This article explains step-by-step how to develop an intelligent ChatGPT-powered chatbot with PHP, covering environment setup, installing the PHP ChatGPT library, writing code to send messages and receive responses via the OpenAI API, and testing the bot with example scripts.
OpenAI has officially launched ChatGPT's real‑time internet browsing and expanded multimodal capabilities, marking a shift from its 2021‑cutoff data model, while also unveiling DALL‑E 3 image generation and highlighting the intense AI competition and massive investment behind these advances.
This article shows programmers how to harness ChatGPT beyond simple Q&A by using advanced prompting techniques for knowledge summarization, task decomposition, code reading, refactoring, generation, unit‑test creation, and plugin integration, turning AI into a practical development assistant.
Amid the rise of generative AI tools like ChatGPT, Stack Overflow announced a 28% staff reduction, shifting focus to its new OverflowAI suite, while a Cornell study links the layoffs to declining site traffic and the broader impact of AI on developer communities.
This article describes how to construct an intelligent customer‑service assistant using ChatGPT for natural‑language understanding, BERT for user‑question classification, and Sentence‑BERT for knowledge‑selection, detailing system architecture, prompt design, model training, performance results, and practical cost reductions.
Recent research suggests GPT‑3 exhibits Theory of Mind abilities, prompting a deep dive into attention mechanisms, neural network fundamentals, emergent capabilities, and the role of compression in large language models, while examining philosophical thought experiments like the Chinese Room to question true machine intelligence.
OpenAI’s latest update adds multimodal voice and image capabilities to ChatGPT, letting users speak or upload pictures for more natural, context‑rich conversations powered by advanced GPT‑3.5 and GPT‑4 models.
This article demonstrates how to use ChatGPT step‑by‑step to design, generate, refine, and test a lightweight Java framework that automatically maps enum descriptions for front‑end consumption, covering problem definition, annotation design, core class implementation, code snippets, and performance evaluation.
Stephen Wolfram explains the inner workings of ChatGPT, covering its transformer architecture, probability‑based word selection, training on massive text corpora, the role of embeddings, neural network layers, attention mechanisms, and the challenges of modeling language, offering a deep technical overview for AI enthusiasts.
The article examines how AI‑generated phrases such as “Regenerate response” and “As an AI language model, I …” have appeared in peer‑reviewed papers, leading to retractions, discusses the scale of the problem, and outlines publisher policies that allow AI assistance when properly disclosed.
This article offers a non‑algorithmic engineer’s clear overview of large language models, explaining ChatGPT’s generative‑pre‑training‑transformer foundation, core mechanisms like attention, practical prompt‑engineering tips, and how enterprises can integrate LLMs into data analysis, smart‑customer service, and other business workflows while noting associated risks.
Co‑authored with ChatGPT, this article explores how large language models reshape software engineering by automating routine coding and bug‑fixing, raising skill demands, boosting productivity through code generation and reviews, creating new AI‑focused roles, highlighting tasks that remain human‑centric, and urging engineers to upskill in AI, data science, ethics, and collaborative problem‑solving to stay competitive in the irreversible AI era.
According to Reuters, OpenAI's ChatGPT experienced a third consecutive month of reduced website visits in August, with global traffic dropping 3.2% to 1.43 billion and average session time falling to seven minutes, while unique visitors edged up slightly and a new Canva plugin was introduced for Plus subscribers.
This article explains the inner workings of ChatGPT, covering how large language models predict the next token using probability distributions, the role of embeddings, the transformer architecture with attention heads, training methods, loss functions, and why such a massive neural network can produce coherent, human‑like language.
This article details JD's low‑code mini‑program platform, describing its MCube architecture, visual drag‑and‑drop canvas, data binding and interaction mechanisms, and how Ui2Code and ChatGPT are used to streamline development while balancing vertical specificity and horizontal generality.
