#structured output
2795 related articles
·3 min
Codex Hooks Explained: The Automation Powerhouse That Hijacks the Entire AI Workflow
Deep dive into Codex Hooks' six lifecycle hook types, covering configuration, local vs global hooks, and practical use cases like security interception and auto-summarization for full AI workflow control.
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·2 min
OpenAI Codex Rate Limits Spark Developer Debate: Pain Points, Signals, and Coping Strategies
OpenAI Codex rate limits spark developer debate. This article analyzes core pain points, OpenAI's communication strategy, possible policy changes, and practical coping tips for developers.
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·2 min
OpenAI Codex Appshots Feature Explained: Double-Tap Command to Capture Full Context
OpenAI Codex launches Appshots: Mac users can double-tap Command to capture any app window's screenshot and full text content, solving the AI coding context transfer problem.
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·3 min
Cursor Auto-Review Mode Explained: Smart Tiered Approval for More Efficient AI Programming
Cursor launches Auto-review mode with smart risk assessment for tiered approval. Low-risk operations auto-approve while high-risk ones need manual confirmation.
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·3 min
The New Builder Mindset in the AI Era: How the Developer Role Is Evolving
OpenAI declares 'developers have evolved.' Explore the new builder mindset: the shift from code writers to product builders, lower barriers, and the rise of full-stack individuals in the AI era.
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·3 min
Firebase AI Logic in Practice: Using Gemini to Automatically Break Down Tasks into Actionable Subtasks
A hands-on guide to Firebase AI Logic and Gemini integration, showing how to automatically break down large tasks into actionable subtasks with structured output and real-time sync.
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·4 min
Five Common Misconceptions in AI Agent Development: An Essential Guide Before You Start
Debunking 5 common AI Agent development misconceptions: Agents aren't smarter ChatGPTs, complexity doesn't equal power, and RAG can't cure hallucinations. Learn the right approach to building Agents.
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·3 min
Firebase Integrates with AI Studio: Four Core Capabilities for Production-Grade Agent Applications
Deep dive into Google Firebase's integration with AI Studio, covering four core capabilities—database auto-configuration, authentication, security rules drafting, and zero-config deployment—for production-grade AI agent apps.
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·4 min
Firebase AI Logic Update: Expanded Model Support and Enhanced Output Integrity Explained
Firebase AI Logic gets major updates at Google I/O, expanding AI model support and enhancing output integrity. Learn how these changes impact developers.
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·3 min
Anthropic Enters Biodefense: How AI Is Safeguarding Global Public Health Security
Anthropic applies AI to biodefense — from early epidemic warnings and rapid vaccine R&D to threat assessment — exploring AI's critical role in global public health security.
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·2 min
The Same Question Behind Six Claude Projects: Why Not Give It a Try?
A developer completed six projects with Claude, all starting from one question: Why not? Exploring the creator's mindset in the AI era and how to build efficient AI-assisted development habits.
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·4 min
OpenAI Swarm Framework Explained: The Core Mechanisms of Function Call and Handoff
Deep dive into OpenAI Swarm multi-agent orchestration framework, explaining Function Call tool invocation and Handoff task transfer mechanisms with local deployment guide.
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·2 min
Andrew Ng's Agent Skills Course Explained: Building Reusable Professional Skills for AI Agents
Deep dive into Andrew Ng's Agent Skills course with Anthropic, covering Skills architecture, progressive loading, MCP integration, and hands-on examples for building reusable AI agent skills.
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Tutorials·3 min
ReAct vs. CodeAct: Two Core Approaches to Agent Tool Calling
In-depth comparison of ReAct and CodeAct — two core Agent tool-calling architectures. From paper principles to code implementation, learn the trade-offs between reasoning+action and code execution.
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Tutorials·3 min
Agent Tuning: A Complete Guide to Training LLMs with Agent Capabilities
A deep dive into Agent Tuning principles and practices, covering why Agent training is needed, the evolution from Prompt to RAG to Agent, development workflows, and cost assessment for private deployment.
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Tutorials·2 min
Building a Financial Analysis Agent with Cursor + MCP: From Architecture Design to Production Deployment
A complete guide to building a financial analysis Agent system from scratch using Cursor AI and MCP protocol, covering three-layer architecture design, MCP Server development, and production deployment.
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Tutorials·2 min
Understanding Function Calling and MCP Through Cursor's System Prompt
Deep dive into Function Calling and MCP working principles through Cursor editor's system prompt analysis, comparing regular tools vs MCP tools and testing Agent capabilities across model sizes.
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Tutorials·2 min
Getting Started with RAG: What Is Retrieval-Augmented Generation and How Does It Solve LLM Hallucination?
RAG (Retrieval-Augmented Generation) is the core solution for LLM hallucination. Learn RAG concepts, how it works, three causes of hallucination, and the complete learning path from basics to Knowledge Graph RAG.
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Tutorials·2 min
How AI Coding Skills Work: From Function Call to Sub-Agent Implementation
Deep dive into how AI coding Skills work: from Function Call to MCP to Skills as sub-agents with on-demand loading, implemented via Spring AI Alibaba.
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Tutorials·2 min
How AI Coding Skills Work: From Function Call to Sub-Agent Implementation
Deep dive into how AI coding Skills work technically, from Function Call to MCP to Skills as sub-agents with on-demand loading, implemented via Spring AI Alibaba.
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