This step‑by‑step guide shows how to obtain a DeepSeek API key, set up an Alibaba Cloud ECS instance, configure the WeChat public platform, clone the open‑source COW project, edit its JSON configuration, and run the service so that a WeChat public account can interact with the DeepSeek large language model.
This article introduces Ollama, an open-source platform for locally running large language models, and walks through installing the software on macOS and Linux, configuring common commands, managing models such as DeepSeek-R1, deploying the Web UI, and using the Python API for interactive inference.
This guide explains how to privately deploy the DeepSeek large language model using a JD Cloud virtual computer, set up Ollama as the LLM service, run various model versions, and integrate local knowledge bases through CherryStudio, Page Assist, and AnythingLLM for offline and network‑enabled AI applications.
This guide walks you through the reasons for deploying a private DeepSeek large‑model, compares full and distilled versions, shows how to purchase a JD Cloud computer, install Ollama, run the model, and integrate a local knowledge base using CherryStudio, Page Assist, and Anything LLM.
This guide walks Java developers through preparing the environment, installing the CodeGPT plugin, configuring DeepSeek with an API key, and using the AI assistant for code generation, completion, explanation, and token usage monitoring within IntelliJ IDEA.
The article explains DeepSeek’s three core modes—V3, R1, and web‑search—details prompt‑engineering techniques for each mode, and demonstrates practical workplace applications such as data visualization, PPT generation, and new‑media copy creation, showing how tailored prompts can boost efficiency.
The rapid rise of DeepSeek’s code‑generation AI has led to significant layoffs among programmers, prompting a shift in career strategy toward embracing AI tools, focusing on non‑automatable skills, and continuous learning to stay relevant in the evolving software industry.
This article analyzes DeepSeek's V3 and R1 large language models, detailing their low‑cost Mixture‑of‑Experts architecture, Multi‑Head Latent Attention redesign, distributed training optimizations, and reasoning‑focused innovations that together challenge traditional GPU/NPU compute demands.
This guide walks you through installing Ollama, pulling the open‑source DeepSeek R1 model, and using LangChain and Streamlit to create a locally hosted Retrieval‑Augmented Generation (RAG) system that can answer questions from uploaded PDFs without any cloud API.
The article explains DeepSeek's two‑stage training pipeline—including massive pre‑training on trillions of tokens and post‑training via instruction tuning and reinforcement learning from human feedback—describes the differences between its V3 instruction model and R1 reasoning model, and highlights performance optimizations and emerging research directions.
This article shares ten actionable techniques for getting the most out of AI assistants—ranging from clearly stating requirements and assigning identities to providing examples, avoiding jargon, giving precise feedback, and combining tools—so users can turn AI into a powerful collaborative partner.
This guide walks you through registering on SiliconFlow, selecting DeepSeek models, installing Cherry Studio, configuring API keys, setting up the environment, and testing the AI assistant, enabling a full‑feature local deployment without high‑end hardware.
This guide walks you through registering on Silicon Flow, accessing DeepSeek models online, and using Cherry Studio to set up a private, low‑spec‑requirement deployment that keeps conversations stored locally for privacy and security.
The article analyzes DeepSeek’s surprise emergence, its claimed sub‑$6 million training cost and performance rivaling OpenAI’s models, while contrasting industry leaders’ investment plans, government bans, and skepticism from Arm’s CEO, offering a comprehensive view of the AI market’s shifting dynamics.
This tutorial walks developers through installing Ollama on a Huawei cloud VM, deploying the DeepSeek large language model, integrating it with the ChatBox web UI, and finally connecting the model to CodeArts IDE via the Continue plugin for seamless AI‑assisted coding.
This guide walks you through installing Ollama, selecting the appropriate DeepSeek‑R1 model size based on GPU memory, running the model locally, and optionally integrating it with Webman AI for a richer user experience.
This article explains what defines a large AI model, compares parameter scales of GPT‑3, GPT‑4 and M6, and analyzes DeepSeek’s recent releases—V3, R1, and Janus‑Pro—highlighting their benchmark performance, reinforcement‑learning techniques, and cost efficiency versus leading proprietary models.
This article explains how the DualPipe scheduling mechanism in DeepSeek‑R1 improves GPU cluster compute‑communication efficiency by using fine‑grained pipeline stages and bidirectional data flow, comparing it with Zero Bubble pipeline parallelism and discussing the challenges of large‑scale distributed training.
