Tagged articles

system engineering

15 articles · Page 1 of 1

Machine Learning Algorithms & Natural Language Processing

Jun 28, 2026 · Artificial Intelligence

DSpark Explained in 10 Essential Concepts: System‑Level Engineering Insights

DSpark, DeepSeek’s new LLM inference framework, combines batch processing, speculative decoding, Eagle‑style draft models and DFlash‑style parallel generation with a lightweight sequential head and hardware‑aware scheduling, delivering 60‑85% speedups while preserving model quality.

Batch ProcessingDeepSeekGPU Optimization

0 likes · 12 min read

DSpark Explained in 10 Essential Concepts: System‑Level Engineering Insights

ITPUB

Jun 24, 2026 · Industry Insights

How a Zero‑GPU, All‑CPU Supercomputer Defied the GPU Trend to Claim World #1

The Ling Sheng supercomputer topped the TOP500 with a 2.19 EFLOPS all‑CPU design, eschewing GPUs, achieving 84.4% scaling efficiency across millions of cores, high energy efficiency, and demonstrating a full‑stack domestic alternative that reshapes the global high‑performance computing landscape.

AI accelerationCPU architectureHigh-performance computing

0 likes · 6 min read

How a Zero‑GPU, All‑CPU Supercomputer Defied the GPU Trend to Claim World #1

DataFunSummit

May 27, 2026 · Artificial Intelligence

How Baidu’s “Sheng Suan” Turns Agents from Outsiders into Business‑Savvy Assistants

The article explains that most AI agents achieve only 80‑90% accuracy in read‑only tasks and cannot handle core production decisions, then details Baidu’s “Sheng Suan” platform which uses a three‑layer business ontology and system‑engineered sandbox, audit, and simulation features to enable agents to execute write operations, citing three real‑world cases where decision latency dropped from months to minutes and accuracy exceeded 95%.

AI agentsCase StudiesEnterprise AI

0 likes · 8 min read

How Baidu’s “Sheng Suan” Turns Agents from Outsiders into Business‑Savvy Assistants

AI Step-by-Step

Apr 3, 2026 · Artificial Intelligence

Why Building AI Agents Requires a Full System‑Engineering Harness

The article explains that simply scaling large language models cannot sustain long‑running, production‑grade AI agents, and that a dedicated Agent Harness—acting as an operating system with orchestration, memory, governance, tool execution, and feedback loops—is essential for reliable, industrial‑scale automation.

AI agentsAgent HarnessGovernance

0 likes · 9 min read

Why Building AI Agents Requires a Full System‑Engineering Harness

Wu Shixiong's Large Model Academy

Nov 27, 2025 · Artificial Intelligence

Why Your Enterprise AI Agent Fails and How to Fix the Four Biggest Pitfalls

This article explains why many enterprise AI agents break down in real projects, identifies four common pitfalls—including mistaking agents for chatbots, lacking schema‑level tool logic, missing memory and variable injection, and absent end‑to‑end pipelines—and offers concrete engineering solutions to build robust, task‑driven agents.

AI AgentEnd-to-End PipelineEnterprise AI

0 likes · 8 min read

Why Your Enterprise AI Agent Fails and How to Fix the Four Biggest Pitfalls

Alibaba Cloud Developer

Oct 30, 2025 · Artificial Intelligence

Why AI Agents Aren’t As Simple As They Appear: Engineering Challenges and Solutions

Building AI agents may seem straightforward with frameworks like LangChain, but hidden complexities in orchestration, memory management, reproducibility, and scalability turn simple demos into fragile systems, requiring systematic engineering, observability, and robust design to achieve reliable, production‑grade intelligent agents.

AI agentsAgent DesignLangChain

0 likes · 21 min read

Why AI Agents Aren’t As Simple As They Appear: Engineering Challenges and Solutions

iQIYI Technical Product Team

Oct 10, 2024 · Artificial Intelligence

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.

AdvertisingCTR PredictionDeep Learning

0 likes · 9 min read

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

Model Perspective

Mar 6, 2024 · Fundamentals

Why Managing a City Is Like Designing a Spaceship: Exploring Complex Systems

An insightful look at how both spacecraft design and city governance exemplify complex systems, distinguishing closed versus open systems, outlining characteristics of complex and mega-complex systems, and linking these concepts to system engineering pioneers like Qian Xuesen and modern large language models.

