A simple, open-source wiki built with ASP.NET Core Razor Pages, designed for learning and live coding demonstrations.
CoreWiki is an open-source wiki application built with ASP.NET Core Razor Pages. It provides a simple, functional wiki platform for creating and managing articles, with features like commenting, user administration, and search-friendly URLs. The project was created as a learning tool to demonstrate real-world ASP.NET Core development during live coding streams.
Developers learning ASP.NET Core, particularly those new to Razor Pages, and anyone seeking a straightforward, self-hosted wiki solution built on the .NET stack.
CoreWiki offers a clean, practical codebase that demonstrates modern ASP.NET Core patterns in a real application. Its educational focus, cross-platform support, and active development via live streams make it a valuable resource for both learning and deployment.
A simple ASP.NET Core wiki that we are working on during live coding streams
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Developed during public coding sessions with extensive archived YouTube playlists, offering real-world examples of ASP.NET Core patterns like CQRS and MediatR.
Runs on Windows, macOS, Linux, and containers, with a Deploy to Azure button and stream discussions on container fixes, making it versatile for various environments.
Encourages pull requests through a contributing guide and 'help-wanted' issues, lowering the barrier for newcomers to engage with open-source .NET development.
Implements key features like Razor Pages, Entity Framework migrations, and role-based authentication, serving as a hands-on reference for learners beyond tutorials.
Lacks advanced wiki capabilities such as rich text editing, file attachments, or detailed version comparison, as it prioritizes educational simplicity over production-ready features.
Progress is tied to live coding streams, which can lead to irregular updates and potential inconsistencies in feature completion or bug fixes, as noted in the stream archive.
As a learning project, it may lack robust error handling, performance optimizations, or thorough documentation for enterprise-scale deployments, focusing instead on core concepts.