我如何对抗AI脑疲劳
How I Fight AI Brain Fry

原始链接: https://sergeykuhne.com/blog/fighting-ai-brain-fry

作者描述了AI编程工作流因同时处理多条线程导致的上下文过载。其解决方法是一个基于Git的优先队列:每个工单是代码旁的Markdown文件,看板状态已提交。代理循环执行调研、构建已批准的工单、测试、浏览器检查和验证部署;敏感更改在分支上进行并需审批;它从不部署到生产环境。需要人类判断的决策进入所有者收件箱。一个Claude artifact从Git重建为展示看板和审查页面的单页,便于加载上下文;裁决返回Git。关键在于决策后关闭每项,使上下文离开工作记忆,焦点回到一次一个任务。挑战仍在于快速提供上下文和防止阻塞的工单循环。总体上,它提高了生产力。

关于“我如何对抗 AI 脑力枯竭”的 Hacker News 讨论,围绕着过度依赖 AI 可能侵蚀思维和解决问题能力的担忧。评论者将脑力锻炼比作身体锻炼,认为手动编码或刷 LeetCode 有助于防止技能萎缩。也有人认为这种担忧被夸大,因为大脑在许多非编码场景中也会得到锻炼,只有编码技能本身在缺乏练习时才会退化。部分人批评作者运行大量并行 AI 代理的做法,建议减少并行工作、投入更强的注意力。该帖还包含一个网站被“Hug of Death”打挂后存档版本的链接,以及关于 AI 撰写博客文章的元评论。总体而言,讨论在探讨 AI 过度使用是否损害认知,以及如何在生产力与刻意的脑力投入之间取得平衡。
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原文

If you work with Claude Code or Codex from the terminal, you know this one.

You start a task. The AI takes time to reply, so you start another one. Then the first one replies, but the reply needs one clarification, one follow up and one side task — and each of them wants its own tab. Then the second tab's reply comes in. You juggle. And all of a sudden you find yourself hopping between 20 threads, trying to keep all those contexts in your brain RAM.

Until you can't. Your brain gets exhausted, your focus smashes, and you don't know anymore what you were doing, where you started, and where you were supposed to finish.

A lot of people are suggesting taking breaks, thinking before typing, limiting scope. I have my own experiment.

I decided this way of working has two major problems: sequencing and context. So I organised my work a bit like a priority queue.

Here is how it actually works.

The queue lives in git. Every ticket is a markdown file in the repo, right next to the code it talks about, and the state of the board is a committed snapshot. Nothing important lives in a chat window and nothing lives in my head. If a session dies, the queue is still there.

An agent works that board in cycles. It researches new tickets, builds the ones a human has tagged as safe to build, runs the tests and a browser check, and verifies what is already deployed. For anything touching sensitive code it builds on a branch and asks first. It never deploys to production — that part is mine.

What it cannot do is decide. So everything that needs me lands in one place I call my owner inbox. A script reads the whole board and hands me a prioritised list of exactly the items waiting on my judgement — review this change, answer this question, rule on this tradeoff.

The reading surface is a Claude artifact. One page, rebuilt from git on every cycle, mirroring the whole board plus a review page per ticket. So I don't scroll back through a terminal trying to reconstruct what a change does. I open one link, read what was built and why, and give my verdict. The verdict goes back into git, onto the card. The page is only a viewer — git is the record.

Then comes the part that matters most.

I take one item. I load its context. I make a decision or request an action. And I close it, by putting it back into the queue.

That closing is the whole trick. The ticket doesn't keep hanging in my memory as a thing to remember about. The context closes with it. So when I pick the next item from the queue, I can actually focus. One thing at a time.

It is still exhausting. But I don't feel distracted or lost.

The challenge was, and still is, the tooling around it. Getting the context served up in a digestible manner, so loading a ticket takes a minute and not twenty. And keeping tickets from going into endless loops because they are blocked waiting for a ruling only I can give.

It's a living organism and I keep improving it. But it felt like a huge step forward in my productivity with AI tools, so I thought it was worth sharing.

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