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Priya Shaw

PROFILE

Priya Shaw

Worked on the confido repository to deliver a cohesive set of front-end enhancements, focusing on a unified header system with animated UI elements and consistent language handling. Leveraged React, JavaScript, and CSS to implement features such as session-based language persistence, responsive language selectors, and robust API data bindings for header components. Addressed storage system improvements and ensured reliable environment configuration, including base URL corrections for API requests. Enhanced modal logic and session expiry handling to improve user experience and application stability. The work emphasized maintainable code, release hygiene, and seamless integration of new features, resulting in a more polished and reliable interface.

Overall Statistics

Feature vs Bugs

77%Features

Repository Contributions

31Total
Bugs
3
Commits
31
Features
10
Lines of code
2,427
Activity Months1

Work History

August 2025

31 Commits • 10 Features

Aug 1, 2025

August 2025 summary: Delivered a cohesive set of front-end refinements for confido, focusing on a consistent header system, robust multi-language handling, and reliable data bindings. Implementations spanned across UI polish, storage, and release hygiene to improve user experience and developer velocity.

Activity

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Quality Metrics

Correctness83.2%
Maintainability84.2%
Architecture76.2%
Performance83.6%
AI Usage30.4%

Skills & Technologies

Programming Languages

CSSJSXJavaScript

Technical Skills

API IntegrationCSSCSS AnimationsComponent DesignComponent DevelopmentConditional RenderingContext APIEnvironment ConfigurationFront End DevelopmentFront-end DevelopmentFrontend DevelopmentModal ComponentsReactSession StorageState Management

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

Aiinhome-Aivista/confido

Aug 2025 Aug 2025
1 Month active

Languages Used

CSSJSXJavaScript

Technical Skills

API IntegrationCSSCSS AnimationsComponent DesignComponent DevelopmentConditional Rendering