Kherndon focused on enhancing documentation and issue tracking for the nod-ai/llm-dev repository, building a centralized source of truth for model testing status and known issues. Using Markdown and leveraging strong documentation skills, Kherndon updated the halo-models.md file to consolidate PASS/FAIL indicators, MLIR references, and links to related issues, improving traceability and release readiness. The work included documenting the llama3.1-405B-FP16-decomposed-TP8 model’s test results, creating and linking new issues for register count overflows, and standardizing the process for tracking memory and compilation issues. The depth of documentation supports faster triage and more effective team collaboration.