fiddler-labs/fiddler-examples
Jun 2025 – Jan 2026
3 Months active
Languages Used
JSONJupyter NotebookPython
Technical Skills
Data ScienceDocumentationJupyter NotebookJupyter NotebooksMachine Learning OperationsNotebook Development
Dhara Goyal
PROFILE
Dhara Goyal
Dhara contributed to the fiddler-labs/fiddler-examples repository by developing features that enhanced baseline management and observability for AI workflows. She introduced a two-step baseline workflow in Python and Jupyter Notebooks, separating data upload from baseline creation to improve reproducibility and onboarding. Dhara also integrated OpenTelemetry to capture message history and tool definitions, enabling deeper analysis and debugging of user-AI interactions. Her work included refining documentation with Unicode support and validating tool usage for reliability. Through focused, reviewable code changes and clear technical writing, Dhara demonstrated depth in AI integration, data analysis, and notebook development, addressing workflow reliability and traceability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
4
Lines of code
1,123
Activity Months3
Your Network
32 peopleShared Repositories
16
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026Summary for 2026-01: Focused on improving observability and business insight for user-AI interactions in fiddler-examples. Delivered an OpenTelemetry enhancement that captures message history attributes for user requests and AI responses, enabling deeper analysis, debugging, and performance tuning of tool interactions. This work strengthens end-to-end traceability and supports data-driven optimization of workflows.
1 Commits • 1 Features
Jan 1, 2026Summary for 2026-01: Focused on improving observability and business insight for user-AI interactions in fiddler-examples. Delivered an OpenTelemetry enhancement that captures message history attributes for user requests and AI responses, enabling deeper analysis, debugging, and performance tuning of tool interactions. This work strengthens end-to-end traceability and supports data-driven optimization of workflows.
January 2026
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025 monthly summary for fiddler-labs/fiddler-examples: Delivered OpenTelemetry integration for LLM tool definitions to enable observability and debugging; improved documentation readability by restoring Unicode symbols; validated tool usage and parameter correctness to enhance reliability; no major bug fixes reported this month; overall impact includes improved observability, developer experience, and documentation quality. Technologies/skills demonstrated: OpenTelemetry, LLM tooling, observability, input validation, Unicode-aware documentation.
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025 monthly summary for fiddler-labs/fiddler-examples: Delivered OpenTelemetry integration for LLM tool definitions to enable observability and debugging; improved documentation readability by restoring Unicode symbols; validated tool usage and parameter correctness to enhance reliability; no major bug fixes reported this month; overall impact includes improved observability, developer experience, and documentation quality. Technologies/skills demonstrated: OpenTelemetry, LLM tooling, observability, input validation, Unicode-aware documentation.
June 2025
11 Commits • 1 Features
Jun 1, 2025June 2025: Delivered key baseline management improvements in fiddler-examples by introducing an explicit two-step baseline workflow that separates data upload from baseline creation, and added static baseline support across quickstart notebooks and fiddler-examples. This work included notebook updates and documentation enhancements, guided by a sequence of commits and code-review-driven fixes. The changes improve reproducibility, onboarding, and overall reliability of baseline experiments, delivering business value by reducing manual steps, accelerating experiment setup, and lowering support overhead. Technologies involved include Python, Jupyter notebooks, and documentation tooling; demonstrated strengths in refactoring, code reviews, and clear technical documentation.
11 Commits • 1 Features
Jun 1, 2025June 2025: Delivered key baseline management improvements in fiddler-examples by introducing an explicit two-step baseline workflow that separates data upload from baseline creation, and added static baseline support across quickstart notebooks and fiddler-examples. This work included notebook updates and documentation enhancements, guided by a sequence of commits and code-review-driven fixes. The changes improve reproducibility, onboarding, and overall reliability of baseline experiments, delivering business value by reducing manual steps, accelerating experiment setup, and lowering support overhead. Technologies involved include Python, Jupyter notebooks, and documentation tooling; demonstrated strengths in refactoring, code reviews, and clear technical documentation.
June 2025
Activity
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Quality Metrics
Correctness93.0%
Maintainability91.4%
Architecture90.0%
Performance85.6%
AI Usage27.2%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPython
Technical Skills
AI integrationData ScienceDocumentationJupyter NotebookJupyter NotebooksMachine Learning OperationsNotebook DevelopmentOpenTelemetryPython ProgrammingPython ScriptingPython scriptingTechnical Writingdata analysisdata visualizationdebugging
Repositories Contributed To
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