This article reviews recent AIGC developments, outlines Soul’s multi‑year experiments with virtual humans, visual generation, large language models, audio synthesis, and product integration, and discusses the strategic balance between using external AI services and building proprietary capabilities.
This article explores how AI-generated content (AIGC) tools like ChatGPT can streamline user research workflows—from drafting study plans and designing questionnaires to basic data analysis and report generation—while highlighting their limitations and best‑practice prompting techniques.
This guide walks you through using Alibaba Cloud's PAI‑DSW and the Free Prompt Editing algorithm to set up a personal AI‑generated content (AIGC) drawing assistant, covering environment setup, instance creation, WebUI parameter tuning, example edits, resource cleanup, and how to share your creations for rewards.
The talk at the 18th Security Identification Technology Expo and Summit outlines three emerging trends for identity verification in the AIGC era: the surge of deep‑fake attacks, the use of generative AI as a new data‑production engine, and the shift toward cross‑device, agent‑based authentication paradigms.
This article explores the rapid rise of ChatGPT, explains how to craft effective prompts using a role‑background‑task‑output formula, demonstrates its applications in writing, style transformation, and various professional scenarios, and introduces a new book and community giveaway related to AI assistants.
The article reviews recent automated quality assessment advances for AIGC image generation, including an aesthetic scoring framework with the APDD dataset and AANSPS network, a human‑preference benchmark (HPD v2 and HPS v2) that outperforms IS/FID, and the Pick‑Score model trained on user‑driven Pick‑a‑Pic data, all enabling faster, unbiased evaluation, cost savings, and more effective model iteration, with ongoing work in home‑improvement AI.
This article explains how AI‑generated content (AIGC) technologies such as text‑to‑image diffusion models, Lora fine‑tuning, and IP‑Adapter style transfer are applied to interior design, dramatically reducing design time, cutting costs, and enabling personalized, high‑quality visualizations for both consumers and furniture merchants.
Learn how to turn simple text prompts into original melodies, lyrics, and album covers for a children’s music collection using Huawei Cloud ModelArts and open‑source MusicGen, with step‑by‑step guidance that requires no coding experience and leverages free cloud resources.
This report examines the rapid rise of AI‑generated content (AIGC) in visual design since early 2023, detailing tool adoption, usage patterns across design roles, practical applications, case studies, knowledge accumulation, and the challenges and future outlook for integrating AI into design workflows.
This tutorial explains how to combine Node.js with OpenAI's AIGC to build a backend service that reads user comments, sends them to a GPT‑3.5‑turbo model for sentiment classification, and outputs whether the sentiment is positive or negative, complete with setup steps and full source code.
This article details how Alibaba's Wanxiang Lab adapted the SDXL diffusion model for large‑scale e‑commerce image generation, introducing the EcomXL series, a weighted‑distillation fine‑tuning method, hierarchical model fusion, specialized ControlNet variants, and the SLAM inference accelerator to achieve high‑quality, controllable product images within three seconds while boosting business metrics.
This article presents OPPO's practical research on deploying multi‑modal pre‑training models across mobile devices and cloud, covering edge image‑text retrieval, text‑image generation and understanding optimizations, and lightweight diffusion model techniques, with detailed algorithmic improvements, performance results, and real‑world application cases.
EcomXL enhances SDXL for large‑scale e‑commerce image generation by leveraging tens of millions of curated images, a two‑stage fine‑tuning with denoising‑weighted distillation and layer‑wise fusion, specialized ControlNets for inpainting and soft‑edge consistency, and the SLAM inference accelerator to achieve sub‑second generation while boosting visual quality and adoption metrics.
This article outlines the JD Insurance UED team's recent explorations and achievements in leveraging AIGC technology to transform creative design workflows, highlighting key insights, visual examples, and recommended readings to inspire broader adoption of AI‑generated content tools across the industry.
This article reviews the definition, key metrics, and major model families of AI‑generated content, details XiaoBu’s recent breakthroughs in image generation, and discusses open research problems such as evaluation gaps, transformer limitations, and the need for richer multimodal intelligence representations.
The article examines how InfiniBand’s specialized features—collective communication, in‑network computing, adaptive routing, congestion control, cut‑through forwarding, shallow buffering, and self‑healing—are optimized for large‑scale AI‑generated content (AIGC) training, delivering higher bandwidth, lower latency, and greater fault tolerance than Ethernet alternatives.
