197 related articles

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 developer stress-tested GPT-5.6 for six weeks across 67 projects, burning $180K-$240K in inference. Real cases of task persistence, Rust rewrites, autonomous browser control — plus honest frontend and 3D shortfalls.

Struggling to learn data science alone? This article explores the value of study partnerships and pairs them with the classic Hands-On ML textbook to offer a phased learning plan from math foundations to deep learning.

Master LangGraph core concepts: nodes, edges, and routing functions. Learn StateGraph, MemorySaver, and ToolNode through a weather-query Hello World example, and understand how LangGraph relates to LangChain and powers Agent workflows.

A deep dive into the Claude Code source code, systematically analyzing the five-layer Harness Engineering architecture: environment, tool, control, memory, and evaluation. Build a stable runtime system for production AI Agents.

OpenAI merged Codex into ChatGPT, killing a developer-beloved AI coding brand. A deep dive into the gains and losses of this brand consolidation.

Want to build an AI Agent but don't know where to start? This guide covers the complete seven-step workflow—from requirements analysis, platform selection, prompt engineering, data storage, and UI building to testing and deployment.

The full GPT-5.6 model lineup is live. How can domestic developers access it at low cost via API relay stations? This article explains the principles, registration, token setup, client integration, and key risks.

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.

OpenAI launches the GPT-5.6 model family with cybersecurity as its biggest highlight. A deep analysis of GPT-5.6's differentiation, double-edged-sword effect, and enterprise strategy.

Kun is an open-source AI coding agent optimized for DeepSeek and domestic users, with nearly 5,000 GitHub stars. Features include requirements drafting, inline diffs, cost visualization, and mobile monitoring. Real-world cache hit rates reached 97%, keeping costs extremely low.

Model capabilities are converging, making inference cost and scalability the new focus of AI competition. A deep analysis of AI infrastructure's core layers.

Why give AI Agents a virtual filesystem and bash instead of more tools? A deep dive into the "Files over Tools" design philosophy, tool bloat, Unix principles, security sandboxing, and hybrid architectures.

A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.

Databricks open-sources Omnigent, a Meta-Harness for orchestrating Claude Code, Codex, and more AI coding assistants together—with built-in guardrails, cross-model workflows, and real-time collaboration. Get started in 10 minutes.

Grok 4.5, GPT-5.5, and Claude go head-to-head on the same coding tasks. A deep comparison of code quality, UI design, and engineering standards to help you choose the right AI coding assistant.

An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.

Resonate's founder proposes "The Prompt is the Platform": as AI agents generate production-grade implementations from abstract specs, engineers' value shifts to specification. A deep dive into deterministic simulation and forbidden-fruit debugging.

Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.

A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.