The Brain Sandwich

Lobsters Hottest News

Summary

一篇关于「脑三明治」工作流的博客文章,提出工程师在使用 AI 编码时应先用脑理解问题、再放手让 AI 执行、最后再用脑审查成果,以避免技能退化并保持代码可审查性。

<p><a href="https://lobste.rs/s/k5omum/brain_sandwich">Comments</a></p>
Original Article
View Cached Full Text

Cached at: 10/18/26, 01:49 PM

# The Brain Sandwich Source: [https://terriblesoftware.org/2026/10/02/the-brain-sandwich/](https://terriblesoftware.org/2026/10/02/the-brain-sandwich/) I was on a 1:1 with Emily, one of our strongest engineers, and we were mostly venting about some of the negatives about AI these days: the crazy amount of code reviews, how the job is changing, and the last one \(which is the most relevant for this post\), how engineers are sometimes[completely removing their brains from the equation](https://terriblesoftware.org/2026/05/27/using-my-fucking-brain/)and just delegating their entire work to AI\. \(Btw, I heard that the cool kids are calling this a[Meat Proxy](https://simonwillison.net/2026/Aug/3/dont-be-a-meat-proxy/)\.\) But anyway, the reason I decided to post about this is that Emily has a very simple framework that she uses for working with AI, which she calls the “Brain Sandwich”, that solves this\. It’s*both*about taking full advantage of AI without letting your engineering skills atrophy\. The idea is very simple, and can be summarized by: whenever you work on something, you should use: 1. Your brain,*and then* 2. AI,*and then* 3. Your brain Explaining a bit more… Before you tell the agent to start building anything, you go there and read and understand it*first*\. You don’t need a complete implementation plan, but you need a rough idea of where you’re going\. Otherwise, AI can bias you in the wrong direction\. Or even worse, it can nudge you into building something that wasn’t even a problem you needed to solve in the first place\! Now that you have all this context,*that’s*the time to bring AI\. By all means, do it\. Fully delegate this part with no shame\! And when the agent brings you the solution, you will know if it’s right, or if it’s too much, or if it’s completely solving the wrong problem, etc\. Then, at the end, your brain comes back\. Because before sending your stuff to other people for review, you need to make the work*reviewable*\. Read the diff, check if the complexity is worth it in this case, improve the freaking PR description, etc\. The rule of thumb is: if you can’t explain what’s changing and why, it’s not ready for someone else to take a look\.

Similar Articles

We made a skill to combat Brain Rot

Reddit r/AI_Agents

作者发布了一套 AI agent 技能(skills),旨在对抗「脑腐」(brain rot)——即在使用 AI agent 构建时把思考外包给 AI、导致产出变成 slop 的问题。这些技能帮助用户在通用构建任务、UI/UX 打磨与算法能力训练中主动思考。

@ba_niu80557: https://x.com/ba_niu80557/status/2068751230667755859

X AI KOLs Timeline

The article explores how increasingly powerful AI models eliminate those whose skills can be encoded into prompts, emphasizing that the truly irreplaceable value lies in tacit knowledge, physical-world operations, and interpersonal trust. Through the example of a friend transitioning from a consultant to a hardware integrator, the author illustrates how proactively yielding to AI-replaceable tasks while deepening expertise in areas beyond AI's reach is key to surviving and thriving in the technological wave.

@dashen_wang: https://x.com/dashen_wang/status/2062318606357303376

X AI KOLs Timeline

The author uses personal experience to introduce a tutorial on architect thinking in the AI era, emphasizing that the ability to understand the underlying essence when abstraction leaks is more critical than tool usage, and shares two modes: assembly thinking and object-oriented thinking.

@dotey: https://x.com/dotey/status/2055097242755706984

X AI KOLs Timeline

Senior developers often fail to communicate effectively with business teams because they overemphasize code complexity, while business teams truly care about eliminating uncertainty. The article suggests developers use "Can we try a faster approach?" to align both sides, and points out that although AI can write code quickly, humans still take responsibility.

Artificial Insanity

Hacker News Top

一篇批评当前生成式AI炒作的评论文章,认为前沿LLM本质上仍是"随机鹦鹉",缺乏真正的推理能力,AI公司夸大其危险与潜力,真正的风险来自人类自身而非机器。