It's 2 a.m. and you're still writing the same CRUD controller, building another authentication flow, and adjusting a database schema—work you could do in your sleep, yet it devours the hours meant for real problem-solving.

When your expertise is senior-level but your day gets consumed by boilerplate, that's a resource allocation problem. Vibe coding fixes that imbalance.

In brief:

• Vibe coding transforms development by pairing your architectural thinking with AI that converts natural language into production-ready code.
• You maintain full control while gaining velocity, describing intent and refining the AI's output rather than wrestling with syntax details.
• The methodology promotes a tight feedback loop where you review, test, and commit code that meets your standards.
• This approach shifts your work to a higher abstraction level where your expertise delivers real value, focusing on architecture and edge cases instead of boilerplate.

What is Vibe Coding?

Vibe coding is a collaborative development approach where developers use natural language prompts to guide AI in generating functional code.

Rather than writing boilerplate manually, you describe desired outcomes—"scaffold a Node REST API with JWT auth and Postgres"—while maintaining control over architecture decisions.

The AI drafts implementations, but you retain responsibility for reviewing, testing, and committing code. This methodology shifts your focus from syntax to system design, allowing you to concentrate on architecture, edge cases, and user impact while the AI handles repetitive implementation patterns.

The Origin and Philosophy of Vibe Coding

Andrej Karpathy popularized the phrase "give in to the vibes" in early 2025, framing this approach as a pragmatic partnership between seasoned developers and large language models rather than a hand-off to a black box. Every prompt converts minutes of natural-language guidance into hours of implementation work.

This approach preserves your review gate. You validate the diff, run the tests, and refine prompts until the output meets your standards. The AI handles pattern matching and rapid synthesis, not architectural decisions.

This reallocation of cognitive budget—away from syntax toward design trade-offs—mirrors productivity leaps brought by compilers and modern IDEs, but with a conversational interface that maintains flow state.

By emphasizing flow-state optimization and contextual code creation, this method treats software as a creative craft. You curate atmosphere, set constraints, and iterate quickly, turning "wouldn't it be cool if...?" moments into running prototypes before inspiration fades.

Comparing Vibe Coding vs. Agentic Coding

Vibe coding and agentic coding represent two distinct AI-assisted development approaches with fundamental differences in developer control.

Vibe coding maintains human oversight throughout the development process: you provide natural language prompts and the AI generates code that you review, modify, and approve before implementation. Think of it as pair programming where you're the senior engineer: you prompt, inspect, and merge.

In contrast, agentic systems promise end-to-end autonomy—you set high-level goals and the AI independently makes decisions about everything from folder structure to deployment with minimal human intervention. Agentic coding effectively hands the repo to a contractor for later review, reducing your involvement to periodic checkpoints rather than continuous collaboration.

Use this approach when you need rapid, iterative progress—prototyping features, refactoring services, or spiking proof of concepts—while retaining ownership of every committed line. Reserve agentic workflows for long-running maintenance tasks like log analysis or migration scripts where contextual judgment is minimal.

The method augments your expertise without diluting it. You remain the architect, reviewer, and the person who signs off on shipping code.

How Vibe Coding Works (In Detail)

Large language models can feel like black boxes, yet this methodology stays transparent by putting you at the center of every iteration. Instead of surrendering control, you run a tight feedback loop that combines AI speed with human judgment.

The Vibe Coding Loop: Step-by-Step

A single loop usually spans minutes, not hours. The process follows this rhythm:

1. Describe intent in natural language
2. Let the model draft code
3. Inspect, test, and diagnose issues
4. Refine the prompt with targeted feedback
5. Repeat until the test suite is green and the code reads like your own

Bad vs. good prompts illustrate why specificity matters:

1// ❌ Bad
2Create a REST API for users.
3
4// ✅ Better
5Create an Express.js REST API with routes for 
6GET /users and POST /users, 
7use MongoDB via Mongoose 7, 
8and load the connection string from process.env.DB_URL. 
9Return 400 on duplicate email.

The refined prompt includes framework versions, environment variables, and error handling—details the AI can't guess. When the model returns code, you immediately run it, often catching issues like unhandled promise rejections. If a bug surfaces, ask the AI:

1The POST /users route throws "duplicate key error" instead of returning 400. 
2Rewrite only that handler to catch MongoError 11000 and respond with res.status(400).

