lbalmelli/keio
Nov 2025 – Nov 2025
1 Month active
Languages Used
MarkdownSysMLUnknown
Technical Skills
AI IntegrationAI integrationData AnalysisDocumentationIoT DevelopmentSystems Engineering
ken-katakura-keio
PROFILE
Ken-katakura-keio
Worked on the lbalmelli/keio repository to deliver two core features: a Class of 2025 user profile with associated assets and a SysML-based fermentation monitoring system for Sansho-zuke. The monitoring system integrated AI analysis and real-time notifications, leveraging data collection and IoT development to provide actionable fermentation insights. Applied systems engineering principles and data visualization techniques to model updates, ensuring maintainability and clarity. Addressed codebase hygiene by removing unused files and improving documentation, which streamlined future development. Used Markdown and SysML to structure documentation and models, demonstrating a disciplined approach to asset management and iterative, cohort-oriented feature delivery.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
9Total
Bugs
1
Commits
9
Features
2
Lines of code
1,035
Activity Months1
Your Network
38 peopleWork History
November 2025
9 Commits • 2 Features
Nov 1, 2025November 2025 performance summary for lbalmelli/keio: Delivered a new Class of 2025 User Profile with assets, advanced the Sansho-zuke fermentation monitoring initiative with a SysML model and AI-enabled analytics (data collection, AI analysis, and real-time notifications), and performed targeted codebase cleanup to reduce clutter and improve maintainability. The work established a foundation for cohort-specific experiences and data-driven fermentation insights, improved repository hygiene, and set the stage for faster future iterations.
9 Commits • 2 Features
Nov 1, 2025November 2025 performance summary for lbalmelli/keio: Delivered a new Class of 2025 User Profile with assets, advanced the Sansho-zuke fermentation monitoring initiative with a SysML model and AI-enabled analytics (data collection, AI analysis, and real-time notifications), and performed targeted codebase cleanup to reduce clutter and improve maintainability. The work established a foundation for cohort-specific experiences and data-driven fermentation insights, improved repository hygiene, and set the stage for faster future iterations.
November 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability97.8%
Architecture100.0%
Performance95.6%
AI Usage42.2%
Skills & Technologies
Programming Languages
MarkdownSysMLUnknown
Technical Skills
AI IntegrationAI integrationData AnalysisDocumentationIoT DevelopmentSystems Engineeringdata visualizationdocumentationfile managementsystems engineering
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