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Deep dive into OpenAI Agents SDK updates covering Harness-Compute separation, Codex-style orchestration, sandbox snapshots, skills system, and multi-agent collaboration with practical demos.

Deep dive into Hermes Agent's core architecture: Agent Loop mechanism, three-layer memory system (Markdown/SQLite/external), Gateway multi-platform integration, context compression, and Cron jobs.

A systematic three-stage AI Agent development roadmap: from Python basics and LLM fundamentals, through five core capabilities like planning and tool use, to hands-on RAG projects for real-world deployment.

Compare Playwright vs Selenium, explore Playwright's DevTools Protocol speed advantages, smart locators, and AI integration via MCP for zero-code web automation testing.

In-depth comparison of Playwright vs Selenium covering DevTools Protocol, async performance, smart locators, and AI MCP integration to help you choose the best automation testing tool.

sakana-mcp wraps Sakana AI Scientist v2 as an MCP server, letting Claude and Cursor act as research directors to orchestrate autonomous research cycles.

Deep dive into BioAgents multi-agent AI framework: how literature analysis and data scientist agents collaborate for autonomous deep research in biological sciences.

AI coding tools quadruple code output, but review time surges 441% and defect rates jump from 9% to 54%. Learn why traditional Code Review fails and how to fix it with layered review and AI-reviews-AI strategies.

A systematic six-week learning roadmap for AI Agent development covering core architecture, ReAct paradigm, multi-agent collaboration, RAG integration, deployment, and hands-on projects.

Deep dive into OpenLLMVTuber, a 10K-star open-source AI virtual character framework integrating ASR, LLM, TTS, and Live2D with voice interruption, visual perception, and modular architecture.

Explore Playwright's core advantages including DevTools Protocol architecture, smart locators, and high-concurrency support, plus how to achieve zero-code automation testing with AI via MCP.

A systematic AI Agent development learning roadmap covering prompt engineering, RAG, multi-Agent collaboration, tool calling, and more—with phased learning advice and 28 hands-on project references.

Five common Claude Code mistakes developers make: copy-pasting code, skipping CLAUDE.md, inefficient prompting, ignoring docs, and poor context management — with fixes.

PeakCode is an open-source AI coding tool combining Claude Code's engineering power with Codex's UX, featuring Web/desktop modes, plugins, Git integration, and automated Agent capabilities.

Tsinghua and Zhipu AI release a full-stack web dev benchmark with three difficulty levels. Top models like Gemini 2.5 Pro see scores plummet from 63 to 11.7 on full-stack tasks, exposing AI's real limits.

Deep dive into MCP (Model Context Protocol): core concepts, four-layer architecture, and hands-on configuration. Learn how MCP transforms AI from a suggestion generator into a true executor.

Anthropic Developer Conference deep dive into three core AI Agent architectures: Build (code execution), Connect (Web Search & MCP), and Optimize, with live demos and multi-tool collaboration examples.

Using a todo app's full development lifecycle as a guide, this article covers 35 core full-stack technologies from frontend to backend, databases, DevOps, cloud deployment, and scaling architecture.

A complete beginner's guide to Vibe Coding: learn how to describe requirements in natural language and build, debug, and launch full apps with zero code using AI platforms like Base44.

Google launches unified AI platform Antigravity, migrating Gemini CLI users to the new Antigravity CLI rebuilt in Go with multi-agent orchestration and async workflows.