This tutorial walks PHP developers through selecting AI coding assistants, crafting prompts, generating functions, classes, tests, and secure code, and offers practical tips for reviewing and refining AI‑produced code to improve productivity and maintain quality.
This comprehensive guide shows backend developers how to collaborate effectively with AI coding tools by building personal context management systems, accurately judging AI output quality, following a structured PRD‑to‑code workflow, leveraging Python scripts and agent prompts, and applying best‑practice documentation techniques to boost productivity and code reliability.
Spec-Driven Development (SDD) proposes writing exhaustive specifications before prompting AI, but experts argue it creates cognitive overload, reduces AI's speed and flexibility, and reverts to waterfall-like processes; instead, small iterative prompts that produce runnable code each cycle are recommended for efficient AI-assisted development.
The article defines Vibe Coding, contrasts it with traditional Spec‑driven development, traces the evolution of human‑AI collaboration from simple resource retrieval to multi‑agent workflows, analyzes current limitations, shares practical tool setups, and forecasts future trends in AI‑augmented software engineering.
This comprehensive guide explains how AI coding assistants like Cursor and Claude Code compute tokens, interact with tools, index codebases with Merkle trees, craft effective prompts, and apply progressive development practices to boost productivity, code quality, and security across real‑world projects.
Boris Cherny, founder of Claude Code, shares a detailed, step‑by‑step set of 13 practical experiences—from parallel multi‑session handling and model configuration to team collaboration, custom slash commands, sub‑agents, and long‑running task automation—demonstrating how to maximize productivity with Claude Code.
A senior Google engineer reveals that Claude Code generated a functional distributed‑agent orchestrator in just an hour, matching a year‑long internal effort, while industry leaders discuss rapid AI‑coding tool advances, workflow tricks, and the expanding partnership between Google and Anthropic.
Qwen Code v0.6.0 introduces an experimental Skills system that can be enabled with a flag, automatically loads custom skill definitions, mirrors Claude Code's architecture, and is available via npm with a daily free request quota.
The article analyzes six emerging trends in Chinese AI coding tools—model capability parity, open tool integration, spec‑driven development, lower entry barriers, self‑validation, and full‑stack automation—arguing that future success depends on end‑to‑end engineering reliability rather than mere code generation or emotional support.
After testing MiniMax M2.1 on a full‑stack e‑commerce order‑management task, the author finds the model correctly implements DDD layers, transaction handling, custom exceptions, and even generates a polished cyber‑punk UI, while its Java‑to‑Go code respects each language’s concurrency model and scores over 88 on the VIBE benchmark.
This guide walks through the entire process of designing, creating, and installing a Claude Skill that automatically optimizes prompts by selecting the best framework, confirming ambiguities with users, and leveraging progressive loading to handle dozens of prompt templates.
Addy Osmani explains that despite AI now writing most code, developers must follow a disciplined, step‑by‑step workflow—including planning, incremental development, context feeding, model selection, rigorous testing, and fine‑grained version control—to avoid chaos and truly boost productivity.
The author shares a 2025 personal usage report for Cursor, revealing a Top 25% activity rank with 1.41 B tokens consumed, and compares this with heavy‑use data from Cursor team members and industry leaders who spend up to 9.1 B tokens, highlighting model preferences and usage patterns.
Cursor, the leading AI‑powered code editor, has acquired Graphite, a modern code‑review platform, promising to merge the inner‑loop code‑writing experience with the outer‑loop review process, enabling self‑driving pull‑requests, stacked PRs, merge queues and AI reviewers that could reshape developers’ workflow.
Vibe coding leverages large‑language AI models to let developers describe ideas in plain language, automatically generating most of the code, while highlighting its rapid rise, practical tools, prototype‑friendly use cases, and the critical limitations around security, transparency, and the need for human oversight.
The article reveals that sampling noise, rather than train‑inference inconsistency, is the primary cause of reward collapse during RL training of MOE models, and demonstrates that suppressing this noise stabilizes training and speeds convergence.
The article examines why AI‑generated code often varies in quality, contrasts Vibe Coding with Spec‑Driven development, and explains how a structured, spec‑centric workflow—including UI specifications, MCP integration, and rule‑based validation—enables stable, high‑quality code generation for enterprise backend systems.
