Building a Full-Stack Notification System Foundation in 10 Minutes with AI

Sometimes the best way to build complex features is to just start coding and let the solution emerge. Recently, I needed to add a comprehensive notification system to a Laravel app - SMS alerts via AWS SNS, database notifications, real-time UI updates, and admin settings. Instead of spending hours planning, I decided to vibe code it with Claude Sonnet 4 in Cursor. The result? A solid foundation built in about 10 minutes that sets the stage for quick refinement prompts.

The Foundation Mindset

Before diving into the implementation details, it's important to understand the mindset that makes this approach so effective. This isn't about building perfect code in one pass - it's about creating a solid foundation that gives us confidence in the approach, then iterating with focused prompts to reach production quality.

This separation of concerns allows for rapid experimentation without getting bogged down in perfectionism. Now let's see how this played out in practice.

The Original Prompt

Here's exactly how I started this entire feature - with one comprehensive prompt to Claude Sonnet 4 in Cursor:

Real-Time Code Review with Git Status

One of the most powerful aspects of AI-assisted development is the ability to do real-time code review. While Claude was generating code, I continuously ran git status and git diff to see exactly what was being created and modified.

# My workflow during AI generation
git status  # See what files are being created/modified
git diff    # Review the actual changes being made
git add -A && git commit -m "AI generated notification foundation"

# This gave me confidence that the code was accurate and expected

The Challenge: Multi-Channel Notifications

The requirement seemed straightforward: "Send me notifications when someone fills out the contact form." But as I started thinking about it, the scope expanded naturally:

• SMS notifications via AWS SNS for immediate alerts
• Database notifications for audit trail and admin dashboard
• Real-time UI with notification bell and badge
• Admin settings to configure notification preferences
• Queue processing for performance
• Proper error handling and fallbacks

In traditional development, this would require extensive planning, architecture diagrams, and careful coordination between frontend and backend. But with AI assistance, I could explore and build iteratively.

Phase 1: The Foundation (AWS SNS Integration)

I started with the most uncertain part - AWS SNS integration. My initial prompt was simple:

The AI immediately understood the scope and suggested a structured approach. Within minutes, I had:

# AWS SDK installation
./vendor/bin/sail composer require aws/aws-sdk-php

# Custom SNS channel creation
./vendor/bin/sail artisan make:notification NewContactRequest

The Custom SNS Channel

The AI helped me create a custom notification channel that handles SMS sending:

<?php
// app/Channels/SnsChannel.php
namespace App\Channels;

use Aws\Sns\SnsClient;
use Illuminate\Notifications\Notification;

class SnsChannel
{
    protected $sns;

    public function __construct()
    {
        $this->sns = new SnsClient([
            'version' => config('services.sns.version'),
            'region' => config('services.sns.region'),
            'credentials' => [
                'key' => config('services.sns.key'),
                'secret' => config('services.sns.secret'),
            ],
        ]);
    }

    public function send($notifiable, Notification $notification)
    {
        $message = $notification->toSns($notifiable);
        
        if (!$message) return;

        return $this->sns->publish([
            'PhoneNumber' => $message['phone'],
            'Message' => $message['message'],
            'MessageAttributes' => [
                'AWS.SNS.SMS.SMSType' => [
                    'DataType' => 'String',
                    'StringValue' => 'Transactional'
                ]
            ]
        ]);
    }
}

Phase 2: Database Notifications & Queue Processing

Next challenge: making notifications persistent and handling the queue processing. The AI guided me through Laravel's notification system:

# Create notifications table
./vendor/bin/sail artisan notifications:table
./vendor/bin/sail artisan migrate

# Add notification settings to users
./vendor/bin/sail artisan make:migration add_notification_settings_to_users_table

The Notification Class

The AI helped me design a notification that works with both database and SMS channels:

<?php
// app/Notifications/NewContactRequest.php
class NewContactRequest extends Notification implements ShouldQueue
{
    use Queueable;
    
    protected $contactRequestData;

    public function __construct(ContactRequest $contactRequest)
    {
        // Store data instead of model to avoid serialization issues
        $this->contactRequestData = [
            'id' => $contactRequest->id,
            'name' => $contactRequest->name,
            'email' => $contactRequest->email,
            'company' => $contactRequest->company,
            'service_type_display' => $contactRequest->service_type_display,
        ];
    }

    public function via(object $notifiable): array
    {
        $channels = ['database'];
        
        if ($notifiable->notification_phone) {
            $channels[] = SnsChannel::class;
        }
        
        return $channels;
    }

    public function toSns(object $notifiable): array
    {
        $message = "🔔 New Contact Request\n\n";
        $message .= "From: {$this->contactRequestData['name']}\n";
        $message .= "Email: {$this->contactRequestData['email']}\n";
        
        if ($this->contactRequestData['company']) {
            $message .= "Company: {$this->contactRequestData['company']}\n";
        }
        
        return [
            'phone' => $notifiable->notification_phone,
            'message' => $message,
        ];
    }
}

Phase 3: Real-Time UI Components

Now for the frontend magic. I wanted a notification bell with badge, dropdown, and real-time updates. My prompt:

The AI suggested a complete solution with AJAX polling and modern UI patterns:

Navigation Bell Component

<!-- Notification Bell in Navigation -->
<div class="relative ml-4">
    <button id="notificationBell" class="relative p-2 text-gray-500 hover:text-gray-700">
        <svg class="w-5 h-5" fill="none" stroke="currentColor" viewBox="0 0 24 24">
            <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" 
                  d="M15 17h5l-5 5v-5z M9.663 17h4.673M12 3v1m6.364 1.636l-.707.707M21 12h-1M4 12H3m3.343-5.657l-.707-.707m2.828 9.9a5 5 0 117.072 0l-.548.547A3.374 3.374 0 0014 18.469V19a2 2 0 11-4 0v-.531c0-.895-.356-1.754-.988-2.386l-.548-.547z"></path>
        </svg>
        <span id="notificationBadge" class="hidden absolute -top-1 -right-1 bg-red-600 text-white rounded-full px-2 py-1 text-xs">
            0
        </span>
    </button>
    
