Jul 27, 2026 · Artificial Intelligence

The Awakening of Large Models: From Classic Language Modeling to Generative AI

This article traces the 56‑year evolution of language models—from ELIZA’s rule‑based scripts and N‑gram statistics to neural embeddings, RNNs, Transformers and the seven‑layer ChatGPT architecture—explaining why the simple next‑token probability definition has remained the core of generative AI, how autoregressive factorization drives training, generation and decoding, why hallucinations arise, and what engineering trade‑offs matter in production.

ChatGPTLLMautoregressive modeling

0 likes · 26 min read

The Awakening of Large Models: From Classic Language Modeling to Generative AI

AndroidPub

Jul 27, 2026 · Artificial Intelligence

Why Clarifying Requirements First Boosts AI Agent Success: The grill‑me Skill in Action

The article explains how the grill‑me skill inserts an interactive requirement‑clarification stage before an AI Agent executes a task, reducing misaligned outputs and rework by asking one focused question at a time, offering suggested answers, and distinguishing factual from decision information.

AI Agentgrill-meprompt engineering

0 likes · 20 min read

Why Clarifying Requirements First Boosts AI Agent Success: The grill‑me Skill in Action

TonyBai

Jul 27, 2026 · Artificial Intelligence

AI Generates 75% of Code—Why Engineers Panic: The Problem Is the Model, Not the Harness

When AI writes most of the code, the promised speed gains mask a deeper issue: current programming models, trained only to pass tests, fail to learn maintainability, leading to declining PR quality, rising bugs, and the collapse of "lights‑off" software factories.

AI codingharness engineeringhuman review

0 likes · 17 min read

AI Generates 75% of Code—Why Engineers Panic: The Problem Is the Model, Not the Harness

Big Data and Microservices

Jul 27, 2026 · Industry Insights

When AI Agents Become Colleagues: Redefining Human‑Machine Collaboration

The article analyzes how AI agents are shifting from simple tools to collaborative colleagues, reshaping human roles, flattening organizational hierarchies, and prompting new governance, skill, and cultural practices backed by reports from Deloitte, Gartner, IDC and real‑world case studies.

AI agentsAI governancedigital employee

0 likes · 13 min read

When AI Agents Become Colleagues: Redefining Human‑Machine Collaboration

AI Engineer Programming

Jul 26, 2026 · Artificial Intelligence

Agent Development Lifecycle (ADLC): Vendor‑Neutral Guide to Build, Test, Deploy, Monitor, and Govern AI Agents

This note outlines a vendor‑agnostic Agent Development Lifecycle (ADLC) that extends traditional SDLC with five stages—Build, Test, Deploy, Monitor, and Govern—detailing layer‑wise tooling choices, evaluation strategies, deployment infrastructure, observability practices, and governance concerns for modern AI agents.

AI lifecycleAgentOpsGovernance

0 likes · 15 min read

Agent Development Lifecycle (ADLC): Vendor‑Neutral Guide to Build, Test, Deploy, Monitor, and Govern AI Agents

Model Perspective

Jul 26, 2026 · Industry Insights

Why Jingdezhen Porcelain Earned UNESCO Heritage: Three Analytical Models

The article explains how Jingdezhen’s hand‑made porcelain secured UNESCO World Heritage status by expanding its feasible material formula, leveraging a 72‑step yield‑multiplication process, and applying a Weber‑style location model that optimizes heavy‑material consumption and light‑material transport.

Feasible RegionIndustrial HeritageJingdezhen

0 likes · 9 min read

Why Jingdezhen Porcelain Earned UNESCO Heritage: Three Analytical Models

dbaplus Community

Jul 26, 2026 · Cloud Native

Will AI Replace Kubernetes? Co‑Founder Brendan Burns on Its Rise and End

Brendan Burns recounts how he convinced Google to back Kubernetes, built the MVP in five days, navigated open‑source governance, tackled technical challenges like Etcd and declarative design, expanded the platform for AI workloads, and reflects on why even successful software like Kubernetes inevitably faces obsolescence.

AI WorkloadsCloud NativeDistributed Systems

0 likes · 31 min read

Will AI Replace Kubernetes? Co‑Founder Brendan Burns on Its Rise and End

The Dominant Programmer

Jul 26, 2026 · Artificial Intelligence

Building Smart Agents with Spring AI Alibaba: A Hands‑On Guide

This article walks through the Spring AI Alibaba Agent Framework (v1.1.2.0), explaining the ReAct reasoning‑acting loop, core APIs, configuration, code examples, testing commands, and common troubleshooting steps so developers can quickly create LLM‑driven agents with tool‑calling and memory support.

Agent FrameworkAlibabaJava

0 likes · 15 min read

Building Smart Agents with Spring AI Alibaba: A Hands‑On Guide

Coder Trainee

Jul 26, 2026 · Fundamentals

Git Team Collaboration Best Practices – Final Summary of the 10‑Part Series

This article concludes a ten‑part Git series by summarizing team collaboration best practices, covering workflow selection, branch naming, commit message conventions, daily development steps, code‑review checklist, command cheat sheet, and a team agreement template to turn Git knowledge into actionable standards.

BranchingCommit MessageTeam Collaboration

0 likes · 10 min read

Git Team Collaboration Best Practices – Final Summary of the 10‑Part Series

Machine Learning Algorithms & Natural Language Processing

Jul 26, 2026 · Artificial Intelligence

EvoX Matches Codex Scores at Just $1.95 per Task – How a Chinese Team Achieved It

The article analyzes why most multi‑agent AI projects fail, introduces EvoX’s swarm‑self‑evolution approach that splits tasks into atomic units, shows benchmark results where EvoX rivals Codex while cutting per‑task cost to $1.95, and explores how information design drives agent self‑organization.

AI agentsEvoXMulti-Agent Systems

0 likes · 12 min read

EvoX Matches Codex Scores at Just $1.95 per Task – How a Chinese Team Achieved It

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