Fast and Hard Code
Armin Ronacher argues that LLMs are profoundly shifting programming, making previously 'hard' languages like Rust and Zig, and low-level technologies like eBPF, more accessible to developers. This accessibility is driving a renewed focus on fast and small software, as AI agents help bridge knowledge gaps and reduce the friction of learning new ecosystems. The Hacker News discussion, however, is heavily skeptical, questioning the true utility and quality of LLM-generated code beyond simple boilerplate.
The Lowdown
Armin Ronacher's article, "Fast and Hard Code," explores how Large Language Models (LLMs) are reshaping programming paradigms, particularly regarding language choice and a renewed emphasis on performance. He posits that LLMs diminish the need for deep familiarity with a language, allowing developers to prioritize factors like performance and marketing appeal.
- LLMs reduce the learning curve for new languages, making language choice less impactful for human developers.
- This shift enables programmers to select languages like Rust and Zig, known for their performance, even if they are unfamiliar with them, with LLMs assisting in code generation and optimization.
- Examples such as Cloudflare's Zig-based Artifacts service and Vercel's fx (a Zig coding agent) are cited as projects leveraging LLM assistance for performance.
- Beyond languages, LLMs are also facilitating engagement with 'harder' low-level technologies, including DWARF files, eBPF, and custom network drivers, which were previously less accessible.
- Ronacher suggests this trend could lead to more developers creating fast and small software, potentially alongside an increase in 'slop' from less experienced use of AI.
Ultimately, the piece suggests that LLMs are democratizing access to complex programming domains, challenging historical gatekeeping by making specialized knowledge more broadly available, and driving a push towards more performant, optimized software.
The Gossip
Statistical Slop or Sensible Solutions?
Commenters largely express skepticism about the quality and utility of LLM-generated code. Many argue that LLMs primarily generate 'boilerplate' or examples that should already exist in documentation, calling much of the output 'slop' that only gets worse when straying from popular idioms. The consensus among these critics is that LLMs don't truly enable developers to tackle 'hard' problems or unfamiliar languages efficiently, but rather regurgitate existing patterns, failing to provide novel or truly robust solutions. Some also fear that this trend reduces the scope for open-source tools and stifles creativity.