continuousactivelearning/vibe
Feb 2025 – Feb 2025
1 Month active
Languages Used
CSSDockerfileHTMLJavaScriptPythonShellTypeScript
Technical Skills
AI IntegrationAI/MLAPI DevelopmentAPI IntegrationAudio ProcessingBackend Development
Farhat Qadri
PROFILE
Farhat Qadri
Worked on the continuousactivelearning/vibe repository to deliver an end-to-end AI Engine-LMS integration, enabling seamless transfer and management of educational content. Developed new API endpoints for video, assessment, and question uploads, and updated the frontend to support secure, authorized interactions. Leveraged Python, FastAPI, and Docker to containerize the AI Engine, streamlining deployment and scaling. Introduced multi-model support with Gemini and Ollama, allowing dynamic service selection and improved input parsing. Enhanced operational readiness by updating deployment documentation and environment configurations. The work focused on backend and frontend reliability, supporting robust LMS integration and facilitating production rollouts for scalable AI-driven learning solutions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
5,180
Activity Months1
Your Network
76 peopleWork History
February 2025
6 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for continuousactivelearning/vibe: Delivered end-to-end AI Engine-LMS integration, containerization for scalable deployments, and multi-model support with Gemini and Ollama; implemented API and frontend enhancements to enable seamless transfer and control of LMS content. Achieved deployment readiness through Dockerization, docker-compose, and updated deployment docs; introduced input parsing improvements and frontend reorganization to support new models. Minor environment variable adjustments were made to improve deployment stability.
6 Commits • 3 Features
Feb 1, 2025February 2025 monthly summary for continuousactivelearning/vibe: Delivered end-to-end AI Engine-LMS integration, containerization for scalable deployments, and multi-model support with Gemini and Ollama; implemented API and frontend enhancements to enable seamless transfer and control of LMS content. Achieved deployment readiness through Dockerization, docker-compose, and updated deployment docs; introduced input parsing improvements and frontend reorganization to support new models. Minor environment variable adjustments were made to improve deployment stability.
February 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture83.4%
Performance76.6%
AI Usage65.0%
Skills & Technologies
Programming Languages
CSSDockerfileHTMLJavaScriptPythonShellTypeScript
Technical Skills
AI IntegrationAI/MLAPI DevelopmentAPI IntegrationAudio ProcessingBackend DevelopmentConfiguration ManagementContainerizationDevOpsDockerDocker ComposeFastAPIFrontend DevelopmentHTMLJavaScript
Repositories Contributed To
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