I'm Not a Coder. I "vibe-coded" This Security Dashboard prototype in 4 Hours Anyway
How Cursor AI and 'vibe coding' are eliminating the last barrier between founders and functional prototypes"
TL;DR: I went from idea to working demo of an AI-powered security dashboard in 4 hours using Cursor AI and "vibe coding." Here's what happened, what worked, what didn't, and why this matters for every founder thinking about technical execution in 2025.
*Disclaimer - I am not a coder. I am not a development expert. This was my way of experimenting with Cursor to satisfy my curiosity.
The Challenge: From Concept to Code in One Afternoon
Earlier this month, I published "AI-Powered Threat Hunting: A Practical Implementation Guide," which went viral among security professionals and investors. The response was overwhelming, but one question kept coming up: "This sounds great in theory, but how complex is it really to build?"
So I decided to find out. Armed with nothing but Cursor AI and the emerging "vibe coding" methodology, I set out to build a functional threat-hunting dashboard from scratch. No extensive planning, no detailed specifications—just natural language prompts and AI assistance.
The goal: Build a real-time security monitoring platform with:
AI-powered threat detection
WebSocket real-time updates
JWT authentication
Interactive dashboard with Material-UI
RESTful API endpoints
Redux state management
The constraint: One afternoon. Four hours max.
The result: A fully functional MVP that actually works.
What is "Vibe Coding" and Why Does It Matter?
Before diving into the build, let's address the elephant in the room. "Vibe coding," coined by AI researcher Andrej Karpathy in early 2025, represents a fundamental shift in how we think about software development.
Traditional coding:
Write detailed specifications → Plan architecture → Code line by line → Debug → Test → Deploy
Vibe coding:
Describe what you want → Let AI generate the foundation → Guide through natural language → Iterate rapidly → Ship
It's not about replacing developers—it's about amplifying human creativity and eliminating the tedious parts that slow down innovation. As one developer put it: "You're no longer just an idea guy; you're a full-stack engineer."
For founders, this changes everything. The technical moat that once required months of development and expensive engineering teams can now be prototyped in hours.
Hour 1: Foundation and Architecture
Starting Point: Empty directory, big ambitions
First Prompt: "Help me create a full-stack AI threat hunting application with Flask backend, React frontend, and PostgreSQL database. I want real-time threat detection with WebSocket updates."
Cursor immediately understood the scope and suggested the project structure:
ai-threat-hunt-demo/
├── backend/
│ ├── app.py
│ ├── models/
│ ├── routes/
│ └── requirements.txt
├── frontend/
│ ├── src/
│ ├── public/
│ └── package.json
└── README.md
What impressed me: Cursor didn't just generate boilerplate. It understood the relationships between components and created a scalable architecture. The Flask backend utilized Blueprint-based routing, while the React frontend employed Redux for state management, and it even recommended proper environment variable handling.
Generated in minutes:
Database models for Events, AIFindings, and Users
Authentication middleware with JWT
Basic API endpoints
React component structure
Redux store configuration
Time invested: 45 minutes
Lines of code generated: ~2,000
Manual coding: Maybe 50 lines of configuration
Hour 2: Database and Backend Logic
The Challenge: Setting up PostgreSQL, creating tables, and implementing the core API logic.
My Approach: Instead of diving into SQL commands and database configurations, I simply told Cursor what I needed:
"I need a PostgreSQL database with tables for security events, AI findings, and users. Events should have source/destination IPs, severity levels, and timestamps. AI findings need confidence scores and summaries."
