During January 2025, Baggepinnen enhanced the robustness and reliability of core numerical workflows in the SciML/Optimization.jl and JuliaSymbolics/Symbolics.jl repositories. He addressed type casting issues in evolutionary optimization by ensuring arguments such as time limits and tolerances were consistently converted to Float64, reducing runtime errors. In Symbolics.jl, he refactored array conversion logic for the Num type, removing stack-overflow-prone methods and improving type parameter handling to yield predictable 2D matrix results. Working primarily in Julia, Baggepinnen applied skills in type systems, array manipulation, and code refactoring, delivering targeted improvements that strengthened type safety and workflow efficiency.