This article presents Baidu's exploration of multimodal content understanding for commercial advertising, detailing the ViCAN pre‑training model, its contrastive and mask‑language learning tasks, integration across recall, ranking and risk‑control pipelines, quantization with MMDict, and future AIGC‑driven generation, all backed by extensive experiments and Q&A.
This article explores how AIGC can streamline the design of high‑impact visual assets for large‑scale events, detailing a real‑world case study of the Guangzhou International Auto Show main key visual, from keyword extraction and mood‑board collection to AI‑assisted concept generation, rendering, and final compositing.
The article presents a 2024 AIGC round‑table where Chinese experts discuss whether to follow a technology‑first or market‑first approach, the challenges of compute, algorithms and data, domestic versus foreign large‑model strategies, multi‑model deployment in enterprises, and criteria for evaluating successful AIGC applications.
The report released at the China AIGC Industry Summit outlines a rapidly expanding AIGC market projected to reach 200 billion RMB in 2024 and a trillion‑scale by 2030, highlighting dominant AI‑native applications, multimodal growth, investment concentration on leading players, and distinct B‑end and C‑end business models.
The inaugural AIGC Innovation Competition, co‑organized by vivo, Nankai University and the China Computer Education Association, invites college students nationwide to showcase generative‑AI applications that transform learning, work and daily life, highlighting AIGC’s shift from AI 1.0 to 2.0 and its cross‑domain potential.
The article analyzes how the surge in AIGC and large language models is reshaping the AI server market, detailing hardware composition, the rise of heterogeneous computing, GPU advantages, demand calculations for models like GPT‑3, and the competitive landscape driving rapid industry growth.
This article details how to create iTaTools, a macOS app and Xcode Source Editor Extension that integrates ChatGPT via AIGC to generate code, UI, and documentation directly in Xcode, complete with implementation steps, Swift code samples, and plugin architecture.
In a series of in‑depth interviews, four senior designers from 58UXD share why they built a design‑system handbook, its value for consistency and innovation, challenges of integrating AIGC, strategies for usability, visual standards, engineering practices, and future plans for continuous iteration.
Since 2023, AI‑generated video research has expanded across six algorithmic categories—text‑to‑video, image‑to‑video, editing, style transfer, human motion, and long‑video generation—highlighting works such as CogVideo, Imagen Video, MagicVideo, ControlVideo, DCTNet, NUWA‑XL and OpenAI’s Sora, while analysis shows short‑clip diffusion models excel, editing remains costly, style transfer is efficient, and truly long, temporally consistent videos remain an open challenge.
This article presents Alibaba's research on using diffusion‑based AIGC techniques, including a training‑free Noise Rectification module and the AtomoVideo model, to automatically convert static product images into high‑quality, detail‑preserving video motions for e‑commerce advertising.
Alibaba’s AI team introduced AtomoVideo, a diffusion‑based image‑to‑video generator enhanced by a training‑free Noise Rectification module that adds and corrects controlled noise to eliminate first‑frame errors, enabling merchants to automatically create high‑fidelity 4‑second 720p product videos with strong temporal consistency for e‑commerce advertising.
The article examines the evolution of AIGC, outlines Baidu’s Comate AI coding assistant, analyzes its underlying technologies and internal adoption, and explores how AI-driven code generation can boost developer efficiency across front‑end, back‑end, and multi‑language projects.
The AI Clothing Graffiti Project in Taobao Life 2 leverages Stable Diffusion, ControlNet, and LoRA to let users generate and stylize clothing designs via text‑image prompts, employing parallel processing, face repair, and content filtering, and has launched successfully, inviting algorithm engineers to join the team.
The article details how JD.com leverages domain‑specific and generic knowledge graphs to enhance multimodal product information, improve controlled text generation, and boost LLM performance for e‑commerce copywriting, covering model architecture, copy‑only mechanisms, token‑type encoding, experimental results, and practical deployment scenarios.
This article presents a comprehensive overview of how knowledge graphs are integrated into e‑commerce AIGC pipelines, covering domain‑specific and generic KG‑driven text generation, model architecture, controllable generation techniques, experimental results, and future directions for large language models in commercial settings.
