A knowledge-sharing platform for Sohu's technology products. As a leading Chinese internet brand with media, video, search, and gaming services and over 700 million users, Sohu continuously drives tech innovation and practice. We’ll share practical insights and tech news here.
This guide warns mobile developers that while VibeCoding can quickly generate SwiftUI, Compose, or Flutter code, they must still address security boundaries, cost implications, compliance rules, performance constraints, data‑schema design, testing, and incident‑response practices before releasing an app to real users.
This article explains how abstract syntax trees (AST) provide a structured representation of source code, how the Language Server Protocol (LSP) standardizes semantic queries, and why AI coding tools like Cursor, Claude Code, and OpenCode rely on LSP instead of simple text search, offering faster, more accurate, and lower‑cost code intelligence.
Superpowers adds a disciplined engineering framework to Claude Code by enforcing requirement clarification, test‑driven development, and isolated sub‑agent execution, turning AI‑generated code from flaky prototypes into traceable, maintainable software.
BrowserAct equips AI agents like Claude Code and Cursor with a reusable skill layer that can open pages, click buttons, fill forms, reuse login sessions, bypass anti‑automation checks, and return structured data, demonstrated through Amazon scraping, GitHub issue reading, smoke testing, and reusable Skill Forge workflows.
The article introduces five Claude Code Skills—pr‑summary, fix‑issue, deep‑research, commit, and explain‑code—that package common development tasks into single commands, showing how context‑aware automation can replace repetitive prompts, streamline pull‑request creation, issue fixing, code investigation, committing, and documentation generation for teams.
The article reveals that 80% of AI voice‑cloning failures stem from poor recording quality, analyzes three fatal sample defects—noise pollution, high‑frequency loss, and invalid segments—and proposes a three‑step “Extract → Enhance → Select” pipeline using BS‑RoFormer, DeepFilterNet3 and NISQA, boosting similarity from 68% to 89%.
The article describes how an AST‑based code‑context service, wrapped by an MCP middleware and built with Spring AI and Eclipse JDT, supplies structured Java code information to large models, addressing the context gaps of diff‑only AI code review and improving accuracy through concrete examples and evaluation.
When sharding databases, independent auto‑increment IDs can collide, causing data integrity issues; this article explains why the conflict occurs and walks through five practical solutions—including UUID, custom auto‑increment steps, segment mode, Snowflake, and Meituan Leaf—detailing their trade‑offs, performance, and implementation steps.
Harness Engineering, the set of systems that surround and extend a large‑language‑model‑based agent, determines real‑world performance far more than the model itself, and mastering its six‑layer architecture, bottlenecks, and practical rollout steps is essential for AI‑agent development and interview preparation.
The author recounts how a bare‑bones Claude implementation missed edge cases, then introduces ten open‑source Claude Code Skills that embed a disciplined workflow—brainstorming, planning, execution, TDD, systematic debugging, parallel reviews, git worktrees, and diagram generation—showing installation commands, real‑world examples, and concrete benefits.