The ACoder platform combines multi‑model AI, a panoramic code‑understanding engine, and a layered knowledge‑management system to automate the entire software‑development lifecycle, delivering 5‑20× overall efficiency gains, up to 24× speed‑up for cross‑platform code migration, and dramatically higher code‑recall accuracy.
The article compares AI‑assisted "Vibe Coding"—which boosts short‑term productivity but introduces intent misalignment, black‑box code, and maintenance debt—with Spec‑Driven Development (SDD), which restores controllability, quality, and collaboration through formal specifications, illustrated by real‑world case studies, a four‑layer ISPI model, and an emerging tooling ecosystem.
This article shares Meituan’s practical approach to controlling the quality of AI‑generated code by using three strategies—unit‑test validation, safety‑net protection for legacy code, and a TDD‑driven workflow—illustrated with real Java examples and detailed test cases.
After two months of using Alibaba’s Qoder AI coding platform, the author explains why it beats alternatives on cost, compliance and maturity, debunks common myths, shares eight hands‑on best‑practice tips, and presents three real‑world case studies that show its impact on backend and frontend development.
This article reviews a comprehensive 303‑page survey on code foundation models, tracing the evolution of code‑focused large language models from 2021 to 2025, comparing general‑purpose and specialized LLMs, and presenting extensive experiments on prompting, fine‑tuning, reinforcement learning, and autonomous coding agents.
This article shares a hands‑on experience of applying SpecKit and AI‑assisted coding to a mature Java backend, detailing the spec‑driven workflow, tool selection, environment setup, step‑by‑step commands, concrete examples, observed benefits, encountered challenges, and practical lessons for future projects.
This article surveys the rapidly expanding AI coding ecosystem, categorizing tools by cloud versus local deployment and interactive versus autonomous operation, then details major IDE plugins, CLI utilities, Vibe‑style products, and asynchronous agents, offering selection guidance and best‑practice tips for developers.
In a candid interview, Qoder founder Shu‑tong shares how Alibaba launched its AI‑coding platform in just 60 days, amassed 500,000 developers, and positioned the product to dominate the real‑software market through spec‑driven workflows, AI agents, and a unified model‑selection engine.
Codex, OpenAI’s software engineering AI agent, leverages the codex-1 model to generate, debug, and submit code via natural language, offering cloud sandbox execution, multi‑entry collaboration, and extensive CLI commands, with detailed feature comparisons, installation steps, configuration guides, and VSCode integration for developers.
In a 1024 Programmer's Day AMA, Xiaohongshu’s tech leaders and Hugging Face co‑founder discussed AI coding as a core skill, open‑source as a catalyst for rapid advancement, and the challenges of achieving human‑like AGI, offering practical takeaways for developers and product teams.
This newsletter curates five insightful articles covering voice agent applications in games and insurance, ten practical tips for building multi‑agent systems with context engineering, a deep dive into AI‑assisted coding from theory to practice, high‑accuracy AI coding workflow design, and the release of Rspack 1.6 for smaller, cleaner build artifacts.
The article analyzes the evolution from single‑agent coding assistants like GitHub Copilot to multi‑agent, AI‑native development ecosystems, detailing architectural designs, practical techniques, enterprise case studies, tool‑form choices, and a five‑layer capability model that together illustrate how AI is shifting from a mere tool to a collaborative partner in software engineering.
The article reviews Cursor's Composer model and Cognition's SWE‑1.5, showing they outperform Haiku 4.5 on SWE‑Bench, deliver token speeds around 950 tokens/s, leverage reinforcement‑learning fine‑tuning, support parallel agents in Cursor 2.0, and provide cost‑effective, high‑quality AI‑assisted coding across multiple real‑world projects.
This article shares a step‑by‑step AI coding workflow, from the underlying beliefs and basic process reconstruction to detailed pipeline implementation, practical tips, and an analysis of AI strengths and limitations, illustrating how AI can dramatically accelerate software delivery.
This comprehensive guide explains the underlying principles of AI coding assistants—including token calculation, tool calling, codebase indexing, and Merkle tree synchronization—while offering actionable best‑practice recommendations for prompt engineering, incremental development, documentation, security compliance, and effective team adoption.
The article reviews eight AI developments—including Tencent's task‑mode‑enabled ima 2.0, DeepSeek's visual‑compression OCR model, Quark's Qwen3‑Max assistant, Kuaishou's three‑layer AI coding ecosystem, Opera Neon’s ODRA agent, USTC‑ByteDance MoGA video generator, real‑time voice deepfakes, and OpenAI’s upcoming music tool—while providing analytical commentary on their broader impact.
