June 2025 monthly performance snapshot for SciML core integration across Optimization.jl and NonlinearSolve.jl. Focused on delivering robust SciPy integration, stabilizing APIs, and improving maintainability and docs to accelerate adoption and reliability. Key achievements: - Implemented the OptimizationSciPy wrapper in SciML/Optimization.jl, introducing local/global optimizers, API completeness improvements, type stability, and SciMLBase integration with tests. - Fixed a bug to standardize optimization objective output as a tuple, ensuring consistent handling in state updates and callbacks. - Enhanced NonlinearSolve.jl with SciPy optimization wrappers (least_squares, root, root_scalar), including new extension modules and concrete algorithm types, and restructured code from extension to lib with tests/docs. - Expanded documentation for SciSciPy/jl integration (installation, methods, troubleshooting) and adjusted COBYLA example accordingly. - Maintenance and setup hygiene across repos: cleanup of OptimizationSciPy environment files, addition of CondaPkg.toml, and dependency/export cleanup for NonlinearSolveSciPy to simplify onboarding and future maintenance.