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AI Architecture Hub
AI Architecture Hub
Apr 21, 2026 · Artificial Intelligence

Why Harness Architecture Turns LLMs into Production‑Ready Agents

This article explains why the Harness architecture—linking prompts, context, and runtime support—is the decisive factor that turns large language models from demo prototypes into reliable production agents, detailing its core capabilities, structural components, execution loop, design trade‑offs, and industry trends.

AI OperationsAgent HarnessLLM engineering
0 likes · 35 min read
Why Harness Architecture Turns LLMs into Production‑Ready Agents
Java Architecture Diary
Java Architecture Diary
Apr 15, 2026 · Operations

Unlock JVM Mysteries: How Arthas and AI Turn Debugging into a One‑Click Process

When a service’s P99 latency spikes to seconds and CPU hits 90% without logs, Arthas lets you inspect the JVM in real time without code changes, and its AI‑driven MCP extension automates command selection, enabling developers to diagnose, trace, decompile, and monitor issues through simple natural‑language prompts and Spring Boot integration.

AI OperationsArthasJVM debugging
0 likes · 8 min read
Unlock JVM Mysteries: How Arthas and AI Turn Debugging into a One‑Click Process
Alibaba Cloud Observability
Alibaba Cloud Observability
Apr 6, 2026 · Artificial Intelligence

How OpenClaw’s New Plugin Reveals Every LLM Decision Step

The OpenClaw CMS plugin 0.1.2 upgrades observability for AI agents by fully restoring multi‑round execution traces, stabilizing concurrent chains, adding STEP spans, and quantifying agent metrics, turning raw trace graphs into actionable insights for debugging, testing, cost control, and cross‑team collaboration.

AI OperationsLLMOpenClaw
0 likes · 8 min read
How OpenClaw’s New Plugin Reveals Every LLM Decision Step
AI Architecture Hub
AI Architecture Hub
Apr 1, 2026 · Artificial Intelligence

How Harness Turns AI Agents from Demo to Production‑Ready Systems

Enterprise AI teams often see impressive results with single‑turn prompts, but when tasks become long‑running and complex, models lose context, produce faulty code, and require heavy manual intervention; the Harness framework provides a full‑lifecycle control system that stabilizes agents, manages knowledge, and ensures reliable production deployment.

AI AgentAI OperationsHarness framework
0 likes · 12 min read
How Harness Turns AI Agents from Demo to Production‑Ready Systems
Data Party THU
Data Party THU
Mar 21, 2026 · Operations

How to Harden and Operate OpenClaw for Reliable Production Use

This guide walks you through the essential steps to transform a freshly installed OpenClaw instance into a stable, production‑ready AI assistant, covering troubleshooting, configuration files, memory persistence, model selection, security hardening, Telegram integration, browser setup, and automated heartbeat and cron management.

AI OperationsConfigurationDeployment
0 likes · 8 min read
How to Harden and Operate OpenClaw for Reliable Production Use
AI Software Product Manager
AI Software Product Manager
Feb 4, 2026 · Artificial Intelligence

Mastering Agent Skills: A Systematic Guide to Large Model Capabilities

This article traces the evolution of large‑model capabilities from early plugins to the standardized Agent Skills framework, explains the core concepts, technical composition, and progressive disclosure mechanism, and provides a step‑by‑step practical guide for building, configuring, and deploying Skills across ecosystems.

AI ArchitectureAI OperationsAgent Skills
0 likes · 11 min read
Mastering Agent Skills: A Systematic Guide to Large Model Capabilities
Bilibili Tech
Bilibili Tech
Nov 28, 2025 · Artificial Intelligence

How We Built an LLM‑Powered AI Hub to Read and Analyze Community Chats

This article details the design and deployment of a multi‑layer LLM system that automatically reads massive creator group chats, extracts structured insights, mitigates hallucinations with dual‑model verification, uses few‑shot prompting for stable output, and delivers real‑time risk alerts and operational reports.

