Open-Earth-Foundation/CityCatalyst
Mar 2025 – Oct 2025
8 Months active
Languages Used
BashCSSCSVHTMLJSONJavaScriptMarkdownPython
Technical Skills
AI Agent DevelopmentAI IntegrationAPI DevelopmentAPI IntegrationAPI TestingAgent Development
Mirco Rudolph
PROFILE
Mirco Rudolph
Mirco developed and maintained the CityCatalyst repository, delivering robust AI-driven data pipelines and multilingual climate action planning tools. He engineered scalable backend systems using Python and Docker, integrating AWS S3 for cloud storage and leveraging FastAPI for resilient API workflows. His work included modernizing ML pipelines, implementing vector store retrieval, and enhancing localization through translation endpoints. Mirco improved CI/CD reliability, introduced comprehensive testing infrastructure, and optimized deployment with Kubernetes manifests. By refactoring code, automating data provisioning, and strengthening observability, he ensured maintainable, production-ready systems that support accurate emissions reporting and rapid, reliable delivery of climate analytics and planning features.
Overall Statistics
Feature vs Bugs
87%Features
Repository Contributions
304Total
Bugs
18
Commits
304
Features
118
Lines of code
1,177,523
Activity Months8
Your Network
24 peopleSame Organization
@openearth.org7
Work History
October 2025
21 Commits • 10 Features
Oct 1, 2025October 2025 monthly summary for Open-Earth-Foundation/CityCatalyst focusing on business value, maintainability, and performance improvements. Delivered structural, deployment, and observability improvements to streamline development, reduce risk, and improve runtime efficiency. Key outcomes include repo restructuring to colocate Colab assets, UV tooling and dependency updates, test/config hygiene, and extensive observability enhancements. Notable fixes and reliability work include a logging output bug fix, manifests updated for rolling updates and startup probes, and lifecycle/workflow refinements. Infrastructure modernization and resource tuning further reduced operational risk and improved performance.
21 Commits • 10 Features
Oct 1, 2025October 2025 monthly summary for Open-Earth-Foundation/CityCatalyst focusing on business value, maintainability, and performance improvements. Delivered structural, deployment, and observability improvements to streamline development, reduce risk, and improve runtime efficiency. Key outcomes include repo restructuring to colocate Colab assets, UV tooling and dependency updates, test/config hygiene, and extensive observability enhancements. Notable fixes and reliability work include a logging output bug fix, manifests updated for rolling updates and startup probes, and lifecycle/workflow refinements. Infrastructure modernization and resource tuning further reduced operational risk and improved performance.
October 2025
September 2025
45 Commits • 15 Features
Sep 1, 2025September 2025 (CityCatalyst): Delivered multilingual support and production readiness; improved CI/CD pipelines; hardened security isolation; fixed critical YAML/debug issues; and enhanced code readability and test structure for long-term velocity. Key outcomes include a translation endpoint with language metadata, production Kubernetes manifests, updated CI/CD workflows, dedicated HIAP API keys, and targeted maintenance that reduces risk and accelerates time-to-market for multilingual plans.
September 2025
45 Commits • 15 Features
Sep 1, 2025September 2025 (CityCatalyst): Delivered multilingual support and production readiness; improved CI/CD pipelines; hardened security isolation; fixed critical YAML/debug issues; and enhanced code readability and test structure for long-term velocity. Key outcomes include a translation endpoint with language metadata, production Kubernetes manifests, updated CI/CD workflows, dedicated HIAP API keys, and targeted maintenance that reduces risk and accelerates time-to-market for multilingual plans.
