Running your FastAPI server from the terminal? Your breakpoints won’t work. Here’s how to properly set up debugging in VS Code so you can step through your code with confidence. The Problem You’ve set a breakpoint in VS Code, started your FastAPI server with uvicorn app.main:app, and tested your endpoint through the Swagger docs. But nothing […]

In relational database systems, deleting data is rarely as simple as removing a row. While SQL provides a straightforward DELETE statement, real-world applications often require stronger guarantees around data recovery, auditing, and referential integrity. This is where the concept of soft delete becomes critical. This article explains what soft delete means, why it is widely

When people talk about AI agents, the focus is usually on models, prompts, or reasoning techniques. But beneath every capable agent is something far less visible—and far more critical: asynchronous execution. AI agents don’t spend most of their time “thinking.”They spend it waiting. Waiting for: If these waits are handled synchronously, the agent blocks. If

Abstract Modern UI frameworks encourage unidirectional data flow, explicit state ownership, and composable architectures. However, when state or callbacks must be passed through multiple intermediate layers that do not directly use them, applications suffer from increased coupling, reduced readability, and poor scalability. In React, this issue is commonly referred to as prop drilling.In Jetpack Compose,

Modern AI-assisted development increasingly depends on external tools—databases, repositories, CI systems, cloud APIs, and internal services. Claude Code supports this integration through the Model Context Protocol (MCP), which allows tools to be exposed to the model as callable capabilities. However, as MCP usage scaled, a structural limitation emerged: tool definition overhead. The Core Issue: Tool

As AI systems evolve from simple chat interfaces into autonomous agents that plan, decide, and act, one uncomfortable truth has become clear: building first and figuring out control later no longer works. This is where PRD-First Development enters the picture—especially within agentic engineering practices. Far from being a theoretical idea, PRD-First is a practical, production-ready

IDisposable vs Using vs Try-With-Resources vs Kotlin use Modern applications interact constantly with files, databases, sockets, streams, and network connections. Managing these resources incorrectly leads to memory leaks, locked files, exhausted connections, and production outages. C#, Java, and Kotlin all solve this problem—but in different, language-specific ways. This article provides a clear, practical comparison of

Modern applications rarely consist of a single API call. They involve authentication, retries, logging, error handling, and monitoring—repeated across every request.This is where interceptors become indispensable. Although each platform implements interceptors differently, the core idea is universal: Intercept execution at a central point to apply cross-cutting logic consistently. This article walks through how interceptors work

Introduction Modern AI agents are no longer just chatbots.They think, use tools, handle real APIs, and manage state safely. In this article, we will build a production-grade GitHub agent using: By the end, you will have a working agent that can: “List my 3 most recent GitHub repositories” —using real GitHub data, not hallucinations. Why

Kotlin 2.3.0 introduces explicit backing fields, a language feature that allows developers to declare the storage field of a property directly, rather than relying on the compiler-generated implicit backing field (field). This change addresses long-standing limitations around property control, initialization, and visibility. Background: Implicit Backing Fields (Before 2.3) Historically, Kotlin properties used implicit backing fields,