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Eino is an open‑source Golang framework for building large‑model applications, offering reusable components, robust orchestration, clean APIs, best‑practice templates, and full‑cycle DevOps tools, with code examples for both Ollama and OpenAI modes, plus streaming and normal output options.
LLMOps, the end-to-end methodology for managing large language models, encompasses a curated set of development, deployment, monitoring, and local management tools—such as LangChain, vLLM, LangSmith, and Ollama—enabling practitioners to efficiently build, scale, and maintain AI applications.
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.
Mojo, a new language from Modular, combines Python-like ease with C++-level speed through static typing, MLIR integration, and hardware acceleration, enabling AI developers to write high-performance models without learning complex low-level languages, and its open-source release promises rapid ecosystem growth.
This article guides frontend developers through AI development, comparing model training, fine‑tuning, prompt engineering, and Retrieval‑Augmented Generation, then explains agent creation via ReAct and tool‑call methods, and showcases Langchain and Flowise as low‑code frameworks for building domain‑specific AI agents.
The article surveys AI development from early GPT experiments to real‑world deployments, explaining how tools like LangChain and Retrieval‑Augmented Generation enable sophisticated agents, multi‑prompt workflows, and function calls for chatbots, education, and creative content while addressing accuracy, resource, and ethical challenges.
Baidu has launched the second Wenxin Cup AI startup competition, inviting global and university teams to develop AI‑native applications and compete for cash prizes—including a new Special Award of up to 50 million RMB—while providing technical guidance, ecosystem resources, and mentorship to accelerate the AI entrepreneurship ecosystem.
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.
This article demonstrates how to use GPT to accelerate code development, automate script creation, and integrate AI-driven suggestions into real‑world business scenarios such as e‑commerce promotion, providing practical tips, example prompts, and ready‑to‑run code snippets.
The article introduces the ChatGPT 0720 update, explains the new Custom Instructions feature, shows how to enable and configure it, compares responses with and without the feature, and provides detailed code examples for implementing similar functionality via system messages in developer tools.
This article explains the limitations of large language models, introduces prompt engineering as a remedy, and provides a comprehensive guide to using the LangChain framework—including models, prompts, chains, agents, vector search, and practical Node.js code examples—to enable LLMs to interact with external tools and data sources.
This technical report presents a comprehensive overview of artificial intelligence evolution, knowledge‑graph construction techniques—including traditional, cross‑lingual and reading‑comprehension based named entity recognition, weak‑supervised and joint relation extraction, attribute completion via multi‑source cues, and conditional knowledge‑graph modeling—highlighting recent research findings and experimental results.
This article introduces Baidu EasyDL's zero‑threshold AI training camp, focusing on an automatic‑driving scene image‑segmentation case study, covering platform features, model selection, data collection, cleaning, annotation, smart labeling, augmentation, deployment, and a live Q&A session.