#closed-loop learning
284 related articles
·5 min
Loop Engineering Explained: The New Programming Paradigm Where AI Prompts Itself
Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.
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·6 min
Harness Engineering: From Context Engineering to the Third Phase of AI Programming
Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.
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·4 min
OpenAI Employee Shares How to Build an AI Work Operating System with Codex
OpenAI's Jason Liu shares how he uses ChatGPT Workbench and Codex to build an AI work OS: Chief of Staff automation, persistent threads, Skills/Plugins, browser control, and app-building methodology.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·3 min
From RAG to Agent Tuning: A Complete Guide to Deploying LLM Agents
A deep dive into LLM Agent frameworks covering RAG, Agent core components (tools, memory, planning), and Agent Tuning workflows with cost considerations for production deployment.
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·3 min
From Frontend to Agent Development: A Complete 6-Week Systematic Learning Path
A 6-week systematic learning path for frontend engineers transitioning to AI Agent development, covering core architecture, ReAct, multi-agent collaboration, RAG integration, and deployment.
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·3 min
Codex Beginner's Guide: Hands-On Review of OpenAI's Programming Assistant with Practical Tips
A detailed guide to OpenAI Codex's core features, real-world usage, comparison with Claude Code, and DeepSeek configuration tips for developers choosing AI programming tools.
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·5 min
Getting Started with Codex: Four Product Forms Explained & How to Choose Between Cursor, Claude Code, and Codex
In-depth analysis of OpenAI Codex's four forms (CLI, web, plugin, app), comparing Codex, Claude Code, and Cursor on price, stability, and use cases to help developers choose the right tool.
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·3 min
How Student Robotics Programs Are Igniting Passion for Future Manufacturing Careers
How student robotics programs use CAD, computer vision, CNC machining, and full tech stacks to spark youth interest in manufacturing and automation careers, bridging STEM education and industry needs.
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·2 min
ag-kit: A Deep Dive into the TypeScript-Based AI Agent Development Toolkit
An in-depth analysis of ag-kit, a TypeScript-based AI Agent development toolkit covering core architecture, modular design, use cases, and tech selection advice for full-stack developers.
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·3 min
Coze Agent Development in Practice: From Beginner to Multi-Agent Collaboration
Coze is ByteDance's homegrown agent-building platform. This article covers getting started with Coze, its comparison with Dify, skill system, workflow orchestration, and multi-agent collaboration.
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·3 min
Building AI Agents with Coze: From Beginner to Multi-Agent Collaboration
Coze is ByteDance's homegrown agent-building platform. This article explains getting started with Coze, comparison with Dify, its skill system, workflow orchestration, and multi-agent collaboration.
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·2 min
Agent Skills Explained: Giving AI Plug-and-Play Capability Packs
Agent Skills is a lightweight open-source format that lets AI agents extend professional capabilities on demand, like loading plugins. Learn what Agent Skills are, the problems they solve, and their modular advantages.
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·8 min
A Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.
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·8 min
The Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
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·6 min
The Complete Guide to Using Claude in China: In-Depth Comparison of 4 Solutions and How to Choose
How can users in China use Claude? This article deeply compares four solutions: official subscription, proxy subscription (WildCard), relay platforms (2233/0011.ai), and API aggregation (OpenRouter).
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·7 min
Context Engineering: A Systematic Approach to Stop AI Agents from 'Talking Nonsense'
Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.
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·6 min
The Boundaries of AI Coding: When Should You Show Yourself a Red Card?
As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.
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·7 min
Alibaba Open-Sources 2.4T Qwen Model as Chinese AI Sweeps the Global Competitive Landscape
Alibaba open-sources a 2.4 trillion parameter Qwen model and launches the Qwen Token Plan. Chinese models surge, Kimi K3 tops global rankings, and China's AI is reshaping the global competitive landscape.
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·3 min
Agent Fine-Tuning in Practice: A Complete Guide to Training Your Own AI Agent
A deep dive into Agent Tuning: from LLM hallucination and staleness issues to RAG vs. Agent architecture, the 4-step fine-tuning process, and cost analysis for building your own AI agent.
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