This collection compiles technical articles that explore diverse AI/ML applications, from deploying large language models on MacBooks and building e‑commerce recommendation engines, to leveraging the LangChain framework, creating AIGC‑driven fashion solutions, and implementing Stable Diffusion for image generation.
The article details Tencent Music's Tianqin Lab research on audio quality AIGC, covering background upgrades, music separation techniques, real‑time super‑resolution, the industry‑first premium master‑track technology, and a Q&A on vocal separation, illustrating practical AI-driven audio enhancements.
This article provides a comprehensive overview of core AI concepts, from basic definitions of AI, AGI, and AIGC to detailed explanations of GPUs, major generative models, leading AI products, and influential companies, helping readers quickly grasp the landscape of AI-generated content.
Databricks has accelerated its generative AI ambitions by acquiring AI startup Einblick, adding to a series of strategic purchases that expand its data governance, model training, and data ingestion capabilities, while launching a new Data Intelligence Platform that merges Lakehouse technology with AI-driven analytics.
The article surveys recent advances in reference‑object‑guided AI image generation, detailing diffusion‑based models such as Dreambooth and Blip‑diffusion, evaluating their trade‑offs, and demonstrating how combining these techniques with 3D reconstruction can realistically insert catalog furniture into users’ rooms, despite viewpoint and depth challenges.
In 2023 JD Retail’s technology team delivered a suite of AI‑powered innovations—including end‑to‑end inventory management, explainable AI for supply chain, privacy‑preserving advertising models, a ReAct‑SFT‑RAG large language model framework, edge AI inference, secure data‑safe‑house infrastructure, and high‑quality 3D modeling pipelines—demonstrating broad academic and industrial impact across multiple domains.
This article presents a comprehensive overview of how knowledge graphs are leveraged in e‑commerce AIGC pipelines, detailing domain‑specific and general graph‑based text generation, model architecture, controllable generation techniques, experimental results, and future directions for large language model integration.
This article presents Baidu's commercial multimodal understanding and AIGC innovations, detailing rich‑media multimodal perception, a unified large‑scale representation framework, scenario‑specific fine‑tuning, and practical applications such as marketing copy, digital‑human video, and poster generation.
This article explores how large language models like ChatGPT can be leveraged to transform natural‑language requirements into UML diagrams using PlantUML, demonstrating the workflow, prompt engineering, iterative training, and the resulting efficiency gains for developers.
This article provides an extensive overview of generative AI concepts such as AIGC and AGI, evaluates various coding assistants and chat models, offers prompt engineering tips, and lists numerous free and paid AI tools and learning resources for developers and everyday users.
The article examines the security challenges introduced by large‑model AIGC, outlines three technical upgrade paths—richer training data, few‑shot model fine‑tuning, and multimodal fusion—and demonstrates practical implementations that dramatically improve detection efficiency, accuracy, and scalability.
AIGC, or AI‑generated content, spans text, image, audio and video creation, evolving from early experiments to rapid growth marked by breakthroughs like ChatGPT and GPT‑3/4, with large models providing the technical foundation, while addressing technical hurdles, ethical concerns, and future application trends worldwide.
In September 2023 Baidu's Mobile Ecosystem User Experience team partnered with Beijing University of Science and Technology for an eight‑week "AIGC and Application Design" course, guiding over a hundred industrial‑design students through AI‑enhanced prototype projects that culminated in dozens of practical, award‑winning solutions.
This tutorial introduces AIGC concepts and walks through practical implementations of tokenization, part‑of‑speech tagging, and named entity recognition using the Transformers library, NLTK, and spaCy on Google Colab, complete with code snippets and visual results.
The article examines the cost, performance, and efficiency challenges of large‑model inference, explains why Kubernetes is becoming the standard platform for AI workloads, and details how the Fluid project provides cloud‑native caching, elastic scaling, and automation to dramatically reduce startup latency and operating expenses.
This article explores the concept of the metaverse and virtual humans, detailing 3D modeling techniques, NLP-driven language understanding, streaming TTS, VR/AR interaction, AIGC content generation, and the deployment of a large‑model intelligent Q&A system with real‑time facial expression synthesis for virtual anchors.
Since July, the author has been developing AIGC projects and observed dramatic image quality improvements driven by rapid advances in large models, open‑source plugins, and deeper understanding of generation pipelines and parameters, prompting a comprehensive summary of the Cyber project’s solutions, industry landscape, and front‑end responsibilities.