AipexBase, the first Chinese open‑source AI‑native backend‑as‑a‑service platform, lets developers and AI agents bypass traditional server, database, and authentication code by using an MCP‑compatible SDK, with step‑by‑step deployment, API key generation, and integration examples that demonstrate end‑to‑end intelligent app creation.
In just five minutes, I used the WeaveFox AI coding platform to recreate a quirky "hand‑counting" calculator by prompting natural language, iterating through form‑based tech‑stack selection, and letting multiple AI agents generate, refine, and deploy the full front‑end application.
The article presents a step‑by‑step 5S documentation standard and 6A workflow that turn AI from a careless code generator into a reliable assistant, cutting bug‑fix time dramatically and delivering up to a 90% efficiency gain in small‑to‑medium projects.
This article reveals the core methodology of applying AI coding to complex client-side development, discusses practical challenges, prompt design, task decomposition, efficiency improvements, and provides actionable guidelines and architectural rules for integrating AI into UI and service layers.
The author compares Claude Code with generic Claude calls in IDEs, details Sonnet 4.5’s benchmark superiority, reveals how the model excels on fresh projects but falters on legacy code riddled with technical debt, and shares practical methods to maximize its value.
This article details how Ant Group’s international credit team applied AI‑assisted code generation using CodeFuse plugins and custom prompts to automatically transform system specifications into Java façade, persistence, and business‑logic code, achieving a 40% reduction in coding effort across multiple projects.
This article reviews the major Manus 1.5 update, detailing its four‑fold speed boost, full‑stack web‑app generation via natural language, and a step‑by‑step experiment that recreates a classic macOS‑style desktop in under ten minutes, while evaluating its design, development, and user‑account features.
This article examines the current state of AI coding, evaluates its technical abilities, engineering safety features, industry market size, and discusses how the rise of AI tools reshapes the roles of junior and senior developers while forecasting future workforce dynamics.
This article outlines five core software design principles—product‑first, natural‑language interaction, iterative feedback, context management, and simplicity—that help developers harness AI‑driven Vibe Coding while avoiding technical debt and ensuring long‑term maintainability.
As large‑model capabilities grow, Vibe Coding lets developers describe requirements in natural language to generate code, but faces challenges of knowledge timeliness and code accuracy, prompting the need for enhanced search tools like Exa.ai and Context7 to ensure reliable, up‑to‑date code generation.
This guide explains how to treat AI coding tools as code reviewers rather than assistants, detailing mindset shifts, practical steps, ideal use cases, collaboration models, and common pitfalls to help developers boost productivity while maintaining code quality.
This talk examines the core challenges facing AI coding agents—limited context windows, knowledge accumulation, and software‑engineering complexity—while outlining practical solutions such as context providing, RAG, fine‑tuning, online learning, feedback loops, and multi‑agent collaboration to move toward truly human‑like, continuously learning coding assistants.
The article analyzes the low adoption rate of AI‑generated code, identifies root causes such as information asymmetry, oversized tasks, missing feedback loops and unclear role boundaries, and proposes a structured documentation and issue‑management workflow to improve AI coding reliability and developer productivity.
The article outlines AI coding adoption (over 62% of developers use it, boosting productivity by 30%‑50%), describes three evolving stages—from code assistants to collaborative and autonomous programming—and explains how Qoder’s spec‑driven, repo‑wiki and cloud‑sandbox architecture seeks to serve real software and deliver up to ten‑fold efficiency gains.
Anthropic's Claude Sonnet 4.5 arrives as the strongest coding model yet, delivering over 30 hours of uninterrupted programming, major gains in reasoning, math and agent tasks, safety‑aligned training, new API features, benchmark‑leading performance, and pricing identical to Sonnet 4.
Anthropic’s Claude Sonnet 4.5 arrives with unchanged pricing but claims top‑tier coding performance, superior reasoning and safety scores, a new Agent SDK for long‑running tasks, and an "Imagine with Claude" preview that lets users generate live software, all backed by benchmark comparisons and real‑world case studies.
This article walks through using Vibe Coding’s AI‑driven workflow with the Trae tool to automatically generate a Vue 2‑based web management dashboard, detailing how to extract UI design rules, create project‑level prompts, run commands, troubleshoot, and refine the interface with iterative prompts.
