Concise monthly summary for 2026-06 focusing on basetenlabs/truss feature work: CLI deployment UX improvements, deterministic post-deployment outputs, and URL exposure; improved onboarding and automation reliability.

May 2026: Focused on configuration modernization and API simplification in basetenlabs/truss-examples. Delivered a migration of example configs from legacy model_cache to the new weights field, and upgraded Kokoro TTS to v1.0 with a simplified API and multilingual support. End-to-end verification was performed for English and Spanish outputs on a T4; Japanese/Mandarin language coverage requires specific voice packs and is documented for follow-up. No explicit bug fixes were recorded in this period; work emphasizes maintainability, scalability, and broader language coverage, positioning us for weight-based deployment patterns.

Month: 2026-04. This monthly review highlights targeted documentation work across basetenlabs/truss-examples and basetenlabs/truss that improves developer onboarding and deployment reliability. The month focused on delivering clear, actionable docs and aligning usage guidance with current tooling and workflows, rather than code churn.

March 2026 monthly summary for basetenlabs/truss focusing on UX improvement and reliability. The main deliverable was correcting the CLI timeout help text to specify minutes instead of hours, clarifying usage and reducing misconfigurations. The change is small, yet improves customer onboarding and reduces support overhead. This work was associated with commit 0ef0afd144a01ed3ad35d5c572e7597badabd90b and aligns with ticket #2227.

February 2026 monthly summary for basetenlabs/truss: Focused on strengthening user guidance and deployment efficiency through documentation enhancements. Consolidated README with deployment workflows, usage patterns, and training notes; introduced a dedicated deployment with Truss section; updated model examples to reflect the latest models; added clarifications on training and usage, and cleaned up contributors section. While no critical bugs were reported this month, the improvements reduce onboarding time and support surface, enabling faster model deployment and adoption of Truss across teams.