The newly released ProgramBench benchmark forces leading LLMs to rebuild full software projects from only usage docs, revealing a 0% full‑completion rate for Claude Opus, GPT‑5, Gemini and others, and exposing the gap between local code generation and true engineering intelligence.
The article introduces two practical GitHub repositories—how-to-train-your-gpt, a step‑by‑step guide that builds a LLaMA‑style GPT model across 12 chapters, and dictionary-of-ai-coding, a plain‑language glossary of AI‑coding terms—showing how they together provide a complete understanding of modern LLM fundamentals and terminology.
The author reproduces Anthropic’s long‑running harness architecture on a Codex + GPT stack, separates planner, generator, and evaluator roles, persists state to concrete artifacts, adds strict execution constraints, and demonstrates that the approach improves task success despite higher costs, while highlighting practical pitfalls and cost‑control strategies.
The article analyzes Anthropic's harness design for long‑running agent coding, reproduces its three‑role architecture, adapts it to Codex and GPT to cut costs, and presents experimental results that confirm the migrated system remains reliable and self‑healing.
This guide walks you through building, training, and fine‑tuning a Transformer‑based large language model entirely from scratch using PyTorch, covering tokenization, self‑attention, multi‑head attention, positional encoding, model architecture, data preparation, training loops, and fine‑tuning on custom lyrics.
Google’s Gemini 3.1 Pro jumps to a 77.1% ARC‑AGI‑2 score—a 148% gain over its predecessor—offering stronger reasoning, agentic workflows, SVG generation and multimodal support, while the article compares its performance with Claude, GPT and outlines preview‑stage caveats.
This article chronicles the rapid progression of GPT models from the 2018 GPT‑1 pre‑training breakthrough through GPT‑2’s multitask learning, GPT‑3’s scaling laws and few‑shot abilities, to GPT‑4’s multimodal capabilities and the 2024 GPT‑4 Turbo, Sora, and GPT‑4o releases, while also explaining core LLM abilities and the decoder‑only architecture of GPT‑2.
This article provides a comprehensive overview of natural language processing, tracing its evolution from early symbolic and statistical stages through deep learning breakthroughs, detailing sequence models, key NLP tasks, text representation methods, and the development of modern architectures like RNN, LSTM, GRU, Transformer, and GPT series.
This guide walks you through checking the current partition layout, converting an MBR‑styled disk to GPT using fdisk and GNU Parted, recreating the partition, formatting it with ext4, mounting it, and finally expanding the filesystem to utilize the full cloud‑disk capacity, all while preserving existing data.
This tutorial walks through the fundamentals of ChatGPT by explaining language modeling, character‑level tokenization, data preprocessing pipelines, the evolution from simple bigram models to scaled dot‑product self‑attention, multi‑head mechanisms, full Transformer blocks, and how to train and generate Shakespeare‑style text with a GPT model.
This guide explains the purpose and advantages of the Parted tool, compares MBR and GPT partition tables, demonstrates interactive and scripted usage with detailed command examples for creating, deleting, formatting, and mounting partitions on large disks, and provides a complete end‑to‑end workflow.
Learn how to create a functional AI agent from scratch in just five days by leveraging GPT for the brain, n8n for automation, CrewAI for multi‑agent orchestration, Cursor for code generation, and Streamlit for a user interface, complete with step‑by‑step instructions and ready‑to‑run Python code.
This article chronicles the rapid development of large language models from the 2017 Transformer breakthrough through the rise of BERT, GPT‑3, ChatGPT, multimodal systems like GPT‑4V/o, and the recent cost‑efficient DeepSeek‑R1, highlighting key architectural innovations, scaling trends, alignment techniques, and their transformative impact on AI research and industry.
This guide explains Linux storage concepts, covering mechanical and solid‑state disks, hot‑plug options, interfaces, and partitioning methods (MBR and GPT), and provides step‑by‑step commands for creating, formatting, and mounting partitions with examples of fstab configuration.