Because every correction is explicit, ownership stays with you.

A quick deploy rounds out the loop. Commit to GitHub, trigger the CI job that runs npm test, and push a Docker image built from a Dockerfile that reads the runtime environment via ARG NODE_ENV—with secrets managed securely outside the repository. The AI can scaffold these files, but you approve each line.

1# Dockerfile excerpt
2ARG NODE_ENV=production
3ENV DB_URL=$DB_URL
4RUN npm ci --omit=dev
5CMD ["node","server.js"]

Lifecycle and Contexts for Vibe Coding

You can ship a weekend MVP with this approach, yet production demands discipline. The method excels during the inception and construction phases where the AI-DLC handles boilerplate so you can validate ideas fast.

For long-lived systems, follow Leanware's advice to define clear objectives and gate every merge with reviews and security scans.

This technique works best for prototypes, proof of concepts, or internal tools you need in days. Requirements are still fluid, and rapid iteration outweighs polish. The risk surface is low—marketing sites, data dashboards, glue services.

Pause or limit this methodology in highly regulated domains like health or finance that require formal audits. Deeply distributed architectures, where a single mis-configured queue can cascade failures, need more careful consideration.

Security-critical code paths such as cryptography or payment processing, deserve traditional development approaches.

Think of this as "code first, refine later"—a lean tactic that trades early velocity for manageable technical debt. Continuous refactoring, thorough testing, and tools like Snyk or Semgrep bring the codebase up to production grade without losing the momentum you gained on day one.

Top Vibe Coding Platforms

You have dozens of AI-assisted environments to choose from, but only a handful balance speed with the level of control this methodology demands. Here's the current landscape, plus how each platform behaves when you push past the demo into real projects.

Major Platforms and Tool Comparison

How to Start Vibe Coding (with Courses)

Fundamentals and Platform Selection

Before opening a prompt window, map your project's technical requirements to a decision matrix that narrows your platform choices. Consider these key factors: project complexity, team size, language requirements, deployment target, budget, and time constraints.

For targeted solutions:

• Rapid UI prototyping: v0 from Vercel or Lovable provide immediate visual results with clean React component export
• Full-stack applications: Bolt offers local runtime control with containerized sandboxes
• Collaborative development: Replit AI enables shared container environments where multiple developers can work simultaneously
• Enterprise requirements: GitHub Copilot Chat integrates with existing toolchains and provides SOC 2 compliance

Account setup involves specific technical steps: generate an API key with appropriate scopes, install the required CLI or editor extension, and configure git integration for version tracking.

Review each platform's privacy policy regarding code submission to AI models—most offer data retention controls. For sensitive codebases, prioritize platforms with local execution options or private tenancy agreements.

Learning Resources and Courses

Most developers don't need courses—official documentation and experimentation work better. That said, structured resources can accelerate specific workflows if you're short on time.

• Codecademy's "Intro to Vibe Coding" (4 hours, beginner-friendly) covers prompt basics and simple projects but assumes minimal coding experience. Skip if you're proficient in JavaScript.
• Microsoft Learn's "Introduction to Vibe Coding" module (2 hours, free) focuses on GitHub Copilot integration—useful for understanding prompt patterns but light on advanced techniques.
• Replit's "Vibe Coding 101" (self-paced, free) includes hands-on projects directly in-browser; good for testing platform capabilities without local setup.

If you prefer learning by doing, skip these entirely and jump to the tutorial in the next section. The highest-value resources are actually platform-specific documentation (Cursor docs, Bolt guides), community Discord servers where experienced users share prompt libraries, and GitHub repos tagged with vibe-coding showing real implementations.

YouTube channels from developers like Fireship and Theo offer practical, no-BS walkthroughs updated regularly. Most paid courses recycle free content—stick with community resources unless you need corporate training credentials for reimbursement.

A Brief Vibe Coding Tutorial

You'll see this methodology in action twice: first with a lightweight to-do list, then with a fuller stack that includes authentication and a database. Both examples follow the AI-driven, human-in-the-loop rhythm—AI proposes code, you review, refine, and ship.

Simple Tutorial: To-Do List App

Start in an empty folder and initialize a project.

1mkdir vibe-todo && cd vibe-todo
2npm init -y
3npm install express@4

Initial prompt

"Create a minimal Express 4 server in index.js with two routes: GET /items returns an in-memory array, POST /items adds an item with text and done=false. The server should listen on port 4000."

