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Fast and Hard Code

Fast and Hard Code

Tech airesearchrustzig Armin Ronacher
LLMs are eroding the barrier to entry for complex, performant systems, allowing developers to build in low-level languages like Rust and Zig regardless of prior experience.
What: Armin Ronacher observes that developers are increasingly utilizing LLM-assisted coding to tackle hardware-level tasks like eBPF, DWARF file parsing, and custom crypto, previously considered gatekept. Recent examples include Cloudflare's pure-Zig Git engine and Vercel's Zig-based 'fx' agent.
Why it matters: The democratization of low-level systems programming indicates a future where architectural choices are driven by marketing and performance obsession rather than the human cost of learning language-specific syntax or idioms.
Deep dive
  • LLMs have reduced the friction of learning new languages, making language choice less consequential for human developers.
  • Performance and small binary size are seeing a resurgence as key engineering priorities, influenced by figures like Mitchell Hashimoto and Charlie Marsh.
  • Hard-to-reach domains like eBPF, custom network drivers, and proprietary crypto are becoming accessible to broader engineering teams through AI assistance.
  • The 'vibe shift' toward performant software is driving adoption of memory-safe but complex languages like Rust and niche languages like Zig.
  • AI-assisted development is accelerating the creation of small, specialized tools that were previously too time-consuming for solo developers to build.
Decoder
  • DWARF: A standardized debugging data format used by compilers and debuggers to associate machine code with source code.
  • eBPF: A technology that allows running sandboxed programs in the Linux kernel without changing kernel source code or loading modules.
  • Git-protocol engine: Software responsible for the low-level handshake and data transfer protocols used by Git to move data between repositories.
Original article

Fast and Hard Code

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.

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