The AIGC Poster Generation Project employs Stable Diffusion enhanced with VAE, ControlNet, LoRA and other extensions to create product posters in four visual styles, exploring outpainting, inpainting, reference‑based diffusion and DreamBooth prototypes, and optimizes detail preservation, super‑resolution text, and masking to achieve over 90% detail fidelity, 95% success rate, and 3–5 second inference per image.
This article examines how AI tools are reshaping creative design teams by boosting efficiency, outlines feasible application areas through quadrant analysis, and presents four detailed case studies that compare traditional and AI‑augmented workflows, highlighting productivity gains and practical considerations.
The 2023 AIGC Year-End Summit highlighted the integration of AI in the insurance industry, with Alibaba's CTO announcing the open-source release of their AI code assistant tool DevPilot to foster industry collaboration.
This article details a step‑by‑step case study of creating, illustrating, bilingualizing, and publishing a children’s picture book using ChatGPT, Midjourney, Photoshop, New Bing, and Kindle Create, highlighting workflow, prompt engineering, and practical publishing challenges.
This article explores how generative AI (AIGC) can be used to synthesize structured data, how synthetic data supports causal inference, and how agent‑based modeling and the YLearn framework together enable advanced causal discovery, effect estimation, and scenario simulation for enterprise AI applications.
Chinese AI talent is in fierce demand, with new AI positions offering average monthly salaries above 46,000 RMB, top roles like ChatGPT researchers reaching 66,000 RMB, and sectors from internet to autonomous driving competing for scarce experts amid a rapidly expanding AIGC market.
Taobao’s Double 11 showcased AI assistants that deliver personalized recommendations, virtual try‑ons, and product comparisons while empowering merchants with AI‑generated content and large‑language‑model tools, illustrating how retrieval‑augmented generation and LoRA address scaling challenges and model hallucinations to create a seamless, data‑driven shopping experience.
The article details Taobao's AI buyer show “淘淘秀,” describing its AIGC‑driven design, technical pipeline—including image generation, avatar synthesis, background replacement—and compares models such as Midjourney, Stable Diffusion, and Roop, while outlining usage flow, challenges, solutions, and future expansion plans.
Fin2.0, NetEase Cloud Music’s AI‑driven design assistant, combines text‑to‑image, text‑to‑icon and text‑to‑copy generation with an internal Stable Diffusion engine and streamlined prompt templates, enabling non‑designers like a colleague to create high‑quality promotional banners in hours while avoiding external costs and data‑security risks.
In this interview, Baidu's search architect Tianbao explains the evolution of text‑to‑image generation since 2022, discusses data preparation, model quality, prompt engineering, multi‑style support, evaluation methods, and predicts when fully AI‑generated video and movies might become mainstream.
In a recent InfoQ Geek Talk, Baidu Search chief architect Tianbao discussed the rapid evolution of AI text‑to‑image technology—highlighting Chinese‑language data preparation, prompt‑engineering challenges, evaluation methods combining human feedback and metrics, and future video‑generation prospects—while announcing openings for visual algorithm engineers.
This article explores powerful AIGC generation models and large language models like ChatGPT, detailing how diffusion models can be applied to robotic planning, introducing AdaptDiffuser, self‑evolving data generation, and embodied AI challenges, while summarizing recent research and practical implementations.
DataFunCon2023 Shenzhen showcases a comprehensive program featuring expert talks on building Data+LLM applications, large-scale storage, cloud‑native architectures, metric systems, data governance, AB testing, and industry‑specific large language model use cases across finance, gaming, advertising, and more, providing valuable insights for practitioners and researchers alike.
This article presents OPPO Andes Smart Cloud's intelligent growth algorithm framework for the smartphone sector, detailing industry background, data and model architecture, four real-world application cases—including AIGC content generation, multimodal recommendation, causal inference, and precise advertising—and summarizing key insights from a technical Q&A session.
This article analyzes how carefully crafted prompts influence the architecture of complex AIGC applications, comparing tools like GitHub Copilot, JetBrains AI Assistant, and Bloop, and presents practical strategies and design patterns for building robust, context‑aware AI‑driven development environments.