This article explains how Ctrip’s AI‑driven Transpiler, built on a ReAct‑inspired multi‑agent framework, automates code transpilation from CRN to xTaro and streamlines large‑scale Java 21 upgrades, dramatically reducing manual effort and improving accuracy across the software development lifecycle.
This roundup highlights Vite’s next‑gen Rolldown engine delivering up to 45% faster builds, AI‑powered coding tools like Comate Zulu and Claude Code enabling solo developers, Browser‑Use’s AI web automation, Alipay’s AI travel assistant built by a four‑person team, ROMA’s dark‑mode adaptation, Kuaishou’s OneSearch generative framework, MIDAS multimodal digital‑human breakthroughs, and the open‑source VoxCPM speech model.
The article details Xiaohongshu’s Dataverse platform evolution into a Data+AI system, highlighting inefficiencies in algorithm and data‑science workflows, the introduction of an interactive notebook, comprehensive data lineage, AI‑coding assistance, and future DataAgent plans to automate data engineering tasks.
The article walks through Alibaba’s AI‑driven development tool Qoder, highlighting its Quest Mode that auto‑generates specs and executes tasks, and the newly upgraded Repo Wiki that creates comprehensive, continuously updated project documentation, demonstrating how these features dramatically streamline both front‑end and back‑end development workflows.
After a hackathon where the author relied entirely on AI tools like Claude Code for documentation, design, and coding, this article shares practical experiences and insights on prompt engineering, task framing, context management, and workflow strategies that boost development efficiency across frontend, backend, and mobile projects.
The 2025 AI Coding market report reveals a shift from experimental tools to production‑grade solutions, driven by rapid model upgrades, rising developer adoption, soaring valuations for startups, and a transition toward autonomous agent‑based platforms that promise end‑to‑end software engineering.
This comprehensive guide shows how development teams can adopt the AI coding tool CodeBuddy, diagnose existing processes, select the right tool, and implement a document‑driven workflow that covers requirements, design, coding, testing, deployment, review, security, debugging and release, delivering faster and higher‑quality software.
As software complexity rises and release cycles shrink, this article explains how combining the Tongyi Lingma AI coding assistant with Alibaba Cloud's Cloud Native AI review can accelerate coding, improve code quality, reduce manual review effort, and integrate seamlessly into CI/CD pipelines.
This article introduces Claude Code UI, a lightweight, responsive visual interface for Claude Code and Cursor CLI that works on desktop, tablet, and mobile, walks through its key features, provides step‑by‑step installation commands, and shares a hands‑on user experience comparison with previous tools.
This article shares five practical techniques for using Wenxin Code's Zulu and Chat agents—covering context provision, automatic command execution, rule constraints, inline chat, and Git commit shortcuts—to make AI‑generated Java code more reliable, maintainable, and aligned with project standards.
This article reviews four essential IntelliJ IDEA plugins—GitHub Copilot, SonarLint, Rainbow Brackets, and Easy Naming—detailing their core advantages, personal experiences, and how they collectively enhance coding efficiency, code quality, readability, and naming consistency for Java developers.
Using gemini-cli’s AI assistance effectively requires balancing manual GEMINI.md directives with the tool’s automatic codebase indexing, a strategy that delivers precise context, role definition, and project-wide insights while avoiding context bloat, ultimately enhancing development speed and code quality.
This guide explains how to install Claude Code and the ClaudeCodeRouter, configure them to use DeepSeek‑V3.1 via the Anthropic API format, run the tool, and troubleshoot common connection and Windows environment issues.
Vibe Coding, a new AI‑centric programming paradigm introduced by Andrej Karpathy, replaces traditional code‑centric development with natural‑language‑driven interactions, enabling developers to act as product‑focused guides while large language models generate code, and discusses tools, workflows, benefits, challenges, and future trends.
In just three weeks, I single‑handedly created a production‑grade Retrieval‑Augmented Generation (RAG) API with FastAPI, leveraging Cursor and Claude Code to automate coding, testing, and deployment, and I share practical insights on AI‑assisted development, high cohesion‑low coupling design, TDD, git worktree parallelism, and agent orchestration.
Anthropic’s internal case study reveals how dozens of teams—from product development and data infrastructure to security, inference, growth marketing, and legal—leveraged Claude Code to automate repetitive tasks, accelerate onboarding, improve code quality, and achieve measurable productivity gains across the organization.