AI OperationsFew‑Shot LearningLLM
0 likes · 14 min read
How We Built an LLM‑Powered AI Hub to Read and Analyze Community Chats
DataFunTalk
DataFunTalk
Sep 28, 2025 · Artificial Intelligence

How Bilibili Leverages Large Language Models to Automate Big Data Operations

This article explores Bilibili’s implementation of a large‑language‑model‑driven intelligent assistant that helps troubleshoot massive offline and real‑time data processing tasks, detailing the platform’s five‑layer architecture, common failure causes, and how AI can streamline issue resolution.

AI OperationsIntelligent Assistantbig data platform
0 likes · 4 min read
How Bilibili Leverages Large Language Models to Automate Big Data Operations
DataFunTalk
DataFunTalk
Sep 27, 2025 · Artificial Intelligence

Bilibili’s AI Assistant: Using Large Language Models to Tackle Massive Data Tasks

This article explains how Bilibili leverages a large‑language‑model‑based intelligent agent to diagnose and resolve failures and slowdowns in its massive big‑data platform, detailing the platform architecture, workload scale, common user issues, and the need for automated assistance.

AI OperationsBilibiliIntelligent Assistant
0 likes · 5 min read
Bilibili’s AI Assistant: Using Large Language Models to Tackle Massive Data Tasks
Architecture & Thinking
Architecture & Thinking
Sep 17, 2025 · Artificial Intelligence

How the 32B ‘Zhiyu’ Model is Revolutionizing Intelligent Operations

The Zhiyu model, a 32‑billion‑parameter SRE‑focused LLM, combines extensive domain knowledge, enhanced professional skills, and deterministic RAG to deliver precise, actionable insights for intelligent operations, backed by a robust multi‑source training pipeline, staged training, and flexible deployment options.

AI OperationsModel TrainingRAG
0 likes · 7 min read
How the 32B ‘Zhiyu’ Model is Revolutionizing Intelligent Operations
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 12, 2025 · Operations

How to Build End‑to‑End Observability for Large‑Model Applications on Alibaba Cloud

This guide explains how to design and implement a complete observability solution for large‑model AI services on Alibaba Cloud, covering architecture, core metrics, logging standards, demo code, log collection, dashboard design, alerting, monitoring tools, troubleshooting SOPs, and recovery procedures.

AI OperationsAlibaba CloudLarge Language Models
0 likes · 21 min read
How to Build End‑to‑End Observability for Large‑Model Applications on Alibaba Cloud
SF Technology Team
SF Technology Team
Jul 29, 2025 · Artificial Intelligence

How SF Tech’s Proprietary Large Models Revolutionize Logistics and AI Operations

The DA Data Intelligence Conference in Shenzhen showcased SF Tech’s breakthroughs in large‑model AI, revealing how their proprietary multimodal models, RAG innovations, and agent platforms dramatically improve logistics decision‑making, resource scheduling, and customer service across multiple industries.

AI OperationsAgent PlatformRAG
0 likes · 11 min read
How SF Tech’s Proprietary Large Models Revolutionize Logistics and AI Operations
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jul 9, 2025 · Operations

How to Build an AI-Powered Ops Assistant with Elasticsearch for Real-Time Log Monitoring

This guide explains how to transform Elasticsearch from a simple log repository into an intelligent operations AI assistant that provides real‑time monitoring, natural‑language query, automated troubleshooting, security threat detection, and low‑code interaction, covering architecture, deployment steps, sample queries, visualization, and resource cleanup.

AI OperationsElasticsearchLog Monitoring
0 likes · 7 min read
How to Build an AI-Powered Ops Assistant with Elasticsearch for Real-Time Log Monitoring
JavaEdge
JavaEdge
Feb 2, 2025 · Artificial Intelligence

Mastering LLMOps: From Model Deployment to Scalable AI Operations

This article explains LLMOps—its goals, core activities, benefits, best practices, and how using an LLMOps platform like Dify can dramatically cut development time, simplify prompt engineering, data preparation, monitoring, and deployment of large language models.

AI OperationsData ManagementLLMOps
0 likes · 13 min read
Mastering LLMOps: From Model Deployment to Scalable AI Operations
Architect
Architect
Jul 13, 2024 · Artificial Intelligence

Practical Guide to Building LLM Products: Prompt Engineering, RAG, Evaluation, and Operations

This article provides a comprehensive, step‑by‑step guide for developing large‑language‑model (LLM) applications, covering prompt design techniques, n‑shot and chain‑of‑thought strategies, retrieval‑augmented generation, structured I/O, workflow optimization, evaluation pipelines, operational best practices, and team organization to create reliable, scalable AI products.

