode for making changes across multiple files
I use Cursor when I need to refactor or when I am working on a feature that touches many files.
Claude and ChatGPT
These are not IDEs. They are reasoning engines.
You paste your code, describe the problem, and they help you debug, refactor, or architect.
They excel at:
- Explaining complex code
- Suggesting architectural improvements
- Writing test cases
- Converting code between languages
Example:
You: This Python script is slow when processing large CSV files. How can I optimize it?
Claude analyzes your code and suggests using pandas chunking, multiprocessing, or switching to Polars.
Replit
Replit is a browser-based IDE with AI built in. You can go from idea to deployed app without leaving the browser.
It is especially good for prototypes, learning, and small projects where you do not want to set up a local environment.
Real Architecture Example
Let me show you how I built a real feature using vibe coding.
Task: Add a rate limiter to our API endpoints.
Here is how the conversation with Cursor went:
Me: I need to add rate limiting to all our Express routes. Use Redis for storage. Limit to 100 requests per minute per IP.
Cursor generated:
const redis = require('redis');
const client = redis.createClient();
const rateLimiter = async (req, res, next) => {
const ip = req.ip;
const key = `ratelimit:${ip}`;
const requests = await client.incr(key);
if (requests === 1) {
await client.expire(key, 60);
}
if (requests > 100) {
return res.status(429).json({
error: 'Too many requests'
});
}
next();
};
module.exports = rateLimiter;
Then I asked: Add logging for when users hit the limit.
It updated:
if (requests > 100) {
console.log(`Rate limit exceeded for IP: ${ip}`);
return res.status(429).json({
error: 'Too many requests',
retryAfter: 60
});
}
Final step: Apply this to all routes.
Cursor modified the main app file:
const rateLimiter = require('./middleware/rateLimiter');
app.use(rateLimiter);
Total time: 3 minutes.
The Architecture of AI Coding Tools
Here is how these systems work under the hood:
┌─────────────────────────────────────────────────────────┐
│ Your Editor/IDE │
│ (VS Code, Cursor, Replit) │
└────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ AI Coding Assistant Plugin │
│ • Captures your code context │
│ • Sends prompts + context to AI │
│ • Receives suggestions │
└────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ AI Model Layer │
│ (GPT-4, Claude, Codex) │
│ • Processes code context │
│ • Generates suggestions │
│ • Returns formatted code │
└────────────┬────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────┐
│ Your Codebase │
│ • Changes applied │
│ • Context updated │
│ • Ready for next iteration │
└─────────────────────────────────────────────────────────┘
The key is context. The more the AI knows about your project, the better its suggestions.
Cursor indexes your entire repo. GitHub Copilot looks at open files and recent edits. Claude works with what you paste.
Workflow Example: Building a REST API
Let me walk through building a simple REST API for a task manager using vibe coding.
Step 1: Define the structure
Me to Cursor: Create an Express API with routes for creating, reading, updating, and deleting tasks. Use MongoDB for storage.
Cursor generates:
const express = require('express');
const mongoose = require('mongoose');
const app = express();
app.use(express.json());
const taskSchema = new mongoose.Schema({
title: String,
description: String,
completed: Boolean,
createdAt: { type: Date, default: Date.now }
});
const Task = mongoose.model('Task', taskSchema);
app.post('/tasks', async (req, res) => {
const task = new Task(req.body);
await task.save();
res.json(task);
});
app.get('/tasks', async (req, res) => {
const tasks = await Task.find();
res.json(tasks);
});
app.put('/tasks/:id', async (req, res) => {
const task = await Task.findByIdAndUpdate(req.params.id, req.body, { new: true });
res.json(task);
});
app.delete('/tasks/:id', async (req, res) => {
await Task.findByIdAndDelete(req.params.id);
res.json({ message: 'Task deleted' });
});
mongoose.connect('mongodb://localhost/taskmanager');
app.listen(3000);
Step 2: Add error handling
Me: Add try-catch blocks and proper error responses.
Cursor updates all routes:
app.post('/tasks', async (req, res) => {
try {
const task = new Task(req.body);
await task.save();
res.json(task);
} catch (error) {
res.status(500).json({ error: error.message });
}
});
Step 3: Add validation
Me: Validate that title is required and completed defaults to false.
Cursor modifies the schema:
const taskSchema = new mongoose.Schema({
title: { type: String, required: true },
description: String,
completed: { type: Boolean, default: false },
createdAt: { type: Date, default: Date.now }
});
Total development time: under 10 minutes.
The Data Flow
Here is how data moves through a vibe coding session:
Developer Intent
|
v
Natural Language Prompt
|
v
AI Model (with code context)
|
v
Generated Code Suggestion
|
v
Developer Review & Edit
|
v
Final Implementation
What Works Best
After months of daily use, here is what I have learned:
-
Be specific in your prompts
- Bad: Make this faster
- Good: Optimize this loop using a hash map instead of nested iteration
-
Give context
- Instead of: Write a login function
- Say: Write a login function that checks credentials against our PostgreSQL users table, returns a JWT token, and logs failed attempts
-
Iterate in small steps
- Do not ask for an entire feature at once
- Build piece by piece, testing as you go
-
Review everything
- AI makes mistakes
- It might use deprecated methods
- It might miss edge cases
The Limits
Vibe coding is not magic. It struggles with:
- Complex business logic that requires domain expertise
- Performance optimization for specialized use cases
- Architectural decisions that involve tradeoffs
- Debugging production issues that need deep system knowledge
You still need to understand what the code does. You still need to think like an engineer.
My Setup
Here is my current workflow:
- Cursor for feature development and refactoring
- GitHub Copilot for autocomplete while editing
- Claude for architecture discussions and code review
- Replit for quick prototypes and experiments
I spend less time typing, more time thinking.
Final Thoughts
Vibe coding is not replacing developers. It is changing what we focus on.
Instead of remembering syntax, we think about architecture.
Instead of writing boilerplate, we design systems.
Instead of debugging typos, we solve real problems.
The tools are here. The question is: are you using them?
What has your experience been with these AI coding tools? Are you skeptical, excited, somewhere in between? Let me know in the comments.
Top comments (1)
Nice overview of vibe coding tools! Curious to know if you had the chance to review Claude vs Codex? If so, which do you prefer?