hud-evals/hud-sdk
Oct 2025 – Oct 2025
1 Month active
Languages Used
DockerfileMarkdownPythonShell
Technical Skills
API IntegrationDockerEnvironment SetupFastAPIHTTP ServicesLLM Evaluation
Gavin Bains
PROFILE
Gavin Bains
Gavin developed a reusable, rubric-based web research environment for the hud-evals/hud-sdk repository, focusing on enabling structured evaluation workflows for agent-based web research. He implemented end-to-end scaffolding using Python, Docker, and configuration files, establishing a reproducible environment where agents can search the web, fetch content, and receive rubric-driven evaluations. The backend leveraged FastAPI and HTTP services to coordinate agent actions and rubric assessments, while integration with the Exa API enabled dynamic web content retrieval. Gavin’s work provided a concrete example environment for rubric evaluation, laying a solid foundation for scalable research experiments and future extensions within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
875
Activity Months1
Your Network
24 peopleShared Repositories
24
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered a reusable Exa-powered rubric-based web research environment within hud-evals/hud-sdk, establishing a tangible example for rubric-driven evaluation. Implemented end-to-end scaffolding (configuration files, Dockerfile, Python backend, and MCP server) enabling agents to search the web, fetch content, submit answers, and receive rubric-based evaluations. This work lays the foundation for scalable evaluation workflows and faster iteration in research and product experiments.
1 Commits • 1 Features
Oct 1, 2025October 2025: Delivered a reusable Exa-powered rubric-based web research environment within hud-evals/hud-sdk, establishing a tangible example for rubric-driven evaluation. Implemented end-to-end scaffolding (configuration files, Dockerfile, Python backend, and MCP server) enabling agents to search the web, fetch content, submit answers, and receive rubric-based evaluations. This work lays the foundation for scalable evaluation workflows and faster iteration in research and product experiments.
October 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileMarkdownPythonShell
Technical Skills
API IntegrationDockerEnvironment SetupFastAPIHTTP ServicesLLM EvaluationWeb Scraping
Repositories Contributed To
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