--- type: "BlogPosting" title: "My 2025 AI Development Workflow: Claude, Codex, Gemini & Google AI Studio" description: "Explore my personal AI development workflow, combining Claude Code, OpenAI Codex, Gemini, Veo, and Google AI Studio. Discover the \"AI Model Ping Pong\" strategy for enhanced results." resource: "https://www.contextstudios.ai/blog/my-ai-development-workflow-2025-how-i-combine-claude-code-codex-gemini-and-google-ai-studio" language: "en" tags: ["KI-Entwicklung", "Claude Code", "OpenAI Codex", "Gemini", "Veo", "Google AI Studio", "Workflow", "Produktivität", "Multi-AI"] generated: by: "process:contextstudios-md/1" at: "2026-10-08T22:27:01.333Z" status: "stable" --- # My 2025 AI Development Workflow: Claude, Codex, Gemini & Google AI Studio Published: 2025-12-28 Tags: KI-Entwicklung, Claude Code, OpenAI Codex, Gemini, Veo, Google AI Studio, Workflow, Produktivität, Multi-AI ![My 2025 AI Development Workflow: Claude, Codex, Gemini & Google AI Studio](https://wary-platypus-754.convex.cloud/api/storage/9014073f-2b6a-4aa3-a55b-2cfacb2c6715) # My AI Development Workflow 2025: How I Combine Claude, Codex, Gemini, and Google AI Studio The future of software development doesn't lie in a single AI tool – but in the intelligent combination of several specialized models. In this article, I'll show you my personal workflow, which I use daily to develop, plan, and visualize projects. ## Why Multi-AI Instead of a Single Tool? Every AI model has its strengths and weaknesses. Claude Code excels at coding, Gemini at image generation, Veo at videos. Instead of relying on a "jack-of-all-trades" tool, I specifically use the best tool for each task – and even have the models check each other's work. > **The result: Better code quality, faster iteration, and more creative solutions.** --- ## My IDE Setup: Antigravity As a foundation, I use **Antigravity** as my primary development environment. ### What makes Antigravity special: - **Claude Code in the Terminal** – Directly integrated for seamless coding - **Claude Code VSCode Extension** – For context-aware code completion - **OpenAI Codex Extension** – As a second opinion and debugging partner This combination gives me the best of both worlds: Anthropic's deep code understanding and OpenAI's rapid iteration. --- ## Claude Code: The Heart of My Development **Claude Code** is my primary tool for all coding and development tasks. ### Strengths of Claude Code: - **Multi-File Refactoring** – Understands complex codebases and can make comprehensive changes - **Context Awareness** – Keeps the entire project context in mind - **Agentic Workflows** – Can independently research, plan, and implement - **MCP Integration** – Connects to external tools and data sources ### Typical Use Cases: - Implementing new features - Performing code reviews - Planning complex refactorings - Developing API integrations - Writing tests Claude Code is particularly strong when it comes to **interconnected, complex tasks** that require a deep understanding of the codebase. --- ## OpenAI Codex: Debugging & Plan Validation I use **OpenAI Codex** specifically for two main purposes: ### 1. Debugging When I encounter a stubborn bug, I get a second opinion from Codex. Often, another model sees problems from a different perspective. ### 2. Plan Validation ("Confirmation AI") Before implementing a major implementation plan, I have Codex review the plan: ```text "Here is my plan for Feature X. Do you see any problems or areas for improvement?" ``` This **second opinion** has saved me from costly mistakes several times. --- ## The "AI Model Ping Pong" Strategy One of my most effective techniques is **AI Model Ping Pong** – the targeted back-and-forth between different AI models. ### How it works: 1. **Claude Code creates** a first draft (code, plan, architecture) 2. **Codex reviews** the draft and provides feedback 3. **Claude Code improves** based on the feedback 4. **Optional: Further iteration** until the result is optimal ### Why it works: - Each model has **different training data** and perspectives - Errors of one model are often recognized by the other - The result is **more robust** than from a single model - Similar to code reviews among humans ### Real-world example: ```text 1. Claude Code: "Here is my implementation for the Authentication system..." 2. Codex Review: "The approach is good, but I see a potential race condition problem with..." 3. Claude Code: "Good point! Here is the improved version with Mutex-Lock..." ``` --- ## GitHub Spec-Kit: Structured Feature Planning with AI A game-changer in my workflow is **[GitHub Spec-Kit](https://github.com/github/spec-kit)** – an open-source toolkit for **Spec-Driven Development**. Instead of directly jumping into coding ("Vibe Coding"), I first write detailed specifications, which then serve as the basis for AI-powered implementation. ### What is Spec-Driven Development? > **The philosophy: Specifications become executable artifacts.** Instead of writing vague prompts like "Build me a login system", I define precisely what the system needs to be able to do – and the AI then implements this spec in a structured way. ### The Spec-Kit Slash Commands After initialization, the following commands are available: | Command | Purpose | |---------|-------| | `/speckit.constitution` | Define the basic principles of the project | | `/speckit.specify` | Define requirements and demands | | `/speckit.plan` | Create a technical implementation strategy | | `/speckit.tasks` | Generate actionable task lists | | `/speckit.implement` | Execute planned development | ### My Spec-Kit Workflow **1. Create CONSTITUTION** - What principles apply to the project? - Which technologies and patterns do we use? - What are no-gos? **2. Write SPECIFY** - What should the feature be able to do? - What user stories are there? - Which edge cases need to be considered? **3. Develop PLAN** - How do we implement this technically? - What architectural decisions do we make? - What dependencies do we need? **4. Generate TASKS** - Break down tasks into small, actionable