Glean 拾遗
中 EN
Submit a link
Daily · timeline

A few picks a day.

Thu, Jul 9, 2026 3picks
← 07-08
Calendar ▾
2026 · 07
MoTuWeThFrSaSu ··12345678910111213141516171819202122232425262728293031
has picks today
06:01

12-step guide to persistent memory for Claude agents

Claude Agent 持久记忆搭建：从基础到自改进的12步指南

A practical 12-step walkthrough for giving Claude agents persistent memory across sessions. Covers four layers: built-in Chat Memory, Project instructions, a lean memory file (CLAUDE.md), and Dreaming – a scheduled background process that consolidates and reorganized memory. Includes setup steps, API calls, and advice on filtering what to remember. Harvey reported ~6x task-completion rate improvement with Dreaming. Ideal for engineers building long-running agents.

x.com · 12 min · Agent Engineering · Ai-Memory · Claude · Context Engineering
06:01

Loop Patterns in Claude Code: A Practical Guide

Claude Code 循环模式：从交互到自动化

The Claude Code team's official blog post introduces four loop modes: turn-based, goal-based, time-based, and proactive loops. It covers how each is triggered, stopped, and when to use them, along with token management and code quality tips. Practical CLI commands and SKILL.md examples are provided. The article emphasizes starting simple and gradually automating repetitive tasks. Essential reading for engineers using or evaluating Claude Code for autonomous development.

x.com · 9 min · Agents · AI Engineering · Ai Tooling · Claude Code · Loop Engineering
06:00

Harness Engineering for Self-Improvement

Harness工程：通往AI递归自我改进的关键路径

This comprehensive survey by Lilian Weng systematically examines the critical role of harness engineering in recursive self-improvement (RSI) for AI systems. A harness is the system layer surrounding a base model that orchestrates execution, context management, tool calling, persistent state, and workflow design. The post synthesizes three design patterns (workflow automation, filesystem as persistent memory, sub-agents and backend jobs) and dives into frontier works: context engineering (ACE, MCE), meta-optimization (Meta-Harness), workflow automation search (ADAS, AFlow), self-improving harnesses (STOP, Self-Harness), and evolutionary search (AlphaEvolve, Darwin Gödel Machine). It concludes with open challenges: weak evaluators, memory management, diversity collapse, reward hacking. Essential reading for AI engineers and agent researchers.

lilianweng.github.io · 42 min · Agent Architecture · Agents · AI Engineering · Context Engineering · LLM · Self-Improvement