This guide explains the purpose and features of the Linux 'parted' tool, compares MBR and GPT partition tables, demonstrates interactive and scripted commands for creating, listing, and deleting partitions, and shows how to format and mount them, providing practical examples for managing large disks.
From the perceptron’s inception in 1958 to modern Transformer-based models like GPT, this article traces the evolution of deep learning, explaining foundational architectures such as DNNs, CNNs, RNNs, LSTMs, attention mechanisms, and recent innovations like DeepSeek’s MLA, highlighting their principles and impact.
This article traces the evolution of artificial intelligence from the 1943 McCulloch‑Pitts neuron model through key milestones such as Turing's test, the Dartmouth conference, the rise of neural networks, deep learning breakthroughs, and recent large language models like GPT‑4, illustrating the field's rapid progress.
This guide explains how to classify storage devices, compare MBR and GPT partition tables, use Linux commands such as lsblk, fdisk, and gdisk to create partitions, format them with ext4 or XFS, and permanently mount them via /etc/fstab, providing a complete workflow for disk management.
This survey acts as a comprehensive portal that organizes AIGC research across seven domains—text, image, and audio generation, cross‑modal association, text‑guided image and audio synthesis, and supporting resources—detailing seminal models such as GPT, Diffusion, CLIP, DALL·E, Stable Diffusion, MusicLM, and key papers that shaped each field.
This article explains what artificial intelligence (AI) is, how it differs from traditional programming, outlines its main categories, introduces machine learning, deep learning, neural network models such as CNN, RNN, and Transformer, describes large models and GPT, and discusses AI’s wide‑range applications and societal implications.
During a phone screening I noticed a candidate’s delayed yet suddenly polished answers, especially a textbook‑style SQL query, which matched GPT output exactly, leading me to suspect and confirm the use of AI in the interview.
The article analyzes the rapid evolution of AI large models—from their role in advancing AGI through massive pre‑training and fine‑tuning, to current market dynamics led by GPT and domestic Chinese players, and finally to future multimodal applications, content‑factory capabilities, and emerging AIGC revenue models projected to reach trillion‑yuan scales by 2030.
This tutorial shows Java engineers how to build a JUnit Rule that captures test failures, extracts the exception stack and source file, and automatically sends the information to OpenAI's GPT for analysis and code‑fix suggestions, complete with reusable data‑model classes and utility methods.
This tutorial teaches Java engineers how to build a JUnit4 Rule that captures test failures, extracts stack traces and source files, sends them to GPT for automated analysis, and logs the AI‑generated code improvement suggestions, complete with full example code and usage.
This article examines how large language models such as GPT and Transformer‑based architectures are reshaping the automotive sector by enhancing in‑vehicle intelligence, streamlining product development, improving customer service, and redefining data analyst roles, while also presenting practical experiments, deployment challenges, and future directions.
Andrej Karpathy released LLM.C, a pure C/CUDA implementation that trains GPT‑2‑style models in about 1,000 lines of code, detailing manual forward/backward passes, memory allocation tricks, SIMD CPU acceleration, CUDA porting, and migration tutorials, while comparing it to PyTorch and discussing broader LLM OS implications.
This article explains how to construct effective SQL prompts by combining personal SQL expertise with large language model capabilities, introduces the open‑source sql‑translator tool for bidirectional SQL‑natural language conversion, and discusses practical productization scenarios.
This article reviews the historical development of artificial intelligence, explains how AI technologies such as large language models are reshaping data processing and analysis, and discusses the practical challenges, trust issues, and governance requirements when applying AI to the data industry.
This article reviews the historical development of artificial intelligence, examines how AI—especially large language models like GPT—can transform data analysis and governance, and outlines the practical challenges, reliability concerns, and future opportunities of integrating AI into the data industry.
This guide walks you through using GPT to craft detailed prompts, refining them on a prompt‑tool platform, and training a custom Midjourney style model via Discord commands, covering data selection, parameter settings, and best‑practice tips.
