Devtest solution.

[AdailtonHolanda]

This PR includes my implementation for the proposed test.

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 35%

Reasoning: The pull request demonstrates characteristics of thoughtful human authorship. The structure of the project is well-organized and adheres to common backend development conventions using FastAPI, SQLAlchemy, and Pydantic. The explanation provided in explanation.md outlines design decisions, tradeoffs considered, and insights on how the system supports future machine learning integration — all of which reflect contextual understanding and clear articulation typical of human developers. However, the code quality and writing style are clear and polished, which could suggest AI assistance or heavy use of tools like Copilot. The commit doesn't include extraneous comments or signature phrasing often associated with ChatGPT-generated content.

Key Indicators:

• Human Indicators:

  • Contextualized and purposeful explanation in explanation.md shows awareness of design tradeoffs and potential ML use cases.
  • Use of docstrings, good naming conventions, and a consistent architecture.
  • CLI and dependency management covered in readme.md, along with testing integration.
  • The explanation includes introspective notes about alternative approaches, suggesting reflective reasoning.

• Possible AI Indicators (but weak):

  • Concise, clean, and correct use of frameworks and documentation might suggest AI-assisted code.
  • The API and DB schema design decisions align with well-documented best practices, which may have been enhanced or suggested via tooling like ChatGPT, but not necessarily fully AI-generated.

Overall, the depth of reasoning and adaptability in the design points more toward human authorship, possibly aided by AI tools during coding.

✅ No strong indicators of AI generation detected