Learn step-by-step how Java developers can obtain a DeepSeek API key, understand the chat and reasoning models, and implement them with Apache HttpClient, covering code examples, key parameters, best practices, and real-world use cases such as smart customer service, education tools, and code generation assistants.
The article outlines eight distinct enterprise strategies for adopting DeepSeek, categorizing them by model maturity, available data types, and specific business challenges, and maps these approaches onto four capability tiers—from basic compliance requirements to advanced multimodal, low‑cost solutions.
The open‑source release of DeepSeek R1 under an MIT license has reshaped the large‑model market, driving cost cuts, prompting rapid responses from global rivals and Chinese cloud providers, and forcing domestic AI firms to rethink differentiation and ecosystem strategies to stay competitive.
This article reviews the development of DeepSeek's Mixture-of-Experts (MoE) models, tracing their evolution from the original DeepSeekMoE V1 through V2 to V3, detailing architectural innovations such as fine‑grained expert segmentation, shared‑expert isolation, load‑balancing losses, device‑limited routing, and the shift from softmax to sigmoid gating.
This article provides a detailed, step‑by‑step tutorial on DeepSeek—a command‑line data processing tool—including its overview, installation on Windows/macOS/Linux, configuration, basic commands for importing, querying, and visualizing data, advanced cleaning and analysis features, practical tips, and a FAQ section.
This guide walks you through deploying various DeepSeek‑R1‑Distill models on Alibaba Cloud's Serverless AI platform CAP, covering supported models, deployment options (Ollama and Transformer), step‑by‑step template and model‑service setups, validation methods, and tips for adding custom models.
This guide shows how to quickly set up Alibaba Cloud's Baileian platform to call DeepSeek‑R1 and DeepSeek‑V3 models for free or at minimal cost, covering API key acquisition, Chatbox client configuration, pricing details, and step‑by‑step usage instructions.
This article explains how to deploy DeepSeek's open‑source large language models—especially DeepSeek‑R1‑Distill—on Volcengine Cloud using three approaches: a containerized VKE solution, a serverless veFaaS setup, and a one‑click Terraform script, complete with step‑by‑step instructions, code snippets, and configuration tips.
This article presents a detailed case study where the ChatDBA AI assistant diagnoses and resolves a gh-ost migration stall caused by large table size and VARCHAR length expansion, compares its response with DeepSeek, and showcases the tool's expert-mode root‑cause analysis and actionable recommendations.
This article breaks down the hardware and software expenses required to deploy a complete DeepSeek large‑language model on‑premises, revealing a total cost of roughly $110,000 and explaining why such an investment is prohibitive for most individual developers but may be justified for well‑funded research or corporate projects.
This guide walks you through preparing a JD Cloud GPU instance, installing NVIDIA drivers, deploying Ollama, running the DeepSeek LLM (including model download and execution), configuring the Chatbox graphical client for interactive queries, and optionally feeding local documents into AnythingLLM for a private knowledge base.
Learn step‑by‑step how to prepare a JD Cloud GPU instance, install GPU drivers, deploy Ollama, run DeepSeek‑R1 models, configure graphical clients like Chatbox on Windows and macOS, and optionally feed local data using AnythingLLM to build an offline knowledge base.
This guide shows how to obtain a free API key from Tencent Cloud's Knowledge Engine, configure OpenAI SDK or a chat client, and call the full‑size 671B DeepSeek‑R1 model without local hardware constraints, with step‑by‑step instructions and sample code.
This week’s AI roundup covers OpenAI’s fully open ChatGPT Search, the launch of Deep Research for automated multi‑step research, NetEase Youdao’s integration of DeepSeek‑R1, Figure’s robot partnership break with OpenAI, low‑cost AI model s1, OpenAI’s Stargate data‑center plans, Google’s antitrust probe, DeepSeek’s traffic surge, and top AI scientist Xu joining Alibaba.