Based on a privacy‑compliant click‑stream panel of 20 million devices across 200+ countries, this report examines ChatGPT’s global usage trends, revealing a decline in overall traffic, dominant programming and education use cases, and the most frequent user commands such as “Write”, “Create”, and “List”.
This article demonstrates how Java developers can leverage ChatGPT to design and iteratively refine a lightweight backend framework that automatically scans annotated enums and generates key‑description maps for front‑end consumption, complete with code examples, performance testing, and deployment guidance.
This tutorial walks through creating a Chrome extension that uses the ChatGPT API to translate selected text, covering setup with OpenAI's quickstart, key code snippets for prompts, content‑script and background service worker, Vite plugin usage, deployment on Vercel, and runtime considerations.
The article introduces the ChatGPT 0720 update, explains the new Custom Instructions feature, shows how to enable and configure it, compares responses with and without the feature, and provides detailed code examples for implementing similar functionality via system messages in developer tools.
This article explains how to embed the ChatGPT API into a low‑code development plugin to automatically translate Chinese variable names into English, preset Prompt templates, and generate fully typed Vue code for CRUD pages, complete with code examples and configuration steps.
This article walks through discovering Bilibili's comment API, programmatically fetching paginated JSON data, converting it into Java POJOs, storing and sorting the comments, and finally feeding the top entries to ChatGPT for automated sentiment and content analysis.
This article walks through creating the Woa project—a .NET 7‑based WeChat Official Account interface that uses Supabase for data storage and messaging, integrates ChatGPT and Claude2, and explains project structure, configuration, deployment, and runtime steps in detail.
In a recent interview, Microsoft CEO Satya Nadella likened the significance of artificial intelligence to Bill Gates' 1995 internet memo, highlighting Microsoft's partnership with OpenAI, the $13 billion investment, and the broader competitive landscape against Google, while emphasizing AI's future impact on technology and society.
This article examines ChatGPT's limitations in real‑time information and answer accuracy, then proposes a retrieval‑augmented method that combines up‑to‑date document search with large language models to deliver more reliable and current responses across various scenarios.
This article explains how Codex, built on the GPT‑3.5 architecture, is trained and fine‑tuned to give ChatGPT the ability to generate code, detailing the data collection, supervised fine‑tuning, evaluation using HumanEval and the pass@k metric, and presenting performance comparisons with GPT‑3 and Codex‑S.
The project implements a ChatGPT‑powered prompt generator for Stable Diffusion that translates non‑English ideas, crafts concise 50‑word positive prompts with structured modifiers, filters prohibited content, leverages few‑shot examples, and is containerized via Docker, dramatically simplifying AI art creation for beginners and multilingual users.
WormGPT, a €60‑per‑month black‑hat AI built on GPT‑J, can generate malicious code, phishing emails and other illegal content, exposing serious security risks and prompting experts to recommend BEC training and stricter email verification to mitigate AI‑driven cyber attacks.
This article explores the practical deployment of ChatGPT‑based products, detailing the model fundamentals, technical architecture, engineering‑focused prompt design, real‑world application scenarios, and the challenges of model generalization, resource consumption, data privacy, interpretability, and ethical considerations.
This article introduces prompt engineering for ChatGPT, explains key design principles, and demonstrates a series of practical applications such as text classification, summarization, role‑playing, terminal emulation, output formatting, temperature control, iterative fine‑tuning, and reverse‑engineering of prompts.
ChatGPT, built on massive text training and simple neural network operations, generates human-like language yet lacks true understanding, prompting integration with Wolfram|Alpha’s precise computational language—a synergy highlighted by Stephen Wolfram’s insights on language structure, AI limits, and future computational possibilities.
This article explains how ChatGPT generates text by predicting the next token using large language models, describes the role of probability, temperature, and attention mechanisms in transformers, and discusses neural network training, embeddings, semantic spaces, and the broader implications for artificial intelligence research.
