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adamkamizelich's Projects

aspnetcorespa icon aspnetcorespa

Asp.Net 5.0 & Angular 11 SPA Fullstack application with plenty of examples. Live demo:

blogger icon blogger

Blog platform using C#, .NET 5 & ASP.NET Core Web API

eshoponcontainers icon eshoponcontainers

Cross-platform .NET sample microservices and container based application that runs on Linux Windows and macOS. Powered by .NET Core 3.0, Docker Containers and Azure Kubernetes Services. Supports Visual Studio, VS for Mac and CLI based environments with Docker CLI, dotnet CLI, VS Code or any other code editor.

eshoponweb icon eshoponweb

Sample ASP.NET Core 5.0 reference application, powered by Microsoft, demonstrating a layered application architecture with monolithic deployment model. Download the eBook PDF from docs folder.

hspa icon hspa

Housing Single Page Application

orderprocessor icon orderprocessor

Simple ordering system using C#, .NET 5, ASP.NET Core Web API and MediatR.

react-admin icon react-admin

A frontend Framework for building B2B applications running in the browser on top of REST/GraphQL APIs, using ES6, React and Material Design

react-firebase-admin icon react-firebase-admin

React ⚛️ starter kit with Firebase 🔥 and Bulma for setting up an admin dashboard - Highly scalable, PWA, Serverless

react.net icon react.net

.NET library for JSX compilation and server-side rendering of React components

restool icon restool

RESTool is an open source UI tool for managing RESTful APIs. It could save you time developing your own internal tools. A live example:

sample-for-can-fulfill-intent-request icon sample-for-can-fulfill-intent-request

Sample related to: "Help Customers Find the Right Skill When No Invocation Name is Used. You can make it easier for customers to find and engage with your US English skills by adding support for the CanFulfillIntentRequest (beta) interface. Using CanFulfillIntentRequest, your skill provides information about its ability to fulfill a given customer request at runtime. Alexa combines this information with a machine-learning model to choose the right skill to use when a customer makes a request without an invocation name. As a result, customers find the right skill faster, using the search terms they say most naturally. "

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