#termination condition
151 related articles
·7 min
Andrew Ng Teams Up with LangChain: Core Principles of LangGraph Agent Development
Andrew Ng and LangChain CEO Harrison Chase present AI Agents in LangGraph, covering five core agent design patterns and LangGraph's graph-based framework for building cyclical agentic workflows.
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·7 min
Andrew Ng Teams Up with LangChain: Core Concepts of LangGraph Agent Development
Andrew Ng and LangChain CEO Harrison Chase's AI Agents in LangGraph course covers five agent design patterns and LangGraph's graph-based framework for building cyclical AI workflows.
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·6 min
LangChain + MCP in Practice: A Pitfall-Avoidance Guide to LLM Selection and Local Deployment
DeepSeek R1 lacks Function Calling and JSON Output by default. Qwen3's programmable thinking modes make it the top open-source agent choice. Key LLM selection pitfalls and MCP protocol updates.
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·6 min
5 Critical Engineering Pitfalls in Enterprise RAG Knowledge Base Deployment: The Real Barriers from Demo to Production
Why do enterprise RAG knowledge bases dazzle in demos but fail in production? This article dissects five critical engineering pitfalls with real-world case studies from million-doc platforms and ops agents.
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·6 min
Codex Beginner's Guide: Four Product Forms Explained, Plus Comparison with Claude Code and Cursor
In-depth analysis of OpenAI Codex's four usage forms, comparing Codex, Claude Code, and Cursor across price, stability, and frontend/backend fit to help developers choose the right AI programming tool.
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After Spending $85,000: Real-World Les…
·2 min
After Spending $85,000: Real-World Lessons and Cost Insights from Lovable's Agentic Coding Journey
Lovable spent $85K on API tokens scaling Agentic Coding. Key lessons: context management, model routing, circuit breakers, and ROI tracking for AI-driven development.
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·6 min
Getting Started with LangChain: A Deep Dive into Model and Agent Core Concepts
Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.
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·7 min
Agent Loop Explained: The Core Runtime Mechanism and Think-Act Cycle of AI Agents
Deep dive into Agent Loop mechanics: the think-act cycle, how agents differ from LLMs, termination conditions, and design principles for building autonomous AI Agent systems.
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·6 min
Dify Self-Hosted Deployment in Practice: Building an Enterprise-Grade AI Application Platform from Scratch
A complete guide to self-hosting Dify, the open-source AI platform: environment setup, Docker Compose deployment, LLM integration, and app building. Runs on just 2 cores and 4GB RAM.
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·3 min
Deep Dive into the /goal Command: The Ultimate Tool for Managing Long-Running AI Agent Tasks
A deep dive into the /goal command in Claude Code and Codex — covering positioning, real-world cases, and a three-element Prompt framework (Goal, Termination Condition, Constraint Rules) for stable long-running AI Agent tasks.
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·4 min
The Four Stages of AI Engineering Evolution: From Prompt to Loop Engineering Explained
A systematic breakdown of the four stages of AI engineering: Prompt Engineering, Context Engineering, Runtime Environment Engineering, and Loop Engineering — with core logic, bottlenecks, and real-world use cases.
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·3 min
AI Programming Power User Workflow: A Practical Guide to Automated Loops and Multi-Model Collaboration
A deep dive into expert AI programming workflows covering Cursor rules, skills systems, automated loops, cloud agent parallel development, and multi-model collaboration strategies.
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·3 min
Four Core Modules of AI Agent Development: An Architecture Guide from Failure to Stable Deployment
Deep dive into four core AI Agent modules: system prompts, tool calling, RAG memory, and ReAct workflow orchestration. Solve hallucinations, loops, and build reliable agents.
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·3 min
What Is AI Loop Programming? A Deep Dive into the Real Value and Hype Behind Loop Engineering
AI Loop Engineering is called a new programming paradigm, but is it truly revolutionary? This article analyzes its core principles, mathematical limitations, real-world details, and the hype behind it.
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·4 min
Kimi K2.7 Code Deep Dive: The Cost-Performance Battle of a Trillion-Parameter Coding Model
Deep dive into Moonshot AI's Kimi K2.7 Code: MoE architecture details, benchmark analysis, API pricing vs Claude/GPT, 6x speed version, and practical guidance for developers evaluating adoption.
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·1 min
The Rolf Loop: A Complete Guide to AI Automated Iterative Programming in Five Lines of Code
A deep dive into the Rolf Loop: learn how five lines of code create an AI automated iterative programming workflow, including use cases, limitations, and best practices.
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·3 min
DAQIRI Platform Explained: Deep Integration of High-Speed Data Acquisition and Real-Time AI Inference
Deep dive into how the DAQIRI platform embeds NVIDIA GPU-accelerated computing into high-speed data acquisition pipelines, enabling real-time AI inference for industrial inspection, scientific experiments, and autonomous driving.
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·4 min
Loop Engineering Explained: Agent Loop Mechanisms and the New Paradigm of AI Agent Development
Deep dive into Loop Engineering: from Agent Loop principles and While loops to Graph structures, covering loop efficiency optimization and termination strategies for AI agent development.
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·4 min
Deep Dive into Six AI Development Tools: Loop Recipe Library, Vercel Agent Framework, and More
Deep analysis of Loop workflow recipes, Vercel's open-source Agent framework, Pyker AI-native project management, Arrow P2P tool, DBX database client, and NVIDIA's Skill Spectre security tool.
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·3 min
DeepSeek ZOI In-Depth Review: An Open-Source Local AI Agent Desktop Workspace
In-depth review of DeepSeek ZOI open-source desktop app covering Code Mode coding Agent, Write Mode, Cone Runtime optimization, setup guide, and cost comparison with Codex and Claude Code.
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