Be intentional about how AI changes your codebase

Original Article ↗ Hacker News Discussion ↗

AI, responsibility, and code review

  • Many argue AI isn’t “making codebases worse”; developers are, by using it without intention or proper review.
  • Strong consensus: if you submit AI-written code, you are fully responsible for it. You should be able to answer any question about the patch.
  • Some compare AI misuse to misusing tools (e.g., a hammer) – the fault is in process, not the tool.

Velocity, slop, and subtle erosion

  • A major concern is AI’s ability to change code faster than teams can absorb or understand it.
  • The danger is less obvious bugs and more gradual erosion of consistency, semantics, and system invariants.
  • AI is good at turning clean “semantic”/pure functions into “pragmatic” ones with side effects, while tests still pass.

Testing, code quality, and linters

  • Several posters say testing alone isn’t enough: AI will optimize for passing tests even if it harms design.
  • There’s debate over whether “code quality” can be fully codified. Some see it as mostly subjective taste; others emphasize what can be enforced: formatting, complexity, patterns.
  • Linters, formatters, and custom project-level rules are seen as key guardrails, especially against AI-generated slop and “optional everything” APIs.

Documentation and “the why”

  • Disagreement on “code is documentation”:
    • Some say code and minimal comments should self-document behavior.
    • Others insist you cannot encode “why” (business context, tradeoffs, historical decisions) in code alone.
  • AI can help generate or summarize documentation, but cannot reconstruct long-term intent or organizational strategy.

Design pitfalls: optional fields and configuration

  • A recurring anti-pattern: AI (and humans) adding lots of optional, nullable parameters instead of enforcing a single, authoritative decision point.
  • This leads to scattered configuration logic, “works on my machine” bugs, and hard-to-reason systems.

How to use AI: agents vs manual coding

  • Some now write most code through agents, in small, reviewed steps, reporting massive productivity gains.
  • Others refuse to let AI write code directly, using it only for diagnosis, search, or inspiration.
  • There’s speculation that “hand-written only” coding will become niche but may persist where precision, performance, and complex invariants dominate (e.g., games, systems code).

AI behavior and codebases

  • AI behaves like a powerful but context-blind junior: good with patterns, bad at hidden assumptions.
  • It mirrors the cleanliness of the repo: tidy codebases guide it into good patterns; messy ones cause it to invent new, inconsistent ones.

UI / site feedback

  • Multiple commenters say the linked site is visually “cool” but functionally broken, especially on mobile Safari.
  • This is cited as an example of “vibe-coded” output: flashy, AI-assisted CSS/animations that fail basic usability and responsiveness.