August 2025
61 Commits • 20 Features
Aug 1, 2025Month: 2025-08 Key features delivered - Vector Store Provisioning and Environment Readiness: Updated to download vector stores, verify local copies, refresh requirements, and run scripts. Commit 81fe091b75d0b92f67b3df460967691b3c06945d - Environment and Workflow Stabilization: Updated workflows for current environments, deprecated unused files, removed debugging info, and adjusted paths to new temp folder. Commits 9abaf30aeb620daefd4ab79e5eb7e9c8b80f99e2, 624651ac8901a3befdf34263310822b9b9f0fcd1, 0e5eab94fcf22659cea12ed49dac29e556562970, 5ca24826ba56922b1ebbf44ff87a0e1fbcfcb5e7, c564af9ac766040c7d6b8263ec7c55af14ef5492 - Agent 3 Plan Creator OpenAI Web Search: Enable Agent 3 plan creator to use OpenAI web search tool. Commit 0f7c36cb526837f37fa89d780ad8c08f7f457dbf - S3 Asset Path Management and Startup: Update startup script to download JSON files from S3 and adjust S3 paths. Commits ca3d0bbb80282486bb882ad5b7d30936271f8b04, 13ecba5a00fb8677de13e4d98889a7a1a0a96fd1 - Test Data Provisioning for Prioritizer: Add test JSON files to prioritizer explanations and integrate test data across tools. Commit 67bcdd527ef1b7d8f8db7dacc0b905c3544463cd - Vector Store Retrieval Tooling Refactor and Prompt Integration: Refactor tooling to Python functions for national vector store retrieval; remove unused tools; update agent/task APIs; added helper for deserialized strategy input; updated prompts and docs. Commits 218b6e9f49f03ce9fcabac4572e2f38dd7acc40b, 54040d87bd31e09616186a1ad1f19961a147f377, 9d324fdf2259ab7d68297450238063861ebe1b12, aaf5805191a3bc5438b0e1d0fb2a8b533cae3f9f, 707db59876aba71b55cf8809ff6f391be34b7567 Major bugs fixed - JSON loading and prompts: Fixed bug in loading from JSON affecting model version updates; fix: prompt bug; hot fix for adaptation missing national strategy var. Commits 3fb44a0e01aded811ec5bc982d1fce3239195be5, 0161660e0e4719535559ca8987743e9adb792764, 988368c840ba9b7b4770145f9f0c769c5774fad6 - Serialize vector results: Fixed _serialize_vector_results and updated downstream code to use new implementation. Commit 39ec25dd3131e5d266962bee09b40777ac6951c4 - Vector store loading error: Fixes to loading vector stores to remove error messages when loading tenants. Commit 221471944e01d240bd98de7515cde20240bd2c9c - Relax Non-Negative Constraint on AFOLU: Removed non-negative constraint on AFOLU to fix validation/breakage. Commit 50a0e23cf03ba78ae60f545984a24594ef879513 - CCRA for Bulk Endpoint: Added CCRA for bulk endpoint. Commit a239806903102e51e2051ca4b037b3436fa3bfc9 Overall impact and accomplishments - Strengthened reliability, observability, and maintainability across data pipelines and AI tooling, enabling faster insight and planning cycles. - Reduced prompt context by routing to a single, most relevant vector result and improved error logging for faster troubleshooting. - Accelerated plan generation and decision support through OpenAI web search integration and vector-store-backed retrieval. Technologies/skills demonstrated - Python tooling modernization, vector store retrieval, and prompt engineering - AI tooling integration (OpenAI web search); plan and prioritizer workflows - Cloud data handling (S3 JSON ingestion) and robust error logging - CI/CD improvements, multi-threading, and parallel processing for scalable endpoints - Documentation, testing data provisioning, and maintainability improvements
61 Commits • 20 Features
Aug 1, 2025Month: 2025-08 Key features delivered - Vector Store Provisioning and Environment Readiness: Updated to download vector stores, verify local copies, refresh requirements, and run scripts. Commit 81fe091b75d0b92f67b3df460967691b3c06945d - Environment and Workflow Stabilization: Updated workflows for current environments, deprecated unused files, removed debugging info, and adjusted paths to new temp folder. Commits 9abaf30aeb620daefd4ab79e5eb7e9c8b80f99e2, 624651ac8901a3befdf34263310822b9b9f0fcd1, 0e5eab94fcf22659cea12ed49dac29e556562970, 5ca24826ba56922b1ebbf44ff87a0e1fbcfcb5e7, c564af9ac766040c7d6b8263ec7c55af14ef5492 - Agent 3 Plan Creator OpenAI Web Search: Enable Agent 3 plan creator to use OpenAI web search tool. Commit 0f7c36cb526837f37fa89d780ad8c08f7f457dbf - S3 Asset Path Management and Startup: Update startup script to download JSON files from S3 and adjust S3 paths. Commits ca3d0bbb80282486bb882ad5b7d30936271f8b04, 13ecba5a00fb8677de13e4d98889a7a1a0a96fd1 - Test Data Provisioning for Prioritizer: Add test JSON files