What if your Azure cloud computing setup could do more for less? Many businesses unknowingly overspend on cloud services due to inefficient configurations and underutilized resources, losing both money and performance potential. The good news is that with the right strategies, you can optimize your Azure cloud computing environment to be smarter, faster, and more cost-effective. This post highlights seven …
Test coverage is a metric that measures how much of your codebase is exercised by tests, providing insight into the effectiveness of your testing efforts. In this blog, we’ll focus on API projects, exploring the types of tests suited for code coverage, realistic goals (and tools to help achieve them), and the minimum coverage needed to reap the benefits of your tests.
Testing your code—down to individual lines—is a critical practice in modern software development. It helps ensure applications remain reliable, stable, and maintainable, all while balancing practical constraints.
I’m currently working on several cloud-native projects hosted on Google Cloud Platform (GCP) that use .NET for the API and React for the UI. These projects rely on GCP’s Identity-Aware Proxy (IAP) to handle authentication, which occurs before any requests reach the Application Load Balancer or the application itself.
While GCP’s IAP offers robust security benefits, configuring a .NET and React application to work seamlessly with it—both locally during development and when deployed to a Cloud Run instance as a Docker container—proved to be more challenging than I expected. The available documentation and resources for this setup are sparse and often fragmented, making it difficult to piece together a clear solution.
This blog post is my way of sharing the lessons I’ve learned along the way. By outlining the steps and solutions that worked for me, I hope to save others from the trial and error I experienced and provide a clearer path for integrating GCP IAP with .NET and React applications.
In the software development space, RAG solutions are being used to enhance productivity and streamline processes. By indexing and searching the entire codebase of a project, these solutions provide relevant, context-aware results from an LLM. Instead of simply suggesting code snippets, RAG-based tools can analyze and generate entire use case implementations across multiple programming languages. The result is a significant boost in productivity, enabling software teams to work faster and more efficiently.
In this blog, we’ll explore how CI/CD automation with tools like JIRA Cloud, Jenkins, Slack, GitHub, SVN, and Postman benefits Agile teams as a whole. Whether you’re new to Agile or are working with an experienced team, automation can significantly reduce “JIRA paperwork” while keeping everyone focused on delivering value. By streamlining repetitive tasks, teams can spend more time on …