Working within a software development team has tremendous benefits, but it also comes with its share of complications. One of those complications is inertia – the more people who get used to a process or a certain set of tools, the more difficult it can be to introduce changes. Maybe this manifests in a positive way, where repetition and familiarity …
This blog walks through building a clean, modular service layer in Flutter to keep mobile apps scalable, maintainable, and resilient to change. Using real examples with third-party APIs, it demonstrates how to abstract logic, structure code for API calls, and easily swap data sources without major rewrites. You’ll also learn strategies like creating request/response classes and centralizing error handling to reduce redundancy and improve code quality.
Artificial intelligence is no longer limited to large data centers and high-powered servers. Today, more organizations are deploying ML models to edge devices such as smartphones, tablets, and IoT hardware. These devices work in environments where connectivity is not guaranteed and computing resources are restricted. By running models directly on edge hardware, businesses reduce latency, enable offline functionality, and improve …
Remember when blockchain was going to change everything? Now AI is the trend dominating every conversation—but some of the most effective programming ideas have been around for decades. This post examines one of COBOL’s lesser-known yet highly practical features: the 88-level field. Far more than a simple boolean, 88-level fields provide a clear, maintainable way to describe data conditions, enforce business rules, and streamline initialization. Through real-world examples, you’ll see how this feature can reduce complexity, eliminate hard-coded values, and make logic more expressive—demonstrating that sometimes proven tools from “old” languages still have lessons worth applying today.
It’s common to face challenges around long-running workflows, state management, and resiliency when you’re building cloud-native applications. Traditional approaches like background services or message queues might work sometimes, but they often require a lot of “glue code” and custom retry logic. This entails not only more work up-front for the developer, but also more maintenance in the long run.
That’s where Azure Durable Functions come in.