10 Tips for AI Editors, App Builders, and the Broader Ecosystem

AI Editors & App Builders
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The broader AI editor and app builder ecosystem moves fast. These tips help you extract real value from tools like Zed, Continue, v0, Bolt, and Lovable — and avoid the common traps.

1. v0: Always Edit After Generating

v0 generates beautiful React components, but they’re starting points, not finished products. The workflow:

  1. Generate the component with a detailed prompt
  2. Copy to your local project
  3. Replace v0’s inline styles with your design system tokens
  4. Connect to your actual data layer (v0 uses mock data)
  5. Add error states and loading states (v0 often skips these)
  6. Add accessibility attributes (aria labels, keyboard navigation)

Treating v0 output as a first draft rather than production code saves you from refactoring later.

2. Bolt: Export Early, Edit Locally

Bolt.new generates full-stack apps in the browser. The trap is continuing to develop inside Bolt’s browser IDE. Instead:

  1. Generate the initial scaffold in Bolt
  2. Download/clone the project immediately
  3. Open in your regular editor (Cursor, VS Code)
  4. Continue development with your full toolchain

Bolt is excellent for the 0-to-1 phase. Your editor is better for the 1-to-100 phase.

3. Continue: Build a Multi-Model Stack

Continue’s power is model flexibility. Set up a tiered approach:

{
  "models": [
    {
      "title": "Claude Sonnet (main)",
      "provider": "anthropic",
      "model": "claude-sonnet-4-20250514"
    },
    {
      "title": "GPT-4o (fast)",
      "provider": "openai",
      "model": "gpt-4o"
    },
    {
      "title": "DeepSeek (cheap)",
      "provider": "deepseek",
      "model": "deepseek-chat"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Ollama Local",
    "provider": "ollama",
    "model": "starcoder2:3b"
  }
}

Use the local model for free Tab completions, DeepSeek for quick questions, GPT-4o for routine tasks, and Claude Sonnet for complex implementations. Switch models per conversation based on what you need.

4. Zed: Use Multi-Buffers for AI-Assisted Refactoring

Zed’s multi-buffer feature lets you view and edit multiple files in a single buffer. Combined with AI:

  1. Search for a pattern across your project (e.g., all API handlers)
  2. Multi-buffer shows all matches in one view
  3. Use AI inline assistance to refactor the pattern across all instances simultaneously

This is faster than editing files one by one and keeps the refactoring consistent.

5. Lovable: Iterate with Screenshots

Lovable responds well to visual feedback. The best workflow:

  1. Generate initial version from a text description
  2. Take a screenshot of the result
  3. Annotate the screenshot with what needs to change (circle elements, add notes)
  4. Upload the annotated screenshot as your next prompt
  5. Repeat until the design matches your vision

This visual iteration loop is faster than trying to describe UI changes in words.

6. Replit Agent: Use for Deployed Prototypes

Replit Agent’s unique advantage is instant deployment. Use it specifically for:

  • Client demos that need a live URL in 30 minutes
  • Internal tools that don’t need to scale
  • Hackathon projects with time pressure
  • Learning projects where you want to share results immediately

Don’t try to build your production app in Replit — use it for what it’s best at: speed to deployed prototype.

7. MCP: Build Custom Integrations

Model Context Protocol lets you extend any compatible AI tool. Practical MCP servers to build:

Database explorer: Let your AI tool query your dev database to understand schemas and write better queries.

API documentation: Serve your OpenAPI spec through MCP so AI suggestions match your actual API contracts.

Issue tracker: Connect Jira/Linear/GitHub Issues so your AI tool can reference tickets when implementing features.

// Simple MCP server skeleton
const server = new Server({ name: "my-project-mcp" });

server.setRequestHandler("tools/list", async () => ({
  tools: [{
    name: "get_open_tickets",
    description: "Get open tickets assigned to the current sprint",
    inputSchema: { type: "object", properties: {} }
  }]
}));

Even simple MCP servers dramatically improve AI output quality because they ground suggestions in your actual project data.

8. JetBrains AI Assistant: Use Context Actions

In JetBrains IDEs, the AI Assistant integrates with context menus:

  • Right-click a function → “Generate Tests”
  • Right-click a class → “Generate Documentation”
  • Right-click a selection → “Explain This Code”
  • Right-click an error → “Suggest Fix”

These context actions are faster than opening a chat panel because they pre-load the relevant code as context.

9. App Builders: Combine for Best Results

Each app builder has strengths. Combine them:

  1. v0 → Generate individual UI components with high design quality
  2. Bolt → Scaffold the full-stack architecture and data layer
  3. Cursor → Assemble the pieces, add business logic, polish

This “best tool for each stage” approach produces better results than using any single tool end-to-end.

10. Track the Ecosystem, Don’t Chase It

New AI dev tools launch weekly. A practical approach:

  • Commit to 2-3 core tools that cover your daily workflow
  • Evaluate new tools quarterly, not immediately on launch
  • Test on a real project, not a todo app
  • Measure actual productivity, not feature count
  • Consider switching costs — learning a new tool takes weeks

The developers who are most productive aren’t using the newest tools. They’re using familiar tools deeply.

What’s your AI tool stack? Which combination works best for your workflow? Share below. :backhand_index_pointing_down: