FGA0138-MDS-Ajax/2024.2-Virgo
Nov 2024 – Feb 2025
4 Months active
Languages Used
MarkdownYAMLJSXJavaScriptSVGBashBatchHTML
Technical Skills
DocumentationMkDocsTechnical WritingYAMLAPI IntegrationExpo Router
oyLeonardo
PROFILE
Oyleonardo
Over four months, 231039113@aluno.unb.br developed and enhanced the FGA0138-MDS-Ajax/2024.2-Virgo repository, focusing on AI-powered image analysis workflows and robust registration experiences. They implemented end-to-end image upload and disease prediction features using Python, TensorFlow, and FastAPI, enabling in-memory inference and detailed result reporting. Their work included React Native front-end development with real-time form validation and onboarding flows, as well as backend API integration for agronomist registration. By improving documentation with MkDocs and streamlining navigation, they increased accessibility and maintainability. The developer demonstrated depth in debugging, model management, and cross-stack integration, delivering reliable, user-focused solutions.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
24Total
Bugs
3
Commits
24
Features
9
Lines of code
3,336
Activity Months4
Your Network
16 people
Work History
February 2025
4 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for FGA0138-MDS-Ajax/2024.2-Virgo focusing on business value and technical achievements. Key features delivered include Image-based Disease Prediction API Enhancements with in-memory TensorFlow processing, richer results (confidence scores and latency), and improved disease translation dictionary readability. Major bugs fixed include API Integration Stability Fix restoring full response structure on the predict endpoint and Correct AI Model Loading ensuring the correct model (lsx_v2.keras) is used. These efforts improved prediction reliability, speed, and user trust, enabling faster, more accurate disease insights from uploaded images. Technologies demonstrated include TensorFlow in-memory inference, Python backend APIs, model management, and translation dictionary maintenance.
4 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for FGA0138-MDS-Ajax/2024.2-Virgo focusing on business value and technical achievements. Key features delivered include Image-based Disease Prediction API Enhancements with in-memory TensorFlow processing, richer results (confidence scores and latency), and improved disease translation dictionary readability. Major bugs fixed include API Integration Stability Fix restoring full response structure on the predict endpoint and Correct AI Model Loading ensuring the correct model (lsx_v2.keras) is used. These efforts improved prediction reliability, speed, and user trust, enabling faster, more accurate disease insights from uploaded images. Technologies demonstrated include TensorFlow in-memory inference, Python backend APIs, model management, and translation dictionary maintenance.
February 2025
January 2025
12 Commits • 5 Features
Jan 1, 2025January 2025 (2025-01) monthly performance summary for FGA0138-MDS-Ajax/2024.2-Virgo. Delivered an end-to-end AI-powered image analysis workflow and comprehensive UI refinements, resulting in smoother user onboarding and a more maintainable codebase. Major bugs fixed across the month included JPEG upload handling, AI backend integration stability, and onboarding UI templates. The work drove clear business value by enabling AI-assisted image processing, improving activation and data quality, and reducing onboarding friction. Technologies demonstrated include React Native frontend, Axios-based API integration, FastAPI backend, AI service integration, image labeling, and robust tooling and documentation improvements.
January 2025
12 Commits • 5 Features
Jan 1, 2025January 2025 (2025-01) monthly performance summary for FGA0138-MDS-Ajax/2024.2-Virgo. Delivered an end-to-end AI-powered image analysis workflow and comprehensive UI refinements, resulting in smoother user onboarding and a more maintainable codebase. Major bugs fixed across the month included JPEG upload handling, AI backend integration stability, and onboarding UI templates. The work drove clear business value by enabling AI-assisted image processing, improving activation and data quality, and reducing onboarding friction. Technologies demonstrated include React Native frontend, Axios-based API integration, FastAPI backend, AI service integration, image labeling, and robust tooling and documentation improvements.
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 (2024-12) monthly summary for FGA0138-MDS-Ajax/2024.2-Virgo focusing on delivering a streamlined registration experience, backend integration for agronomist onboarding, and frontend routing stability. Key outcomes include enhanced UX with real-time form validation, a new home page and backend integration for agronomist registration, and robust post-merge routing fixes that improve navigation and user feedback.
5 Commits • 2 Features
Dec 1, 2024December 2024 (2024-12) monthly summary for FGA0138-MDS-Ajax/2024.2-Virgo focusing on delivering a streamlined registration experience, backend integration for agronomist onboarding, and frontend routing stability. Key outcomes include enhanced UX with real-time form validation, a new home page and backend integration for agronomist registration, and robust post-merge routing fixes that improve navigation and user feedback.
December 2024
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for FGA0138-MDS-Ajax/2024.2-Virgo. Delivered enhancements to meeting minutes documentation with embedded YouTube videos and improved MkDocs navigation, consolidating meeting records and improving accessibility for cross-team review. Added new minutes files and refined initial meeting details, withMkDocs navigation updates to streamline discovery and onboarding.
November 2024
3 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for FGA0138-MDS-Ajax/2024.2-Virgo. Delivered enhancements to meeting minutes documentation with embedded YouTube videos and improved MkDocs navigation, consolidating meeting records and improving accessibility for cross-team review. Added new minutes files and refined initial meeting details, withMkDocs navigation updates to streamline discovery and onboarding.
Activity
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Quality Metrics
Correctness83.4%
Maintainability83.4%
Architecture80.0%
Performance75.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
BashBatchHTMLJSXJavaScriptMarkdownPythonSVGTypeScriptYAML
Technical Skills
AI IntegrationAI/Machine LearningAPI DevelopmentAPI IntegrationAutomationAxiosBackend DevelopmentDebuggingDevOpsDocumentationExpoExpo RouterFastAPIFile HandlingFile Upload Handling
Repositories Contributed To
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