AI applications

JD Tech

May 8, 2025 · Artificial Intelligence

The Emerging Boom of Large Model Applications and Why 2025 Will Be the Turning Point

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.

2025AI applicationsDeepSeek

0 likes · 10 min read

The Emerging Boom of Large Model Applications and Why 2025 Will Be the Turning Point

Tencent Technical Engineering

Apr 14, 2025 · Artificial Intelligence

MCP Protocol: Technical Principles and Business Applications

The article examines the Model Context Protocol (MCP), detailing its microkernel‑based technical architecture, development timeline from Anthropic’s 2024 release to industry adoption, hands‑on implementation examples, and business use cases such as multi‑agent QQ robots, highlighting MCP’s potential to standardize AI tool integration across industries.

AI applicationsAI architectureBusiness Implementation

0 likes · 14 min read

MCP Protocol: Technical Principles and Business Applications

Beijing SF i-TECH City Technology Team

Apr 7, 2025 · Artificial Intelligence

LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing

This article explores the application of Large Language Models (LLM) in text information detection and extraction, focusing on blue-collar recruitment data processing. It details the implementation of LLM through prompt engineering, RAG enhancement, and model fine-tuning to improve data cleaning efficiency and accuracy.

AI applicationsLLMNatural Language Processing

0 likes · 31 min read

LLM Application in Text Information Detection and Extraction: A Case Study of Blue-Collar Recruitment Data Processing

DataFunSummit

Apr 7, 2025 · Artificial Intelligence

Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents

This article examines how Retrieval‑Augmented Generation (RAG) and multi‑agent technologies narrow the gap between large language models and practical deployment, highlighting their roles in operations automation, financial risk control, intelligent data governance, database localization, edge inference, and future AI‑driven solutions.

AI applicationsAgentsFinancial Risk Management

0 likes · 8 min read

Bridging the Gap Between Large Models and Real‑World Applications with RAG and Agents

DaTaobao Tech

Mar 26, 2025 · Artificial Intelligence

Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies

The article surveys Retrieval‑Augmented Generation (RAG) as a solution to large language model limits—such as outdated knowledge, hallucinations, and security risks—by integrating vector‑database retrieval with LLM generation, and discusses related tools, multi‑agent frameworks, prompt engineering, fine‑tuning methods, and emerging optimization trends.

AI applicationsLLMmulti-agent systems

0 likes · 29 min read

Overview of Retrieval-Augmented Generation (RAG) and Related AI Technologies

Sohu Tech Products

Mar 19, 2025 · Databases

Redis Vector Search Technology for AI Applications: Implementation and Best Practices

The article explains how Redis vector search, powered by RedisSearch’s FLAT and HNSW algorithms and supporting various data types and precisions, enables fast AI-driven similarity queries for text, image, and audio, and provides implementation guidance, optimization tips, and a real‑world customer‑service use case.

AI applicationsDatabase OptimizationHNSW

0 likes · 17 min read

Redis Vector Search Technology for AI Applications: Implementation and Best Practices

Cognitive Technology Team

Mar 9, 2025 · Artificial Intelligence

AGI Learning Framework and Practical AI Application Guide

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.

AGIAI applicationsLarge Models

0 likes · 7 min read

AGI Learning Framework and Practical AI Application Guide

DataFunTalk

Jan 26, 2025 · Artificial Intelligence

58.com’s LingXi Large Language Model Platform: Development, Deployment, and Performance Optimizations

Since the launch of ChatGPT, 58.com has built a Model‑as‑a‑Service platform called LingXi that trains and serves domain‑specific large language models, supports over a hundred internal scenarios with daily inference exceeding ten million calls, and continuously improves performance through quantization, GPU optimization, model miniaturization, and advanced AI applications such as interview assistants, voice agents, and RAG‑enabled agents.

AI PlatformAI applicationsLLM

0 likes · 9 min read

58.com’s LingXi Large Language Model Platform: Development, Deployment, and Performance Optimizations

DataFunSummit

Dec 17, 2024 · Artificial Intelligence

Exploring Baidu PaddlePaddle's Multimodal Large Model Innovations and the PaddleMIX Development Kit

This article presents Baidu's latest advances in multimodal large models, detailing their capabilities, architectural evolution, real‑world applications, and the open‑source PaddleMIX toolkit that streamlines data processing, training, fine‑tuning, and high‑performance inference for developers.

AI applicationsData ProcessingLarge Models

0 likes · 20 min read

Exploring Baidu PaddlePaddle's Multimodal Large Model Innovations and the PaddleMIX Development Kit

DataFunTalk

Dec 10, 2024 · Artificial Intelligence

Tencent Large Language Model Applications: RAG, GraphRAG, and Agent Technologies

This article explores Tencent's large language model deployments across various business scenarios, detailing the principles and practical implementations of Retrieval‑Augmented Generation (RAG), GraphRAG for role‑playing, and Agent technologies, while also covering model fine‑tuning, knowledge‑base construction, and evaluation methods.

AI applicationsGraphRAGTencent

0 likes · 15 min read

Tencent Large Language Model Applications: RAG, GraphRAG, and Agent Technologies

DevOps

Dec 8, 2024 · Artificial Intelligence

Understanding Fine-Tuning in Machine Learning: Concepts, Importance, Steps, and Applications

This article explains fine‑tuning in machine learning, covering its definition, why it matters, the role of pre‑trained models, detailed step‑by‑step procedures, advantages, and diverse applications such as NLP, computer vision, speech and finance, with practical examples like face recognition and object detection.