The assistant generates index.js. Your first review spots a missing body parser. Ask for a fix:

"Update the code to handle JSON request bodies using express.json() and add basic error handling."

1// index.js
2const express = require('express');
3const app = express();
4app.use(express.json());
5
6let items = [];
7
8// list items
9app.get('/items', (req, res) => res.json(items));
10
11// add item
12app.post('/items', (req, res) => {
13  const { text } = req.body;
14  if (!text) return res.status(400).json({ error: 'text required' });
15  const item = { id: Date.now(), text, done: false };
16  items.push(item);
17  res.status(201).json(item);
18});
19
20// global error handler
21app.use((err, _req, res, _next) => res.status(500).json({ error: err.message }));
22
23app.listen(4000, () => console.log('listening on 4000'));

Save, then run:

1node index.js

Common friction points include CORS issues if you plan to hit the API from a browser—add npm i cors and app.use(require('cors')()). Environment variables should replace the hard-coded port with process.env.PORT || 4000 before deploying. Install nodemon for a smoother development loop with hot reload.

Deployment

Create a Dockerfile:

1FROM node:18-alpine
2WORKDIR /app
3COPY package*.json ./
4RUN npm ci --omit=dev
5COPY . .
6EXPOSE 4000
7CMD ["node", "index.js"]

Build and run locally:

1docker build -t vibe-todo .
2docker run -p 4000:4000 vibe-todo

Push the image to any registry or deploy to a micro-VM host. The API is live; front-end work can iterate independently.

Advanced Example: Full-Stack App Prototype

Scaffold a "Book Tracker" with Next.js 14, PostgreSQL via Supabase, and email/password auth. This demands more architectural choices, so the prompts become higher-level while retaining oversight.

Create the workspace:

1npx create-next-app@latest book-tracker --ts --app
2cd book-tracker
3npm install @supabase/supabase-js@2

Architectural prompt

"Set up Supabase: 1) initialize client, 2) environment variables for SUPABASE_URL and SUPABASE_ANON_KEY, 3) helper in lib/supabase.ts. Then build an auth flow with AuthProvider and a protected /dashboard route."

Review the generated code. Two red flags appear:

1. Secrets are committed—instruct the model: "Move secrets to .env.local and add it to .gitignore."
2. The dashboard fetches books without pagination—ask: "Refactor the getBooks function to support range queries and handle Supabase rate limits gracefully."

1// lib/supabase.ts
2import { createClient } from '@supabase/supabase-js';
3
4export const supabase = createClient(
5  process.env.SUPABASE_URL as string,
6  process.env.SUPABASE_ANON_KEY as string,
7  { realtime: { params: { eventsPerSecond: 5 } } }
8);

1// lib/books.ts
2export async function getBooks(offset = 0, limit = 20) {
3  const { data, error } = await supabase
4    .from('books')
5    .select('*')
6    .order('created_at', { ascending: false })
7    .range(offset, offset + limit - 1);
8
9  if (error) throw error;
10  return data;
11}

Debugging a real issue

Running npm run dev surfaces a 429 error. Prompt: "Suggest an exponential back-off strategy for getBooks."

The assistant returns a promise-based wrapper with retry logic. You merge it, then write unit tests for edge cases—timeouts, empty results—using Jest. AI can draft the tests; you verify assertions align with the happy path and failure modes.

Production considerations

Here's what you need to keep in mind for production:

• Environment security: Store Supabase keys in the hosting provider's secrets manager rather than committing them to your repository
• HTTPS requirements: Next.js on Vercel includes HTTPS by default; custom hosts require TLS termination
• Monitoring: Implement Vercel Analytics or your preferred APM solution for tracking performance
• Version control: Create a git tag before each AI-assisted development session for clear rollback points
• Security scanning: Run npm audit and static analysis tools before merging changes, as AI can overlook vulnerable dependencies
• Monorepo considerations: When integrating with larger codebases:
  • Isolate the Supabase client to prevent duplicate instances
  • Align TypeScript configuration across modules
  • Apply your organization's ESLint rules consistently
  • Treat AI as a junior developer: helpful and fast, but requiring thorough code review

With these two projects you've experienced this methodology's rapid loop—from natural-language intent to running software—while maintaining the quality gates needed for real deployments.