In March 2023 Stack Overflow’s traffic fell nearly 14% while ChatGPT’s usage surged, prompting the company’s CEO to announce a 10% layoff and sparking a broader analysis of AI‑driven tools like GitHub Copilot reshaping how developers seek code solutions.
OpenAI’s newly opened Code Interpreter for ChatGPT Plus users lets the model execute Python code, enabling data upload, analysis, visualization, and file conversion, which many see as a game‑changing tool that could democratize data analytics while still facing limitations like paywalls and no internet access.
This article explains the inner workings of ChatGPT, covering its probabilistic token generation, transformer architecture, attention mechanisms, embeddings, training process, and the mathematical principles that enable a massive neural network to produce coherent, human‑like text.
OpenAI’s newly released Code Interpreter plugin lets ChatGPT write, run, and test code, offering file handling, a built‑in Python environment with popular libraries, and a secure sandbox, while showcasing ten impressive tricks—from converting GIFs to MP4s to turning data into interactive webpages.
ChatGPT’s new Code Interpreter lets GPT‑4 Plus users upload files, have the model write, run, and debug Python code in a private sandbox, enabling non‑programmers to automate complex tasks such as data analysis, image‑to‑video conversion, and PDF generation through natural language prompts.
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.
The article examines how ChatGPT’s strong design and coding capabilities boost programmer productivity, explores historical parallels with past programming revolutions, and discusses whether AI will ultimately reduce development team sizes or simply reshape the software industry’s demand and job landscape.
An anonymous developer built WinGPT, a C‑based ChatGPT client that runs on Windows 3.1 and later 16‑or 32‑bit Windows versions, using native TLS 1.3 to connect to OpenAI’s API, highlighting the challenges of retro UI controls, Winsock dependency, and icon creation on legacy systems.
The article examines the so‑called “grandma loophole”—a prompt‑injection technique that convinces ChatGPT, Bing, and other LLMs to generate Windows and Office activation keys, explores related exploits across platforms, and discusses the broader implications for AI security and ongoing mitigation efforts.
This article evaluates ChatGPT's performance on the 2023 national high school mathematics exam by presenting each question, the model's answers, reference solutions, and a detailed scoring analysis that reveals its strengths, limitations, and potential university admission implications.
This article explores whether AI agents such as ChatGPT can help business owners quickly perform data analysis by linking large language models with local MySQL databases via LangChain, presenting the experimental setup, schema creation, sample queries, results, and practical insights.
This article explains what GPT is, how it is trained, its strengths and limitations, the various risks it poses, and provides practical guidance on safely adopting large language models like ChatGPT within development teams, including code‑level analysis examples.
This article curates recent front‑end engineering insights, including a ChatGPT‑driven pair‑programming approach using the driver‑observer model, a rapid 200‑line Vue‑like framework built with AI assistance, practical JavaScript code‑cleanliness techniques, strategies for optimistic UI design, and a lightweight method for measuring user‑experience metrics.
This article explains what AIGC (AI‑Generated Content) is, compares it with PGC and UGC, explores AI applications in low‑code platforms, and shows how front‑end engineers can use large language models and prompt engineering to dramatically boost development efficiency.
This article explains the fundamentals of recommendation systems, their digital representation, how ChatGPT and large language models are applied to enhance recommendation performance, and highlights emerging trends such as conversational recommendation and a recommended book on the subject.
The article examines how AI-generated misinformation spreads across platforms—from misleading answers on Bing and Stack Overflow to fabricated news stories—highlighting the resulting contamination of online information, the risks to model training, and emerging efforts to detect and curb such low‑quality AI output.
The article examines how AI-generated content is flooding the Chinese internet with unverified information, causing misinformation across platforms like Bing, Zhihu, Stack Overflow and Reddit, and discusses research showing that training models on such data can degrade future AI performance.
Meta’s chief AI scientist Yann LeCun argues that large‑language models like ChatGPT are far from human intelligence, lacking real‑world understanding and even falling short of a dog’s cleverness, while experts debate AI’s risks, benefits, and the need for regulation.
