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WasmGC introduces native garbage‑collection support to WebAssembly, enabling Kotlin and Dart to run as true native languages on the web, dramatically shrinking bundle sizes, speeding startup, eliminating memory‑leak cycles, and improving interop, with full browser support now standard across Chrome, Firefox, and Safari.
This tutorial explains the three-stage evolution from Prompt Engineering to Context Engineering and finally Harness Engineering, detailing their motivations, core components, practical implementations, and why stable, end‑to‑end AI agents require a full harness to manage tasks, context, tools, execution, state, and error recovery.
This article explains the architecture and evolution of a feed push system, covering the basic push model, its early implementation, performance trade‑offs, and a series of optimizations—including lazy loading, length control, delayed push, active‑inactive fan filtering, and hot‑cold separation—to improve read efficiency, reduce storage costs, and handle massive fan bases.
WebAssembly extends JavaScript by handling heavy computation, binary processing, cross‑language reuse, and secure sandboxing, making it a practical engineering tool for performance‑critical, privacy‑sensitive, multi‑platform, and plugin‑based web applications while complementing rather than replacing JavaScript.
In early 2026 the AI community witnessed a sharp shift away from Model Context Protocol (MCP) toward CLI‑first toolchains, as engineers highlight token inflation, fragmented authentication, and loss of composability in MCP, while praising the low‑friction, text‑based, and easily debuggable nature of command‑line interfaces for building robust AI agents.
This article details the design and implementation of an AI‑powered automated code‑review system for GitLab Merge Requests, covering background problems, layered architecture, diff parsing, prompt engineering, comment management, rate‑limiting, concurrency control, and the measurable improvements achieved.
This guide walks through the core concepts of Cursor’s AI‑assisted development workflow—Rules, Skills, Subagents, and the Model Context Protocol (MCP)—explaining their purposes, types, configuration steps, practical examples, and when each should be used to streamline iOS projects.
This article walks through creating an AI skill that leverages natural‑language queries to search GitHub repositories, explains how the skill parses input, applies synonym expansion and relevance filtering, and presents results in a table with stars, license and links, all demonstrated with a live example.
This guide walks you through creating an Agent Skill that turns OpenCode into a code‑repository expert, covering OpenCode installation, skill‑creator setup, DeepWiki integration, SKILL.md design, disambiguation, hallucination safeguards, and practical examples for querying Ascend inference ecosystem repositories.
This article explains the concept of edge computing, why models are deployed to devices, the advantages of using Python scripts on Android/iOS, and provides a step‑by‑step guide to implementing a Python interpreter, NumCpp integration, feature management, dynamic deployment, and business impact through real‑time strategy updates.
