Using AI to write better code more slowly

Original Article ↗ Hacker News Discussion ↗

Roles AI Plays in Coding

  • Used as tutor, rubber duck, and design partner: explain unfamiliar tech, critique plans, suggest alternatives, generate practice problems, and help with specs/PRDs.
  • Popular as code reviewer and bug-finder: spot corner cases, security issues, performance bottlenecks, inconsistent patterns, and missing tests.
  • Often employed for boilerplate, repetitive refactors, test generation, documentation, and comments, while humans handle architecture and critical paths.

Workflows and Practices

  • Many describe multi-step loops: human drafts requirements → AI proposes plan → human refines → AI implements in small chunks → AI(s) review → tests/benchmarks → further refactors.
  • Some orchestrate multiple models in series or parallel (implementation vs review vs specialized audits) and use skills/guardrails (e.g., “don’t hand-roll, prefer libraries”; “treat each change as a PR with tests”).
  • Others prefer minimalism: write most code by hand, let AI fill small gaps, review code, or generate specs from conversations.

Speed, Productivity, and Cost

  • Some claim substantial speedups (e.g., “v3-quality in v1 time”, hitting OKRs earlier) even with long AI review loops.
  • Others report parity or slower progress versus manual coding, but with higher final quality and more explored alternatives.
  • Token cost and provider limits/outages are real constraints; people discuss cheaper models, local models, and being “token-efficient” as a skill.

Code Quality, Comprehension, and Learning

  • Thread strongly emphasizes staying “aware” of all generated code and maintaining a mental model; fear of cognitive offloading and “anchoring” on AI’s first attempt.
  • Opinions split on AI code quality: some say modern models beat most humans on small snippets; others find it technically correct but ugly, overengineered, or context-blind.
  • Several report genuine learning: new patterns, idioms, and designs from reading AI code and arguing through trade-offs.

Skepticism and Risks

  • Concerns about “slop”: large, poorly factored AI-generated code, endless review loops, and accumulating tech debt that no one truly understands.
  • Worries about burnout from supervising many agents in parallel, constant context switching, and pressure to ship more with less time.
  • Ethical and organizational fears: dependence on vendors, layoffs justified by AI, management pushing unsupervised agentic coding, and loss of craftsmanship and “taste.”