Senior Android Developer & Interviewer, regularly sharing original tech articles, learning resources, and practical interview guides. Welcome to follow and contribute!
The article explains how the grill‑me skill inserts an interactive requirement‑clarification stage before an AI Agent executes a task, reducing misaligned outputs and rework by asking one focused question at a time, offering suggested answers, and distinguishing factual from decision information.
This article demystifies Kotlin's Flow, StateFlow, SharedFlow, and Channel by explaining their emission, storage, and loss mechanisms, illustrating common pitfalls with concrete code examples, and providing practical guidelines for choosing the right asynchronous data container in Android apps.
The article analyzes how Android evolves in 2026 from a simple app platform into a full‑blown intelligence system, covering on‑device AI via AICore and Gemini Nano, hybrid inference, intent‑based computing, the new AppFunctions framework, AI‑enhanced Chrome, and a transformed developer workflow.
The article traces four evolutionary jumps in AI engineering—from Prompt to Context, Harness, and finally Loop Engineering—explaining how Loop Engineering replaces manual prompting with self‑driving closed‑loop systems, outlines its five‑module architecture, memory layer, and the four conditions and safeguards needed for production‑grade AI agents.
The article examines Kotlin Multiplatform's adoption in production, highlights its quality and staffing benefits, presents survey reuse statistics, and then delves into iOS‑specific debugging hurdles, Objective‑C interop limits, missing API support, native dependency gaps, concurrency complexities, and offers cautious rollout recommendations.
Android 17 introduces MemoryLimiter, a system‑level app memory cap that silently kills processes exceeding device‑based limits, making unoptimized Bitmaps the biggest risk; the article explains the mechanism, detection methods, and five concrete Bitmap‑optimisation strategies plus new Android Studio tools to keep apps alive.
After trying Opus 4.7, the author shows that the old "pair‑programming" style of feeding AI tiny prompts wastes time, and explains a delegation workflow—clear goals, constraints, acceptance criteria, effort levels, permission handling, and verification loops—that lets AI independently deliver reliable web and mobile features.
The article analyzes five common agent architecture paradigms, explains their strengths and weaknesses, recommends suitable frameworks for each, and provides a five‑step decision process to help teams select the most appropriate architecture for their business needs.
This guide explains how to decide whether a piece of state belongs in a Composable or a ViewModel by distinguishing UI state from business state, using clear principles, lifecycle considerations, and concrete code examples such as search bars, expandable cards, and drag‑and‑drop scenarios.
The article traces the evolution of the "Harness" concept from traditional test harnesses to modern AI agent engineering, explains the Planner‑Generator‑Evaluator architecture, evaluates its trade‑offs, and argues that Harness Engineering is a transitional technique rather than mere hype.
