Use Fable 5 as orchestrator and Opus + Codex to execute (to save fable usage):
Fable 5 (max reasoning) = orchestrator
Opus = deep reasoning subagent
Sonnet = mechanical work subagent
Codex = peer Sr. engineer, different perspective
Setup:
1. Set Fable 5 as your main model In Claude Code: /model → Fable 5 → reasoning /effort to max
2. Create 2 subagents with /agents In Claude Code:
deep-reasoner → pinned to opus "Use for reasoning-heavy phases, architecture, debugging complex issues, algorithm design. Think thoroughly, return a concise conclusion the orchestrator can act on."
fast-worker → pinned to sonnet "Use for mechanical tasks, boilerplate, tests, formatting, simple edits. Execute efficiently."
3. Add OpenAI's official Codex plugin (install codex cli in your computer first), In Claude Code type:
/plugin marketplace add openai/codex-plugin-cc
/plugin install codex@openai-codex
/codex:setup
4. Drop this in your CLAUDE.md in your folder:
## Orchestration workflow
You (Fable) are the orchestrator. Plan, decompose, synthesize.
Reasoning-heavy phases → deep-reasoner
Mechanical work → fast-worker
Codex (/codex:rescue --background) is a cracked engineer on par with deep-reasoner, from a different perspective. Treat as a peer, not a reviewer.
High-stakes decisions: task Opus + Codex on the same problem in parallel, synthesize the best of both, without showing either the other's answer. Keep your own context lean.
5. Then prompt Fable like a tech lead: "Goal: [what you want] Context: [files, constraints] You're the lead. Delegate reasoning to deep-reasoner, grunt work to fast-worker, fresh-perspective problems to Codex. Show me your plan first, then execute."
That's it.
New skill: /animation-vocabulary
Helps you get better animations from an AI by telling it exactly what you want by using the right words.
"morph", "rubber-banding", "layout animation", and more.
分享了这么多AI编程工具,今天转载一篇不一样的硬核干货。
这篇在推特上很火的文章叫《如何在2026年成为一名AI工程师(无需CS学位)》。作者 Khairallah 是一位拥有一线交付经验的 Web3 与 AI 领域资深开发者。
他提出了一个直击当下痛点的事实:在技术频繁更迭的时代,传统的大学教育大纲早已跟不上大模型的进化速度。科技行业里薪酬极高的 AI 构建岗位,根本不在乎你的毕业证写了什么,只看你亲手交付过什么产品。
这篇文章提供了一个非常务实且不同的视角,去重新看待人与 AI 的关系:
很多人都在焦虑“既然 AI 已经能写大量代码,人类为什么还要学习编程?”作者给出的答案很简单——因为 AI 永远需要指挥官。
决定产品走向、设计系统架构、评估 AI 的输出是否准确,这些才是人类的商业价值所在。AI 工具的普及不会让你失业,反而会成倍放大你的产出。外行只看 AI 运行成功一次的噱头,而专业工程师却在解决它运行一万次时的稳定性和成本控制。
原文信息量很大,包含了从零开始的12个月转型路线图。
强烈建议大家挂上“沉浸式翻译”插件去读英文原文。如果你也在 AI 时代感到焦虑,这篇文章或许能给你一个不一样的思考视角。
As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:
1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales
Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.
A healthy team needs a mix of these, depending on the product:
- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2
Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?