Large Language ModelsQian Xuesenopen vs closed systems

0 likes · 9 min read

Why Managing a City Is Like Designing a Spaceship: Exploring Complex Systems

Architects Research Society

Jul 4, 2023 · Fundamentals

Design Patterns and Interface Coupling in Enterprise Service Environments

The article explains the concept of software design patterns, their role in managing coupling and interfaces, and provides best‑practice guidelines for enterprise‑level service design, illustrating how MITRE system engineers should select and apply patterns to achieve loosely‑coupled, extensible systems.

Design PatternsEnterprise ArchitectureSoftware engineering

0 likes · 13 min read

Design Patterns and Interface Coupling in Enterprise Service Environments

ByteDance SYS Tech

Nov 15, 2022 · Cloud Native

How ByteDance’s STE Team Tackles Linux Kernel Memory Waste and Drives Cloud‑Native Innovation

This interview reveals how ByteDance’s STE team, a youthful group of Linux kernel engineers, identified and solved memory‑management redundancy, contributed the HVO and VDUSE projects to the open‑source community, and leveraged these advances to boost cloud‑native performance and reliability across the company.

Cloud NativeLinux kernelOpen-source

0 likes · 15 min read

How ByteDance’s STE Team Tackles Linux Kernel Memory Waste and Drives Cloud‑Native Innovation

Alimama Tech

Dec 22, 2021 · Artificial Intelligence

Performance Optimization of Advertising Deep Learning Systems: Algorithm, System, and Hardware Co‑Design

The paper presents a holistic algorithm‑system‑hardware co‑design for advertising deep‑learning inference, combining model pruning, approximate computing, kernel fusion, scheduling and PCIe transfer optimizations with GPU and NPU upgrades, achieving up to five‑fold speed‑up and significantly higher latency‑bounded QPS for large‑scale ad services.

Algorithmic OptimizationGPUNPU

0 likes · 24 min read

Performance Optimization of Advertising Deep Learning Systems: Algorithm, System, and Hardware Co‑Design

ITFLY8 Architecture Home

Jul 11, 2021 · Fundamentals

How to Design Large-Scale Systems: From Business to Technical Architecture

This article explains the fundamental differences between large and simple systems, clarifies why large-system design is more than just distributed design, and outlines a step-by-step approach covering business, system, and technology layers, including business, application, data, and technical architecture.

large-scale architecturesoftware designsystem engineering

0 likes · 8 min read

How to Design Large-Scale Systems: From Business to Technical Architecture

Open Source Linux

Jul 23, 2020 · Operations

5 Essential Steps to Become a Successful DevOps Engineer

This article outlines the five key practices—adopting a developer mindset, mastering system engineering, gaining cloud experience, learning containers, and developing soft skills—required to become an effective DevOps engineer in today’s rapidly evolving tech landscape.

Cloud ComputingContainersOperations

0 likes · 6 min read

5 Essential Steps to Become a Successful DevOps Engineer

JD Tech Talk

Jun 5, 2020 · Information Security

From System Engineering to Anti‑Black‑Market: Lin Yuansheng’s Journey in JD’s Intelligent Risk Detection

Lin Yuansheng, JD’s first post‑doctoral researcher, leverages his system‑engineering background and big‑data analytics to build models that identify black‑market accounts, improve network risk perception, and demonstrate how academic research can be rapidly applied to real‑world e‑commerce security challenges.

Risk Detectiondata modelingsystem engineering

0 likes · 8 min read

From System Engineering to Anti‑Black‑Market: Lin Yuansheng’s Journey in JD’s Intelligent Risk Detection

Art of Distributed System Architecture Design

Oct 16, 2015 · Artificial Intelligence

Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang

This talk shares the experience of a small machine‑learning team at Dangdang, describing how they built a recommendation system from scratch, the tools and processes they used, the challenges of limited personnel, and the many pitfalls they encountered while iterating toward a production‑ready solution.

ML pipelinebest practicesrecommendation

0 likes · 21 min read

Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang