#AutoGen framework
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·3 min
Agent Skills Architecture Explained: A New Paradigm for Modular AI Agent Development
A deep dive into Agent Skills architecture: core concepts, components, and how it works. Clarifies common misconceptions about Skills vs. MCP, and compares Skills with Multi-Agent architecture.
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MiniMax Model Real-World Test: The Ultimate Cost-Effective Choice for Ultra-Long Research Tasks
Developer tests MiniMax model running 16 hours on research tasks at a fraction of GPT-4o and Claude costs. Analysis of cost advantages, use cases, and multi-model strategies.
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Google Co-Scientist Explained: A Gemin…
·2 min
Google Co-Scientist Explained: A Gemini-Powered Multi-Agent AI Research System
Deep dive into Google's Co-Scientist: a Gemini-powered multi-agent AI system that autonomously generates hypotheses, conducts agent debates, and iteratively evolves research directions.
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What Is AI Loop? The Paradigm Shift from Agents to Loop-Based Architecture
Deep dive into AI Loop architecture: how continuous-running agent swarms differ from traditional AI Agents, with applications in software development, cybersecurity, and beyond.
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BioAgents: A Multi-Agent AI Framework for Autonomous Research in Biological Sciences
Deep dive into BioAgents multi-agent AI framework: how literature analysis and data scientist agents collaborate for autonomous deep research in biological sciences.
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Sakana AI × Sumitomo Mitsui: Multi-AI Agent Collaboration Auto-Generates Proposals, Boosting Efficiency 100x
Sakana AI and SMBC developed a multi-AI Agent proposal auto-generation app, reducing creation time from 1-2 weeks to hours. Deep dive into the multi-Agent architecture and its implications for financial AI.
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Core Analysis of Agent Skill: Progressive Disclosure & Middleware Practical Guide
Deep dive into Agent Skill's core design—Progressive Disclosure—with detailed middleware and dynamic tool implementation, Multi-Agent comparison, and practical tips.
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·4 min
AI Agent Development: A Complete Six-Week Systematic Learning Roadmap
A systematic six-week learning roadmap for AI Agent development covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, deployment, and hands-on projects.
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The Complete Skill Map for AI Application Engineers: A Progressive Roadmap from Fundamentals to Production
A systematic breakdown of the complete skill structure for AI application engineers, covering Python & deep learning fundamentals, small model engineering, LLM fine-tuning, Agent development, and enterprise projects.
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Is the 748-Episode AI Large Language Model Tutorial on Bilibili Worth Watching? A Full Breakdown of Content Structure and Study Tips
In-depth analysis of Bilibili's 748-episode AI LLM tutorial covering RAG, Agent, and fine-tuning. Includes content structure breakdown and practical study tips for beginners.
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DeepSeek Researcher Summarizes 10 Rules for Using AI Agents
A DeepSeek researcher shares 10 universal rules for using AI agents, covering the shift from execution to judgment, memory file systems, human-AI collaboration boundaries, and more.
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Agent Harness: A New AI Agent Paradigm from Prompt Engineering to Execution Environment Orchestration
Deep dive into Agent Harness Engineering: how loop execution and context isolation overcome the bottlenecks of prompt and context engineering in modern AI coding agents like Cursor.
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TraeHarness Open-Sourced: 18 AI Agents Form a Virtual Development Team
TraeHarness is an open-source multi-agent framework with 18 specialized AI Agents simulating a real software team, covering requirements, architecture, development, testing, and deployment.
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In-Depth Guide to the Codex Chinese Manual: A Complete Walkthrough from Beginner to Advanced
In-depth breakdown of ByteDance's 198-page Codex Chinese manual covering installation, Commands, MCP workflows, Skills templates, and multi-Agent collaboration.
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·3 min
MCP vs Skills: Understanding the Key to AI Testing Efficiency
Understand the key differences between MCP (Model Context Protocol) and Skills through practical analogies and real testing scenarios to boost AI-driven test automation efficiency.
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Codex and Claude Code Dual-Engine: A Practical Guide to AI-Powered Engineering
A deep dive into AI engineering with Codex and Claude Code: Vibe Coding limitations, Chinese LLM rankings, Skill-driven development, and enterprise project practices.
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·3 min
GitHub AI Weekly Rankings: Agent Infrastructure Projects Dominate, Toolchain Ecosystem Taking Shape
June 2026 Week 1 GitHub AI trends: Agent infrastructure projects like Hedron and MarkItDown dominate, as context compression, document parsing, memory systems, and aesthetic toolchains rapidly mature.
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Claude Code Workflow in Action: 68 Sub-Agents Working Concurrently
Hands-on test of Claude Code's Workflow mode with 68 concurrent sub-agents. Covers setup, write-review separation, real concurrency results, and token costs.
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Stop Writing Prompts by Hand: Let AI Agents Prompt Themselves
Deep dive into the AI coding paradigm shift: from hand-crafted prompts to self-prompting agent loops. Learn how agent self-review and proactive context fetching enable scalable, high-quality AI coding.
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·3 min
New Book Breakdown: "Claude Code in Practice: The Way of Harness Engineering" — Master AI Programming Engineering in Ten Chapters
Deep breakdown of the new book on Claude Code engineering, covering Harness concepts, four-layer architecture, five-layer memory, sub-agents, hooks, MCP protocol, and CI/CD integration.
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