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DZone.com Feed Recent posts on DZone.com How to Build an Asynchronous AI-Content Review Workflow in C# 23 Sep 2026, 15:00 by Brian O'Neill For application users, waiting for an upload to finish entering a web portal is familiar enough. Waiting again while the application analyzes that upload, however, can leave a user wondering whether anything is happening at all. The application can reassure the user by confirming the upload was received before analysis finishes, but it still needs a way to prevent the uploaded content from being published or used downstream until it's been evaluated against content policies and approved. We can accomplish this for a user-facing upload application by simply separating the submission process from the evaluation process. That allows the application to save the upload in private staging and queue it for background analysis before returning information to the frontend. With this approach, the uploaded content remains unavailable to downstream workflows until after the application records a content approval decision. ‍ Three Hidden Traps That Shape Software Engineering Decisions 23 Sep 2026, 14:00 by Otavio Santana Software engineering is often seen as a technical field focused on designing systems, writing code, selecting architectures, defining APIs, and optimizing for performance, scalability, and maintainability. However, many of the most challenging problems are not technical, but human. As engineers advance into leadership roles such as senior engineer, Staff Engineer, architect, or engineering manager, the challenge extends beyond technical solutions. Leaders must also understand how people make decisions, how teams respond to change, and how individual behavior can influence software quality. ‍ How to Prevent Retry Storms With Retry Budgets in Distributed Systems 23 Sep 2026, 13:00 by Uthej Mopathi Retries are one of the simplest ways to make a distributed system appear more reliable. A transient connection failure, overloaded replica, or short-lived network interruption can disappear after another attempt, which is why retry support exists in major RPC frameworks and cloud SDKs.  The danger begins when every layer makes the same decision independently. A mobile client retries an API gateway, the gateway retries a service, that service retries another service, and the final dependency retries a database call. The original request has not become more important, but the system has multiplied the work required to fail. AWS describes a five-deep service stack in which three attempts at each layer can drive 243 calls against the database when the deepest dependency is failing. Google’s SRE guidance similarly warns that retries can amplify overload and contribute to cascading failure.  ‍ What Full-Stack AI Engineering Means in Real Projects 23 Sep 2026, 12:00 by Paul Schloss Quick answer: Full-stack AI engineering is the practice of building an AI feature from end to end: the data and retrieval layer, the model and orchestration layer, the application interface, and the production monitoring that keeps it working. It treats a model as one component inside a larger system rather than the product itself. I've spent the past few years watching teams move from "we called an LLM API and shipped a demo" to "we run this thing in production, and it can't fall over on a Tuesday." The gap between those two states is where full-stack AI development actually lives. This article walks through what that work involves, where projects tend to break, and how to think about the decisions that matter in 2026. ‍ ‍ You are receiving this email because you subscribed to this feed at blogtrottr.com. By using Blogtrottr, you agree to our terms. If you no longer wish to receive these emails, you can unsubscribe from this feed, edit this subscription, or manage all your subscriptions.