After four months of using the AI code assistant Cursor, the author shares a practical methodology that combines clear requirement articulation, pre‑planning, custom rules, and various Model Context Protocol (MCP) tools—such as feedback‑enhanced, sequential‑thinking, and better‑tapd—to streamline development, reduce token waste, and improve collaboration.
This article describes how a development team integrated a suite of AI‑driven workflows—including AI‑generated documentation, code scaffolding, rule‑based IDE guidance, MCP server connectivity, and memory banks—to reduce repetitive tasks, accelerate coding, improve code review, and streamline operations across the entire software lifecycle.
This guide breaks down a practical, step‑by‑step methodology for using AI to quickly turn ideas into functional products, covering tech‑stack choices, lightweight PRDs, disciplined version control, example‑driven prompts, and rapid error‑handling to avoid common pitfalls.
This article shares practical experiences and best‑practice recommendations for leveraging AI coding tools—especially Claude Code—including prompt engineering, task categorisation, context management, memory handling, command usage, and collaborative workflows to dramatically accelerate software development.
This article analyzes how AI‑powered coding tools are reshaping the software industry, presenting rapid growth data, seven under‑discussed conflicts—from product form to pricing models—and the broader implications for developers, enterprises, and the future of programming.
This article explains how to replace the luck‑based Vibe Coding approach with a structured Spec workflow—using Kiro, Claude Code, or other AI IDEs—to turn vague prompts into clear requirements, designs, task lists, and incremental, reviewable code, dramatically improving productivity and quality.
This article evaluates the open‑source Qwen3‑Coder‑480B‑A35B model, comparing its programming and agentic capabilities to Claude 4 and other leading models, detailing its architecture, token length, reinforcement‑learning‑after‑training technique, ecosystem tools, and real‑world code‑generation case studies.
In this detailed case study I walk through using the AI coding assistant Cursor to automatically generate a React + Vite replica of the Juejin homepage, covering project initialization, dependency fixes, component creation, layout tweaks, mock data integration, infinite scrolling, and final polishing steps.
Google DeepMind’s $2.4 billion reverse acqui‑hire of Windsurf, targeting its talent and licensing its AI coding technology, highlights a stark shift in startup equity dynamics, leaving most Windsurf employees without benefits and raising concerns about future competition in the AI code‑generation market.
This in‑depth review examines Kimi K2’s new focus on agent and coding abilities, comparing its performance on 3D HTML generation, code generation, and real‑world agent tasks against Claude 4 and Gemini 2.5, while also evaluating cost, openness, and practical usability for developers.
The author demonstrates how the Cursor AI coding assistant was used to migrate a Vue 3 horizontal‑scrollbar plugin to Vue 2, covering dependency replacement, code refactoring, documentation updates, build fixes, npm publishing, and adding new features through precise prompts.
This article examines the rise of terminal AI coding assistants, comparing Google’s Gemini CLI and Anthropic’s Claude Code across features, context length, cost, performance, platform support, and usage tips, helping developers decide which tool best fits their productivity and project needs.
2025 marks the explosive rise of AI coding, with over 10,000 tools transforming development from code writing to code reviewing, introducing concepts like Vibe Coding, and offering diverse IDE and conversational platforms that automate tasks, streamline workflows, and reshape the role of developers across the industry.
This article presents thirteen essential development tools—from IntelliJ IDEA and AI‑assisted Cursor to Docker, Git, and Jenkins—explaining how each can dramatically improve coding speed, debugging, testing, and deployment, and includes practical code snippets and real‑world performance data.
Google unveiled Gemini CLI, a terminal‑based AI assistant with a generous free tier and cross‑platform support, while Zed introduced a fast, configurable Rust‑based debugger for multiple languages, and Microsoft open‑sourced a Rust rewrite of its classic MS‑DOS editor, now running on modern OSes.
This article provides an in‑depth comparison of the AI‑powered coding assistants Cursor and Augment, examining their core features, code‑completion capabilities, context awareness, enterprise security, pricing models, and ideal user scenarios to help developers choose the best tool for their projects.
This article compares Google Jules and Anthropic Claude Code, examining their design philosophies, cloud versus local workflows, feature sets, integration methods, strengths, limitations, and ideal use cases to help developers decide which AI coding assistant best matches their development processes.
