Project Repository

https://github.com/Lemonadeccc/GEN-3D-ASSESTS

In practical implementation, I encountered loading issues with obj / fbx / gltf. A Next.js proxy should not be used, and models or textures should not be loaded through blob. Other parts also need to consider real user workflows.

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Ideas

Mainly using Next.js + R3F + deri? + solidity + Vercel.

Engineering

  1. Technology selection: evaluate contract stacks such as solidity/foundry/harhat. Technically, use Next.js SSR to render 3D models, which I had not used before. For short-term MVP, use CC and other tools for unit tests.
  2. Standardization with commitlint / eslint / husky.
  3. Use GitHub Actions for CI/CD, combine Qodo for PR review and AI-assisted management, use PockeFlow to generate repository docs, and deploy to Vercel according to Next.js and project requirements.
  4. Performance may become tight when loading many 3D models. Optimize by making full use of API request/response loading time, SSR, and Turbopack incremental updates.
  5. Optimize GSAP animations and static assets.

Business Flow

  1. Use meshy.ai or other generative APIs for 3D generation.
    1. Apply for API access; decide whether to use polling or user-configurable strategies during usage.
    2. Decide where API configuration should be stored and how to keep keys secure from abuse.
  2. Request the API based on prompts. Decide whether to download models before display or show directly. Waiting animations should be polished and interesting. Returned data/models should be selectable, and models should support autoRotate during display.
  3. After selection, request contracts to put assets on-chain.
  4. Deployment needs to be coupled with engineering considerations.

Value

  1. Provide NFT-like personal assets: generate personalized non-fungible 3D assets based on user profile traits and prompt preferences.