source-academy/backend
Dec 2025 – Dec 2025
1 Month active
Languages Used
Elixir
Technical Skills
AI integrationAPI developmentElixirbackend developmentdatabase management
Arul
PROFILE
Arul
Arul developed an AI-powered marking system for the source-academy/backend repository, enabling automated student feedback generation using a language model. The solution incorporated Elixir for backend development and focused on secure API integration, including encryption and management of sensitive keys. Arul expanded the database schema and APIs to persist AI-generated comments and prompts, while implementing robust error handling for OpenAI interactions. Observability was enhanced through comprehensive input and output logging, and test coverage was updated to ensure reliability. The system allowed per-course enablement of AI grading, supporting future scalability and maintainability through careful migration adjustments and peer-reviewed code improvements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,018
Activity Months1
Your Network
5 peopleShared Repositories
5
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary: Delivered a scalable AI-assisted marking capability within the backend that automates student feedback via a language model, with controls to enable/disable AI grading per course and secure handling of API keys. Expanded the data model and APIs to persist AI-generated comments and associated prompts, and improved observability through comprehensive logging and error handling. Implemented encryption for sensitive keys, updated tests, and adjusted migrations for reliability and future growth.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary: Delivered a scalable AI-assisted marking capability within the backend that automates student feedback via a language model, with controls to enable/disable AI grading per course and secure handling of API keys. Expanded the data model and APIs to persist AI-generated comments and associated prompts, and improved observability through comprehensive logging and error handling. Implemented encryption for sensitive keys, updated tests, and adjusted migrations for reliability and future growth.
December 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
Elixir
Technical Skills
AI integrationAPI developmentElixirbackend developmentdatabase management
Repositories Contributed To
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