AutoDev Remote Agent, an open‑source AI coding assistant that runs as a GitHub Action MCP service, assists with issue analysis, task planning, code generation, architecture design, and testing, while offering a sandboxed environment, tool chaining, and future plans for automated code writing, testing, and deployment.
A Qodo survey of 609 developers reveals that while AI coding tools dramatically increase productivity for a small elite, most developers see only moderate gains and many remain hesitant due to trust issues and hallucinations.
In a three‑day sprint, ByteDance’s VP Hong Dingkun built an English‑learning app using the AI‑coding platform TRAE, illustrating how large‑model‑driven code completion, natural‑language programming, and AI‑enhanced development can dramatically boost productivity, democratize coding, and push the limits of software intelligence.
This article explores Visual Studio Code’s recent AI integration, detailing the new chat interface, shortcut symbols, and MCP configuration options that let developers and even non‑programmers leverage AI assistance directly within the editor.
This article shares a step‑by‑step guide on planning, documenting, templating, configuring, and rule‑setting for the Cursor AI coding assistant, showing how thorough preparation transforms it from a source of buggy code into a reliable development partner.
This article walks through the Model Context Protocol (MCP) concepts, demonstrates how Cursor leverages Docs and Rules to develop a weather‑service MCP server in TypeScript, explores a gold‑price prediction project, and shares practical tips, advantages, and limitations of AI‑assisted coding.
This article evaluates Anthropic's Claude 4 (especially Claude‑4‑Sonnet) within the Cursor IDE, presenting benchmark scores on SWE‑bench, detailed prompts for UI, frontend, architecture, and backend generation, visual results, and a balanced list of strengths and remaining issues.
This guide outlines a systematic workflow for using the Cursor AI coding assistant—starting with precise requirement planning, creating project documentation, leveraging starter kits, injecting context files, configuring granular project rules, and finally achieving stable, maintainable code output.
This article walks through Cursor 0.49’s new auto‑generated Project Rules feature, explains its key updates, provides step‑by‑step instructions for creating rules from chat context, showcases real‑world tests on front‑end projects, and offers practical best‑practice recommendations and cautions for developers.
Claude 3.7 Sonnet, announced by Anthropic, introduces a hybrid reasoning architecture that dramatically boosts code generation, offering rapid response and extended thinking modes, enabling massive line‑of‑code outputs, complex game and physics simulations, and an integrated Claude Code tool that automates engineering tasks and bridges large codebases.
Google’s Firebase Studio is a browser‑accessible, cloud‑native development environment that leverages Gemini AI to generate, debug, and test full‑stack applications across backend, frontend, and mobile frameworks, offering project templates, customizable environments, and one‑click deployment to Google Cloud services.
The article examines how AI agents differ from static workflows, outlines the ideal characteristics for agent tasks, explores codebase indexing, RAG and Function Call techniques, and introduces the Model Context Protocol (MCP) as a standardized, efficient bridge between large language models and enterprise tooling for next‑generation AI‑driven software development.
This article explores the rise of chat‑based programming with Cursor, explains its three core breakthroughs, demonstrates building a Sudoku game in 16 seconds, introduces the Model Context Protocol (MCP), and provides ten practical tips—from terminal chat to Git‑based code review—that help developers harness AI for faster, clearer, and more creative coding.
This article examines the rise of chat‑style programming, explains how Cursor’s AI‑driven features such as natural‑language coding, Agent mode, and Model Context Protocol (MCP) transform the software development workflow, and provides practical tips for leveraging the tool to boost productivity and achieve a flow state.
Large language models often struggle with coding tasks, failing to stop when encountering obstacles, ignoring black‑box testing principles, and making unnecessary refactors; this article examines those blind spots, offers practical examples, and suggests strategies such as preparatory refactoring, stateless tools, and careful prompting to improve AI‑assisted development.
This article explains what the Model Context Protocol (MCP) is, lists popular MCP service marketplaces, and provides step‑by‑step instructions for adding global or project‑level MCP servers in Cursor, including JSON configuration, verification, usage examples, tips, and troubleshooting.
This step‑by‑step guide shows how to set up VS Code with the Cline and Continue extensions, configure the free DeepSeek API, generate website code using AI, and deploy the site via GitHub Pages, enabling anyone to create a personal website without paying for AI tools.
