#payment integration
437 related articles
·2 min
Claude Code for QA Engineers: A Practical Handbook from Basics to Engineering-Level Implementation
A practical Claude Code handbook built for QA engineers — covering environment setup, spec-driven development, Skill encapsulation, MCP integration, and AI test sub-agents across 10 core modules.
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·3 min
Zero-Code Cinematic Websites: A Complete Guide Using GPT Image 2 + Claude Code
No coding needed: use GPT Image 2 for assets, Claude Code for development, and Vercel for free deployment to build a cinematic scroll-driven brand website.
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·4 min
Understanding CI/CD from Scratch: Core Principles and Practical Introduction to Automated Deployment
A clear, practical guide to CI/CD: from Waterfall to DevOps, manual vs. automated deployment, and a full Jenkins + RuoYi hands-on learning path for beginners.
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·4 min
Loop Engineering in Practice: Designing and Deploying a Multi-Agent Collaborative Development System
A deep dive into Loop Engineering: how multi-agent collaborative dev systems achieve automated coding loops through workflow scheduling, step isolation, and validation.
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·2 min
Beware of the 'Free GPT-5.6' Scam: A Full Breakdown of AI Fraud Tactics
Videos promoting 'Free GPT-5.6 SOL' are circulating on Bilibili. This article exposes the fictional product, fake benchmarks, and manipulation tactics behind this AI scam.
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·4 min
GPT-5.6 + Codex Tested: 3 Prompts to Edit a 15-Minute Video, Efficiency Doubled
OpenAI's GPT-5.6 and Codex integration tested: 3 prompts to edit a 15-min video in just 20 minutes. Full breakdown of performance, Token costs, GPT Work automation, and Chat Card real-world results.
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·3 min
OpenAI Merges ChatGPT and Codex: The Birth of an AI OS and Four Core Strategies
OpenAI merges ChatGPT and Codex into a Super App, unveiling the early blueprint of an AI OS. A deep-dive into four core strategies: Loop workflows, tool stack economy, multi-threading, and Sites.
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Designing APIs for AI Agents: A Paradi…
·3 min
Designing APIs for AI Agents: A Paradigm Shift from Human Interfaces to Agent-Friendly Design
When AI Agents become the primary API callers, traditional interface design assumptions break down. This article explores agent-friendly API design principles and how MCP is driving this paradigm shift.
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·2 min
AI Skill Auto-Generates Test Cases: Say Goodbye to Manually Transcribing PRDs
Test engineers: use the AI Skill 'Doc-based Test Case Generator' to auto-generate structured test cases from PRDs or screenshots, covering boundary values, negative scenarios, and more.
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·2 min
AI Truly "Gets Its Hands On" Your Phone: Android Remote Control MCP v1.9.0 Deep Dive
Android Remote Control MCP v1.9.0 adds ChatGPT support, OAuth 2.1 auth, and major WebView reliability fixes. Let AI Agents operate any phone app like a human.
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·3 min
Tencent Hunyuan Hy3 Free Access Guide: 5 Channels to Try It at No Cost
Tencent Hunyuan Hy3 is live — here are 5 free access channels including WorkBuddy, Hermes Agent, OpenRouter, SiliconFlow, and WeChat Mini Program (up to 100M tokens).
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·2 min
Getting Started with Claude Code: How AI Coding Assistants Went from Chatting to Actually Getting Things Done
What is Claude Code? This guide covers 5 core differences from ordinary AI chat tools, including context reading, code execution, MCP tool calls, and a Cursor comparison.
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·3 min
Chain of Thought (CoT) Explained: Teaching AI to Reason Step by Step
A deep dive into Chain of Thought (CoT) prompting: from prompt construction to reasoning chain generation, revealing the three mechanisms behind AI's improved reasoning. Covers math, medical, and financial use cases.
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·3 min
AgentScope 2.0 Deep Dive: Six Core Upgrades for Production-Grade Multi-Agent Development
AgentScope 2.0 by Alibaba's Tongyi Lab delivers six major upgrades: typed event streaming, dangerous instruction interception, human-in-the-loop, concurrent execution, workspace system, and agent-as-a-service for production-grade multi-agent development.
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AI Agent Human-in-the-Loop (HITL) Desi…
·3 min
AI Agent Human-in-the-Loop (HITL) Design: Risk Classification and Best Practices
A deep dive into AI Agent Human-in-the-Loop design: balancing automation with oversight using risk tiers, async approval, and confidence thresholds.
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·3 min
LangChain 1.3 in Practice: A Complete Learning Path from LLMs to Agent Development
A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.
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·4 min
LangChain V1.3 in Practice: Master AI Engineering Thinking and Break Free from Tool Dependency
LangChain V1.3 course deep-dive: why engineering thinking beats tool-chasing. Covers RAG accuracy myths, Token cost control, and LangChain/LangGraph/Deep Agent breakdowns.
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·3 min
How to Test High-Risk Actions in AI Agents Before Going Live: A Complete Strategy Beyond Mocking
Mock testing can't cover the real side effects of high-risk, irreversible AI Agent actions. Learn sandbox environments, shadow mode, dry run, HITL, and more.
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·3 min
AI Agent Beginner's Guide: LLMs vs. Agents, and a Full Breakdown of Development Tools
What is an AI Agent? This guide explains the key differences between LLMs and Agents, breaks down the Agent formula (LLM + Workflow + Knowledge Base), and compares tools like Dify, Coze, LangChain, and LlamaIndex.
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·3 min
LangChain V1.3 Hands-On Tutorial: A Complete Breakdown of RAG, Multi-Agents, and Engineering Mindset
An in-depth look at LangChain V1.3's core philosophy: from RAG to multi-agent workflows. Master LangGraph, Chain, and DeepAgent, learn token control and Human-in-the-loop, and become a true master of AI app development.
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