to prioritizer explanations and integrate test data across tools. Commit 67bcdd527ef1b7d8f8db7dacc0b905c3544463cd - Vector Store Retrieval Tooling Refactor and Prompt Integration: Refactor tooling to Python functions for national vector store retrieval; remove unused tools; update agent/task APIs; added helper for deserialized strategy input; updated prompts and docs. Commits 218b6e9f49f03ce9fcabac4572e2f38dd7acc40b, 54040d87bd31e09616186a1ad1f19961a147f377, 9d324fdf2259ab7d68297450238063861ebe1b12, aaf5805191a3bc5438b0e1d0fb2a8b533cae3f9f, 707db59876aba71b55cf8809ff6f391be34b7567 Major bugs fixed - JSON loading and prompts: Fixed bug in loading from JSON affecting model version updates; fix: prompt bug; hot fix for adaptation missing national strategy var. Commits 3fb44a0e01aded811ec5bc982d1fce3239195be5, 0161660e0e4719535559ca8987743e9adb792764, 988368c840ba9b7b4770145f9f0c769c5774fad6 - Serialize vector results: Fixed _serialize_vector_results and updated downstream code to use new implementation. Commit 39ec25dd3131e5d266962bee09b40777ac6951c4 - Vector store loading error: Fixes to loading vector stores to remove error messages when loading tenants. Commit 221471944e01d240bd98de7515cde20240bd2c9c - Relax Non-Negative Constraint on AFOLU: Removed non-negative constraint on AFOLU to fix validation/breakage. Commit 50a0e23cf03ba78ae60f545984a24594ef879513 - CCRA for Bulk Endpoint: Added CCRA for bulk endpoint. Commit a239806903102e51e2051ca4b037b3436fa3bfc9 Overall impact and accomplishments - Strengthened reliability, observability, and maintainability across data pipelines and AI tooling, enabling faster insight and planning cycles. - Reduced prompt context by routing to a single, most relevant vector result and improved error logging for faster troubleshooting. - Accelerated plan generation and decision support through OpenAI web search integration and vector-store-backed retrieval. Technologies/skills demonstrated - Python tooling modernization, vector store retrieval, and prompt engineering - AI tooling integration (OpenAI web search); plan and prioritizer workflows - Cloud data handling (S3 JSON ingestion) and robust error logging - CI/CD improvements, multi-threading, and parallel processing for scalable endpoints - Documentation, testing data provisioning, and maintainability improvements
August 2025
July 2025
25 Commits • 8 Features
Jul 1, 2025July 2025 — CityCatalyst: Delivered key features to scale operations, improve resilience, and enable smarter planning. Notable work includes bulk processing enhancements with API alignment, API resilience via rate limiting and improved error handling, dynamic localization support, and planning tooling integration. Ranking enhancements with CCRA integration and test prioritization further strengthened decision-making capabilities, while infrastructure cleanup and CI/CD improvements improved maintainability and deployment reliability. Overall, these efforts increased throughput, reduced silent failures, and empowered faster, more reliable planning and execution.
July 2025
25 Commits • 8 Features
Jul 1, 2025July 2025 — CityCatalyst: Delivered key features to scale operations, improve resilience, and enable smarter planning. Notable work includes bulk processing enhancements with API alignment, API resilience via rate limiting and improved error handling, dynamic localization support, and planning tooling integration. Ranking enhancements with CCRA integration and test prioritization further strengthened decision-making capabilities, while infrastructure cleanup and CI/CD improvements improved maintainability and deployment reliability. Overall, these efforts increased throughput, reduced silent failures, and empowered faster, more reliable planning and execution.
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly work summary for Open-Earth-Foundation/CityCatalyst focused on delivering a robust testing infrastructure, CI/CD improvements, and API documentation clarity to drive reliability, faster feedback, and reduced deployment risk. Implemented testing infrastructure integrated into CI/CD, added environment variables for testing, updated dependencies, and streamlined pipelines by removing unused coverage reporting. Clarified the OpenAI Web Search Tool API docstring to require a two-letter ISO country code to reduce misconfigurations. These changes improved code quality, developer experience, and business value by enabling safer deployments and more predictable integrations.
