airqo-platform/AirQo-api
Nov 2024 – Sep 2025
8 Months active
Languages Used
PythonTextMarkdownJSONtextJavaScriptSQLShell
Technical Skills
AI IntegrationAPI DevelopmentAPI IntegrationBackend DevelopmentCachingCloud Integration
wabinyai
PROFILE
Wabinyai
Raja Castro developed and maintained the airqo-platform/AirQo-api repository, delivering robust backend features for air quality monitoring and analytics. Over eight months, he built scalable reporting and spatial analysis APIs, integrated AI-driven report generation using Python and Google Generative AI, and optimized sensor placement with machine learning and geospatial data. His work included implementing city-level AQI heatmaps with Redis caching, enhancing data modeling and validation, and streamlining dependency management for reliability. Raja improved documentation and code maintainability, enabling smoother onboarding and future development. His technical approach emphasized modular API design, efficient data processing, and production-ready cloud integration for scalable deployments.
Overall Statistics
Feature vs Bugs
81%Features
Repository Contributions
106Total
Bugs
8
Commits
106
Features
34
Lines of code
7,283
Activity Months8
Your Network
8 people
Work History
September 2025
3 Commits • 2 Features
Sep 1, 2025Concise monthly summary for AirQo-api (Sept 2025).
3 Commits • 2 Features
Sep 1, 2025Concise monthly summary for AirQo-api (Sept 2025).
September 2025
August 2025
51 Commits • 14 Features
Aug 1, 2025August 2025 — AirQo API: Delivered high-impact features to improve performance, reliability, and data capabilities, while fixing critical stability issues. Key features include cache performance improvements with 30-minute caching, GCP-based data modeling and city validation, a new data bucketing system, and location lookup with category support. Major bug fixes addressed core error handling, batch Redis caching issues, and local environment diff saving to stabilize operations. Overall, these efforts reduce latency, enable scalable analytics for city-level data, and strengthen the platform's readiness for production workloads. Technologies and skills demonstrated include caching strategies, cloud integration (GCP), data modeling and concurrency, configurable architecture, and robust error handling.
August 2025
51 Commits • 14 Features
Aug 1, 2025August 2025 — AirQo API: Delivered high-impact features to improve performance, reliability, and data capabilities, while fixing critical stability issues. Key features include cache performance improvements with 30-minute caching, GCP-based data modeling and city validation, a new data bucketing system, and location lookup with category support. Major bug fixes addressed core error handling, batch Redis caching issues, and local environment diff saving to stabilize operations. Overall, these efforts reduce latency, enable scalable analytics for city-level data, and strengthen the platform's readiness for production workloads. Technologies and skills demonstrated include caching strategies, cloud integration (GCP), data modeling and concurrency, configurable architecture, and robust error handling.
July 2025
12 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for airqo-platform/AirQo-api. Focused on delivering scalable AQI insights across cities with robust caching and city-level customization, while enhancing model management for fresh, accurate predictions. Key features delivered: - AQI Heatmap Generation and Caching: unified heatmap across cities including data fetch, model training/prediction, image generation/visualization, and caching to boost performance. Shorter cache intervals and a switch to a fresher, more accurate prediction model were implemented. - Per-City AQI Heatmap API Endpoints: city-specific heatmap data endpoints with grid-aware data handling, improved error handling, and Redis-backed caching for faster responses. - AQI Model Persistence and City-level Retraining: model loading for pre-trained city models, selective retraining, and a retrain_cities mechanism to keep predictions up-to-date with efficient compute. Major bugs fixed (and stability improvements): - Enhanced error handling in per-city APIs and grid data workflows to reduce failed requests. - Reliability gains from Redis caching and cache-time adjustments to minimize stale data and failed fetches. Overall impact and accomplishments: - Enables scalable, city-level AQI insights with faster responses and fresher predictions, supporting data-driven decisions for more cities. - Improves maintainability and lifecycle management of city-specific models with a streamlined retraining process. Technologies/skills demonstrated: - Python-based API design, Redis caching, model persistence, selective retraining workflows, data fetching pipelines, image generation/visualization, and robust error handling.
