This blog post series discusses and compares iterative solvers and numerical methods, specifically exploring the usage of Fortran versus Python in teaching numerical linear algebra. It argues that Fortran may reduce cognitive load for students, details optimizations for the Jacobi method, and investigates the performance differences between Gauss-Seidel and Jacobi implementations. It emphasizes understanding algorithm efficiency rather than just coding skill, and each post contributes to a comprehensive exploration of numerical methods.