This article analyzes the 2026 AI ecosystem, detailing free token allocations across more than 30 domestic and international large‑model platforms, compares their limits, models, and access requirements, and provides practical code snippets, workflow recommendations, and safety tips for developers seeking cost‑free LLM access.
This article outlines the common pain points developers face with Claude Code, introduces the open‑source "claude-code-best-practice" project, details its core features, workflow comparisons, practical tips, reusable configuration templates, and provides step‑by‑step instructions for rapid adoption.
A data‑driven comparison of OpenAI's GPT‑5.4, Anthropic's Claude Opus 4.6, and Google Gemini shows how each model performs on desktop‑agent, coding, and multimodal benchmarks, reveals pricing differences, and offers concrete recommendations for developers, startups, and enterprise users.
This article explains how to create, configure, and use one‑click VSCode code snippets for multiple languages, covering snippet structure, configuration options, examples, special characters, built‑in variables and functions, and how to migrate snippets across projects.
Effective technical documentation requires choosing a searchable knowledge‑base for long‑term storage, writing only value‑adding docs such as architecture designs, feature guides, and onboarding material, following standardized templates and clear formatting rules, assigning owners for regular maintenance, and applying best‑practice principles like accuracy, completeness, and the pyramid structure.
This article shares Guan LeiMing’s 15‑year open‑source journey and practical guidance on how developers can select, contribute to, and benefit from open‑source projects, illustrated by a real‑world DataX‑OBS plugin case on Huawei Cloud.
The article critiques the conventional bottom‑up, theory‑heavy machine‑learning curriculum for developers, argues that costly degrees and deep math are unnecessary, and proposes a top‑down, project‑focused approach using modern tools, repeatable processes, suitable datasets, and practical resources to quickly build end‑to‑end ML solutions.
