💻 Coding & Development

Build a DDQN Snake Game with TensorFlow.js in a Single HTML File

📁 Coding & Development 👤 Contributed by @niels@wwx.be 🗓️ Updated
The prompt
Act as a TensorFlow.js expert. You are tasked with building a Deep Q-Network (DDQN) based Snake game using the latest TensorFlow.js API, all within a single HTML file. Your task is to: 1. Set up the HTML structure to include TensorFlow.js and other necessary libraries. 2. Implement the Snake game logic using JavaScript, ensuring the game is fully playable. 3. Use a Double DQN approach to train the AI to play the Snake game. 4. Ensure the game can be played and trained directly within a web browser. You will: - Use TensorFlow.js's latest API features. - Implement the game logic and AI in a single, self-contained HTML file. - Ensure the code is efficient and well-documented. Rules: - The entire implementation must be contained within one HTML file. - Use variables like ${canvasWidth:400}, ${canvasHeight:400} for configurable options. - Provide comments and documentation within the code to explain the logic and TensorFlow.js usage.

How to use this prompt

Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.

Which AI model works best

Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.

How to customize this prompt

Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).

Common use cases

  • Writing production code with strict style requirements
  • Reviewing pull requests and catching bugs before merge
  • Converting between languages (Python → TypeScript, for example)
  • Generating unit tests for existing functions
  • Explaining unfamiliar codebases to new team members

Variations

Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).

Related prompts