The article analyzes the emerging "skill" trend—encapsulating human expertise as callable modules—and explains how it reshapes product design, turns AI into an autonomous project manager, and forces humans to become structured, API‑like nodes within collaborative systems.
The openJiuwen community introduces Coordination Engineering, a new paradigm that lets multiple AI agents form autonomous, self‑organizing teams through the Agent Team Engine, encapsulated in reusable Team Skills and shared via the Team Skills Hub, with examples ranging from renovation planning to multi‑disciplinary medical consultations.
The article introduces the “LLM Fallacy,” explaining how relying on large language models can make individuals mistakenly attribute AI‑generated results to their own skills, leading to overestimated competence across tasks such as writing, coding, and analysis, and discusses implications for education, hiring, and skill development.
Jiuwen Claw advances AI coordination engineering by unveiling Coordination Engineering and the first standardized multi‑agent capability package, Team Skills, which codifies collaboration workflows, offers a creator tool and hub for reusable, cross‑framework team skills such as a medical expert consultation team.
As AI code generators master syntax, the article argues that programmers must cultivate computational thinking—decomposition, abstraction, pattern recognition, and algorithm design—to stay indispensable, offering concrete examples, research findings, and practical guidelines for effective AI collaboration.
This article explains multi‑agent AI collaboration, outlines its core characteristics, breaks down the technical workflow of task decomposition, role assignment, communication and conflict resolution, compares leading frameworks, and showcases three practical scenarios—from financial report automation to game NPC ecosystems and intelligent customer service.
The article analyzes Anthropic’s Co‑work, showing how the new AI collaboration system moves beyond answering questions to actively managing emails, calendars, documents, and Slack, turning fragmented digital tools into a unified workflow that lets knowledge workers treat AI as a true partner rather than a simple query engine.
The article examines the emerging skillification trend—encapsulating human expertise as callable AI skills—explaining how this shift transforms product design from feature collections to ability networks, repositions AI from advisor to task‑orchestrator, and forces humans to become well‑defined, API‑like contributors.
This article analyzes a PM‑centric AI collaboration framework that consolidates scattered knowledge, reduces repetitive interruptions, and enables a single product manager to efficiently support a twenty‑person team by structuring context, optimizing prompt usage, and standardizing workflows across product, engineering, design, and operations.
OpenSpec is a CLI tool that introduces a spec‑driven workflow between developers and AI assistants, outlining clear "what" and "how" stages, change proposal management, integration with Cursor for automated documentation, and a complete cycle of proposing, reviewing, implementing, and archiving changes.
The article analyzes how traditional project kick‑off meetings cause silent information loss, proposes the PRD Agent as a single source of truth that anchors all documentation, automates mechanical tasks with AI, creates reusable expert skills, and ensures every AI‑generated answer is fully traceable, dramatically improving collaboration efficiency.
The article explains why single‑agent LLMs struggle with complex tasks and demonstrates how OpenClaw's multi‑agent architecture—featuring persistent, sub‑ and ACP agents, isolated workspaces, and cost‑aware model selection—enables parallel role‑focused collaboration, scalability, and significant efficiency gains.
TradingAgents introduces a multi‑agent architecture that lets specialized LLM experts—researchers, analysts, traders and risk managers—collaborate to analyse markets, manage risk and execute trades, offering a new AI‑driven collaboration paradigm for quantitative finance while providing explainable decisions and enterprise‑grade stability.
The article explains why vague language hurts AI‑assisted teamwork, illustrates the problem with a real Bilibili collaboration case, and proposes three concrete principles—specify format, provide full context, and treat every request as a prompt—to dramatically reduce rework and improve efficiency.
The article analyzes the emerging "Harness Engineering" paradigm, explaining why large‑language models need a surrounding harness of file systems, code execution, sandboxing, memory, and context management to become useful autonomous agents and how this concept may shape AI development through 2026.
The author shares practical experiences of collaborating with AI coding assistants, outlines the current gaps between today’s tools and an ideal Jarvis‑like system, and demonstrates a template‑driven Next.js workflow that lets AI generate a complete feature in five minutes while offering concrete decision‑making rules and task classifications.
Routa is an engineering‑focused multi‑agent framework that separates tasks, state, events, and execution into controllable modules, enabling open‑ecosystem AI coding agents to collaborate through structured specifications, event‑driven coordination, and verifiable tool interfaces rather than fragile prompt stitching.
Prompt engineering serves as the bridge between humans and AI, requiring cognitive decomposition, instruction engineering, and model alignment; the article outlines essential skills, precise language, scenario adaptation, iterative optimization, ethical safeguards, symbol conventions, template examples, tuning tools, FAQs, and learning resources.
This guide explains Claude Code’s experimental Agent Teams feature, compares it with Subagents, shows when to use each, walks through enabling the feature, configuring tmux split‑pane or in‑process modes, and provides best‑practice tips, troubleshooting steps, and a complete end‑to‑end example building a visual analysis platform.
Claude Opus 4.6 and GPT‑5.3‑Codex both introduce Agent Teams that let a single user orchestrate up to 16 parallel AI agents, cutting latency by 78%, boosting accuracy to 78.4%, and enabling feats like building a C compiler for the Linux kernel, with Kimi K2.5 offering a more user‑friendly, zero‑code alternative.
This article demonstrates a step‑by‑step, multi‑round dialogue with the AI assistant TRAE SOLO to progressively configure UnoCSS for a UniApp + Vue3 + Vite project, covering initial planning, dependency installation, platform‑specific fixes, shortcut creation, dynamic class handling, and component development.
Microsoft announced that starting February 2026 employees in the Puget Sound area must work on-site at least three days a week, a policy that will expand globally, aiming to boost collaboration, innovation, and hybrid work experiences amid a broader industry shift away from full remote work.
The article explores Ant Group’s agentUniverse team’s experience applying multi‑agent technology in finance, covering background on large language models, the agentUniverse framework, real‑world implementations, and the advantages of coordinated multi‑agent collaboration for complex analytical and decision‑making tasks.
This article presents a practical AI collaboration framework for developers, detailing three core work scenarios, three counter‑intuitive techniques, implementation steps, and deeper mindset shifts that together can dramatically accelerate programming tasks and expand team capabilities.
This recap of Chen Tian's AI‑focused meetup explores why knowledge still matters, presents four practical learning methods, outlines effective AI prompting techniques, redefines the developer’s role from coder to conductor, and offers a five‑stage AI collaboration workflow for modern software creation.
This article examines the shortcomings of the previous Cursor IDE rule set, introduces a three‑layer architecture for AI‑assisted coding, defines a standardized rule format and execution protocol, and provides best‑practice guidelines and phased rollout plans to ensure consistent, high‑quality code generation.
This article explains why current AI models struggle to cooperate, introduces the Model Context Protocol (MCP) as a universal “translation standard” for AI interoperability, and outlines its core features, practical benefits, limitations, and steps developers and users should take to adopt it.
Bill Gates predicts AI agents will soon replace most apps, prompting designers to rethink user experience, explore new interaction devices, and master AI communication skills, while emphasizing accessible design principles to thrive in a rapidly evolving digital landscape.
MetaGPT is a meta‑programming framework that encodes standard operating procedures as prompts, enabling LLM‑driven multi‑agent systems to automatically generate software artifacts, coordinate roles, and build complex applications like a Blackjack CLI with near‑perfect task completion.
On May 16, 360 and Zhipu AI announced a strategic partnership to co‑develop the trillion‑parameter models 360GLM and 360GPT, positioning them as China’s answer to Microsoft‑OpenAI by combining large‑scale pre‑training, bilingual capabilities, and integration with 360’s search and browser ecosystem.
