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Big Data and Microservices

Focused on big data architecture, AI applications, and cloud‑native microservice practices, we dissect the business logic and implementation paths behind cutting‑edge technologies. No obscure theory—only battle‑tested methodologies: from data platform construction to AI engineering deployment, and from distributed system design to enterprise digital transformation.

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Latest from Big Data and Microservices

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Big Data and Microservices

Oct 10, 2026 · Industry Insights

AI Enters Value Realization: 18 Cases Show Shift from Perception to Execution

This daily observation report analyzes 18 AI application cases across manufacturing, healthcare, agriculture, transportation, finance, governance, and consumer hardware on October 10, 2026, revealing a shift from perception to optimization, AI's penetration into rural areas, and the rise of on-device AI hardware, with data flywheels and domain knowledge as key enablers.

AI AgentsAI applicationsData Flywheel

0 likes · 37 min read

AI Enters Value Realization: 18 Cases Show Shift from Perception to Execution

Big Data and Microservices

Oct 9, 2026 · Industry Insights

AI Agents Shift from Chat to Execution: 14 Real-World Deployments Across 10 Sectors (Oct 9, 2026)

This daily observation tracks 14 AI deployments across manufacturing, healthcare, agriculture, transport, finance, governance, rural development, terminals, and investment promotion, revealing a shift from conversational AI to autonomous agents, with data flywheels and domain knowledge as key enablers.

AI AgentsAI deploymentData Flywheel

0 likes · 26 min read

AI Agents Shift from Chat to Execution: 14 Real-World Deployments Across 10 Sectors (Oct 9, 2026)

Big Data and Microservices

Oct 9, 2026 · Industry Insights

AI Splits Retail: Discovery to Agents, Ownership Stays with Retailers

This article analyzes how AI agents split retail into a discovery layer (given to AI) and an ownership layer (kept by retailers), citing conversion rates flipping from -38% to +42%, case studies from Amazon, Walmart, and Shopify, and a five-question framework for retailers.

AI AgentsAI in retailconversion rate

0 likes · 21 min read

AI Splits Retail: Discovery to Agents, Ownership Stays with Retailers

Big Data and Microservices

Oct 8, 2026 · Industry Insights

AI Scheduling Brain Unifies Logistics' Six Siloed Segments

This article analyzes how AI scheduling layers like SF Express's Super Brain and JD Logistics's Super Brain 2.0 integrate storage, picking, palletizing, sorting, transportation, and delivery into a unified decision engine, replacing fragmented optimizations with real-time global orchestration that cuts costs by millions.

AI schedulingLarge Language ModelsMulti-Agent Systems

0 likes · 18 min read

AI Scheduling Brain Unifies Logistics' Six Siloed Segments

Big Data and Microservices

Oct 6, 2026 · Industry Insights

AI in Chemicals: Why PID Autonomous Control Trumps Molecule Discovery

This article analyzes two distinct AI paths in chemical engineering—formula R&D for new materials and process control for optimizing existing plants—highlighting that the critical frontier is PID autonomous decision-making, not molecule discovery, and provides a framework for evaluating AI projects through data comparability, control handover stages, and lab-to-production gaps.

AI in chemical engineeringPID autonomous decision-makingformula R&D

0 likes · 21 min read

AI in Chemicals: Why PID Autonomous Control Trumps Molecule Discovery

Big Data and Microservices

Oct 5, 2026 · Industry Insights

AI in Steel: Why Scheduling Beats Visual Inspection as First Win

This analysis reveals why AI in steel manufacturing first optimizes scheduling and logistics rather than visual inspection, detailing a three-layer architecture where mechanism models remain the foundation, and presenting quantitative results from major Chinese steelmakers showing cost reductions, efficiency gains, and the critical role of data readiness.

Data InfrastructureDigital transformationMechanism Models

0 likes · 21 min read

AI in Steel: Why Scheduling Beats Visual Inspection as First Win

Big Data and Microservices

Oct 4, 2026 · Industry Insights

AI in Auto: Urban NOA Shift Makes Data Loop Speed the Real Differentiator

The article analyzes the shift of automotive AI competition from highway to complex urban driving, where 64.1% of mid-to-high-end ADAS are now urban NOA, and argues that the true competitive edge lies in data closed-loop iteration speed (5 days to 12 hours) rather than disengagement rates, detailing a three-layer vehicle intelligence architecture and the AI+Industry Five Questions framework.

AI in automotiveHorizon RoboticsRobotaxi

0 likes · 18 min read

AI in Auto: Urban NOA Shift Makes Data Loop Speed the Real Differentiator

Big Data and Microservices

Oct 3, 2026 · Industry Insights

AI Agents Slash Chip Defect Analysis to 5 Minutes; KPIs Now Track Deployment

This article analyzes how AI agents are transforming semiconductor design and verification, highlighting a 38-hour to 5.4-minute reduction in defect root-cause analysis at Hefei Jinhua, SK Hynix tying AI deployment to engineer KPIs, and a three-layer EDA framework where agents propose solutions but deterministic sign-off remains with physics-based tools and humans.

AI AgentsAI in manufacturingEDA

0 likes · 17 min read

AI Agents Slash Chip Defect Analysis to 5 Minutes; KPIs Now Track Deployment

Big Data and Microservices

Oct 2, 2026 · Industry Insights

How a Trillion-Parameter AI Model Saves Grid Electricity

This article analyzes three layers of AI application in power grids—prediction, optimization, and autonomous coordination—using case studies like China's Guangming Power Model to show how AI reduces curtailment, optimizes dispatch, and cuts reserve capacity, while warning that AI's own energy cost must be offset by grid savings.

AI in energyLarge Language Modelsenergy storage arbitrage

0 likes · 16 min read

How a Trillion-Parameter AI Model Saves Grid Electricity

Big Data and Microservices

Sep 30, 2026 · Industry Insights

Three Steps to Smart Factory: From Rule Checks to Autonomous Agent Loops

The article outlines three maturity layers of AI in manufacturing — rule-based inspection, AI visual inspection, and autonomous agent closed-loops — using real-world cases like Deli Group's 42x efficiency gain and PetroChina's 90% root-cause accuracy to show why most factories stall at layer two and what it takes to reach true self-optimizing production.

AI AgentsAI in manufacturingcase study

0 likes · 13 min read

Three Steps to Smart Factory: From Rule Checks to Autonomous Agent Loops