steps - Set priorities - Identify dependencies between tasks **5. Execute IMPLEMENT** - AI implements based on the spec - Structured, predictable results - Less "Vibe Coding", more precision ### Why Spec-Kit has revolutionized my workflow **Before (Vibe Coding):** ```text Prompt: "Build me a User-Authentication-System" → AI improvises → Unpredictable results → A lot of rework needed ``` **After (Spec-Driven):** ```text Spec: "Authentication-System with the following requirements: - OAuth2 with Google and GitHub - Session-based with Redis - Rate-Limiting: 5 attempts/minute - 2FA optional via TOTP - Password-Reset via Email - Audit-Log for all Auth-Events" → AI has clear specifications → Predictable, complete implementation → Less iteration needed ``` ### Spec-Kit + AI Model Ping Pong The combination of Spec-Kit and my ping-pong strategy is particularly powerful: 1. **Write Spec with Claude Code** – Formulate detailed requirements 2. **Have Codex review Spec** – Identify gaps and ambiguities 3. **Improve Spec** – Based on feedback 4. **Create Plan with Claude Code** – Plan technical implementation 5. **Validate Plan with Codex** – Check alternative approaches 6. **Start Implementation** – With a solid foundation ### Supported AI Assistants Spec-Kit works with virtually all modern AI coding tools: - ✅ Claude Code - ✅ GitHub Copilot - ✅ Cursor - ✅ Windsurf - ✅ Google Gemini - ✅ OpenAI Codex ### Three Scenarios for Spec-Kit | Scenario | Description | When to use? | |----------|--------------|--------------| | **Greenfield** | New project from scratch | Startups, new Products | | **Creative Exploration** | Test parallel implementations | Prototyping, Experiments | | **Brownfield** | Extend existing systems | Legacy code, Refactoring | ### Quick Start with Spec-Kit ```bash # Installation npx specify init # Or install persistently npm install -g specify # Initialize project specify init my-project # Then use the Slash Commands /speckit.constitution /speckit.specify /speckit.plan /speckit.tasks /speckit.implement ``` > Spec-Kit has fundamentally changed the way I plan and implement features. Instead of "Vibe Coding", I now work with **precise specifications** that lead to **predictable, high-quality results**. --- ## Gemini: Image Generation & 3D Illustrations For **visual assets**, I use **Google Gemini**. ### Areas of Application: - **Hero Images** for blog posts and landing pages - **3D Illustrations** for technical concepts - **Icons and UI elements** in a consistent style - **Infographics** for complex workflows ### Workflow with Gemini: 1. Describe the concept (detailed prompt) 2. Generate initial variations 3. Select and refine the best version 4. Export in various formats Gemini's strength lies in its **consistency** – once a style is established, I can maintain it across many assets. --- ## Veo: Professional Video Generation For **video content**, I rely on **Google Veo**. ### Areas of Application: - **Product Demos** and Feature Showcases - **Explainer Videos** for complex concepts - **Social Media Content** (short clips) - **Background Animations** for Websites ### Veo Workflow: 1. Create storyboard/concept 2. Prompt scene by scene 3. Generate and review clips 4. Post-processing and editing Veo is particularly useful for **rapid prototyping** – before I invest in expensive video production, I first test concepts with AI-generated videos. --- ## Google AI Studio: Mockups & Animation Concepts **Google AI Studio** is my go-to tool for creative concept work. ### Website Mockups - Landing Page Designs - UI Concepts - Responsive Layouts ### Animation Concepts - **Scrollytelling Animations** – How should content appear when scrolling? - **3D Animations** – Concept visualization before implementation - **Micro-Interactions** – Button Hovers, Loading States, Transitions ### Workflow: 1. Description of the desired animation/mockup 2. AI Studio generates visual concepts 3. Iteration until the desired result 4. Export as a reference for development This saves an enormous amount of time in the **concept phase** – instead of iterating for hours in Figma, I quickly generate several variants and then decide. ## Summary: My Tool Stack | Task | Primary Tool | Backup/Validation | |---------|---------------|-------------------| | Coding & Development | Claude Code | - | | Debugging | Codex | Claude Code | | Feature Specification | Spec-Kit + Claude Code | Codex | | Plan Validation | Codex | - | | Image Generation | Gemini | - | | 3D Illustrations | Gemini | - | | Video Generation | Veo | - | | Mockups & Animations | Google AI Studio | - | --- ## Conclusion: The Future is Multi-AI + Spec-Driven The days when a single tool could handle all tasks are over. I achieve the best results through: 1. **Specialization** – The best tool for each task 2. **Specification** – Spec-Kit for structured, predictable results 3. **Validation** – AI models check each other 4. **Integration** – Seamless workflow between the tools 5. **Iteration** – AI Model Ping Pong for optimal results > This workflow has **doubled** my productivity and significantly improved the quality of my work. The investment in learning multiple tools – especially Spec-Kit – pays off. --- ## Your Next Steps 1. **Start with a Tool** – Claude Code or Codex as a base 2. **Install Spec-Kit** – `npx specify init` for structured planning 3. **Add Specialization** – Gemini for images, Veo for videos 4. **Establish Ping Pong** – Have models review each other 5. **Iterate your Workflow** – Find out what works for you The AI landscape is evolving rapidly – but the principles of Multi-AI usage and Spec-Driven Development will remain. --- *Are you already using Spec-Kit or other structured planning tools in your AI workflow? Share your experiences in the comments!* ## Related - [AI Development](https://www.contextstudios.ai/ai-development.md) - [AI Workflow Automation](https://www.contextstudios.ai/ai-workflow-automation.md) - [LLM Integration](https://www.contextstudios.ai/llm-integration.md) - [AI Consulting](https://www.contextstudios.ai/ai-consulting.md)