---

title: 'Changelog for 2024' description: 'Changelog for 2024' author: name: 'ZJY' src: '/avatar.jpg' date: '2025-02-10' image: '/blog/tech-hero.png'

⚡️ Solo Development of an AI Platform | Latest Tech Stack Experience

📝 Project Background

Recently, I've been developing a Chat to Code platform, primarily focused on AI-assisted code development. As a solo developer, technology selection is crucial - the right tech stack can multiply development efficiency!

🛠 Core Tech Stack Breakdown

1. Frontend Technology

• Next.js 14 App Router

  • Server components for faster page loads
  • Integrated API routes, no separate backend needed
  • Route middleware perfect for authentication

  // middleware.ts example
  export function middleware(request: NextRequest) {
    const token = request.cookies.get('token');
    if (!token) {
      return NextResponse.redirect(new URL('/login', request.url));
    }
  }
  

• Vercel AI SDK

  • Simple streaming response implementation
  • Built-in prompt management
  • Multi-model support (OpenAI, Anthropic, Cohere)

  // AI streaming response example
  const response = await ai.streamComplete({
    messages: messages,
    model: 'gpt-4',
    maxTokens: 2000,
  });
  

2. Data Layer

• Prisma + PostgreSQL
  • Type safety as the biggest advantage
  • Automatic schema synchronization
  • Excellent query builder experience

  // Prisma query example
  const user = await prisma.user.findUnique({
    where: { email },
    include: { projects: true },
  });
  

3. UI/UX

• Shadcn UI + Tailwind CSS
  • On-demand component imports, small bundle size
  • Flexible theme customization
  • Simple responsive design

  <Card className="transition-shadow hover:shadow-lg">
    <CardHeader>
      <CardTitle>Project Statistics</CardTitle>
    </CardHeader>
    <CardContent>
      <div className="grid grid-cols-2 gap-4">{/* Stats content */}</div>
    </CardContent>
  </Card>
  

🎯 Practical Experience Sharing

1. AI Implementation Key Points

• Redis for caching conversation history
• AI context management
• Code highlighting and real-time completion

// AI context management example
const context = await redis.lrange(`chat:${chatId}`, 0, -1);
const messages = context.map((msg) => JSON.parse(msg));

2. Performance Optimization Experience

• next/image for optimized image loading
• Component-level lazy loading
• Edge functions for API routes

// Edge function example
export const runtime = 'edge';
export async function GET() {
  // Handle request
}

3. Development Efficiency Tips

• Using VSCode + GitHub Copilot
• Husky for commit checks
• pnpm for dependency management

📊 Real-world Results

• First paint loading time < 2s
• AI response latency < 500ms
• Code completion accuracy > 85%

🔍 Lessons Learned

1. Next.js App Router

• Mind the distinction between server and client components
• Careful planning of route caching strategies
• Attention to dynamic route parameter handling

2. Prisma Usage

• Watch out for N+1 query problems
• Remember migrations after model updates
• Connection pool configuration is crucial

3. AI Integration

• Handle streaming response error retries
• Mind token limits
• Prompt engineering is key

💡 Development Recommendations

1. Technology Selection

   • Choose mainstream, stable tech stack
   • Prioritize developer experience
   • Consider long-term maintenance costs

2. Architecture Design

   • Modular design
   • Consider scalability
   • Plan for monitoring integration

3. Deployment & Operations

   • Use Vercel for automatic deployment
   • Configure error monitoring
   • Implement proper logging