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Wed, Jul 8, 2026 3picks
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06:00

Agent harness engineering with Claude: 14-step roadmap from one agent to a self-improving system

构建 Agent 框架的 14 步路线图:从单代理到自我改进系统

This article presents a 14-step roadmap for building an Agent harness with Claude, from a single agent to a self-improving system. The author argues that the harness — comprising model, tools, permissions, and initial context — is the foundation for any loop, and a weak harness leads to slop. It covers concrete practices: file structure (.claude/), CLAUDE.md for standing facts, settings.json for permission pre-approvals, subagents for isolated contexts, skills for reusable procedures, hooks for deterministic enforcement, and memory for cross-session learning. The final steps add loops and dynamic workflows, closing the feedback loop where output → lessons → skills → better output. The article targets engineers who run or plan to run multi-agent code generation systems.

06:00

A Field Guide to Fable: Finding Your Unknowns

Claude Fable 使用心得:如何系统性地发现未知盲区

The author shares hands-on experience with Claude Fable for agentic coding, emphasizing that the prompt (map) never fully matches the codebase (territory). He categorizes unknowns into four types (known knowns, known unknowns, unknown knowns, unknown unknowns) and provides practical techniques to systematically discover them: blindspot passes, brainstorming & prototypes, interviews, references, implementation plans, implementation notes, pitches, and quizzes. Ends with a real example of editing the Fable launch video. Suitable for engineers using AI-assisted coding.

06:00

Human-in-the-Loop Workflow Design: From Approval Fatigue to Planned Review

人机协作工作流:从审批疲劳到计划级干预的设计

Based on an analysis of 400,000 Claude Code sessions, this article reveals that 93% of permission prompts are approved, leading to 'consent fatigue' where humans are nominally in the loop but functionally tuned out. The author proposes restructuring the workflow into three layers: input (precise task description, constraints, examples), steering (plan-level review instead of per-action approval), and output review (defining quality criteria and self-assessing). A single evaluation checkpoint improved generation quality by 8–10% in controlled tests. The article provides actionable steps to move from per-action approval to strategic intervention, targeting AI engineers and agent developers.