The article shares the author’s hands‑on experiments with ChatGPT, demonstrating how the AI model can generate code, translate scripts, create test cases, assist with debugging, and improve game‑testing workflows while also comparing its strengths to New Bing and discussing safety considerations.
This guide explains why prompt quality determines AI output, introduces a four-part prompt template, shows real examples for travel planning, interview simulation, and workflow assistance, and teaches advanced techniques like continuation and reward‑punishment commands to fine‑tune ChatGPT responses.
Ziroom demonstrates how large language models like ChatGPT, combined with WhisperX transcription and LangChain-powered pipelines, can automate meeting minutes, generate summaries, and build intelligent knowledge bases, outlining implementation steps, code snippets, and practical challenges for internal AI adoption.
The article recaps a Beijing offline salon where experts and open‑source contributors discussed ChatGPT desktop applications, the development and deployment of ChatGPT‑Next‑Web, large‑language‑model challenges, the VisualGLM multimodal model, and product design considerations, providing technical insights and community perspectives on AI advancements.
This article explains what AIGC is, explores the current challenges of front‑end productivity, and shows how AI—especially ChatGPT—can be integrated into low‑code platforms through prompt engineering, enabling automatic page generation, layout editing, and component creation to dramatically boost development efficiency.
This article explains prompt engineering for large language models like ChatGPT, covering its definition, essential techniques such as diverse prompting strategies, problem restatement, background provision, gradient prompting, example inclusion, role‑playing, and the importance of systematic experimentation and quantitative evaluation to achieve high‑quality, task‑specific AI outputs.
This article explains how ChatGPT, as a powerful AI tool, can assist software testers by automatically generating test cases, locating bugs, creating automated test scripts, and producing test reports, thereby reducing costs and improving testing efficiency and accuracy.
This article explains how artificial intelligence tools such as ChatGPT, NewBing and MidJourney can streamline the entire UI design workflow—covering user research, concept generation, prompt engineering, style control, and rapid iteration—to produce higher‑quality, user‑focused interfaces faster.
Recent reports indicate a noticeable decline in ChatGPT Plus’s GPT‑4 performance, especially in coding accuracy, prompting speculation about model scaling pain, AI alignment trade‑offs, and OpenAI’s GPU‑limited roadmap that includes cheaper models, longer context windows, finetuning, and multimodal extensions.
This article explores why ChatGPT has become a breakthrough AI technology, reviews past AI‑database integrations, and envisions multiple future use cases—including natural‑language SQL generation, performance tuning, schema and architecture design, data analysis, and governance—showing how developers, DBAs, and analysts might benefit.
An online CSA seminar on May 30 examined ChatGPT’s security impact, presenting a whitepaper and four AI‑security interaction dimensions, while experts discussed telecom‑operator security‑GPT models, safe vertical‑domain large‑model training, and future industry implications.
This article explains why crafting good prompts matters more than memorizing facts, introduces prompt engineering for large language models, and provides practical techniques—such as diverse prompting strategies, problem restatement, background provision, gradient prompting, role‑playing, and systematic evaluation—to help users get the most out of ChatGPT and similar AI systems.
This tutorial walks developers through setting up an OpenAI account, obtaining an API key, configuring the Python environment, and writing a complete Python script that uses the ChatGPT API to create an interactive chatbot, complete with code examples and execution tips.
This guide explains how to craft effective ChatGPT prompts to produce markdown‑based mind maps, and details three methods—online Markmap tool, VSCode Markmap extension, and XMind import—for visualizing and editing the resulting diagrams.
This article demonstrates how to leverage ChatGPT's ability to produce textual code snippets together with PlantUML to automatically create various UML diagrams—such as sequence, class, flow, state, and use‑case diagrams—from Java‑style pseudocode, providing a step‑by‑step workflow and example prompts.