This step‑by‑step tutorial shows how to build a custom nutrition‑analysis GPT—named “Nutri Buddy”—by logging into ChatGPT, creating a GPT, uploading knowledge files, configuring an Action plugin that calls the Boohee food API for calorie, protein and carbohydrate data, and then publishing the assistant with private, link‑only, or public visibility.
This article provides a comprehensive overview of natural language processing and language‑model research, tracing the historical development from early rule‑based and statistical N‑gram models through neural network approaches such as RNN, LSTM, ELMo, and Transformer, and detailing the architectures, strengths, and limitations of the GPT series up to GPT‑4, while also discussing evaluation metrics, practical applications, and future challenges.
This article surveys the development of natural language processing from early rule‑based systems through statistical n‑gram models, neural language models, RNNs, LSTMs, ELMo, Transformers and BERT, and then details the architecture, training methods, advantages and limitations of the GPT‑1, GPT‑2, GPT‑3, ChatGPT and GPT‑4 models, concluding with a discussion of future challenges and references.
This article demonstrates how to use GPT to accelerate code development, automate script creation, and integrate AI-driven suggestions into real‑world business scenarios such as e‑commerce promotion, providing practical tips, example prompts, and ready‑to‑run code snippets.
The guide explains GPT’s core capabilities—natural language mastery, domain reasoning, and code generation—while detailing its limits such as prompt sensitivity, token caps, and lack of memory, then offers engineering workarounds like systematic prompting, chain‑of‑thought, external memory, tool integration, safety checks, and a six‑layer architecture for building robust commercial AI applications.
This article details Huolala's one‑stop monitoring platform called Monitor, covering its multi‑cloud architecture, data collection pipelines, real‑time business monitoring, unified alarm handling, and future AI‑driven enhancements, while sharing concrete metrics, incident case studies, and practical implementation steps for large‑scale observability.
This article traces the evolution of AI‑generated content (AIGC) from early RNN‑based Seq2Seq models through the transformative impact of the Transformer architecture, covering key milestones such as UniLM, T5, BART, the GPT series, InstructGPT, and the emergence of ChatGPT.
This article reviews the evolution of the GPT family from GPT‑1 to GPT‑3, comparing encoder‑decoder architectures, explaining the shift from supervised fine‑tuning to zero‑shot and few‑shot learning, and highlighting the architectural and training innovations that enabled large‑scale language models.
This article explains GPT's two‑stage learning—unsupervised generative pre‑training on large raw corpora followed by discriminative fine‑tuning on labeled tasks—detailing the underlying Transformer decoder architecture, loss functions, and task‑specific input transformations.
This article demonstrates how GPT can be leveraged in real‑world development to generate Flutter UI code, write automation scripts, and integrate AI‑driven search and recommendation features into e‑commerce platforms, offering practical tips, example code, and best‑practice guidelines.
The article examines the rapid rise of GPT models, their technical capabilities and limitations, and how their integration is reshaping software interaction from command‑line to GUI‑plus‑Language UI, offering frontend engineers new opportunities, practical examples, and guidance on leveraging large‑model AI in product design.
PromptAppGPT is a low‑code, prompt‑driven framework that leverages GPT‑3/4 and DALL‑E to enable fast natural‑language‑driven app creation, automatic UI generation, multi‑task execution, plugin extensibility, and integrated code editing, compilation, and execution capabilities.
This article provides a detailed technical review of the evolution of GPT models, the Transformer architecture, large language model training methods, emergent abilities such as in‑context learning and chain‑of‑thought, multimodal extensions, and the challenges of data, scaling, and alignment, offering a holistic view for researchers and practitioners.
The article explains prompt‑engineering fundamentals—definitions, instruction, context, and output formatting—and showcases tricks like few‑shot, chain‑of‑thought, and ReAct, then demonstrates testing with OpenAI APIs, token management, LangChain integration, and low‑code applications such as AI‑generated SQL, API gateways, DSL‑driven UI, chart creation, and vector‑based semantic search.
