423 related articles

Can AI really replace programmers? This article explains Harness Engineering principles and its three evolutionary stages, revealing real pain points of enterprise AI programming.

LangChain open-sources OpenWiki, a tool that auto-generates and maintains AI-readable wiki documentation for codebases via a single command, powered by Git history and agents.md integration.

A deep dive into Loop Engineering for AI Agents — what loop feedback mechanisms are, how they differ from Harness Engineering, and a complete guide from principles to production implementation.
Morph Reflexes: Building Real-Time Beh…
Morph Reflexes is an open-source AI agent monitoring tool that uses multi-head classifiers for real-time trace classification — enabling safety guardrails, quality scoring, and training data filtering.

Deep dive into LangChain 1.0's architecture: LangChain framework, LangGraph multi-Agent orchestration, and LangSmith observability platform, with hands-on RAG and intelligent customer service projects.

Alibaba Cloud vs Volcano Engine TTS: why "I want both" is the mature engineering decision. Dual-engine routing design, priority trap debugging, and vibecoding-powered implementation.

Vercel's Chief of Software Andrew Qu explains the eve Agent framework's design philosophy, covering Skills modularity, Sandboxes security, and agent-readable websites—revealing the paradigm shift from instruction-driven to goal-driven software.
AI Engineer World's Fair Closing Day: …
AIEWF closing day recap: the agent loops debate, the State of AI Engineering report, and a keynote on what to build next — covering AI engineering's key divides and trends.

Learn how to orchestrate Claude Code custom commands to chain content research and social media publishing agents into a fully automated workflow with one command.

Andrew Ng and LangChain CEO Harrison Chase present AI Agents in LangGraph, covering five core agent design patterns and LangGraph's graph-based framework for building cyclical agentic workflows.

Andrew Ng and LangChain CEO Harrison Chase's AI Agents in LangGraph course covers five agent design patterns and LangGraph's graph-based framework for building cyclical AI workflows.

Why do enterprise RAG knowledge bases dazzle in demos but fail in production? This article dissects five critical engineering pitfalls with real-world case studies from million-doc platforms and ops agents.

Gas Town is an open-source multi-agent workspace manager built in Go with 16,000+ GitHub Stars. This article analyzes its architecture, Go language advantages, and typical multi-agent collaboration scenarios.

In-depth analysis of OpenAI Codex's four usage forms, comparing Codex, Claude Code, and Cursor across price, stability, and frontend/backend fit to help developers choose the right AI programming tool.

A developer built Manifest Studio to automate the full pipeline from AI code generation to app deployment, using a Skill mechanism for one-sentence publishing.

A deep dive into Harness Engineering architecture: building an AI procurement assistant on ERP systems, covering multi-agent orchestration, MCP protocol, ASGI deployment, and sandbox isolation.

Master full-stack AI development with Vercel: from LLM, RAG, and vector embeddings to AI SDK, AI Gateway, and v0 — build production-ready AI web apps end to end.

Full comparison of Hermes Agent vs Open Cloud: lower token usage, 200+ model support, auto Skill encapsulation, WeChat/DingTalk integration. A cost-effective AI Agent alternative for long-term deployment.

A detailed four-stage competency model for AI Agent development: from Python/RAG basics (15K) to workflow orchestration (20K), inference optimization (30K), and Agent cluster governance (40K RMB).

Deep dive into LangChain's core Model and Agent concepts, covering unified model interfaces, agent tool calling, middleware mechanisms, and key principles for building LLM applications.