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.
I’ve worked on many small projects over the years, but these are the ones I’m most proud of. Some are open source, some came from real workflow problems, and all of them helped shape how I think about AI systems, documentation, and developer infrastructure.
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.
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.
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.
Technical educator / 120K+ views
Practical writing and sessions on Azure AI Search, Azure OpenAI, RAG, fine-tuning, safety, and production LLM workflows.
AI localization workloads across production repositories.
GitHub-native localization pull requests across Microsoft OSS.
Technical learning content kept synchronized for global learners.
Azure AI and LLM engineering writing read by developers.
I care about queues, retries, timeouts, review loops, formatting drift, observability, and the small operational details that decide whether AI work keeps working.
The best AI systems make output easier to review, retry, and trust. They do not hide uncertainty from the people responsible for shipping.
Writing, talks, and open-source work are part of the system. Good infrastructure becomes more useful when its tradeoffs are visible.