AI applicationsPretrainingfine-tuning

0 likes · 16 min read

Understanding Fine-Tuning in Machine Learning: Concepts, Importance, Steps, and Applications

ZhongAn Tech Team

Dec 1, 2024 · Artificial Intelligence

AI Weekly Digest Issue 4: Market Insights, Industry Solutions, and Emerging Technologies

The fourth AI weekly newsletter reviews recent industry news—including Jensen Huang's robot era vision and Tesla's Optimus plans—introduces Claude's new style‑customization feature, explores AI‑enhanced input methods, and evaluates DeepSeek's R1‑Lite model performance on complex reasoning tasks.

AIAI applicationsClaude

0 likes · 10 min read

AI Weekly Digest Issue 4: Market Insights, Industry Solutions, and Emerging Technologies

iKang Technology Team

Nov 12, 2024 · Artificial Intelligence

AI Applications in Medical Imaging for Enhanced Disease Detection

AI-driven medical imaging leverages deep learning and massive datasets to detect lesions—often earlier than human eyes— with high accuracy and speed, reducing fatigue‑related errors and workload, while challenges such as data quality, interpretability, privacy, and regulatory compliance must be addressed for widespread clinical adoption.

AIAI applicationsAI in Healthcare

0 likes · 5 min read

AI Applications in Medical Imaging for Enhanced Disease Detection

DataFunSummit

Oct 28, 2024 · Artificial Intelligence

Exploration and Practice of Multimodal Large Models at 360

This article presents 360's comprehensive exploration of image‑text multimodal large models, covering background concepts, research routes, three generations of model development, proprietary architectures like SEEChat, 360VL and Inner‑Adaptor, and real‑world AI applications across various products and services.

AI applicationsLarge Language ModelsModel Architecture

0 likes · 19 min read

Exploration and Practice of Multimodal Large Models at 360

Baidu Tech Salon

Oct 24, 2024 · Artificial Intelligence

Insights from Baidu CTO Wang Haifeng at the 2024 World Science and Development Forum on AI Advances

At the 2024 World Science and Development Forum, Baidu CTO Wang Haifeng highlighted the rapid maturation of agent technology and large-model AI, the emergence of AGI, the continued relevance of scaling laws, and Baidu’s Wenxin Model 4.0 Turbo powering billions of daily interactions across diverse industries while emphasizing people-centric, responsible AI development.

AGIAI applicationsArtificial Intelligence

0 likes · 6 min read

Insights from Baidu CTO Wang Haifeng at the 2024 World Science and Development Forum on AI Advances

Refining Core Development Skills

Sep 21, 2024 · Artificial Intelligence

Using GLM-4-Plus Large Model API: Features, Code Samples, and Practical Application Scenarios

This article introduces the rapid rise of large language models, highlights the advantages of the GLM-4-Plus model—including superior language understanding, long‑text handling, and enhanced reasoning—explains how to obtain API credentials, demonstrates request parameters and curl examples, and showcases diverse real‑world use cases such as code generation, social‑media copy, travel planning, and interview question creation.

AI applicationsAPI usageGLM-4-Plus

0 likes · 17 min read

Using GLM-4-Plus Large Model API: Features, Code Samples, and Practical Application Scenarios

Code Mala Tang

Sep 12, 2024 · Artificial Intelligence

Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js

This article explains the core concepts of Retrieval‑Augmented Generation (RAG), walks through its implementation steps with LangChain.js—including text chunking, embedding, storage, retrieval, and generation—and showcases practical use cases, challenges, and best practices for building reliable AI‑powered applications.

AI applicationsEmbeddingLLM

0 likes · 16 min read

Boost LLM Accuracy with Retrieval‑Augmented Generation Using LangChain.js

Kuaishou Tech

Jul 31, 2024 · Artificial Intelligence

Kuaishou’s Kolors Text‑to‑Image Model: Architecture, Evaluation, and Real‑World Applications

The article presents a comprehensive overview of Kuaishou’s Kolors (formerly 可图) multimodal generative model, detailing its data collection strategy, diffusion‑based architecture, evaluation metrics, derived capabilities such as prompt refinement and interactive generation, and a range of practical applications from AI‑powered live‑stream gifts to virtual try‑on, while also offering strategic advice for the domestic visual‑generation community.

AI applicationsKolorsdiffusion models

0 likes · 27 min read

Kuaishou’s Kolors Text‑to‑Image Model: Architecture, Evaluation, and Real‑World Applications

Model Perspective

Jul 31, 2024 · Artificial Intelligence

How Monte Carlo Tree Search Powers AlphaGo and Beyond: A Deep Dive

Monte Carlo Tree Search (MCTS) is a statistical heuristic algorithm that builds decision trees through selection, expansion, simulation, and backpropagation, enabling breakthroughs like AlphaGo’s victory and finding applications in robotics, autonomous driving, finance, and bioinformatics.

AI applicationsAlphaGoMCTS

0 likes · 7 min read

How Monte Carlo Tree Search Powers AlphaGo and Beyond: A Deep Dive

DevOps

Jul 21, 2024 · Artificial Intelligence

LLM Fundamentals, Applications, Prompt Engineering, RAG, and Agentic Workflows

This article provides a comprehensive overview of large language models (LLMs), covering their basic concepts, relationship with NLP, development history, parameter scaling, offline deployment, practical applications, prompt‑engineering frameworks, retrieval‑augmented generation, LangChain integration, agents, workflow orchestration, and future directions toward multimodal AI and AGI.

AI applicationsArtificial IntelligenceLLM

0 likes · 36 min read

LLM Fundamentals, Applications, Prompt Engineering, RAG, and Agentic Workflows