This article examines the debate over whether large language models truly possess intelligence, arguing that next‑token prediction functions as a form of lossless data compression whose efficiency reflects intelligence, and it surveys research on knowledge extraction, neuron semantics, circuit competition, scaling effects, and the broader philosophical implications of GPT as a mirror of the world’s parameters.
Using eight years of Chinese recruitment data and GPT‑based labeling, the study quantifies how likely various occupations are to be automated, revealing that high‑growth, knowledge‑intensive roles such as translation, design, and programming face the greatest replacement risk while blue‑collar manufacturing jobs are least vulnerable.
Since its 2015 founding, OpenAI has built a suite of generative AI models—including GPT, DALL‑E, and Whisper—exposed via simple REST APIs, enabling developers to integrate advanced language, vision, and speech capabilities without deep ML expertise, while offering fine‑tuning, SDKs, and Azure integration.
Using eight years of Chinese recruitment data, the article analyses how large language models like GPT could automate specific tasks across thousands of jobs, presents the most and least vulnerable occupations, and explores the relationship between wage growth, job experience, and AI replaceability.
Using eight years of Chinese recruitment data mapped to O*net, the study breaks down occupations into thousands of tasks, lets GPT score each for automation potential, and reveals the top and bottom jobs by AI replacement risk along with the economic factors that drive these trends.
This article provides a comprehensive overview of the development of large language models, reviewing classic papers from GPT‑1 through GPT‑4, discussing open‑source implementations such as LLaMA, Alpaca, GLM, and ChatGLM, and analyzing training methods, datasets, and future research directions.
This article explains what GPT (Generative Pre‑trained Transformer) is, traces its development from early neural networks to the latest GPT‑4 models, and details the three‑stage training pipeline of unsupervised learning, supervised fine‑tuning, and reinforcement learning with human feedback.
Despite sensational claims that GPT will eliminate programmers, this analysis argues that AI will reshape rather than eradicate the profession, emphasizing the need for developers to adopt full‑stack and low‑code skills, diversify into business and management roles, and adapt to evolving market demands.
The article analyzes how GPT and AI tools will reshape programmers' work by boosting coding efficiency, altering job structures, creating new software demand, and urging developers to adopt full‑stack, low‑code, and business‑oriented skills to stay competitive.
This article explores the concept and evolution of large foundation models, their transformative impact on AI-generated content, the underlying technologies such as transformers, diffusion, and CLIP, and discusses the challenges, emerging abilities, and future prospects of these models across multiple modalities.
This article examines how large language models such as GPT‑1 through GPT‑4 succeed largely due to high‑quality, large‑scale training data, and explains the Data‑centric AI framework—training data development, inference data development, and data maintenance—while discussing prompt engineering, data‑driven improvements, and future trends in AI.
This article chronicles Ilya Sutskever’s two‑decade rise from a young researcher to a leading figure in artificial intelligence, highlighting his early mentorship, breakthroughs in image recognition, language translation, the founding of OpenAI, and the development of GPT and DALL‑E models.
This article explores whether AI constitutes a new technological revolution, examines recent breakthroughs like GPT‑4 and AIGC, predicts emerging AI‑driven industries, discusses potential societal impacts, and reflects on the path toward artificial general intelligence and future AI‑human coexistence.
This article provides a comprehensive overview of the GPT series, explains the differences between prompt learning and instruction learning, details the three‑stage training pipeline of InstructGPT/ChatGPT—including supervised fine‑tuning, reward‑model training, and PPO‑based reinforcement learning—examines their strengths, weaknesses, and future research directions, and discusses the broader impact of these models on AI development.
This article provides a concise technical overview of GPT, explaining how word vectors are constructed, how the Transformer architecture with self‑attention and feed‑forward layers processes these vectors, and how GPT‑2 and GPT‑3 extend the model with decoder‑only and large‑scale designs.
This article demonstrates how to use Reinforcement Learning from Human Feedback (RLHF) with a PPO algorithm and a sentiment‑analysis model to train a language model that generates positive product reviews, covering task definition, data sampling, reward evaluation, model optimization, and experimental results.
