Backend tech guide and AI engineering practice covering fundamentals, databases, distributed systems, high concurrency, system design, plus AI agents and large-model engineering.
This guide explains how to safely and effectively use Claude Code by structuring CLAUDE.md, managing layered permissions, leveraging Auto Mode, Sub‑Agents, Skills, MCP, and Git worktrees, and adopting disciplined prompting and workflow practices to avoid project‑breaking mistakes.
The article analyzes the sudden suspension of Anthropic's Claude Fable 5 due to U.S. export controls, contrasts it with Zhipu's rapid full release of GLM‑5.2 for the GLM Coding Plan, and discusses the models' capabilities, competitive landscape, and the surprising market valuations driving developer choices.
This article walks through connecting the free Agnes AI multimodal API to Claude Code, detailing the required setup, a small Java code‑generation task, image and video generation examples, skill creation, and performance observations to help developers evaluate its suitability for their workflows.
Kimi Work, the desktop agent mode of Kimi’s AI client, can read local files, control browsers via WebBridge, schedule tasks, and run up to 300 parallel sub‑agents, which the author demonstrates through an AI‑driven stock‑analysis radar and a custom Skill, noting strong capabilities but slower speed and stability issues.
In 2026 the backend job market is undergoing a dramatic shift, with AI deep‑binding, salary polarization and a surge in demand for AI‑engineered talent, prompting many developers to consider a new AI algorithm engineer track that promises higher salaries and rapid career growth.
CodeGraph builds a local code knowledge graph with tree‑sitter and SQLite, allowing Claude Code, Codex and similar AI tools to query a pre‑indexed graph instead of repeatedly scanning files, which the author’s benchmarks show reduces token consumption by 59%, cuts cost 35%, speeds responses by 49% and improves routing detection for many web frameworks.
The article presents a step‑by‑step workflow for safely using AI coding assistants like Claude Opus 4.8, covering Git preparation, lightweight specifications, rule files, reusable Skills, multi‑agent coordination, permission controls, and evidence‑based verification to keep AI‑produced code trustworthy.
The article walks through a hands‑on assessment of MiniMax M3, using a real Redis SCAN outage to test fault diagnosis, then reproducing the SCAN algorithm in Go and finally constructing a lightweight Redis monitoring panel, while comparing its performance to GLM‑5.1.
The author recounts how using Codex to analyze JavaGuide's performance triggered a site-wide outage, then details the step‑by‑step investigation—from cache misconfigurations and hashed asset handling to a VuePress hydration bug—and shares practical lessons on combining AI assistance with disciplined engineering practices.
The article examines the pitfalls of "Vibe Coding"—quick AI‑generated code that skips testing and security—introduces the disciplined "Spec Coding" approach, and details how Addy Osmani’s open‑source agent‑skills project adds engineering rigor to AI coding through reusable skills, slash commands, and multi‑agent reviews.
