Focused on hardcore practical AI technologies (OpenClaw, ClaudeCode, LLMs, etc.) and HarmonyOS development. No hype—just real-world tips, pitfall chronicles, and productivity tools. Follow to transform workflows with code.
The article analyzes Andrej Karpathy’s introduction of “Claw” as a new AI‑agent layer, explains its architecture, rapid industry adoption, the shift toward mutable codebases, local deployment benefits, and how these trends reshape software engineering principles in the AI era.
This step‑by‑step guide shows how to deploy OpenClaw on an Alibaba Cloud ECS instance, replace its default model with GLM‑4.7, configure a Feishu bot, and verify a continuously running AI assistant that can handle code, web browsing, and system tasks.
This article explains the complementary roles of MCP and Skill for Claude, compares them with real‑world analogies, outlines typical use cases, lists advantages and disadvantages, and provides decision guidance on when to use each or combine them.
The article shares ten practical Claude Code techniques—including parallel sessions, Plan Mode, CLAUDE.md, custom Skills, Subagents, terminal tweaks, and AI‑assisted debugging—illustrated with step‑by‑step commands and examples, helping developers boost efficiency and tailor the tool to their own workflows.
This article analyzes the ten most popular Claude Skills on GitHub, detailing their core capabilities, ideal user groups, installation methods, and how they can boost AI‑assistant productivity up to four times for developers, teams, and designers.
This article explains ArkUI's component lifecycle hooks, the low‑level FrameNode API, LazyForEach rendering strategy with key generation, the full rendering pipeline, custom measure/layout methods, event bubbling, differences between property and explicit animations, and the purpose of XComponent for native rendering.
This article explains ArkTS's strict type restrictions, its use of ESObject for unknown JS types, special limits on interfaces, classes, and decorators, as well as its memory model, closure rules, and the differences between Record and Map containers, highlighting how these design choices improve compilation and runtime efficiency.
This comprehensive guide explores Support Vector Machines—from their historical roots and core mathematical principles to practical Python implementations, visualization techniques, and diverse applications such as image recognition, text classification, bioinformatics, and financial risk assessment—while also weighing their strengths and limitations.
This article provides a comprehensive guide to the K‑Nearest Neighbors algorithm, covering its intuitive principle, step‑by‑step workflow, distance metrics, strategies for selecting the optimal K via cross‑validation, Python implementation with scikit‑learn, advantages, limitations, and diverse application scenarios.
This comprehensive guide explores the K‑Means clustering algorithm, covering its mathematical foundation, step‑by‑step procedure, centroid initialization strategies, practical implementation with Python’s Scikit‑learn on the Iris dataset, evaluation metrics, optimization techniques, and diverse applications ranging from image segmentation to bioinformatics.
