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Report abuseI contribute to open-source AI tooling, with a focus on evaluation reliability, observability, and agent infrastructure. Much of my work addresses failures that produce plausible but incorrect results, such as invalid judge scores, missing trace data, and errors hidden by fallback paths.
I am an Area Triager at TruLens for Feedback functions and metrics. I reproduce reported issues, review pull requests in my area, and help contributors navigate evaluation and metric behavior. The role and scope are listed in the official maintainer roster.
230+ merged pull requests across 60+ upstream repositories. My contributions include code fixes, regression tests, documentation, and review follow-up.
A selection of merged work:
| Project | Contribution | Pull request |
|---|---|---|
| TruLens | Kept unparseable answerability verdicts from being scored as successful abstentions. | #2864 |
| Inspect Evals | Computed Humanity's Last Exam calibration error per attempt for evaluations with multiple epochs. | #2132 |
| Microsoft PyRIT | Introduced explicit, typed iteration state for GCG attack optimization. | #2467 |
| Opik | Added namespaced delimiters around evaluated model output in judge prompts. | #8195 |
| OpenInference | Recorded replayed reasoning items in OpenAI Agents input traces for continuation turns. | #3677 |
| XGrammar | Corrected positional JSON Schema prefixItems handling, including valid shorter prefixes. | #834 |
I start with a reproducible failure and trace it to the relevant API contract or existing behavior. For code fixes, I prioritize regression tests that fail on the original version, small diffs, and validation against the repository's checks. I follow patches through maintainer review and contribute issue triage and code reviews alongside implementation.
Most repositories on this account are forks used for upstream contributions.
Debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards.
A framework for few-shot evaluation of language models.
The Python Risk Identification Tool for generative AI (PyRIT) is an open source framework built to empower security professionals and engineers to proactively identify risks in generative AI systems.
Fast, Flexible and Portable Structured Generation
Collection of evals for Inspect AI
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