Cursor's Magic: It generated not just the database schema, but also:
# Complete database models with relationships
class Event(db.Model):
id = db.Column(db.Integer, primary_key=True)
event_type = db.Column(db.String(255), nullable=False)
source_ip = db.Column(db.String(255), nullable=False)
destination_ip = db.Column(db.String(255), nullable=False)
severity = db.Column(db.String(50), nullable=False)
timestamp = db.Column(db.DateTime, nullable=False)
class AIFinding(db.Model):
id = db.Column(db.Integer, primary_key=True)
summary = db.Column(db.Text, nullable=False)
confidence = db.Column(db.String(50), nullable=False)
created_at = db.Column(db.DateTime, nullable=False)
And the API endpoints:
@dashboard.route('/api/dashboard', methods=['GET'])
def get_dashboard_data():
time_threshold = datetime.utcnow() - timedelta(hours=24)
recent_events = Event.query.filter(Event.timestamp >= time_threshold).all()
recent_findings = AIFinding.query.order_by(AIFinding.created_at.desc()).limit(10).all()
return jsonify({
'events': [event.to_dict() for event in recent_events],
'findings': [finding.to_dict() for finding in recent_findings],
'metrics': calculate_metrics(recent_events, recent_findings)
})
The Real Kicker: When I mentioned I wanted real-time updates, Cursor automatically implemented WebSocket integration with Flask-SocketIO. It understood that real-time threat detection requires bidirectional communication and configured everything accordingly.
Time invested: 60 minutes
Major features implemented:
Complete database schema
Authentication with JWT tokens
RESTful API with 8 endpoints
WebSocket real-time communication
Error handling and validation
Hour 3: Frontend Development and Real-Time Features
The Reality Check: I'm primarily a dated Unix/Solaris/Shell scripter but my JavaScript skills are... limited. This is where vibe coding really shines.
The Prompt: "Create a Material-UI dashboard that displays real-time security events and AI findings. I want metrics cards, event timelines, and live updates via WebSocket."
What Cursor Delivered:
A complete React application with:
// Redux store configuration
const store = configureStore({
reducer: {
auth: authReducer,
threats: threatsReducer,
analysis: analysisReducer,
},
});
// Dashboard component with real-time updates
function Dashboard() {
const [dashboardData, setDashboardData] = useState({
metrics: { totalEvents: 0, activeThreats: 0, highConfidence: 0, lowConfidence: 0 },
events: [],
findings: []
});
useEffect(() => {
const loadDashboardData = async () => {
try {
const data = await fetchDashboardData();
setDashboardData(data);
} catch (error) {
console.error('Error loading dashboard data:', error);
}
};
loadDashboardData();
websocketService.connect(); // Real-time updates
}, []);
return (
<Container maxWidth="xl">
<Box sx={{ py: 4 }}>
<Grid container spacing={3}>
<Grid item xs={12}>
<MetricsPanel metrics={dashboardData.metrics} />
</Grid>
<Grid item xs={12} md={6}>
<EventTimeline events={dashboardData.events} />
</Grid>
<Grid item xs={12} md={6}>
<FindingsList findings={dashboardData.findings} />
</Grid>
</Grid>
</Box>
</Container>
);
}
The Surprise: Cursor didn't just generate the components—it understood the relationships between them. When I mentioned "real-time updates," it automatically:
Implemented WebSocket connections
Created proper event handlers
Added Redux actions for state management
Included error handling for connection failures
Time invested: 75 minutes
Frontend completed:
Responsive Material-UI dashboard
Real-time WebSocket integration
Redux state management
Protected routes with authentication
Interactive data visualization
Hour 4: Integration, Testing, and Polish
The Final Push: Getting everything working together and adding those crucial finishing touches.
Challenge #1:
Database Connection Issues - Error: psql: FATAL: role "your_db_user" does not exist
Instead of diving into PostgreSQL documentation, I simply told Cursor: "The database connection is failing with a user error. Help me fix the configuration."
Cursor immediately suggested creating a .env file with the correct credentials and showed me how to update the backend configuration. Problem solved in 5 minutes.
Challenge #2:
Redux Integration - The front end was showing a blank page after login.
My prompt: "The dashboard is blank after login. I think it's a Redux issue." Cursor diagnosed the problem instantly—missing Redux Provider in the root component—and provided the fix:
// index.js
import { Provider } from 'react-redux';
import store from './store';
root.render(
<Provider store={store}>
<App />
</Provider>
);
Challenge #3:
Real-Time Updates - "The WebSocket isn't pushing updates to the frontend properly."
