#Harness orchestration layer
162 related articles
·3 min
Codex++ with Free API: A Complete Guide to Using Codex at Zero Cost
Learn how to use Codex for free with Codex++ and free APIs. Complete setup guide covering text, image, and video generation with a real-world creative workflow example.
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·4 min
Harness Engineering in Practice: Integrating AI-Driven Development into Enterprise Workflows
A deep dive into Harness Engineering methodology — from Prompt Engineering to Context Engineering to Harness Engineering — covering enterprise setup, Skill systems, and pipeline-style AI programming.
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·3 min
Harness Engineering in Practice: Building an Enterprise E-Commerce System with Claude Code — A Complete Walkthrough
A deep dive into Harness Engineering for AI programming, from concept to implementation. Build an enterprise Java e-commerce system using Claude Code with Skill-driven AI development pipelines.
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·2 min
Perplexity Computer Integrates Deep Research as a Native Skill: A New Paradigm for AI Agent Capability Fusion
Perplexity integrates Deep Research as a native skill in Computer, enabling automatic invocation without manual mode switching. Analyzing the Agent Harness design philosophy and AI capability fusion trends.
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·3 min
Hermes Agent Deep Dive: A Self-Evolving Agent Architecture with Practical Implementation Guide
Deep dive into Hermes Agent's core architecture: four-layer memory system, Skill self-evolution mechanism, Harness Engineering methodology, OpenCloud comparison, and Feishu integration tutorial.
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·3 min
Harness AI Engineering Programming: A Detailed Methodology for Enterprise-Level Project Development
Deep dive into Harness AI Engineering Programming methodology, covering SDD, Skill development patterns, and core practices for enterprise-level AI-assisted development.
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·3 min
Nexent Open-Source Platform: Zero-Code Auto-Generation of Production-Grade AI Agents
Deep dive into Nexent's open-source platform for zero-code production-grade AI Agent generation, covering Harness Engineering, built-in controls, use cases, and comparisons with AutoGen and CrewAI.
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·3 min
Deep Dive into Claude Cloud Agent Memory System Architecture: CMA Design Philosophy and Technical Choices
Deep analysis of Anthropic's Cloud Managed Agents memory architecture, covering file-first strategy, memory store reuse, Dreaming async consolidation, and key differences from Claude Code's memory system.
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·4 min
Agent Beginner's Guide: Architecture Principles and Practical Efficiency Tips
A deep dive into AI Agent principles, core architecture, and practical applications. Learn how Agents differ from LLMs and how to leverage Agent Skills to boost productivity.
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·3 min
Claude Code Enterprise E-commerce in Practice: A Methodology for Taking AI Programming from Demo to Industrial-Grade
A deep dive into engineering methodology for enterprise e-commerce development with Claude Code and Harness AI, covering architecture, code quality, and CI/CD practices.
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·2 min
Claude Code Dynamic Workflows: A Complete Guide to Six Patterns and Ten Scenarios
Deep dive into Claude Code's dynamic workflow mechanism covering Agent, Parallel, and Pipeline functions, six orchestration patterns, and ten real-world scenarios with cost control tips.
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·3 min
A Practical Guide for Agent Engineers: The Three-Phase Core Methodology for AI Programming
Master the three-phase methodology for Agent engineers: Ideation, Iteration, and Evolution. Build reliable AI programming systems without over-engineering.
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·3 min
The New Builder Mindset in the AI Era: How the Developer Role Is Evolving
OpenAI declares 'developers have evolved.' Explore the new builder mindset: the shift from code writers to product builders, lower barriers, and the rise of full-stack individuals in the AI era.
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Tutorials·2 min
MCP Protocol in Practice: Building an AI Investment Analysis Agent with TradingView + Notion
A detailed guide on MCP protocol vs Skills, integrating TradingView and Notion MCPs to build an automated investment analysis Agent with market scanning, backtesting, and report generation.
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Product Reviews·2 min
JCode: Deep Dive into a Multi-AI Agent Collaborative Programming Framework
Deep dive into JCode, an open-source Coding Agent Harness designed for multi-Agent collaboration. Features Agent Memory, Swarm collaboration, multi-Provider access, and self-evolution with just 14ms first-frame latency and 117MB for 10 sessions.
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Product Reviews·3 min
6 Developer Tools Roundup: AI Coding Cost Tracking, Agent Virtual File Systems, and More
Roundup of 6 developer tools: CodeBurn for AI coding token cost tracking, Mirage virtual file system for Agents, Boring SSH tunnel manager, PeerTrace file tree renderer, DataTab font-based data visualization, and Flu TypeScript Agent framework.
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Industry Insights·2 min
Cloud AI Agent Architecture Design: Far More Than Moving Local Agents to a Server
Building cloud AI Agents requires entirely new architectural thinking. This article analyzes three core infrastructure components—durable execution platforms, execution frameworks, and dev environment tools—to help teams avoid common pitfalls when migrating from local to cloud.
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Tutorials·3 min
Complete Learning Roadmap for Java Developers Transitioning to AI Engineering
A detailed five-phase learning roadmap for Java developers transitioning to AI engineering, covering Spring AI, LangChain4j, RAG core technology, and Agent development.
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Industry Insights·4 min
Google I/O 2026 Deep Dive: From Super Apps to the Battle for Ecosystem Dominance
Deep analysis of Google I/O 2026: Gemini 3.5 Flash, Omni video tools, Spark personal Agent, and how Google, OpenAI, and Anthropic are competing for AI ecosystem dominance.
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Deep Dives·3 min
Harness Engineering Explained: Building Stable and Efficient Work Systems for AI
Deep dive into Harness Engineering: how to build execution environments, toolchains, and feedback loops for AI. From Prompt Engineering to system-level engineering for stable AI production.
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