This tutorial walks you through obtaining a DeepSeek API key, installing Docker, configuring the chatgpt‑on‑wechat project, creating a custom docker‑compose.yml, and deploying the Cow application to enable DeepSeek-powered chat in your personal WeChat, complete with code snippets and screenshots.
deepseek4j 1.3 introduces web‑search capability, streaming responses, system prompts, expanded multi‑platform support, enhanced SSE debugging, and upcoming features like API‑key rotation and resilience, enabling Java developers to integrate DeepSeek models effortlessly while focusing on business logic.
DeepSeek, a Chinese AI startup, has rapidly risen to global prominence by releasing high‑performance large language models such as V2, V3, and R1, which combine innovative architectures, dramatically lower training costs, and an open‑source strategy that challenges established AI giants and reshapes industry dynamics.
This article examines DeepSeek R1’s large‑scale reinforcement‑learning approach, its training pipeline that combines rule‑based scaling and deep‑reasoning SFT data, and why its open‑source, low‑cost replication of OpenAI o1 marks a pivotal step toward more efficient, democratized AI models.
The article examines DeepSeek’s rapid rise, its open‑source R1 model and distilled variants, the resurgence of AI PCs, hardware support from Nvidia, AMD and others, and how this ecosystem is reshaping personal AI experiences and the broader large‑model landscape.
This document, authored by a postdoctoral team at Tsinghua University's New Media Research Center, provides a detailed, image‑rich tutorial on using the DeepSeek AI model, aiming to help users better understand and apply the technology through clear visual instructions.
This article dissects the Multi‑Token Prediction (MTP) technique used in DeepSeek‑R1, contrasting it with traditional next‑token prediction, detailing Meta’s MTP design, DeepSeek’s adapted architecture, loss weighting, and why MTP is applied only during training to boost efficiency and model capability.
This article provides an in‑depth analysis of DeepSeek’s model distillation technology, covering its definition, core principles, innovative strategies, architecture design, training optimizations, benchmark results, efficiency gains, and the remaining challenges of applying distillation to large language models and multimodal data.
This step‑by‑step guide shows Java developers how to prepare the environment, install the CodeGPT plugin, configure DeepSeek API keys, set up chat and inference models in IntelliJ IDEA, and then use the assistant for code generation, completion, explanation, and troubleshooting, complete with screenshots and example code.
The article reviews DeepSeek‑R1’s training methodology—including cold‑start data collection, multi‑stage RL fine‑tuning, SFT data generation, and model distillation—highlights its performance comparable to OpenAI‑o1‑1217, and discusses key contributions, reward design, successful experiments, and failed attempts.
The article provides an in‑depth technical analysis of DeepSeek R1, explaining how it reproduces OpenAI o1's reasoning abilities through rule‑based large‑scale reinforcement learning, mixed SFT data, and efficient scaling, while discussing its broader impact on AI model development and capability density trends.
This guide explains how to set up DeepSeek in the popular Java IDE IntelliJ IDEA by installing and configuring the open‑source Continue plugin and the CodeGPT plugin, covering installation steps, activation, API key acquisition, and basic usage examples.
This tutorial explains how to prepare the environment, install the CodeGPT plugin, configure DeepSeek with an API key, set up chat and inference models in IntelliJ IDEA, and use the AI assistant for code generation, completion, explanation, and token usage monitoring, all illustrated with Java examples.
This guide shows how the DeepSeek AI model combined with Jianying video editor can automate topic selection, script writing, voice‑over, editing and distribution, boosting short‑video creation efficiency by up to tenfold and enabling creators of any skill level to produce professional‑grade content at scale.
This guide presents seven practical prompt engineering tricks—ranging from precise requirement definition and contextual background provision to step‑by‑step decomposition, keyword tagging, iterative follow‑ups, tone/style adjustments, and model switching—that dramatically improve the relevance and quality of DeepSeek’s responses for work, learning, and creative tasks.
DeepSeek analyzes eight 2025 wealth trends—housing prices, A‑share market, gold, hot careers, emerging cities, automotive pricing, and economic confidence—providing a clear framework, data‑backed scenarios, and practical recommendations for investors and job seekers.
The article explains that DeepSeek is an open‑source LLM that many sellers monetize by offering paid installation packages, outlines the model lineup and size options, and provides a step‑by‑step guide to install and run DeepSeek locally with Ollama and Open WebUI.