This article examines how a team leveraged AI image‑generation tools like Midjourney and Stable Diffusion to streamline the creation of visual assets for city consumption coupons, addressing high‑frequency campaigns, inconsistent styles, and tight deadlines while improving efficiency and user experience.
The article presents DiffUTE, an end‑to‑end self‑supervised multilingual text‑editing diffusion model that leverages fine‑grained position and glyph guidance together with large language model control to achieve high‑quality, high‑fidelity text modifications in images, and demonstrates its effectiveness through extensive experiments and real‑world deployments at Ant Group.
FlashAttention‑2 is an IO‑aware exact attention algorithm that cuts GPU HBM traffic through tiling and recomputation, optimizes non‑matmul FLOPs, expands sequence‑parallelism and warp‑level work distribution, delivering up to 2× speedup over FlashAttention, near‑GEMM efficiency, and enabling longer‑context Transformer training and inference for AIGC with fastunet and negligible accuracy loss.
This speech explores the emergence of AIGC and AGI in design, discussing prompt engineering, affordance, AI‑native design, storytelling, ethical challenges, and future directions for leveraging generative AI to boost creativity and productivity.
Senior VP Shang Chun shares recent global employment trends, the impact of AIGC tools, evolving front‑end hiring requirements, and detailed Tubi interview design—including multiple coding rounds, culture assessment, and practical project tasks—to help engineers navigate the current job market and identify strong opportunities.
This article reviews the development of AIGC and generative AI technologies—including image, text, and molecular generation—explains key model advances such as diffusion and large language models, discusses their impact on drug discovery, and outlines current challenges, opportunities, and future directions.
This article presents Baidu's commercial multimodal understanding framework and AIGC innovations, detailing rich-media multimodal perception, the VICAN‑12B multimodal representation‑generation model, scenario‑specific fine‑tuning, feature quantization for ranking, and practical applications such as marketing content generation, digital‑human video creation, and poster synthesis.
This article explores how generative AI (AIGC) can be used to synthesize structured data, how synthetic data and agent‑based modeling support causal inference, and introduces the YLearn framework for end‑to‑end causal learning, highlighting practical use cases and research directions.
This article explores the rise of AIGC-driven 3D model creation for e‑commerce, reviewing foundational technologies like NeRF, Point‑E, Shap‑E, DreamFusion and Magic3D, detailing practical experiments, hardware requirements, current challenges, and future industry implications.
This article explores how generative AI (AIGC) can be used to synthesize structured data, how such synthetic data enhances causal inference tasks, and how agent‑based modeling and the YLearn framework together enable a two‑way synergy between AIGC and causal learning for enterprise AI solutions.
This article explores how AI-powered tools such as Midjourney and Photoshop enable designers to rapidly generate high‑quality 3D assets, streamline visual workflows, meet urgent client demands, and expand creative possibilities while emphasizing the need for solid aesthetic fundamentals.
The article reviews Baidu's 2023 generative AI initiatives, details the inaugural Baidu Commercial AI Technology Innovation Competition co‑hosted with the China AI Society and NVIDIA, highlights winning teams' technical approaches in conversion prediction and inference optimization, and shares insights from industry leaders on future AI talent and innovation.
This article explains how designers can shorten 3D design cycles by using AIGC tools to train LoRA adapters for Stable Diffusion, covering the theory, required plugins, data preparation, labeling rules, training steps, result tweaking, and practical applications.
This article summarizes NetEase Cloud's CTO Xu Hangsheng's presentation on AGI and AIGC, detailing how generative AI enhances each phase of the software development lifecycle—from requirement analysis and design to coding, testing, deployment, and support—while offering practical tips, Copilot‑style tools, and real‑world case studies.
The article details how Baidu's Fengchao team leverages large language models, including a transition from DNN embeddings to ERNIE, introduces multi‑level tokenization and discrete core‑word inputs, and applies prompt learning and AIGC techniques to improve search advertising satisfaction and industry‑specific relevance modeling.
The article examines Baidu’s inaugural Commercial AI Innovation Competition, highlighting its focus on AIGC commercial applications such as conversion behavior prediction and inference performance optimization, and explores how large‑scale models like PaddleBox and AI‑native tools are poised to transform content creation, marketing, and enterprise operations.