    <div id="notificationDropdown" class="hidden absolute right-0 mt-2 w-80 bg-white rounded-md shadow-lg border">
        <!-- Dropdown content -->
    </div>
</div>

Real-Time JavaScript

The AI provided a complete JavaScript solution with polling and AJAX updates:

// Real-time notification polling
function pollNotifications() {
    fetch('/manage/notifications/unread-count')
        .then(response => response.json())
        .then(data => updateNotificationBadge(data.count))
        .catch(error => console.error('Error:', error));
}

function loadNotifications() {
    fetch('/manage/notifications/recent')
        .then(response => response.json())
        .then(data => {
            updateNotificationList(data.notifications);
            updateNotificationBadge(data.unread_count);
        });
}

// Poll every 30 seconds
setInterval(pollNotifications, 30000);

Phase 4: Admin Dashboard Integration

The system needed proper admin interfaces. The AI suggested a complete admin experience:

Profile Settings Interface

The AI designed a tabbed interface for comprehensive settings management:

<?php
// Profile Settings Controller
public function updateNotificationSettings(Request $request)
{
    $validated = $request->validate([
        'notification_phone' => 'nullable|string|max:255',
        'notifications_enabled' => 'boolean',
        'sms_notifications' => 'boolean',
        'email_notifications' => 'boolean',
        'notification_settings' => 'nullable|array',
    ]);

    // Handle checkbox states properly
    $validated['notifications_enabled'] = $request->has('notifications_enabled');
    $validated['sms_notifications'] = $request->has('sms_notifications');
    
    Auth::user()->update($validated);
    
    return redirect()->back()->with('success', 'Settings updated!');
}

The File Structure That Emerged

Here's what the AI helped me create across the entire application stack:

Backend Infrastructure:
├── app/Channels/SnsChannel.php                           # Custom SMS channel
├── app/Notifications/NewContactRequest.php               # Notification class
├── app/Console/Commands/TestNotification.php             # Testing command
├── config/services.php                                   # AWS configuration
└── database/migrations/
    ├── add_notification_settings_to_users_table.php      # User settings
    └── create_notifications_table.php                    # Notification storage

Controllers & Logic:
├── app/Http/Controllers/ContactController.php            # Trigger notifications
├── app/Http/Controllers/Admin/NotificationController.php # Admin management
├── app/Http/Controllers/Admin/ProfileSettingsController.php # Settings
├── app/Http/Controllers/Admin/DashboardController.php    # Dashboard stats
└── app/Models/User.php                                   # User model updates

Frontend & Views:
├── resources/views/layouts/app.blade.php                 # Navigation bell
├── resources/views/admin/notifications/index.blade.php   # Notification list
├── resources/views/admin/profile/index.blade.php         # Settings page
├── resources/views/admin/dashboard.blade.php             # Dashboard updates
└── routes/web.php                                        # All new routes

Documentation:
└── NOTIFICATION_SETUP.md                                # Complete setup guide

Testing the Complete System

The AI even helped me create a comprehensive testing command:

# Test the complete flow
./vendor/bin/sail artisan test:notification --phone="+1234567890"

# Process the queue
./vendor/bin/sail artisan queue:work --once

# Check results
./vendor/bin/sail artisan tinker --execute="echo 'Notifications: ' . \App\Models\User::first()->notifications()->count();"

Why This Approach Works

This notification system would typically take an experienced developer 2-3 days to plan, architect, and implement properly. With AI-assisted vibe coding, it was completed in a few hours. Here's why this approach is so effective:

The Safety Net Strategy

Throughout this process, I committed frequently with descriptive messages:

git commit -m "Phase 1: AWS SNS integration working"
git commit -m "Phase 2: Database notifications and queue processing"  
git commit -m "Phase 3: Real-time UI with notification bell"
git commit -m "Phase 4: Complete admin dashboard integration"

This gave me the confidence to experiment boldly, knowing I could always roll back to a working state.

Lessons for Your Next Vibe Coding Session

The Reality: Foundation vs Production-Ready

Let's be clear about what we accomplished: this isn't production-ready code, but it's an excellent foundation that sets up the next phase of refinement prompts. The AI created a working system that demonstrates all the key concepts and provides a solid base for iteration.

The Time Reality Check

Here's an honest comparison of development timelines for this notification system:

When to Use This Approach

Vibe coding with AI assistance works best for:

• MVP features where speed matters more than perfect architecture
• Exploratory development when you're not sure of the best approach
• Learning new technologies like AWS services or new Laravel features
• Full-stack features that touch multiple layers of the application
• Prototyping to validate ideas quickly

It's not ideal for mission-critical systems that need extensive planning, or when you're working with a large team that needs detailed specifications.

Try It Yourself

Next time you need to build a moderately complex feature, try the AI-assisted foundation approach:

1. Start with a comprehensive, high-level prompt describing the entire feature
2. Use git status and git diff for real-time code review
3. Let the AI build the complete foundation across all layers
4. Commit frequently with descriptive messages
5. Test the foundation to ensure core functionality works
6. Use follow-up prompts to refine and polish for production

You might be surprised at how solid a foundation you can create in just a few minutes. The key is thinking of AI-assisted development as a two-phase process: rapid foundation building, followed by targeted refinement prompts.