In a candid hour‑long interview, SemiAnalysis founder Dylan Patel predicts OpenAI will be the first to achieve artificial superintelligence (ASI), while dissecting GPT‑4.5’s failure, Meta’s costly AI missteps, Apple’s strategic lag, and the shifting partnership between OpenAI and Microsoft.
On June 16, the China Academy of Information and Communications Technology released the first edition of the Intelligent Agent Industry Map, highlighting over 200 companies, detailing foundational, generic, and specialized layers, and showcasing 360’s Nano AI super‑search agent and MCP toolbox as key examples of the emerging AI‑agent ecosystem.
This article demystifies the Transformer architecture for beginners, explaining its relationship to large models, the self‑attention and multi‑head attention mechanisms, positional encoding, and the roles of Encoder and Decoder components, using clear analogies and visual diagrams to aid comprehension.
The article examines whether to pursue a computer science degree amid rapid AI advances, presenting data on unemployment rates, predicting that AI will generate most code, and recommending top students consider research while ordinary students weigh interdisciplinary alternatives unless driven by genuine passion.
Elon Musk, speaking at Y Combinator's AI Startup School, warned that digital superintelligence could arrive as early as this year or next, comparing current government work to cleaning a beach versus an impending AI tsunami, and outlined his bold predictions for AI‑driven economies, robotics, and hardware challenges.
The article argues that the AI‑driven Software Engineering 3.0 era has quietly begun, detailing how large‑model agents now understand requirements and business logic, accelerating productivity and reshaping development practices far earlier than the anticipated 2030 milestone.
The article explains how current software remains fragmented and clunky, introduces AI-driven agents that can understand natural language and personalize responses, defines agents in computer science, outlines a step‑by‑step workflow for building agent applications on a platform, and describes the core perception‑decision‑action‑learning framework that powers them.
Meta CEO Mark Zuckerberg is assembling a Superintelligence team and investing over $10 billion in Scale AI, aiming for AGI while deepening government ties, delaying flagship models, and pledging massive future funding amid intensifying industry competition.
This article examines DeepSeek’s various versions, detailing their core modules, underlying principles, architectural diagrams, and performance metrics, offering practical guidance for enthusiasts, professionals, and practitioners while inspiring further exploration of artificial intelligence innovations.
Apple’s WWDC revealed delayed Siri updates, a new Xcode 26 with built‑in ChatGPT, and a Foundation Models framework, but investors were disappointed, causing a $750 billion market‑cap drop and sparking criticism that Apple’s AI strategy lags behind competitors.
This article explores how Remote Direct Memory Access (RDMA) technology transforms high‑performance computing, artificial intelligence, and cloud storage by eliminating data copies, bypassing the kernel, and offloading protocols to hardware, while reviewing key metrics, product ecosystems, real‑world use cases, challenges, and future trends.
The article explains how rapid AI advances and the AIAgent architecture are reshaping software development, outlines four key technical drivers—embedding, Transformer scaling laws, scenario Moore's law, and LLM OS—and discusses the security, professionalism, and responsibility challenges enterprises face when deploying AI‑native applications.
This article explains how DeepSeek's large‑model‑driven search system overcomes traditional keyword‑matching limits, improves long‑tail query coverage, and delivers personalized, accurate government service results through intent parsing, knowledge‑graph retrieval, and generative optimization.
This comprehensive article examines Retrieval‑Augmented Generation (RAG), covering its historical evolution, core theory, implementation variants, practical code examples, diverse applications, current controversies, and future research directions within the AI and NLP landscape.
The article provides an in‑depth technical overview of Remote Direct Memory Access (RDMA), covering its zero‑copy, kernel‑bypass, and protocol‑offload features, hardware and software ecosystems, and its impact on high‑performance computing, artificial intelligence, cloud storage, finance, and edge computing.
This article traces the history of graphics GPUs from their 1980s origins to modern AI and high‑performance computing roles, examines China's emerging GPU market and its challenges, and reviews the key graphics and compute standards shaping the industry today.
Agentic AI, the emerging autonomous AI technology, differs from traditional AI agents by offering self‑directed decision‑making, adaptability, and goal‑orientation, promising to automate recruitment, performance evaluation, and employee experience, while 2025 is poised to become the year of AI agents in HR, demanding responsible implementation.