Vibe Coding Examples

A well-crafted prompt is the steering wheel of vibe coding. You tell the model where to go, inspect the route it picks, then nudge it until the output matches your mental model. Below are concrete prompt iterations that show how that back-and-forth works in practice.

Example 1: Pagination Implementation

Initial prompt: "Add pagination to my /users endpoint." The AI returns code that fetches 100 users per page with no total count.

Refined prompt: "Paginate /users with page and limit query parameters, include totalCount in the JSON response."

Result: This produces improved output:

1app.get('/users', async (req, res) => {
2  const { page = 1, limit = 20 } = req.query;
3  const [rows] = await db.query(
4    'SELECT SQL_CALC_FOUND_ROWS * FROM users LIMIT ? OFFSET ?',
5    [limit, (page - 1) * limit]
6  );
7  const [[{ 'FOUND_ROWS()': totalCount }]] = await db.query('SELECT FOUND_ROWS()');
8  res.json({ data: rows, totalCount });
9});

Example 2: Cursor-Based Pagination

Initial prompt: "Create pagination for my posts endpoint."

Issue: Assumes offset-based pagination, which performs poorly at scale.

Refined prompt: "Implement cursor-based pagination using MongoDB: return 20 posts per page, use _id as cursor, include hasNextPage boolean and nextCursor in response metadata."

Result: Efficient pagination that scales to large datasets without the performance degradation of OFFSET queries.

1app.get('/posts', async (req, res) => {
2  const { cursor, limit = 20 } = req.query;
3  const query = cursor ? { _id: { $lt: cursor } } : {};
4  
5  const posts = await Post.find(query)
6    .sort({ _id: -1 })
7    .limit(parseInt(limit) + 1);
8  
9  const hasNextPage = posts.length > limit;
10  const data = hasNextPage ? posts.slice(0, -1) : posts;
11  const nextCursor = hasNextPage ? data[data.length - 1]._id : null;
12  
13  res.json({ data, hasNextPage, nextCursor });
14});

Example 3: WebSocket Reconnection Logic

Initial prompt: "Reconnect WebSocket if it drops."

Issue: Generates infinite reconnect loops without backoff.

Refined prompt: "Add WebSocket reconnection with exponential backoff starting at 1 second, max delay 30 seconds, max 5 attempts, emit connection status events."

Result: Production-ready reconnection logic that won't hammer your server during outages.

1class ReconnectingWebSocket {
2  constructor(url) {
3    this.url = url;
4    this.reconnectAttempts = 0;
5    this.maxAttempts = 5;
6    this.connect();
7  }
8  
9  connect() {
10    this.ws = new WebSocket(this.url);
11    
12    this.ws.onclose = () => {
13      if (this.reconnectAttempts < this.maxAttempts) {
14        const delay = Math.min(1000 * Math.pow(2, this.reconnectAttempts), 30000);
15        this.reconnectAttempts++;
16        this.emit('reconnecting', { attempt: this.reconnectAttempts, delay });
17        setTimeout(() => this.connect(), delay);
18      } else {
19        this.emit('failed', { attempts: this.reconnectAttempts });
20      }
21    };
22    
23    this.ws.onopen = () => {
24      this.reconnectAttempts = 0;
25      this.emit('connected');
26    };
27  }
28}

Example 4: Database Optimization

Initial prompt: "Speed up product searches."

Issue: AI suggests LIKE '%term%' queries that table-scan millions of rows.

Refined prompt: "Create a case-insensitive GIN index on products.name for PostgreSQL and switch queries to use ILIKE term%, include EXPLAIN ANALYZE output showing performance improvement."

Result: Query latency drops from 900ms to 75ms with proper index usage and verification.

1-- Create GIN index for case-insensitive search
2CREATE EXTENSION IF NOT EXISTS pg_trgm;
3CREATE INDEX idx_products_name_gin ON products 
4USING gin(name gin_trgm_ops);
5
6-- Optimized query
7SELECT * FROM products 
8WHERE name ILIKE $1 || '%'
9ORDER BY name 
10LIMIT 20;
11
12-- EXPLAIN ANALYZE shows:
13-- Index Scan using idx_products_name_gin (cost=8.00..75.23 rows=20)
14-- Execution time: 75ms (vs 900ms full table scan)

Example 5: API Rate Limiting

Initial prompt: "Make sure nobody can DDoS us."