This article examines DeepSeek R1’s three breakthroughs, its low‑cost optimizations that bypass CUDA, and the resulting impact on the AI ecosystem, then provides a detailed technical review of seven open‑source reproductions—Open‑R1, Tiny‑Zero, SimpleScaling‑S1, and simpleRL‑reason—covering their architectures, reinforcement‑learning pipelines, and code implementations.
This article provides a detailed technical analysis of DeepSeek's R1 large language model, covering its background, architecture, training methods, hardware optimizations, performance claims, user impressions, deployment options, and the challenges of reproducing its results.
The article explains how DeepSeek‑R1 improves upon the Zero version by introducing expert‑crafted cold‑start data and a four‑phase multi‑stage training pipeline, resulting in markedly better reasoning, coding, and general knowledge performance across benchmark tests.
This guide introduces DeepSeek, outlines its cutting‑edge open‑source LLMs, and provides step‑by‑step instructions for accessing the admin backend, adding and configuring DeepSeek models, setting API endpoints and keys, and enabling frontend access.
This article shows how AI tools like DeepSeek can dramatically speed up data‑engineering tasks—such as fixing long‑running SQL queries, building real‑time data pipelines with Flink, and deciphering legacy stored procedures—while offering concrete prompts, real‑world case studies, and five time‑saving techniques.
This guide walks you through registering a SiliconFlow account, obtaining API keys, installing the Continue plugin for VS Code or JetBrains IDEs, configuring DeepSeek models (R1, V3, V2.5) in the plugin’s JSON settings, and testing the integration, with screenshots and full configuration code.
This guide explains how to deploy a production‑ready DeepSeek‑R1 inference service on Alibaba Cloud ACK using KServe, covering model preparation, storage configuration, service deployment, observability, autoscaling, model acceleration, gray‑release and GPU‑shared inference.
This article analyses DeepSeek's V3 and R1 models, explaining how their innovative MoE architecture, Multi‑Head Latent Attention, low‑cost training strategies, and distributed‑training optimizations deliver high‑performance large language models while reducing GPU/NPU demand and sparking industry excitement.
This article examines DeepSeek's advanced Transformer‑based architecture, dynamic routing, MoE system, multi‑stage training, efficient inference, multimodal capabilities, real‑world applications, technical challenges, and future prospects, providing a comprehensive technical analysis of the model's strengths and limitations.
This guide walks Java developers through preparing the environment, installing the CodeGPT proxy plugin, adding the DeepSeek AI plugin, configuring API keys and model endpoints, and using the assistant for code generation, completion, explanation, and troubleshooting within IntelliJ IDEA.
This tutorial guides beginners through the complete process of integrating DeepSeek's large language model API into a WeChat public account, covering API key acquisition, WeChat platform configuration, free Alibaba Cloud ECS setup, code deployment, dependency installation, configuration file editing, and final server verification.
This guide outlines the hardware requirements for AI models ranging from lightweight 1.5 B parameters to massive 671 B models, detailing CPU cores, memory, GPU recommendations, storage needs, optimization tips, deployment suggestions, and suitable application scenarios.
This guide walks you through preparing a Tencent Cloud account, creating a Cloud Studio workspace, installing Ollama, downloading and running the DeepSeek‑R1 large language model, interacting via terminal or API, and managing resources and model versions.
This article provides a step‑by‑step guide to installing the DeepSeek AI code‑assistant plugin (via CodeGPT) in IntelliJ IDEA, configuring the required Python environment and API key, using its code‑completion, explanation, and question‑answer features, and also includes usage statistics and a brief promotion of a paid plugin bundle.
This tutorial walks you through obtaining a DeepSeek API key, installing Docker, configuring the open‑source chatgpt‑on‑wechat project, creating a docker‑compose file, and deploying the service so you can chat with DeepSeek directly from your WeChat client.
This guide shares over ten practical DeepSeek prompting strategies—ranging from precise questioning and multi‑turn dialogue to structured outputs and feedback—to dramatically improve the relevance and efficiency of AI responses for everyday tasks.
This guide walks you through obtaining DeepSeek API access, installing the Continue and CodeGPT plugins in IntelliJ IDEA, configuring them with your API key, and understanding the associated usage costs, enabling AI‑assisted development to boost productivity.
This guide walks you through multiple ways to use DeepSeek’s full‑capacity model—including direct web platforms and step‑by‑step API integration with Tencent Cloud, ByteDance Volcano Engine, and Alibaba Cloud Bailei—so you can get fast, free AI responses without hitting common pitfalls.
