J. Omar Pavez

[ghost]

Hi! I wrote my solution focusing on the elevator demand, as other states are not relevant to the problem. I made two schemas for this solution:

Elevator

Represents an elevator in the system:

• id: Unique identifier
• building_id: Identifier for the building
• max_floor: Maximum floor the elevator can reach
• min_floor: Minimum floor the elevator can reach

ElevatorDemand

Represents a demand for an elevator:

• id: Unique identifier
• timestamp: When the demand occurred
• floor: Which floor the demand came from
• direction: Whether the person wanted to go up or down
• elevator_id: Which elevator responded to this demand (optional)

The application was written in Python using FastAPI and SQLAlchemy for the DB, FastAPI was my first choice because it's a extremely powerful framework that not only is fast but it allows for fast development thanks to Pydantic you get validation, serialization, docs, etc out of the box. The entire web app is in a Docker file and uses Alembic for migrations. Also (with the help of AI) wrote some tests using Pytest with mocks. As with any FastAPI application, you can see the entire API docs in http://localhost:8000/docs . You run the server with the docker_run.bat or .sh script in the scripts folder.

Check documentation.md for setup instructions and more details. If you have any questions please let me know

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 55%

Reasoning: The overall structure, content, and technical quality of the pull request are strong and consistent with well-documented human or AI-assisted professional work. The submission includes numerous properly configured project files (e.g., Dockerfile, docker-compose.yml, alembic config, scripts), with inline comments and clearly defined code and data models. While competent, it lacks telltale signs of fully automated authorship such as unnatural phrasing, inconsistent documentation, or repetitive patterns.

Moreover, the author explicitly mentions in the pull request description that some tests were written "with the help of AI," which indicates that at least part of the PR was AI-assisted, though not entirely AI-generated. The level of contextual awareness and project integration suggests human oversight and direction.

Key Indicators:

• Mention of AI assistance: The description candidly states "Also (with the help of AI) wrote some tests...", pointing to partial AI involvement.
• Natural flow and nuance: The architecture and descriptive comments reflect structured human reasoning (e.g., on elevator resting floors or analytics endpoints).
• Code complexity with consistent design: The inclusion of domain-specific logic (elevator demand prediction), test mocking patterns, and thoughtful data modeling supports likely human authorship or strong human oversight.
• Absence of excessive verbosity or duplication — common in AI-generated content.

In conclusion, the work was likely produced with a blend of manual effort and AI tools (e.g., Copilot), which is common in modern software development workflows. It is not entirely AI-generated, but AI assistance is evident.

✅ No strong indicators of AI generation detected

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 35%

Reasoning: The pull request description shows a strong understanding of the problem domain and outlines a thoughtful design approach, including schema modeling, tool selection (FastAPI, SQLAlchemy, Alembic), and development decisions. The mention of how specific technologies aid development, such as Pydantic's features and the setup details, reflect a personal development workflow. The presence of a small admission—"with the help of AI"—suggests the author did likely use AI assistance in writing tests, but not in generating the entire pull request content. The overall tone and structure are personal and context-specific, indicating a human author, potentially with minor AI assistance.

Key Indicators:

• Use of personal voice and explanation (e.g., "I wrote my solution", "FastAPI was my first choice").
• Customized solution design with domain-relevant schema explanation.
• Mentions of specific setup details, such as Docker scripts, which suggest practical development experience.
• Admits partial use of AI for testing, which implies transparency and supports human authorship for the bulk of the work.
• No unnatural sentence flow or typical AI-patterned phrasing detected.

✅ No strong indicators of AI generation detected

[github-actions]

AI Detection Analysis 🔍

Confidence Score: 45%

Reasoning: The content of the pull request appears to be largely consistent with what a motivated and experienced software developer might produce when implementing a full-stack prototype application. It showcases a coherent and extensive domain model (elevator and demand tracking), organized code structure (following standard FastAPI conventions), extensive documentation, and well-integrated modern tools like Docker, Alembic, and Pytest. However, there is a brief mention ("with the help of AI") in the description, suggesting at least partial AI involvement. The implementation of repetitive scaffolding (e.g., Alembic scripts, schemas, Docker configs) could plausibly have been accelerated or templated using AI tools such as GitHub Copilot or ChatGPT.

Key Indicators:

• Human-authored indicators:

  • Personal tone in the description ("Hi! I wrote my solution...").
  • Mention of decision-making rationale: "FastAPI was my first choice because..."
  • Explicit note regarding minor AI usage ("with the help of AI").
  • Clear, structured documentation and domain-driven design with meaningful class names.
  • Modular architecture and Docker integration indicate practical software engineering experience.

• Possible AI-generated or assisted indicators:

  • Certain sections like Alembic env.py, Dockerfiles, and schema definitions reflect common patterns that match boilerplate produced by AI tools.
  • High consistency and completeness across different components of the application (typical in AI-assisted generation to cover all required parts).
  • Test scaffolding using mocks and schemas follows a template-like process.

Overall, it leans toward being primarily human-authored work with possible assistance from AI tools for boilerplate or test code.

✅ No strong indicators of AI generation detected