AI OperationsLLMProduct Development
0 likes · 54 min read
Practical Guide to Building LLM Products: Prompt Engineering, RAG, Evaluation, and Operations
iQIYI Technical Product Team
iQIYI Technical Product Team
May 31, 2024 · Artificial Intelligence

How Opal Turns iQIYI’s ML Workflow into a Unified AI Platform

Opal is iQIYI's end‑to‑end machine‑learning platform that integrates feature production, sample construction, model training, and deployment with big‑data services, addressing duplicated effort, weak data processing, and fragmented pipelines to boost efficiency across recommendation, advertising, and risk‑control scenarios.

AI OperationsBig Data IntegrationDistributed Training
0 likes · 19 min read
How Opal Turns iQIYI’s ML Workflow into a Unified AI Platform
DataFunSummit
DataFunSummit
May 10, 2024 · Artificial Intelligence

LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions

This article introduces LLMOps by defining large language model operations, explains the three stages of LLM development, details modern fine‑tuning methods such as PEFT, Adapter, Prefix, Prompt and LoRA, outlines the architecture for building LLM applications, discusses the main difficulties of agent‑based deployments, and presents practical solutions including Prompt IDE, low‑code deployment, monitoring and cost control.

AI OperationsFine-tuningLLMOps
0 likes · 14 min read
LLMOps: Definition, Fine‑tuning Techniques, Application Architecture, Challenges and Solutions
Baidu Geek Talk
Baidu Geek Talk
Dec 6, 2023 · Industry Insights

From MLOps to LMOps: Challenges and Solutions for Large‑Model Operations

This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.

AI OperationsLMOpsMLOps
0 likes · 23 min read
From MLOps to LMOps: Challenges and Solutions for Large‑Model Operations
Baidu Intelligent Cloud Tech Hub
Baidu Intelligent Cloud Tech Hub
Aug 8, 2023 · Artificial Intelligence

Unlocking LMOps: How Enterprises Can Master Large Model Operations

This article explains the evolution from traditional machine learning to the current large‑model era, introduces LMOps concepts and key technologies, compares them with MLOps, and showcases Baidu Cloud's Qianfan platform as a practical solution for building, deploying, and managing large language models in industry.

AI OperationsBaidu CloudLMOps
0 likes · 22 min read
Unlocking LMOps: How Enterprises Can Master Large Model Operations
Efficient Ops
Efficient Ops
Nov 7, 2022 · Artificial Intelligence

Unlocking AI Project Success with the New MLOps Maturity Assessment

This article outlines the background, standards, evaluation items, process, and registration details of a newly launched MLOps development management maturity assessment designed to accelerate AI model delivery and improve operational efficiency across teams.

AI OperationsMLOpsModel Deployment
0 likes · 6 min read
Unlocking AI Project Success with the New MLOps Maturity Assessment
Didi Tech
Didi Tech
Apr 26, 2021 · Artificial Intelligence

Model Quality Assurance Practices at DiDi: Challenges, Solutions, and Evaluation

DiDi’s shift to machine‑learning‑driven ride‑hailing services revealed major QA challenges—data and feature quality, model verification, and API stability—prompting a four‑pillar framework and a unified “Strategy‑Center 1.0” platform to systematically monitor, evaluate, and improve model effectiveness, bias paths, and feature discovery.

AI OperationsFeature EvaluationModel Evaluation
0 likes · 8 min read
Model Quality Assurance Practices at DiDi: Challenges, Solutions, and Evaluation
Efficient Ops
Efficient Ops
Nov 22, 2018 · Artificial Intelligence

How AI Transforms Log Management: Building an Intelligent Log Center for AIOps

This article explores how AI-driven AIOps can turn massive operational log data into actionable insights, detailing the five‑level AI capability model, real‑world implementation scenarios, and industry case studies that demonstrate the value of an intelligent log center.

AI OperationsIT OperationsLog Management
0 likes · 10 min read
How AI Transforms Log Management: Building an Intelligent Log Center for AIOps