Building a Cooking Game with 3 Specialized AI Tools

What It Is

A developer recently created a browser-based cooking game by orchestrating three distinct AI tools, each handling a specific aspect of development. Claude Code managed the project structure and codebase organization, Gemini implemented the game mechanics and conditional logic, and Flux generated the visual sprites and graphical elements. The finished product demonstrates how specialized AI tools can collaborate on a single project, with each handling tasks aligned to its strengths. The game itself is playable at https://infinite-kitchen.com/kitchen and showcases what’s possible when developers treat AI tools as a coordinated toolkit rather than relying on a single solution.

This approach represents a shift in how developers might structure AI-assisted projects. Instead of prompting one model to handle everything from architecture to assets, the workflow distributes responsibilities based on each tool’s documented capabilities. Claude handles structural decisions and file organization, Gemini processes the branching logic that makes gameplay responsive, and Flux produces consistent visual assets without the anatomical distortions that plague some image generators.

Why It Matters

This multi-tool strategy addresses a fundamental limitation in current AI development: no single model excels at everything. Game development particularly exposes these gaps, requiring strong architectural planning, precise conditional logic, and coherent visual design. Forcing one AI to juggle all three often produces mediocre results across the board.

Indie developers and small teams benefit most from this approach. Budget constraints typically force choices between hiring specialists or accepting compromised quality. A coordinated AI workflow provides access to specialized capabilities without the overhead of multiple contractors. The cooking game example proves that production-ready results are achievable without a traditional development team.

The broader development ecosystem gains a template for AI orchestration. As more developers document which tools handle specific tasks effectively, the community builds collective knowledge about optimal AI combinations. This matters because it accelerates the shift from “AI can code” to “AI can handle distinct parts of a development pipeline reliably.”

Getting Started

Developers interested in replicating this approach should start by mapping project requirements to tool strengths. For a simple game prototype:

Structure and Setup (Claude Code):

// Initialize project structure const gameConfig = {
 canvas: document.getElementById('gameCanvas'),
 width: 800,
 height: 600,
 scenes: ['kitchen', 'storage', 'serving']
};

Game Logic (Gemini):

// Implement cooking mechanics function processCookingAction(ingredient, cookTime, temperature) {
 if (temperature > 180 && cookTime < 5) {
 return 'burned';
 } else if (temperature >= 160 && cookTime >= 3) {
 return 'perfect';
 }
 return 'undercooked';
}

Visual Assets (Flux): Generate sprites by prompting for consistent style: “pixel art cooking ingredient, 32x32, transparent background, top-down view.”

The key is maintaining clear handoffs between tools. Export Claude’s structure as a base repository, feed that context to Gemini for logic implementation, then integrate Flux-generated assets into the final build. Version control becomes critical when multiple AI tools contribute to the same codebase.

Context

Alternative approaches exist. Developers could use Unity with ChatGPT for scripting, or Godot with Midjourney for assets. The three-tool combination isn’t inherently superior, but it offers specific advantages: Claude’s code organization tends toward cleaner architecture than ChatGPT’s quick solutions, Gemini handles nested conditionals with fewer logic errors, and Flux maintains visual consistency better than Midjourney’s artistic variations.

Limitations remain significant. AI-generated game logic requires extensive testing since models don’t inherently understand game balance or player experience. Visual assets need manual review for consistency, and structural decisions from Claude still require developer judgment to validate. The workflow also introduces coordination overhead - managing three different interfaces and prompt styles adds complexity compared to single-tool development.

For projects beyond simple games, this pattern scales differently. Complex applications might need five or six specialized tools, at which point coordination costs outweigh benefits. The sweet spot appears to be projects with three to four distinct domains where tool specialization provides clear advantages over generalist approaches.