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This article explores DeepSeek's open‑source large‑model breakthroughs, PingCAP's AI‑enhanced database roadmap, TiDB.AI's retrieval‑augmented generation framework, the unified TiDB data engine, and practical Q&A insights on knowledge‑graph construction, vector search, and AI‑driven SQL generation.
This article provides a comprehensive overview of web‑scraping technologies, compares popular tools such as requests, BeautifulSoup and Selenium, introduces AI‑assisted crawling with DeepSeek, and walks through practical steps for using BrightData’s platform to collect industry data, complete with ready‑to‑run Python code.
This step‑by‑step tutorial shows how to install Ollama, pull the DeepSeek‑R1 model, create a Spring Boot project with the Spring AI Ollama starter, code a ChatController, and test a local AI "Hello World" integration, illustrating AI‑enhanced backend development.
Version 0.9.0 of Ollama introduces a ‘thinking’ control that lets users view and manage the AI model’s reasoning process, with detailed CLI commands, REST API usage, model support list, scripting options, and advanced Modelfile configurations for models like DeepSeek R1 and Qwen 3.
The article announces the official release of Spring AI 1.0, explains the meaning of GA, provides the Maven dependency configuration, describes how Spring AI simplifies AI integration for Java applications, and outlines its key features and supported model types.
DeepSeek quietly launched the R1 0528 model, which early testers report matches OpenAI’s o3 in benchmarks and style, while adding deeper chain‑of‑thought reasoning, better writing output, and extended thinking windows, and the announcement is followed by a promotion for the GOPS Global Ops Conference.
The article provides a detailed technical overview of DeepSeek's flagship large language models, DeepSeek‑V3 and DeepSeek‑R1, describing their MoE architecture, training frameworks, reinforcement‑learning based fine‑tuning, inference optimizations, and the broader impact of these innovations on the AI landscape while also promoting related books and resources.
Spring AI 1.0, the first production‑grade Java AI framework, introduces ready‑to‑use APIs, seamless model integration, enterprise‑level RAG engine, smart tool calling, and three development modes, empowering developers to rapidly build, customize, and fully control AI applications with major model providers like OpenAI, Anthropic, DeepSeek.
This tutorial walks beginners through obtaining a DeepSeek API key, setting up an Alibaba Cloud ECS instance, configuring the server and WeChat public platform, installing required Python dependencies, editing configuration files, and finally running the chatbot so the public account can interact with the DeepSeek large‑language model.
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.
This article provides a comprehensive overview of DeepSeek's model distillation technology, detailing its definition, key innovations, architecture, training methods, performance gains, and the remaining challenges such as the implicit performance ceiling and multimodal data distillation.
This article introduces Spring AI, explains its core features for integrating various AI models, and walks through creating a Spring Boot chat application that connects to the DeepSeek model using both synchronous and streaming endpoints.
Amid the AI wave, large language models like DeepSeek R1 are poised to explode by 2025, driven by open-source, low-cost access and superior reasoning, with successful deployment requiring four key factors—domain expertise, knowledge bases, robust search, and engineered agent architectures—to unlock value beyond simple chat.
DeepSeek has quietly open‑sourced a new mathematics‑focused large language model, DeepSeek‑Prover‑V2 (available in 671B and 7B variants), achieving 88.9% on MiniF2F and strong results on PutnamBench, alongside the high‑quality ProverBench dataset and a novel recursive theorem‑proving pipeline.
DeepSeek‑Prover‑V2‑671B, a 671 billion‑parameter AI model released on Hugging Face, dramatically advances formal mathematical theorem proving with MoE architecture, FP8 quantization, 163 k token context, superior performance over GPT‑4 Turbo and other models, and broad implications for research and industry.
While most eyes remain on familiar AI giants, China’s Alibaba and DeepSeek are unveiling open‑source video and inference models that run on consumer GPUs, sparking a regulatory scramble and threatening the dominance of closed‑source AI, heralding a rapid, disruptive shift across the industry.
This guide demonstrates how to create a Spring Boot backend that integrates DeepSeek's large language model via the Spring AI OpenAI starter, covering project setup, dependency configuration, API key management, and a sample controller that provides AI-powered chat responses such as weather forecasts.
This step‑by‑step tutorial shows how to set up CUDA 12.4, install required packages, prepare a JSON dataset and custom reward, troubleshoot out‑of‑memory errors, and launch DeepSeek R1 training on an 8‑GPU A100 cluster using Accelerate, Deepspeed zero‑3 and vLLM configurations.
In a side‑by‑side test on the Trae platform, the author used Claude Sonnet 3.5 to create a functional Twitter‑to‑image‑card browser extension in roughly twenty minutes, while DeepSeek‑R1 required multiple prompt iterations, manual bug fixes, and still produced visual glitches, demonstrating Claude’s superior reliability for frontend plugin generation.
The article introduces DeepSeek's new V3-0324 model, highlights its performance gains and new features like function calling and standardized JSON output, demonstrates Chinese and frontend coding tests, provides Java code examples for AI integration, and concludes with a summary of its business impact.