快准狠的代码
Fast and Hard Code

原始链接: https://lucumr.pocoo.org/2026/8/22/fast-hard-code/

大语言模型(LLM)的兴起正从根本上改变开发者的编程方式,使得编程语言的选择变得愈发不重要。由于 AI 智能体可以轻松转换代码或处理程序员不熟悉的语言,开发者不再受限于学习语法所带来的“摩擦力”。 这种转变促使更多项目开始采用 Rust 和 Zig 等“难度较高”的高性能语言。得益于大语言模型在优化代码以及处理 eBPF、自定义加密和 DWARF 文件等复杂领域方面的卓越能力,那些曾经被认为过于困难或难以入门的技术,如今已在广大开发者的触及范围内。 在行业追求“轻量、快速、高效”软件的氛围推动下,开发者正利用人工智能绕过传统的准入门槛。虽然这种复杂编程的平民化可能会导致“劣质内容”增加,但它同时也让新一代开发者能够构建起以往无法涉足的高性能系统。

抱歉。
这篇 Hacker News 的讨论聚焦于 AI 辅助的“氛围编程”(vibe coding)——即用户在无需深厚专业知识的情况下生成软件——究竟是传统编程的终结,还是生产力新纪元的开启。 支持者认为 AI 是一种“力量倍增器”,使开发者无需精通语言的每一个细微差别,就能着手处理雄心勃勃的项目,例如构建操作系统或使用 eBPF 等复杂技术。他们认为这降低了准入门槛,让创作者能够专注于架构而非语法。 然而,怀疑论者警告称,跳过“深挖”过程(即从底层原理学习系统运作方式)会导致代码脆弱且难以维护。他们认为,缺乏领域知识,开发者就无法有效引导 AI、审查其输出或调试关键问题。一个反复被提及的担忧是,在密码学等敏感任务上依赖 AI 是危险的,因为“能运行”的代码并不等同于“安全”或“正确”的代码。 归根结底,共识在于:尽管 AI 可以加速开发,但设计、验证和理解的责任依然在人类手中。那些对技术有深刻理解的“钻研者”将继续成长,而其他人则面临产出无法维护的“AI 垃圾”的风险。
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原文

written on August 22, 2026

One of the memes on Twitter is that “programming is solved now.” I’m not sure to what degree it is, but one thing is pretty clear: the act of familiarizing yourself with a language no longer matters and some of the friction that mattered for humans does not matter for agents.

As a result, LLMs make language choice much less consequential than it used to be. If you don’t like the choice, you can seemingly rewrite it in another language and you can make it pick a language that you, as a programmer, are entirely unfamiliar with.

Which in turn means that people can, and do, choose based on the marketing of languages much more. As a long-term Rust programmer I found it quite fascinating to see people now ship Rust code who previously might not have chosen it. I attribute at least one part of this to two recent vibe shifts: there is a lot more talk about wanting fast software, and about LLMs being exceptional at optimizing code without regressing behavior.

Folks like Mitchell Hashimoto, Charlie Marsh, Jarred Sumner, Daniel Lemire and quite a few others always carried a certain level of obsession with fast and performant software and they also all happen to be receptive to agents writing code. Maybe as a result, or unrelated others are now joining in. That’s because with things like autoresearch you don’t even necessarily need to know all the tricks: you just need to put an agent on it — though knowledge greatly helps!

If you look around, there are plenty of projects that want to be fast and small, and they increasingly pick “hard languages”. And it’s not just Rust that is benefiting. Even Zig — despite the fact that the creators and parts of the core community are pretty negative on the whole AI thing — is too. For instance Cloudflare’s new Artifacts service uses a pure-Zig Git-protocol engine, compiled to a roughly 100 KB WebAssembly module and Vercel released fx, a Zig coding agent advertised to be small and fast. From what I can tell, all these projects are largely LLM-assisted.

But it’s not just people picking less common languages but also that they are increasingly working with “much harder” technologies. All of a sudden I have seen people do some really impressive stuff with DWARF files, eBPF, custom network drivers, custom crypto and really old computing hardware. Many of these things were previously off-limits for lots of developers. In some cases (eg: crypto) you were even pushed away because those things were intentionally gatekept by the people in the know.

So maybe the world will have more slop, but it might also have more developers in it, that want things to be fast and small.

This entry was tagged ai, programming and thoughts

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