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06:00

Stop Being the Loop: How to Make Claude Work While You Sleep

把 Claude 从对话工具变成自动化工人:循环工程入门

Boris Cherny, who built Claude Code at Anthropic, no longer writes prompts by hand—he writes loops. This guide explains what a real loop is: a small system that runs Claude repeatedly until a job is done, complete with self-checking, state persistence, and automatic stopping. Unlike cron jobs, loops contain a decision-maker (Claude) that can adapt mid-stream. The article covers Claude Code's /goal (loop until done) and /loop (repeat on a schedule) commands, and provides a paste-ready charter template with sections for goals, work sources, work instructions, self-verification, memory, and stop conditions. Ideal for engineers transitioning from prompting to building persistent, autonomous AI workflows.

06:00

Agentic Code Review

AI生成代码泛滥后,代码审查才是真正的核心技能

When coding agents produce thousands of lines of often solid code in minutes, the engineering bottleneck shifts from writing to trusting, making review the most leveraged skill in software. Multi-source 2026 data (Faros AI, CodeRabbit, GitClear, GitHub) shows: AI users generate ~4x raw output but only ~12% more delivered value; code churn up 861%, defect rate from 9% to 54%, review duration up 441.5%, and zero-review merges up 31.3%. The article argues the fix is not to stop using AI but to tier review effort by blast radius: light for solo no-user projects, heavy for large enterprises. Specific advice: triage PRs upfront, require evidence before review, watch test rewrites, run two differently-structured AI reviewers in parallel, and upgrade humans from line-level review to spot-checking and auditing. The durable skill is understanding a system well enough to stand behind it.

addyosmani.com · 29 min · Agent Engineering · AI Engineering · Code Review · Developer Tools · Productivity
06:00

The Orchestration Tax: When Agents Parallelize but Your Attention Doesn't

编排税:当AI智能体并行时,注意力的串行瓶颈

Starting agents is cheap, but closing the loop (review, merge, judgment) must route through a single serial processor: you. The author frames human attention as the GIL for AI agents — you can spawn 20 agents, but your throughput to main is bounded by your serial review rate. Amdahl's Law applies: the non-parallelizable judgment fraction caps any speedup. Strategies include: scaling agent count to review rate, not UI; sorting tasks into async-able vs. judgment-heavy; batching reviews; making agents prove correctness with tests/screenshots. The insight: busy ≠ productive. Unpaid orchestration tax accumulates both technical debt and cognitive debt, silently eroding your mental model of the system.