This roundup highlights ten recent AI developments, from Apple's Matrix3D model that creates 3D scenes from photos, to Qwen's Deep Research assistant, Tencent's CodeBuddy 3.0, ByteDance's Seed1.5‑VL, Step Star's open‑source Step1X‑3D, Google's iOS icon refresh, Apple's eye‑tracking scrolling test, Chrome's upcoming Gemini AI assistant, Shanghai's AI Identity Ecosystem Alliance, and Kuaishou's Keling AI 2.0 topping the global video‑generation leaderboard.
Marking its 30th birthday, Java remains the leading language for enterprise software, with a vast talent pool, continuous performance innovations from Azul, and growing relevance in cloud‑native and AI applications, proving that reliability and ecosystem strength often outweigh fleeting technological hype.
The article announces the Spring AI 1.0 release, provides the Maven BOM dependency, compares Spring AI with LangChain4j for Java AI development, and outlines upcoming integration, performance, and observability improvements for both frameworks amid rapid large‑model advancements.
In his OceanBase developer conference keynote, Ant Group CTO He Zhengyu analyzes how the explosion of data fuels AGI progress, outlines four key data challenges—cost, scarcity, multimodality, and quality assessment—and argues that overcoming them will turn data companies into AI leaders.
This article explores Zheng Naning's lecture on machine behavior, embodied intelligence, and their challenges, outlining AI development stages, the need for explainable and cooperative machine actions, and the specific hurdles and frameworks for achieving safe, adaptive autonomous driving in dynamic environments.
Microsoft announced at Build 2025 that the GitHub Copilot Extension for VSCode will be released as open‑source under the MIT license, detailing the integration of AI agent capabilities into VSCode, the motivations behind the move, and the upcoming roadmap for community‑driven development.
At the 2025 Taipei International Computer Expo, Nvidia CEO Jensen Huang outlined the company's shift from a chipmaker to an AI infrastructure leader, introduced the concept of physical AI, and detailed upcoming hardware, software, and strategic initiatives that could reshape data centers, robotics, and autonomous driving.
The article explores Google’s A2A protocol and the MCP framework, comparing their roles as complementary gears that enable AI agents to collaborate, extend capabilities, and interact with external resources, illustrated with a smart‑customer‑service scenario and future outlooks.
This article analyzes the concept, core functions, industry chain, infrastructure components, and rapid market growth of AI Data Centers, highlighting their distinction from traditional data centers, regional concentration in eastern China, and projected investment and compute capacity through 2028.
This presentation explores how combining knowledge graphs with DeepSeek large‑model agents can revolutionize enterprise knowledge management, detailing DeepSeek’s technical strengths, the graph‑model complementarity paradigm, various knowledge types, practical frameworks, case studies, and future outlooks for AI‑enhanced intelligent systems.
The article introduces JianYi, a self‑developed image segmentation and matting system for Taobao's home‑decoration business that supports product, human, and panoramic segmentation with multi‑modal interaction, achieving high‑precision real‑time performance and powering AI tools such as "Jiazuo" and "Fang Wo Jia".
The May 2024 TIOBE ranking shows Python soaring to a record 25.35% share, widening the gap over C++ and cementing its dominance in AI, data science, and automation, while highlighting the continued relevance of languages like Java, C++, and R for various development needs.
In a May 14 interview, OpenAI chief scientist Jakub Pachocki discusses how reasoning models will evolve over the next five years, the growing role of reinforcement learning, whether AI truly “thinks,” upcoming open‑source model releases, and his shifting timeline for achieving artificial general intelligence.
This article surveys the rapid emergence of AI agents, outlining their projected 2025 breakthrough, market momentum, key frameworks such as Manus and MCP, the four core abilities of perception, planning, tool use, and memory, and the evolving landscape of multimodal and autonomous AI systems.
The U.S. Treasury is examining Benchmark Capital’s $75 million investment in Chinese AI startup Manus AI to determine if it falls under new regulations governing foreign investments in advanced technologies, amid growing tech competition and scrutiny of U.S.–China AI ties.
Trump’s administration plans to overturn the Biden-era AI chip export curbs, proposing a streamlined global licensing system that critics say could boost US innovation but also raise security concerns, while market reactions see Nvidia and AMD shares rise and Chinese AI chip development accelerates.
