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Vibe Coding Principles

Focused, concise and context-rich prompts are the key to effective vibe coding.
  1. Provide Essential Context - Share product requirements, user journey, and constraints upfront
  2. Decompose Into Small Tasks - Break your project into isolated, single-purpose tasks. Solve only one task per session
  3. Restart Sessions Often - Start new chats for new features or when stuck. Use /compact (Claude Code) or /compress (Gemini CLI) to summarize before restarting

Quick Setup

Requirements: Project Setup:
  1. Download Inworld templates
  2. Open your project in your development environment:
    • AI IDEs: Cursor, Windsurf, etc.
    • IDEs with coding agents: Visual Studio Code (with Zencoder, GitHub Copilot), JetBrains (with AI Assistant), etc.
    • CLI tools: Claude Code, Gemini CLI, etc.
  3. Create a copy of the .env-sample, rename it to .env, and add your base64 API key
  4. Install dependencies - In the root folder of your project, run this terminal command:
yarn install
  1. Run a simple example - Test your setup with this basic LLM example:
cd templates/ts/cli
yarn basic-llm "Tell me a short joke"
  1. View traces and logs - Visit the Inworld Portal to monitor your graph executions, observe latency, and debug issues. Learn more about logs and traces.

Vibe Code with Inworld Runtime

Follow these 4 steps for each new feature: Start fresh → Find template → Build incrementally → Troubleshoot as needed.

Step 1: Set Context

Enter this prompt to ask the AI to familiarize itself with the codebase.

Step 2: Choose Template

Finding the best template to start from.
I want to build [describe your app and what it does].

User journey: [what users input → what they get back]

Please:
1. Find the closest template in this codebase
2. Outline the changes needed to make it into my app
3. Create a simple implementation plan

Keep the solution as simple as possible while meeting the user journey needs.

Step 3: Build Incrementally

Start implementation with the recommended template.
Build the app incrementally using the recommended template.
- Begin with the identified template
- Implement one node modification per iteration
- Test and demonstrate each change before proceeding
- Request approval before adding complex features or custom nodes

Step 4: Troubleshooting

When your AI agent gets stuck, try these troubleshooting options:
  1. Search templates for patterns:
I'm stuck on [specific issue - e.g., "connecting TTS node to LLM output"]. 

Search the templates folder for:
- Similar node configurations or patterns
- Examples that handle [your specific data type/flow]
- Working implementations I can adapt

Show me the relevant code snippets and explain how to adapt them to my case.
  1. Revert to last checkpoint: Use “Restore Checkpoint” in supported tools, or git reset/manual backups in others
  2. Restart the session: Summarize progress first, then start fresh
Summarize: 
- What we've built so far (features, templates used, key configurations)
- Current blockers or challenges preventing progress
- Next immediate task to tackle
  1. Document successful solutions: Save useful patterns as reusable rules. In Cursor, you can create rules by prompting the agent:
/Generate Cursor Rules

Document the fix we just implemented in this format:

**Problem:** [Describe the exact error or issue that was blocking progress]
**Solution:** [Explain the specific approach, pattern, or code change that resolved it]
**Context:** [Define when this rule applies - specific scenarios, node types, or conditions]
**Example:** [Include the actual code snippet or configuration that worked]