I built a programming language using Claude Code

Original Article ↗ Hacker News Discussion ↗

Role of Programming Languages When LLMs Write Code

  • Debate over whether language choice still matters if humans neither read nor write most code.
  • Many argue it does: performance (e.g., Rust vs Python), safety guarantees, and constraints still shape system behavior.
  • Others note that if the human can’t read or reason about the generated code, the benefits of a sophisticated language may be lost.

Language Design for AI vs Humans

  • Several see more value in new, specialized languages: LLMs can learn niche syntaxes from docs and examples without the usual human-adoption barrier.
  • Others counter that without substantial training data, LLMs perform worse, and stuffing language specs into context is inefficient.
  • There’s interest in languages that:
    • Make invalid states unrepresentable.
    • Emphasize concurrency safety and performance “knobs.”
    • Are concise but still human-readable (not extreme code-golf; not Java-level verbosity).
  • Disagreement over whether terse syntaxes or token-efficient designs actually help models much.

Quality, Testing, and Guardrails

  • Strong skepticism about relying on LLM-written code plus LLM-written tests as “guardrails”; tests can be wrong and still all pass.
  • Several report that LLMs often hallucinate APIs, mishandle edge cases (e.g., float lexing), or quietly add fallbacks/mock paths that mask failures.
  • Formal methods and stronger static guarantees are mentioned as missing but desirable.

Practical Experiences & Productivity

  • Multiple accounts of using Claude/Codex to:
    • Build toy languages, interpreters, or DSLs quickly.
    • Prototype games, frameworks, and large systems far faster than solo coding.
  • Others question claims of massive productivity gains, citing studies showing more modest boosts and personal experience of frequent errors.

Ownership, Copyright, and Ethics

  • One subthread notes that fully machine-generated code may not be copyrightable under current US guidance.
  • Some worry about AI dependence leading to fewer new foundational tools and a potential “long dark teatime” for human engineering.

Behavioral Concerns

  • Several compare LLM prompting to gambling: unpredictable results, “just one more prompt” compulsion, and the sense that “the house always wins.”