Issue: Produces vague, ineffective middleware.

Refined prompt: "Implement per-IP rate limiting at 100 requests per minute using Redis storage with sliding window algorithm, return 429 status with Retry-After header, block duration of 10 minutes."

Result: Production-ready RateLimiterRedis middleware with proper HTTP semantics and distributed state management.

1const { RateLimiterRedis } = require('rate-limiter-flexible');
2const Redis = require('ioredis');
3
4const redisClient = new Redis({ host: 'localhost', port: 6379 });
5
6const rateLimiter = new RateLimiterRedis({
7  storeClient: redisClient,
8  keyPrefix: 'ratelimit',
9  points: 100,
10  duration: 60,
11  blockDuration: 600,
12});
13
14app.use(async (req, res, next) => {
15  try {
16    const clientIP = req.ip;
17    await rateLimiter.consume(clientIP);
18    next();
19  } catch (rejRes) {
20    res.set('Retry-After', String(Math.round(rejRes.msBeforeNext / 1000)));
21    res.status(429).json({ 
22      error: 'Too many requests',
23      retryAfter: rejRes.msBeforeNext 
24    });
25  }
26});

Example 6: Authentication Middleware

Initial prompt: "Add JWT authentication."

Issue: Generated token validation without refresh logic or proper error codes.

Refined prompt: "Implement JWT middleware with access tokens (15min expiry) and refresh tokens (7 days), store refresh tokens in httpOnly cookies, return 401 for expired access tokens and 403 for invalid signatures, include token rotation on refresh."

Result: Complete authentication flow following security best practices with proper token lifecycle management.

1const jwt = require('jsonwebtoken');
2
3const generateTokens = (userId) => {
4  const accessToken = jwt.sign({ userId }, process.env.ACCESS_SECRET, { expiresIn: '15m' });
5  const refreshToken = jwt.sign({ userId }, process.env.REFRESH_SECRET, { expiresIn: '7d' });
6  return { accessToken, refreshToken };
7};
8
9const authMiddleware = (req, res, next) => {
10  const token = req.headers.authorization?.split(' ')[1];
11  
12  if (!token) return res.status(401).json({ error: 'No token provided' });
13  
14  try {
15    const decoded = jwt.verify(token, process.env.ACCESS_SECRET);
16    req.userId = decoded.userId;
17    next();
18  } catch (err) {
19    if (err.name === 'TokenExpiredError') {
20      return res.status(401).json({ error: 'Token expired' });
21    }
22    return res.status(403).json({ error: 'Invalid token' });
23  }
24};
25
26app.post('/refresh', (req, res) => {
27  const refreshToken = req.cookies.refreshToken;
28  
29  try {
30    const decoded = jwt.verify(refreshToken, process.env.REFRESH_SECRET);
31    const tokens = generateTokens(decoded.userId);
32    
33    res.cookie('refreshToken', tokens.refreshToken, { 
34      httpOnly: true, 
35      secure: true, 
36      sameSite: 'strict' 
37    });
38    res.json({ accessToken: tokens.accessToken });
39  } catch (err) {
40    res.status(403).json({ error: 'Invalid refresh token' });
41  }
42});

The Pattern

Specify context (your stack), constraints (performance targets, security requirements), and success criteria (response format, error handling), then iterate until the code meets production standards.

Treat each prompt refinement as a code review conversation with a junior developer who needs precise requirements. The more specific your constraints, the closer the first output gets to production-ready code.

Vibe Coding Best Practices

AI tools deliver a 55% speed boost for early-stage developers, but you still own the outcome. These practices let you move fast without sacrificing clarity, safety, or long-term stability.

Craft Effective Prompts and Feedback Loops

Treat every prompt like a protocol specification: tighter specs mean less rework. I use a simple CT-FV loop—Context, Task, Format, Validation. Give the model concrete context (framework version, file path, business rule). State the task in one sentence.

Declare the expected format—function signature, test suite, or plain English explanation. Spell out validation criteria so the model can self-check.

Large features don't fit in one prompt. Break them into chunks the model can handle: "Generate an Express router for /invoice" comes before "add pagination and rate limiting." After each response, ask the model to justify unfamiliar decisions.

Treating it as a junior developer surfaces hidden assumptions and prevents silent errors. When outputs drift, summarize the agreed-upon architecture in your next message to re-anchor the conversation—an approach that mirrors the human-in-the-loop checkpoints of AI-Driven Development.

