Things I’ve built and want to keep building on.

Projects and educational resources I build and maintain, alongside contributions to other open-source projects. Explore my role in each project, the code, and the work behind it.

Projects and resources I build

Localizeflow

Founder / AI infrastructure / 44M+ words processed

A GitHub-native localization workflow service that watches documentation repositories, processes translation workloads outside fragile CI paths, and opens reviewable pull requests.

Co-op Translator

Microsoft OSS maintainer / 55 languages

An open-source CLI and Python API for translating Markdown, notebooks, and image text while preserving structure, links, frontmatter, and maintainer review loops.

Azure AI writing and workshops

Technical educator / 120K+ views

Practical writing and sessions on Azure AI Search, Azure OpenAI, RAG, fine-tuning, safety, and production LLM workflows.

ai-healthcheck

Lightweight health checks for OpenAI models, designed for practical validation of AI service availability and behavior.

az-ai-healthcheck

Health checks for Azure OpenAI and Azure AI Vision that help teams verify provider connectivity before deeper workflows run.

Open-source contributions

Microsoft PhiCookBook

Contributor / developer education / 40 closed PRs

Tutorials and workflow improvements around Phi fine-tuning, Prompt Flow, Azure AI Studio, Azure AI Foundry, and Responsible AI evaluation.

Apache Fesod

Contributions around tests, documentation correctness, build quality, and project hygiene in Apache open-source workflows.

Impact

44M+ words processed

AI localization workloads across production repositories.

835+ automated PRs

GitHub-native localization pull requests across Microsoft OSS.

55 languages

Technical learning content kept synchronized for global learners.

120K+ public views

Azure AI and LLM engineering writing read by developers.

Operating style

Production evidence over demo polish

I care about queues, retries, timeouts, review loops, formatting drift, observability, and the small operational details that decide whether AI work keeps working.

Automation with human control

The best AI systems make output easier to review, retry, and trust. They do not hide uncertainty from the people responsible for shipping.

Open systems that teach

Writing, talks, and open-source work are part of the system. Good infrastructure becomes more useful when its tradeoffs are visible.