All-Hands-AI/agent-sdk
Mar 2026 – Mar 2026
1 Month active
Languages Used
Python
Technical Skills
AI DevelopmentAI model integrationMachine LearningPythonmachine learningunit testing
Mario Young
PROFILE
Mario Young
During March 2026, Maye Co enhanced the All-Hands-AI/agent-sdk repository by integrating Gemini-3.1 pro-preview into the suite of supported reasoning models. She refined the handling of model parameters, ensuring that temperature and top_p are selectively ignored for specific models, which improved configuration flexibility and reduced the risk of misconfiguration. Her work included updating and modernizing unit tests to validate the new model integration, accelerating experimentation with advanced reasoning models. Leveraging Python and machine learning expertise, Maye focused on expanding model capabilities and strengthening code quality, demonstrating depth in AI development and robust test-driven engineering practices throughout the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
9
Activity Months1
Your Network
51 peopleShared Repositories
51
Work History
March 2026
2 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for All-Hands-AI/agent-sdk. Delivered Gemini-3.1 pro-preview model support and refined parameter handling for reasoning models, with tests updated to recognize and validate the new model. No explicit critical bugs fixed this month; focus was on expanding capabilities, improving configuration flexibility, and strengthening test coverage. Overall impact: extended model capabilities, reduced risk of misconfigurations, and accelerated experimentation with advanced reasoning models. Technologies/skills demonstrated include model integration, test modernization, parameter handling, and collaborative code quality improvements.
2 Commits • 2 Features
Mar 1, 2026March 2026 monthly summary for All-Hands-AI/agent-sdk. Delivered Gemini-3.1 pro-preview model support and refined parameter handling for reasoning models, with tests updated to recognize and validate the new model. No explicit critical bugs fixed this month; focus was on expanding capabilities, improving configuration flexibility, and strengthening test coverage. Overall impact: extended model capabilities, reduced risk of misconfigurations, and accelerated experimentation with advanced reasoning models. Technologies/skills demonstrated include model integration, test modernization, parameter handling, and collaborative code quality improvements.
March 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI DevelopmentAI model integrationMachine LearningPythonmachine learningunit testing
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