Integrating ChatGPT with low‑code platforms—using it to translate domain models, generate DSL specifications, and fill code snippets—can automate up to 90 % of routine coding while still requiring human review for complex logic, data sensitivity, and best‑practice compliance.
This article explains how AI large models such as ChatGPT can assist developers throughout the full workflow—from requirement analysis, technical design, and code generation to testing, deployment, and operations—while highlighting practical tips, potential pitfalls, and security considerations.
The article examines ChatGPT's limitations in timeliness and factual accuracy, especially for security‑related queries, and proposes a method that combines external document search with the model to deliver up‑to‑date, reliable answers across intelligent‑assistant scenarios.
The article examines the growing controversy surrounding ChatGPT, highlighting data‑security risks, the spread of false information, corporate and governmental restrictions, and fears that AI could displace jobs, while also proposing technical, legislative, and public‑awareness measures to prepare for the AI era.
The panel examines how large language models like ChatGPT reshape software engineering, covering safety risks, model training stages, prompt‑engineering challenges, teaching reforms, and future human‑AI collaboration, while weighing technical trade‑offs and practical solutions.
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 walks you through building an intelligent documentation system that stores markdown files in Embedbase, creates contextual prompts, and uses ChatGPT to answer user queries, covering setup, code integration, and deployment steps for a full‑stack solution.
This article explains the fundamentals of GPT models, contrasts large models with traditional AI, details ChatGPT's architecture and token processing, outlines its limitations, and presents a five‑layer framework for applying ChatGPT across chat, language, text, reasoning, and private model use cases.
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 article explains step‑by‑step how to turn a static Markdown documentation site into an AI‑enhanced, interactive knowledge base by storing content in Embedbase, retrieving semantically similar passages, constructing context‑aware prompts, and invoking ChatGPT through a custom Nextra search component.
The article shows how AI large‑language models such as ChatGPT can support every stage of the software development lifecycle—from extracting requirements and designing solutions to generating code, tests, deployment scripts, and operational diagnostics—while warning about model inaccuracies, hallucinations, intellectual‑property and privacy risks.
Apple has prohibited its staff from using ChatGPT and other generative AI tools over data‑leakage concerns, mirroring similar policies at Amazon, Microsoft and JPMorgan, while simultaneously developing its own large‑model‑powered Siri features under the codename Bobcat.
OpenAI has released a free iOS ChatGPT app with Whisper‑powered voice input and syncable chat history, promises an Android version in the coming weeks, and highlights speed improvements, privacy features, and its impact on third‑party alternatives.
This guide explains what ChatGPT plugins are, why they matter, how to enable them, and showcases popular extensions that let the model fetch real‑time data, access private knowledge bases, automate tasks, and even generate images or code.
This article demonstrates how to use ChatGPT as a Linux terminal, showing how to issue commands like uname, ip, and ping, displaying the responses, and discussing the implications of AI-powered command‑line access.
This article explains what ChatGPT is, describes how it can improve various software testing activities such as API, unit, mobile, integration, and end‑to‑end testing, highlights its context‑driven capabilities, and outlines the key limitations that testers should be aware of.
This article shows how a concise 10‑line shell script, created with ChatGPT, can continuously capture MySQL QPS, TPS, and connection counts and append the metrics to a log file for performance monitoring.
This article explains practical principles and techniques for crafting clear, specific prompts for ChatGPT, demonstrates how to structure prompts with separators and output formats, and provides a step‑by‑step guide—including code examples—for installing the OpenAI Python library, obtaining an API key, and writing a reusable get_completion function to interact with the model.
This article provides a comprehensive analysis of the Transformer model behind ChatGPT, covering its origin, core mechanisms such as embedding, positional encoding, self‑attention, multi‑head attention, a step‑by‑step translation example, and the broader implications for AI research and industry.
The article analyzes how Stack Overflow's monthly traffic has fallen sharply in 2023, contrasting this trend with the explosive growth of AI services like ChatGPT and GitHub, and discusses the platform's strategic responses including content bans, layoffs, and new AI‑focused product plans.
