255 related articles

No coding needed: master Claude Code workflows with folder structure, sub-agents, third-party connectors, and scheduled routines to build your own AI automation OS.

LLM JSON output unstable in your Agent? This guide covers 6 engineering layers: constrained decoding, validation retry, fake tool calls, Logit Masking, Schema contracts, and anti-pattern locking.

How should test engineers choose AI tools? This guide breaks down the pitfalls of pure AI solutions and recommends a hybrid strategy using tools like DeepSeek, TRAE, Claude Code, and Skill encapsulation.

An orchestration Agent looped for hours, firing thousands of LLM calls and burning weeks of budget. Learn the root causes and practical defenses: circuit breakers, tiered budgets, and iteration limits.

The open-source project "Interview System" offers 204 RAG interview questions, 12 architecture approaches, and deep analysis of 6 failure modes. Prepare systematically for RAG engineer roles.

A systematic guide to the four-stage AI Agent development path: core concepts, principle paradigms like ReAct, RL and multi-agent optimization, and real-world projects. Mastering Agent development is the true hardcore edge in today's LLM field.

Resume full of RAG and Agent but keep failing interviews? The issue is you only run demos and can't explain production engineering challenges. This article breaks down data cleaning, hybrid retrieval, hallucination protection, and agent loop breakers.

An in-depth guide to building an AI-driven second brain with Obsidian + Hermes Agent. Covers living files, VPS deployment, core memory mechanisms, and skill visualization.

A user's American Express card was auto-charged 171 times by an AI service, totaling nearly $1,800 with no warning. This article analyzes pay-as-you-go risks and offers practical protection: spending limits, virtual cards, and automation monitoring.

What is an AI Agent's harness? This article systematically dissects the core components of agent frameworks: context management, tool use, control loops, and caching strategies—revealing why the same model performs so differently across harnesses.

Superpowers is a trending GitHub project that builds an agentic skills framework with Shell scripts, injecting software engineering methodology into AI coding workflows. A deep dive into its architecture and value.

A full review of Claude Sonnet 5: major agentic gains, benchmarks near Opus 4.8, but a Tokenizer switch inflates real costs, nearly erasing the price gap with Opus. We break down the pricing traps.

GPT-5.6 is officially released with core upgrades including programmatic tool calling, autonomous subagent delegation, and higher token information density. A hands-on card game build reveals its Agentic power.

A complete guide to Dify's core features and 1.8.0 deployment. Covers 5 app types, Docker setup, Workflow vs Chatflow differences, and RAG knowledge bases for beginners.

An in-depth look at the three core eras of AI Agent development: reliable tool calling, coherent long-task execution, and autonomous orchestration with metacognition. Helps developers match tasks to model capabilities.

A major breakthrough in AI coding! Hands-on tests show new LLMs generating a Minecraft clone in 90 minutes and a TMNT game in 30 minutes, building 3D scenes, animation, and game logic in one shot.

An in-depth analysis of the "any Agent as an orchestrator" design philosophy, exploring the technical implementation of multi-Agent collaboration, context management, and workflow automation.

A complete AI Agent learning roadmap covering agent principles, prompt engineering, RAG, multi-agent systems, and hands-on projects — from zero to real-world deployment.

Real debugging case: when 400MB of source code and 40K files caused an infinite crash loop, MiniMax M3, DeepSeek, and Hunyuan all gave wrong answers. GPT-4.1 mini found the root cause after an hour of deep reasoning.

Deep dive into LangChain's three modules: Chain pipelines, LangGraph state graphs, and autonomous planning Agents. From RAG to ReAct — build your AI architecture thinking.