Learning-Mode-AI/Learning-Mode-AI
Dec 2024 – Mar 2025
4 Months active
Languages Used
MarkdownCSSGoJavaScript
Technical Skills
Architecture Decision RecordsDocumentationSystem DesignAPI IntegrationBackend DevelopmentCSS
Muhmdk
PROFILE
Muhmdk
Muhammadlive_ contributed to the Learning-Mode-AI/Learning-Mode-AI repository by building features that automate video transcript generation, streamline Stripe-based subscription management, and enhance user experience for video learning. He designed an architecture decision record for integrating Amazon Transcribe, enabling fallback transcript creation to improve accessibility. Using JavaScript, Go, and Redis, he implemented backend workflows for Stripe webhook handling and persisted subscription states for reliable access control. On the frontend, he improved video processing feedback, error handling, and quiz interactions, focusing on maintainability and user engagement. His work demonstrated depth in system design, API integration, and robust error management across backend and frontend.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
13Total
Bugs
0
Commits
13
Features
9
Lines of code
1,654
Activity Months4
Your Network
13 people
Work History
March 2025
5 Commits • 3 Features
Mar 1, 2025March 2025 highlights for Learning-Mode-AI/Learning-Mode-AI: - Key features delivered: Auto Toggle Learning Mode on Video Navigation; Video Processing Robustness and Error Handling; Quiz Experience Improvements. - Major bugs fixed: Modal overlay stability after LEA-79 error; improved error messaging for video processing and token-limit scenarios. - Overall impact: Smoother cross-content UX, higher reliability, reduced user friction, and stronger quiz integrity, supporting scalable usage and onboarding of new content. - Technologies/skills demonstrated: Frontend URL/video load event handling, backend input validation, enhanced error handling, modal overlay UI, and quiz state management with randomized options.
5 Commits • 3 Features
Mar 1, 2025March 2025 highlights for Learning-Mode-AI/Learning-Mode-AI: - Key features delivered: Auto Toggle Learning Mode on Video Navigation; Video Processing Robustness and Error Handling; Quiz Experience Improvements. - Major bugs fixed: Modal overlay stability after LEA-79 error; improved error messaging for video processing and token-limit scenarios. - Overall impact: Smoother cross-content UX, higher reliability, reduced user friction, and stronger quiz integrity, supporting scalable usage and onboarding of new content. - Technologies/skills demonstrated: Frontend URL/video load event handling, backend input validation, enhanced error handling, modal overlay UI, and quiz state management with randomized options.
March 2025
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 – Delivered end-to-end Stripe-based subscription features for Learning-Mode-AI. Implemented Stripe webhook integration, persisted subscription state in Redis, and established tier-based access control with a focus on maintainability. This work enables automated monetization, reliable access provisioning, and scalable future enhancements while reducing manual subscription overhead.
February 2025
3 Commits • 1 Features
Feb 1, 2025February 2025 – Delivered end-to-end Stripe-based subscription features for Learning-Mode-AI. Implemented Stripe webhook integration, persisted subscription state in Redis, and established tier-based access control with a focus on maintainability. This work enables automated monetization, reliable access provisioning, and scalable future enhancements while reducing manual subscription overhead.
January 2025
4 Commits • 4 Features
Jan 1, 2025January 2025 — Key outcomes for Learning-Mode-AI/Learning-Mode-AI: delivered user-focused video processing feedback, backend cleanup, enhanced AI chat UX, and Stripe webhook groundwork enabling payments and event handling. These efforts improved long-operation feedback, faster access to summarized content, richer AI interactions, and payment readiness for monetization.
4 Commits • 4 Features
Jan 1, 2025January 2025 — Key outcomes for Learning-Mode-AI/Learning-Mode-AI: delivered user-focused video processing feedback, backend cleanup, enhanced AI chat UX, and Stripe webhook groundwork enabling payments and event handling. These efforts improved long-operation feedback, faster access to summarized content, richer AI interactions, and payment readiness for monetization.
January 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: December 2024 (Learning-Mode-AI/Learning-Mode-AI) Key features delivered: - Transcript generation capability planning with Amazon Transcribe. Published an Architecture Decision Record outlining a fallback mechanism to automatically generate transcripts for videos that lack transcripts. The ADR covers problem statement, objectives, current system analysis, requirements, options, and a proposed solution leveraging Amazon Transcribe. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Established a scalable, architecture-driven path to automatic transcripts, boosting accessibility, searchability, and user engagement while reducing reliance on manual transcription. - Created a reusable design artifact (ADR) that enables faster, consistent decision-making for future transcription-related work and similar fallback capabilities. - Strengthened cross-team alignment around transcript strategy and vendor choice (Amazon Transcribe). Technologies/skills demonstrated: - Architecture Decision Records and requirements-driven design - Trade-off analysis and option comparison - AWS product concepts (Amazon Transcribe) and integration planning - Documentation discipline, ADR formation, and roadmap-oriented thinking
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: December 2024 (Learning-Mode-AI/Learning-Mode-AI) Key features delivered: - Transcript generation capability planning with Amazon Transcribe. Published an Architecture Decision Record outlining a fallback mechanism to automatically generate transcripts for videos that lack transcripts. The ADR covers problem statement, objectives, current system analysis, requirements, options, and a proposed solution leveraging Amazon Transcribe. Major bugs fixed: - None reported this month. Overall impact and accomplishments: - Established a scalable, architecture-driven path to automatic transcripts, boosting accessibility, searchability, and user engagement while reducing reliance on manual transcription. - Created a reusable design artifact (ADR) that enables faster, consistent decision-making for future transcription-related work and similar fallback capabilities. - Strengthened cross-team alignment around transcript strategy and vendor choice (Amazon Transcribe). Technologies/skills demonstrated: - Architecture Decision Records and requirements-driven design - Trade-off analysis and option comparison - AWS product concepts (Amazon Transcribe) and integration planning - Documentation discipline, ADR formation, and roadmap-oriented thinking
Activity
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Quality Metrics
Correctness85.4%
Maintainability83.0%
Architecture80.0%
Performance78.4%
AI Usage29.2%
Skills & Technologies
Programming Languages
CSSGoJavaScriptMarkdown
Technical Skills
API IntegrationArchitecture Decision RecordsBackend DevelopmentBrowser Extension DevelopmentCSSConfiguration ManagementDatabase ManagementDocumentationError HandlingFront End DevelopmentFront-end DevelopmentFrontend DevelopmentJavaScriptLocal StorageReact
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