The document provides an introduction to MvvmCross, which is a framework that implements the Model-View-ViewModel (MVVM) pattern for .NET platforms. It discusses MVVM theory, .NET implementations of MVVM patterns like INotifyPropertyChanged, and how MvvmCross enables cross-platform development through features like portable class libraries, plugins, and interface-driven development. It also provides examples of code evolution using MvvmCross and showcases real-world applications that have been developed with it.
It provides information on LIP's mission to provide opportunities for all through microfinance initiatives with minimized due diligence costs. It outlines LIP's history, partnerships with microfinance institutions, and efforts to develop a web-based evaluation system called LMES to assess small MFIs with low-cost evaluations. The biggest challenges are mitigating information risks like fraud and ensuring the system's capabilities. LIP believes mathematical modeling can help overcome objections to quantifying important immeasurable factors.
The document provides an introduction to MvvmCross, which is a framework that implements the Model-View-ViewModel (MVVM) pattern for .NET platforms. It discusses MVVM theory, .NET implementations of MVVM patterns like INotifyPropertyChanged, and how MvvmCross enables cross-platform development through features like portable class libraries, plugins, and interface-driven development. It also provides examples of code evolution using MvvmCross and showcases real-world applications that have been developed with it.
It provides information on LIP's mission to provide opportunities for all through microfinance initiatives with minimized due diligence costs. It outlines LIP's history, partnerships with microfinance institutions, and efforts to develop a web-based evaluation system called LMES to assess small MFIs with low-cost evaluations. The biggest challenges are mitigating information risks like fraud and ensuring the system's capabilities. LIP believes mathematical modeling can help overcome objections to quantifying important immeasurable factors.