The 4th China AI Competition results conference highlighted how generative AI (AIGC) is being integrated into Ant Group's financial services to automate workflows, enhance marketing creativity, and improve claim processing, promising exponential efficiency gains, new customer experiences, and a transformed industry ecosystem.
The article shows that in the AIGC era a programmer who leverages AI tools like Copilot and GPT‑4 can deliver projects far faster and more cost‑effectively than a veteran relying on manual coding, urging developers to upskill, adopt AI‑assisted workflows, and focus on higher‑level design and ethical considerations.
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 shares recent AIGC practices in e‑commerce, detailing product copy generation using GPT‑based models, image creation with Stable Diffusion, the evolution of large language models, technical solutions, experimental results, and future opportunities for AI‑driven automation in online retail.
Baidu’s chief architect Li Shuanglong outlined how AIGC, driven by advanced large‑language and multimodal models, is already powering commercial tools such as automated copywriting, 2D digital‑human video creation and lead‑generation chatbots, while emphasizing future progress in engineering scalability, algorithmic fidelity, data quality, and scenario‑focused applications.
The interview with ZhongAn Insurance CTO Jiang Jiyun discusses how the company leverages AIGC technologies such as large language models, embeddings, and prompt engineering to enhance marketing, intelligent customer service, and data security, while highlighting practical challenges and best practices for AI adoption in the insurance sector.
This article explores how generative AI (AIGC) is reshaping front‑end engineering by shifting paradigms from imperative to declarative, then to an AIGC‑driven workflow, and offers practical examples, tool comparisons, and strategic advice for the next‑generation front‑end developer.
The 2023 Trusted AI White Paper, released at the World AI Conference in Shanghai, outlines the rapid advances and challenges of trustworthy artificial intelligence, presents a structured evaluation framework, and details five chapters covering AI development stages, industry practices, large‑model and AIGC security, assessment tools, and future recommendations.
This article presents a comprehensive multi-modal video understanding system for AIGC video generation, detailing technical architecture, GCN-based semi-supervised learning, and practical applications across automotive content scenarios.
This article analyzes how AI‑generated content (AIGC) is merging with low‑code, software development, and various innovation processes, presenting data on cost reductions, efficiency gains, a classification of innovation types, TRIZ methodology steps, and a forward‑looking view of AI‑driven production‑relation transformations.
Databricks' $1.3 billion acquisition of MosaicML brings the startup's open‑source MPT models and high‑efficiency training stack into the Lakehouse platform, reflecting a strategic push to embed generative AI across enterprises while emphasizing data control, cost reduction, and open‑source policies.
This article presents OPPO's Andes Smart Cloud team's intelligent growth algorithm architecture, covering industry background, data pipelines, model designs such as uplift, PU‑learning, multimodal AIGC, and their practical applications in content supply, recommendation, precise audience targeting, and ad bidding, followed by a summary and Q&A.
The A2M Internet Architecture and AI Summit in Shanghai showcased 24 topics and 84 talks, highlighting AIGC applications across enterprise collaboration, large‑scale model integration, 3D visual content, music entertainment, and the broader impact of LLMs on software development and digital transformation.
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 how AI‑generated content (AIGC) reshapes document search by combining traditional indexing with modern embedding and prompt‑tuning techniques, reviews key components such as LangChain and Supabase, compares existing AI‑search products, and discusses the future blend of classic and AI‑driven search.
DataFun will host the DataFun Summit 2023 on June 17‑18, featuring three chairs and eight presenters who will discuss core topics such as large language model research, multimodal generation, reinforcement learning, tool learning, distributed training, and industry applications, with free registration via QR code.
The DataFun Summit 2023 brings together leading experts to discuss large models and AIGC, featuring a keynote on voice cloning technology for AI sales assistants that covers multi‑speaker, multi‑accent cloning, naturalness optimization, and deployment strategies.
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.
The article explains how AIGC models demand massive GPU compute, why network bandwidth and latency become the critical limiting factors, and how the Distributed Disaggregated Chassis (DDC) architecture addresses these challenges with scalable, high‑throughput networking solutions.
This article explores the rapid rise of AI painting tools such as Midjourney and Stable Diffusion, compares their features, outlines new design workflows, and presents four detailed case studies—from 2D‑to‑3D character creation to poster and H5 header generation—showing how designers can boost productivity and creativity.
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.