Enforce Quality, Security, and Review Standards

AI hallucinates insecure patterns, hard-coded secrets, and outdated dependencies. A few guardrails keep production safe:

• Validate input and sanitize output for every external interface
• Enforce authentication and authorization checks on all protected routes
• Rate-limit public APIs
• Externalize secrets via environment variables or vaults
• Pin dependency versions and scan them with Snyk or Dependabot
• Instrument logging and metrics from day one

Run automated linters like ESLint, static analysis with Semgrep, and dynamic scanning via OWASP ZAP in your CI pipeline. Complement them with unit and integration tests. Prompting the model to scaffold tests first makes later refactors safer.

A simple example shows the principle:

1// ❌ Dangerous: secret lives in source
2const apiKey = "sk-test-123456";
3
4// ✅ Safe: secret loaded at runtime
5import 'dotenv/config';
6const apiKey = process.env.STRIPE_API_KEY;

Your pull request review remains non-negotiable. Use the checklist above, query the model for clarifications, and require a green build before merging.

Manage Long-Term Code Health

Rapid generation can snowball into debt if you ignore grooming. Schedule periodic "cleanup sprints" where you prompt the model to standardize naming, extract shared utilities, or update deprecated libraries. Consistent formatting via Prettier or Black keeps the diff noise low, while concise commit messages and feature branches preserve history for future teammates.

Dependency freshness matters: automate npm audit or pip-audit, and capture upgrade guides in the repository wiki. For structural shifts, ask the model to draft migration scripts, then you review and test. Document decisions in README files or ADRs that explain why a pattern exists—future you will thank present you.

Combining disciplined prompting with rigorous review and scheduled refactoring gives you this methodology's velocity without gambling on code quality.

Being a Developer in the Vibe Coding Era

You're stepping into a workflow where natural-language prompts replace boilerplate keystrokes and AI suggestions land in your editor moments after you think of them. That speed forces a shift in what it means to be "the developer on the team."

Evolving Skills and Team Roles

This approach moves you up the stack—from implementer to architect. The AI drafts entire CRUD layers in seconds, making your differentiator system design, edge-case handling, and code review discipline.

Prompt engineering, architectural reasoning, and security triage become daily habits. Early adopters often report faster project turnaround than with traditional workflows, yet still rely on senior engineers for oversight and refactoring guidance.

Junior roles evolve too: instead of writing repetitive code, they curate prompts, maintain test suites, and learn design patterns earlier. Team structures flatten as pair programming becomes pairing with an AI assistant.

Your leadership lies in orchestrating that collaboration—translating business goals into precise prompts, validating outputs, and safeguarding long-term maintainability.

Adapting and Thriving

Treat side projects as sandboxes for experimenting with new prompting patterns. Build a personal library of tested prompts, iterate quickly, and measure outcomes rather than line counts. Follow thought leaders, join Discord channels, and hack on community showcases to stay current.

Rapid iteration beats polished first drafts—ship, observe, refine. Keep your expertise in focus: this methodology is a power tool, not a replacement. The real advantage is freedom to tackle larger architectural puzzles while the AI handles scaffolding, letting you deliver stronger, more creative solutions in less time.

Vibe Coding Meets Headless CMS: Building Faster with Strapi

This rapid prototyping approach combines naturally with Strapi's API-first architecture. Strapi generates REST and GraphQL endpoints from your content models, and natural-language prompts can be used to help generate React, Vue, or Next.js components that consume those endpoints, though developer oversight and iteration are typically required to produce robust code.

You describe the interface behavior, and your AI assistant generates components that authenticate with JWT tokens and fetch from /api routes.

Prompt example: "Create a React dashboard that fetches articles from /api/articles, handles pagination, and submits new posts with authenticated requests." Your model returns table components with data fetching hooks and form validation.

Strapi manages content modeling, user permissions, and file uploads—the backend complexity that rapid AI-generated prototypes typically skip.

Run npx create-strapi@latest to set up your backend, then start prompting for frontend components. Your development cycle accelerates when both tools handle their strengths: Strapi for production APIs, AI-assisted development for rapid UI iteration.

Try the Live Demo

Strapi Launchpad demo comes with Strapi 5 in the back, Next.js 14, TailwindCSS and Aceternity UI in the front.

• Start your demo