Monash-FIT3170/2025W1-QualAI
Mar 2025 – May 2025
2 Months active
Languages Used
BashMarkdownPythonTextCSSDockerfileHTMLJSON
Technical Skills
AI/MLAPI DevelopmentBackend DevelopmentBug FixingDatabase ManagementDependency Management
Kays Beslen
PROFILE
Kays Beslen
During two months on the Monash-FIT3170/2025W1-QualAI repository, Kieran Beswick developed and integrated core backend and frontend features supporting AI-assisted document retrieval and chatbot workflows. He implemented a TypeScript MongoDB client API with robust CRUD operations, built a React-based document management UI, and established a Flask backend with file upload and API endpoints. His work included integrating OpenAI Whisper for transcription, sentence-transformer embeddings, and Neo4j vector storage, enabling advanced data processing and retrieval. Kieran also reinforced CI/CD pipelines using Docker, GitHub Actions, and docker-compose, ensuring reliable automated testing and deployment. The solutions demonstrated strong code organization and maintainability.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
24Total
Bugs
1
Commits
24
Features
11
Lines of code
23,484
Activity Months2
Your Network
133 peopleSame Organization
@student.monash.edu116
Work History
May 2025
16 Commits • 6 Features
May 1, 2025May 2025 monthly summary for Monash-FIT3170/2025W1-QualAI: Delivered core backend, frontend, and data access capabilities, established AI-assisted retrieval, and hardened CI/CD. Key features delivered include a TypeScript MongoDB Client API with CRUD wrappers and collection utilities, a robust document retrieval workflow (default MONGO_URI handling, safe behavior when documents are not found, and _id stripping from results), a Document Management UI for viewing/renaming/deleting documents via a context menu, and an Ollama-based AI chatbot with vectorized storage and an end-to-end file upload pipeline. Foundational backend and frontend work includes a Flask-based app skeleton with initial upload routes, frontend refactor moving components to a centralized src/gui, and a reinforced CI/CD/ deployment pipeline using GitHub Actions, Dockerfiles, and docker-compose for automated testing and deployment. These efforts collectively improve data accessibility, developer productivity, end-user workflows, and deployment reliability.
16 Commits • 6 Features
May 1, 2025May 2025 monthly summary for Monash-FIT3170/2025W1-QualAI: Delivered core backend, frontend, and data access capabilities, established AI-assisted retrieval, and hardened CI/CD. Key features delivered include a TypeScript MongoDB Client API with CRUD wrappers and collection utilities, a robust document retrieval workflow (default MONGO_URI handling, safe behavior when documents are not found, and _id stripping from results), a Document Management UI for viewing/renaming/deleting documents via a context menu, and an Ollama-based AI chatbot with vectorized storage and an end-to-end file upload pipeline. Foundational backend and frontend work includes a Flask-based app skeleton with initial upload routes, frontend refactor moving components to a centralized src/gui, and a reinforced CI/CD/ deployment pipeline using GitHub Actions, Dockerfiles, and docker-compose for automated testing and deployment. These efforts collectively improve data accessibility, developer productivity, end-user workflows, and deployment reliability.
May 2025
March 2025
8 Commits • 5 Features
Mar 1, 2025March 2025 performance highlights for Monash-FIT3170/2025W1-QualAI: Delivered 5 feature spikes and tooling to accelerate AI research and prototyping across Whisper-based transcription, sentence-transformer embeddings, Neo4j vector storage, and a Flask API, plus documentation improvements. Major bug fix: vector database node removal handling corrected. Result: faster experimentation cycles, improved data handling, and stronger testability across the CI-friendly spike workflow.
March 2025
8 Commits • 5 Features
Mar 1, 2025March 2025 performance highlights for Monash-FIT3170/2025W1-QualAI: Delivered 5 feature spikes and tooling to accelerate AI research and prototyping across Whisper-based transcription, sentence-transformer embeddings, Neo4j vector storage, and a Flask API, plus documentation improvements. Major bug fix: vector database node removal handling corrected. Result: faster experimentation cycles, improved data handling, and stronger testability across the CI-friendly spike workflow.
Activity
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Quality Metrics
Correctness86.2%
Maintainability86.6%
Architecture84.6%
Performance75.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
BashCSSDockerfileHTMLJSONJavaScriptMarkdownPythonShellText
Technical Skills
AI IntegrationAI/MLAPI DevelopmentBackend DevelopmentBackend TestingBug FixingCI/CDCode OrganizationCode RefactoringCode RestructuringComponent IntegrationDatabase IntegrationDatabase ManagementDependency ManagementDevOps
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