Cursor not only fixed the WebSocket implementation but also added proper error handling and reconnection logic. It is understood that production applications need robust real-time communication.
Time invested: 60 minutes
Final features added:
Database connection and sample data
Complete authentication flow
Working real-time updates
Error handling and user feedback
Basic security measures
The Final Result: An AI Security Platform Demo
Security Dashboard Demo portal:
Sample output from synthetic data
| Brute Force Login Attempts | Multiple failed login attempts from a single IP in a short period. | { "timestamp": "2024-05-29T10:15:00Z", "event_type": "login_failed", "source_ip": "203.0.113.45", "username": "admin", "details": "Failed login attempt" } |
| Suspicious Lateral Movement | User logs into multiple machines they don’t normally access. | { "timestamp": "2024-05-29T10:20:00Z", "event_type": "remote_login", "source_ip": "10.0.0.5", "dest_ip": "10.0.0.23", "username": "jdoe", "details": "Remote desktop connection" } |
| Data Exfiltration | Large amounts of data sent to an external IP. | { "timestamp": "2024-05-29T10:30:00Z", "event_type": "data_transfer", "source_ip": "10.0.0.10", "dest_ip": "198.51.100.99", "bytes_sent": 104857600, "details": "Unusually large outbound transfer" } |
| Malware Beaconing | Regular, periodic connections to a known malicious domain. | { "timestamp": "2024-05-29T10:45:00Z", "event_type": "dns_query", "source_ip": "10.0.0.15", "domain": "malicious.example.com", "details": "Known C2 domain" } |
| Privilege Escalation | A user account suddenly gains admin privileges. | { "timestamp": "2024-05-29T11:00:00Z", "event_type": "privilege_change", "username": "jdoe", "old_role": "user", "new_role": "admin", "details": "User role changed" } |After 4 hours, I had a fully functional security dashboard with:
✅ Real-time threat detection - Events streaming live to the dashboard
✅ AI-powered analysis - Automatic threat assessment with confidence scores
✅ Modern UI - Material-UI components with responsive design
✅ Authentication - JWT-based login system
✅ WebSocket integration - Live updates without page refresh
✅ RESTful API - 8 endpoints for complete data management
✅ Redux state management - Proper global state handling
Total lines of code: ~3,500
Lines I personally wrote: ~200 (mostly configuration and customization)
AI-generated code percentage: 94%
What Worked Brilliantly
1. Natural Language Architecture - Instead of writing technical specifications, I could describe features in plain English. "I want real-time updates" became a complete WebSocket implementation with error handling.
2. Context Awareness - Cursor understood the relationships between components. When I mentioned authentication, it automatically secured the API endpoints and created protected routes in the front end.
3. Error Resolution - Database issues, configuration problems, and integration bugs were resolved with simple natural language descriptions of the symptoms.
4. Full-Stack Coherence - The backend and frontend worked together seamlessly, with matching data structures and API contracts.
What Still Needs Human Oversight
1. Security Considerations - While Cursor implemented JWT authentication and basic security measures, production security requires human review and oversight. The AI-generated good foundations but didn't consider all edge cases.
2. Business Logic Complexity - The AI analysis algorithms were basic. For production-grade threat detection, you need domain expertise that AI can't provide yet.
3. Performance Optimization - The generated code works but isn't optimized for scale. Database queries, caching strategies, and WebSocket management need human refinement.
4. Testing Strategy - Cursor generated functional code but minimal testing. Production applications need comprehensive test suites.
The Founder's Perspective: What This Really Means
As someone who has advised dozens of startups and seen countless technical roadblocks kill promising ideas, this experience has fundamentally changed how I think about early-stage development.
Speed to Market is Everything - Four hours from idea to working prototype means you can validate concepts before your competitors even finish their planning meetings. In a world where product-market fit is everything, this speed advantage is massive.
Technical Debt vs. Technical Discovery - Traditional wisdom says "move fast and break things" leads to technical debt. But vibe coding changes the equation. The AI generates cleaner, more consistent code than most early-stage development teams. You're not accumulating debt—you're discovering what works.