12 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for airqo-platform/AirQo-api. Focused on delivering scalable AQI insights across cities with robust caching and city-level customization, while enhancing model management for fresh, accurate predictions. Key features delivered: - AQI Heatmap Generation and Caching: unified heatmap across cities including data fetch, model training/prediction, image generation/visualization, and caching to boost performance. Shorter cache intervals and a switch to a fresher, more accurate prediction model were implemented. - Per-City AQI Heatmap API Endpoints: city-specific heatmap data endpoints with grid-aware data handling, improved error handling, and Redis-backed caching for faster responses. - AQI Model Persistence and City-level Retraining: model loading for pre-trained city models, selective retraining, and a retrain_cities mechanism to keep predictions up-to-date with efficient compute. Major bugs fixed (and stability improvements): - Enhanced error handling in per-city APIs and grid data workflows to reduce failed requests. - Reliability gains from Redis caching and cache-time adjustments to minimize stale data and failed fetches. Overall impact and accomplishments: - Enables scalable, city-level AQI insights with faster responses and fresher predictions, supporting data-driven decisions for more cities. - Improves maintainability and lifecycle management of city-specific models with a streamlined retraining process. Technologies/skills demonstrated: - Python-based API design, Redis caching, model persistence, selective retraining workflows, data fetching pipelines, image generation/visualization, and robust error handling.
July 2025
May 2025
6 Commits • 1 Features
May 1, 2025May 2025: AirQo-api delivered ML-driven sensor placement optimization within polygon boundaries, including justifications for selected sites and enforcement of a minimum inter-sensor distance. This feature uses geographic data and ML to identify optimal deployment layouts and provide rationale for candidate sites, supporting data-driven decision making and deployment efficiency. The work progressed from grid-to-sites translation to integration-ready components, laying groundwork for scoring/validation and future rollout.
May 2025
6 Commits • 1 Features
May 1, 2025May 2025: AirQo-api delivered ML-driven sensor placement optimization within polygon boundaries, including justifications for selected sites and enforcement of a minimum inter-sensor distance. This feature uses geographic data and ML to identify optimal deployment layouts and provide rationale for candidate sites, supporting data-driven decision making and deployment efficiency. The work progressed from grid-to-sites translation to integration-ready components, laying groundwork for scoring/validation and future rollout.
February 2025
6 Commits • 1 Features
Feb 1, 2025February 2025: Focused API improvements in AirQo-api with a strong emphasis on spatial analytics capabilities and reliability. Delivered documentation-driven enhancements and a critical API change that improves payload handling and overall reliability for spatial analysis workflows, positioning the platform for smoother internal adoption and ML-assisted site selection reasoning.
6 Commits • 1 Features
Feb 1, 2025February 2025: Focused API improvements in AirQo-api with a strong emphasis on spatial analytics capabilities and reliability. Delivered documentation-driven enhancements and a critical API change that improves payload handling and overall reliability for spatial analysis workflows, positioning the platform for smoother internal adoption and ML-assisted site selection reasoning.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 focused on documentation and maintainability improvements in the backend API to reduce future tech debt and accelerate onboarding for new engineers. The primary effort was enhancing the SiteCategoryModel documentation, clarifying data structures, variable purposes, priority lists, and the OSM data querying/processing steps to improve readability and maintainability across AirQo-api.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 focused on documentation and maintainability improvements in the backend API to reduce future tech debt and accelerate onboarding for new engineers. The primary effort was enhancing the SiteCategoryModel documentation, clarifying data structures, variable purposes, priority lists, and the OSM data querying/processing steps to improve readability and maintainability across AirQo-api.
December 2024
5 Commits • 2 Features
Dec 1, 2024December 2024 — AirQo-api: Delivered two major AI integration upgrades and cleanup, focusing on business value and maintainability. 1) Deprecation of Hugging Face/LLM integrations with a cleaned-up API surface, removing transformers/torch usage, token dependency, and environment variable; dependencies streamlined (e.g., sentencepiece); endpoint renamed to reflect consolidated functionality. 2) Migrated report generation from OpenAI to Google Generative AI, removing OpenAI config and integration. These changes reduce external dependencies and operational risk, simplify deployment, and improve reliability and security for AI-powered reporting. Key achievements across the month include the commits associated with HF cleanup and the migration to Google Gen AI. Technologies demonstrated: API design and refactor, dependency management, cloud AI services (Google Generative AI), and secure configuration management.
