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Sat, Jun 27, 2026 3picks
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06:00

ECC: Cross-Harness Agent Operating System for Claude Code, Cursor & Beyond

跨 AI 编码助手的智能体增强操作系统——规则、技能与安全审计

ECC is a comprehensive agent harness operating system designed to enhance multiple AI coding assistants including Claude Code, Cursor, Codex, OpenCode, and GitHub Copilot. It's not a standalone AI tool but a collection of 260+ skills, 67+ agents, persistent memory, continuous learning, cost optimization, and security auditing features. By providing unified rules, hooks, and MCP configurations across platforms, it addresses issues of inconsistent agent behavior, context loss, and inadequate security. Ideal for professional developers deeply using AI coding assistants and teams seeking standardized agent engineering practices.

github.com · 94 min · Agent Engineering · Claude Code · Context Engineering · Developer Tools · Mcp · Observability
06:00

Human in the /loop

人类在循环中：如何设置能自主运行并通知你的AI编码循环

The author shares a practical workflow for coding with AI agents: define a verifiable 'definition of done' (model eval score, QA pass, green tests, performance benchmark), wrap it in a loop for the agent to iterate autonomously, and get notified via Slack when a decision is needed or the task completes. Loops run in the cloud, not on the local machine. The author runs 3-5 long loops concurrently plus shorter tasks. For engineers looking to level up from one-shot agent interactions to long-running autonomous optimization tasks.

x.com · 4 min · Agent Engineering · Ai Tooling · Context Engineering · Loop Engineering · Mcp
06:00

Loop Engineering for Beginners: From Concept to Minimal Loop

小白也能上手的Loop Engineering：从概念到最小闭环实践

This article is a beginner-friendly tutorial that demystifies Loop Engineering: it's not a buzzword but a structured framework for human-AI collaboration, formalizing repetitive actions like goal-setting, stepwise execution, quality checks, feedback loops, and stop conditions. It clearly distinguishes between ordinary prompting (one-shot) and looping (sustained closure), then walks through a minimal viable example—building a personal knowledge base using the LLM Wiki approach. The author stresses that the 'check' step is the heart of any loop, and that without inspection criteria, automation just generates garbage. It also warns against common beginner mistakes such as mistaking long prompts for loops, overly ambitious goals, vague standards, lack of human sign-off, and granting excessive permissions. The article is instructional rather than deep, but it provides a ready-to-use template and the seven-element checklist (goal, input, execute, check, feedback, record, stop). It's best suited for engineers new to agentic workflows who want a structured starting point.

x.com · 7 min · Agent Workflow · Beginner Tutorial · LLM Wiki · Loop Engineering · Prompt Engineering