How I write code using Cursor

Original Article ↗ Hacker News Discussion ↗

Perceived benefits of Cursor / LLM coding tools

  • Major time-saver for boilerplate, glue code, tests, simple CRUD, API wrappers, UI scaffolding, and repetitive data-munging.
  • Lets many devs “think at architecture level” and stay in flow instead of context-switching to docs, search, or Stack Overflow.
  • Especially helpful when exploring unfamiliar languages/libraries (e.g., Rust testing libs, Web APIs, SQL dialects, React/Next stacks).
  • Tab-completion and multi-file edits are praised for “next action” suggestions (e.g., updating all call sites after a signature change).
  • Some report completing small apps or games in a fraction of the time, spending most effort on product design and testing.
  • Strong use case as a smarter search/assistant: explaining errors, sketching options, generating commands for tools like pandoc or jq.

Concerns and limitations

  • Accuracy drops sharply for unique, business-heavy or messy problems (e.g., complex banking integrations, basket pricing rules).
  • Tends to generate “average”, tutorial-like code: misses edge cases, may use deprecated APIs, and can balloon complexity during debugging.
  • Large, legacy or monorepo codebases remain challenging: limited context, weak understanding of conventions, risk of duplication and wrong abstractions.
  • Some find inline suggestions visually distracting or overly aggressive, feeling like “another person touching my code.”
  • Multi-step autonomous workflows (e.g., Cline running tests and editing) can be impressive but also risky and prone to subtle misunderstandings.

Impact on learning and developer skill

  • Split views:
    • Pro: offloads tedious work, lets experienced devs focus on design, and can even surface simpler solutions or tests they’d miss.
    • Con: risks “skill atrophy” and stunted growth for juniors who never struggle through fundamentals or read docs deeply.
  • Many emphasize that LLM output must be reviewed like a junior’s code; it doesn’t replace understanding.

Comparisons and ecosystem

  • Cursor seen by fans as a “next generation” over Copilot: deeper repo indexing, multi-file edits, tuned models, and better completions.
  • Others feel VS Code + plugins (Copilot/Continue/Supermaven/Cline) or JetBrains + AI are equivalent or preferable.
  • Context/RAG strategies help but don’t fully solve “grok the whole system” problems; long context alone not sufficient.

Organizational & ethical issues

  • Strong concern about sending proprietary code to third-party servers; some companies ban such tools, others allow them widely.
  • Environmental cost of LLMs is raised by a few as a reason not to use them for trivial tasks.