This article explains what ChatGPT is, how it builds on natural-language-processing and the Transformer-based GPT series, details its model-size growth, architectural enhancements, multilingual support, and walks through the tokenization-to-generation pipeline that enables coherent AI-driven conversations.
This tutorial shows how to quickly integrate ChatGPT and Stable Diffusion AIGC models into a WeChat public account or mini‑program using the AIGCaaS platform, covering registration, API Explorer usage, code customization, asynchronous handling, and deployment with Python Flask.
The article outlines how ChatGPT 4.0 can significantly improve product managers' workflow across research, planning, design, project execution, and iteration by providing prompt engineering techniques, practical examples, and actionable recommendations while emphasizing security and information‑risk considerations.
ChatGPT’s web interface has been increasingly showing “Something went wrong” errors after idle periods, and this guide explains two remedies: using an OpenAI API key for more stable access, and installing the KeepChatGPT Chrome/Tampermonkey extension, which suppresses the error and adds several usability enhancements.
This article explains how ChatGPT works by covering the fundamentals of natural language processing, generative language models, deep learning, the Transformer architecture, attention mechanisms, few‑shot learning, and the reinforcement‑learning techniques that align its outputs with human preferences.
This article compiles a wide range of practical ChatGPT applications—including grammar correction, translation, code explanation, error fixing, creative generation, and more—illustrated with descriptions, code snippets, and images to help users explore the model's versatile capabilities.
The article explains how ChatGPT’s surprising step‑by‑step reasoning, known as Chain of Thought, emerged as a technical breakthrough, links it to model scaling, cognitive System 1/2 theory, and the influence of code data in training large language models.
This article translates Ibrahim John’s book on prompt engineering for ChatGPT, explaining a wide range of prompting techniques—including instruction, role, seed‑word, zero‑shot, few‑shot, and reinforcement‑learning prompts—through clear English descriptions, formulas, and illustrative examples to help readers obtain high‑quality model outputs.
This guide shows front‑end developers how to generate realistic mock data with ChatGPT, export it as JSON, and instantly serve a full RESTful mock API using json‑server, covering schema design, routing, filtering, pagination, sorting, and query operators.
A study by Quebec researchers shows that ChatGPT often produces insecure code across C, C++, Python, and Java, warns that the model rarely flags these issues unless explicitly asked, and highlights ethical inconsistencies in its handling of vulnerable code.
The article traces the evolution from Turing's seminal AI test through the rise of ChatGPT, explains how large language models rely on knowledge graphs built from massive unstructured data, and examines the challenges and advantages of modern graph databases for high‑performance, flexible, and explainable AI applications.
The article traces the evolution from Turing's seminal AI test through ChatGPT's massive adoption, explains how large language models rely on knowledge graphs built via graph databases, and highlights the technical challenges and advantages of high‑performance, flexible, low‑code graph database solutions for real‑time AI applications.
The article explains why ChatGPT’s “Something went wrong” error appears frequently, compares using an OpenAI API key versus installing the KeepChatGPT Chrome extension, and provides a step‑by‑step guide to install Tampermonkey and the extension to permanently stop the error.
This guide walks you through preparing an OpenAI account, obtaining an API key, using the Completion endpoint with example JSON, calling the API from plain JavaScript/HTML, and integrating the same service into a Spring Boot application with Maven dependencies and Java code.
This article explains how to use Server‑Sent Events (SSE) and the EventSource API to stream ChatGPT responses in real time, covering both browser‑side JavaScript examples and Node.js backend proxy code, along with configuration tips for Nginx and compatibility notes.
At a live demo, OpenAI co‑founder Greg Brockman revealed ChatGPT’s upcoming Plugins, Browsing, and Code Interpreter features that let the model generate images, add items to a shopping cart, post to Twitter, fact‑check answers, and automatically analyze large datasets, illustrating a true AutoGPT experience.