5 Commits • 2 Features
Dec 1, 2024December 2024 — AirQo-api: Delivered two major AI integration upgrades and cleanup, focusing on business value and maintainability. 1) Deprecation of Hugging Face/LLM integrations with a cleaned-up API surface, removing transformers/torch usage, token dependency, and environment variable; dependencies streamlined (e.g., sentencepiece); endpoint renamed to reflect consolidated functionality. 2) Migrated report generation from OpenAI to Google Generative AI, removing OpenAI config and integration. These changes reduce external dependencies and operational risk, simplify deployment, and improve reliability and security for AI-powered reporting. Key achievements across the month include the commits associated with HF cleanup and the migration to Google Gen AI. Technologies demonstrated: API design and refactor, dependency management, cloud AI services (Google Generative AI), and secure configuration management.
December 2024
November 2024
22 Commits • 10 Features
Nov 1, 2024Month: 2024-11 — AirQo-api monthly summary focused on delivering a robust, AI-assisted, and observable reporting stack, while improving maintainability and data access. Key features delivered: - API Endpoint for Report Generation: created endpoints for generating reports with create and fetch capabilities, enabling automated reporting and spatial analysis insights. - AI/LLM Based Reporting with Gemini: integrated Gemini to generate and augment reports, reducing manual drafting and enabling smarter insights. - Logging Enhancements: added comprehensive logging across the reporting system to improve observability and troubleshooting. - URL Handling for Reporting Endpoints: enhanced URL handling to improve access, routing, and security of report endpoints. - Pandas-GBQ Integration: added support for pandas-gbq data access to enable scalable analytics against BigQuery. - Daily Mean Statistics: implemented daily mean calculations to support time-series data analysis. - Report Generation Module: established the core reporting module architecture to enable scalable reporting workflows. - User Chat Functionality: added user chat functionality to streamline data queries and collaboration. - Dependency Management Improvements: improved handling of dependencies to reduce build issues and drift. - Grumming Module: introduced grumming module (experimental/contextual use TBD). Major bugs fixed: - Maintenance and PR Hygiene: cleanup and alignment of code review and requirements references in the batch, improving governance and release hygiene. Overall impact and accomplishments: - Delivered end-to-end reporting capabilities with AI augmentation, improved observability, and scalable data access, enabling faster data-driven decisions. - Strengthened cross-functional collaboration through improved PR hygiene and clearer documentation. Technologies/skills demonstrated: - REST API design and integration, AI/LLM (Gemini) integration, logging and observability, URL routing, pandas-gbq data access, daily time-series analytics, and dependency management.
November 2024
22 Commits • 10 Features
Nov 1, 2024Month: 2024-11 — AirQo-api monthly summary focused on delivering a robust, AI-assisted, and observable reporting stack, while improving maintainability and data access. Key features delivered: - API Endpoint for Report Generation: created endpoints for generating reports with create and fetch capabilities, enabling automated reporting and spatial analysis insights. - AI/LLM Based Reporting with Gemini: integrated Gemini to generate and augment reports, reducing manual drafting and enabling smarter insights. - Logging Enhancements: added comprehensive logging across the reporting system to improve observability and troubleshooting. - URL Handling for Reporting Endpoints: enhanced URL handling to improve access, routing, and security of report endpoints. - Pandas-GBQ Integration: added support for pandas-gbq data access to enable scalable analytics against BigQuery. - Daily Mean Statistics: implemented daily mean calculations to support time-series data analysis. - Report Generation Module: established the core reporting module architecture to enable scalable reporting workflows. - User Chat Functionality: added user chat functionality to streamline data queries and collaboration. - Dependency Management Improvements: improved handling of dependencies to reduce build issues and drift. - Grumming Module: introduced grumming module (experimental/contextual use TBD). Major bugs fixed: - Maintenance and PR Hygiene: cleanup and alignment of code review and requirements references in the batch, improving governance and release hygiene. Overall impact and accomplishments: - Delivered end-to-end reporting capabilities with AI augmentation, improved observability, and scalable data access, enabling faster data-driven decisions. - Strengthened cross-functional collaboration through improved PR hygiene and clearer documentation. Technologies/skills demonstrated: - REST API design and integration, AI/LLM (Gemini) integration, logging and observability, URL routing, pandas-gbq data access, daily time-series analytics, and dependency management.
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.8%
Architecture80.6%
Performance76.4%
AI Usage25.2%
Skills & Technologies
Programming Languages
GitJSONJavaScriptMarkdownPythonSQLShellTexttext
Technical Skills
AI IntegrationAPI DevelopmentAPI IntegrationAir Quality MonitoringBackend DevelopmentCachingCloud ConfigurationCloud IntegrationCloud StorageCode CleanupCode DocumentationCode RefactoringConcurrencyConfiguration ManagementData Analysis
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