May 2026 summary for llm-d/llm-d-benchmark: Key features delivered include Model Service Configuration Enhancements with tolerations, YAML parsing fixes, and a backward-compatible DeploymentBaseConfig update; Deployment and Serving Correctness Fixes addressing vLLM HuggingFace protocol model references and harness serviceAccount precedence; and Dynamic Inference Scheduler Image Naming enabling custom scheduler images. Impact: improved deployment reliability, faster startup, and broader image portability, with reduced validation errors and safer service account handling. Technologies demonstrated: Kubernetes deployment templating, YAML decoding/validation, DeploymentBaseConfig schema evolution, vLLM/HuggingFace integration, and robust service account precedence logic.

April 2026 monthly summary for repo ai-dynamo/aiperf: Delivered a new Tool Call Response Data Model and integrated token tracking into performance metrics and endpoint logic, enabling precise visibility into tool call tokens across timing and token-count metrics. This work improves observability, cost awareness, and performance diagnostics. Implemented via commit 361c784262ce1898dad21eb503c1739b5b0891c7 with endpoint-level integration and data model changes.