JetBrains’ IntelliJ IDEA 2026.1 EAP 3 finally moves deleted files to the system recycle bin, a feature requested for six years, and the release also bundles extensive Spring, Java, Kotlin, editor, AI, platform, and performance improvements that reshape the IDE experience.
Bram Cohen, the BitTorrent protocol creator, lambasts Anthropic’s Claude team for embracing “Vibe Coding”—an AI‑only development style that, he argues, erodes code quality, inflates technical debt, and hides fundamental software engineering responsibilities.
The article explains how to avoid the extremes of fully automated AI or no AI at all by defining clear Human-in-the-Loop patterns, identifying irreversible, high‑responsibility, and high‑exception steps, and applying tailored approval, edit, and escalation nodes in finance, contract, and other critical business processes.
A recent anthropology‑funded experiment shows that AI assistance speeds up seasoned programmers but actually slows skill acquisition for novices, with no overall productivity gain and significantly lower test scores, especially in debugging tasks.
This article reviews essential IntelliJ IDEA plugins for Java/Spring developers, detailing core features, configuration tips, and trade‑offs for each plugin, and recommends a prioritized installation order to boost productivity while monitoring performance impact.
This guide explains step‑by‑step how to craft effective prompts for AI tools to automatically produce comprehensive API test plans, covering preparation of API details, core test‑plan components, prompt design principles, example prompts, iterative refinement, execution, and validation of the resulting test plan.
This article explains Bilibili's massive data platform architecture, the common offline‑task failures and slowdowns users encounter, and how the company applies a large‑language‑model‑driven intelligent assistant to diagnose and resolve these engineering problems efficiently.
This article walks through using AI to streamline every stage of JMeter performance testing—from automatically generating test plans and JMX scripts, creating realistic parameter data, and writing BeanShell/Groovy logic, to real‑time log analysis, adaptive load control, automated root‑cause analysis, and professional report generation—while highlighting verification, confidentiality, and context‑size considerations.
This article explains Bilibili's massive big‑data platform architecture, the common offline‑task failures and slowdowns users encounter, and how a large‑language‑model‑driven intelligent assistant is being built to diagnose and resolve these issues automatically.
This article explains how Bilibili's massive video platform leverages a large‑language‑model‑driven assistant to diagnose and resolve offline task failures and slowdowns within its five‑layer, storage‑compute separated big data architecture, improving operational efficiency for thousands of daily queries.
This article explains Bilibili's massive data platform architecture, the common reasons offline tasks fail or slow down, and how the company is exploring large‑language‑model‑driven assistants to automatically diagnose and resolve these engineering issues.
This article explains Bilibili's massive video platform data architecture, the huge daily workload of offline and real‑time tasks, common user problems like task failures and slowdowns, their root causes, and how a large language model assistant is being used to automate troubleshooting.
The recent arXiv paper by Scott Aaronson and Freek Witteveen proves that black‑box amplification for QMA protocols cannot surpass a double‑exponential bound, highlighting the ultimate limits of quantum proof verification and outlining open questions about QMA versus QMA₁.
Bilibili’s massive video platform relies on a five‑layer, storage‑compute separated big‑data architecture, handling hundreds of thousands of daily tasks, and now leverages large‑language‑model assistants to automatically diagnose and resolve frequent task failures and performance slowdowns.
The article introduces shadcn‑ui‑mcp‑server, a Node.js tool that implements a Model Context Protocol to let AI assistants read shadcn/ui component documentation, source code, and demos, enabling richer code suggestions and full‑page layout generation within editors like VS Code, Continue, and Cursor.
Google’s T5‑based machine‑learning tool transforms code‑review comments into automatic edit suggestions, reducing the average 60‑minute per‑review effort, boosting resolution rates from 4.9 % to 7.5 %, and delivering multi‑role efficiency gains that translate into hundreds of person‑years saved annually.
The article explains how Tencent’s Wuji low‑code platform uses a de‑templated, data‑first approach, automatic UI inference, progressive development layers, and AI assistance to let developers quickly build and customize a wide range of internal systems while maintaining integration and openness.
This guide walks you through obtaining DeepSeek API access, installing the Continue and CodeGPT plugins in IntelliJ IDEA, configuring them with your API key, and understanding the associated usage costs, enabling AI‑assisted development to boost productivity.
This tutorial shows how to use the jq command‑line tool to extract each user's name from a JSON array, illustrating simple commands such as jq '.users[].name' data.json, describing the author's move from manual IDE methods, and emphasizing the importance of knowing jq exists and leveraging AI to generate scripts from plain‑language descriptions.
This article reviews the main insights from the book “Efficient Paper Writing: AI‑Assisted Academic Writing”, covering AI prompting frameworks, the value of expert knowledge, practical research‑method tips, and how AI can boost productivity while preserving scholarly rigor.
This article shares practical tips for writing clean frontend code, using AI assistants for code improvement, understanding business requirements, reading source code of popular libraries, and mastering front‑end infrastructure such as tooling, monitoring, CI/CD, and technology selection to ace technical interviews.
This article explores how software test engineers can leverage ChatGPT for tasks such as requirement analysis, test case creation, defect reporting, functional test generation, API test case generation, and automation script writing, thereby improving testing speed, accuracy, and overall quality.
This article explores how software test engineers can leverage ChatGPT to improve efficiency across the testing workflow, including requirement analysis, test case creation, defect reporting, functional and API test generation, and automation script development, while providing code examples and practical tips for integrating AI assistance into testing processes.
This article explores how ChatGPT can boost developers' productivity by generating code, refactoring and debugging, explaining code line‑by‑line, clarifying programming concepts, and automatically creating test cases, encouraging programmers to embrace AI as a daily tool.
This article recounts how a software test engineer leveraged ChatGPT to generate and refine test ideas for a mobile contacts export feature, highlighting the impact of query phrasing, timing, and AI response variability on test coverage and reliability.
This guide shows how to leverage ChatGPT together with tools like VS Code, Markmap, and PlantUML to automatically create complete, non‑redundant test case mind maps that cover boundary conditions, scenario coverage, and eliminate duplicate tests, boosting testing efficiency.
This guide walks designers through the fundamentals, organization, and validation of UX semantic writing, presenting three practical design methods, AI‑assisted writing techniques, and multiple case studies that illustrate how to improve clarity, conversion, and user experience across products.
This article chronicles a designer's hands‑on experiment using ChatGPT as a collaborative partner across a design‑driven project, detailing how AI‑generated questions, market analysis, strategy formulation, and execution support can boost efficiency while highlighting the model's current limitations in visual input and nuanced decision‑making.
Facing a rapidly aging population, Baidu’s Big Font app combines AI‑driven large‑font interfaces, high‑contrast colors, extended audio cues, simplified gestures, and voice‑over features to create an accessible, emotionally supportive mobile experience that addresses seniors’ visual, auditory, motor, and cognitive challenges.
