Month: 2025-09 — In tenstorrent/tt-metal, delivered focused model performance improvements and updated demo content updates. Key work included metric corrections and accuracy enhancements for ttnn.gelu, supported by commits that updated model metrics. Major bugs fixed centered on metric calculations and reporting accuracy, reducing QA gaps and improving reliability for dashboards and demos. Overall impact: clearer performance signals for customers, faster decision-making, and a solid foundation for next iterations. Technologies demonstrated include ML metrics instrumentation, metric-driven debugging, performance tuning, and demo-content delivery.

July 2025 (tt-llk) performance snapshot: Core math capabilities expanded through cross-repo consolidation, improving modularity, test coverage, and reuse. Delivered GELU and exp-related operations within tt-llk, enabling faster feature delivery and consistent testing across the stack.