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly work summary for Open-Earth-Foundation/CityCatalyst focused on delivering a robust testing infrastructure, CI/CD improvements, and API documentation clarity to drive reliability, faster feedback, and reduced deployment risk. Implemented testing infrastructure integrated into CI/CD, added environment variables for testing, updated dependencies, and streamlined pipelines by removing unused coverage reporting. Clarified the OpenAI Web Search Tool API docstring to require a two-letter ISO country code to reduce misconfigurations. These changes improved code quality, developer experience, and business value by enabling safer deployments and more predictable integrations.
June 2025
May 2025
5 Commits • 3 Features
May 1, 2025May 2025 — CityCatalyst performance summary. Delivered three core enhancements that advance data accuracy, storage reliability, and workflow control, delivering measurable business value for emissions reporting and operations. Key outcomes: - Enhanced Emissions Data Model and Rankings: updated scope emission calculation, added new GHG inventory fields, refreshed rankings to reflect latest data; improves accuracy and availability of emissions insights for reporting and decision-making. - Cloud Storage Migration for Large Files: moved local PDF data to S3, updated docs; reduces on-prem storage and simplifies access to large assets. - Pipeline Workflow Selective Execution: enables selective execution of pipeline stages to optimize prioritization, reduce compute waste, and improve deployment velocity. - Bug fix: corrected the scope emissions calculation formula, aligning results with updated GHG inventory and rankings; shipped as part of the emissions feature.
May 2025
5 Commits • 3 Features
May 1, 2025May 2025 — CityCatalyst performance summary. Delivered three core enhancements that advance data accuracy, storage reliability, and workflow control, delivering measurable business value for emissions reporting and operations. Key outcomes: - Enhanced Emissions Data Model and Rankings: updated scope emission calculation, added new GHG inventory fields, refreshed rankings to reflect latest data; improves accuracy and availability of emissions insights for reporting and decision-making. - Cloud Storage Migration for Large Files: moved local PDF data to S3, updated docs; reduces on-prem storage and simplifies access to large assets. - Pipeline Workflow Selective Execution: enables selective execution of pipeline stages to optimize prioritization, reduce compute waste, and improve deployment velocity. - Bug fix: corrected the scope emissions calculation formula, aligning results with updated GHG inventory and rankings; shipped as part of the emissions feature.
April 2025
56 Commits • 19 Features
Apr 1, 2025April 2025 – CityCatalyst performance snapshot: Key features delivered include city data and inventory maintenance, API and data schema enhancements for climate actions with multilingual support, and improvements to the adaptation effectiveness scoring pipeline and rankings. Major bugs fixed encompassed Dockerfile and SQL corrections, biome data handling improvements, missing mitigation actions explanation field, and logging level adjustments. Overall impact: improved data accuracy for city locodes and inventories, reliable API-driven climate actions workflows, more trustworthy scoring and ranking outputs, and strengthened observability and maintainability. Technologies/skills demonstrated: Python data pipelines, ML model integration, API design, multilingual routing, Docker/SQL troubleshooting, advanced logging, translations, and code hygiene.
56 Commits • 19 Features
Apr 1, 2025April 2025 – CityCatalyst performance snapshot: Key features delivered include city data and inventory maintenance, API and data schema enhancements for climate actions with multilingual support, and improvements to the adaptation effectiveness scoring pipeline and rankings. Major bugs fixed encompassed Dockerfile and SQL corrections, biome data handling improvements, missing mitigation actions explanation field, and logging level adjustments. Overall impact: improved data accuracy for city locodes and inventories, reliable API-driven climate actions workflows, more trustworthy scoring and ranking outputs, and strengthened observability and maintainability. Technologies/skills demonstrated: Python data pipelines, ML model integration, API design, multilingual routing, Docker/SQL troubleshooting, advanced logging, translations, and code hygiene.