This article provides a comprehensive overview of the GPT series and explains how InstructGPT and ChatGPT are built by combining supervised fine‑tuning, reward modeling, and Proximal Policy Optimization, detailing their datasets, training pipeline, performance advantages, limitations, and future research directions.
ChatGPT, a dialogue-focused language model, builds on the GPT family and employs techniques such as Reinforcement Learning from Human Feedback (RLHF), the TAMER framework, and a three-stage training pipeline (supervised fine‑tuning, reward modeling, and PPO reinforcement learning) to achieve advanced conversational capabilities.
This article surveys the evolution of AI‑generated content for intelligent writing, covering its definition, key technologies from RNN Seq2Seq to Transformer‑based models such as UniLM, T5, BART and GPT series, evaluation datasets and metrics, product deployments by Datagrand, and the remaining challenges and future directions.
This article traces OpenAI’s progression from GPT‑1 through GPT‑3, Codex, InstructGPT, and ChatGPT, highlighting how increasing model scale, prompt‑based task integration, and human‑feedback alignment have driven the evolution toward more capable, generalizable language intelligence aimed at achieving artificial general intelligence.
This tutorial explains how to use Linux's fdisk and parted commands to view, create, modify, and delete disk partitions, covering MBR and GPT limitations, essential command syntax, interactive options, and safety precautions for reliable system administration.
This tutorial walks you through Linux disk partitioning using fdisk and parted, covering MBR vs GPT limits, command syntax, interactive operations, example workflows for creating, listing, modifying, and deleting partitions, and essential safety warnings.
This guide explains the differences between MBR and GPT, shows how to list, create, modify, and delete partitions using fdisk for smaller disks and parted for larger than 2 TB, and provides practical command examples and safety tips.
This article introduces essential concepts behind operating system startup—including BIOS, UEFI, MBR, GPT, and the GRUB bootloader—explaining their roles, differences, and how they interact to enable Linux (and other OS) boot processes on modern hardware.
This guide explains how to install gdisk on CentOS, create a GPT partition on a new 10 TB data disk, format it, mount it, and set up automatic mounting at boot using the disk's UUID, with step‑by‑step commands and screenshots.
This article explains how UEFI, the Unified Extensible Firmware Interface, supersedes traditional BIOS by offering faster boot times, enhanced security through separate boot partitions and Secure Boot, graphical interfaces, greater storage capacity, and more flexible boot options for modern computers.
The guide shows how to enlarge storage on a Tencent Cloud Linux VM by adding and expanding CBS cloud disks using three approaches—direct bare‑device formatting, MBR partitioning for disks under 2 TB, and GPT partitioning for larger disks—detailing step‑by‑step commands, online vs offline resizing, and best‑practice recommendations.
This article reviews the evolution of language models from bag‑of‑words to BERT, compares ELMo, GPT, and BERT architectures, and details how NetEase Yanxuan applies pre‑trained models to classification, text matching, sequence labeling, and generative tasks in production.
This article reviews the historical development of natural language processing language models, covering expert rule‑based systems, statistical n‑grams, smoothing techniques, neural network models such as NNLM, RNN, word2vec, GloVe, ELMo, and the transformer‑based breakthroughs of GPT, BERT and GPT‑2, and summarizes their impact on modern NLP tasks.
Natural language processing, a cornerstone of AI, relies on language models to capture linguistic features; this article reviews classic pre‑training models—ELMo, GPT, and BERT—explaining their architectures, training objectives, and how they boost downstream NLP tasks despite data‑scarcity challenges.
This guide explains how to manage large Linux disks by using GPT partition tables, choosing XFS or EXT4 filesystems, performing partitioning with parted, formatting with appropriate mkfs commands, handling e2fsprogs version requirements, and considerations for lazy init and snapshot impacts.
This article explains the physical components of hard disk drives, the concepts of tracks, cylinders and sectors, compares MBR and GPT partition schemes, outlines common file system types, and provides practical command‑line examples using fdisk and parted for creating, modifying, and deleting partitions on Linux.