OpenAI announced that Fidji Simo, former Instacart CEO and longtime Meta executive, will join as Application CEO reporting directly to Sam Altman, while Altman remains CEO focusing on research, compute, and safety, signaling a strategic shift toward product scaling and AGI development.
The article reviews Anthropic’s controversial report that links lobsters, pregnant women, and banned chips to illustrate absurd claims about China’s AI capabilities, arguing that US export restrictions on high‑performance GPUs are essential to maintain America’s lead in artificial intelligence.
This article introduces Spring AI's multimodal capabilities, explains the Message API for handling text, image, audio, and video inputs, and provides step‑by‑step Spring Boot examples for image analysis, ID card extraction, and structured JSON output of car‑color counts.
The article reviews NVIDIA's GPU architecture progression—from Volta's pioneering Tensor Cores through Turing, Ampere, Hopper, and the latest Blackwell and Rubin designs—highlighting key innovations, performance gains for deep learning, and related resource updates for AI practitioners.
This article analyzes whether reinforcement learning enhances large language model reasoning, compares findings from DeepSeek-Math, a Tsinghua‑Shanghai Jiao‑Tong paper, and Qwen3, and outlines practical training pipelines—including Seed‑Thinking‑v1.5, DeepSeek‑R1, Kimi‑K1.5, and Qwen3—that aim to endow LLMs with robust reasoning capabilities.
The article critiques AI giants’ claims of nearing human-level intelligence, highlighting research that shows current models rely on massive rule memorization rather than genuine reasoning, leading to brittleness in navigation, mathematics, and adaptability, and emphasizing the need to understand these limitations for future progress.
OpenAI announced new shopping capabilities for ChatGPT, improving product recommendation, visual presentation, and direct purchase links, while Alibaba released the Qwen3 series of large and MoE language models with detailed parameter counts and benchmark performance, highlighting rapid advancements in consumer‑focused AI applications.
This weekly technology digest highlights significant advancements in artificial intelligence, including OpenAI's GPT-4o upgrades, Tencent's Hunyuan 3D v2.5 release, and major funding rounds for xAI and Manus, alongside expert discussions on the future evolution of AI agent networks and the theoretical possibility of machine consciousness.
This article examines how Retrieval‑Augmented Generation (RAG), AI agents, and multimodal large‑model techniques are reshaping AI‑industry integration, discusses their technical challenges and practical implementations, and outlines future development directions across algorithms, products, and domain‑specific applications.
The DeepSeek‑R1T‑Chimera model merges DeepSeek‑R1 reasoning with V3‑0324 architecture, reusing most V3 weights and swapping only the blue‑highlighted R1 routing experts, achieving the same intelligence as R1 while reducing output tokens by about 40% and running faster, all without any fine‑tuning or distillation.
A small tech firm, tngtech, released an open‑source model fusion called DeepSeek‑R1T‑Chimera that merges R1 inference with V3‑0324 without fine‑tuning, distillation, or prompts, achieving the same intelligence as R1 while reducing token output by 40% and speeding up inference.
Sam Altman revealed that excessive politeness toward ChatGPT, such as saying “please” and “thank you,” can drive up server workload and electricity use, potentially costing OpenAI tens of millions of dollars, while highlighting broader concerns about AI energy consumption and user behavior.
The article reviews Herbert Simon’s four influential decision‑making models—Subjective Expected Utility, Behavioral, Intuitive, and Darwinian—explaining their key features, applications, and limitations, and shows how they illuminate rational and adaptive choices in economics, psychology, and organizational behavior.
Mechanize, a new AI startup founded by Epoch AI co‑founder Tamay Besiroglu, aims to fully automate all white‑collar work and the global economy, targeting a $60 trillion labor market, but faces technical hurdles, investor scrutiny, and widespread criticism over its radical vision.
In a recent interview, Microsoft CTO Kevin Scott discusses the company’s AI progress, emphasizes that scaling laws have not yet reached their limits, predicts a future dominated by many specialized AI agents managed by knowledgeable product managers, and highlights the surprising impact of China’s DeepSeek project.
The article analyzes the emerging "Era of Experience" in AI, arguing that reliance on static human data limits progress and that reinforcement learning‑based experiential learning—exemplified by AlphaZero—offers a path toward surpassing human knowledge, while outlining the technical, safety, and ethical challenges ahead.