The article evaluates DeepSeek‑R1’s strong reasoning, high performance, native Chinese training and low cost, then explores how such large language models can reshape B2C and B2B services, propose a new “intelligent data store” architecture, and outline comprehensive organizational and strategic changes enterprises must adopt to thrive in the AI era.
This guide explains why DeepSeek is a valuable AI assistant, outlines the challenges of high demand, and provides step‑by‑step instructions for creating, configuring, testing, and publishing a DeepSeek AI assistant within the DingTalk platform to ensure stable access.
This guide shows how to start DeepSeek large‑language models on cnb.cool in just 5‑10 seconds without downloading, using a simple three‑step process that includes forking the repository, selecting a model branch, and running Ollama or Docker commands, plus options for long‑term cloud deployment.
The article examines DeepSeek’s rapid rise, the large‑scale malicious attacks it suffered, and then provides a detailed, cloud‑native security observability guide using Alibaba Cloud services such as DDoS protection, WAF, CLB, SAS, and SLS for logging, monitoring, anomaly detection, and alert response.
The large‑scale malicious attack on DeepSeek exposed severe service disruptions, highlighted the AI industry's security vulnerabilities, and prompted a detailed walkthrough of building a comprehensive, observable security stack on Alibaba Cloud using DDoS protection, WAF, load balancing, log services, and anomaly detection.
This comprehensive guide walks you through DeepSeek's features, installation on Windows, macOS, and Linux, configuration details, basic commands for data import, querying, and visualization, as well as advanced cleaning, analysis, plugin extensions, troubleshooting tips, and a handy command cheat sheet.
This article walks you through everything you need to know about DeepSeek R1—including its different model sizes, hardware requirements, installation tools like Ollama, LM Studio and Docker, and how to set up a visual interface with Open‑WebUI or Dify—for offline, private, and cost‑effective AI inference.
This guide walks you through preparing the environment, installing the CodeGPT plugin, configuring DeepSeek with API keys, and using its code generation, completion, explanation, and question‑answer features inside IntelliJ IDEA, while also showing how to monitor token usage.
This article introduces deepseek4j, a Spring‑Boot‑compatible library that fully supports DeepSeek R1’s chain‑of‑thought and billing features, offering reactive streaming, easy configuration, and a built‑in debugging page, with step‑by‑step setup and code examples to help developers quickly integrate the model.
This guide walks Java developers through preparing the environment, installing the CodeGPT plugin, configuring DeepSeek API keys, and using the DeepSeek AI assistant within IntelliJ IDEA to generate, complete, explain, and troubleshoot code, while also showing usage statistics.
This step‑by‑step tutorial shows beginners how to install Ollama, deploy the DeepSeek large language model locally, and configure the Chatbox AI client to create a functional AI assistant on Windows, macOS, Linux, or mobile devices within ten minutes.
This guide walks you through installing Ollama, downloading the DeepSeek model, and configuring the Chatbox AI client so you can run a powerful local AI assistant on Windows, macOS, or Linux within minutes.
DeepSeek reshapes the AI landscape by replacing brute‑force compute scaling with algorithmic breakthroughs such as a novel MoE architecture, memory compression, active‑learning data pipelines, and open‑source tooling, delivering dramatically lower training and inference costs while enabling edge deployment and a vibrant developer ecosystem.
This tutorial explains why a personal knowledge base is essential, then walks you through installing Cherry Studio, configuring DeepSeek chat and embedding models, creating and populating the knowledge base, and finally using an AI assistant to retrieve information efficiently.
This article provides a comprehensive analysis of DeepSeek's large‑language‑model technology, covering the company's background, model capabilities, remarkably low training and inference costs, and the core architectural and algorithmic innovations such as MoE, MLA attention, FP8 mixed‑precision, and the DualPipe pipeline that enable efficient large‑scale AI deployment.
This article explains how knowledge distillation enables massive AI models to be compressed and deployed efficiently, covering its principles, classification dimensions, implementation steps, innovative practices at DeepSeek, real‑world applications, and future research directions.
