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09:19

Orchestrating AI Code Review at Scale

Cloudflare 多智能体代码审查实战：7 个专项 Agent 并行，30 天跑完 13 万次 review

Cloudflare built an AI code review system on OpenCode, orchestrating up to 7 domain-specific agents (security, performance, docs, etc.) via a coordinator. Over 30 days it processed 131k+ reviews with a median latency of 3m39s and average cost of $1.19. The post dives deep into plugin architecture, risk tiers, circuit breakers, incremental re-reviews, prompt injection prevention, and honest limitations. Suitable for engineers exploring AI-assisted development and CI/CD integration at scale.

blog.cloudflare.com · 51 min · AI · Cloudflare · LLM
06:00

Andrej Karpathy says 99% of AI users miss 7 basics. Full breakdown.

Andrej Karpathy 亲述：99% 的 AI 用户不知道的 7 个基本功

Andrej Karpathy — OpenAI co-founder, former Tesla AI head — argues the bottleneck for most AI users isn't the model or the prompt, but the lack of a system around it. This breakdown covers his 7 practical rules: provide full context instead of magic prompts; curate a proper CLAUDE.md; adopt a /raw, /wiki, and config three-layer memory; permanently save strong outputs as reference pages; maintain index.md and log.md for long projects; treat AI as a super-intern with no taste, working in small verified steps; and add one line to render research as navigable HTML. Aimed at engineers stuck in prompt tweaking loops, these habits take an afternoon to set up and compound fast.

x.com · 8 min · AI · LLM
06:00

Claude Subagents vs. Agent Teams, explained

Claude 子代理 vs 智能体团队：多智能体架构该如何选

Compares two Claude multi-agent paradigms: sub-agents (fire-and-forget, isolated context, return compressed results) for embarrassingly parallel tasks, and agent teams (persistent, direct peer communication, shared task list) for ongoing coordination. Provides design principles: decompose by context boundaries, not by roles; start simple and add complexity only when measurable. Covers five orchestration patterns, three situations where multi-agent systems are justified, and common failure modes. Practical advice with code examples for engineers building LLM‑powered agents.

x.com · 11 min · Agents · AI · LLM
06:00

The Complete Guide to /goal, /loop, /schedule & Stop Hooks in Claude Code

Claude Code 自主运行完全指南：/goal、/loop、/schedule 与 Stop Hook

A complete guide to four autonomous Claude Code commands that eliminate per‑step babysitting. /goal sets a success condition checked by a fast evaluator after each turn; /loop runs on a fixed time interval; /schedule creates background tasks independent of an open session; Stop Hooks let you programmatically decide when Claude may stop (e.g., via test‑suite scripts). The article provides templates, real examples, condition‑writing rules, and the /goal + Auto mode combination for fully unattended work. It contrasts use‑cases and offers a decision matrix for choosing the right command, turning Claude from a prompt‑driven assistant into an autonomous coding agent.

x.com · 9 min · Agents · AI
06:00

Claude Code /goal: Autonomous Task Completion Without Babysitting

Claude Code 的 /goal 命令：告别手动“继续”，让 AI 自主完成编码任务

The /goal command in Claude Code enables autonomous task completion by letting it loop turn after turn until a verifiable condition is met. An evaluator model (Claude Haiku by default) checks the transcript each turn. The post explains how to write effective goals (specific, measurable, output-verifiable), project setup tips (CLAUDE.md, hooks, Auto Mode), and common pitfalls like vague goals causing token waste and evaluator hallucination. It compares /goal with /loop and stop hooks. For developers tired of nudging AI, this is a practical guide to hands-off coding sessions.

x.com · 5 min · Agents · AI · LLM
06:00

Building self-improving tax agents with Codex

用 Codex 构建自改进税务 AI：生产反馈闭环实践

OpenAI and Thrive Holdings co-developed Tax AI for Crete's accounting firms, using a Codex-driven self-improvement loop. The system processed 7,000 returns with 97% accuracy and 50% throughput increase, cutting one senior accountant's prep time from 180 to 15 hours. The design relies on three pillars: practitioner feedback, production traces, and a Codex iteration cycle. A detailed rental property example shows how practitioner corrections become eval targets, then Codex investigates and proposes fixes. Practical for teams building self-improving agents in expert domains.

openai.com · 15 min · Agents · AI · Performance