This guide demonstrates how to quickly generate TreeMind tree diagrams and word‑cloud visualizations using Wenxin Yiyan 4.5 by enabling the Call Tool option in X1, providing example prompts, and showing the resulting images, highlighting AI’s role as a helpful assistant.
This article explains the MCP and A2A communication protocols, compares their purposes, and explores how they can be combined to build multi‑agent AI systems with coordinated task management, tool integration, and security safeguards.
This article examines whether artificial intelligence can truly enhance human creativity by defining both concepts, outlining how AI tools assist creators, discussing controversies such as originality and emotional depth, and envisioning a collaborative future where AI augments rather than replaces human inventive capacity.
The article explains Google’s newly open‑sourced Agent‑to‑Agent (A2A) protocol and Anthropic’s Model Context Protocol (MCP), detailing their core functions, real‑world use cases, and how they complement each other to enable seamless collaboration and integration among AI agents and external tools.
This article walks readers through the fundamentals of large language models, covering what tokens are, how tokenization works, the conversion of tokens to numeric IDs, the transformer architecture—including positional encoding, self‑attention, feed‑forward networks and softmax—and explains how these components enable next‑token prediction.
This article explores the newly popular Quasar Alpha AI model, detailing its high-performance, multimodal capabilities, speculative origins, and provides a step‑by‑step guide to obtain an OpenRouter API key, configure a Spring Boot environment, and call the model using Java code.
Stanford’s Human‑Centric AI Institute released the 456‑page 2025 AI Index, highlighting twelve 2024 trends—from benchmark breakthroughs and rising inference efficiency to record private investment, expanding medical and autonomous‑driving applications, and growing global competition—showing AI is moving beyond mere possibility.
This article introduces the concept of AI-powered Data Agents, outlines their key features and use cases across enterprise analytics, data governance, and intelligent customer service, and reviews recent implementations from Google, Alibaba Cloud, and ByteDance, highlighting their impact on modern data-driven workflows.
Meta AI Vice President Ahmad Al‑Dahle addressed recent criticisms of the newly released Llama 4 model, denying claims of test‑set cheating, explaining quality variations as post‑release optimization, and acknowledging internal concerns that led to staff resignations and calls for transparency.
In this candid interview, 83‑year‑old Turing‑Award winner Jeffrey Ullman reflects on his decades‑long impact on compilers, databases, and algorithms, discusses the unpredictable nature of technological revolutions, explores the rise of large language models, parallel computing, prompt engineering, and the challenges of adapting education and software engineering to rapid AI‑driven change.
This article outlines the rapid growth of AIGC technologies, describes key image‑generation and language models, demonstrates step‑by‑step design workflows, explores user‑experience research enhancements, and envisions future business uses while offering practical tips for mastering AI‑generated content.
A new study from the a‑m‑team introduces “Think Twice”, a test‑time multi‑round reasoning technique that, without additional training or model changes, repeatedly prompts large language models to self‑correct, yielding notable accuracy gains across benchmarks such as AIME, MATH‑500, GPQA‑Diamond and LiveCodeBench, while also producing shorter, more confident answers.
Industry leaders including Microsoft’s CTO, IBM’s CEO, and Salesforce’s founder discuss how AI will reshape software development, predicting that up to 95% of code could be AI‑generated by 2030, while emphasizing the continued need for human engineers to guide and design.
In a detailed interview, OpenAI CEO Sam Altman explains the delayed release of upcoming models o3 and o4‑mini, previews the imminent launch of GPT‑5 on a free tier, outlines the company’s shift toward a consumer‑tech business model, and shares his views on AGI, hallucinations, regulation, competition, and career advice for the next generation.
This article explains Retrieval‑Augmented Generation (RAG), its role in mitigating large language model knowledge cutoff and hallucination, outlines the evolution from naive to advanced, modular, graph, and agentic RAG, and discusses future directions such as intelligent and multi‑modal RAG systems.
The Claude research team introduced a novel "circuit tracing" technique that builds substitute models and attribution graphs to expose the internal reasoning steps of large language models, uncovering capabilities such as multilingual understanding, long‑term planning, multi‑step inference, and hidden mathematical computation strategies.
This article explores how understanding others' mental states—from basic intentions to recursive mindreading—can be modeled with Bayesian inference and applied to robots for predicting human behavior in scenarios like pedestrian crossing, shopping assistance, and multi‑agent games.