April 2025
March 2025
86 Commits • 41 Features
Mar 1, 2025March 2025 CityCatalyst monthly summary focusing on business value, technical achievements, and impact. Delivered a set of ML, data, and deployment enhancements that improve analytics reliability, scalability, and localization readiness while cleaning up the codebase for maintainability. Key highlights across the month: - ML Pipeline improvements with defined-test-split weights and scaler adjustments; updated model weights and removal of unnecessary scaling for tree-based models; scaler files cleaned. - Data preprocessing enhancements including filling missing values with 0 and updating the ML comparator to skip rows with empty values, improving data quality for training and inference. - Data model and workflow refinements, including updated model weights, rankings, tournament flow adjustments, and ensuring data is reset to the full dataset after testing for robust experiments. - Delivery, tooling, and deployment optimizations: migrated pipeline execution from Bash to Python, added Python-based pipeline scripts, implemented downloading of the vectorstore from S3, and synchronized frontend data provisioning/assets to S3. - Localization, translation, and testing readiness: introduced a multilingual translation framework and prompts, added AT files with reduced hazards, and updated language data to support broader localization. Impact and business value: - Increased analytics reliability and speed due to streamlined ML weights, robust data preprocessing, and improved data handling. - Scalable data processing and deployment workflows with bulk CCAR processing and S3-based asset/vectorstore management. - Improved localization readiness through translation tooling and multilingual data coverage, expanding reach and reducing time-to-market for localized features. - Stronger code health and maintainability from comprehensive cleanup, deprecation, and standardized tooling. Technologies/skills demonstrated: - Python-based pipeline orchestration, S3/vectorstore management, and data handling improvements. - ML weight management and feature scaling decisions for tree vs non-tree models. - Data quality controls, error handling improvements, and robust testing practices (AT files and multilingual prompts). - Localization tooling, translation prompts, and multi-language data pipelines.
March 2025
86 Commits • 41 Features
Mar 1, 2025March 2025 CityCatalyst monthly summary focusing on business value, technical achievements, and impact. Delivered a set of ML, data, and deployment enhancements that improve analytics reliability, scalability, and localization readiness while cleaning up the codebase for maintainability. Key highlights across the month: - ML Pipeline improvements with defined-test-split weights and scaler adjustments; updated model weights and removal of unnecessary scaling for tree-based models; scaler files cleaned. - Data preprocessing enhancements including filling missing values with 0 and updating the ML comparator to skip rows with empty values, improving data quality for training and inference. - Data model and workflow refinements, including updated model weights, rankings, tournament flow adjustments, and ensuring data is reset to the full dataset after testing for robust experiments. - Delivery, tooling, and deployment optimizations: migrated pipeline execution from Bash to Python, added Python-based pipeline scripts, implemented downloading of the vectorstore from S3, and synchronized frontend data provisioning/assets to S3. - Localization, translation, and testing readiness: introduced a multilingual translation framework and prompts, added AT files with reduced hazards, and updated language data to support broader localization. Impact and business value: - Increased analytics reliability and speed due to streamlined ML weights, robust data preprocessing, and improved data handling. - Scalable data processing and deployment workflows with bulk CCAR processing and S3-based asset/vectorstore management. - Improved localization readiness through translation tooling and multilingual data coverage, expanding reach and reducing time-to-market for localized features. - Stronger code health and maintainability from comprehensive cleanup, deprecation, and standardized tooling. Technologies/skills demonstrated: - Python-based pipeline orchestration, S3/vectorstore management, and data handling improvements. - ML weight management and feature scaling decisions for tree vs non-tree models. - Data quality controls, error handling improvements, and robust testing practices (AT files and multilingual prompts). - Localization tooling, translation prompts, and multi-language data pipelines.
Activity
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Quality Metrics
Correctness87.0%
Maintainability87.6%
Architecture83.6%
Performance79.6%
AI Usage25.4%
Skills & Technologies
Programming Languages
BashCSSCSVDockerfileHTMLJSONJavaScriptJupyter NotebookMarkdownPython
Technical Skills
AI Agent DevelopmentAI IntegrationAI Model ConfigurationAI/MLAPI DesignAPI DevelopmentAPI IntegrationAPI TestingAWS EKSAWS S3AWS SDKAgent DevelopmentAgent-based SystemsAlgorithm DevelopmentAlgorithm Optimization
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