1375 related articles

Claude Code, Codex, or Cursor? This in-depth comparison covers each tool's positioning, ideal users, and how to combine them for maximum productivity in your AI coding workflow.

In-depth analysis of GPT 5.6 Soul: multi-sub-agent parallel architecture, Ultra Mode coding in practice, the controversy behind its 91.9% Terminal Bench score, and the trend of frontier AI entering government review.

Hands-on with GPT-5.6 Sol: auto-generate real-time voice anime characters from one prompt, write physics engines from scratch, and build unfamiliar toolchains autonomously. In-depth review of coding, agentic tasks, benchmarks, and its hallucination weakness.
GPT-5.6 Trio Launches: Luna, Terra, an…
OpenAI officially launches the GPT-5.6 family: Luna, Terra, and Sol, with 1M token context and a focus on long-running agentic performance. A deep dive into three-tier pricing, Agents' Last Exam results, the SWE-Bench Pro controversy, and new API features like programmatic tool calling and native multi-agent support.

AI programming burning through Tokens too fast? This guide covers the open-source Hand of Labor project's multi-model approach: Codex plans, DeepSeek executes—cutting AI coding costs while boosting output quality.

Deep dive into the Cocos Creator AI Agent Plugin: three-level context compression, tool approval, multi-agent parallelism, and DeepSeek-powered Mario scene building — all in one test.

Anthropic's open-source Claude Cookbooks project offers runnable Jupyter Notebook examples covering RAG, Tool Use, multimodal processing, and more—helping developers master Claude API best practices.

FableCut is an open-source browser video editor built on a zero-dependency architecture and programmable AI-agent-driven design. A deep dive for AI automation developers.

Master Codex's Goal command mechanism. Use five standard project settings—agents.md, context.md, active-context and more—to solve context forgetting and hallucination in long-running Agents and maximize your weekly quota.

AI Job Search is an open source, Claude Code-powered job search automation tool with over 20K GitHub stars. It offers resume tailoring, cover letter generation, dual-Agent QA, and keeps humans in control of submission.

A hands-on comparison of AI models—Fable 5, DeepSeek V4 Flash, GLM 5.2, Qwen 3.6—building a sales CRM. The priciest cost $27.69, the cheapest just 30 cents. A deep dive into open-source LLM coding value.

Limited time but want to learn AI systematically? This guide maps out a practical learning path for working IT pros—from AI application engineering and prompt engineering to RAG and Agents.

An in-depth analysis of the AI-driven software testing paradigm: with Skill and CLI as the core hub, supporting both platformized management and digital employees, helping testing teams transform from script writers into capability builders.

A hands-on analysis of the Hermes 2.0 hybrid multi-agent system: can multi-model collaboration beat a single top-tier LLM? We break down how the Mixture of Experts (MoE) architecture works, AgentOS features, and model-agnostic design.

GPT 5.6 updates Codex with Sol/Terra/Luna model tiers, Ultra thinking mode, 350K context, and stronger autonomous loops. Full hands-on review of all core upgrades.

Many people learn tons of fragmented content yet remain confused. This article maps out the complete AI Agent knowledge landscape—from LLM and prompt basics, tool calling, and RAG to LangChain and multi-agent collaboration—with a clear learning order.

A proven 4-step roadmap to becoming an AI Agent engineer: stable LLM calls, tool use (RAG + Function Calling), production engineering, and resume optimization.

Learning AI Agent development is no longer daunting! This article outlines the simplest practical path: master just enough Python, grasp core LLM concepts, then build your first Agent with LangChain.

A comprehensive analysis of GPT-5.6: the Sol/Terra/Luna product-line positioning, MoE architecture, 1.5M token context, coding agent benchmarks, and the shift from Chatbot to Agent.

An in-depth analysis of AI agent development based on Langchain.js—comparing workflow agents and Agent Loops, deconstructing the TypeScript implementation path of an OpenClaw-like engine, covering structured output, MCP, and LangGraph.