Prompt engineering uses in‑context learning to turn large language models into precise, task‑aware assistants by providing well‑crafted prompts that guide the model’s probability distribution, reduce hallucinations, and unlock hidden knowledge without any parameter tuning.
This article examines the limitations of current vision‑language and reasoning models, proposes a visual reasoning model (VRM) that can process images and perform deep logical inference, and discusses architecture, training methods, reinforcement‑learning reward designs, and practical challenges.
The article analyzes the challenges of deploying large language models on cloud servers—such as latency, security, and constant connectivity—and explains how near‑memory computing architectures (PNM, PIM, CIM) can integrate storage and processing to enable efficient, high‑performance edge AI deployments, outlining the trade‑offs of each approach.
This article examines how large language models are transforming financial risk management by mapping various risk categories to LLM capabilities, showcasing practical use cases across market, credit, reputation, operational, and anti‑fraud domains, while also discussing technical challenges, data‑space concepts, and future prospects.
The article introduces Spring AI as an emerging tool for Java developers, explains its background, goals, and core components such as data processing, model training, deployment and monitoring, showcases application scenarios like NLP, image processing, recommendation systems and predictive analytics, and also includes promotional offers for AI resources and community groups.
This article introduces Spring AI, a Spring ecosystem module that simplifies building, training, and deploying AI applications for Java developers, covering its background, goals, core components such as data processing, model training, deployment, practical code examples, use cases, advantages, challenges, and future outlook.
This article introduces Spring AI, explains its background, goals, core components such as data processing, model training, deployment and monitoring, showcases practical use cases like NLP, image processing and recommendation systems, and discusses its advantages, challenges, and future outlook for Java developers.
DeepSeek’s R1 inference mode replaces linear chain‑of‑thought with a transparent, multi‑path tree reasoning system, offering layered analysis, intent understanding, memory management, emotion detection, and hallucination mitigation, illustrated through a practical example of buying authentic cigarettes and detailed technical breakdowns.
NVIDIA’s GTC 2025 keynote outlines the four AI waves—from perception to physical AI—while highlighting the company’s latest Blackwell chips, DGX Spark/Station computers, Dynamo inference accelerator, robotics collaborations, GM autonomous‑driving partnership, and AI‑native 6G efforts, underscoring massive data‑center investment and future challenges.
Meituan's Big Model Bodu Internship Program invites master’s and doctoral students specializing in big‑model AI to undertake 3‑12‑month research‑focused internships that provide real‑world data, extensive computational resources, mentorship from leading researchers, opportunities for joint publications and patents, housing subsidies for non‑local participants, and the possibility of full‑time conversion after interview, with applications allowing up to three positions and results communicated via email or SMS.
MiniMax’s rapid 2025 releases—including a video model, open‑source LLM, and high‑fidelity voice model—showcase its multimodal linear‑attention architecture that handles up to 4 million tokens, earns a16z recognition, and signals China’s growing influence in open‑source AI innovation.
By 2025, AIGC is poised to transform full‑stack development, automating requirement analysis, code generation, testing, and deployment, thereby accelerating productivity while prompting developers to upskill and collaborate with AI rather than fear replacement.
This weekly technology digest highlights significant advancements in artificial intelligence, including OpenAI's Python-enabled o1 model, Google's open-source Gemma 3, and Alibaba's AI-driven Quark application, alongside major industry partnerships, expert forecasts on AI agent proliferation, and emerging developments in robotics and wearable technology.
This article explains the Model Context Protocol (MCP) released by Anthropic, describes its three core components, outlines the problems it solves for AI agents, and details four MCP server implementations—github, fetch, sequential‑thinking, and tavily—along with usage commands and code examples.
The article analyzes how Chinese researchers dominate AI research worldwide, detailing their roles in US tech giants, Chinese model teams, talent‑attraction policies in both countries, and the strategic implications of this "internal" competition for the future of artificial intelligence.
The 2024 Chinese Two Sessions emphasized artificial intelligence as a strategic priority, discussing AI safety regulations, industry applications, talent shortages, and policy proposals from leaders such as DeepSeek, Xiaomi, and academic experts, highlighting the drive to integrate AI across manufacturing, agriculture, healthcare, and education.
This article explains the historical challenges of natural language processing, from rule‑based and statistical models to recurrent networks and their limitations, then introduces the Transformer architecture, its self‑attention mechanism, multi‑head attention, and supporting layers, illustrating how it overcomes previous issues and enables efficient parallel training.
