# Apply Technique: Hybrid AI Coding: Using Claude Code for Quality and Codex for Speed
## What This Is
For complex coding tasks, using two specialized AI assistants is more effective than one. This technique uses an AI like OpenAI Codex to rapidly generate the initial functional code, and then uses a different AI, like Claude Code, to review, refactor, and improve the code's quality for production use.
Source: https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://newsletter.semianalysis.com/p/claude-code-is-the-inflection-point) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Analyze the user's current task to determine if a multi-agent workflow is appropriate. For new features or complex logic, designate one agent (e.g., OpenAI Codex) for initial drafting and another (e.g., Claude Code) for review.
- Execute the drafting phase: Use an execution-focused agent to generate a first draft of the code, focusing on functional completeness and addressing the core requirements.
- Initiate the review phase: Pass the generated code to a reasoning-focused agent. Instruct it to perform a comprehensive review, focusing on refactoring for production-readiness, adding documentation, improving architecture, and identifying potential bugs or security vulnerabilities.
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it