The New Technical Co-Founder - For non-technical founders, this is a transformational experience. You can now build sophisticated prototypes to attract technical talent, secure investor interest, or validate market demand without upfront engineering costs.
Resource Allocation Revolution - Instead of spending months building an MVP that might be wrong, you can spend hours building multiple prototypes to test different approaches. This shifts resources from development to experimentation—a much better investment.
The Limitations: Where Human Expertise Still Matters
Let's be honest about what vibe coding can't do yet:
Domain Expertise - Cursor helped me build a security dashboard, but it couldn't tell me what constitutes a meaningful threat pattern. Business logic still requires human intelligence.
Strategic Architecture - While the AI created a good component structure, deciding between microservices vs. monolith, choosing the right database, or planning for scale still requires experience.
Integration Complexity - My dashboard operates independently, but integrating it with enterprise systems, third-party APIs, or existing security tools requires a thorough understanding of the organizational context.
Regulatory Compliance - Building a healthcare app? Financial services platform? AI can generate HIPAA-compliant code structures, but understanding regulatory requirements needs human expertise.
The Competitive Implications
If you're a founder or investor, here's what keeps me up at night: Every startup now has access to this technology.
The barrier to building sophisticated prototypes just dropped to nearly zero. Your competitors can test ideas as fast as they can think of them. The startups that win will be those that:
Iterate faster - More experiments, faster feedback loops
Focus on problems, not solutions - Let AI handle implementation
Emphasize design and user experience - Where human creativity still dominates
Build domain expertise - Understanding customer needs AI can't replicate
Looking Forward: The Vibe Coding Revolution
This isn't just about faster development—it's about democratizing technical execution. When implementation barriers disappear, competition shifts to:
Problem identification - Finding real customer pain points
User experience design - Creating intuitive, delightful interfaces
Business model innovation - Monetizing solutions effectively
Go-to-market execution - Reaching customers efficiently
For investors, this changes due diligence. Technical risk decreases, but market risk becomes paramount. The question isn't "Can this team build it?" but "Should this solution exist?"
For founders, this is liberating. You can spend less time worrying about technical feasibility and more time understanding customer problems. The constraints that shaped startup strategy for decades are evaporating.
The Tools That Made This Possible
While Cursor AI was the star of this experiment, the broader ecosystem enabled the rapid development:
Cursor AI: The AI-powered IDE that made vibe coding possible
Material-UI: Component library that provides professional design out of the box
Redux Toolkit: Simplified state management for complex applications
Flask-SocketIO: Real-time communication without the complexity
PostgreSQL: Robust database that "just works"
Each tool eliminated categories of complexity that would have consumed hours in traditional development.
Practical Takeaways for Founders
If you're inspired to try vibe coding for your startup, here's what I learned:
Start Simple, Think Big - Begin with core functionality and let the AI suggest architecture improvements. Don't over-engineer from the start.
Embrace the Conversation - Vibe coding is conversational. Describe problems, not solutions. Let the AI propose technical approaches.
Test Early, Test Often - With rapid prototyping, you can test assumptions quickly. Build multiple versions and see what resonates.
Plan for Human Review - AI-generated code needs human oversight before production. Use the speed for experimentation, not final deployment.
Focus on the Unique - Let AI handle standard functionality. Invest human creativity in features that differentiate your solution.
The Bottom Line
Four hours. One afternoon. A fully functional “BASIC” AI-powered security platform.
This isn't a tech demo or marketing stunt—it's a glimpse into how software development is evolving. The startups that embrace this shift will build faster, iterate more quickly, and outpace competitors still operating within old paradigms.
However, remember that tools don't build companies; people do. Vibe coding gives you superpowers, but you still need to find real problems worth solving and customers willing to pay for solutions.
The revolution isn't that AI can code—it's that founders can now focus entirely on what matters: building something people actually want.
Ready to experiment with vibe coding? Start with a simple project, embrace the conversation with AI, and prepare to rethink everything you know about technical execution. The future of startups just got a lot more interesting.
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What would you build in 4 hours? Let me know in the comments.