The article defines AI, AGI, and AIGC, outlines their core characteristics, functions, and typical application scenarios, compares their scopes, explains how AIGC is an AI application while AGI represents a broader, still‑theoretical goal, and summarizes their interrelationships.
This article provides a comprehensive analysis of DeepSeek, covering its foundational AI research, technology stack, product offerings, and the broader upstream, midstream, and downstream AI industry landscape, including hardware, server, cloud, and market trends.
This article outlines a systematic AGI learning framework across five capability levels, recommends key papers and books, and provides practical steps for engineers to combine study with hands‑on large‑model projects, identify suitable use‑cases, and stay competitive in the evolving AI landscape.
An analysis of Perforce's 2025 Java Developer Productivity Report reveals that over half of Java developers face deployment delays and tool shortages, while AI tools like ChatGPT and GitHub Copilot are increasingly adopted for code completion, refactoring, and documentation, signaling a strategic shift toward AI in Java development.
This article chronicles the rapid development of large language models from the 2017 Transformer breakthrough through successive milestones such as BERT, GPT‑3, ChatGPT, multimodal GPT‑4 variants, open‑weight releases, and the cost‑efficient DeepSeek‑R1, highlighting key architectural innovations, training paradigms, alignment techniques, and industry impact.
The article evaluates the Manus agent’s current strengths in information‑gathering tasks, contrasts collaborative versus fully‑delegated agent models, identifies structural and context limitations that hinder professional‑grade outputs, and speculates on how future agents might bridge this gap.
The ACM awarded Andrew Barto and Richard Sutton the 2024 Turing Award for pioneering reinforcement learning, detailing their seminal contributions, academic biographies, and urgent warnings about AI safety in a comprehensive overview.
The author experiments with reinforcement‑learning‑from‑human‑feedback on a 0.5B Qwen instruct model using Logic‑RL and Open‑R1, discovers that reward mis‑design and curriculum learning cause the model to produce overly short or incorrect reasoning chains on knight‑and‑knave puzzles, and analyses the underlying causes.
The article analyzes DeepSeek's open‑source model launch, its rapid user growth, and presents a comprehensive list of 100 practical AI use cases across sectors—grouped by frequency and adoption stage—to illustrate the model's market impact and future potential.
The article outlines DeepSeek's four‑level capability hierarchy—basic multimodal data fusion and dynamic governance, intermediate domain modeling with causal reasoning and multi‑objective optimization, advanced complex system modeling with digital twins and multi‑agent coordination, and ultimate autonomous evolution features such as concept‑space exploration and self‑programming.
Microsoft CEO Satya Nadella emphasizes that AI’s real success should be judged by its ability to boost global GDP, likening the search for a transformative “killer app” to the historic impact of email and Excel on economic productivity.
Amid rapid advances in cloud, AI, big data, and DevOps, this 2024 guide outlines the ten essential technologies—ranging from multi-language programming and database mastery to distributed systems, microservices, and security—that IT architects need to master to stay competitive and drive digital transformation.
This article examines why Retrieval‑Augmented Generation (RAG) is needed, compares traditional RAG, GraphRAG, and the DeepSearcher framework across architecture, data organization, retrieval mechanisms, result generation, efficiency and accuracy, and provides step‑by‑step implementation guides and experimental results using vector and graph databases.
The article presents two context‑enhanced methods—ECUP for full‑chain uplift modeling and NLGR for neighbor‑list reranking—demonstrating significant gains in coupon issuance and ranking on Meituan’s aggregation page through extensive offline and online experiments.
DeepSeek’s OpenSourceWeek introduced FlashMLA, a GPU‑optimized MLA decoding kernel for Hopper GPUs that leverages FlashAttention and CUTLASS to dramatically improve large‑model inference performance, with early adoption showing up to 30% higher compute utilization and doubled speed in some scenarios.
This step‑by‑step guide explains how to use Alibaba Cloud's MaxCompute, DataWorks, and the AI platform PAI to build a custom dataset, fine‑tune the DeepSeek‑R1 distilled model, and deploy the resulting model for practical applications.
This article provides an in‑depth overview of DeepSeek, covering its core multimodal and multilingual features, long‑context capabilities, domain optimizations, security, main functions, diverse application scenarios, and practical usage via web interface or API integration.
