The article details how the security team rebuilt its AI knowledge base to overcome retrieval accuracy and injection efficiency issues, outlining a six‑step process, governance mechanisms, and measurable outcomes that turned a static repository into a self‑sustaining, agent‑friendly supply system.
Cloudflare built a three‑layer AI engineering stack—platform, knowledge, and governance—that lets 3,600 engineers (95% of R&D) use AI tools at scale, reduces inference cost by 77% with Workers AI, intercepts over 240,000 standard violations, and keeps per‑review cost under $1 while maintaining high security.
In a candid YC Startup School interview, Jeff Dean explains how AI is moving from ever‑larger models to long‑running, self‑evolving agents, emphasizing inference hardware, data‑movement costs, context engineering, and the strategic 1% rule that guides startups to focus on problems where generic models still fail.
This article defines AI Data Engineering as a data supply system for large models and agents, extending traditional data engineering with semantic modeling, RAG, controlled data services, permission governance, and feedback loops to make data understandable, retrievable, callable, and auditable for reliable enterprise AI deployment.
In the first half of 2026, 360 AI Institute saw six papers accepted at ICLR, CVPR, ICML and ECCV, covering multimodal understanding, agents, image generation and editing, while Chinese contributions reached 43.7% of ICLR submissions, highlighting a global move toward more precise and controllable artificial intelligence.
The article explains how AI has moved from chat interfaces to full‑workflow automation, outlines a formula for building valuable AI agents, reviews emerging Chinese AI‑agent products, and provides five practical tips and a step‑by‑step framework for turning everyday tasks into repeatable, testable Skills.
At ICML 2026, ten closed‑door sessions with leading researchers uncovered emerging signals—from next‑generation diffusion language models and data‑centric AI to AI‑driven finance, autonomous agents, AI as an operating system, and AI for science—highlighting the directions that will shape AI research and deployment over the next few years.
The article demystifies why many users get disappointing results with AI by detailing the essential concepts—Tools (local and remote), the Model Context Protocol (MCP), Rules, Skills as reusable recipes, and the different Agent types—so readers can precisely describe tasks and fully leverage AI capabilities.
This article walks through the inner workings of Claude Code, explains why it behaves unpredictably out of the box, and provides a step‑by‑step guide—including CLAUDE.md configuration, three operating modes, Hooks, Skills, Agents, and cost‑saving techniques—to transform the tool into a reliable, project‑aware AI coding assistant.
This article dissects why Claude Code often behaves unpredictably, then walks through a step‑by‑step configuration of CLAUDE.md, work‑mode switching, Hooks, Skills, and Agents, plus cost‑saving tips and real‑world workflow examples, enabling developers to harness the AI assistant safely and efficiently.
This article walks through selecting the most suitable AI coding agent, provides step‑by‑step installation commands for Claude Code, OpenCode, Pi Agent and Hermes, details how to configure six domestic large language models, introduces the cc‑switch tool for quick model swapping, compares model capabilities, and offers troubleshooting tips.
LangChain is an LLM application framework that standardizes development steps into reusable components linked by a unified syntax (LCEL), offering modules such as Models, Prompts, Chains, Agents, Tools, and Memory, and shows measurable benefits like 17% lower latency and halved development time for multi‑step workflows.
The weekly tech roundup covers Anthropic’s flagship Claude Fable 5 and Mythos 5 models—showing record‑high benchmark scores but a two‑fold price increase—while also reviewing GPT‑5.6’s internal tests, Meshy’s world‑first 3D Agent, Kimi Work’s local AI assistant, Tencent Cloud’s Agent strategy, token‑cost cuts for overseas AI teams, Apple’s side‑AI breakthrough, and the HRM‑Text model that challenges scaling laws.
This article walks through how VS Code’s GitHub Copilot now supports Bring‑Your‑Own‑Key (BYOK) integration, allowing developers to attach custom models such as MiniMax, DeepSeek, Gemini, Claude or OpenAI, control costs, configure agents, skills and instructions, and monitor usage via logs and flow charts.
Thorsten Ball outlines twelve decisive observations about the AI era, arguing that abundant code, the rise of autonomous agents, shifting bottlenecks, and new value drivers will fundamentally rewrite software development, organization, and engineer roles.
The article analyzes how the surge of AI agents—projected to generate 80% of API calls—forces backend systems to evolve from MVC‑style monoliths toward a new core foundational unit that unifies APIs, workflows, observability, and shared state across diverse frameworks.
The article reviews the GitHub project claude-code-templates, which aggregates over 100 reusable Claude Code assets—including agents, commands, MCPs, settings, hooks, and skills—into an npm‑like repository and a web dashboard, showing how a single npx command can install a complete development stack, detailing usage examples, pros, cons, and target audiences.
