hud-evals/hud-sdk
Sep 2025 – Feb 2026
2 Months active
Languages Used
MarkdownPython
Technical Skills
Asynchronous ProgrammingBackend DevelopmentDocumentationEnvironment VariablesEvent-Driven ArchitecturePython
genteki
PROFILE
Genteki
During their work on the hud-evals/hud-sdk repository, Zhang Kaiyuan developed and integrated a centralized GLM Computer Tool, streamlining agent-based workflows and enabling scalable GLM operations. They implemented an event-driven callback mechanism with asynchronous support, enhancing extensibility and automation reliability. Zhang improved environment-based configuration for history management, reducing storage and rendering costs, and consolidated GLM functions for cleaner architecture. Their technical approach emphasized robust Python development, leveraging asynchronous programming, type hinting, and static analysis tools like Pyright. Through careful code refactoring, documentation updates, and bug fixes, Zhang delivered maintainable, well-structured solutions that improved system stability and onboarding for GLM-enabled features.
Overall Statistics
Feature vs Bugs
69%Features
Repository Contributions
36Total
Bugs
5
Commits
36
Features
11
Lines of code
5,035
Activity Months2
Your Network
25 peopleShared Repositories
25
Work History
February 2026
31 Commits • 10 Features
Feb 1, 2026February 2026 (2026-02) - hud-evals/hud-sdk Key features delivered - GLM Computer Tool (GLM-CUA) integration: added GLM Computer Tool, created GLMCUA core, and integrated GLM_CUA into the AgentType enum; simplified GLMCUA defaults for easier adoption. - GLM History management enhancements: introduced environment-based GLM history controls (GLM_MAX_HISTORY_SCREENSHOTS, GLM_HISTORY_IMAGE_SCALE); reduced history capture to 2 screenshots at 25% scale to reduce storage and render costs. - GLM Tool-Calling pattern and compatibility: implemented the new tool-calling pattern, updated for glm4.6v, adjusted action space schema, and registered openai_computer as a native tool for operator use. - GLM Tool-Calling robustness fixes: patches for broken GLM tool-calling and argument handling (_fix_xmkl_args) to improve resilience against mixed xml/json inputs. Major bugs fixed - Dead code elimination and code hygiene: removed unused checks, fixed formatting inconsistencies, and stabilized Pyright tests. - Minor maintenance: additional formatting cleanups and removal of redundant xml checks. Overall impact and accomplishments - Centralized GLM operations into a single computer tool, enabling scalable GLM-based workflows within the HUD SDK. Improvements in reliability, startup readiness, and maintainability, with storage/performance gains from history and rescaling refinements. Documentation and initialization improvements streamline onboarding for GLM-enabled features. Technologies/skills demonstrated - Python tooling and GLM integration patterns; tool-calling architecture and compatibility updates; environment/config management; static typing and test stability (Pyright); code quality tooling (ruff); documentation and onboarding improvements.
31 Commits • 10 Features
Feb 1, 2026February 2026 (2026-02) - hud-evals/hud-sdk Key features delivered - GLM Computer Tool (GLM-CUA) integration: added GLM Computer Tool, created GLMCUA core, and integrated GLM_CUA into the AgentType enum; simplified GLMCUA defaults for easier adoption. - GLM History management enhancements: introduced environment-based GLM history controls (GLM_MAX_HISTORY_SCREENSHOTS, GLM_HISTORY_IMAGE_SCALE); reduced history capture to 2 screenshots at 25% scale to reduce storage and render costs. - GLM Tool-Calling pattern and compatibility: implemented the new tool-calling pattern, updated for glm4.6v, adjusted action space schema, and registered openai_computer as a native tool for operator use. - GLM Tool-Calling robustness fixes: patches for broken GLM tool-calling and argument handling (_fix_xmkl_args) to improve resilience against mixed xml/json inputs. Major bugs fixed - Dead code elimination and code hygiene: removed unused checks, fixed formatting inconsistencies, and stabilized Pyright tests. - Minor maintenance: additional formatting cleanups and removal of redundant xml checks. Overall impact and accomplishments - Centralized GLM operations into a single computer tool, enabling scalable GLM-based workflows within the HUD SDK. Improvements in reliability, startup readiness, and maintainability, with storage/performance gains from history and rescaling refinements. Documentation and initialization improvements streamline onboarding for GLM-enabled features. Technologies/skills demonstrated - Python tooling and GLM integration patterns; tool-calling architecture and compatibility updates; environment/config management; static typing and test stability (Pyright); code quality tooling (ruff); documentation and onboarding improvements.
February 2026
September 2025
5 Commits • 1 Features
Sep 1, 2025September 2025 performance summary for hud-sdk: Implemented a robust BaseTool Callback Mechanism to support event-driven extensibility with add/remove/trigger capabilities, including asynchronous callbacks and detailed usage docs. Fixed a Pyautogui executor DISPLAY environment variable formatting bug to ensure reliable graphical operations. Updated type hints and documentation to reflect new capabilities, enhancing maintainability and developer experience. These changes improve automation reliability, extension points for customers, and overall system stability.
September 2025
5 Commits • 1 Features
Sep 1, 2025September 2025 performance summary for hud-sdk: Implemented a robust BaseTool Callback Mechanism to support event-driven extensibility with add/remove/trigger capabilities, including asynchronous callbacks and detailed usage docs. Fixed a Pyautogui executor DISPLAY environment variable formatting bug to ensure reliable graphical operations. Updated type hints and documentation to reflect new capabilities, enhancing maintainability and developer experience. These changes improve automation reliability, extension points for customers, and overall system stability.
Activity
Loading activity data...
Quality Metrics
Correctness93.8%
Maintainability90.6%
Architecture91.2%
Performance90.4%
AI Usage33.8%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AI DevelopmentAI IntegrationAI integrationAPI developmentAPI integrationAgent architectureAsynchronous ProgrammingBackend DevelopmentClass designCode FormattingCode RefactoringDocumentationEnum usageEnvironment VariablesEvent-Driven Architecture
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline