Fast and Hard Code
lucumr.pocoo.org
August 23, 2026
2 min read
48/100
Summary
Large language models are reducing the importance of developers’ prior familiarity with programming languages and lowering barriers to rewriting software in unfamiliar languages. The trend is making language choice less consequential for some projects and may be encouraging choices driven by a language’s marketing or perceived performance characteristics. Rust, Zig, and other languages often viewed as harder to use are gaining attention for projects that prioritize speed and small binaries. Cloudflare’s Artifacts service uses a Git-protocol engine written entirely in Zig and compiled into an approximately 100 KB WebAssembly module. Vercel released fx, a Zig-based coding agent marketed as small and fast. The source text characterizes these projects as largely LLM-assisted. LLM agents are also being used to optimize code without intended behavior regressions and to work with specialized technologies including DWARF debugging data, eBPF, custom network drivers, cryptography, and older hardware. Developers including Mitchell Hashimoto, Charlie Marsh, Jarred Sumner, and Daniel Lemire are identified as long-standing advocates for high-performance software who are receptive to agents writing code. The source text argues that automated research and coding tools can let more developers pursue performance work, while noting that relevant technical knowledge remains helpful.
Key Takeaways
- LLMs can help developers write or rewrite software in programming languages they do not already know, reducing the weight of language familiarity in technology choices.
- Cloudflare’s Artifacts service uses a pure-Zig Git-protocol engine compiled to an approximately 100 KB WebAssembly module.
- Vercel released fx, a Zig coding agent marketed as small and fast.
- LLM-assisted development is expanding access to specialized areas such as eBPF, DWARF files, custom drivers, cryptography, and older computing hardware.
What the discussion said
The thread treated the article less as a claim that programming is finished than as a test of what AI changes about technical ambition. Several commenters have watched chat models and coding assistants lower the entry barrier to work once reserved for specialists: systems tooling, unfamiliar languages, compiler-backed Rust analysis, and sizeable application rewrites. The optimistic case is not that developers can stop thinking, but that an experienced developer can explore more designs, take on formerly impractical projects, and use a model as a fast research and implementation partner. The strongest pushback is about the gap between producing code and owning it. Readers argued that unfamiliar-language output cannot be responsibly reviewed, debugged, evolved, or trusted merely because it compiles. This becomes acute in cryptography and other high-consequence systems, where a plausible implementation can hide fatal mistakes. One experiment produced a sophisticated linting approach from broad requirements, but its author explicitly limited that workflow to a domain where missed findings cause no harm. Commenters also stressed that model performance follows the density and maturity of public training material: established math, algorithms, and bindings fare well, while newer language features and polished UI remain weak. The broad middle ground is that AI shifts programmers toward wider domain knowledge and sharper evaluation skills rather than making either unnecessary.
Where opinion split
The central fight is whether AI makes deep knowledge of a programming language and its underlying systems largely optional. Optimists say agents can absorb language friction and let capable people pursue much larger ideas, while curiosity-driven specialists will still push the frontier. Skeptics say code is only useful when its owner can inspect its assumptions, catch language-specific traps, and maintain it after the model's first pass.
Community Sentiment
Positives
- Coding assistants are letting developers attempt systems projects, full rewrites, and niche tooling that previously died under their sheer implementation cost.
- Conversational model use can act as a serious force multiplier for experienced developers, accelerating delivery while leaving human judgment on the critical path.
- Models can surface stronger technical approaches than a developer would initially choose, such as using compiler name resolution instead of fragile source parsing for workspace-wide analysis.
- Rich public prior art gives models real leverage in mathematics, algorithms, and established low-level interfaces, making cross-language experimentation less intimidating.
Concerns
- Generated code in an unfamiliar language is hard to review or maintain, so treating language knowledge as obsolete turns reliability into an article of faith.
- Custom cryptography remains a high-stakes danger zone: a convincing implementation can conceal subtle failures that only deep expertise exposes.
- Agent-built applications can invent data, mishandle alignment, and corrupt caches, illustrating how a polished first pass can mask basic correctness defects.
- Models lag on cutting-edge language features and often produce generic UI because their competence tracks the availability and quality of training examples.