This week’s AI roundup highlights rapid technical iteration and market rollout, including SpaceX’s $60 billion acquisition of Cursor, the release of Moon’s Dark Side flagship model KimiK2.6, new Windows 11 preview agents, policy pushes from China’s State Council, and multiple major model launches and investigations across the globe.
A rapid roundup of today’s AI news shows the industry moving beyond marginal model gains toward lower cost and latency, agents entering task and browser workflows, redesign of the design‑code gap, 3D/web expansion, and open‑source tools reaching smaller teams.
This in‑depth guide explains how to unlock the full power of Claude Code by understanding the dual .claude directories, crafting effective CLAUDE.md and CLAUDE.local.md files, organizing modular rules, hooks, skills, and agents, and configuring permissions with settings.json for secure, repeatable AI‑assisted development.
This guide explains the purpose of the .claude directory, details how CLAUDE.md, rules, hooks, skills, agents, and settings.json work together, and provides step‑by‑step instructions for configuring Claude Code to follow project‑specific policies and improve productivity.
The article explains how the .claude/ directory serves as Claude Code’s control center—detailing its two‑level structure, the role of CLAUDE.md, rules, commands, skills, agents, and settings.json, and provides a step‑by‑step guide to configure and extend it for team and personal workflows.
The article analyzes Matt Van Horn’s workflow that replaces traditional IDEs with a planning‑first approach using markdown task files, voice input, parallel research agents, and a custom "last30days" engine, showing how AI can orchestrate research, planning, execution, and verification to boost productivity across code and non‑code tasks.
Understanding the .claude directory—its project‑level and global subfolders, the roles of CLAUDE.md, rules, commands, skills, agents, and settings.json—lets you turn Claude Code from a black‑box chat tool into a configurable, team‑aligned coding partner that respects permissions and workflow conventions.
In 2026 the AI industry undergoes a paradigm shift as agents move from demos to large‑scale commercial use, embodied robots enter factories, and compact models reshape efficiency, signaling a new era of AI‑driven productivity across every sector.
OpenClaw is an AI‑driven automation platform that lets users describe tasks in natural language—like extracting PDF data or generating PPTs—and the system’s agents select appropriate skills to execute the work, aiming to simplify repetitive document workflows without coding.
The 2025 State of AI report from Artificial Analysis outlines five core trends—intensified competition, the rise of autonomous agents, native speech models, mainstream inference models, and booming image/video generation—showing how costs have plummeted, capabilities have surged, and AI is reshaping every industry.
This article traces the history of artificial intelligence from the human brain and the first computer, through the birth of AI, the rise of machine learning and AI models, to the transformer‑driven explosion of large language models, multimodal systems, agents, and the challenges that lie ahead.
Oh‑My‑OpenCode replaces a single AI coder with six specialized agents—Sisyphus, Oracle, Explore, Librarian, Visual Engineering, and Writing—automating task decomposition, parallel execution, and expert analysis, delivering 3‑8× speedups across code understanding, bug fixing, refactoring, documentation, and full‑stack development, as shown in real‑world case studies.
The article explains OpenAI’s new agent primitives—Skills, a hosted Shell environment, and server‑side Compaction—detailing how they enable long‑running, reliable AI agents, provides practical design patterns and tips, and compares this approach with the open‑source OpenClaw framework.
This roadmap outlines a step‑by‑step learning path for building, optimizing, and safely deploying large language model systems, covering fundamentals, vector stores, RAG, advanced techniques, fine‑tuning, inference speed, deployment, observability, agents, and production safeguards.
This article introduces the Agents‑Companion paradigm for Text‑to‑SQL, detailing how self‑describing database agents autonomously mine schema, statistics and semantics to generate high‑quality evidence, thereby bridging the gap between academic research and industrial deployment and significantly improving query accuracy.
This article walks you through the entire process of creating Claude Code plugins—from understanding why plugins are needed, setting up the directory structure, writing commands, agents, skills, and hooks, to testing, debugging, advanced features, and publishing—providing concrete code examples, configuration files, and best‑practice recommendations.
This article explains GitHub's AI ecosystem—Models, Copilot, Agents, Spaces, Spark, Instructions, Skills, and the Model Context Protocol—clarifying each component, their relationships, and practical steps for developers to integrate them into their workflow.
This article explains how product managers can demystify and apply the three core AI techniques—Retrieval‑Augmented Generation, fine‑tuning, and agents—using the Dify platform, showing step‑by‑step setups, practical benefits, cost considerations, and when to choose each approach.
This comprehensive article traces AI from its biological roots and early computers through the birth of artificial intelligence, the rise of machine learning, the emergence of large language models, multimodal agents, and finally explores current breakthroughs, practical applications, and future directions.
This article explains the fundamentals of AI‑generated content (AIGC), the Retrieval‑Augmented Generation (RAG) technique, Function Calling, autonomous agents, and the Model Context Protocol (MCP), highlighting their evolution, technical workflows, limitations, and real‑world examples for developers.
An analysis of the 5,984 papers accepted at NeurIPS 2025 shows a decisive move from ever‑larger models toward agents, reasoning‑focused LLMs, efficiency engineering, AI for Science, and trustworthy AI, signaling the transition from a research‑toy era to an engineering‑driven AI ecosystem.
Anthropic’s enterprise guide shows how early adopters boost productivity—20‑35% faster customer service, 30‑50% higher content output, 15% less coding time—and outlines a four‑step framework, prompt‑engineering formula, and agent roadmap to turn AI into measurable business value.
Artificial Analysis’s Q3 2025 AI report reveals a rapidly accelerating industry across the entire stack, with US and Chinese labs neck‑and‑neck, fierce competition among OpenAI, Google, Anthropic, xAI, DeepSeek and Alibaba, cost‑efficient models, booming multimodal agents, and a hardware race led by NVIDIA’s Blackwell accelerators.
LangGraph extends LangChain by letting developers define stateful, multi‑agent LLM workflows as graphs with nodes, edges, and shared state, and the article walks through core concepts, typical use cases, and a detailed example that shows how to define state, nodes, edges, compile and run the graph.
This article explains the core concept of LangChain agents—combining an LLM, a set of tools, and a reasoning‑action loop—to enable dynamic decision‑making, tool invocation, and iterative observation for solving complex, multi‑step tasks.
Alibaba Cloud and Alibaba Baicheng Technology jointly released a 200k‑word whitepaper authored by 15 experts and 40 frontline engineers that dissects AI‑native application architecture across the full DevOps lifecycle, outlines 11 critical components, highlights challenges such as debugging, latency, security and cost, and proposes concrete engineering solutions.
This comprehensive guide walks you through ClaudeCode, an Anthropic command‑line AI programming assistant, covering its core concepts, step‑by‑step installation, Chinese‑specific configuration, essential commands, custom agents, hooks, MCP server setup, workflow modes, and additional tools to transform ClaudeCode into a powerful end‑to‑end development partner.
This repository offers a comprehensive, step‑by‑step LangChain tutorial series that walks developers through installation, the LangChain Expression Language, streaming, parallel execution, callbacks, serialization, model customization, prompt templates, memory, multimodal support, and advanced tools like LangGraph and LangSmith, enabling the creation of sophisticated AI applications.
The 2025 H1 AI Core Achievements and Trends report reveals how agents are reshaping productivity, models are gaining inference power and becoming smaller, reinforcement learning is overtaking pre‑training, and industry competition is intensifying, with China and the US narrowing their technology gap.
This article presents a thorough evaluation of the open‑source JoyAgent‑JDGenie multi‑agent AI framework, covering its background, test cases for restaurant recommendation and travel planning, deployment steps, performance metrics, and concluding recommendations, highlighting its efficiency, ease of deployment, and result quality.
This article explores practical AI engineering strategies, detailing the three AI application modes—Embedding, Copilot, and Agents—along with prompt engineering, model selection, function calling, RAG, workflow design, and multi‑agent architectures to boost business efficiency and user experience.
This article provides a concise overview of the latest AI breakthroughs—including AIGC, multimodal technology, Retrieval‑Augmented Generation (RAG), intelligent agents with function‑calling models, and the Model Context Protocol (MCP)—explaining their principles, relationships, and practical implications for developers outside the AI field.
This article traces the evolution of large language models from the GPT‑4 era through 2024‑2025, highlighting the shift from pure scaling to efficiency‑focused architectures, the rise of reasoning‑centric "thinking" models, and the emergence of agentic capabilities that enable tools and real‑world interaction.
The article explains how large AI models are evolving from simple chatbots into reasoning-powered agents integrated with massive domain knowledge, creating powerful “industrial brains” that can understand and solve complex real‑world problems, reshaping the future of IT and the internet industry.
In a post‑I/O interview, Sundar Pichai explains how Google’s new AI products—from multimodal Gemini to AI agents like Flow—signal a second‑stage platform shift where AI becomes a universal interface, reshaping search, content creation, and the future integration of agents across consumer and enterprise ecosystems.
The 2025 Q1 AI report from Artificial Analysis highlights six major trends—including a thousand‑fold drop in inference cost, the rise of MoE models, the growing parity of Chinese open‑source labs, the emergence of autonomous AI agents, native multimodal capabilities, and the trade‑off between performance, cost, and context windows—painting a picture of a rapidly evolving, increasingly competitive AI ecosystem.
This article surveys eight popular large‑language‑model (LLM) agent development frameworks—OpenAI Agents SDK, LangGraph, LlamaIndex, AutoGen, Pydantic AI, SmolAgents, Camel, and CrewAI—explaining each’s key features and providing concrete Python code to integrate the MCP Server for tool access.
The podcast recap dissects a year of rapid AI change, highlighting surprise‑fast open‑source model releases, shifting foundation‑model dynamics, the rise of GPT wrappers, over‑hyped agents, undervalued memory, product‑market fit debates, infrastructure opportunities, and lingering mysteries like RL in non‑verifiable domains.
This tutorial walks through the limitations of simple prompt usage, introduces LangChain as a framework for building full‑featured LLM applications, explains its core concepts and components, and provides step‑by‑step code examples for installing, configuring, and running a basic LangChain demo.
Satya Nadella predicts that conventional software applications, including SaaS, will disappear as AI agents directly interact with databases, reshaping how we work, access data, and automate tasks, while raising new questions about trust, permissions, and the future of the software industry.
In 2025, large language models will see three key trends—agents becoming pervasive in daily life and industry, the emergence of efficient small models for edge and specialized tasks, and the integration of multimodal capabilities that combine text, images, and audio to enable more natural human‑machine interaction.
DB‑GPT is an open‑source AI‑native data application framework that unifies multi‑model management, RAG, agents, and workflow orchestration to simplify building large‑model‑driven data solutions, offering features such as private Q&A, multi‑source analytics, automated fine‑tuning, and robust privacy security.
LangChain is a modular framework that streamlines integration of large language models by providing unified model interfaces, prompt optimization, memory handling, indexing, chains, and agents, enabling developers to quickly build and deploy sophisticated NLP applications such as text generation, information extraction, and dynamic tool‑driven workflows across various industries.
This article documents the end‑to‑end workflow of using large multimodal models and specialized agents to automatically generate a storyboard, compose original music and lyrics, produce keyframes, and assemble a complete music video, while highlighting the remaining manual steps and future automation possibilities.
LangChain is an open‑source framework that provides Python and JavaScript SDKs, templates, and services such as LangServe and LangSmith to compose models, embeddings, prompts, indexes, memory, chains, and agents via a concise expression language, enabling rapid prototyping, debugging, and deployment of LLM‑driven applications.
This guide walks AI engineers through a rapid 10‑minute boot‑strap of LangChain, explaining its purpose, core concepts, design questions, environment setup, and step‑by‑step code examples that cover APIs, chains, memory, retrieval‑augmented generation, tools, agents, and the overall architecture.
This article provides a detailed examination of large language models, covering their underlying technologies, capabilities and constraints, model families, training processes, cloud and edge deployment challenges, agent architectures, and emerging trends, offering practical insights for developers, product managers, and researchers.
This article introduces LangChain—a Python framework for building LLM‑driven applications—explains its core components such as models, indexes, chains, memory, and agents, and provides practical code examples for document summarization, retrieval‑augmented QA, and future development directions.
This article explains what LangChain is, outlines its core components such as Models, Indexes, Chains, Memory and Agents, and demonstrates how to build LLM‑driven applications with detailed Python code snippets, visual diagrams, and future development suggestions.
This article examines how the rapid rise of generative AI reshapes application engineering by outlining AI's core characteristics, the challenges developers face, the evolution of prompt and chain-of-thought techniques, the emergence of agents and tool integration, and the future direction toward AI‑centric computing architectures.
This article introduces the LangChain framework, explains its core concepts such as models, prompts, agents, memory, indexes, and tools, provides detailed code examples for each component, and demonstrates practical applications ranging from chatbots to image generation, helping readers understand and build powerful LLM-powered solutions.
This article explains what LangChain is, outlines its core components such as Models, Indexes, Chains, Memory and Agents, provides practical code examples for building summarization and QA pipelines, and discusses future directions for LLM‑centric development.
This tutorial introduces LangChain, an open‑source Python framework that provides unified model access, prompt management, memory, retrieval, and tool integration, enabling developers to quickly prototype AI‑driven applications using large language models and various external data sources.
This article introduces LangChain, explains why it’s useful for building applications with large language models, walks through installation, API‑key setup, model and embedding selection, prompt engineering, chaining, memory, agents, and vector‑store indexing, and provides runnable Python code examples throughout.
This article explores how large AI models can act as autonomous agents equipped with tools, demonstrates practical LangChain examples for remote server queries and RSS parsing, explains the ReAct reasoning‑acting loop, and shows how decorators and object‑oriented patterns enable seamless AI‑driven programming.
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.
The article introduces LangChain, a framework that unifies language models, prompts, memory, retrieval, and tool‑driven agents into composable chains, illustrating its core components, code examples, end‑to‑end applications such as retrieval‑augmented QA and image generation, and outlining future uses in customer service, recommendation, and automated code review.