An in‑depth comparison of DeepSeek’s R1 model variants—from 1.5B to 671B—covers parameter scale, accuracy, training and inference costs, and ideal use cases, followed by a detailed contrast with the V3 version’s design goals, architecture, training methods, performance and application scenarios.
This step‑by‑step guide shows non‑technical users how to install Ollama, pull the desired DeepSeek‑R1 model version, run it from the terminal, and optionally connect the free Chatbox desktop client for a visual chat interface, all without external network dependencies.
This article examines DeepSeek’s frequent service interruptions, contrasts the inherent reliability challenges of AIGC products with traditional internet applications, and proposes adopting proven isolation, rate‑limiting, and elastic‑scaling techniques to improve AI service availability and user experience.
This guide explains how to deploy the full 671B DeepSeek R1 model on local hardware using Ollama, leveraging dynamic quantization to shrink model size, detailing hardware requirements, step‑by‑step installation, configuration, performance observations, and practical recommendations.
This article provides a comprehensive tutorial on locally deploying the full‑size DeepSeek R1 671B model using Ollama, covering dynamic quantization options, hardware specifications, detailed installation commands, configuration files, performance observations, and practical recommendations for consumer‑grade systems.
The article reviews DeepSeek’s V‑series papers, explaining how scaling‑law insights, Grouped Query Attention, a depth‑first design, loss‑free load balancing, multi‑token prediction and Multi‑Head Latent Attention together enable economical mixture‑of‑experts LLMs that rival closed‑source models while cutting compute and hardware costs.
Within just 20 days of launch, DeepSeek has amassed over 20 million daily active users, secured support from China’s four major cloud providers and leading global chip makers, and sparked a wave of AI ecosystem development that highlights both market potential and technological collaboration across the industry.
This step‑by‑step guide shows how to install IntelliJ IDEA, add the CodeGPT plugin, configure DeepSeek API keys and URLs, and use the DeepSeek Coder template for AI‑assisted code completion within the IDE.
This article presents ten detailed techniques for effectively using DeepSeek's large language models—including mode selection, model comparisons, knowledge updates, prompt engineering, RAG, file uploads, API access, and open‑source resources—while offering concrete examples and code snippets for each feature.
The article enumerates DeepSeek’s extensive technical optimizations—including Grouped Query Attention, Multi‑head Latent Attention, Mixture‑of‑Experts, 4D parallelism, quantization, and multi‑token prediction—that together enable cheap, high‑performance large language models.
This article provides a comprehensive guide to locally deploying DeepSeek R1 models using Ollama on Windows and Linux, covering model variants, hardware requirements, installation steps, command‑line operations, visual client options, usage examples, performance tuning, and best‑practice recommendations for developers and enterprises.
An in‑depth recap of a five‑hour Lex Fridman podcast reveals DeepSeek’s breakthrough R1 model, its cost‑saving MoE and MLA techniques, the geopolitical chip export battle, market reactions, and broader AI industry trends, offering a comprehensive analysis of technology, economics, and future implications.
This article provides a comprehensive, step‑by‑step tutorial on DeepSeek—a versatile command‑line data processing and analysis tool—covering its core features, installation on Windows/macOS/Linux, configuration options, basic commands, advanced functions, practical tips, and troubleshooting advice.
This tutorial shows how to log into Alibaba Cloud PAI, navigate to the Model Gallery, select a DeepSeek model such as the distilled DeepSeek‑R1‑Distill‑Qwen‑7B, and deploy it with a single click using vLLM or BladeLLM, providing endpoint and token details for immediate use.
This guide walks you through DeepSeek’s core capabilities—including installation on Windows, macOS, and Linux, configuration of storage paths, API keys, and logging levels, as well as data import, cleaning, analysis, visualization, batch processing, scheduling, and plugin extensions—providing concrete command examples and troubleshooting tips.
The author explores how DeepSeek V3 can assist in designing a task scheduling system's sharding logic, describing system components, slot allocation, and the AI's step‑by‑step code generation, analysis, and testing process.
DeepSeek’s open‑source R1 model shattered the prevailing belief that closed‑source giants like OpenAI dominate AI progress by introducing a pure reinforcement‑learning‑driven inference breakthrough with its GRPO algorithm, sparking global excitement, prompting political concern, and leading the company to aggressively hire engineers in Beijing and Hangzhou with competitive 14‑month salaries despite demanding top‑conference publications.
