Technology

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,

When I decided to learn backend development with Python, I kept hearing one name repeatedly: FastAPI. People described it as fast, modern, and developer-friendly.What I didn’t expect was how quickly I could go from zero to a working, documented API—with almost no boilerplate. This post walks through how I started with FastAPI, why it felt

This article explains how and why Jetpack Compose UI can be rendered off-screen (for example, into a Bitmap) and what actually works in real projects today.It avoids fantasy APIs and clearly separates supported approaches from fragile hacks. The real problem (not the romantic one) When developers say “off-screen rendering in Compose”, they usually want one

Every year, developers debate which programming language is “the best.”And every year, the conversation goes in circles—lists, rankings, arguments, counterarguments. By 2026, one thing is clear: there is no single best programming language. What exists instead is a fragmented but stable landscape where different languages dominate different kinds of work, driven by economics, infrastructure, security,

Jetpack Compose has fundamentally changed how Android developers build UI. Its declarative model, composable functions, and Kotlin-first approach dramatically reduce boilerplate and improve readability. However, with that power comes subtle performance pitfalls. One of the most common—and least understood—is inline lambdas that capture state, which can lead to unnecessary recomposition, reduced UI performance, and even